Set Theory Some Basics and a Glimpse of Some Advanced Techniques
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Calibrating Determinacy Strength in Levels of the Borel Hierarchy
CALIBRATING DETERMINACY STRENGTH IN LEVELS OF THE BOREL HIERARCHY SHERWOOD J. HACHTMAN Abstract. We analyze the set-theoretic strength of determinacy for levels of the Borel 0 hierarchy of the form Σ1+α+3, for α < !1. Well-known results of H. Friedman and D.A. Martin have shown this determinacy to require α+1 iterations of the Power Set Axiom, but we ask what additional ambient set theory is strictly necessary. To this end, we isolate a family of Π1-reflection principles, Π1-RAPα, whose consistency strength corresponds 0 CK exactly to that of Σ1+α+3-Determinacy, for α < !1 . This yields a characterization of the levels of L by or at which winning strategies in these games must be constructed. When α = 0, we have the following concise result: the least θ so that all winning strategies 0 in Σ4 games belong to Lθ+1 is the least so that Lθ j= \P(!) exists + all wellfounded trees are ranked". x1. Introduction. Given a set A ⊆ !! of sequences of natural numbers, consider a game, G(A), where two players, I and II, take turns picking elements of a sequence hx0; x1; x2;::: i of naturals. Player I wins the game if the sequence obtained belongs to A; otherwise, II wins. For a collection Γ of subsets of !!, Γ determinacy, which we abbreviate Γ-DET, is the statement that for every A 2 Γ, one of the players has a winning strategy in G(A). It is a much-studied phenomenon that Γ -DET has mathematical strength: the bigger the pointclass Γ, the stronger the theory required to prove Γ -DET. -
1 Elementary Set Theory
1 Elementary Set Theory Notation: fg enclose a set. f1; 2; 3g = f3; 2; 2; 1; 3g because a set is not defined by order or multiplicity. f0; 2; 4;:::g = fxjx is an even natural numberg because two ways of writing a set are equivalent. ; is the empty set. x 2 A denotes x is an element of A. N = f0; 1; 2;:::g are the natural numbers. Z = f:::; −2; −1; 0; 1; 2;:::g are the integers. m Q = f n jm; n 2 Z and n 6= 0g are the rational numbers. R are the real numbers. Axiom 1.1. Axiom of Extensionality Let A; B be sets. If (8x)x 2 A iff x 2 B then A = B. Definition 1.1 (Subset). Let A; B be sets. Then A is a subset of B, written A ⊆ B iff (8x) if x 2 A then x 2 B. Theorem 1.1. If A ⊆ B and B ⊆ A then A = B. Proof. Let x be arbitrary. Because A ⊆ B if x 2 A then x 2 B Because B ⊆ A if x 2 B then x 2 A Hence, x 2 A iff x 2 B, thus A = B. Definition 1.2 (Union). Let A; B be sets. The Union A [ B of A and B is defined by x 2 A [ B if x 2 A or x 2 B. Theorem 1.2. A [ (B [ C) = (A [ B) [ C Proof. Let x be arbitrary. x 2 A [ (B [ C) iff x 2 A or x 2 B [ C iff x 2 A or (x 2 B or x 2 C) iff x 2 A or x 2 B or x 2 C iff (x 2 A or x 2 B) or x 2 C iff x 2 A [ B or x 2 C iff x 2 (A [ B) [ C Definition 1.3 (Intersection). -
1.5. Set Functions and Properties. Let Л Be Any Set of Subsets of E
1.5. Set functions and properties. Let A be any set of subsets of E containing the empty set ∅. A set function is a function µ : A → [0, ∞] with µ(∅)=0. Let µ be a set function. Say that µ is increasing if, for all A, B ∈ A with A ⊆ B, µ(A) ≤ µ(B). Say that µ is additive if, for all disjoint sets A, B ∈ A with A ∪ B ∈ A, µ(A ∪ B)= µ(A)+ µ(B). Say that µ is countably additive if, for all sequences of disjoint sets (An : n ∈ N) in A with n An ∈ A, S µ An = µ(An). n ! n [ X Say that µ is countably subadditive if, for all sequences (An : n ∈ N) in A with n An ∈ A, S µ An ≤ µ(An). n ! n [ X 1.6. Construction of measures. Let A be a set of subsets of E. Say that A is a ring on E if ∅ ∈ A and, for all A, B ∈ A, B \ A ∈ A, A ∪ B ∈ A. Say that A is an algebra on E if ∅ ∈ A and, for all A, B ∈ A, Ac ∈ A, A ∪ B ∈ A. Theorem 1.6.1 (Carath´eodory’s extension theorem). Let A be a ring of subsets of E and let µ : A → [0, ∞] be a countably additive set function. Then µ extends to a measure on the σ-algebra generated by A. Proof. For any B ⊆ E, define the outer measure ∗ µ (B) = inf µ(An) n X where the infimum is taken over all sequences (An : n ∈ N) in A such that B ⊆ n An and is taken to be ∞ if there is no such sequence. -
Certain Ideals Related to the Strong Measure Zero Ideal
