DOCSLIB.ORG

Class Subset Selection for Partial Domain Adaptation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Class Subset Selection for Partial Domain Adaptation Class Subset Selection for Partial Domain Adaptation Fariba Zohrizadeh Mohsen Kheirandishfard Farhad Kamangar Department of Computer Science and Engineering, University of Texas at Arlington {fariba.zohrizadeh,mohsen.kheirandishfard,farhad.kamangar}@uta.edu Abstract performance on the target domain classification task. In the absence of target labels, unsupervised domain Domain adaptation is the task of transferring knowl- adaptation (UDA) seeks to bridge different domains by edge from a labeled source dataset to an unlabeled tar- learning feature representations that are discriminative get dataset. Partial domain adaptation (PDA) investi- and domain-invariant [1, 12, 21]. gates the scenarios in which the target label space is a Recently, various approaches have been proposed subset of the source label space. The main purpose of to combine both domain adaptation and deep feature the PDA is to identify the shared classes between the learning in a unified framework for exploiting more domains and promote learning transferable knowledge transferable knowledge across domains [6, 7, 15, 18, 31, from these classes. Inspired by the idea of subset selec- 37] (see [34] for a comprehensive survey on deep domain tion, we propose an adversarial PDA approach which adaptation methods). A class of deep domain adapta- aims to not only automatically select the most relevant tion methods aims to reduce the misfit between the subset of source domain classes but also ignore the sam- distributions of the source and target domains through ples that are less transferable across the domains. In minimizing discrepancy measures such as maximum the absence of target labels, the proposed approach is mean discrepancy [15, 18], correlation distance [27, 29], able to effectively learn domain-invariant feature repre- etc. In this way, they map the domains into the same sentations, which in turn can facilitate and enhance the latent space, which results in learning feature repre- classification performance in the target domain. Em- sentations that are domain-invariant. A new line of pirical results on Office-31 and Office-Home datasets research has recently emerged which uses the concept demonstrate the high potential of the proposed approach of generative adversarial networks [13] to align feature in addressing different partial domain adaptation tasks. distributions across the domains and learn discrimina- tors that are able to predict the domain labels of dif- ferent samples [19, 23, 35]. Specifically, these methods try to generate feature representations that are difficult 1. Introduction for the discriminators to differentiate. Deep neural networks have demonstrated superior Despite the advantages offered by the existing UDA performance in a variety of machine learning prob- methods, they mostly exhibit superior performance lems such as semantic image segmentation [5, 11, 17], in scenarios in which the source and target domains object detection, and classification [16, 24, 30], etc. share the same label space. With the goal of consider- These impressive achievements heavily depend on the ing more realistic cases, [4] introduced partial domain availability of large amounts of labeled training data. adaptation (PDA) as a new adaptation scenario which However, in many applications, the acquisition of suffi- assumes the target domain label space is a subset of the cient labeled data is difficult and time-consuming. One source domain label space. The primary challenge in potential solution to reduce the labeling consumption PDA is to identify and reject the source domain classes is to build an effective predictive model using richly- that do not appear in the target domain, known as out- annotated datasets from different but related domains. lier classes, since they may have negative impacts on However, this paradigm generally suffers from the do- the transfer performance [3, 22]. Addressing this chal- main shift between the distributions of the source and lenge enables the PDA methods to effectively transfer the target datasets. As a result, deep networks trained models learned on large labeled datasets (e.g. Ima- on labeled source datasets often exhibit unsatisfactory geNet) to small-scale datasets from different but re- 66 lated domains. recently developed which simultaneously promote pos- In this paper, we propose an adversarial approach itive transfer from the common classes between the do- for partial domain adaptation, which aims to not only mains and alleviate the negative transfer from the out- automatically reject the outlier source classes, but also lier classes [3, 4, 38]. Selective adversarial network [3] down-weight the relative importance of irrelevant sam- trains separate domain discriminators for each source ples, i.e. those samples that are highly dissimilar across class to align the distributions of the source and target different domains. Our method uses the same network domains across the shared label space and to ignore architecture as partial