Quantum Logic and Partially Ordered Abelian Groups
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14. Least Upper Bounds in Ordered Rings Recall the Definition of a Commutative Ring with 1 from Lecture 3
14. Least upper bounds in ordered rings Recall the definition of a commutative ring with 1 from lecture 3 (Definition 3.2 ). Definition 14.1. A commutative ring R with unity consists of the following data: a set R, • two maps + : R R R (“addition”), : R R R (“multiplication”), • × −→ · × −→ two special elements 0 R = 0 , 1R = 1 R, 0 = 1 such that • ∈ 6 (1) the addition + is commutative and associative (2) the multiplication is commutative and associative (3) distributive law holds:· a (b + c) = ( a b) + ( a c) for all a, b, c R. (4) 0 is an additive identity. · · · ∈ (5) 1 is a multiplicative identity (6) for all a R there exists an additive inverse ( a) R such that a + ( a) = 0. ∈ − ∈ − Example 14.2. The integers Z, the rationals Q, the reals R, and integers modulo n Zn are all commutative rings with 1. The set M2(R) of 2 2 matrices with the usual matrix addition and multiplication is a noncommutative ring with 1 (where× “1” is the identity matrix). The set 2 Z of even integers with the usual addition and multiplication is a commutative ring without 1. Next we introduce the notion of an integral domain. In fact we have seen integral domains in lecture 3, where they were called “commutative rings without zero divisors” (see Lemma 3.4 ). Definition 14.3. A commutative ring R (with 1) is an integral domain if a b = 0 = a = 0 or b = 0 · ⇒ for all a, b R. (This is equivalent to: a b = a c and a = 0 = b = c.) ∈ · · 6 ⇒ Example 14.4. -
Morphological Image Processing Introduction
Morphological Image Processing Introduction • In many areas of knowledge Morphology deals with form and structure (biology, linguistics, social studies, etc) • Mathematical Morphology deals with set theory • Sets in Mathematical Morphology represents objects in an Image 2 Mathematic Morphology • Used to extract image components that are useful in the representation and description of region shape, such as – boundaries extraction – skeletons – convex hull (italian: inviluppo convesso) – morphological filtering – thinning – pruning 3 Mathematic Morphology mathematical framework used for: • pre-processing – noise filtering, shape simplification, ... • enhancing object structure – skeletonization, convex hull... • segmentation – watershed,… • quantitative description – area, perimeter, ... 4 Z2 and Z3 • set in mathematic morphology represent objects in an image – binary image (0 = white, 1 = black) : the element of the set is the coordinates (x,y) of pixel belong to the object a Z2 • gray-scaled image : the element of the set is the coordinates (x,y) of pixel belong to the object and the gray levels a Z3 Y axis Y axis X axis Z axis X axis 5 Basic Set Operators Set operators Denotations A Subset B A ⊆ B Union of A and B C= A ∪ B Intersection of A and B C = A ∩ B Disjoint A ∩ B = ∅ c Complement of A A ={ w | w ∉ A} Difference of A and B A-B = {w | w ∈A, w ∉ B } Reflection of A Â = { w | w = -a for a ∈ A} Translation of set A by point z(z1,z2) (A)z = { c | c = a + z, for a ∈ A} 6 Basic Set Theory 7 Reflection and Translation Bˆ = {w ∈ E 2 : w -
Dichotomy Between Deterministic and Probabilistic Models in Countably Additive Effectus Theory
Dichotomy between deterministic and probabilistic models in countably additive effectus theory Kenta Cho National Institute of Informatics, Japan Bas Westerbaan University College London John van de Wetering Radboud University Nijmegen June 6, 2020 such that § hom-sets tf : A Ñ Bu are convex sets, § and the scalars ts : I Ñ I u are the real unit interval r0; 1s Special operations: § States StpAq :“ t! : I Ñ Au § Effects EffpAq :“ tp : A Ñ I u § p ˝ ! is probability that p holds on state ! Generalized Probabilistic Theories GPTs are generalisations of quantum theory. They consist of § systems A; B; C;:::, § the `empty system' I , § operations f : A Ñ B, Special operations: § States StpAq :“ t! : I Ñ Au § Effects EffpAq :“ tp : A Ñ I u § p ˝ ! is probability that p holds on state ! Generalized Probabilistic Theories GPTs are generalisations of quantum theory. They consist of § systems A; B; C;:::, § the `empty system' I , § operations f : A Ñ B, such that § hom-sets tf : A Ñ Bu are convex sets, § and the scalars ts : I Ñ I u are the real unit interval r0; 1s Generalized Probabilistic Theories GPTs are generalisations of quantum theory. They consist of § systems A; B; C;:::, § the `empty system' I , § operations f : A Ñ B, such that § hom-sets tf : A Ñ Bu are convex sets, § and the scalars ts : I Ñ I u are the real unit interval r0; 1s Special operations: § States StpAq :“ t! : I Ñ Au § Effects EffpAq :“ tp : A Ñ I u § p ˝ ! is probability that p holds on state ! Solution: allow more general sets of scalars ts : I Ñ I u. -
