Haar Null and Haar Meager Sets: a Survey and New Results
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Lectures on Fractal Geometry and Dynamics
Lectures on fractal geometry and dynamics Michael Hochman∗ March 25, 2012 Contents 1 Introduction 2 2 Preliminaries 3 3 Dimension 3 3.1 A family of examples: Middle-α Cantor sets . 3 3.2 Minkowski dimension . 4 3.3 Hausdorff dimension . 9 4 Using measures to compute dimension 13 4.1 The mass distribution principle . 13 4.2 Billingsley's lemma . 15 4.3 Frostman's lemma . 18 4.4 Product sets . 22 5 Iterated function systems 24 5.1 The Hausdorff metric . 24 5.2 Iterated function systems . 26 5.3 Self-similar sets . 31 5.4 Self-affine sets . 36 6 Geometry of measures 39 6.1 The Besicovitch covering theorem . 39 6.2 Density and differentiation theorems . 45 6.3 Dimension of a measure at a point . 48 6.4 Upper and lower dimension of measures . 50 ∗Send comments to [email protected] 1 6.5 Hausdorff measures and their densities . 52 1 Introduction Fractal geometry and its sibling, geometric measure theory, are branches of analysis which study the structure of \irregular" sets and measures in metric spaces, primarily d R . The distinction between regular and irregular sets is not a precise one but informally, k regular sets might be understood as smooth sub-manifolds of R , or perhaps Lipschitz graphs, or countable unions of the above; whereas irregular sets include just about 1 everything else, from the middle- 3 Cantor set (still highly structured) to arbitrary d Cantor sets (irregular, but topologically the same) to truly arbitrary subsets of R . d For concreteness, let us compare smooth sub-manifolds and Cantor subsets of R . -
Appendix A. Measure and Integration
Appendix A. Measure and integration We suppose the reader is familiar with the basic facts concerning set theory and integration as they are presented in the introductory course of analysis. In this appendix, we review them briefly, and add some more which we shall need in the text. Basic references for proofs and a detailed exposition are, e.g., [[ H a l 1 ]] , [[ J a r 1 , 2 ]] , [[ K F 1 , 2 ]] , [[ L i L ]] , [[ R u 1 ]] , or any other textbook on analysis you might prefer. A.1 Sets, mappings, relations A set is a collection of objects called elements. The symbol card X denotes the cardi- nality of the set X. The subset M consisting of the elements of X which satisfy the conditions P1(x),...,Pn(x) is usually written as M = { x ∈ X : P1(x),...,Pn(x) }.A set whose elements are certain sets is called a system or family of these sets; the family of all subsystems of a given X is denoted as 2X . The operations of union, intersection, and set difference are introduced in the standard way; the first two of these are commutative, associative, and mutually distributive. In a { } system Mα of any cardinality, the de Morgan relations , X \ Mα = (X \ Mα)and X \ Mα = (X \ Mα), α α α α are valid. Another elementary property is the following: for any family {Mn} ,whichis { } at most countable, there is a disjoint family Nn of the same cardinality such that ⊂ \ ∪ \ Nn Mn and n Nn = n Mn.Theset(M N) (N M) is called the symmetric difference of the sets M,N and denoted as M #N. -
6.436J Lecture 13: Product Measure and Fubini's Theorem
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 13 10/22/2008 PRODUCT MEASURE AND FUBINI'S THEOREM Contents 1. Product measure 2. Fubini’s theorem In elementary math and calculus, we often interchange the order of summa tion and integration. The discussion here is concerned with conditions under which this is legitimate. 1 PRODUCT MEASURE Consider two probabilistic experiments described by probability spaces (�1; F1; P1) and (�2; F2; P2), respectively. We are interested in forming a probabilistic model of a “joint experiment” in which the original two experiments are car ried out independently. 1.1 The sample space of the joint experiment If the first experiment has an outcome !1, and the second has an outcome !2, then the outcome of the joint experiment is the pair (!1; !2). This leads us to define a new sample space � = �1 × �2. 1.2 The �-field of the joint experiment Next, we need a �-field on �. If A1 2 F1, we certainly want to be able to talk about the event f!1 2 A1g and its probability. In terms of the joint experiment, this would be the same as the event A1 × �1 = f(!1; !2) j !1 2 A1; !2 2 �2g: 1 Thus, we would like our �-field on � to include all sets of the form A1 × �2, (with A1 2 F1) and by symmetry, all sets of the form �1 ×A2 (with (A2 2 F2). This leads us to the following definition. Definition 1. We define F1 ×F2 as the smallest �-field of subsets of �1 ×�2 that contains all sets of the form A1 × �2 and �1 × A2, where A1 2 F1 and A2 2 F2. -
