Generating Borel Sets by Balls
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Set Theory, Including the Axiom of Choice) Plus the Negation of CH
ANNALS OF ~,IATltEMATICAL LOGIC - Volume 2, No. 2 (1970) pp. 143--178 INTERNAL COHEN EXTENSIONS D.A.MARTIN and R.M.SOLOVAY ;Tte RockeJ~'ller University and University (.~t CatiJbrnia. Berkeley Received 2 l)ecemt)er 1969 Introduction Cohen [ !, 2] has shown that tile continuum hypothesis (CH) cannot be proved in Zermelo-Fraenkel set theory. Levy and Solovay [9] have subsequently shown that CH cannot be proved even if one assumes the existence of a measurable cardinal. Their argument in tact shows that no large cardinal axiom of the kind present;y being considered by set theorists can yield a proof of CH (or of its negation, of course). Indeed, many set theorists - including the authors - suspect that C1t is false. But if we reject CH we admit Gurselves to be in a state of ignorance about a great many questions which CH resolves. While CH is a power- full assertion, its negation is in many ways quite weak. Sierpinski [ 1 5 ] deduces propcsitions there called C l - C82 from CH. We know of none of these propositions which is decided by the negation of CH and only one of them (C78) which is decided if one assumes in addition that a measurable cardinal exists. Among the many simple questions easily decided by CH and which cannot be decided in ZF (Zerme!o-Fraenkel set theory, including the axiom of choice) plus the negation of CH are tile following: Is every set of real numbers of cardinality less than tha't of the continuum of Lebesgue measure zero'? Is 2 ~0 < 2 ~ 1 ? Is there a non-trivial measure defined on all sets of real numbers? CIhis third question could be decided in ZF + not CH only in the unlikely event t Tile second author received support from a Sloan Foundation fellowship and tile National Science Foundation Grant (GP-8746). -
Algebra I Chapter 1. Basic Facts from Set Theory 1.1 Glossary of Abbreviations
Notes: c F.P. Greenleaf, 2000-2014 v43-s14sets.tex (version 1/1/2014) Algebra I Chapter 1. Basic Facts from Set Theory 1.1 Glossary of abbreviations. Below we list some standard math symbols that will be used as shorthand abbreviations throughout this course. means “for all; for every” • ∀ means “there exists (at least one)” • ∃ ! means “there exists exactly one” • ∃ s.t. means “such that” • = means “implies” • ⇒ means “if and only if” • ⇐⇒ x A means “the point x belongs to a set A;” x / A means “x is not in A” • ∈ ∈ N denotes the set of natural numbers (counting numbers) 1, 2, 3, • · · · Z denotes the set of all integers (positive, negative or zero) • Q denotes the set of rational numbers • R denotes the set of real numbers • C denotes the set of complex numbers • x A : P (x) If A is a set, this denotes the subset of elements x in A such that •statement { ∈ P (x)} is true. As examples of the last notation for specifying subsets: x R : x2 +1 2 = ( , 1] [1, ) { ∈ ≥ } −∞ − ∪ ∞ x R : x2 +1=0 = { ∈ } ∅ z C : z2 +1=0 = +i, i where i = √ 1 { ∈ } { − } − 1.2 Basic facts from set theory. Next we review the basic definitions and notations of set theory, which will be used throughout our discussions of algebra. denotes the empty set, the set with nothing in it • ∅ x A means that the point x belongs to a set A, or that x is an element of A. • ∈ A B means A is a subset of B – i.e. -
Lecture Notes
