Nonstandard Approach to Hausdorff Measure Theory and an Analysis Of
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Measure-Theoretic Probability I
Measure-Theoretic Probability I Steven P.Lalley Winter 2017 1 1 Measure Theory 1.1 Why Measure Theory? There are two different views – not necessarily exclusive – on what “probability” means: the subjectivist view and the frequentist view. To the subjectivist, probability is a system of laws that should govern a rational person’s behavior in situations where a bet must be placed (not necessarily just in a casino, but in situations where a decision must be made about how to proceed when only imperfect information about the outcome of the decision is available, for instance, should I allow Dr. Scissorhands to replace my arthritic knee by a plastic joint?). To the frequentist, the laws of probability describe the long- run relative frequencies of different events in “experiments” that can be repeated under roughly identical conditions, for instance, rolling a pair of dice. For the frequentist inter- pretation, it is imperative that probability spaces be large enough to allow a description of an experiment, like dice-rolling, that is repeated infinitely many times, and that the mathematical laws should permit easy handling of limits, so that one can make sense of things like “the probability that the long-run fraction of dice rolls where the two dice sum to 7 is 1/6”. But even for the subjectivist, the laws of probability should allow for description of situations where there might be a continuum of possible outcomes, or pos- sible actions to be taken. Once one is reconciled to the need for such flexibility, it soon becomes apparent that measure theory (the theory of countably additive, as opposed to merely finitely additive measures) is the only way to go. -
Interaction of the Past of Parallel Universes
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by CERN Document Server Interaction of the Past of parallel universes Alexander K. Guts Department of Mathematics, Omsk State University 644077 Omsk-77 RUSSIA E-mail: [email protected] October 26, 1999 ABSTRACT We constructed a model of five-dimensional Lorentz manifold with foliation of codimension 1 the leaves of which are four-dimensional space-times. The Past of these space-times can interact in macroscopic scale by means of large quantum fluctuations. Hence, it is possible that our Human History consists of ”somebody else’s” (alien) events. In this article the possibility of interaction of the Past (or Future) in macroscopic scales of space and time of two different universes is analysed. Each universe is considered as four-dimensional space-time V 4, moreover they are imbedded in five- dimensional Lorentz manifold V 5, which shall below name Hyperspace. The space- time V 4 is absolute world of events. Consequently, from formal standpoints any point-event of this manifold V 4, regardless of that we refer it to Past, Present or Future of some observer, is equally available to operate with her.Inotherwords, modern theory of space-time, rising to Minkowsky, postulates absolute eternity of the World of events in the sense that all events exist always. Hence, it is possible interaction of Present with Past and Future as well as Past can interact with Future. Question is only in that as this is realized. The numerous articles about time machine show that our statement on the interaction of Present with Past is not fantasy, but is subject of the scientific study. -
Geometric Integration Theory Contents
Steven G. Krantz Harold R. Parks Geometric Integration Theory Contents Preface v 1 Basics 1 1.1 Smooth Functions . 1 1.2Measures.............................. 6 1.2.1 Lebesgue Measure . 11 1.3Integration............................. 14 1.3.1 Measurable Functions . 14 1.3.2 The Integral . 17 1.3.3 Lebesgue Spaces . 23 1.3.4 Product Measures and the Fubini–Tonelli Theorem . 25 1.4 The Exterior Algebra . 27 1.5 The Hausdorff Distance and Steiner Symmetrization . 30 1.6 Borel and Suslin Sets . 41 2 Carath´eodory’s Construction and Lower-Dimensional Mea- sures 53 2.1 The Basic Definition . 53 2.1.1 Hausdorff Measure and Spherical Measure . 55 2.1.2 A Measure Based on Parallelepipeds . 57 2.1.3 Projections and Convexity . 57 2.1.4 Other Geometric Measures . 59 2.1.5 Summary . 61 2.2 The Densities of a Measure . 64 2.3 A One-Dimensional Example . 66 2.4 Carath´eodory’s Construction and Mappings . 67 2.5 The Concept of Hausdorff Dimension . 70 2.6 Some Cantor Set Examples . 73 i ii CONTENTS 2.6.1 Basic Examples . 73 2.6.2 Some Generalized Cantor Sets . 76 2.6.3 Cantor Sets in Higher Dimensions . 78 3 Invariant Measures and the Construction of Haar Measure 81 3.1 The Fundamental Theorem . 82 3.2 Haar Measure for the Orthogonal Group and the Grassmanian 90 3.2.1 Remarks on the Manifold Structure of G(N,M).... 94 4 Covering Theorems and the Differentiation of Integrals 97 4.1 Wiener’s Covering Lemma and its Variants . -
Version of 21.8.15 Chapter 43 Topologies and Measures II The