ERGODIC THEORY and ENTROPY Contents 1. Introduction 1 2
ERGODIC THEORY AND ENTROPY JACOB FIEDLER Abstract. In this paper, we introduce the basic notions of ergodic theory, starting with measure-preserving transformations and culminating in as a statement of Birkhoff's ergodic theorem and a proof of some related results. Then, consideration of whether Bernoulli shifts are measure-theoretically iso- morphic motivates the notion of measure-theoretic entropy. The Kolmogorov- Sinai theorem is stated to aid in calculation of entropy, and with this tool, Bernoulli shifts are reexamined. Contents 1. Introduction 1 2. Measure-Preserving Transformations 2 3. Ergodic Theory and Basic Examples 4 4. Birkhoff's Ergodic Theorem and Applications 9 5. Measure-Theoretic Isomorphisms 14 6. Measure-Theoretic Entropy 17 Acknowledgements 22 References 22 1. Introduction In 1890, Henri Poincar´easked under what conditions points in a given set within a dynamical system would return to that set infinitely many times. As it turns out, under certain conditions almost every point within the original set will return repeatedly. We must stipulate that the dynamical system be modeled by a measure space equipped with a certain type of transformation T :(X; B; m) ! (X; B; m). We denote the set we are interested in as B 2 B, and let B0 be the set of all points in B that return to B infinitely often (meaning that for a point b 2 B, T m(b) 2 B for infinitely many m). Then we can be assured that m(B n B0) = 0. This result will be proven at the end of Section 2 of this paper. In other words, only a null set of points strays from a given set permanently. -
Uniqueness for the Signature of a Path of Bounded Variation and the Reduced Path Group
ANNALS OF MATHEMATICS Uniqueness for the signature of a path of bounded variation and the reduced path group By Ben Hambly and Terry Lyons SECOND SERIES, VOL. 171, NO. 1 January, 2010 anmaah Annals of Mathematics, 171 (2010), 109–167 Uniqueness for the signature of a path of bounded variation and the reduced path group By BEN HAMBLY and TERRY LYONS Abstract We introduce the notions of tree-like path and tree-like equivalence between paths and prove that the latter is an equivalence relation for paths of finite length. We show that the equivalence classes form a group with some similarity to a free group, and that in each class there is a unique path that is tree reduced. The set of these paths is the Reduced Path Group. It is a continuous analogue of the group of reduced words. The signature of the path is a power series whose coefficients are certain tensor valued definite iterated integrals of the path. We identify the paths with trivial signature as the tree-like paths, and prove that two paths are in tree-like equivalence if and only if they have the same signature. In this way, we extend Chen’s theorems on the uniqueness of the sequence of iterated integrals associated with a piecewise regular path to finite length paths and identify the appropriate ex- tended meaning for parametrisation in the general setting. It is suggestive to think of this result as a noncommutative analogue of the result that integrable functions on the circle are determined, up to Lebesgue null sets, by their Fourier coefficients. -
Nilsequences, Null-Sequences, and Multiple Correlation Sequences
Nilsequences, null-sequences, and multiple correlation sequences A. Leibman Department of Mathematics The Ohio State University Columbus, OH 43210, USA e-mail: [email protected] April 3, 2013 Abstract A (d-parameter) basic nilsequence is a sequence of the form ψ(n) = f(anx), n ∈ Zd, where x is a point of a compact nilmanifold X, a is a translation on X, and f ∈ C(X); a nilsequence is a uniform limit of basic nilsequences. If X = G/Γ be a compact nilmanifold, Y is a subnilmanifold of X, (g(n))n∈Zd is a polynomial sequence in G, and f ∈ C(X), we show that the sequence ϕ(n) = g(n)Y f is the sum of a basic nilsequence and a sequence that converges to zero in uniformR density (a null-sequence). We also show that an integral of a family of nilsequences is a nilsequence plus a null-sequence. We deduce that for any d invertible finite measure preserving system (W, B,µ,T ), polynomials p1,...,pk: Z −→ Z, p1(n) pk(n) d and sets A1,...,Ak ∈B, the sequence ϕ(n)= µ(T A1 ∩ ... ∩ T Ak), n ∈ Z , is the sum of a nilsequence and a null-sequence. 0. Introduction Throughout the whole paper we will deal with “multiparameter sequences”, – we fix d ∈ N and under “a sequence” will usually understand “a two-sided d-parameter sequence”, that is, a mapping with domain Zd. A (compact) r-step nilmanifold X is a factor space G/Γ, where G is an r-step nilpo- tent (not necessarily connected) Lie group and Γ is a discrete co-compact subgroup of G. -
