Arithmetic, Geometry, Cryptography and Coding Theory
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Mathematics Is a Gentleman's Art: Analysis and Synthesis in American College Geometry Teaching, 1790-1840 Amy K
Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 2000 Mathematics is a gentleman's art: Analysis and synthesis in American college geometry teaching, 1790-1840 Amy K. Ackerberg-Hastings Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Higher Education and Teaching Commons, History of Science, Technology, and Medicine Commons, and the Science and Mathematics Education Commons Recommended Citation Ackerberg-Hastings, Amy K., "Mathematics is a gentleman's art: Analysis and synthesis in American college geometry teaching, 1790-1840 " (2000). Retrospective Theses and Dissertations. 12669. https://lib.dr.iastate.edu/rtd/12669 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margwis, and improper alignment can adversely affect reproduction. in the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. -
Modern Coding Theory: the Statistical Mechanics and Computer Science Point of View
Modern Coding Theory: The Statistical Mechanics and Computer Science Point of View Andrea Montanari1 and R¨udiger Urbanke2 ∗ 1Stanford University, [email protected], 2EPFL, ruediger.urbanke@epfl.ch February 12, 2007 Abstract These are the notes for a set of lectures delivered by the two authors at the Les Houches Summer School on ‘Complex Systems’ in July 2006. They provide an introduction to the basic concepts in modern (probabilistic) coding theory, highlighting connections with statistical mechanics. We also stress common concepts with other disciplines dealing with similar problems that can be generically referred to as ‘large graphical models’. While most of the lectures are devoted to the classical channel coding problem over simple memoryless channels, we present a discussion of more complex channel models. We conclude with an overview of the main open challenges in the field. 1 Introduction and Outline The last few years have witnessed an impressive convergence of interests between disciplines which are a priori well separated: coding and information theory, statistical inference, statistical mechanics (in particular, mean field disordered systems), as well as theoretical computer science. The underlying reason for this convergence is the importance of probabilistic models and/or probabilistic techniques in each of these domains. This has long been obvious in information theory [53], statistical mechanics [10], and statistical inference [45]. In the last few years it has also become apparent in coding theory and theoretical computer science. In the first case, the invention of Turbo codes [7] and the re-invention arXiv:0704.2857v1 [cs.IT] 22 Apr 2007 of Low-Density Parity-Check (LDPC) codes [30, 28] has motivated the use of random constructions for coding information in robust/compact ways [50]. -
O Modular Forms As Clues to the Emergence of Spacetime in String
PHYSICS ::::owan, C., Keehan, S., et al. (2007). Health spending Modular Forms as Clues to the Emergence of Spacetime in String 1anges obscure part D's impact. Health Affairs, 26, Theory Nathan Vander Werf .. , Ayanian, J.Z., Schwabe, A., & Krischer, J.P. Abstract .nd race on early detection of cancer. Journal ofth e 409-1419. In order to be consistent as a quantum theory, string theory requires our universe to have extra dimensions. The extra dimensions form a particu lar type of geometry, called Calabi-Yau manifolds. A fundamental open ions ofNursi ng in the Community: Community question in string theory over the past two decades has been the problem ;, MO: Mosby. of establishing a direct relation between the physics on the worldsheet and the structure of spacetime. The strategy used in our work is to use h year because of lack of insurance. British Medical methods from arithmetic geometry to find a link between the geome try of spacetime and the physical theory on the string worldsheet . The approach involves identifying modular forms that arise from the Omega Fact Finder: Economic motives with modular forms derived from the underlying conformal field letrieved November 20, 2008 from: theory. The aim of this article is to first give a motivation and expla nation of string theory to a general audience. Next, we briefly provide some of the mathematics needed up to modular forms, so that one has es. (2007). National Center for Health the tools to find a string theoretic interpretation of modular forms de : in the Health ofAmericans. Publication rived from K3 surfaces of Brieskorm-Pham type. -
Number Theory
