Matroid Enumeration for Incidence Geometry
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Robot Vision: Projective Geometry
Robot Vision: Projective Geometry Ass.Prof. Friedrich Fraundorfer SS 2018 1 Learning goals . Understand homogeneous coordinates . Understand points, line, plane parameters and interpret them geometrically . Understand point, line, plane interactions geometrically . Analytical calculations with lines, points and planes . Understand the difference between Euclidean and projective space . Understand the properties of parallel lines and planes in projective space . Understand the concept of the line and plane at infinity 2 Outline . 1D projective geometry . 2D projective geometry ▫ Homogeneous coordinates ▫ Points, Lines ▫ Duality . 3D projective geometry ▫ Points, Lines, Planes ▫ Duality ▫ Plane at infinity 3 Literature . Multiple View Geometry in Computer Vision. Richard Hartley and Andrew Zisserman. Cambridge University Press, March 2004. Mundy, J.L. and Zisserman, A., Geometric Invariance in Computer Vision, Appendix: Projective Geometry for Machine Vision, MIT Press, Cambridge, MA, 1992 . Available online: www.cs.cmu.edu/~ph/869/papers/zisser-mundy.pdf 4 Motivation – Image formation [Source: Charles Gunn] 5 Motivation – Parallel lines [Source: Flickr] 6 Motivation – Epipolar constraint X world point epipolar plane x x’ x‘TEx=0 C T C’ R 7 Euclidean geometry vs. projective geometry Definitions: . Geometry is the teaching of points, lines, planes and their relationships and properties (angles) . Geometries are defined based on invariances (what is changing if you transform a configuration of points, lines etc.) . Geometric transformations -
Study Guide for the Midterm. Topics: • Euclid • Incidence Geometry • Perspective Geometry • Neutral Geometry – Through Oct.18Th
Study guide for the midterm. Topics: • Euclid • Incidence geometry • Perspective geometry • Neutral geometry { through Oct.18th. You need to { Know definitions and examples. { Know theorems by names. { Be able to give a simple proof and justify every step. { Be able to explain those of Euclid's statements and proofs that appeared in the book, the problem set, the lecture, or the tutorial. Sources: • Class Notes • Problem Sets • Assigned reading • Notes on course website. The test. • Excerpts from Euclid will be provided if needed. • The incidence axioms will be provided if needed. • Postulates of neutral geometry will be provided if needed. • There will be at least one question that is similar or identical to a homework question. Tentative large list of terms. For each of the following terms, you should be able to write two sentences, in which you define it, state it, explain it, or discuss it. Euclid's geometry: - Euclid's definition of \right angle". - Euclid's definition of \parallel lines". - Euclid's \postulates" versus \common notions". - Euclid's working with geometric magnitudes rather than real numbers. - Congruence of segments and angles. - \All right angles are equal". - Euclid's fifth postulate. - \The whole is bigger than the part". - \Collapsing compass". - Euclid's reliance on diagrams. - SAS; SSS; ASA; AAS. - Base angles of an isosceles triangle. - Angles below the base of an isosceles triangle. - Exterior angle inequality. - Alternate interior angle theorem and its converse (we use the textbook's convention). - Vertical angle theorem. - The triangle inequality. - Pythagorean theorem. Incidence geometry: - collinear points. - concurrent lines. - Simple theorems of incidence geometry. - Interpretation/model for a theory. -
Arxiv:1609.06355V1 [Cs.CC] 20 Sep 2016 Outlaw Distributions And
