Algebraic Automata Theory
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LOGIC and P-RECOGNIZABLE SETS of INTEGERS 1 Introduction
LOGIC AND p-RECOGNIZABLE SETS OF INTEGERS V´eronique Bruy`ere∗ Georges Hansel Christian Michaux∗† Roger Villemairez Abstract We survey the properties of sets of integers recognizable by automata when they are written in p-ary expansions. We focus on Cobham's theorem which characterizes the sets recognizable in different bases p and on its generaliza- tion to Nm due to Semenov. We detail the remarkable proof recently given by Muchnik for the theorem of Cobham-Semenov, the original proof being published in Russian. 1 Introduction This paper is a survey on the remarkable theorem of A. Cobham stating that the only sets of numbers recognizable by automata, independently of the base of repre- sentation, are those which are ultimately periodic. The proof given by Cobham, even if it is elementary, is rather difficult [15]. In his book [24], S. Eilenberg proposed as a challenge to find a more reasonable proof. Since this date, some researchers found more comprehensible proofs for subsets of N, and more generally of Nm.Themore recent works demonstrate the power of first-order logic in the study of recognizable sets of numbers [54, 49, 50]. One aim of this paper is to collect, from B¨uchi to Muchnik's works [9, 54], all the base-dependence properties of sets of numbers recognizable by finite automata, with some emphasis on logical arguments. It contains several examples and some logical proofs. In particular, the fascinating proof recently given by A. Muchnik is detailed ∗This work was partially supported by ESPRIT-BRA Working Group 6317 ASMICS and Co- operation Project C.G.R.I.-C.N.R.S. -
An Introduction to Operad Theory
AN INTRODUCTION TO OPERAD THEORY SAIMA SAMCHUCK-SCHNARCH Abstract. We give an introduction to category theory and operad theory aimed at the undergraduate level. We first explore operads in the category of sets, and then generalize to other familiar categories. Finally, we develop tools to construct operads via generators and relations, and provide several examples of operads in various categories. Throughout, we highlight the ways in which operads can be seen to encode the properties of algebraic structures across different categories. Contents 1. Introduction1 2. Preliminary Definitions2 2.1. Algebraic Structures2 2.2. Category Theory4 3. Operads in the Category of Sets 12 3.1. Basic Definitions 13 3.2. Tree Diagram Visualizations 14 3.3. Morphisms and Algebras over Operads of Sets 17 4. General Operads 22 4.1. Basic Definitions 22 4.2. Morphisms and Algebras over General Operads 27 5. Operads via Generators and Relations 33 5.1. Quotient Operads and Free Operads 33 5.2. More Examples of Operads 38 5.3. Coloured Operads 43 References 44 1. Introduction Sets equipped with operations are ubiquitous in mathematics, and many familiar operati- ons share key properties. For instance, the addition of real numbers, composition of functions, and concatenation of strings are all associative operations with an identity element. In other words, all three are examples of monoids. Rather than working with particular examples of sets and operations directly, it is often more convenient to abstract out their common pro- perties and work with algebraic structures instead. For instance, one can prove that in any monoid, arbitrarily long products x1x2 ··· xn have an unambiguous value, and thus brackets 2010 Mathematics Subject Classification. -
Discriminants and the Monoid of Quadratic Rings