MEASURE THEORY AND STOCHASTIC PROCESSES Lecture Notes José Melo, Susana Cruz Faculdade de Engenharia da Universidade do Porto Programa Doutoral em Engenharia Electrotécnica e de Computadores March 2011 Contents 1 Probability Space3 1 Sample space Ω .....................................4 2 σ-eld F .........................................4 3 Probability Measure P .................................6 3.1 Measure µ ....................................6 3.2 Probability Measure P .............................7 4 Learning Objectives..................................7 5 Appendix........................................8 2 Chapter 1 Probability Space Let's consider the experience of throwing a dart on a circular target with radius r (assuming the dart always hits the target), divided in 4 dierent areas as illustrated in Figure 1.1. 4 3 2 1 Figure 1.1: Circular Target The circles that bound the regions 1, 2, 3, and 4, have radius of, respectively, r , r , 3r , and . 4 2 4 r Therefore, the probability that a dart lands in each region is: 1 , 3 , 5 , P (1) = 16 P (2) = 16 P (3) = 16 7 . P (4) = 16 For this kind of problems, the theory of discrete probability spaces suces. However, when it comes to problems involving either an innitely repeated operation or an innitely ne op- eration, this mathematical framework does not apply. This motivates the introduction of a measure-theoretic probability approach to correctly describe those cases. We dene the proba- bility space (Ω; F;P ), where Ω is the sample space, F is the event space, and P is the probability 3 measure. Each of them will be described in the following subsections. 1 Sample space Ω The sample space Ω is the set of all the possible results or outcomes ! of an experiment or observation. -
Borel Structure in Groups and Their Dualso
BOREL STRUCTURE IN GROUPS AND THEIR DUALSO GEORGE W. MACKEY Introduction. In the past decade or so much work has been done toward extending the classical theory of finite dimensional representations of com- pact groups to a theory of (not necessarily finite dimensional) unitary repre- sentations of locally compact groups. Among the obstacles interfering with various aspects of this program is the lack of a suitable natural topology in the "dual object"; that is in the set of equivalence classes of irreducible representations. One can introduce natural topologies but none of them seem to have reasonable properties except in extremely special cases. When the group is abelian for example the dual object itself is a locally compact abelian group. This paper is based on the observation that for certain purposes one can dispense with a topology in the dual object in favor of a "weaker struc- ture" and that there is a wide class of groups for which this weaker structure has very regular properties. If .S is any topological space one defines a Borel (or Baire) subset of 5 to be a member of the smallest family of subsets of 5 which includes the open sets and is closed with respect to the formation of complements and countable unions. The structure defined in 5 by its Borel sets we may call the Borel structure of 5. It is weaker than the topological structure in the sense that any one-to-one transformation of S onto 5 which preserves the topological structure also preserves the Borel structure whereas the converse is usually false. -
Ergodicity and Metric Transitivity
Chapter 25 Ergodicity and Metric Transitivity Section 25.1 explains the ideas of ergodicity (roughly, there is only one invariant set of positive measure) and metric transivity (roughly, the system has a positive probability of going from any- where to anywhere), and why they are (almost) the same. Section 25.2 gives some examples of ergodic systems. Section 25.3 deduces some consequences of ergodicity, most im- portantly that time averages have deterministic limits ( 25.3.1), and an asymptotic approach to independence between even§ts at widely separated times ( 25.3.2), admittedly in a very weak sense. § 25.1 Metric Transitivity Definition 341 (Ergodic Systems, Processes, Measures and Transfor- mations) A dynamical system Ξ, , µ, T is ergodic, or an ergodic system or an ergodic process when µ(C) = 0 orXµ(C) = 1 for every T -invariant set C. µ is called a T -ergodic measure, and T is called a µ-ergodic transformation, or just an ergodic measure and ergodic transformation, respectively. Remark: Most authorities require a µ-ergodic transformation to also be measure-preserving for µ. But (Corollary 54) measure-preserving transforma- tions are necessarily stationary, and we want to minimize our stationarity as- sumptions. So what most books call “ergodic”, we have to qualify as “stationary and ergodic”. (Conversely, when other people talk about processes being “sta- tionary and ergodic”, they mean “stationary with only one ergodic component”; but of that, more later. Definition 342 (Metric Transitivity) A dynamical system is metrically tran- sitive, metrically indecomposable, or irreducible when, for any two sets A, B n ∈ , if µ(A), µ(B) > 0, there exists an n such that µ(T − A B) > 0. -
POINT-COFINITE COVERS in the LAVER MODEL 1. Background Let
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 138, Number 9, September 2010, Pages 3313–3321 S 0002-9939(10)10407-9 Article electronically published on April 30, 2010 POINT-COFINITE COVERS IN THE LAVER MODEL ARNOLD W. MILLER AND BOAZ TSABAN (Communicated by Julia Knight) Abstract. Let S1(Γ, Γ) be the statement: For each sequence of point-cofinite open covers, one can pick one element from each cover and obtain a point- cofinite cover. b is the minimal cardinality of a set of reals not satisfying S1(Γ, Γ). We prove the following assertions: (1) If there is an unbounded tower, then there are sets of reals of cardinality b satisfying S1(Γ, Γ). (2) It is consistent that all sets of reals satisfying S1(Γ, Γ) have cardinality smaller than b. These results can also be formulated as dealing with Arhangel’ski˘ı’s property α2 for spaces of continuous real-valued functions. The main technical result is that in Laver’s model, each set of reals of cardinality b has an unbounded Borel image in the Baire space ωω. 1. Background Let P be a nontrivial property of sets of reals. The critical cardinality of P , denoted non(P ), is the minimal cardinality of a set of reals not satisfying P .A natural question is whether there is a set of reals of cardinality at least non(P ), which satisfies P , i.e., a nontrivial example. We consider the following property. Let X be a set of reals. U is a point-cofinite cover of X if U is infinite, and for each x ∈ X, {U ∈U: x ∈ U} is a cofinite subset of U.1 Having X fixed in the background, let Γ be the family of all point- cofinite open covers of X. -
Dynkin (Λ-) and Π-Systems; Monotone Classes of Sets, and of Functions – with Some Examples of Application (Mainly of a Probabilistic flavor)
Dynkin (λ-) and π-systems; monotone classes of sets, and of functions { with some examples of application (mainly of a probabilistic flavor) Matija Vidmar February 7, 2018 1 Dynkin and π-systems Some basic notation: Throughout, for measurable spaces (A; A) and (B; B), (i) A=B will denote the class of A=B-measurable maps, and (ii) when A = B, A_B := σA(A[B) will be the smallest σ-field on A containing both A and B (this notation has obvious extensions to arbitrary families of σ-fields on a given space). Furthermore, for a measure µ on F, µf := µ(f) := R fdµ will signify + − the integral of an f 2 F=B[−∞;1] against µ (assuming µf ^ µf < 1). Finally, for a probability space (Ω; F; P) and a sub-σ-field G of F, PGf := PG(f) := EP[fjG] will denote the conditional + − expectation of an f 2 F=B[−∞;1] under P w.r.t. G (assuming Pf ^ Pf < 1; in particular, for F 2 F, PG(F ) := P(F jG) = EP[1F jG] will be the conditional probability of F under P given G). We consider first Dynkin and π-systems. Definition 1. Let Ω be a set, D ⊂ 2Ω a collection of its subsets. Then D is called a Dynkin system, or a λ-system, on Ω, if (i) Ω 2 D, (ii) fA; Bg ⊂ D and A ⊂ B, implies BnA 2 D, and (iii) whenever (Ai)i2N is a sequence in D, and Ai ⊂ Ai+1 for all i 2 N, then [i2NAi 2 D. -