Representation of the Dirac Delta Function in C(R)
Representation of the Dirac delta function in C(R∞) in terms of infinite-dimensional Lebesgue measures Gogi Pantsulaia∗ and Givi Giorgadze I.Vekua Institute of Applied Mathematics, Tbilisi - 0143, Georgian Republic e-mail: [email protected] Georgian Technical University, Tbilisi - 0175, Georgian Republic g.giorgadze Abstract: A representation of the Dirac delta function in C(R∞) in terms of infinite-dimensional Lebesgue measures in R∞ is obtained and some it’s properties are studied in this paper. MSC 2010 subject classifications: Primary 28xx; Secondary 28C10. Keywords and phrases: The Dirac delta function, infinite-dimensional Lebesgue measure. 1. Introduction The Dirac delta function(δ-function) was introduced by Paul Dirac at the end of the 1920s in an effort to create the mathematical tools for the development of quantum field theory. He referred to it as an improper functional in Dirac (1930). Later, in 1947, Laurent Schwartz gave it a more rigorous mathematical definition as a spatial linear functional on the space of test functions D (the set of all real-valued infinitely differentiable functions with compact support). Since the delta function is not really a function in the classical sense, one should not consider the value of the delta function at x. Hence, the domain of the delta function is D and its value for f ∈ D is f(0). Khuri (2004) studied some interesting applications of the delta function in statistics. The purpose of the present paper is an introduction of a concept of the Dirac delta function in the class of all continuous functions defined in the infinite- dimensional topological vector space of all real valued sequences R∞ equipped arXiv:1605.02723v2 [math.CA] 14 May 2016 with Tychonoff topology and a representation of this functional in terms of infinite-dimensional Lebesgue measures in R∞. -
Sparse Sets Alexander Shen, Laurent Bienvenu, Andrei Romashchenko
Sparse sets Alexander Shen, Laurent Bienvenu, Andrei Romashchenko To cite this version: Alexander Shen, Laurent Bienvenu, Andrei Romashchenko. Sparse sets. JAC 2008, Apr 2008, Uzès, France. pp.18-28. hal-00274010 HAL Id: hal-00274010 https://hal.archives-ouvertes.fr/hal-00274010 Submitted on 17 Apr 2008 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Journees´ Automates Cellulaires 2008 (Uzes),` pp. 18-28 SPARSE SETS LAURENT BIENVENU 1, ANDREI ROMASHCHENKO 2, AND ALEXANDER SHEN 1 1 LIF, Aix-Marseille Universit´e,CNRS, 39 rue Joliot Curie, 13013 Marseille, France E-mail address: [email protected] URL: http://www.lif.univ-mrs.fr/ 2 LIP, ENS Lyon, CNRS, 46 all´eed’Italie, 69007 Lyon, France E-mail address: [email protected] URL: http://www.ens-lyon.fr/ Abstract. For a given p > 0 we consider sequences that are random with respect to p-Bernoulli distribution and sequences that can be obtained from them by replacing ones by zeros. We call these sequences sparse and study their properties. They can be used in the robustness analysis for tilings or computations and in percolation theory. -
1 Probability Spaces
BROWN UNIVERSITY Math 1610 Probability Notes Samuel S. Watson Last updated: December 18, 2015 Please do not hesitate to notify me about any mistakes you find in these notes. My advice is to refresh your local copy of this document frequently, as I will be updating it throughout the semester. 1 Probability Spaces We model random phenomena with a probability space, which consists of an arbitrary set , a collection1 of subsets of , and a map P : [0, 1], where and P satisfy certain F F! F conditions detailed below. An element ! is called an outcome, an element E is 2 2 F called an event, and we interpret P(E) as the probability of the event E. To connect this setup to the way you usually think about probability, regard ! as having been randomly selected from in such a way that, for each event E, the probability that ! is in E (in other words, that E happens) is equal to P(E). If E and F are events, then the event “E and F” corresponds to the ! : ! E and ! F , f 2 2 g abbreviated as E F. Similarly, E F is the event that E happens or F happens, and \ [ E is the event that E does not happen. We refer to E as the complement of E, and n n sometimes denote2 it by Ec. To ensure that we can perform these basic operations, we require that is closed under F them. In other words, E must be an event whenever E is an event (that is, E n n 2 F whenever E ). -
Review of Set Theory and Probability Space
