The Performances of the Fixed Constraints Transform Applied in Text Compression Experimental Results and Comparisons
Bibliographic record
Abstract
The Fixed Constraints Transform (FCT) encodes the text based on a dictionary. This dictionary is used to accomplish the connections between the words in the text and their corresponding transforms. The dictionary is generated one time and it is saved in a binary form for a better word-indexing speed. This method is strictly designed for text compression and it has maximum performances when the text has normal formatting - in a phrase, only the first word starts with upper case, and it continues with lower case. Because the algorithm is based on modification of the words in the text, on unaltered signs of punctuation, on spaces and other special characters, the algorithms performance is given by the ratio between the number of letters in the text and the total number of characters. FCT has close performances with other frequently used transforms - Star, Burrows-Wheeler, etc. - in terms of compression, but it has better execution speed. The applied algorithms of lossless data compression for testing are: RLE (Run-Length Encoding), arithmetic, PPMd (Prediction by Partial Matching), BZip2, Deflate (WinZip), LAMA, and RAR. The following indicators of compression performance were measured: the requested time for transform generation, the compression rate, and the requested time for compression. The text files used for evaluating the performance are from the Calgary Corpus. FCT leads to compression performances close to the ones obtained by the usual transforms used as pre-compression methods (BWT, Star Transform and derivatives). FCT is suited for the use of a chain of processors that have as purpose lossless data compression. The transform itself does not do a performing compression, but - most important - it helps a compression algorithm applied after it with the fact that it eliminates some redundant information and specific features to the idioms written in a certain language. FCT is an efficient method of data processing with notable results that can be very easily implemented and used in a lossless compression chain both for stream sequences and files in usual applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".