“mcs-ftl” — 2010/9/8 — 0:40 — page 81 — #87 4 Number Theory Number theory is the study of the integers. Why anyone would want to study the integers is not immediately obvious. First of all, what’s to know? There’s 0, there’s 1, 2, 3, and so on, and, oh yeah, -1, -2, . Which one don’t you understand? Sec- ond, what practical value is there in it? The mathematician G. H. Hardy expressed pleasure in its impracticality when he wrote: [Number theorists] may be justified in rejoicing that there is one sci- ence, at any rate, and that their own, whose very remoteness from or- dinary human activities should keep it gentle and clean. Hardy was specially concerned that number theory not be used in warfare; he was a pacifist. You may applaud his sentiments, but he got it wrong: Number Theory underlies modern cryptography, which is what makes secure online communication possible. Secure communication is of course crucial in war—which may leave poor Hardy spinning in his grave. It’s also central to online commerce. Every time you buy a book from Amazon, check your grades on WebSIS, or use a PayPal account, you are relying on number theoretic algorithms. Number theory also provides an excellent environment for us to practice and apply the proof techniques that we developed in Chapters 2 and 3. Since we’ll be focusing on properties of the integers, we’ll adopt the default convention in this chapter that variables range over the set of integers, Z. 4.1 Divisibility The nature of number theory emerges as soon as we consider the divides relation a divides b iff ak b for some k: D The notation, a b, is an abbreviation for “a divides b.” If a b, then we also j j say that b is a multiple of a. -
FROM HARMONIC ANALYSIS to ARITHMETIC COMBINATORICS: a BRIEF SURVEY the Purpose of This Note Is to Showcase a Certain Line Of
FROM HARMONIC ANALYSIS TO ARITHMETIC COMBINATORICS: A BRIEF SURVEY IZABELLA ÃLABA The purpose of this note is to showcase a certain line of research that connects harmonic analysis, speci¯cally restriction theory, to other areas of mathematics such as PDE, geometric measure theory, combinatorics, and number theory. There are many excellent in-depth presentations of the vari- ous areas of research that we will discuss; see e.g., the references below. The emphasis here will be on highlighting the connections between these areas. Our starting point will be restriction theory in harmonic analysis on Eu- clidean spaces. The main theme of restriction theory, in this context, is the connection between the decay at in¯nity of the Fourier transforms of singu- lar measures and the geometric properties of their support, including (but not necessarily limited to) curvature and dimensionality. For example, the Fourier transform of a measure supported on a hypersurface in Rd need not, in general, belong to any Lp with p < 1, but there are positive results if the hypersurface in question is curved. A classic example is the restriction theory for the sphere, where a conjecture due to E. M. Stein asserts that the Fourier transform maps L1(Sd¡1) to Lq(Rd) for all q > 2d=(d¡1). This has been proved in dimension 2 (Fe®erman-Stein, 1970), but remains open oth- erwise, despite the impressive and often groundbreaking work of Bourgain, Wol®, Tao, Christ, and others. We recommend [8] for a thorough survey of restriction theory for the sphere and other curved hypersurfaces. Restriction-type estimates have been immensely useful in PDE theory; in fact, much of the interest in the subject stems from PDE applications. -
Modern Coding Theory: the Statistical Mechanics and Computer Science Point of View
Modern Coding Theory: The Statistical Mechanics and Computer Science Point of View Andrea Montanari1 and R¨udiger Urbanke2 ∗ 1Stanford University, [email protected], 2EPFL, ruediger.urbanke@epfl.ch February 12, 2007 Abstract These are the notes for a set of lectures delivered by the two authors at the Les Houches Summer School on ‘Complex Systems’ in July 2006. They provide an introduction to the basic concepts in modern (probabilistic) coding theory, highlighting connections with statistical mechanics. We also stress common concepts with other disciplines dealing with similar problems that can be generically referred to as ‘large graphical models’. While most of the lectures are devoted to the classical channel coding problem over simple memoryless channels, we present a discussion of more complex channel models. We conclude with an overview of the main open challenges in the field. 1 Introduction and Outline The last few years have witnessed an impressive convergence of interests between disciplines which are a priori well separated: coding and information theory, statistical inference, statistical mechanics (in particular, mean field disordered systems), as well as theoretical computer science. The underlying reason for this convergence is the importance of probabilistic models and/or probabilistic techniques in each of these domains. This has long been obvious in information theory [53], statistical mechanics [10], and statistical inference [45]. In the last few years it has also become apparent in coding theory and theoretical computer science. In the first case, the invention of Turbo codes [7] and the re-invention of Low-Density Parity-Check (LDPC) codes [30, 28] has motivated the use of random constructions for coding information in robust/compact ways [50]. -
The Packing Problem in Statistics, Coding Theory and Finite Projective Spaces: Update 2001
