Our Sponsors
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
Simplicial Sets, Nerves of Categories, Kan Complexes, Etc
SIMPLICIAL SETS, NERVES OF CATEGORIES, KAN COMPLEXES, ETC FOLING ZOU These notes are taken from Peter May's classes in REU 2018. Some notations may be changed to the note taker's preference and some detailed definitions may be skipped and can be found in other good notes such as [2] or [3]. The note taker is responsible for any mistakes. 1. simplicial approach to defining homology Defnition 1. A simplical set/group/object K is a sequence of sets/groups/objects Kn for each n ≥ 0 with face maps: di : Kn ! Kn−1; 0 ≤ i ≤ n and degeneracy maps: si : Kn ! Kn+1; 0 ≤ i ≤ n satisfying certain commutation equalities. Images of degeneracy maps are said to be degenerate. We can define a functor: ordered abstract simplicial complex ! sSet; K 7! Ks; where s Kn = fv0 ≤ · · · ≤ vnjfv0; ··· ; vng (may have repetition) is a simplex in Kg: s s Face maps: di : Kn ! Kn−1; 0 ≤ i ≤ n is by deleting vi; s s Degeneracy maps: si : Kn ! Kn+1; 0 ≤ i ≤ n is by repeating vi: In this way it is very straightforward to remember the equalities that face maps and degeneracy maps have to satisfy. The simplical viewpoint is helpful in establishing invariants and comparing different categories. For example, we are going to define the integral homology of a simplicial set, which will agree with the simplicial homology on a simplical complex, but have the virtue of avoiding the barycentric subdivision in showing functoriality and homotopy invariance of homology. This is an observation made by Samuel Eilenberg. To start, we construct functors: F C sSet sAb ChZ: The functor F is the free abelian group functor applied levelwise to a simplical set. -
The ACT Pipeline from Abstraction to Application
Addressing hypercomplexity: The ACT pipeline from abstraction to application David I. Spivak (Joint with Brendan Fong, Paolo Perrone, Evan Patterson) Department of Mathematics Massachusetts Institute of Technology 0 / 25 Introduction Outline 1 Introduction Why? How? What? Plan of the talk 2 Some new applications 3 Catlab.jl 4 Conclusion 0 / 25 Introduction Why? What's wrong? As problem complexity increases, good organization becomes crucial. (Courtesy of Spencer Breiner, NIST) 1 / 25 Introduction Why? The problem is hypercomplexity The world is becoming more complex. We need to deal with more and more diverse sorts of interaction. Rather than closed dynamical systems, everything is open. The state of one system affects those of systems in its environment. Examples are everywhere. Designing anything requires input from more diverse stakeholders. Software is developed by larger teams, touching a common codebase. Scientists need to use experimental data obtained by their peers. (But the data structure and assumptions don't simply line up.) We need to handle this new complexity; traditional methods are faltering. 2 / 25 Introduction How? How might we deal with hypercomplexity? Organize the problem spaces effectively, as objects in their own right. Find commonalities in the various problems we work on. Consider the ways we build complex problems out of simple ones. Think of problem spaces and solutions as mathematical entities. With that in hand, how do you solve a hypercomplex problem? Migrating subproblems to groups who can solve them. Take the returned solutions and fit them together. Ensure in advance that these solutions will compose. Do this according the mathematical problem-space structure. -
Diagrammatics in Categorification and Compositionality
Diagrammatics in Categorification and Compositionality by Dmitry Vagner Department of Mathematics Duke University Date: Approved: Ezra Miller, Supervisor Lenhard Ng Sayan Mukherjee Paul Bendich Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Mathematics in the Graduate School of Duke University 2019 ABSTRACT Diagrammatics in Categorification and Compositionality by Dmitry Vagner Department of Mathematics Duke University Date: Approved: Ezra Miller, Supervisor Lenhard Ng Sayan Mukherjee Paul Bendich An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Mathematics in the Graduate School of Duke University 2019 Copyright c 2019 by Dmitry Vagner All rights reserved Abstract In the present work, I explore the theme of diagrammatics and their capacity to shed insight on two trends|categorification and compositionality|in and around contemporary category theory. The work begins with an introduction of these meta- phenomena in the context of elementary sets and maps. Towards generalizing their study to more complicated domains, we provide a self-contained treatment|from a pedagogically novel perspective that introduces almost all concepts via diagrammatic language|of the categorical machinery with which we may express the broader no- tions found in the sequel. The work then branches into two seemingly unrelated disciplines: dynamical systems and knot theory. In particular, the former research defines what it means to compose dynamical systems in a manner analogous to how one composes simple maps. The latter work concerns the categorification of the slN link invariant. In particular, we use a virtual filtration to give a more diagrammatic reconstruction of Khovanov-Rozansky homology via a smooth TQFT. -