Lecture 5: Binary Morphology
Lecture 5: Binary Morphology c Bryan S. Morse, Brigham Young University, 1998–2000 Last modified on January 15, 2000 at 3:00 PM Contents 5.1 What is Mathematical Morphology? ................................. 1 5.2 Image Regions as Sets ......................................... 1 5.3 Basic Binary Operations ........................................ 2 5.3.1 Dilation ............................................. 2 5.3.2 Erosion ............................................. 2 5.3.3 Duality of Dilation and Erosion ................................. 3 5.4 Some Examples of Using Dilation and Erosion . .......................... 3 5.5 Proving Properties of Mathematical Morphology .......................... 3 5.6 Hit-and-Miss .............................................. 4 5.7 Opening and Closing .......................................... 4 5.7.1 Opening ............................................. 4 5.7.2 Closing ............................................. 5 5.7.3 Properties of Opening and Closing ............................... 5 5.7.4 Applications of Opening and Closing .............................. 5 Reading SH&B, 11.1–11.3 Castleman, 18.7.1–18.7.4 5.1 What is Mathematical Morphology? “Morphology” can literally be taken to mean “doing things to shapes”. “Mathematical morphology” then, by exten- sion, means using mathematical principals to do things to shapes. 5.2 Image Regions as Sets The basis of mathematical morphology is the description of image regions as sets. For a binary image, we can consider the “on” (1) pixels to all comprise a set of values from the “universe” of pixels in the image. Throughout our discussion of mathematical morphology (or just “morphology”), when we refer to an image A, we mean the set of “on” (1) pixels in that image. The “off” (0) pixels are thus the set compliment of the set of on pixels. By Ac, we mean the compliment of A,or the off (0) pixels. -
Hit-Or-Miss Transform
Hit-or-miss transform • Used to extract pixels with specific neighbourhood configurations from an image • Grey scale extension exist • Uses two structure elements B1 and B2 to find a given foreground and background configuration, respectively C HMT B X={x∣B1x⊆X ,B2x⊆X } • Example: 1 Morphological Image Processing Lecture 22 (page 1) 9.4 The hit-or-miss transformation Illustration... Morphological Image Processing Lecture 22 (page 2) Objective is to find a disjoint region (set) in an image • If B denotes the set composed of X and its background, the• match/hit (or set of matches/hits) of B in A,is A B =(A X) [Ac (W X)] ¯∗ ª ∩ ª − Generalized notation: B =(B1,B2) • Set formed from elements of B associated with B1: • an object Set formed from elements of B associated with B2: • the corresponding background [Preceeding discussion: B1 = X and B2 =(W X)] − More general definition: • c A B =(A B1) [A B2] ¯∗ ª ∩ ª A B contains all the origin points at which, simulta- • ¯∗ c neously, B1 found a hit in A and B2 found a hit in A Hit-or-miss transform C HMT B X={x∣B1x⊆X ,B2x⊆X } • Can be written in terms of an intersection of two erosions: HMT X= X∩ X c B B1 B2 2 Hit-or-miss transform • Simple example usages - locate: – Isolated foreground pixels • no neighbouring foreground pixels – Foreground endpoints • one or zero neighbouring foreground pixels – Multiple foreground points • pixels having more than two neighbouring foreground pixels – Foreground contour points • pixels having at least one neighbouring background pixel 3 Hit-or-miss transform example • Locating 4-connected endpoints SEs for 4-connected endpoints Resulting Hit-or-miss transform 4 Hit-or-miss opening • Objective: keep all points that fit the SE. -