Version of 21.8.15 Chapter 43 Topologies and measures II The first chapter of this volume was ‘general’ theory of topological measure spaces; I attempted to distinguish the most important properties a topological measure can have – inner regularity, τ-additivity – and describe their interactions at an abstract level. I now turn to rather more specialized investigations, looking for features which offer explanations of the behaviour of the most important spaces, radiating outwards from Lebesgue measure. In effect, this chapter consists of three distinguishable parts and two appendices. The first three sections are based on ideas from descriptive set theory, in particular Souslin’s operation (§431); the properties of this operation are the foundation for the theory of two classes of topological space of particular importance in measure theory, the K-analytic spaces (§432) and the analytic spaces (§433). The second part of the chapter, §§434-435, collects miscellaneous results on Borel and Baire measures, looking at the ways in which topological properties of a space determine properties of the measures it carries. In §436 I present the most important theorems on the representation of linear functionals by integrals; if you like, this is the inverse operation to the construction of integrals from measures in §122. The ideas continue into §437, where I discuss spaces of signed measures representing the duals of spaces of continuous functions, and topologies on spaces of measures. The first appendix, §438, looks at a special topic: the way in which the patterns in §§434-435 are affected if we assume that our spaces are not unreasonably complex in a rather special sense defined in terms of measures on discrete spaces. -
Hausdorff Dimension of Singular Vectors
HAUSDORFF DIMENSION OF SINGULAR VECTORS YITWAH CHEUNG AND NICOLAS CHEVALLIER Abstract. We prove that the set of singular vectors in Rd; d ≥ 2; has Hausdorff dimension d2 d d+1 and that the Hausdorff dimension of the set of "-Dirichlet improvable vectors in R d2 d is roughly d+1 plus a power of " between 2 and d. As a corollary, the set of divergent t t −dt trajectories of the flow by diag(e ; : : : ; e ; e ) acting on SLd+1 R= SLd+1 Z has Hausdorff d codimension d+1 . These results extend the work of the first author in [6]. 1. Introduction Singular vectors were introduced by Khintchine in the twenties (see [14]). Recall that d x = (x1; :::; xd) 2 R is singular if for every " > 0, there exists T0 such that for all T > T0 the system of inequalities " (1.1) max jqxi − pij < and 0 < q < T 1≤i≤d T 1=d admits an integer solution (p; q) 2 Zd × Z. In dimension one, only the rational numbers are singular. The existence of singular vectors that do not lie in a rational subspace was proved by Khintchine for all dimensions ≥ 2. Singular vectors exhibit phenomena that cannot occur in dimension one. For instance, when x 2 Rd is singular, the sequence 0; x; 2x; :::; nx; ::: fills the torus Td = Rd=Zd in such a way that there exists a point y whose distance in the torus to the set f0; x; :::; nxg, times n1=d, goes to infinity when n goes to infinity (see [4], chapter V). -
Interim Report IR-07-027 Adaptive Dynamics for Physiologically
International Institute for Tel: +43 2236 807 342 Applied Systems Analysis Fax: +43 2236 71313 Schlossplatz 1 E-mail: [email protected] A-2361 Laxenburg, Austria Web: www.iiasa.ac.at Interim Report IR-07-027 Adaptive dynamics for physiologically structured population models Michel Durinx ([email protected]) Johan A.J. Metz ([email protected]) Géza Meszéna ([email protected]) Approved by Ulf Dieckmann Leader, Evolution and Ecology Program October 2007 Interim Reports on work of the International Institute for Applied Systems Analysis receive only limited review. Views or opinions expressed herein do not necessarily represent those of the Institute, its National Member Organizations, or other organizations supporting the work. IIASA STUDIES IN ADAPTIVE DYNAMICS NO. 133 The Evolution and Ecology Program at IIASA fosters the devel- opment of new mathematical and conceptual techniques for un- derstanding the evolution of complex adaptive systems. Focusing on these long-term implications of adaptive processes in systems of limited growth, the Evolution and Ecology Program brings together scientists and institutions from around the world with IIASA acting as the central node. EEP Scientific progress within the network is collected in the IIASA Studies in Adaptive Dynamics series. No. 1 Metz JAJ, Geritz SAH, Meszéna G, Jacobs FJA, van No. 11 Geritz SAH, Metz JAJ, Kisdi É, Meszéna G: The Dy- Heerwaarden JS: Adaptive Dynamics: A Geometrical Study namics of Adaptation and Evolutionary Branching. IIASA of the Consequences of Nearly Faithful Reproduction. IIASA Working Paper WP-96-077 (1996). Physical Review Letters Working Paper WP-95-099 (1995). -
Near-Death Experiences and the Theory of the Extraneuronal Hyperspace