数理解析研究所講究録 第 1595 巻 2008 年 37-46 37 Certain ideals related to the strong measure zero ideal 大阪府立大学理学系研究科 大須賀昇 (Noboru Osuga) Graduate school of Science, Osaka Prefecture University 1 Motivation and Basic Definition In 1919, Borel[l] introduced the new class of Lebesgue measure zero sets called strong measure zero sets today. The family of all strong measure zero sets become $\sigma$-ideal and is called the strong measure zero ideal. The four cardinal invariants (the additivity, covering number, uniformity and cofinal- ity) related to the strong measure zero ideal have been studied. In 2002, Yorioka[2] obtained the results about the cofinality of the strong measure zero ideal. In the process, he introduced the ideal $\mathcal{I}_{f}$ for each strictly increas- ing function $f$ on $\omega$ . The ideal $\mathcal{I}_{f}$ relates to the structure of the real line. We are interested in how the cardinal invariants of the ideal $\mathcal{I}_{f}$ behave. $Ma\dot{i}$ly, we te interested in the cardinal invariants of the ideals $\mathcal{I}_{f}$ . In this paper, we deal the consistency problems about the relationship between the cardi- nal invariants of the ideals $\mathcal{I}_{f}$ and the minimam and supremum of cardinal invariants of the ideals $\mathcal{I}_{g}$ for all $g$ . We explain some notation which we use in this paper. Our notation is quite standard. And we refer the reader to [3] and [4] for undefined notation. For sets X and $Y$, we denote by $xY$ the set of all functions $homX$ to Y. -
Sets, Functions
Sets 1 Sets Informally: A set is a collection of (mathematical) objects, with the collection treated as a single mathematical object. Examples: • real numbers, • complex numbers, C • integers, • All students in our class Defining Sets Sets can be defined directly: e.g. {1,2,4,8,16,32,…}, {CSC1130,CSC2110,…} Order, number of occurence are not important. e.g. {A,B,C} = {C,B,A} = {A,A,B,C,B} A set can be an element of another set. {1,{2},{3,{4}}} Defining Sets by Predicates The set of elements, x, in A such that P(x) is true. {}x APx| ( ) The set of prime numbers: Commonly Used Sets • N = {0, 1, 2, 3, …}, the set of natural numbers • Z = {…, -2, -1, 0, 1, 2, …}, the set of integers • Z+ = {1, 2, 3, …}, the set of positive integers • Q = {p/q | p Z, q Z, and q ≠ 0}, the set of rational numbers • R, the set of real numbers Special Sets • Empty Set (null set): a set that has no elements, denoted by ф or {}. • Example: The set of all positive integers that are greater than their squares is an empty set. • Singleton set: a set with one element • Compare: ф and {ф} – Ф: an empty set. Think of this as an empty folder – {ф}: a set with one element. The element is an empty set. Think of this as an folder with an empty folder in it. Venn Diagrams • Represent sets graphically • The universal set U, which contains all the objects under consideration, is represented by a rectangle. -
A Bayesian Level Set Method for Geometric Inverse Problems
Interfaces and Free Boundaries 18 (2016), 181–217 DOI 10.4171/IFB/362 A Bayesian level set method for geometric inverse problems MARCO A. IGLESIAS School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK E-mail: [email protected] YULONG LU Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK E-mail: [email protected] ANDREW M. STUART Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK E-mail: [email protected] [Received 3 April 2015 and in revised form 10 February 2016] We introduce a level set based approach to Bayesian geometric inverse problems. In these problems the interface between different domains is the key unknown, and is realized as the level set of a function. This function itself becomes the object of the inference. Whilst the level set methodology has been widely used for the solution of geometric inverse problems, the Bayesian formulation that we develop here contains two significant advances: firstly it leads to a well-posed inverse problem in which the posterior distribution is Lipschitz with respect to the observed data, and may be used to not only estimate interface locations, but quantify uncertainty in them; and secondly it leads to computationally expedient algorithms in which the level set itself is updated implicitly via the MCMC methodology applied to the level set function – no explicit velocity field is required for the level set interface. Applications are numerous and include medical imaging, modelling of subsurface formations and the inverse source problem; our theory is illustrated with computational results involving the last two applications. -
COVERING PROPERTIES of IDEALS 1. Introduction We Will Discuss The