Homotopical Categories: from Model Categories to ( ,)-Categories ∞
HOMOTOPICAL CATEGORIES: FROM MODEL CATEGORIES TO ( ;1)-CATEGORIES 1 EMILY RIEHL Abstract. This chapter, written for Stable categories and structured ring spectra, edited by Andrew J. Blumberg, Teena Gerhardt, and Michael A. Hill, surveys the history of homotopical categories, from Gabriel and Zisman’s categories of frac- tions to Quillen’s model categories, through Dwyer and Kan’s simplicial localiza- tions and culminating in ( ;1)-categories, first introduced through concrete mod- 1 els and later re-conceptualized in a model-independent framework. This reader is not presumed to have prior acquaintance with any of these concepts. Suggested exercises are included to fertilize intuitions and copious references point to exter- nal sources with more details. A running theme of homotopy limits and colimits is included to explain the kinds of problems homotopical categories are designed to solve as well as technical approaches to these problems. Contents 1. The history of homotopical categories 2 2. Categories of fractions and localization 5 2.1. The Gabriel–Zisman category of fractions 5 3. Model category presentations of homotopical categories 7 3.1. Model category structures via weak factorization systems 8 3.2. On functoriality of factorizations 12 3.3. The homotopy relation on arrows 13 3.4. The homotopy category of a model category 17 3.5. Quillen’s model structure on simplicial sets 19 4. Derived functors between model categories 20 4.1. Derived functors and equivalence of homotopy theories 21 4.2. Quillen functors 24 4.3. Derived composites and derived adjunctions 25 4.4. Monoidal and enriched model categories 27 4.5. -
Category Theory Cheat Sheet
Musa Al-hassy https://github.com/alhassy/CatsCheatSheet July 5, 2019 Even when morphisms are functions, the objects need not be sets: Sometimes the objects . are operations —with an appropriate definition of typing for the functions. The categories Reference Sheet for Elementary Category Theory of F -algebras are an example of this. “Gluing” Morphisms Together Categories Traditional function application is replaced by the more generic concept of functional A category C consists of a collection of “objects” Obj C, a collection of “(homo)morphisms” composition suggested by morphism-arrow chaining: HomC(a; b) for any a; b : Obj C —also denoted “a !C b”—, an operation Id associating Whenever we have two morphisms such that the target type of one of them, say g : B A a morphism Ida : Hom(a; a) to each object a, and a dependently-typed “composition” is the same as the source type of the other, say f : C B then “f after g”, their composite operation _ ◦ _ : 8fABC : Objg ! Hom(B; C) ! Hom(A; B) ! Hom(A; C) that is morphism, f ◦ g : C A can be defined. It “glues” f and g together, “sequentially”: required to be associative with Id as identity. It is convenient to define a pair of operations src; tgt from morphisms to objects as follows: f g f◦g C − B − A ) C − A composition-Type f : X !C Y ≡ src f = X ^ tgt f = Y src; tgt-Definition Composition is the basis for gluing morphisms together to build more complex morphisms. Instead of HomC we can instead assume primitive a ternary relation _ : _ !C _ and However, not every two morphisms can be glued together by composition. -
Yoneda's Lemma for Internal Higher Categories
YONEDA'S LEMMA FOR INTERNAL HIGHER CATEGORIES LOUIS MARTINI Abstract. We develop some basic concepts in the theory of higher categories internal to an arbitrary 1- topos. We define internal left and right fibrations and prove a version of the Grothendieck construction and of Yoneda's lemma for internal categories. Contents 1. Introduction 2 Motivation 2 Main results 3 Related work 4 Acknowledgment 4 2. Preliminaries 4 2.1. General conventions and notation4 2.2. Set theoretical foundations5 2.3. 1-topoi 5 2.4. Universe enlargement 5 2.5. Factorisation systems 8 3. Categories in an 1-topos 10 3.1. Simplicial objects in an 1-topos 10 3.2. Categories in an 1-topos 12 3.3. Functoriality and base change 16 3.4. The (1; 2)-categorical structure of Cat(B) 18 3.5. Cat(S)-valued sheaves on an 1-topos 19 3.6. Objects and morphisms 21 3.7. The universe for groupoids 23 3.8. Fully faithful and essentially surjective functors 26 arXiv:2103.17141v2 [math.CT] 2 May 2021 3.9. Subcategories 31 4. Groupoidal fibrations and Yoneda's lemma 36 4.1. Left fibrations 36 4.2. Slice categories 38 4.3. Initial functors 42 4.4. Covariant equivalences 49 4.5. The Grothendieck construction 54 4.6. Yoneda's lemma 61 References 71 Date: May 4, 2021. 1 2 LOUIS MARTINI 1. Introduction Motivation. In various areas of geometry, one of the principal strategies is to study geometric objects by means of algebraic invariants such as cohomology, K-theory and (stable or unstable) homotopy groups. -