Digital Image Processing
Digital Image Processing Lecture # 11 Morphological Operations 1 Image Morphology 2 Introduction Morphology A branch of biology which deals with the form and structure of animals and plants Mathematical Morphology A tool for extracting image components that are useful in the representation and description of region shapes The language of mathematical morphology is Set Theory 3 Morphology: Quick Example Image after segmentation Image after segmentation and morphological processing 4 Introduction Morphological image processing describes a range of image processing techniques that deal with the shape (or morphology) of objects in an image Sets in mathematical morphology represents objects in an image. E.g. Set of all white pixels in a binary image. 5 Introduction foreground: background I(p )c I(p)0 This represents a digital image. Each square is one pixel. 6 Set Theory The set space of binary image is Z2 Each element of the set is a 2D vector whose coordinates are the (x,y) of a black (or white, depending on the convention) pixel in the image The set space of gray level image is Z3 Each element of the set is a 3D vector: (x,y) and intensity level. NOTE: Set Theory and Logical operations are covered in: Section 9.1, Chapter # 9, 2nd Edition DIP by Gonzalez Section 2.6.4, Chapter # 2, 3rd Edition DIP by Gonzalez 7 Set Theory 2 Let A be a set in Z . if a = (a1,a2) is an element of A, then we write aA If a is not an element of A, we write aA Set representation A{( a1 , a 2 ),( a 3 , a 4 )} Empty or Null set A 8 Set Theory Subset: if every element of A is also an element of another set B, the A is said to be a subset of B AB Union: The set of all elements belonging either to A, B or both CAB Intersection: The set of all elements belonging to both A and B DAB 9 Set Theory Two sets A and B are said to be disjoint or mutually exclusive if they have no common element AB Complement: The set of elements not contained in A Ac { w | w A } Difference of two sets A and B, denoted by A – B, is defined as c A B { w | w A , w B } A B i.e. -
A Note on Embedding a Partially Ordered Ring in a Division Algebra William H
proceedings of the american mathematical society Volume 37, Number 1, January 1973 A NOTE ON EMBEDDING A PARTIALLY ORDERED RING IN A DIVISION ALGEBRA WILLIAM H. REYNOLDS Abstract. If H is a maximal cone of a ring A such that the subring generated by H is a commutative integral domain that satisfies a certain centrality condition in A, then there exist a maxi- mal cone H' in a division ring A' and an order preserving mono- morphism of A into A', where the subring of A' generated by H' is a subfield over which A' is algebraic. Hypotheses are strengthened so that the main theorems of the author's earlier paper hold for maximal cones. The terminology of the author's earlier paper [3] will be used. For a subsemiring H of a ring A, we write H—H for {x—y:x,y e H); this is the subring of A generated by H. We say that H is a u-hemiring of A if H is maximal in the class of all subsemirings of A that do not contain a given element u in the center of A. We call H left central if for every a e A and he H there exists h' e H with ah=h'a. Recall that H is a cone if Hn(—H)={0], and a maximal cone if H is not properly contained in another cone. First note that in [3, Theorem 1] the commutativity of the hemiring, established at the beginning of the proof, was only exploited near the end of the proof and was not used to simplify the earlier details. -
Algorithmic Semi-Algebraic Geometry and Topology – Recent Progress and Open Problems
