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Record W128333381

Parallelism, Program Size, Time, and Temperature in Self-Assembly

2010· article· en· W128333381 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuearXiv (Cornell University) · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Biological Computing
Canadian institutionsnot available
Fundersnot available
KeywordsTileSquare (algebra)Upper and lower boundsCombinatoricsParallelism (grammar)Binary logarithmComputer scienceOrder (exchange)Parallel computingDiscrete mathematicsMathematicsGeometryMaterials science
DOInot available

Abstract

fetched live from OpenAlex

We settle a number of questions in variants of Winfree’s abstract Tile Assembly Model (aTAM), a model of molecular algorithmic self-assembly. In the “hierarchical” aTAM, two assemblies, both consisting of multiple tiles, are allowed to aggregate together, whereas in the “seeded” aTAM, tiles attach one at a time to a growing assembly. Adleman, Cheng, Goel, and Huang (Running Time and Program Size for Self-Assembled Squares, STOC 2001) showed how to assemble an n × n square in O(n) time in the seeded aTAM using O( logn log logn ) unique tile types, showed that both of these parameters are optimal, and asked whether the hierarchical aTAM could allow a tile system to use the ability to form large assemblies in parallel before they attach to break the Ω(n) lower bound for assembly time. We show there is a tile system with the optimal O( log n log logn ) tile types that assembles an n × n square using O(log n) parallel “stages”, which is close to the optimal Ω(log n) stages, forming the final n×n from four n/2×n/2 squares, which are themselves recursively formed from n/4×n/4 squares, etc. However, despite this nearly maximal parallelism, the system requires superlinear time to assemble the square. We leave open the question of whether some hierarchical tile system can break the Ω(n) assembly time lower bound for assembling an n× n square. We extend the definition of partial order tile systems studied by Adleman et al. in a natural way to hierarchical assembly and show that no hierarchical partial order tile system can build any shape with diameter N in less than time Ω(N), demonstrating that in this case the hierarchical model affords no speedup whatsoever over the seeded model. We also strengthen the Ω(N) time lower bound for deterministic seeded systems of Adleman et al. to nondeterministic seeded systems. We then investigate the relationship between the temperature of a tile system and its size. We show that a tile system can in general require temperature that is exponentially greater than its number of tile types. On the other hand, for the special case of 2-cooperative systems, in which all binding events involve at most 2 sides of tiles, it suffices to use temperature linear in the number of tile types. We show that there is a polynomial-time algorithm that, given any tile system T specified by its desired binding behavior, finds a temperature and binding energies (at most exponential in the number of tile types of T ) that realize this behavior or reports that no such energies exist. This result is applied to show that there is a polynomial-time algorithm that, given an n× n square Sn, determines the smallest (non-hierarchical “seeded”) system (at any temperature) that is deterministic and self-assembles Sn. This answers an open question of Adleman, Cheng, Goel, Huang, Kempe, Moisset de Espanes, and Rothemund (Combinatorial Optimization Problems in Self-Assembly, STOC 2002). The first author was supported by the Molecular Programming Project under NSF grant 0832824, the second and fourth authors were supported by NSF Computing Innovation Fellowships, and the third author was supported by NSERC Discovery Grant R2824A01 and the Canada Research Chair in Biocomputing to Lila Kari. California Institute of Technology, Pasadena, CA, USA, holinc@gmail.com, ddoty@caltech.edu University of Western Ontario, Dept. of Computer Science, London, ON, Canada, N6A 5B7, sseki@csd.uwo.ca. University of Washington, Dept. of Computer Science, Seattle, WA, USA, dsolov@u.washington.edu.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.173
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it