adversarial domain adaptation the outlier classes. Partial adversarial domain adapta- (PADA) [4] and incorporates two additional regular- tion (PADA) [4] proposed a new architecture which ization terms to boost the target domain classification assigns a weight to each source domain class based performance. Inspired by the idea of subset selection, on the target label prediction and automatically re- the first regularization is a row-sparsity term on the duces the weights of the outlier classes. Importance output of the classifier, which promotes the selection of weighted adversarial nets [38] develops a two-domain a small subset of classes that are in common between classifier strategy to estimate the relative importance the source and target domains. The second regular- of the source domain samples. ization is a minimum entropy term which utilizes the Closely related to our work, transferable atten- output of the discriminator to down-weight the relative tion for domain adaptation (TADA) [35] proposed an importance of irrelevant samples from both domains. attention-based mechanism for UDA, which can high- We empirically observe that our method can effectively light transferable regions or images. Unlike TADA, enhance the target classification accuracy on different our method is focused on the PDA problem and uti- PDA tasks. lizes a different network architecture with a novel loss function that efficiently assigns weights to both classes 2. Related Work and samples. Our method differs from PADA [4] in the sense that we incorporate two novel regularization To date, various deep unsupervised domain adapta- terms which not only able to discover and reject the tion methods have been developed to extract domain- outlier classes more effectively but also down-weight invariant feature representations from different do- the relative importance of the irrelevant samples in the mains. Some studies [9, 15, 18, 20, 36] have proposed to training procedure. minimize the maximum mean discrepancy between the source and target distributions. In [28], a correlation 3. Problem Formulation alignment (CORAL) method is proposed that utilizes a linear transformation to match the second-order statis- This section briefly reviews two well-established do- tics between the domains. [29] presented an extension main adaptation methods and then provides a detailed of the CORAL method that aligns correlations of layer explanation on how our proposed method relates to i i ns activations in deep networks by learning a non-linear them. Let {(xs, ys)}i=1 be a set of ns sample points i transformation. Despite the practical success of the drawn i.i.d from the source domain Ds, where xs de- th i aforementioned methods in aligning the domain distri- notes the i source image with label ys. Similarly, let i nt butions, it is shown that they are unable to completely {xt}i=1 be a set of nt sample points collected i.i.d from i th eliminate the domain shift [7, 37]. the target domain Dt, where xt indicates the i target Recently, adversarial learning has been widely em- image. To clarify notation, let X =Xs∪Xt be the set of i ns ployed to enhance the performance of UDA methods entire images from both domains, where Xs = {xs}i=1 i nt [2, 8, 10, 19, 32]. The basic idea behind the adversarial- and Xt = {xt }i=1. The UDA methods assume that based methods is to train a discriminator for predicting the source and target domains possess the same label domain labels and a deep network for extracting fea- space, denoted as Cs and Ct, respectively. In the ab- tures that are indistinguishable by the discriminator. sence of target labels, the primary goal of the UDA is By doing so, the discrepancy between the source and to learn domain-invariant feature representations that target domains can be efficiently eliminated, which re- can reduce the domain shift. One promising direction sults in significant improvement in the overall classifi- to achieve this goal is to train a domain adversarial cation performance [8, 23, 32]. [39] developed an incre- neural network [8] which consists of a discriminator Gd mental adversarial scheme which gradually reduces the for predicting the domain labels, a feature extractor gap between the domain distributions by iteratively se- Gf for confusing the discriminator by learning trans- lecting the high confidence pseudo-labeled target sam- ferable feature representations, and a classifier Gy that ples to enlarge the training set. classifies the source domain samples. Training the ad- Towards the task of PDA, great studies have been versarial network is equivalent to solve the following 67 Figure 1: Overview of the proposed adversarial network for partial transfer learning. The network consists of a feature extractor, a classifier, and a domain discriminator, denoted by Gf , Gy, and Gd, respectively. The blue and green arrows depict the source flow and target flow. Loss functions Ly, Ld, Le, and L∞ denote the classification loss, the discriminative loss, the entropy loss, and the selection loss. Best viewed in color. optimization problem classifier trained along such feature extractor can gen- eralize well to the target domain.
