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Record W2963809654 · doi:10.19086/da.611

Computing automorphism groups of shifts using atypical equivalence classes

2016· article· en· W2963809654 on OpenAlex

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Bibliographic record

VenueDiscrete Analysis · 2016
Typearticle
Languageen
FieldComputer Science
TopicCellular Automata and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutomorphismMathematicsSigmaCombinatoricsBijectionDiscrete mathematicsInvariant (physics)Dynamical systems theoryType (biology)AlphabetPhysics

Abstract

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Computing automorphism groups of shifts, using atypical equivalence classes, Discrete Analysis 2016:3, 24 pp. Symbolic dynamics is about dynamical systems of the following type. Let $A$ be an alphabet, and let $\sigma$ be the left shift map from $A^{\mathbb Z}$ to itself. Giving $A$ the discrete topology and $A^{\mathbb Z}$ the product topology, if $X$ is a closed $\sigma$-invariant subset of $A^{\mathbb Z}$, then $(X,\sigma)$ is a dynamical system. Of particular interest are _minimal_ systems of this type: that is, systems where $X$ is the closure of the set of all shifts of a single doubly infinite word $(a_n)_{n\in\mathbb{Z}}$ in $A$. The properties of the word turn out to be interestingly related to properties of the dynamical system. A key parameter for such a system is the _complexity function_ $p:\mathbb{N}\to\mathbb{N}$, where for each positive integer $n$, $p(n)$ is defined to be the number of distinct subwords of the form $(a_m,a_{m+1},\dots,a_{m+n-1})$. In particular, the rate of growth of this function is important. An _automorphism_ of a dynamical system $(X,\sigma)$ is a continuous bijection from $X$ to $X$ that commutes with $\sigma$. A trivial example of an automorphism is $\sigma$ itself, or indeed any power of $\sigma$. In the past few years, there has been a lot of work on showing that dynamical systems $(X,\sigma)$ for which the complexity function grows slowly have automorphism groups that are in some sense small. However, even in the lowest nontrivial complexity (that of nonconstant, sublinear complexity), we do not have a complete understanding of the automorphism group, and in general there is no method that gives a complete characterization of this group. In this paper, the authors focus on the particular class of substitution systems of constant length, which are the systems whose infinite words are obtained by iterating a substitution infinitely many times on some letter in the alphabet. An interesting result in the paper is an algorithm to compute the automorphism group in this situation, along with the use of this algorithm to compute all conjugacies between two shifts generated by constant length substitutions. The proof uses dynamical methods to reduce the problem to combinatorial arguments. Another result is that for a minimal system with complexity that grows at most linearly the quotient of the automorphism group by the group generated by $\sigma$ is finite. It is not clear whether the techniques used in this paper can be generalized to a significantly wider class of systems. However, the difficulty of computing the automorphism group in general is such that any new non-trivial examples are useful and instructive.

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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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.287
Teacher spread0.263 · 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