![Cubical Sets and the Topological Topos Arxiv:1610.05270V1 [Cs.LO]](https://docslib.b-cdn.net/cover/9297/cubical-sets-and-the-topological-topos-arxiv-1610-05270v1-cs-lo-709297.webp)
Cubical Sets and the Topological Topos Arxiv:1610.05270V1 [Cs.LO]
Cubical sets and the topological topos Bas Spitters Aarhus University October 18, 2016 Abstract Coquand's cubical set model for homotopy type theory provides the basis for a com- putational interpretation of the univalence axiom and some higher inductive types, as implemented in the cubical proof assistant. This paper contributes to the understand- ing of this model. We make three contributions: 1. Johnstone's topological topos was created to present the geometric realization of simplicial sets as a geometric morphism between toposes. Johnstone shows that simplicial sets classify strict linear orders with disjoint endpoints and that (classically) the unit interval is such an order. Here we show that it can also be a target for cubical realization by showing that Coquand's cubical sets classify the geometric theory of flat distributive lattices. As a side result, we obtain a simplicial realization of a cubical set. 2. Using the internal `interval' in the topos of cubical sets, we construct a Moore path model of identity types. 3. We construct a premodel structure internally in the cubical type theory and hence on the fibrant objects in cubical sets. 1 Introduction Simplicial sets from a standard framework for homotopy theory. The topos of simplicial sets is the classifying topos of the theory of strict linear orders with endpoints. Cubical arXiv:1610.05270v1 [cs.LO] 17 Oct 2016 sets turn out to be more amenable to a constructive treatment of homotopy type theory. We consider the cubical set model in [CCHM15]. This consists of symmetric cubical sets with connections (^; _), reversions ( ) and diagonals. In fact, to present the geometric realization clearly, we will leave out the reversions. -
INTRODUCTION to TEST CATEGORIES These Notes Were
INTRODUCTION TO TEST CATEGORIES TALK BY MAREK ZAWADOWSKI; NOTES BY CHRIS KAPULKIN These notes were taken and LATEX'd by Chris Kapulkin, from Marek Za- wadowski's lecture that was an introduction to the theory of test categories. In modern homotopy theory it is common to work with the category op Sets∆ of simplicial sets instead of the category category Top of topo- logical spaces. These categories are Quillen equivalent, however the former enjoys many good properties that the latter lacks and which make it a good framework to address many homotopy-theoretic questions. It is natural to ask: what is so special about the category ∆? In these notes we will try to characterize categories that can equally well as ∆ serve as an environ- ment for homotopy theory. The examples of such categories include, among others, the cube category and Joyal's Θ. These notes are organized as follows. In sections1 and2 we review some standard results about the nerve functor(s) and the homotopy category, re- spectively. In section3 we introduce test categories, provide their character- ization, and give some examples. In section4 we define test functors and as in the previous section: provide their characterization and some examples. 1. Background on Nerve Functors op The nerve functor N : Cat / Sets∆ is given by: N (C)n = Cat(j[n];C); where j : ∆ ,−! Cat is the obvious inclusion. It has a left adjoint given by the left Kan extension: C:=Lanyj op ? ) Sets∆ o Cat c N = y j ∆ Note that this basic setup depends only on Cat being cocomplete and j being an arbitrary covariant functor. -
Lecture Notes on Simplicial Homotopy Theory
Lectures on Homotopy Theory The links below are to pdf files, which comprise my lecture notes for a first course on Homotopy Theory. I last gave this course at the University of Western Ontario during the Winter term of 2018. The course material is widely applicable, in fields including Topology, Geometry, Number Theory, Mathematical Pysics, and some forms of data analysis. This collection of files is the basic source material for the course, and this page is an outline of the course contents. In practice, some of this is elective - I usually don't get much beyond proving the Hurewicz Theorem in classroom lectures. Also, despite the titles, each of the files covers much more material than one can usually present in a single lecture. More detail on topics covered here can be found in the Goerss-Jardine book Simplicial Homotopy Theory, which appears in the References. It would be quite helpful for a student to have a background in basic Algebraic Topology and/or Homological Algebra prior to working through this course. J.F. Jardine Office: Middlesex College 118 Phone: 519-661-2111 x86512 E-mail: [email protected] Homotopy theories Lecture 01: Homological algebra Section 1: Chain complexes Section 2: Ordinary chain complexes Section 3: Closed model categories Lecture 02: Spaces Section 4: Spaces and homotopy groups Section 5: Serre fibrations and a model structure for spaces Lecture 03: Homotopical algebra Section 6: Example: Chain homotopy Section 7: Homotopical algebra Section 8: The homotopy category Lecture 04: Simplicial sets Section 9: -
