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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 OpenAlex
Devin Lui, Ahmed Badr, Ashish Khisti

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

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.667
Threshold uncertainty score0.530

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 science0.0010.000
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 head0.074
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

Quick stats

Citations8
Published2011
Admission routes1
Has abstractyes

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