DISCRIMINANTS AND THE MONOID OF QUADRATIC RINGS JOHN VOIGHT Abstract. We consider the natural monoid structure on the set of quadratic rings over an arbitrary base scheme and characterize this monoid in terms of discriminants. Quadratic field extensions K of Q are characterized by their discriminants. Indeed, there is a bijection 8Separable quadratic9 < = ∼ × ×2 algebras over Q −! Q =Q : up to isomorphism ; p 2 ×2 Q[ d] = Q[x]=(x − d) 7! dQ wherep a separable quadratic algebra over Q is either a quadratic field extension or the algebra Q[ 1] ' Q × Q of discriminant 1. In particular, the set of isomorphism classes of separable quadratic extensions of Q can be given the structure of an elementary abelian 2-group, with identity element the class of Q × Q: we have simply p p p Q[ d1] ∗ Q[ d2] = Q[ d1d2] p × ×2 up to isomorphism. If d1; d2; dp1d2 2pQ n Q then Q( d1d2) sits as the third quadratic subfield of the compositum Q( d1; d2): p p Q( d1; d2) p p p Q( d1) Q( d1d2) Q( d2) Q p Indeed, ifpσ1 isp the nontrivial elementp of Gal(Q( d1)=Q), then therep is a unique extension of σ1 to Q( d1; d2) leaving Q( d2) fixed, similarly with σ2, and Q( d1d2) is the fixed field of the composition σ1σ2 = σ2σ1. This characterization of quadratic extensions works over any base field F with char F 6= 2 and is summarized concisely in the Kummer theory isomorphism H1(Gal(F =F ); {±1g) = Hom(Gal(F =F ); {±1g) ' F ×=F ×2: On the other hand, over a field F with char F = 2, all separable quadratic extensions have trivial discriminant and instead they are classified by the (additive) Artin-Schreier group F=}(F ) where }(F ) = fr + r2 : r 2 F g Date: January 17, 2021. -
Language and Automata Theory and Applications
LANGUAGE AND AUTOMATA THEORY AND APPLICATIONS Carlos Martín-Vide Characterization • It deals with the description of properties of sequences of symbols • Such an abstract characterization explains the interdisciplinary flavour of the field • The theory grew with the need of formalizing and describing the processes linked with the use of computers and communication devices, but its origins are within mathematical logic and linguistics A bit of history • Early roots in the work of logicians at the beginning of the XXth century: Emil Post, Alonzo Church, Alan Turing Developments motivated by the search for the foundations of the notion of proof in mathematics (Hilbert) • After the II World War: Claude Shannon, Stephen Kleene, John von Neumann Development of computers and telecommunications Interest in exploring the functions of the human brain • Late 50s XXth century: Noam Chomsky Formal methods to describe natural languages • Last decades Molecular biology considers the sequences of molecules formed by genomes as sequences of symbols on the alphabet of basic elements Interest in describing properties like repetitions of occurrences or similarity between sequences Chomsky hierarchy of languages • Finite-state or regular • Context-free • Context-sensitive • Recursively enumerable REG ⊂ CF ⊂ CS ⊂ RE Finite automata: origins • Warren McCulloch & Walter Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5:115-133, 1943 • Stephen C. Kleene. Representation of events in nerve nets and -
2020 SIGACT REPORT SIGACT EC – Eric Allender, Shuchi Chawla, Nicole Immorlica, Samir Khuller (Chair), Bobby Kleinberg September 14Th, 2020
2020 SIGACT REPORT SIGACT EC – Eric Allender, Shuchi Chawla, Nicole Immorlica, Samir Khuller (chair), Bobby Kleinberg September 14th, 2020 SIGACT Mission Statement: The primary mission of ACM SIGACT (Association for Computing Machinery Special Interest Group on Algorithms and Computation Theory) is to foster and promote the discovery and dissemination of high quality research in the domain of theoretical computer science. The field of theoretical computer science is the rigorous study of all computational phenomena - natural, artificial or man-made. This includes the diverse areas of algorithms, data structures, complexity theory, distributed computation, parallel computation, VLSI, machine learning, computational biology, computational geometry, information theory, cryptography, quantum computation, computational number theory and algebra, program semantics and verification, automata theory, and the study of randomness. Work in this field is often distinguished by its emphasis on mathematical technique and rigor. 