COVERING PROPERTIES OF IDEALS MAREK BALCERZAK, BARNABAS´ FARKAS, AND SZYMON GLA¸B Abstract. M. Elekes proved that any infinite-fold cover of a σ-finite measure space by a sequence of measurable sets has a subsequence with the same property such that the set of indices of this subsequence has density zero. Applying this theorem he gave a new proof for the random-indestructibility of the density zero ideal. He asked about other variants of this theorem concerning I-almost everywhere infinite-fold covers of Polish spaces where I is a σ-ideal on the space and the set of indices of the required subsequence should be in a fixed ideal J on !. We introduce the notion of the J-covering property of a pair (A;I) where A is a σ- algebra on a set X and I ⊆ P(X) is an ideal. We present some counterexamples, discuss the category case and the Fubini product of the null ideal N and the meager ideal M. We investigate connections between this property and forcing-indestructibility of ideals. We show that the family of all Borel ideals J on ! such that M has the J- covering property consists exactly of non weak Q-ideals. We also study the existence of smallest elements, with respect to Katˇetov-Blass order, in the family of those ideals J on ! such that N or M has the J-covering property. Furthermore, we prove a general result about the cases when the covering property \strongly" fails. 1. Introduction We will discuss the following result due to Elekes [8]. -
Generalized Stochastic Processes with Independent Values
GENERALIZED STOCHASTIC PROCESSES WITH INDEPENDENT VALUES K. URBANIK UNIVERSITY OF WROCFAW AND MATHEMATICAL INSTITUTE POLISH ACADEMY OF SCIENCES Let O denote the space of all infinitely differentiable real-valued functions defined on the real line and vanishing outside a compact set. The support of a function so E X, that is, the closure of the set {x: s(x) F 0} will be denoted by s(,o). We shall consider 3D as a topological space with the topology introduced by Schwartz (section 1, chapter 3 in [9]). Any continuous linear functional on this space is a Schwartz distribution, a generalized function. Let us consider the space O of all real-valued random variables defined on the same sample space. The distance between two random variables X and Y will be the Frechet distance p(X, Y) = E[X - Yl/(l + IX - YI), that is, the distance induced by the convergence in probability. Any C-valued continuous linear functional defined on a is called a generalized stochastic process. Every ordinary stochastic process X(t) for which almost all realizations are locally integrable may be considered as a generalized stochastic process. In fact, we make the generalized process T((p) = f X(t),p(t) dt, with sp E 1, correspond to the process X(t). Further, every generalized stochastic process is differentiable. The derivative is given by the formula T'(w) = - T(p'). The theory of generalized stochastic processes was developed by Gelfand [4] and It6 [6]. The method of representation of generalized stochastic processes by means of sequences of ordinary processes was considered in [10]. -
The Universal Finite Set 3
THE UNIVERSAL FINITE SET JOEL DAVID HAMKINS AND W. HUGH WOODIN Abstract. We define a certain finite set in set theory { x | ϕ(x) } and prove that it exhibits a universal extension property: it can be any desired particular finite set in the right set-theoretic universe and it can become successively any desired larger finite set in top-extensions of that universe. Specifically, ZFC proves the set is finite; the definition ϕ has complexity Σ2, so that any affirmative instance of it ϕ(x) is verified in any sufficiently large rank-initial segment of the universe Vθ ; the set is empty in any transitive model and others; and if ϕ defines the set y in some countable model M of ZFC and y ⊆ z for some finite set z in M, then there is a top-extension of M to a model N in which ϕ defines the new set z. Thus, the set shows that no model of set theory can realize a maximal Σ2 theory with its natural number parameters, although this is possible without parameters. Using the universal finite set, we prove that the validities of top-extensional set-theoretic potentialism, the modal principles valid in the Kripke model of all countable models of set theory, each accessing its top-extensions, are precisely the assertions of S4. Furthermore, if ZFC is consistent, then there are models of ZFC realizing the top-extensional maximality principle. 1. Introduction The second author [Woo11] established the universal algorithm phenomenon, showing that there is a Turing machine program with a certain universal top- extension property in models of arithmetic. -
Product Specifications and Ordering Information CMS 2019 R2 Condition Monitoring Software
Product specifications and ordering information CMS 2019 R2 Condition Monitoring Software Overview SETPOINT® CMS Condition Monitoring Software provides a powerful, flexible, and comprehensive solution for collection, storage, and visualization of vibration and condition data from VIBROCONTROL 8000 (VC-8000) Machinery Protection System (MPS) racks, allowing trending, diagnostics, and predictive maintenance of monitored machinery. The software can be implemented in three different configurations: 1. CMSPI (PI System) This implementation streams data continuously from all VC-8000 racks into a connected PI Server infrastructure. It consumes on average 12-15 PI tags per connected vibration transducer (refer to 3. manual S1176125). Customers can use their CMSHD/SD (Hard Drive) This implementation is used existing PI ecosystem, or for those without PI, a when a network is not available stand-alone PI server can be deployed as a self- between VC-8000 racks and a contained condition monitoring solution. server, and the embedded flight It provides the most functionality (see Tables 1 recorder in each VC-8000 rack and 2) of the three possible configurations by will be used instead. The flight recorder is a offering full integration with process data, solid-state hard drive local to each VC-8000 rack integrated aero/thermal performance monitoring, that can store anywhere from 1 – 12 months of integrated decision support functionality, nested data*. The data is identical to that which would machine train diagrams and nearly unlimited be streamed to an external server, but remains in visualization capabilities via PI Vision or PI the VC-8000 rack until manually retrieved via ProcessBook, and all of the power of the OSIsoft removable SDHC card media, or by connecting a PI System and its ecosystem of complementary laptop to the rack’s Ethernet CMS port and technologies. -
Old Notes from Warwick, Part 1
Measure Theory, MA 359 Handout 1 Valeriy Slastikov Autumn, 2005 1 Measure theory 1.1 General construction of Lebesgue measure In this section we will do the general construction of σ-additive complete measure by extending initial σ-additive measure on a semi-ring to a measure on σ-algebra generated by this semi-ring and then completing this measure by adding to the σ-algebra all the null sets. This section provides you with the essentials of the construction and make some parallels with the construction on the plane. Throughout these section we will deal with some collection of sets whose elements are subsets of some fixed abstract set X. It is not necessary to assume any topology on X but for simplicity you may imagine X = Rn. We start with some important definitions: Definition 1.1 A nonempty collection of sets S is a semi-ring if 1. Empty set ? 2 S; 2. If A 2 S; B 2 S then A \ B 2 S; n 3. If A 2 S; A ⊃ A1 2 S then A = [k=1Ak, where Ak 2 S for all 1 ≤ k ≤ n and Ak are disjoint sets. If the set X 2 S then S is called semi-algebra, the set X is called a unit of the collection of sets S. Example 1.1 The collection S of intervals [a; b) for all a; b 2 R form a semi-ring since 1. empty set ? = [a; a) 2 S; 2. if A 2 S and B 2 S then A = [a; b) and B = [c; d). -
Chapter I Set Theory
Chapter I Set Theory There is surely a piece of divinity in us, something that was before the elements, and owes no homage unto the sun. Sir Thomas Browne One of the benefits of mathematics comes from its ability to express a lot of information in very few symbols. Take a moment to consider the expression d sin( θ). dθ It encapsulates a large amount of information. The notation sin( θ) represents, for a right triangle with angle θ, the ratio of the opposite side to the hypotenuse. The differential operator d/dθ represents a limit, corresponding to a tangent line, and so forth. Similarly, sets are a convenient way to express a large amount of information. They give us a language we will find convenient in which to do mathematics. This is no accident, as much of modern mathematics can be expressed in terms of sets. 1 2 CHAPTER I. SET THEORY 1 Sets, subsets, and set operations 1.A What is a set? A set is simply a collection of objects. The objects in the set are called the elements . We often write down a set by listing its elements. For instance, the set S = 1, 2, 3 has three elements. Those elements are 1, 2, and 3. There is a special symbol,{ }, that we use to express the idea that an element belongs to a set. For instance, we∈ write 1 S to mean that “1 is an element of S.” For∈ the set S = 1, 2, 3 , we have 1 S, 2 S, and 3 S.