The Mixed-Radix Chinese Remainder Theorem and Its Applications to Residue Comparison
Bibliographic record
Abstract
The Chinese remainder theorem (CRT) and mixed-radix conversion (MRC) are two classic theorems used to convert a residue number to its binary correspondence for a given moduli set {P_n, · · · , P_2, P_1}. The MRC is a weighted number system and it requires operations modulo P_i only and hence magnitude comparison is easily performed. However, the calculation of the mixed-radix coefficients in the MRC is a strictly sequential process and involves complex divisions. Thus the residue-to-binary (R/B) conversions and residue comparisons based on the MRC require large delay. In contrast, the R/B conversion and residue comparison based on the CRT are fully parallel processes. However, the CRT requires large operations modulo M = P_n · · · P_2P_1. In this paper, a new mixed-radix CRT is proposed which possesses both the advantages of the CRT and the MRC, which are parallel processing, small operations modulo P_i only, and the efficiency of making modulo comparison. Based on the proposed CRT, new residue comparators are developed for the three-moduli set {2^n − 1, 2^n, 2^n + 1}. The FPGA implementation results show that the proposed modulo comparators are about 20% faster and smaller than one of the previous best designs.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".