Outlaw distributions and locally decodable codes Jop Bri¨et∗ Zeev Dvir† Sivakanth Gopi‡ September 22, 2016 Abstract Locally decodable codes (LDCs) are error correcting codes that allow for decoding of a single message bit using a small number of queries to a corrupted encoding. De- spite decades of study, the optimal trade-off between query complexity and codeword length is far from understood. In this work, we give a new characterization of LDCs using distributions over Boolean functions whose expectation is hard to approximate (in L∞ norm) with a small number of samples. We coin the term ‘outlaw distribu- tions’ for such distributions since they ‘defy’ the Law of Large Numbers. We show that the existence of outlaw distributions over sufficiently ‘smooth’ functions implies the existence of constant query LDCs and vice versa. We also give several candidates for outlaw distributions over smooth functions coming from finite field incidence geometry and from hypergraph (non)expanders. We also prove a useful lemma showing that (smooth) LDCs which are only required to work on average over a random message and a random message index can be turned into true LDCs at the cost of only constant factors in the parameters. 1 Introduction Error correcting codes (ECCs) solve the basic problem of communication over noisy channels. They encode a message into a codeword from which, even if the channel partially corrupts it, arXiv:1609.06355v1 [cs.CC] 20 Sep 2016 the message can later be retrieved. With one of the earliest applications of the probabilistic method, formally introduced by Erd˝os in 1947, pioneering work of Shannon [Sha48] showed the existence of optimal (capacity-achieving) ECCs. -
Connections Between Graph Theory, Additive Combinatorics, and Finite
UNIVERSITY OF CALIFORNIA, SAN DIEGO Connections between graph theory, additive combinatorics, and finite incidence geometry A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Mathematics by Michael Tait Committee in charge: Professor Jacques Verstra¨ete,Chair Professor Fan Chung Graham Professor Ronald Graham Professor Shachar Lovett Professor Brendon Rhoades 2016 Copyright Michael Tait, 2016 All rights reserved. The dissertation of Michael Tait is approved, and it is acceptable in quality and form for publication on microfilm and electronically: Chair University of California, San Diego 2016 iii DEDICATION To Lexi. iv TABLE OF CONTENTS Signature Page . iii Dedication . iv Table of Contents . .v List of Figures . vii Acknowledgements . viii Vita........................................x Abstract of the Dissertation . xi 1 Introduction . .1 1.1 Polarity graphs and the Tur´annumber for C4 ......2 1.2 Sidon sets and sum-product estimates . .3 1.3 Subplanes of projective planes . .4 1.4 Frequently used notation . .5 2 Quadrilateral-free graphs . .7 2.1 Introduction . .7 2.2 Preliminaries . .9 2.3 Proof of Theorem 2.1.1 and Corollary 2.1.2 . 11 2.4 Concluding remarks . 14 3 Coloring ERq ........................... 16 3.1 Introduction . 16 3.2 Proof of Theorem 3.1.7 . 21 3.3 Proof of Theorems 3.1.2 and 3.1.3 . 23 3.3.1 q a square . 24 3.3.2 q not a square . 26 3.4 Proof of Theorem 3.1.8 . 34 3.5 Concluding remarks on coloring ERq ........... 36 4 Chromatic and Independence Numbers of General Polarity Graphs . -
COMBINATORICS, Volume
http://dx.doi.org/10.1090/pspum/019 PROCEEDINGS OF SYMPOSIA IN PURE MATHEMATICS Volume XIX COMBINATORICS AMERICAN MATHEMATICAL SOCIETY Providence, Rhode Island 1971 Proceedings of the Symposium in Pure Mathematics of the American Mathematical Society Held at the University of California Los Angeles, California March 21-22, 1968 Prepared by the American Mathematical Society under National Science Foundation Grant GP-8436 Edited by Theodore S. Motzkin AMS 1970 Subject Classifications Primary 05Axx, 05Bxx, 05Cxx, 10-XX, 15-XX, 50-XX Secondary 04A20, 05A05, 05A17, 05A20, 05B05, 05B15, 05B20, 05B25, 05B30, 05C15, 05C99, 06A05, 10A45, 10C05, 14-XX, 20Bxx, 20Fxx, 50A20, 55C05, 55J05, 94A20 International Standard Book Number 0-8218-1419-2 Library of Congress Catalog Number 74-153879 Copyright © 1971 by the American Mathematical Society Printed in the United States of America All rights reserved except those granted to the United States Government May not be produced in any form without permission of the publishers Leo Moser (1921-1970) was active and productive in various aspects of combin• atorics and of its applications to number theory. He was in close contact with those with whom he had common interests: we will remember his sparkling wit, the universality of his anecdotes, and his stimulating presence. This volume, much of whose content he had enjoyed and appreciated, and which contains the re• construction of a contribution by him, is dedicated to his memory. CONTENTS Preface vii Modular Forms on Noncongruence Subgroups BY A. O. L. ATKIN AND H. P. F. SWINNERTON-DYER 1 Selfconjugate Tetrahedra with Respect to the Hermitian Variety xl+xl + *l + ;cg = 0 in PG(3, 22) and a Representation of PG(3, 3) BY R. -
