Bibliographic record
Abstract
This project contains Python3 code that illustrates the algorithms from the paper "Robot positioning using torus packing for multisets" by (in alphabetical order) Chung Shue Chen (Nokia Bell Labs, France) Élie de Panafieu (Nokia Bell Labs, France) Peter Keevash, (Mathematical Institute, University of Oxford, UK) Sean Kennedy (Nokia Bell Labs, Canada) Adrian Vetta (McGill University, Canada) presented at the International Colloquium on Automata, Languages and Programming, ICALP Track A 2024. In the terminology of "Universal cycles for combinatorial structures" (Fan Chung, Persi Diaconis, Ron Graham, 1992) and "Universal Cycle Packings and Coverings for k-Subsets of an n-Set" (Michał Dȩbski, Zbigniew Lonc, 2016), our algorithm outputs a universal cycle packing of dimension d for multisets. Let us now detail what it means. It inputs integer parameters 'd', 'b' and 't' and outputs a colored grid. Let k = db + 1, m = 2bdt, s = (2bd)(d-2) * t(d-1) and n = (2ms + 1)**(b-1) * (2ms - 1/s) + 1/s. The grid has dimension 'd' and size 'n'. Each pixel receives a color in {0, 1, ..., k-1}. The grid is considered as a torus. The main property is that no two d-dimensional subsquare of size m correspond to the same multiset of colors.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".