Near-Death Experiences and the Theory of the Extraneuronal Hyperspace Linz Audain, J.D., Ph.D., M.D. George Washington University The Mandate Corporation, Washington, DC ABSTRACT: It is possible and desirable to supplement the traditional neu rological and metaphysical explanatory models of the near-death experience (NDE) with yet a third type of explanatory model that links the neurological and the metaphysical. I set forth the rudiments of this model, the Theory of the Extraneuronal Hyperspace, with six propositions. I then use this theory to explain three of the pressing issues within NDE scholarship: the veridicality, precognition and "fear-death experience" phenomena. Many scholars who write about near-death experiences (NDEs) are of the opinion that explanatory models of the NDE can be classified into one of two types (Blackmore, 1993; Moody, 1975). One type of explana tory model is the metaphysical or supernatural one. In that model, the events that occur within the NDE, such as the presence of a tunnel, are real events that occur beyond the confines of time and space. In a sec ond type of explanatory model, the traditional model, the events that occur within the NDE are not at all real. Those events are merely the product of neurobiochemical activity that can be explained within the confines of current neurological and psychological theory, for example, as hallucination. In this article, I supplement this dichotomous view of explanatory models of the NDE by proposing yet a third type of explanatory model: the Theory of the Extraneuronal Hyperspace. This theory represents a Linz Audain, J.D., Ph.D., M.D., is a Resident in Internal Medicine at George Washington University, and Chief Executive Officer of The Mandate Corporation. -
Beyond Lebesgue and Baire IV: Density Topologies and a Converse
Beyond Lebesgue and Baire IV: Density topologies and a converse Steinhaus-Weil Theorem by N. H. Bingham and A. J. Ostaszewski On the centenary of Hausdorff’s Mengenlehre (1914) and Denjoy’s Approximate continuity (1915) Abstract. The theme here is category-measure duality, in the context of a topological group. One can often handle the (Baire) category case and the (Lebesgue, or Haar) measure cases together, by working bi-topologically: switching between the original topology and a suitable refinement (a density topology). This prompts a systematic study of such density topologies, and the corresponding σ-ideals of negligibles. Such ideas go back to Weil’s classic book, and to Hashimoto’s ideal topologies. We make use of group norms, which cast light on the interplay between the group and measure structures. The Steinhaus- Weil interior-points theorem (‘on AA−1’) plays a crucial role here; so too does its converse, the Simmons-Mospan theorem. Key words. Steinhaus-Weil property, Weil topology, shift-compact, density topology, Hashimoto ideal topology, group norm Mathematics Subject Classification (2000): Primary 26A03; 39B62. 1 Introduction This paper originates from several sources. The first three are our earlier studies ‘Beyond Lebesgue and Baire I-III’ ([BinO1,2], [Ost3]), the general arXiv:1607.00031v2 [math.GN] 8 Nov 2017 theme of which is the similarity (indeed, duality) between measure and ca- tegory, and the primacy of category/topology over measure in many areas. The second three are recent studies by the second author on the Effros Open Mapping Principle ([Ost4,5,6]; §6.4 below). The Steinhaus-Weil property (critical for regular variation: see [BinGT, Th. -
23. Dimension Dimension Is Intuitively Obvious but Surprisingly Hard to Define Rigorously and to Work With
58 RICHARD BORCHERDS 23. Dimension Dimension is intuitively obvious but surprisingly hard to define rigorously and to work with. There are several different concepts of dimension • It was at first assumed that the dimension was the number or parameters something depended on. This fell apart when Cantor showed that there is a bijective map from R ! R2. The Peano curve is a continuous surjective map from R ! R2. • The Lebesgue covering dimension: a space has Lebesgue covering dimension at most n if every open cover has a refinement such that each point is in at most n + 1 sets. This does not work well for the spectrums of rings. Example: dimension 2 (DIAGRAM) no point in more than 3 sets. Not trivial to prove that n-dim space has dimension n. No good for commutative algebra as A1 has infinite Lebesgue covering dimension, as any finite number of non-empty open sets intersect. • The "classical" definition. Definition 23.1. (Brouwer, Menger, Urysohn) A topological space has dimension ≤ n (n ≥ −1) if all points have arbitrarily small neighborhoods with boundary of dimension < n. The empty set is the only space of dimension −1. This definition is mostly used for separable metric spaces. Rather amazingly it also works for the spectra of Noetherian rings, which are about as far as one can get from separable metric spaces. • Definition 23.2. The Krull dimension of a topological space is the supre- mum of the numbers n for which there is a chain Z0 ⊂ Z1 ⊂ ::: ⊂ Zn of n + 1 irreducible subsets. DIAGRAM pt ⊂ curve ⊂ A2 For Noetherian topological spaces the Krull dimension is the same as the Menger definition, but for non-Noetherian spaces it behaves badly. -