Review of Set Theory and Probability Space i) Set A set is a collection of objects. These objects are called elements of the set. ii) Subset A subset b of a set a is a set whose elements are also elements of a . iii) Space “Space” S is the largest set and all other sets under consideration are subsets of S . iv) Null Set O is an empty or null set. O contains no elements. Set Operations A set b is a subset of a , b ⊂ a , or the set a contains b , a ⊃ b , if all elements of b are also elements of a . That is, If b ⊂ a , and c ⊂ b , then c ⊂ a . The following relationship holds: a c a ⊂ a , 0 ⊂ a , a ⊂ S b i) Equality S a = b iff a ⊂ b and b ⊂ a . Examples of subsets. ii)Union (Sum) The union of two sets a and b is a set consisting of all elements of a or of b or of both. The union operation satisfies the following properties: a ∪ b = b ∪ a , (Commutative), a ∪ b a ∪ a = a , a b a ∪ 0 = a , An example of union of sets a and b. ME529 1 G. Ahmadi a ∪ S = S , ()a ∪ b ∪ c = a ∪ (b ∪ c) = a ∪ b ∪ c , (Associative). iii) Intersection (Product) The intersection of two sets a and b is a set consisting of all elements that are common to the sets a and b . The intersection operation satisfies the following properties: a ∩ b = b ∩ a , (Commutative), a ∩ a = a , a a ∩ b b a ∩ 0 = 0 , An example of intersection of sets a and b. -
Linz 2013 — Non-Classical Measures and Integrals
LINZ 2013 racts Abst 34 Fuzzy Theory Set th Non-Classical Measures Linz Seminar on Seminar Linz Bildungszentrum St. Magdalena, Linz, Austria Linz, Magdalena, St. Bildungszentrum February 26 – March 2, 2013 2, March – 26 February and Integrals Erich Peter Klement Radko Mesiar Abstracts Endre Pap Editors LINZ 2013 — NON-CLASSICAL MEASURES AND INTEGRALS Dedicated to the memory of Lawrence Neff Stout ABSTRACTS Radko Mesiar, Endre Pap, Erich Peter Klement Editors Printed by: Universit¨atsdirektion, Johannes Kepler Universit¨at, A-4040 Linz 2 Since their inception in 1979, the Linz Seminars on Fuzzy Set Theory have emphasized the development of mathematical aspects of fuzzy sets by bringing together researchers in fuzzy sets and established mathematicians whose work outside the fuzzy setting can provide directions for further research. The philos- ophy of the seminar has always been to keep it deliberately small and intimate so that informal critical discussions remain central. LINZ 2013 will be the 34th seminar carrying on this tradition and is devoted to the theme “Non-Classical Measures and Integrals”. The goal of the seminar is to present and to discuss recent advances in non-classical measure theory and corresponding integrals and their various applications in pure and applied mathematics. A large number of highly interesting contributions were submitted for pos- sible presentation at LINZ 2013. In order to maintain the traditional spirit of the Linz Seminars — no parallel sessions and enough room for discussions — we selected those thirty-three submissions which, in our opinion, fitted best to the focus of this seminar. This volume contains the abstracts of this impressive selection. -
Dirac Delta Function of Matrix Argument
Dirac Delta Function of Matrix Argument ∗ Lin Zhang Institute of Mathematics, Hangzhou Dianzi University, Hangzhou 310018, PR China Abstract Dirac delta function of matrix argument is employed frequently in the development of di- verse fields such as Random Matrix Theory, Quantum Information Theory, etc. The purpose of the article is pedagogical, it begins by recalling detailed knowledge about Heaviside unit step function and Dirac delta function. Then its extensions of Dirac delta function to vector spaces and matrix spaces are discussed systematically, respectively. The detailed and elemen- tary proofs of these results are provided. Though we have not seen these results formulated in the literature, there certainly are predecessors. Applications are also mentioned. 1 Heaviside unit step function H and Dirac delta function δ The materials in this section are essential from Hoskins’ Book [4]. There are also no new results in this section. In order to be in a systematic way, it is reproduced here. The Heaviside unit step function H is defined as 1, x > 0, H(x) := (1.1) < 0, x 0. arXiv:1607.02871v2 [quant-ph] 31 Oct 2018 That is, this function is equal to 1 over (0, +∞) and equal to 0 over ( ∞, 0). This function can − equally well have been defined in terms of a specific expression, for instance 1 x H(x)= 1 + . (1.2) 2 x | | The value H(0) is left undefined here. For all x = 0, 6 dH(x) H′(x)= = 0 dx ∗ E-mail: [email protected]; [email protected] 1 corresponding to the obvious fact that the graph of the function y = H(x) has zero slope for all x = 0.