Continuity in Enriched Categories and Metric Model Theory
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An Axiomatic Approach to Physical Systems
' An Axiomatic Approach $ to Physical Systems & % Januari 2004 ❦ ' Summary $ Mereology is generally considered as a formal theory of the part-whole relation- ship concerning material bodies, such as planets, pickles and protons. We argue that mereology can be considered more generally as axiomatising the concept of a physical system, such as a planet in a gravitation-potential, a pickle in heart- burn and a proton in an electro-magnetic field. We design a theory of sets and physical systems by extending standard set-theory (ZFC) with mereological axioms. The resulting theory deductively extends both `Mereology' of Tarski & L`esniewski as well as the well-known `calculus of individuals' of Leonard & Goodman. We prove a number of theorems and demonstrate that our theory extends ZFC con- servatively and hence equiconsistently. We also erect a model of our theory in ZFC. The lesson to be learned from this paper reads that not only is a marriage between standard set-theory and mereology logically respectable, leading to a rigorous vin- dication of how physicists talk about physical systems, but in addition that sets and physical systems interact at the formal level both quite smoothly and non- trivially. & % Contents 0 Pre-Mereological Investigations 1 0.0 Overview . 1 0.1 Motivation . 1 0.2 Heuristics . 2 0.3 Requirements . 5 0.4 Extant Mereological Theories . 6 1 Mereological Investigations 8 1.0 The Language of Physical Systems . 8 1.1 The Domain of Mereological Discourse . 9 1.2 Mereological Axioms . 13 1.2.0 Plenitude vs. Parsimony . 13 1.2.1 Subsystem Axioms . 15 1.2.2 Composite Physical Systems . -
On the Boundary Between Mereology and Topology
On the Boundary Between Mereology and Topology Achille C. Varzi Istituto per la Ricerca Scientifica e Tecnologica, I-38100 Trento, Italy [email protected] (Final version published in Roberto Casati, Barry Smith, and Graham White (eds.), Philosophy and the Cognitive Sciences. Proceedings of the 16th International Wittgenstein Symposium, Vienna, Hölder-Pichler-Tempsky, 1994, pp. 423–442.) 1. Introduction Much recent work aimed at providing a formal ontology for the common-sense world has emphasized the need for a mereological account to be supplemented with topological concepts and principles. There are at least two reasons under- lying this view. The first is truly metaphysical and relates to the task of charac- terizing individual integrity or organic unity: since the notion of connectedness runs afoul of plain mereology, a theory of parts and wholes really needs to in- corporate a topological machinery of some sort. The second reason has been stressed mainly in connection with applications to certain areas of artificial in- telligence, most notably naive physics and qualitative reasoning about space and time: here mereology proves useful to account for certain basic relation- ships among things or events; but one needs topology to account for the fact that, say, two events can be continuous with each other, or that something can be inside, outside, abutting, or surrounding something else. These motivations (at times combined with others, e.g., semantic transpar- ency or computational efficiency) have led to the development of theories in which both mereological and topological notions play a pivotal role. How ex- actly these notions are related, however, and how the underlying principles should interact with one another, is still a rather unexplored issue. -
A Translation Approach to Portable Ontology Specifications
Knowledge Systems Laboratory September 1992 Technical Report KSL 92-71 Revised April 1993 A Translation Approach to Portable Ontology Specifications by Thomas R. Gruber Appeared in Knowledge Acquisition, 5(2):199-220, 1993. KNOWLEDGE SYSTEMS LABORATORY Computer Science Department Stanford University Stanford, California 94305 A Translation Approach to Portable Ontology Specifications Thomas R. Gruber Knowledge System Laboratory Stanford University 701 Welch Road, Building C Palo Alto, CA 94304 [email protected] Abstract To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse — definitions of classes, relations, functions, and other objects — is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how -
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. -
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]. -
Semantics of First-Order Logic
CSC 244/444 Lecture Notes Sept. 7, 2021 Semantics of First-Order Logic A notation becomes a \representation" only when we can explain, in a formal way, how the notation can make true or false statements about some do- main; this is what model-theoretic semantics does in logic. We now have a set of syntactic expressions for formalizing knowledge, but we have only talked about the meanings of these expressions in an informal way. We now need to provide a model-theoretic semantics for this syntax, specifying precisely what the possible ways are of using a first-order language to talk about some domain of interest. Why is this important? Because, (1) that is the only way that we can verify that the facts we write down in the representation actually can mean what we intuitively intend them to mean; representations without formal semantics have often turned out to be incoherent when examined from this perspective; and (2) that is the only way we can judge whether proposed methods of making inferences are reasonable { i.e., that they give conclusions that are guaranteed to be true whenever the premises are, or at least are probably true, or at least possibly true. Without such guarantees, we will probably end up with an irrational \intelligent agent"! Models Our goal, then, is to specify precisely how all the terms, predicates, and formulas (sen- tences) of a first-order language can be interpreted so that terms refer to individuals, predicates refer to properties and relations, and formulas are either true or false. So we begin by fixing a domain of interest, called the domain of discourse: D. -