Studies on Enumeration of Acyclic Substructures in Graphs and Hypergraphs (グラフや超グラフに含まれる非巡回部分構造の列挙に関する研究)
CORE Metadata, citation and similar papers at core.ac.uk Provided by Hokkaido University Collection of Scholarly and Academic Papers Studies on Enumeration of Acyclic Substructures in Graphs and Hypergraphs (グラフや超グラフに含まれる非巡回部分構造の列挙に関する研究) Kunihiro Wasa February 2016 Division of Computer Science Graduate School of Information Science and Technology Hokkaido University Abstract Recently, due to the improvement of the performance of computers, we can easily ob- tain a vast amount of data defined by graphs or hypergraphs. However, it is difficult to look over all the data by mortal powers. Moreover, it is impossible for us to extract useful knowledge or regularities hidden in the data. Thus, we address to overcome this situation by using computational techniques such as data mining and machine learning for the data. However, we still confront a matter that needs to be dealt with the expo- nentially many substructures in input data. That is, we have to consider the following problem: given an input graph or hypergraph and a constraint, output all substruc- tures belonging to the input and satisfying the constraint without duplicates. In this thesis, we focus on this kind of problems and address to develop efficient enumeration algorithms for them. Since 1950's, substructure enumeration problems are widely studied. In 1975, Read and Tarjan proposed enumeration algorithms that list spanning trees, paths, and cycles for evaluating the electrical networks and studying program flow. Moreover, due to the demands from application area, enumeration algorithms for cliques, subtrees, and subpaths are applied to data mining and machine learning. In addition, enumeration problems have been focused on due to not only the viewpoint of application but also of the theoretical interest. -
Collineations in Perspective
Collineations in Perspective Now that we have a decent grasp of one-dimensional projectivities, we move on to their two di- mensional analogs. Although they are more complicated, in a sense, they may be easier to grasp because of the many applications to perspective drawing. Speaking of, let's return to the triangle on the window and its shadow in its full form instead of only looking at one line. Perspective Collineation In one dimension, a perspectivity is a bijective mapping from a line to a line through a point. In two dimensions, a perspective collineation is a bijective mapping from a plane to a plane through a point. To illustrate, consider the triangle on the window plane and its shadow on the ground plane as in Figure 1. We can see that every point on the triangle on the window maps to exactly one point on the shadow, but the collineation is from the entire window plane to the entire ground plane. We understand the window plane to extend infinitely in all directions (even going through the ground), the ground also extends infinitely in all directions (we will assume that the earth is flat here), and we map every point on the window to a point on the ground. Looking at Figure 2, we see that the lamp analogy breaks down when we consider all lines through O. Although it makes sense for the base of the triangle on the window mapped to its shadow on 1 the ground (A to A0 and B to B0), what do we make of the mapping C to C0, or D to D0? C is on the window plane, underground, while C0 is on the ground. -
Enumeration of Maximal Cliques from an Uncertain Graph