A TEXTBOOK of TOPOLOGY Lltld
SEIFERT AND THRELFALL: A TEXTBOOK OF TOPOLOGY lltld SEI FER T: 7'0PO 1.OG 1' 0 I.' 3- Dl M E N SI 0 N A I. FIRERED SPACES This is a volume in PURE AND APPLIED MATHEMATICS A Series of Monographs and Textbooks Editors: SAMUELEILENBERG AND HYMANBASS A list of recent titles in this series appears at the end of this volunie. SEIFERT AND THRELFALL: A TEXTBOOK OF TOPOLOGY H. SEIFERT and W. THRELFALL Translated by Michael A. Goldman und S E I FE R T: TOPOLOGY OF 3-DIMENSIONAL FIBERED SPACES H. SEIFERT Translated by Wolfgang Heil Edited by Joan S. Birman and Julian Eisner @ 1980 ACADEMIC PRESS A Subsidiary of Harcourr Brace Jovanovich, Publishers NEW YORK LONDON TORONTO SYDNEY SAN FRANCISCO COPYRIGHT@ 1980, BY ACADEMICPRESS, INC. ALL RIGHTS RESERVED. NO PART OF THIS PUBLICATION MAY BE REPRODUCED OR TRANSMITTED IN ANY FORM OR BY ANY MEANS, ELECTRONIC OR MECHANICAL, INCLUDING PHOTOCOPY, RECORDING, OR ANY INFORMATION STORAGE AND RETRIEVAL SYSTEM, WITHOUT PERMISSION IN WRITING FROM THE PUBLISHER. ACADEMIC PRESS, INC. 11 1 Fifth Avenue, New York. New York 10003 United Kingdom Edition published by ACADEMIC PRESS, INC. (LONDON) LTD. 24/28 Oval Road, London NWI 7DX Mit Genehmigung des Verlager B. G. Teubner, Stuttgart, veranstaltete, akin autorisierte englische Ubersetzung, der deutschen Originalausgdbe. Library of Congress Cataloging in Publication Data Seifert, Herbert, 1897- Seifert and Threlfall: A textbook of topology. Seifert: Topology of 3-dimensional fibered spaces. (Pure and applied mathematics, a series of mono- graphs and textbooks ; ) Translation of Lehrbuch der Topologic. Bibliography: p. Includes index. 1. -
The Hardness of the Hidden Subset Sum Problem and Its Cryptographic Implications
The Hardness of the Hidden Subset Sum Problem and Its Cryptographic Implications Phong Nguyen and Jacques Stern Ecole´ Normale Sup´erieure Laboratoire d’Informatique 45 rue d’Ulm, 75230 Paris Cedex 05 France fPhong.Nguyen,[email protected] http://www.dmi.ens.fr/~fpnguyen,sterng/ Abstract. At Eurocrypt’98, Boyko, Peinado and Venkatesan presented simple and very fast methods for generating randomly distributed pairs of the form (x; gx mod p) using precomputation. The security of these methods relied on the potential hardness of a new problem, the so-called hidden subset sum problem. Surprisingly, apart from exhaustive search, no algorithm to solve this problem was known. In this paper, we exhibit a security criterion for the hidden subset sum problem, and discuss its implications on the practicability of the precomputation schemes. Our results are twofold. On the one hand, we present an efficient lattice-based attack which is expected to succeed if and only if the parameters satisfy a particular condition that we make explicit. Experiments have validated the theoretical analysis, and show the limitations of the precomputa- tion methods. For instance, any realistic smart-card implementation of Schnorr’s identification scheme using these precomputations methods is either vulnerable to the attack, or less efficient than with traditional pre- computation methods. On the other hand, we show that, when another condition is satisfied, the pseudo-random generator based on the hidden subset sum problem is strong in some precise sense which includes at- tacks via lattice reduction. Namely, using the discrete Fourier transform, we prove that the distribution of the generator’s output is indistinguish- able from the uniform distribution. -
Problem Set 1 This Problem Set Is Due on Friday, September 25
