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Record W2100137244 · doi:10.1109/cwit.2011.5872144

Streaming codes for a double-link burst erasure channel

2011· article· en· W2100137244 on OpenAlexaff
Devin Lui, Ahmed Badr, Ashish Khisti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCommunication sourceComputer scienceErasureErasure codeNetwork packetComputer networkChannel (broadcasting)Binary erasure channelOnline codesFountain codeLink (geometry)Code (set theory)Upper and lower boundsRaptor codeDecoding methodsChannel capacityAlgorithmLinear codeBlock codeMathematics

Abstract

fetched live from OpenAlex

A sender and receiver are connected by two links, which both pass through a burst erasure channel. The channel induces an erasure burst of length B onto both links, but the bursts are separated by d time units. Source packets arrive at the sender, and are encoded with a streaming code such that the receiver can decode with a delay T. If source packet s[t] arrives at the sender at time t, then the receiver must be able to decode s[t] by time t+T from its received packets. Given the parameters B, T and d, we find the upper bound for the rate of the streaming code, and also discover codes that can operate at capacity for certain parameter values. The code constructions also internally make use of SCo codes. Finally, we find that by exploiting the dependence of the burst erasure locations on either link, we can achieve a higher rate than if we simply used single-link SCo codes on each link.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.284
Teacher spread0.211 · 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 designSimulation or modeling
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

Citations8
Published2011
Admission routes1
Has abstractyes

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