Contemporary Mathematics Algorithmic Semi-algebraic Geometry and Topology – Recent Progress and Open Problems Saugata Basu Abstract. We give a survey of algorithms for computing topological invari- ants of semi-algebraic sets with special emphasis on the more recent devel- opments in designing algorithms for computing the Betti numbers of semi- algebraic sets. Aside from describing these results, we discuss briefly the back- ground as well as the importance of these problems, and also describe the main tools from algorithmic semi-algebraic geometry, as well as algebraic topology, which make these advances possible. We end with a list of open problems. Contents 1. Introduction 1 2. Semi-algebraic Geometry: Background 3 3. Recent Algorithmic Results 10 4. Algorithmic Preliminaries 12 5. Topological Preliminaries 22 6. Algorithms for Computing the First Few Betti Numbers 41 7. The Quadratic Case 53 8. Betti Numbers of Arrangements 66 9. Open Problems 69 Acknowledgment 71 References 71 1. Introduction This article has several goals. The primary goal is to provide the reader with a thorough survey of the current state of knowledge on efficient algorithms for computing topological invariants of semi-algebraic sets – and in particular their Key words and phrases. Semi-algebraic Sets, Betti Numbers, Arrangements, Algorithms, Complexity . The author was supported in part by NSF grant CCF-0634907. Part of this work was done while the author was visiting the Institute of Mathematics and its Applications, Minneapolis. 2000 Mathematics Subject Classification Primary 14P10, 14P25; Secondary 68W30 c 0000 (copyright holder) 1 2 SAUGATA BASU Betti numbers. At the same time we want to provide graduate students who intend to pursue research in the area of algorithmic semi-algebraic geometry, a primer on the main technical tools used in the recent developments in this area, so that they can start to use these themselves in their own work. -
Embedding Two Ordered Rings in One Ordered Ring. Part I1
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector JOURNAL OF ALGEBRA 2, 341-364 (1966) Embedding Two Ordered Rings in One Ordered Ring. Part I1 J. R. ISBELL Department of Mathematics, Case Institute of Technology, Cleveland, Ohio Communicated by R. H. Bruck Received July 8, 1965 INTRODUCTION Two (totally) ordered rings will be called compatible if there exists an ordered ring in which both of them can be embedded. This paper concerns characterizations of the ordered rings compatible with a given ordered division ring K. Compatibility with K is equivalent to compatibility with the center of K; the proof of this depends heavily on Neumann’s theorem [S] that every ordered division ring is compatible with the real numbers. Moreover, only the subfield K, of real algebraic numbers in the center of K matters. For each Ks , the compatible ordered rings are characterized by a set of elementary conditions which can be expressed as polynomial identities in terms of ring and lattice operations. A finite set of identities, or even iden- tities in a finite number of variables, do not suffice, even in the commutative case. Explicit rules will be given for writing out identities which are necessary and sufficient for a commutative ordered ring E to be compatible with a given K (i.e., with K,). Probably the methods of this paper suffice for deriving corresponding rules for noncommutative E, but only the case K,, = Q is done here. If E is compatible with K,, , then E is embeddable in an ordered algebra over Ks and (obviously) E is compatible with the rational field Q. -
Grayscale Mathematical Morphology Václav Hlaváč
Grayscale mathematical morphology Václav Hlaváč Czech Technical University in Prague Czech Institute of Informatics, Robotics and Cybernetics 160 00 Prague 6, Jugoslávských partyzánů 1580/3, Czech Republic http://people.ciirc.cvut.cz/hlavac, [email protected] also Center for Machine Perception, http://cmp.felk.cvut.cz Courtesy: Petr Matula, Petr Kodl, Jean Serra, Miroslav Svoboda Outline of the talk: Set-function equivalence. Top-hat transform. Umbra and top of a set. Geodesic method. Ultimate erosion. Gray scale dilation, erosion. Morphological reconstruction. A quick informal explanation 2/42 Grayscale mathematical morphology is the generalization of binary morphology for images with more gray levels than two or with voxels. 3 The point set A ∈ E . The first two coordinates span in the function (point set) domain and the third coordinate corresponds to the function value. The concepts supremum ∨ (also the least upper bound), resp. infimum ∧ (also the greatest lower bound) play a key role here. Actually, the related operators max, resp. min, are used in computations with finite sets. Erosion (resp. dilation) of the image (with the flat structuring) element assigns to each pixel the minimal (resp. maximal) value in the chosen neighborhood of the current pixel of the input image. The structuring element (function) is a function of two variables. It influences how pixels in the neighborhood of the current pixel are taken into account. The value of the (non-flat) structuring element is added (while dilating), resp. subtracted (while eroding) when the maximum, resp. minimum is calculated in the neighborhood. Grayscale mathematical morphology explained via binary morphology 3/42 It is possible to introduce grayscale mathematical morphology using the already explained binary (black and white only) mathematical morphology. -
