PACKING DIMENSION and CARTESIAN PRODUCTS 1. Introduction Marstrand's Product Theorem
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SUBFRACTALS INDUCED by SUBSHIFTS a Dissertation
SUBFRACTALS INDUCED BY SUBSHIFTS A Dissertation Submitted to the Graduate Faculty of the North Dakota State University of Agriculture and Applied Science By Elizabeth Sattler In Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Major Department: Mathematics May 2016 Fargo, North Dakota NORTH DAKOTA STATE UNIVERSITY Graduate School Title SUBFRACTALS INDUCED BY SUBSHIFTS By Elizabeth Sattler The supervisory committee certifies that this dissertation complies with North Dakota State Uni- versity's regulations and meets the accepted standards for the degree of DOCTOR OF PHILOSOPHY SUPERVISORY COMMITTEE: Dr. Do˘ganC¸¨omez Chair Dr. Azer Akhmedov Dr. Mariangel Alfonseca Dr. Simone Ludwig Approved: 05/24/2016 Dr. Benton Duncan Date Department Chair ABSTRACT In this thesis, a subfractal is the subset of points in the attractor of an iterated function system in which every point in the subfractal is associated with an allowable word from a subshift on the underlying symbolic space. In the case in which (1) the subshift is a subshift of finite type with an irreducible adjacency matrix, (2) the iterated function system satisfies the open set condition, and (3) contractive bounds exist for each map in the iterated function system, we find bounds for both the Hausdorff and box dimensions of the subfractal, where the bounds depend both on the adjacency matrix and the contractive bounds on the maps. We extend this result to sofic subshifts, a more general subshift than a subshift of finite type, and to allow the adjacency matrix n to be reducible. The structure of a subfractal naturally defines a measure on R . -
March 12, 2011 DIAGONALLY NON-RECURSIVE FUNCTIONS and EFFECTIVE HAUSDORFF DIMENSION 1. Introduction Reimann and Terwijn Asked Th
March 12, 2011 DIAGONALLY NON-RECURSIVE FUNCTIONS AND EFFECTIVE HAUSDORFF DIMENSION NOAM GREENBERG AND JOSEPH S. MILLER Abstract. We prove that every sufficiently slow growing DNR function com- putes a real with effective Hausdorff dimension one. We then show that for any recursive unbounded and nondecreasing function j, there is a DNR function bounded by j that does not compute a Martin-L¨ofrandom real. Hence there is a real of effective Hausdorff dimension 1 that does not compute a Martin-L¨of random real. This answers a question of Reimann and Terwijn. 1. Introduction Reimann and Terwijn asked the dimension extraction problem: can one effec- tively increase the information density of a sequence with positive information den- sity? For a formal definition of information density, they used the notion of effective Hausdorff dimension. This effective version of the classical Hausdorff dimension of geometric measure theory was first defined by Lutz [10], using a martingale defini- tion of Hausdorff dimension. Unlike classical dimension, it is possible for singletons to have positive dimension, and so Lutz defined the dimension dim(A) of a binary sequence A 2 2! to be the effective dimension of the singleton fAg. Later, Mayor- domo [12] (but implicit in Ryabko [15]), gave a characterisation using Kolmogorov complexity: for all A 2 2!, K(A n) C(A n) dim(A) = lim inf = lim inf ; n!1 n n!1 n where C is plain Kolmogorov complexity and K is the prefix-free version.1 Given this formal notion, the dimension extraction problem is the following: if 2 dim(A) > 0, is there necessarily a B 6T A such that dim(B) > dim(A)? The problem was recently solved by the second author [13], who showed that there is ! an A 2 2 such that dim(A) = 1=2 and if B 6T A, then dim(B) 6 1=2. -
A Note on Pointwise Dimensions