Hausdorff Measure
Hausdorff Measure Jimmy Briggs and Tim Tyree December 3, 2016 1 1 Introduction In this report, we explore the the measurement of arbitrary subsets of the metric space (X; ρ); a topological space X along with its distance function ρ. We introduce Hausdorff Measure as a natural way of assigning sizes to these sets, especially those of smaller \dimension" than X: After an exploration of the salient properties of Hausdorff Measure, we proceed to a definition of Hausdorff dimension, a separate idea of size that allows us a more robust comparison between rough subsets of X. Many of the theorems in this report will be summarized in a proof sketch or shown by visual example. For a more rigorous treatment of the same material, we redirect the reader to Gerald B. Folland's Real Analysis: Modern techniques and their applications. Chapter 11 of the 1999 edition served as our primary reference. 2 Hausdorff Measure 2.1 Measuring low-dimensional subsets of X The need for Hausdorff Measure arises from the need to know the size of lower-dimensional subsets of a metric space. This idea is not as exotic as it may sound. In a high school Geometry course, we learn formulas for objects of various dimension embedded in R3: In Figure 1 we see the line segment, the circle, and the sphere, each with it's own idea of size. We call these length, area, and volume, respectively. Figure 1: low-dimensional subsets of R3: 2 4 3 2r πr 3 πr Note that while only volume measures something of the same dimension as the host space, R3; length, and area can still be of interest to us, especially 2 in applications. -
Arxiv:1902.02232V1 [Cond-Mat.Mtrl-Sci] 6 Feb 2019
Hyperspatial optimisation of structures Chris J. Pickard∗ Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, United Kingdom and Advanced Institute for Materials Research, Tohoku University 2-1-1 Katahira, Aoba, Sendai, 980-8577, Japan (Dated: February 7, 2019) Anticipating the low energy arrangements of atoms in space is an indispensable scientific task. Modern stochastic approaches to searching for these configurations depend on the optimisation of structures to nearby local minima in the energy landscape. In many cases these local minima are relatively high in energy, and inspection reveals that they are trapped, tangled, or otherwise frustrated in their descent to a lower energy configuration. Strategies have been developed which attempt to overcome these traps, such as classical and quantum annealing, basin/minima hopping, evolutionary algorithms and swarm based methods. Random structure search makes no attempt to avoid the local minima, and benefits from a broad and uncorrelated sampling of configuration space. It has been particularly successful in the first principles prediction of unexpected new phases of dense matter. Here it is demonstrated that by starting the structural optimisations in a higher dimensional space, or hyperspace, many of the traps can be avoided, and that the probability of reaching low energy configurations is much enhanced. Excursions into the extra dimensions are progressively eliminated through the application of a growing energetic penalty. This approach is tested on hard cases for random search { clusters, compounds, and covalently bonded networks. The improvements observed are most dramatic for the most difficult ones. Random structure search is shown to be typically accelerated by two orders of magnitude, and more for particularly challenging systems. -
NEGLIGIBLE SETS for REAL CONNECTIVITY FUNCTIONS Jack B
NEGLIGIBLE SETS FOR REAL CONNECTIVITY FUNCTIONS jack b. brown Introduction. Only functions from the interval 1= [0, l] into I will be considered in this paper, and no distinction will be made be- tween a function and its graph. For real functions / from / into I, the property of connectivity is intermediate to that of continuity and the Darboux property, and is equivalent to the property of / being a connected set in the plane. For some connectivity functions g from I into I, it can be observed that there is a subset Af of / such that every function from / into I which agrees with g on 7 —Af is a connectivity function (in this case, M will be said to be g-negli- gible). For example, if Af is a Cantor subset of I and g is a function from I into I such that if x belongs to some component (a, b) of I— M, g (x) = | si n {cot [tr (x —a) / (b —a ]} |, then M is g-negligible. The following theorems will be proved: Theorem 1. If M is a subset of I, then the following statements are equivalent: (i) there is a connectivity function g from I into I such that M is g-negligible, and (ii) every subinterval of I contains c-many points of I—M (c denotes the cardinality of the continuum). Theorem 2. If g is a connectivity function from I into I, then the following statements are equivalent: (i) g is dense in I2, (ii) every nowhere dense subset of I is g-negligible, and (iii) there exists a dense subset of I which is g-negligible.