The packing problem in statistics, coding theory and finite projective spaces: update 2001 J.W.P. Hirschfeld School of Mathematical Sciences University of Sussex Brighton BN1 9QH United Kingdom http://www.maths.sussex.ac.uk/Staff/JWPH [email protected] L. Storme Department of Pure Mathematics Krijgslaan 281 Ghent University B-9000 Gent Belgium http://cage.rug.ac.be/ ls ∼ [email protected] Abstract This article updates the authors’ 1998 survey [133] on the same theme that was written for the Bose Memorial Conference (Colorado, June 7-11, 1995). That article contained the principal results on the packing problem, up to 1995. Since then, considerable progress has been made on different kinds of subconfigurations. 1 Introduction 1.1 The packing problem The packing problem in statistics, coding theory and finite projective spaces regards the deter- mination of the maximal or minimal sizes of given subconfigurations of finite projective spaces. This problem is not only interesting from a geometrical point of view; it also arises when coding-theoretical problems and problems from the design of experiments are translated into equivalent geometrical problems. The geometrical interest in the packing problem and the links with problems investigated in other research fields have given this problem a central place in Galois geometries, that is, the study of finite projective spaces. In 1983, a historical survey on the packing problem was written by the first author [126] for the 9th British Combinatorial Conference. A new survey article stating the principal results up to 1995 was written by the authors for the Bose Memorial Conference [133]. -
Arxiv:1111.6055V1 [Math.CO] 25 Nov 2011 2 Definitions
Adinkras for Mathematicians Yan X Zhang Massachusetts Institute of Technology November 28, 2011 Abstract Adinkras are graphical tools created for the study of representations in supersym- metry. Besides having inherent interest for physicists, adinkras offer many easy-to-state and accessible mathematical problems of algebraic, combinatorial, and computational nature. We use a more mathematically natural language to survey these topics, suggest new definitions, and present original results. 1 Introduction In a series of papers starting with [8], different subsets of the “DFGHILM collaboration” (Doran, Faux, Gates, Hübsch, Iga, Landweber, Miller) have built and extended the ma- chinery of adinkras. Following the spirit of Feyman diagrams, adinkras are combinatorial objects that encode information about the representation theory of supersymmetry alge- bras. Adinkras have many intricate links with other fields such as graph theory, Clifford theory, and coding theory. Each of these connections provide many problems that can be compactly communicated to a (non-specialist) mathematician. This paper is a humble attempt to bridge the language gap and generate communication. We redevelop the foundations in a self-contained manner in Sections 2 and 4, using different definitions and constructions that we consider to be more mathematically natural for our purposes. Using our new setup, we prove some original results and make new interpretations in Sections 5 and 6. We wish that these purely combinatorial discussions will equip the readers with a mental model that allows them to appreciate (or to solve!) the original representation-theoretic problems in the physics literature. We return to these problems in Section 7 and reconsider some of the foundational questions of the theory. -
Pure Mathematics
Why Study Mathematics? Mathematics reveals hidden patterns that help us understand the world around us. Now much more than arithmetic and geometry, mathematics today is a diverse discipline that deals with data, measurements, and observations from science; with inference, deduction, and proof; and with mathematical models of natural phenomena, of human behavior, and social systems. The process of "doing" mathematics is far more than just calculation or deduction; it involves observation of patterns, testing of conjectures, and estimation of results. As a practical matter, mathematics is a science of pattern and order. Its domain is not molecules or cells, but numbers, chance, form, algorithms, and change. As a science of abstract objects, mathematics relies on logic rather than on observation as its standard of truth, yet employs observation, simulation, and even experimentation as means of discovering truth. The special role of mathematics in education is a consequence of its universal applicability. The results of mathematics--theorems and theories--are both significant and useful; the best results are also elegant and deep. Through its theorems, mathematics offers science both a foundation of truth and a standard of certainty. In addition to theorems and theories, mathematics offers distinctive modes of thought which are both versatile and powerful, including modeling, abstraction, optimization, logical analysis, inference from data, and use of symbols. Mathematics, as a major intellectual tradition, is a subject appreciated as much for its beauty as for its power. The enduring qualities of such abstract concepts as symmetry, proof, and change have been developed through 3,000 years of intellectual effort. Like language, religion, and music, mathematics is a universal part of human culture. -
From Arithmetic to Algebra