A Note on Pointwise Dimensions Neil Lutz∗ Department of Computer Science, Rutgers University Piscataway, NJ 08854, USA [email protected] April 6, 2017 Abstract This short note describes a connection between algorithmic dimensions of individual points and classical pointwise dimensions of measures. 1 Introduction Effective dimensions are pointwise notions of dimension that quantify the den- sity of algorithmic information in individual points in continuous domains. This note aims to clarify their relationship to the classical notion of pointwise dimen- sions of measures, which is central to the study of fractals and dynamics. This connection is then used to compare algorithmic and classical characterizations of Hausdorff and packing dimensions. See [15, 17] for surveys of the strong ties between algorithmic information and fractal dimensions. 2 Pointwise Notions of Dimension arXiv:1612.05849v2 [cs.CC] 5 Apr 2017 We begin by defining the two most well-studied formulations of algorithmic dimension [5], the effective Hausdorff and packing dimensions. Given a point x ∈ Rn, a precision parameter r ∈ N, and an oracle set A ⊆ N, let A A n Kr (x) = min{K (q): q ∈ Q ∩ B2−r (x)} , where KA(q) is the prefix-free Kolmogorov complexity of q relative to the oracle −r A, as defined in [10], and B2−r (x) is the closed ball of radius 2 around x. ∗Research supported in part by National Science Foundation Grant 1445755. 1 Definition. The effective Hausdorff dimension and effective packing dimension of x ∈ Rn relative to A are KA(x) dimA(x) = lim inf r r→∞ r KA(x) DimA(x) = lim sup r , r→∞ r respectively [11, 14, 1]. -
Perfect Filtering and Double Disjointness
ANNALES DE L’I. H. P., SECTION B HILLEL FURSTENBERG YUVAL PERES BENJAMIN WEISS Perfect filtering and double disjointness Annales de l’I. H. P., section B, tome 31, no 3 (1995), p. 453-465 <http://www.numdam.org/item?id=AIHPB_1995__31_3_453_0> © Gauthier-Villars, 1995, tous droits réservés. L’accès aux archives de la revue « Annales de l’I. H. P., section B » (http://www.elsevier.com/locate/anihpb) implique l’accord avec les condi- tions générales d’utilisation (http://www.numdam.org/conditions). Toute uti- lisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit conte- nir la présente mention de copyright. Article numérisé dans le cadre du programme Numérisation de documents anciens mathématiques http://www.numdam.org/ Ann. Inst. Henri Poincaré, Vol. 31, n° 3, 1995, p. 453-465. Probabilités et Statistiques Perfect filtering and double disjointness Hillel FURSTENBERG, Yuval PERES (*) and Benjamin WEISS Institute of Mathematics, the Hebrew University, Jerusalem. ABSTRACT. - Suppose a stationary process ~ Un ~ is used to select from several stationary processes, i.e., if Un = i then we observe Yn which is the n’th variable in the i’th process. when can we recover the selecting sequence ~ Un ~ from the output sequence ~Yn ~ ? RÉSUMÉ. - Soit ~ Un ~ un processus stationnaire utilise pour la selection de plusieurs processus stationnaires, c’ est-a-dire si Un = i alors on observe Yn qui est le n-ième variable dans le i-ième processus. Quand peut-on reconstruire ~ Un ~ à partir de ~ Yn ~ ? 1. INTRODUCTION Suppose a discrete-time stationary stochastic signal ~ Un ~, taking integer values, is transmitted over a noisy channel. -
Fractal Geometry
Fractal Geometry Special Topics in Dynamical Systems | WS2020 Sascha Troscheit Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Wien, Austria [email protected] July 2, 2020 Abstract Classical shapes in geometry { such as lines, spheres, and rectangles { are only rarely found in nature. More common are shapes that share some sort of \self-similarity". For example, a mountain is not a pyramid, but rather a collection of \mountain-shaped" rocks of various sizes down to the size of a gain of sand. Without any sort of scale reference, it is difficult to distinguish a mountain from a ragged hill, a boulder, or ever a small uneven pebble. These shapes are ubiquitous in the natural world from clouds to lightning strikes or even trees. What is a tree but a collection of \tree-shaped" branches? A central component of fractal geometry is the description of how various properties of geometric objects scale with size. Dimension theory in particular studies scalings by means of various dimensions, each capturing a different characteristic. The most frequent scaling encountered in geometry is exponential scaling (e.g. surface area and volume of cubes and spheres) but even natural measures can simultaneously exhibit very different behaviour on an average scale, fine scale, and coarse scale. Dimensions are used to classify these objects and distinguish them when traditional means, such as cardinality, area, and volume, are no longer appropriate. We shall establish fundamen- tal results in dimension theory which in turn influences research in diverse subject areas such as combinatorics, group theory, number theory, coding theory, data processing, and financial mathematics. -