1 Enumeration of Maximal Cliques from an Uncertain Graph Arko Provo Mukherjee #1, Pan Xu ∗2, Srikanta Tirthapura #3 # Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA Coover Hall, Ames, IA, USA 1 [email protected] 3 [email protected] ∗ Department of Computer Science, University of Maryland A.V. Williams Building, College Park, MD, USA 2 [email protected] Abstract—We consider the enumeration of dense substructures (maximal cliques) from an uncertain graph. For parameter 0 < α < 1, we define the notion of an α-maximal clique in an uncertain graph. We present matching upper and lower bounds on the number of α-maximal cliques possible within a (uncertain) graph. We present an algorithm to enumerate α-maximal cliques whose worst-case runtime is near-optimal, and an experimental evaluation showing the practical utility of the algorithm. Index Terms—Graph Mining, Uncertain Graph, Maximal Clique, Dense Substructure F 1 INTRODUCTION of the most basic problems in graph mining, and has been applied in many settings, including in finding overlapping Large datasets often contain information that is uncertain communities from social networks [14, 17, 18, 19], finding in nature. For example, given people A and B, it may not overlapping multiple protein complexes [20], analysis of be possible to definitively assert a relation of the form “A email networks [21] and other problems in bioinformat- knows B” using available information. Our confidence in ics [22, 23, 24]. such relations are commonly quantified using probability, and we say that the relation exists with a probability of p, for While the notion of a dense substructure and methods some value p determined from the available information. -
Keith E. Mellinger
Keith E. Mellinger Department of Mathematics home University of Mary Washington 1412 Kenmore Avenue 1301 College Avenue, Trinkle Hall Fredericksburg, VA 22401 Fredericksburg, VA 22401-5300 (540) 368-3128 ph: (540) 654-1333, fax: (540) 654-2445 [email protected] http://www.keithmellinger.com/ Professional Experience • Director of the Quality Enhancement Plan, 2013 { present. UMW. • Professor of Mathematics, 2014 { present. Department of Mathematics, UMW. • Interim Director, Office of Academic and Career Services, Fall 2013 { Spring 2014. UMW. • Associate Professor, 2008 { 2014. Department of Mathematics, UMW. • Department Chair, 2008 { 2013. Department of Mathematics, UMW. • Assistant Professor, 2003 { 2008. Department of Mathematics, UMW. • Research Assistant Professor (Vigre post-doc), 2001 - 2003. Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, IL, 60607-7045. Position partially funded by a VIGRE grant from the National Science Foundation. Education • Ph.D. in Mathematics: August 2001, University of Delaware, Newark, DE. { Thesis: Mixed Partitions and Spreads of Projective Spaces • M.S. in Mathematics: May 1997, University of Delaware. • B.S. in Mathematics: May 1995, Millersville University, Millersville, PA. { minor in computer science, option in statistics Grants and Awards • Millersville University Outstanding Young Alumni Achievement Award, presented by the alumni association at MU, May 2013. • Mathematical Association of America's Carl B. Allendoerfer Award for the article, -
1 Definition and Models of Incidence Geometry
1 Definition and Models of Incidence Geometry (1.1) Definition (geometry) A geometry is a set S of points and a set L of lines together with relationships between the points and lines. p 2 1. Find four points which belong to set M = f(x;y) 2 R jx = 5g: 2. Let M = f(x;y)jx;y 2 R;x < 0g: p (i) Find three points which belong to set N = f(x;y) 2 Mjx = 2g. − 4 L f 2 Mj 1 g (ii) Are points P (3;3), Q(6;4) and R( 2; 3 ) belong to set = (x;y) y = 3 x + 2 ? (1.2) Definition (Cartesian Plane) L Let E be the set of all vertical and non-vertical lines, La and Lk;n where vertical lines: f 2 2 j g La = (x;y) R x = a; a is fixed real number ; non-vertical lines: f 2 2 j g Lk;n = (x;y) R y = kx + n; k and n are fixed real numbers : C f 2 L g The model = R ; E is called the Cartesian Plane. C f 2 L g 3. Let = R ; E denote Cartesian Plane. (i) Find three different points which belong to Cartesian vertical line L7. (ii) Find three different points which belong to Cartesian non-vertical line L15;p2. C f 2 L g 4. Let P be some point in Cartesian Plane = R ; E . Show that point P cannot lie simultaneously on both L and L (where a a ). a a0 , 0 (1.3) Definition (Poincar´ePlane) L Let H be the set of all type I and type II lines, aL and pLr where type I lines: f 2 j g aL = (x;y) H x = a; a is a fixed real number ; type II lines: f 2 j − 2 2 2 2 g pLr = (x;y) H (x p) + y = r ; p and r are fixed R, r > 0 ; 2 H = f(x;y) 2 R jy > 0g: H f L g The model = H; H will be called the Poincar´ePlane. -