Load more

Recommended publications
  • Surviving Set Theory: a Pedagogical Game and Cooperative Learning Approach to Undergraduate Post-Tonal Music Theory
    Surviving Set Theory: A Pedagogical Game and Cooperative Learning Approach to Undergraduate Post-Tonal Music Theory DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Angela N. Ripley, M.M. Graduate Program in Music The Ohio State University 2015 Dissertation Committee: David Clampitt, Advisor Anna Gawboy Johanna Devaney Copyright by Angela N. Ripley 2015 Abstract Undergraduate music students often experience a high learning curve when they first encounter pitch-class set theory, an analytical system very different from those they have studied previously. Students sometimes find the abstractions of integer notation and the mathematical orientation of set theory foreign or even frightening (Kleppinger 2010), and the dissonance of the atonal repertoire studied often engenders their resistance (Root 2010). Pedagogical games can help mitigate student resistance and trepidation. Table games like Bingo (Gillespie 2000) and Poker (Gingerich 1991) have been adapted to suit college-level classes in music theory. Familiar television shows provide another source of pedagogical games; for example, Berry (2008; 2015) adapts the show Survivor to frame a unit on theory fundamentals. However, none of these pedagogical games engage pitch- class set theory during a multi-week unit of study. In my dissertation, I adapt the show Survivor to frame a four-week unit on pitch- class set theory (introducing topics ranging from pitch-class sets to twelve-tone rows) during a sophomore-level theory course. As on the show, students of different achievement levels work together in small groups, or “tribes,” to complete worksheets called “challenges”; however, in an important modification to the structure of the show, no students are voted out of their tribes.
    [Show full text]
  • Pitch-Class Set Theory: an Overture
    Chapter One Pitch-Class Set Theory: An Overture A Tale of Two Continents In the late afternoon of October 24, 1999, about one hundred people were gathered in a large rehearsal room of the Rotterdam Conservatory. They were listening to a discussion between representatives of nine European countries about the teaching of music theory and music analysis. It was the third day of the Fourth European Music Analysis Conference.1 Most participants in the conference (which included a number of music theorists from Canada and the United States) had been looking forward to this session: meetings about the various analytical traditions and pedagogical practices in Europe were rare, and a broad survey of teaching methods was lacking. Most felt a need for information from beyond their country’s borders. This need was reinforced by the mobility of music students and the resulting hodgepodge of nationalities at renowned conservatories and music schools. Moreover, the European systems of higher education were on the threshold of a harmoni- zation operation. Earlier that year, on June 19, the governments of 29 coun- tries had ratifi ed the “Bologna Declaration,” a document that envisaged a unifi ed European area for higher education. Its enforcement added to the urgency of the meeting in Rotterdam. However, this meeting would not be remembered for the unusually broad rep- resentation of nationalities or for its political timeliness. What would be remem- bered was an incident which took place shortly after the audience had been invited to join in the discussion. Somebody had raised a question about classroom analysis of twentieth-century music, a recurring topic among music theory teach- ers: whereas the music of the eighteenth and nineteenth centuries lent itself to general analytical methodologies, the extremely diverse repertoire of the twen- tieth century seemed only to invite ad hoc approaches; how could the analysis of 1.
    [Show full text]
  • The Axiom of Choice and Its Implications
    THE AXIOM OF CHOICE AND ITS IMPLICATIONS KEVIN BARNUM Abstract. In this paper we will look at the Axiom of Choice and some of the various implications it has. These implications include a number of equivalent statements, and also some less accepted ideas. The proofs discussed will give us an idea of why the Axiom of Choice is so powerful, but also so controversial. Contents 1. Introduction 1 2. The Axiom of Choice and Its Equivalents 1 2.1. The Axiom of Choice and its Well-known Equivalents 1 2.2. Some Other Less Well-known Equivalents of the Axiom of Choice 3 3. Applications of the Axiom of Choice 5 3.1. Equivalence Between The Axiom of Choice and the Claim that Every Vector Space has a Basis 5 3.2. Some More Applications of the Axiom of Choice 6 4. Controversial Results 10 Acknowledgments 11 References 11 1. Introduction The Axiom of Choice states that for any family of nonempty disjoint sets, there exists a set that consists of exactly one element from each element of the family. It seems strange at first that such an innocuous sounding idea can be so powerful and controversial, but it certainly is both. To understand why, we will start by looking at some statements that are equivalent to the axiom of choice. Many of these equivalences are very useful, and we devote much time to one, namely, that every vector space has a basis. We go on from there to see a few more applications of the Axiom of Choice and its equivalents, and finish by looking at some of the reasons why the Axiom of Choice is so controversial.