Russell David Lyons
Russell David Lyons Education Case Western Reserve University, Cleveland, OH B.A. summa cum laude with departmental honors, May 1979, Mathematics University of Michigan, Ann Arbor, MI Ph.D., August 1983, Mathematics Sumner Myers Award for best thesis in mathematics Specialization: Harmonic Analysis Thesis: A Characterization of Measures Whose Fourier-Stieltjes Transforms Vanish at Infinity Thesis Advisers: Hugh L. Montgomery, Allen L. Shields Employment Indiana University, Bloomington, IN: James H. Rudy Professor of Mathematics, 2014{present. Indiana University, Bloomington, IN: Adjunct Professor of Statistics, 2006{present. Indiana University, Bloomington, IN: Professor of Mathematics, 1994{2014. Georgia Institute of Technology, Atlanta, GA: Professor of Mathematics, 2000{2003. Indiana University, Bloomington, IN: Associate Professor of Mathematics, 1990{94. Stanford University, Stanford, CA: Assistant Professor of Mathematics, 1985{90. Universit´ede Paris-Sud, Orsay, France: Assistant Associ´e,half-time, 1984{85. Sperry Research Center, Sudbury, MA: Researcher, summers 1976, 1979. Hampshire College Summer Studies in Mathematics, Amherst, MA: Teaching staff, summers 1977, 1978. Visiting Research Positions University of Calif., Berkeley: Visiting Miller Research Professor, Spring 2001. Microsoft Research: Visiting Researcher, Jan.{Mar. 2000, May{June 2004, July 2006, Jan.{June 2007, July 2008{June 2009, Sep.{Dec. 2010, Aug.{Oct. 2011, July{Oct. 2012, May{July 2013, Jun.{Oct. 2014, Jun.{Aug. 2015, Jun.{Aug. 2016, Jun.{Aug. 2017, Jun.{Aug. 2018. Weizmann Institute of Science, Rehovot, Israel: Rosi and Max Varon Visiting Professor, Fall 1997. Institute for Advanced Studies, Hebrew University of Jerusalem, Israel: Winston Fellow, 1996{97. Universit´ede Lyon, France: Visiting Professor, May 1996. University of Wisconsin, Madison, WI: Visiting Associate Professor, Winter 1994. -
Affine Embeddings of Cantor Sets on the Line Arxiv:1607.02849V3
Affine Embeddings of Cantor Sets on the Line Amir Algom August 5, 2021 Abstract Let s 2 (0; 1), and let F ⊂ R be a self similar set such that 0 < dimH F ≤ s . We prove that there exists δ = δ(s) > 0 such that if F admits an affine embedding into a homogeneous self similar set E and 0 ≤ dimH E − dimH F < δ then (under some mild conditions on E and F ) the contraction ratios of E and F are logarithmically commensurable. This provides more evidence for Conjecture 1.2 of Feng et al.(2014), that states that these contraction ratios are logarithmically commensurable whenever F admits an affine embedding into E (under some mild conditions). Our method is a combination of an argument based on the approach of Feng et al.(2014) with a new result by Hochman(2016), which is related to the increase of entropy of measures under convolutions. 1 Introduction Let A; B ⊂ R. We say that A can be affinely embedded into B if there exists an affine map g : R ! R; g(x) = γ · x + t for γ 6= 0; t 2 R, such that g(A) ⊆ B. The motivating problem of this paper to show that if A and B are two Cantor sets and A can be affinely embedded into B, then the there should be some arithmetic dependence between the scales appearing in the multiscale structures of A and B. This phenomenon is known to occur e.g. when A and B are two self similar sets satisfying the strong separation condition that are Lipschitz equivalent, see Falconer and Marsh(1992). -
Characterization of Cutoff for Reversible Markov Chains