From arithmetic to algebra Slightly edited version of a presentation at the University of Oregon, Eugene, OR February 20, 2009 H. Wu Why can’t our students achieve introductory algebra? This presentation specifically addresses only introductory alge- bra, which refers roughly to what is called Algebra I in the usual curriculum. Its main focus is on all students’ access to the truly basic part of algebra that an average citizen needs in the high- tech age. The content of the traditional Algebra II course is on the whole more technical and is designed for future STEM students. In place of Algebra II, future non-STEM would benefit more from a mathematics-culture course devoted, for example, to an understanding of probability and data, recently solved famous problems in mathematics, and history of mathematics. At least three reasons for students’ failure: (A) Arithmetic is about computation of specific numbers. Algebra is about what is true in general for all numbers, all whole numbers, all integers, etc. Going from the specific to the general is a giant conceptual leap. Students are not prepared by our curriculum for this leap. (B) They don’t get the foundational skills needed for algebra. (C) They are taught incorrect mathematics in algebra classes. Garbage in, garbage out. These are not independent statements. They are inter-related. Consider (A) and (B): The K–3 school math curriculum is mainly exploratory, and will be ignored in this presentation for simplicity. Grades 5–7 directly prepare students for algebra. Will focus on these grades. Here, abstract mathematics appears in the form of fractions, geometry, and especially negative fractions. -
SE 015 465 AUTHOR TITLE the Content of Arithmetic Included in A
DOCUMENT' RESUME ED 070 656 24 SE 015 465 AUTHOR Harvey, John G. TITLE The Content of Arithmetic Included in a Modern Elementary Mathematics Program. INSTITUTION Wisconsin Univ., Madison. Research and Development Center for Cognitive Learning, SPONS AGENCY National Center for Educational Research and Development (DHEW/OE), Washington, D.C. BUREAU NO BR-5-0216 PUB DATE Oct 71 CONTRACT OEC-5-10-154 NOTE 45p.; Working Paper No. 79 EDRS PRICE MF-$0.65 HC-$3.29 DESCRIPTORS *Arithmetic; *Curriculum Development; *Elementary School Mathematics; Geometric Concepts; *Instruction; *Mathematics Education; Number Concepts; Program Descriptions IDENTIFIERS Number Operations ABSTRACT --- Details of arithmetic topics proposed for inclusion in a modern elementary mathematics program and a rationale for the selection of these topics are given. The sequencing of the topics is discussed. (Author/DT) sJ Working Paper No. 19 The Content of 'Arithmetic Included in a Modern Elementary Mathematics Program U.S DEPARTMENT OF HEALTH, EDUCATION & WELFARE OFFICE OF EDUCATION THIS DOCUMENT HAS SEEN REPRO Report from the Project on Individually Guided OUCED EXACTLY AS RECEIVED FROM THE PERSON OR ORGANIZATION ORIG INATING IT POINTS OF VIEW OR OPIN Elementary Mathematics, Phase 2: Analysis IONS STATED 00 NOT NECESSARILY REPRESENT OFFICIAL OFFICE Of LOU Of Mathematics Instruction CATION POSITION OR POLICY V lb. Wisconsin Research and Development CENTER FOR COGNITIVE LEARNING DIE UNIVERSITY Of WISCONSIN Madison, Wisconsin U.S. Office of Education Corder No. C03 Contract OE 5-10-154 Published by the Wisconsin Research and Development Center for Cognitive Learning, supported in part as a research and development center by funds from the United States Office of Education, Department of Health, Educa- tion, and Welfare. -
The Use of Coding Theory in Computational Complexity 1 Introduction
The Use of Co ding Theory in Computational Complexity Joan Feigenbaum ATT Bell Lab oratories Murray Hill NJ USA jfresearchattcom Abstract The interplay of co ding theory and computational complexity theory is a rich source of results and problems This article surveys three of the ma jor themes in this area the use of co des to improve algorithmic eciency the theory of program testing and correcting which is a complexity theoretic analogue of error detection and correction the use of co des to obtain characterizations of traditional complexity classes such as NP and PSPACE these new characterizations are in turn used to show that certain combinatorial optimization problems are as hard to approximate closely as they are to solve exactly Intro duction Complexity theory is the study of ecient computation Faced with a computational problem that can b e mo delled formally a complexity theorist seeks rst to nd a solution that is provably ecient and if such a solution is not found to prove that none exists Co ding theory which provides techniques for robust representation of information is valuable b oth in designing ecient solutions and in proving that ecient solutions do not exist This article surveys the use of co des in complexity theory The improved upp er b ounds obtained with co ding techniques include b ounds on the numb er of random bits used by probabilistic algo rithms and the communication complexity of cryptographic proto cols In these constructions co des are used to design small sample spaces that approximate the b ehavior