Universal Data Compression with Side Information at the Decoder by Using Traditional Universal Lossless Compression Algorithms
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we investigate universal data compression with side information at the decoder by leveraging traditional universal data compression algorithms. Specifically, consider a source network with feedback in which a finite alphabet source X = {X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> } <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=0</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sup> is to be encoded and transmitted, and another finite alphabet source Y = {Y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> } <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=0</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sup> available only to the decoder as the side information correlated with X. Assuming that the encoder and decoder share a uniform i.i.d. (independent and identically distributed) random database that is independent of (X, Y), we propose a string matching-based (variable-rate) block coding algorithm with a simple progressive encoder for the feedback source network. Instead of using standard joint typicality decoding, this algorithm derives its decoding rule from the codeword length function of a traditional universal lossless coding algorithm. As a result, neither the encoder nor the decoder assumes any prior knowledge of the joint distribution of (X, Y) or even the achievable rates. It is proven that for any (X, Y) in the class of all stationary, ergodic source-side information pairs with finite alphabet, the average number of bits per letter transmitted from the encoder to the decoder (compression rate) goes arbitrarily close to the conditional entropy rate H(X|Y) of X given Y asymptotically, and the average number of bits per letter transmitted from the decoder to the encoder (feedback rate) goes to 0 asymptotically.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it