Characterization of cutoff for reversible Markov chains Riddhipratim Basu ∗ Jonathan Hermon y Yuval Peres z Abstract A sequence of Markov chains is said to exhibit (total variation) cutoff if the conver- gence to stationarity in total variation distance is abrupt. We consider reversible lazy chains. We prove a necessary and sufficient condition for the occurrence of the cutoff phenomena in terms of concentration of hitting time of \worst" (in some sense) sets of stationary measure at least α, for some α (0; 1). 2 We also give general bounds on the total variation distance of a reversible chain at time t in terms of the probability that some \worst" set of stationary measure at least α was not hit by time t. As an application of our techniques we show that a sequence of lazy Markov chains on finite trees exhibits a cutoff iff the product of their spectral gaps and their (lazy) mixing-times tends to . 1 Keywords: Cutoff, mixing-time, finite reversible Markov chains, hitting times, trees, maximal inequality. arXiv:1409.3250v4 [math.PR] 18 Feb 2015 ∗Department of Statistics, UC Berkeley, California, USA. E-mail: [email protected]. Supported by UC Berkeley Graduate Fellowship. yDepartment of Statistics, UC Berkeley, USA. E-mail: [email protected]. zMicrosoft Research, Redmond, Washington, USA. E-mail: [email protected]. 1 1 Introduction In many randomized algorithms, the mixing-time of an underlying Markov chain is the main component of the running-time (see [21]). We obtain a tight bound on tmix() (up to an absolute constant independent of ) for lazy reversible Markov chains in terms of hitting times of large sets (Proposition 1.7,(1.6)). -
Divergence Points of Self-Similar Measures and Packing Dimension
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Advances in Mathematics 214 (2007) 267–287 www.elsevier.com/locate/aim Divergence points of self-similar measures and packing dimension I.S. Baek a,1,L.Olsenb,∗, N. Snigireva b a Department of Mathematics, Pusan University of Foreign Studies, Pusan 608-738, Republic of Korea b Department of Mathematics, University of St. Andrews, St. Andrews, Fife KY16 9SS, Scotland, UK Received 14 December 2005; accepted 7 February 2007 Available online 1 March 2007 Communicated by R. Daniel Mauldin Abstract Rd ∈ Rd log μB(x,r) Let μ be a self-similar measure in . A point x for which the limit limr0 log r does not exist is called a divergence point. Very recently there has been an enormous interest in investigating the fractal structure of various sets of divergence points. However, all previous work has focused exclusively on the study of the Hausdorff dimension of sets of divergence points and nothing is known about the packing dimension of sets of divergence points. In this paper we will give a systematic and detailed account of the problem of determining the packing dimensions of sets of divergence points of self-similar measures. An interesting and surprising consequence of our results is that, except for certain trivial cases, many natural sets of divergence points have distinct Hausdorff and packing dimensions. © 2007 Elsevier Inc. All rights reserved. MSC: 28A80 Keywords: Fractals; Multifractals; Hausdorff measure; Packing measure; Divergence points; Local dimension; Normal numbers 1. -
Amber L. Puha, Phd June 2021 Department of Mathematics Craven Hall 6130 California State University San Marcos Office Phone: 1-760-750-4201 333 S
Amber L. Puha, PhD June 2021 Department of Mathematics Craven Hall 6130 California State University San Marcos Office Phone: 1-760-750-4201 333 S. Twin Oaks Valley Road Department Phone: 1-760-750-8059 San Marcos, CA 92096-0001 https://faculty.csusm.edu/apuha/ [email protected] Appointments 2010{present, Professor, Department of Mathematics, CSU San Marcos 2020-2021, Sabbatical Leave of Absence, Research Associate and Teaching Visitor Department of Mathematics, UCSD 2013-2014, Sabbatical Leave of Absence, Research Associate and Teaching Visitor Department of Mathematics, UCSD 2010-2011, Professional Leave of Absence 2009{2011, Associate Director, Institute for Pure and Applied Mathematics (IPAM), UCLA 2004-2010, Associate Professor, Department of Mathematics, CSU San Marcos 2009-2010, Professional Leave of Absence 2005-2006, Sabbatical Leave of Absence, Research Associate and Teaching Visitor, Department