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 . -
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 -
Solutions to Inclass Problems Week 2, Wed
Massachusetts Institute of Technology 6.042J/18.062J, Fall ’05: Mathematics for Computer Science September 14 Prof. Albert R. Meyer and Prof. Ronitt Rubinfeld revised September 13, 2005, 1279 minutes Solutions to InClass Problems Week 2, Wed. Problem 1. For each of the logical formulas, indicate whether or not it is true when the domain of discourse is N (the natural numbers 0, 1, 2, . ), Z (the integers), Q (the rationals), R (the real numbers), and C (the complex numbers). ∃x (x2 =2) ∀x ∃y (x2 =y) ∀y ∃x (x2 =y) ∀x�= 0∃y (xy =1) ∃x ∃y (x+ 2y=2)∧ (2x+ 4y=5) Solution. Statement N Z Q R √ C ∃x(x2= 2) f f f t(x= 2) t ∀x∃y(x2=y) t t t t(y=x2) t ∀y∃x(x2=y) f f f f(take y <0)t ∀x�=∃ 0y(xy= 1) f f t t(y=/x 1 ) t ∃x∃y(x+ 2y= 2)∧ (2x+ 4y=5)f f f f f � Problem 2. The goal of this problem is to translate some assertions about binary strings into logic notation. The domain of discourse is the set of all finitelength binary strings: λ, 0, 1, 00, 01, 10, 11, 000, 001, . (Here λdenotes the empty string.) In your translations, you may use all the ordinary logic symbols (including =), variables, and the binary symbols 0, 1 denoting 0, 1. A string like 01x0yof binary symbols and variables denotes the concatenation of the symbols and the binary strings represented by the variables. For example, if the value of xis 011 and the value of yis 1111, then the value of 01x0yis the binary string 0101101111. -
Current Version, Ch01-03
Digital Logic and Computer Organization Neal Nelson c May 2013 Contents 1 Numbers and Gates 5 1.1 Numbers and Primitive Data Types . .5 1.2 Representing Numbers . .6 1.2.1 Decimal and Binary Systems . .6 1.2.2 Binary Counting . .7 1.2.3 Binary Conversions . .9 1.2.4 Hexadecimal . 11 1.3 Representing Negative Numbers . 13 1.3.1 Ten's Complement . 14 1.3.2 Two's Complement Binary Representation . 18 1.3.3 Negation in Two's Complement . 20 1.3.4 Two's Complement to Decimal Conversion . 21 1.3.5 Decimal to Two's Complement Conversion . 22 1.4 Gates and Circuits . 22 1.4.1 Logic Expressions . 23 1.4.2 Circuit Expressions . 24 1.5 Exercises . 26 2 Logic Functions 29 2.1 Functions . 29 2.2 Logic Functions . 30 2.2.1 Primitive Gate Functions . 31 2.2.2 Evaluating Logic Expressions and Circuits . 31 2.2.3 Logic Tables for Expressions and Circuits . 32 2.2.4 Expressions as Functions . 34 2.3 Equivalence of Boolean Expressions . 36 2.4 Logic Functional Completeness . 36 2.5 Boolean Algebra . 37 2.6 Tables, Expressions, and Circuits . 37 1 2.6.1 Disjunctive Normal Form . 38 2.6.2 Logic Expressions from Tables . 39 2.7 Exercises . 41 3 Dataflow Logic 43 3.1 Data Types . 44 3.1.1 Unsigned Int . 45 3.1.2 Signed Int . 45 3.1.3 Char and String . 45 3.2 Data Buses . 46 3.3 Bitwise Functions . 48 3.4 Integer Functions . -
Discrete Mathematics for Computer Science I
University of Hawaii ICS141: Discrete Mathematics for Computer Science I Dept. Information & Computer Sci., University of Hawaii Originals slides by Dr. Baek and Dr. Still, adapted by J. Stelovsky Based on slides Dr. M. P. Frank and Dr. J.L. Gross Provided by McGraw-Hill ICS 141: Discrete Mathematics I – Fall 2011 4-1 University of Hawaii Lecture 4 Chapter 1. The Foundations 1.3 Predicates and Quantifiers ICS 141: Discrete Mathematics I – Fall 2011 4-2 Topic #3 – Predicate Logic Previously… University of Hawaii In predicate logic, a predicate is modeled as a proposional function P(·) from subjects to propositions. P(x): “x is a prime number” (x: any subject) P(3): “3 is a prime number.” (proposition!) Propositional functions of any number of arguments, each of which may take any grammatical role that a noun can take P(x,y,z): “x gave y the grade z” P(Mike,Mary,A): “Mike gave Mary the grade A.” ICS 141: Discrete Mathematics I – Fall 2011 4-3 Topic #3 – Predicate Logic Universe of Discourse (U.D.) University of Hawaii The power of distinguishing subjects from predicates is that it lets you state things about many objects at once. e.g., let P(x) = “x + 1 x”. We can then say, “For any number x, P(x) is true” instead of (0 + 1 0) (1 + 1 1) (2 + 1 2) ... The collection of values that a variable x can take is called x’s universe of discourse or the domain of discourse (often just referred to as the domain). ICS 141: Discrete Mathematics I – Fall 2011 4-4 Topic #3 – Predicate Logic Quantifier Expressions University of Hawaii Quantifiers provide a notation that allows us to quantify (count) how many objects in the universe of discourse satisfy the given predicate.