1. Awards ▪ 2020 Gödel Prize: This was awarded to Robin A. Moser and Gábor Tardos for their paper “A constructive proof of the general Lovász Local Lemma”, Journal of the ACM, Vol 57 (2), 2010. The Lovász Local Lemma (LLL) is a fundamental tool of the probabilistic method. It enables one to show the existence of certain objects even though they occur with exponentially small probability. The original proof was not algorithmic, and subsequent algorithmic versions had significant losses in parameters. This paper provides a simple, powerful algorithmic paradigm that converts almost all known applications of the LLL into randomized algorithms matching the bounds of the existence proof. The paper further gives a derandomized algorithm, a parallel algorithm, and an extension to the “lopsided” LLL. -
Binary Integer Programming and Its Use for Envelope Determination
Binary Integer Programming and its Use for Envelope Determination By Vladimir Y. Lunin1,2, Alexandre Urzhumtsev3,† & Alexander Bockmayr2 1 Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Pushchino, Moscow Region, 140292 Russia 2 LORIA, UMR 7503, Faculté des Sciences, Université Henri Poincaré, Nancy I, 54506 Vandoeuvre-les-Nancy, France; [email protected] 3 LCM3B, UMR 7036 CNRS, Faculté des Sciences, Université Henri Poincaré, Nancy I, 54506 Vandoeuvre-les-Nancy, France; [email protected] † to whom correspondence must be sent Abstract The density values are linked to the observed magnitudes and unknown phases by a system of non-linear equations. When the object of search is a binary envelope rather than a continuous function of the electron density distribution, these equations can be replaced by a system of linear inequalities with respect to binary unknowns and powerful tools of integer linear programming may be applied to solve the phase problem. This novel approach was tested with calculated and experimental data for a known protein structure. 1. Introduction Binary Integer Programming (BIP in what follows) is an approach to solve a system of linear inequalities in binary unknowns (0 or 1 in what follows). Integer programming has been studied in mathematics, computer science, and operations research for more than 40 years (see for example Johnson et al., 2000 and Bockmayr & Kasper, 1998, for a review). It has been successfully applied to solve a huge number of large-scale combinatorial problems. The general form of an integer linear programming problem is max { cTx | Ax ≤ b, x ∈ Zn } (1.1) with a real matrix A of a dimension m by n, and vectors c ∈ Rn, b ∈ Rm, cTx being the scalar product of the vectors c and x. -
Making a Faster Curry with Extensional Types
Making a Faster Curry with Extensional Types Paul Downen Simon Peyton Jones Zachary Sullivan Microsoft Research Zena M. Ariola Cambridge, UK University of Oregon [email protected] Eugene, Oregon, USA [email protected] [email protected] [email protected] Abstract 1 Introduction Curried functions apparently take one argument at a time, Consider these two function definitions: which is slow. So optimizing compilers for higher-order lan- guages invariably have some mechanism for working around f1 = λx: let z = h x x in λy:e y z currying by passing several arguments at once, as many as f = λx:λy: let z = h x x in e y z the function can handle, which is known as its arity. But 2 such mechanisms are often ad-hoc, and do not work at all in higher-order functions. We show how extensional, call- It is highly desirable for an optimizing compiler to η ex- by-name functions have the correct behavior for directly pand f1 into f2. The function f1 takes only a single argu- expressing the arity of curried functions. And these exten- ment before returning a heap-allocated function closure; sional functions can stand side-by-side with functions native then that closure must subsequently be called by passing the to practical programming languages, which do not use call- second argument. In contrast, f2 can take both arguments by-name evaluation. Integrating call-by-name with other at once, without constructing an intermediate closure, and evaluation strategies in the same intermediate language ex- this can make a huge difference to run-time performance in presses the arity of a function in its type and gives a princi- practice [Marlow and Peyton Jones 2004]. -
