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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 OpenAlexfundno aff
Reem Yassawi, Anthony Quas, Ethan M. Coven

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

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0040.008
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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.

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2016
Admission routes1
Has abstractyes

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