of Mathematics, UCSD 1999-2004, Assistant Professor, Department of Mathematics, CSU San Marcos 2000-2001 & Spring 2002, Professional Leave of Absence, National Science Foundation Mathematical Sciences Postdoctoral Fellow Research Interests Probability Theory and Stochastic Processes, with emphasis on Stochastic Networks Professional Awards 2021-2024, PI, National Science Foundation Single-Investigator Award, DMS-2054505, $232,433 2020{2021, CSUSM Research, Scholarship and Creative Activity (RSCA) Grant, $7,200 2019{2020, CSUSM Research, Scholarship and Creative Activity (RSCA) Grant, $2,000 2019, National Scholastic Surfing Association, -
Fractal-Bits
fractal-bits Claude Heiland-Allen 2012{2019 Contents 1 buddhabrot/bb.c . 3 2 buddhabrot/bbcolourizelayers.c . 10 3 buddhabrot/bbrender.c . 18 4 buddhabrot/bbrenderlayers.c . 26 5 buddhabrot/bound-2.c . 33 6 buddhabrot/bound-2.gnuplot . 34 7 buddhabrot/bound.c . 35 8 buddhabrot/bs.c . 36 9 buddhabrot/bscolourizelayers.c . 37 10 buddhabrot/bsrenderlayers.c . 45 11 buddhabrot/cusp.c . 50 12 buddhabrot/expectmu.c . 51 13 buddhabrot/histogram.c . 57 14 buddhabrot/Makefile . 58 15 buddhabrot/spectrum.ppm . 59 16 buddhabrot/tip.c . 59 17 burning-ship-series-approximation/BossaNova2.cxx . 60 18 burning-ship-series-approximation/BossaNova.hs . 81 19 burning-ship-series-approximation/.gitignore . 90 20 burning-ship-series-approximation/Makefile . 90 21 .gitignore . 90 22 julia/.gitignore . 91 23 julia-iim/julia-de.c . 91 24 julia-iim/julia-iim.c . 94 25 julia-iim/julia-iim-rainbow.c . 94 26 julia-iim/julia-lsm.c . 98 27 julia-iim/Makefile . 100 28 julia/j-render.c . 100 29 mandelbrot-delta-cl/cl-extra.cc . 110 30 mandelbrot-delta-cl/delta.cl . 111 31 mandelbrot-delta-cl/Makefile . 116 32 mandelbrot-delta-cl/mandelbrot-delta-cl.cc . 117 33 mandelbrot-delta-cl/mandelbrot-delta-cl-exp.cc . 134 34 mandelbrot-delta-cl/README . 139 35 mandelbrot-delta-cl/render-library.sh . 142 36 mandelbrot-delta-cl/sft-library.txt . 142 37 mandelbrot-laurent/DM.gnuplot . 146 38 mandelbrot-laurent/Laurent.hs . 146 39 mandelbrot-laurent/Main.hs . 147 40 mandelbrot-series-approximation/args.h . 148 41 mandelbrot-series-approximation/emscripten.cpp . 150 42 mandelbrot-series-approximation/index.html . -
Front Matter
Cambridge University Press 978-1-107-13411-9 — Fractals in Probability and Analysis Christopher J. Bishop , Yuval Peres Frontmatter More Information CAMBRIDGE STUDIES IN ADVANCED MATHEMATICS 162 Editorial Board B. BOLLOBAS,´ W. FULTON, F. KIRWAN, P. SARNAK, B. SIMON, B. TOTARO FRACTALS IN PROBABILITY AND ANALYSIS A mathematically rigorous introduction to fractals which emphasizes examples and fundamental ideas. Building up from basic techniques of geometric measure theory and probability, central topics such as Hausdorff dimension, self-similar sets and Brownian motion are introduced, as are more specialized topics, including Kakeya sets, capacity, percolation on trees and the Traveling Salesman Theorem. The broad range of techniques presented enables key ideas to be highlighted, without the distraction of excessive technicalities. The authors incorporate some novel proofs which are simpler than those available elsewhere. Where possible, chapters are designed to be read independently so the book can be used to teach a variety of courses, with the clear structure offering students an accessible route into the topic. Christopher J. Bishop is a professor in the Department of Mathematics at Stony Brook University. He has made contributions to the theory of function algebras, Kleinian groups, harmonic measure, conformal and quasiconformal mapping, holomorphic dynamics and computational geometry. Yuval Peres is a Principal Researcher at Microsoft Research in Redmond, WA. He is particularly known for his research in topics such as fractals and Hausdorff measure, random walks, Brownian motion, percolation and Markov chain mixing times. In 2016 he was elected to the National Academy of Science. © in this web service Cambridge University Press www.cambridge.org Cambridge University Press 978-1-107-13411-9 — Fractals in Probability and Analysis Christopher J.