Finite Projective Geometry 2Nd Year Group Project
Finite Projective Geometry 2nd year group project. B. Doyle, B. Voce, W.C Lim, C.H Lo Mathematics Department - Imperial College London Supervisor: Ambrus Pal´ June 7, 2015 Abstract The Fano plane has a strong claim on being the simplest symmetrical object with inbuilt mathematical structure in the universe. This is due to the fact that it is the smallest possible projective plane; a set of points with a subsets of lines satisfying just three axioms. We will begin by developing some theory direct from the axioms and uncovering some of the hidden (and not so hidden) symmetries of the Fano plane. Alternatively, some projective planes can be derived from vector space theory and we shall also explore this and the associated linear maps on these spaces. Finally, with the help of some theory of quadratic forms we will give a proof of the surprising Bruck-Ryser theorem, which shows that if a projective plane has order n congruent to 1 or 2 mod 4, then n is the sum of two squares. Thus we will have demonstrated fascinating links between pure mathematical disciplines by incorporating the use of linear algebra, group the- ory and number theory to explain the geometric world of projective planes. 1 Contents 1 Introduction 3 2 Basic Defintions and results 4 3 The Fano Plane 7 3.1 Isomorphism and Automorphism . 8 3.2 Ovals . 10 4 Projective Geometry with fields 12 4.1 Constructing Projective Planes from fields . 12 4.2 Order of Projective Planes over fields . 14 5 Bruck-Ryser 17 A Appendix - Rings and Fields 22 2 1 Introduction Projective planes are geometrical objects that consist of a set of elements called points and sub- sets of these elements called lines constructed following three basic axioms which give the re- sulting object a remarkable level of symmetry. -
Cramer Benjamin PMET
Just-in-Time-Teaching and other gadgets Richard Cramer-Benjamin Niagara University http://faculty.niagara.edu/richcb The Class MAT 443 – Euclidean Geometry 26 Students 12 Secondary Ed (9-12 or 5-12 Certification) 14 Elementary Ed (1-6, B-6, or 1-9 Certification) The Class Venema, G., Foundations of Geometry , Preliminaries/Discrete Geometry 2 weeks Axioms of Plane Geometry 3 weeks Neutral Geometry 3 weeks Euclidean Geometry 3 weeks Circles 1 week Transformational Geometry 2 weeks Other Sources Requiring Student Questions on the Text Bonnie Gold How I (Finally) Got My Calculus I Students to Read the Text Tommy Ratliff MAA Inovative Teaching Exchange JiTT Just-in-Time-Teaching Warm-Ups Physlets Puzzles On-line Homework Interactive Lessons JiTTDLWiki JiTTDLWiki Goals Teach Students to read a textbook Math classes have taught students not to read the text. Get students thinking about the material Identify potential difficulties Spend less time lecturing Example Questions For February 1 Subject line WarmUp 3 LastName Due 8:00 pm, Tuesday, January 31. Read Sections 5.1-5.4 Be sure to understand The different axiomatic systems (Hilbert's, Birkhoff's, SMSG, and UCSMP), undefined terms, Existence Postulate, plane, Incidence Postulate, lie on, parallel, the ruler postulate, between, segment, ray, length, congruent, Theorem 5.4.6*, Corrollary 5.4.7*, Euclidean Metric, Taxicab Metric, Coordinate functions on Euclidean and taxicab metrics, the rational plane. Questions Compare Hilbert's axioms with the UCSMP axioms in the appendix. What are some observations you can make? What is a coordinate function? What does it have to do with the ruler placement postulate? What does the rational plane model demonstrate? List 3 statements about the reading.