MA 2210 Fall 2015 - Problem set 1 This problem set is due on Friday, September 25. All parts (#) count 10 points. Solve the problems in order and please turn in for full marks (140 points) • Problems 1, 2, 6, 8, 9 in full • Problem 3, either #1 or #2 (not both) • Either Problem 4 or Problem 5 (not both) • Problem 7, either #1 or #2 (not both) 1. Let D be the dyadic grid on R and m denote the Lebesgue outer measure on R, namely for A ⊂ R ( ¥ ¥ ) [ m(A) = inf ∑ `(Ij) : A ⊂ Ij; Ij 2 D 8 j : j=1 j=1 −n #1 Let n 2 Z. Prove that m does not change if we restrict to intervals with `(Ij) ≤ 2 , namely ( ¥ ¥ ) (n) (n) [ −n m(A) = m (A); m (A) := inf ∑ `(Ij) : A ⊂ Ij; Ij 2 D 8 j;`(Ij) ≤ 2 : j=1 j=1 N −n #2 Let t 2 R be of the form t = ∑n=−N kn2 for suitable integers N;k−N;:::;kN. Prove that m is invariant under translations by t, namely m(A) = m(A +t) 8A ⊂ R: −n −m Hints. For #2, reduce to the case t = 2 for some n. Then use #1 and that Dm = fI 2 D : `(I) = 2 g is invariant under translation by 2−n whenever m ≥ n. d d 2. Let O be the collection of all open cubes I ⊂ R and define the outer measure on R given by ( ¥ ¥ ) [ n(A) = inf ∑ jRnj : A ⊂ R j; R j 2 O 8 j n=0 n=0 where jRj is the Euclidean volume of the cube R. -
STAT 571 Assignment 1 Solutions 1. If Ω Is a Set and C a Collection Of
STAT 571 Assignment 1 solutions 1. If Ω is a set and a collection of subsets of Ω, let be the intersection of all σ-algebras that contain . ProveC that is the σ-algebra generatedA by . C A C Solution: Let α α A be the collection of all σ-algebras that contain , and fA j 2 g C set = α. We first show that is a σ-algebra. There are three things to A \ A A α A prove. 2 (a) For every α A, α is a σ-algebra, so Ω α, and hence Ω α A α = . 2 A 2 A 2 \ 2 A A (b) If B , then B α for every α A. Since α is a σ-algebra, we have c 2 A 2 A 2 A c B α. But this is true for every α A, so we have B . 2 A 2 2 A (c) If B1;B2;::: are sets in , then B1;B2;::: belong to α for each α A. A A 2 Since α is a σ-algebra, we have 1 Bn α. But this is true for every A [n=1 2 A α A, so we have 1 Bn . 2 [n=1 2 A Thus is a σ-algebra that contains , and it must be the smallest one since α for everyA α A. C A ⊆ A 2 2. Prove that the set of rational numbers Q is a Borel set in R. Solution: For every x R, the set x is the complement of an open set, and hence Borel. -
(Measure Theory for Dummies) UWEE Technical Report Number UWEETR-2006-0008
A Measure Theory Tutorial (Measure Theory for Dummies) Maya R. Gupta {gupta}@ee.washington.edu Dept of EE, University of Washington Seattle WA, 98195-2500 UWEE Technical Report Number UWEETR-2006-0008 May 2006 Department of Electrical Engineering University of Washington Box 352500 Seattle, Washington 98195-2500 PHN: (206) 543-2150 FAX: (206) 543-3842 URL: http://www.ee.washington.edu A Measure Theory Tutorial (Measure Theory for Dummies) Maya R. Gupta {gupta}@ee.washington.edu Dept of EE, University of Washington Seattle WA, 98195-2500 University of Washington, Dept. of EE, UWEETR-2006-0008 May 2006 Abstract This tutorial is an informal introduction to measure theory for people who are interested in reading papers that use measure theory. The tutorial assumes one has had at least a year of college-level calculus, some graduate level exposure to random processes, and familiarity with terms like “closed” and “open.” The focus is on the terms and ideas relevant to applied probability and information theory. There are no proofs and no exercises. Measure theory is a bit like grammar, many people communicate clearly without worrying about all the details, but the details do exist and for good reasons. There are a number of great texts that do measure theory justice. This is not one of them. Rather this is a hack way to get the basic ideas down so you can read through research papers and follow what’s going on. Hopefully, you’ll get curious and excited enough about the details to check out some of the references for a deeper understanding.