Abstract Mathematical Morphology Based on Structuring Element
Abstract Mathematical morphology based on structuring element: Application to morpho-logic Marc Aiguier1 and Isabelle Bloch2 and Ram´on Pino-P´erez3 1. MICS, CentraleSupelec, Universit´eParis Saclay, France [email protected] 2. LTCI, T´el´ecom Paris, Institut Polytechnique de Paris, France [email protected] 3. Departemento de Matematicas, Facultad de Ciencias, Universidad de Los Andes, M´erida, Venezuela [email protected] Abstract A general definition of mathematical morphology has been defined within the algebraic framework of complete lattice theory. In this frame- work, dealing with deterministic and increasing operators, a dilation (re- spectively an erosion) is an operation which is distributive over supremum (respectively infimum). From this simple definition of dilation and ero- sion, we cannot say much about the properties of them. However, when they form an adjunction, many important properties can be derived such as monotonicity, idempotence, and extensivity or anti-extensivity of their composition, preservation of infimum and supremum, etc. Mathemati- cal morphology has been first developed in the setting of sets, and then extended to other algebraic structures such as graphs, hypergraphs or sim- plicial complexes. For all these algebraic structures, erosion and dilation are usually based on structuring elements. The goal is then to match these structuring elements on given objects either to dilate or erode them. One of the advantages of defining erosion and dilation based on structuring ele- arXiv:2005.01715v1 [math.CT] 4 May 2020 ments is that these operations are adjoint. Based on this observation, this paper proposes to define, at the abstract level of category theory, erosion and dilation based on structuring elements. -
Arxiv:Quant-Ph/0611110 V1 10 Nov 2006
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by CERN Document Server COORDINATING QUANTUM AGENTS’ PERSPECTIVES: CONVEX OPERATIONAL THEORIES, QUANTUM INFORMATION, AND QUANTUM FOUNDATIONS H. BARNUM1 1Computer and Computational Sciences Division CCS-3, MS B256, Los Alamos National Laboratory, Los Alamos 87545, USA E-mail: [email protected] In this paper, I propose a project of enlisting quantum information science as a source of task- oriented axioms for use in the investigation of operational theories in a general framework capable of encompassing quantum mechanics, classical theory, and more. Whatever else they may be, quantum states of systems are compendia of probabilities for the outcomes of possible operations we may perform on the systems: “operational theories.” I discuss appropriate general frameworks for such theories, in which convexity plays a key role. Such frameworks are appropriate for investigating what things look like from an “inside view,” i.e. for describing perspectival information that one subsystem of the world can have about another. Understanding how such views can combine, and whether an overall “geometric” picture (“outside view”) coordinating them all can be had, even if this picture is very different in nature from the structure of the perspectives within it, is the key to understanding whether we may be able to achieve a unified, “objective” physical view in which quantum mechanics is the appropriate description for certain perspectives, or whether quantum mechanics is truly telling us we must go beyond this “geometric” conception of physics. The nature of information, its flow and processing, as seen from various operational persepectives, is likely to be key to understanding whether and how such coordination and unification can be achieved.