Self-Similarity in the Foundations
Self-similarity in the Foundations Paul K. Gorbow Thesis submitted for the degree of Ph.D. in Logic, defended on June 14, 2018. Supervisors: Ali Enayat (primary) Peter LeFanu Lumsdaine (secondary) Zachiri McKenzie (secondary) University of Gothenburg Department of Philosophy, Linguistics, and Theory of Science Box 200, 405 30 GOTEBORG,¨ Sweden arXiv:1806.11310v1 [math.LO] 29 Jun 2018 2 Contents 1 Introduction 5 1.1 Introductiontoageneralaudience . ..... 5 1.2 Introduction for logicians . .. 7 2 Tour of the theories considered 11 2.1 PowerKripke-Plateksettheory . .... 11 2.2 Stratifiedsettheory ................................ .. 13 2.3 Categorical semantics and algebraic set theory . ....... 17 3 Motivation 19 3.1 Motivation behind research on embeddings between models of set theory. 19 3.2 Motivation behind stratified algebraic set theory . ...... 20 4 Logic, set theory and non-standard models 23 4.1 Basiclogicandmodeltheory ............................ 23 4.2 Ordertheoryandcategorytheory. ...... 26 4.3 PowerKripke-Plateksettheory . .... 28 4.4 First-order logic and partial satisfaction relations internal to KPP ........ 32 4.5 Zermelo-Fraenkel set theory and G¨odel-Bernays class theory............ 36 4.6 Non-standardmodelsofsettheory . ..... 38 5 Embeddings between models of set theory 47 5.1 Iterated ultrapowers with special self-embeddings . ......... 47 5.2 Embeddingsbetweenmodelsofsettheory . ..... 57 5.3 Characterizations.................................. .. 66 6 Stratified set theory and categorical semantics 73 6.1 Stratifiedsettheoryandclasstheory . ...... 73 6.2 Categoricalsemantics ............................... .. 77 7 Stratified algebraic set theory 85 7.1 Stratifiedcategoriesofclasses . ..... 85 7.2 Interpretation of the Set-theories in the Cat-theories ................ 90 7.3 ThesubtoposofstronglyCantorianobjects . ....... 99 8 Where to go from here? 103 8.1 Category theoretic approach to embeddings between models of settheory . -
COMPUTER MODELS and AUTOMATA THEORY in BIOLOGY and MEDICINE Ion C
Mathematical Modelling, Vol. 7, pp. 1513-1577,1986 Printed in the U,S.A. All rights reserved. Copyright C 1986 Pergamon Journals Ltd. REVIEW ARTICLE COMPUTER MODELS AND AUTOMATA THEORY IN BIOLOGY AND MEDICINE Ion C. Baianu University of Ulinois at Urbana Physical Chemistry and NMR Laboratories 567 Bevier Hall, 905 S. Goodwin Ave .Urbana, UIinois 61801 (Received 27 February 1985; revised 12 September 1985) 1. INTRODUCTION The applications of computers to biological and biomedical problem solving goes back to the very beginnings of computer science, automata theory [1], and mathematical biology [2]. With the advent of more versatile and powerful computers, biological and biomedical applications of computers have proliferated so rapidly that it would be virtually impossible to compile a comprehensive review of all developments in this field. Limitations of computer simulations in biology have also come under close scrutiny, and claims have been made that biological systems have limited information processing power [3]. Such general conjectures do not, however, deter biologists and biomedical researchers from developing new computer applications in biology and medicine. Microprocessors are being widely employed in biological laboratories both for automatic data acquisition/processing and modeling; one particular area, which is of great biomedical interest, involves fast digital image processing and is already established for routine clinical examinations in radiological and nuclear medicine centers, Powerful techniques for biological research are routinely employing dedicated, on-line microprocessors or array processors; among such techniques are: Fourier-transform nuclear magnetic resonance (NMR), NMR imaging (or tomography), x-ray tomography, x-ray diffraction, high performance liquid chromatography, differential scanning calorimetry and mass spectrometry. -
A Mathematical Theory of Computation?
