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  1. Computation, Physics and Beyond.M. Dinneen, B. Khoussainov & A. Nies (eds.) - 2012 - Springer.
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  • Fixed point theorems on partial randomness.Kohtaro Tadaki - 2012 - Annals of Pure and Applied Logic 163 (7):763-774.
  • Cone avoidance and randomness preservation.Stephen G. Simpson & Frank Stephan - 2015 - Annals of Pure and Applied Logic 166 (6):713-728.
  • Effectively closed sets of measures and randomness.Jan Reimann - 2008 - Annals of Pure and Applied Logic 156 (1):170-182.
    We show that if a real x2ω is strongly Hausdorff -random, where h is a dimension function corresponding to a convex order, then it is also random for a continuous probability measure μ such that the μ-measure of the basic open cylinders shrinks according to h. The proof uses a new method to construct measures, based on effective continuous transformations and a basis theorem for -classes applied to closed sets of probability measures. We use the main result to derive a (...)
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  • Mass problems and initial segment complexity.W. M. Phillip Hudelson - 2014 - Journal of Symbolic Logic 79 (1):20-44.
  • Propagation of partial randomness.Kojiro Higuchi, W. M. Phillip Hudelson, Stephen G. Simpson & Keita Yokoyama - 2014 - Annals of Pure and Applied Logic 165 (2):742-758.
    Let f be a computable function from finite sequences of 0ʼs and 1ʼs to real numbers. We prove that strong f-randomness implies strong f-randomness relative to a PA-degree. We also prove: if X is strongly f-random and Turing reducible to Y where Y is Martin-Löf random relative to Z, then X is strongly f-random relative to Z. In addition, we prove analogous propagation results for other notions of partial randomness, including non-K-triviality and autocomplexity. We prove that f-randomness relative to a (...)
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  • Calibrating randomness.Rod Downey, Denis R. Hirschfeldt, André Nies & Sebastiaan A. Terwijn - 2006 - Bulletin of Symbolic Logic 12 (3):411-491.
    We report on some recent work centered on attempts to understand when one set is more random than another. We look at various methods of calibration by initial segment complexity, such as those introduced by Solovay [125], Downey, Hirschfeldt, and Nies [39], Downey, Hirschfeldt, and LaForte [36], and Downey [31]; as well as other methods such as lowness notions of Kučera and Terwijn [71], Terwijn and Zambella [133], Nies [101, 100], and Downey, Griffiths, and Reid [34]; higher level randomness notions (...)
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