SHEAVES, TOPOSES, LANGUAGES Acceleration Based on A
Chapter 7 Logic of behavior: Sheaves, toposes, and internal languages 7.1 How can we prove our machine is safe? Imagine you are trying to design a system of interacting components. You wouldn’t be doing this if you didn’t have a goal in mind: you want the system to do something, to behave in a certain way. In other words, you want to restrict its possibilities to a smaller set: you want the car to remain on the road, you want the temperature to remain in a particular range, you want the bridge to be safe for trucks to pass. Out of all the possibilities, your system should only permit some. Since your system is made of components that interact in specified ways, the possible behavior of the whole—in any environment—is determined by the possible behaviors of each of its components in their local environments, together with the precise way in which they interact.1 In this chapter, we will discuss a logic wherein one can describe general types of behavior that occur over time, and prove properties of a larger-scale system from the properties and interaction patterns of its components. For example, suppose we want an autonomous vehicle to maintain a distance of some safe R from other objects. To do so, several components must interact: a 2 sensor that approximates the real distance by an internal variable S0, a controller that uses S0 to decide what action A to take, and a motor that moves the vehicle with an 1 The well-known concept of emergence is not about possibilities, it is about prediction. -
UNIVERSITY of CALIFORNIA RIVERSIDE the Grothendieck
UNIVERSITY OF CALIFORNIA RIVERSIDE The Grothendieck Construction in Categorical Network Theory A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Mathematics by Joseph Patrick Moeller December 2020 Dissertation Committee: Dr. John C. Baez, Chairperson Dr. Wee Liang Gan Dr. Carl Mautner Copyright by Joseph Patrick Moeller 2020 The Dissertation of Joseph Patrick Moeller is approved: Committee Chairperson University of California, Riverside Acknowledgments First of all, I owe all of my achievements to my wife, Paola. I couldn't have gotten here without my parents: Daniel, Andrea, Tonie, Maria, and Luis, or my siblings: Danielle, Anthony, Samantha, David, and Luis. I would like to thank my advisor, John Baez, for his support, dedication, and his unique and brilliant style of advising. I could not have become the researcher I am under another's instruction. I would also like to thank Christina Vasilakopoulou, whose kindness, energy, and expertise cultivated a deeper appreciation of category theory in me. My expe- rience was also greatly enriched by my academic siblings: Daniel Cicala, Kenny Courser, Brandon Coya, Jason Erbele, Jade Master, Franciscus Rebro, and Christian Williams, and by my cohort: Justin Davis, Ethan Kowalenko, Derek Lowenberg, Michel Manrique, and Michael Pierce. I would like to thank the UCR math department. Professors from whom I learned a ton of algebra, topology, and category theory include Julie Bergner, Vyjayanthi Chari, Wee-Liang Gan, Jos´eGonzalez, Jacob Greenstein, Carl Mautner, Reinhard Schultz, and Steffano Vidussi. Special thanks goes to the department chair Yat-Sun Poon, as well as Margarita Roman, Randy Morgan, and James Marberry, and many others who keep the whole thing together. -
Mathematics and the Brain: a Category Theoretical Approach to Go Beyond the Neural Correlates of Consciousness
entropy Review Mathematics and the Brain: A Category Theoretical Approach to Go Beyond the Neural Correlates of Consciousness 1,2,3, , 4,5,6,7, 8, Georg Northoff * y, Naotsugu Tsuchiya y and Hayato Saigo y 1 Mental Health Centre, Zhejiang University School of Medicine, Hangzhou 310058, China 2 Institute of Mental Health Research, University of Ottawa, Ottawa, ON K1Z 7K4 Canada 3 Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 310036, China 4 School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria 3800, Australia; [email protected] 5 Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria 3800, Australia 6 Advanced Telecommunication Research, Soraku-gun, Kyoto 619-0288, Japan 7 Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Osaka 565-0871, Japan 8 Nagahama Institute of Bio-Science and Technology, Nagahama 526-0829, Japan; [email protected] * Correspondence: georg.northoff@theroyal.ca All authors contributed equally to the paper as it was a conjoint and equally distributed work between all y three authors. Received: 18 July 2019; Accepted: 9 October 2019; Published: 17 December 2019 Abstract: Consciousness is a central issue in neuroscience, however, we still lack a formal framework that can address the nature of the relationship between consciousness and its physical substrates. In this review, we provide a novel mathematical framework of category theory (CT), in which we can define and study the sameness between different domains of phenomena such as consciousness and its neural substrates. CT was designed and developed to deal with the relationships between various domains of phenomena.