A Mathematical Theory of Computation? Simone Martini Dipartimento di Informatica { Scienza e Ingegneria Alma mater studiorum • Universit`adi Bologna and INRIA FoCUS { Sophia / Bologna Lille, February 1, 2017 1 / 57 Reflect and trace the interaction of mathematical logic and programming (languages), identifying some of the driving forces of this process. Previous episodes: Types HaPOC 2015, Pisa: from 1955 to 1970 (circa) Cie 2016, Paris: from 1965 to 1975 (circa) 2 / 57 Why types? Modern programming languages: control flow specification: small fraction abstraction mechanisms to model application domains. • Types are a crucial building block of these abstractions • And they are a mathematical logic concept, aren't they? 3 / 57 Why types? Modern programming languages: control flow specification: small fraction abstraction mechanisms to model application domains. • Types are a crucial building block of these abstractions • And they are a mathematical logic concept, aren't they? 4 / 57 We today conflate: Types as an implementation (representation) issue Types as an abstraction mechanism Types as a classification mechanism (from mathematical logic) 5 / 57 The quest for a \Mathematical Theory of Computation" How does mathematical logic fit into this theory? And for what purposes? 6 / 57 The quest for a \Mathematical Theory of Computation" How does mathematical logic fit into this theory? And for what purposes? 7 / 57 Prehistory 1947 8 / 57 Goldstine and von Neumann [. ] coding [. ] has to be viewed as a logical problem and one that represents a new branch of formal logics. Hermann Goldstine and John von Neumann Planning and Coding of problems for an Electronic Computing Instrument Report on the mathematical and logical aspects of an electronic computing instrument, Part II, Volume 1-3, April 1947. -
On the Hall Algebra of Semigroup Representations Over F1
ON THE HALL ALGEBRA OF SEMIGROUP REPRESENTATIONS OVER F1 MATT SZCZESNY Abstract. Let A be a finitely generated semigroup with 0. An A–module over F1 (also called an A–set), is a pointed set (M, ∗) together with an action of A. We define and study the Hall algebra HA of the category CA of finite A–modules. HA is shown to be the universal enveloping algebra of a Lie algebra nA, called the Hall Lie algebra of CA. In the case of the hti - the free monoid on one generator hti, the Hall algebra (or more precisely the Hall algebra of the subcategory of nilpotent hti-modules) is isomorphic to Kreimer’s Hopf algebra of rooted forests. This perspective allows us to define two new commutative operations on rooted forests. We also consider the examples when A is a quotient of hti by a congruence, and the monoid G ∪{0} for a finite group G. 1. introduction The aim of this paper is to define and study the Hall algebra of the category of set- theoretic representations of a semigroup. Classically, Hall algebras have been studied in the context abelian categories linear over finite fields Fq. Given such a category A, finitary in the sense that Hom(M, N)andExt1(M, N) are finite-dimensional ∀ M, N ∈ A (and therefore finite sets), we may construct from A an associative algebra HA defined 1 over Z , called the Ringel-Hall algebra of A. Asa Z–module, HA is freely generated by the isomorphism classes of objects in A, and its structure constants are expressed in terms of the number of extensions between objects. -
Solving Multiclass Learning Problems Via Error-Correcting Output Codes
Journal of Articial Intelligence Research Submitted published Solving Multiclass Learning Problems via ErrorCorrecting Output Co des Thomas G Dietterich tgdcsorstedu Department of Computer Science Dearborn Hal l Oregon State University Corval lis OR USA Ghulum Bakiri ebisaccuobbh Department of Computer Science University of Bahrain Isa Town Bahrain Abstract Multiclass learning problems involve nding a denition for an unknown function f x whose range is a discrete set containing k values ie k classes The denition is acquired by studying collections of training examples of the form hx f x i Existing ap i i proaches to multiclass learning problems include direct application of multiclass algorithms such as the decisiontree algorithms C and CART application of binary concept learning algorithms to learn individual binary functions for each of the k classes and application of binary concept learning algorithms with distributed output representations This pap er compares these three approaches to a new technique in which errorcorrecting co des are employed as a distributed output representation We show that these output representa tions improve the generalization p erformance of b oth C and backpropagation on a wide range of multiclass learning tasks We also demonstrate that this approach is robust with resp ect to changes in the size of the training sample the assignment of distributed represen tations to particular classes and the application of overtting avoidance techniques such as decisiontree pruning Finally we show thatlike