    [Show full text]
  • Formal Construction of a Set Theory in Coq
    Saarland University Faculty of Natural Sciences and Technology I Department of Computer Science Masters Thesis Formal Construction of a Set Theory in Coq submitted by Jonas Kaiser on November 23, 2012 Supervisor Prof. Dr. Gert Smolka Advisor Dr. Chad E. Brown Reviewers Prof. Dr. Gert Smolka Dr. Chad E. Brown Eidesstattliche Erklarung¨ Ich erklare¨ hiermit an Eides Statt, dass ich die vorliegende Arbeit selbststandig¨ verfasst und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe. Statement in Lieu of an Oath I hereby confirm that I have written this thesis on my own and that I have not used any other media or materials than the ones referred to in this thesis. Einverstandniserkl¨ arung¨ Ich bin damit einverstanden, dass meine (bestandene) Arbeit in beiden Versionen in die Bibliothek der Informatik aufgenommen und damit vero¨ffentlicht wird. Declaration of Consent I agree to make both versions of my thesis (with a passing grade) accessible to the public by having them added to the library of the Computer Science Department. Saarbrucken,¨ (Datum/Date) (Unterschrift/Signature) iii Acknowledgements First of all I would like to express my sincerest gratitude towards my advisor, Chad Brown, who supported me throughout this work. His extensive knowledge and insights opened my eyes to the beauty of axiomatic set theory and foundational mathematics. We spent many hours discussing the minute details of the various constructions and he taught me the importance of mathematical rigour. Equally important was the support of my supervisor, Prof. Smolka, who first introduced me to the topic and was there whenever a question arose.
    [Show full text]
  • Equivalents to the Axiom of Choice and Their Uses A
    EQUIVALENTS TO THE AXIOM OF CHOICE AND THEIR USES A Thesis Presented to The Faculty of the Department of Mathematics California State University, Los Angeles In Partial Fulfillment of the Requirements for the Degree Master of Science in Mathematics By James Szufu Yang c 2015 James Szufu Yang ALL RIGHTS RESERVED ii The thesis of James Szufu Yang is approved. Mike Krebs, Ph.D. Kristin Webster, Ph.D. Michael Hoffman, Ph.D., Committee Chair Grant Fraser, Ph.D., Department Chair California State University, Los Angeles June 2015 iii ABSTRACT Equivalents to the Axiom of Choice and Their Uses By James Szufu Yang In set theory, the Axiom of Choice (AC) was formulated in 1904 by Ernst Zermelo. It is an addition to the older Zermelo-Fraenkel (ZF) set theory. We call it Zermelo-Fraenkel set theory with the Axiom of Choice and abbreviate it as ZFC. This paper starts with an introduction to the foundations of ZFC set the- ory, which includes the Zermelo-Fraenkel axioms, partially ordered sets (posets), the Cartesian product, the Axiom of Choice, and their related proofs. It then intro- duces several equivalent forms of the Axiom of Choice and proves that they are all equivalent. In the end, equivalents to the Axiom of Choice are used to prove a few fundamental theorems in set theory, linear analysis, and abstract algebra. This paper is concluded by a brief review of the work in it, followed by a few points of interest for further study in mathematics and/or set theory. iv ACKNOWLEDGMENTS Between the two department requirements to complete a master's degree in mathematics − the comprehensive exams and a thesis, I really wanted to experience doing a research and writing a serious academic paper.
