[no title]
Bibliographic record
Abstract
$ \newcommand{\N}{{\mathbb N}} \newcommand{\spt}{\mathsf{\Sigma^\mathsf{P}_2}} $ We investigate the role of nondeterminism in Winfree's abstract Tile Assembly Model (aTAM), in particular how its use in tile systems affects the resource requirements. We show that for infinitely many $c\in\N$, there is a finite shape $S$ that is self-assembled by a tile system (meaning that all of the various terminal assemblies produced by the tile system have shape $S$) with $c$ tile types, but every directed tile system that self-assembles $S$ (i. e., has only one terminal assembly, whose shape is $S$) needs more than $c$ tile types. We extend the technique to prove our main theorem, that the problem of finding the minimum number of tile types that self-assemble a given finite shape is $\spt$-complete. We then show an analogous “computability theoretic” result: there is an infinite shape $S$ that is self-assembled by a tile system but not by any directed tile system. An extended abstract of this paper appeared in SODA'11.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".