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Record W2104496560 · doi:10.1017/s0143385706000228

Asymptotic randomization of sofic shifts by linear cellular automata

2006· article· en· W2104496560 on OpenAlexaff
Marcus Pivato, Reem Yassawi

Bibliographic record

VenueErgodic Theory and Dynamical Systems · 2006
Typearticle
Languageen
FieldComputer Science
TopicCellular Automata and Applications
Canadian institutionsTrent University
Fundersnot available
KeywordsCombinatoricsAbelian groupMathematics

Abstract

fetched live from OpenAlex

Let ${\mathbb{M}}={\mathbb{Z}}^D$ be a $D$ -dimensional lattice, and let $({\mathcal{A}},+)$ be an abelian group. ${\mathcal{A}}^{\mathbb{M}}$ is then a compact abelian group under componentwise addition. A continuous function $\Phi:{\mathcal{A}}^{\mathbb{M}}\longrightarrow{\mathcal{A}}^{\mathbb{M}}$ is called a linear cellular automaton if there is a finite subset ${\mathbb{F}}\subset{\mathbb{M}}$ and non-zero coefficients $\varphi_{\textsf{f}}\in{\mathcal{Z}}$ so that, for any ${\bf{a}}\in{\mathcal{A}}^{\mathbb{M}}\, \Phi({\bf{a}}) = \sum_{{\textsf{f}}\in{\mathbb{F}}}\varphi_{\textsf{f}}\cdot\sigma^{{\textsf{f}}}({\bf{a}})$ . Suppose that $\mu$ is a probability measure on ${\mathcal{A}}^{\mathbb{M}}$ whose support is a subshift of finite type or sofic shift. We provide sufficient conditions (on $\Phi$ and $\mu$ ) under which $\Phi$ asymptotically randomizes $\mu$ , meaning that $\mathrm{wk}^*-\lim_{{\mathbb{J}}\ni j\rightarrow\infty} \Phi^j\mu = \eta$ , where $\eta$ is the Haar measure on ${\mathcal{A}}^{\mathbb{M}}$ , and ${\mathbb{J}}\subset{\mathbb{N}}$ has Cesàro density one. In the case when $\Phi=1+\sigma$ and ${\mathcal{A}}=({{\mathbb{Z}}_{/p}})^s$ ( $p$ prime), we provide a condition on $\mu$ that is both necessary and sufficient. We then use this to construct zero-entropy measures which are randomized by $1+\sigma$ .

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)0.009
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.003
GPT teacher head0.188
Teacher spread0.186 · 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

Citations18
Published2006
Admission routes1
Has abstractyes

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