    [Show full text]
  • P0138R2 2016-03-04 Reply-To: [email protected]
    P0138R2 2016-03-04 Reply-To: [email protected] Construction Rules for enum class Values Gabriel Dos Reis Microsoft Abstract This paper suggests a simple adjustment to the existing rules governing conversion from the underlying type of a scoped enumeration to said enumeration. This effectively supports programming styles that rely on definition of new distinct integral types out of existing integer types, without the complexity of anarchic integer conversions, while retaining all the ABI characteristics and benefits of the integer types, especially for system programming. 1 INTRODUCTION There is an incredibly useful technique for introducing a new integer type that is almost an exact copy, yet distinct type in modern C++11 programs: an enum class with an explicitly specified underlying type. Example: enum class Index : uint32_t { }; // Note: no enumerator. One can use Index as a new distinct integer type, it has no implicit conversion to anything (good!) This technique is especially useful when one wants to avoid the anarchic implicit conversions C++ inherited from C. For all practical purposes, Index acts like a "strong typedef" in C++11. There is however, one inconvenience: to construct a value of type Index, the current language spec generally requires the use a cast -- either a static_cast or a functional notation cast. This is both conceptually wrong and practically a serious impediment. Constructing an Index value out of uint32_t is not a cast, no more than we consider struct ClassIndex { uint32_t val; }; ClassIndex idx { 42 }; // OK ClassIndex idx2 = { 7 }; // OK a cast. It is a simple construction of a value of type ClassIndex, with no narrowing conversion.
    [Show full text]
  • CE160 Determinacy Lesson Plan Vukazich
    Lesson Plan Instructor: Steven Vukazich Lesson: Evaluating the Determinacy of Planar Structures Timeframe: 50 minutes Materials needed: Paper Pencil Eraser Objectives: Basic: 1. Remember the number of independent equations of equilibrium available for a general planar (two-dimensional) structure (prerequisite course CE 95) 2. Remember the number of unknown reactive forces associated with common structural supports (e.g. pin support, roller support from prerequisite course CE 95) 3. Isolate and draw a Free Body Diagram (FBD) of a structural system (prerequisite course CE 95) Advanced: 1. Identify individual “pieces” of structure required to be isolated, draw FBDs of each “piece”, and evaluate determinacy 2. Idealize and evaluate determinacy of an actual planar structure presented 3. Modify the planar process to evaluate determinacy of a general three-dimensional structure Background: Read chapter material that reviews basic objectives 1, 2, and 3; Read chapter material and notes (and possible video lecture) provided by the instructor to introduce advanced learning objective 1. Procedure [Time needed, include additional steps if needed]: Pre-Class Individual Space Activities and Resources: Steps Purpose Estimated Learning Time Objective Step 1: Review basic statics 20 minutes Basic (pre-requisite Objectives Read section in Leet text material) and to 1,2,3 introduce the new material Step 2: In depth review and 30 Basic discussion of new minutes Objectives Read instructor notes (see notes below – these material 1,2,3 could be turned into a Camtasia video) Step 3: Reinforce the 10 Basic review and minutes Objective Complete example problem in instructor notes. extension to new 3 material CE 160 Notes – Determinacy for Planar (two-dimensional) Structures Recall from statics that for a planar structure there are 3 independent equilibrium equations available to solve for unknown forces per free body diagram (FBD).
    [Show full text]
  • Cardinality and the Nature of Infinity
    Cardinality and The Nature of Infinity Recap from Last Time Functions ● A function f is a mapping such that every value in A is associated with a single value in B. ● For every a ∈ A, there exists some b ∈ B with f(a) = b. ● If f(a) = b0 and f(a) = b1, then b0 = b1. ● If f is a function from A to B, we call A the domain of f andl B the codomain of f. ● We denote that f is a function from A to B by writing f : A → B Injective Functions ● A function f : A → B is called injective (or one-to-one) iff each element of the codomain has at most one element of the domain associated with it. ● A function with this property is called an injection. ● Formally: If f(x0) = f(x1), then x0 = x1 ● An intuition: injective functions label the objects from A using names from B. Surjective Functions ● A function f : A → B is called surjective (or onto) iff each element of the codomain has at least one element of the domain associated with it. ● A function with this property is called a surjection. ● Formally: For any b ∈ B, there exists at least one a ∈ A such that f(a) = b. ● An intuition: surjective functions cover every element of B with at least one element of A. Bijections ● A function that associates each element of the codomain with a unique element of the domain is called bijective. ● Such a function is a bijection. ● Formally, a bijection is a function that is both injective and surjective.
    [Show full text]
  • A Tale of Two Set Theories
    1 The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-23250-4_4 arXiv:1907.08368v1 [cs.LO] 18 Jul 2019 2 A Tale of Two Set Theories Chad E. Brown1 and Karol Pąk2[0000−0002−7099−1669] 1 Czech Technical University in Prague 2 University of Białystok [email protected] Abstract. We describe the relationship between two versions of Tarski- Grothendieck set theory: the first-order set theory of Mizar and the higher-order set theory of Egal. We show how certain higher-order terms and propositions in Egal have equivalent first-order presentations. We then prove Tarski’s Axiom A (an axiom in Mizar) in Egal and construct a Grothendieck Universe operator (a primitive with axioms in Egal) in Mizar. Keywords: Formalized Mathematics, Theorem Proving, Set Theory, Proof Checking, Mizar 1 Introduction We compare two implemented versions of Tarski-Grothendieck (TG) set theory. The first is the first-order TG implemented in Mizar [3,15] axiomatized using Tarski’s Axiom A [24,25]. The other is the higher-order TG implemented in Egal [7] axiomatized using Grothendieck universes [17]. We discuss what would be involved porting Mizar developments into Egal and vice versa. We use Egal’s Grothendieck universes (along with a choice operator) to prove Tarski’s Axiom A in Egal. Consequently the Egal counterpart of each of Mizar’s axioms is provable in Egal and so porting from Mizar to Egal should always be possible in principle. In practice one would need to make Mizar’s implicit reasoning using its type system explicit, a nontrivial task outside the scope of this paper.
    [Show full text]
  • Nonconvergence, Covariance Constraints, and Class Enumeration in Growth Mixture Models
    Nonconvergence, Covariance Constraints, and Class Enumeration in Growth Mixture Models Daniel McNeish1, Jeffrey R. Harring2, & Daniel J. Bauer3 1Arizona State University, Tempe, USA 2University of Maryland, College Park, USA 3University of North Carolina, Chapel Hill, USA Contact Information Daniel McNeish, PO Box 871104, Arizona State University, Tempe, AZ 85287. Email: [email protected] Acknowledgements The authors were supported by the Institute of Educational Sciences program in statistical and research methodology, project number R305D190011. The content is solely the responsibility of the authors and does not represent the official views of the Institute of Educational Sciences. The data in the empirical were supplied by the original authors with a Creative Common BY 4.0 International license that allows use provided that proper attribution is provided to the original authors. We thank these authors for their transparency and commitment to making scientific research an open process. Abstract Growth mixture models (GMMs) are a popular method to identify latent classes of growth trajectories. One shortcoming of GMMs is nonconvergence, which often leads researchers to apply covariance equality constraints to simplify estimation. This approach is criticized because it introduces a dubious homoskedasticity assumption across classes. Alternative methods have been proposed to reduce nonconvergence without imposing covariance equality constraints, and though studies have shown that these methods perform well when the correct number of classes is known, research has not examined whether they can accurately identify the number of classes. Given that selecting the number of classes tends to be the most difficult aspect of GMMs, more information about class enumeration performance is crucial to assess the potential utility of these methods.
    [Show full text]
  • Florida State University Libraries
    )ORULGD6WDWH8QLYHUVLW\/LEUDULHV 2020 Justifying Alternative Foundations for Mathematics Jared M. Ifland Follow this and additional works at DigiNole: FSU's Digital Repository. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY COLLEGE OF ARTS & SCIENCES JUSTIFYING ALTERNATIVE FOUNDATIONS FOR MATHEMATICS By JARED M. IFLAND A Thesis submitted to the Department of Philosophy in partial fulfillment of the requirements for graduation with Honors in the Major Degree Awarded: Spring, 2020 The members of the Defense Committee approve the thesis of Jared M. Ifland defended on July 17, 2020. ______________________________ Dr. James Justus Thesis Director ______________________________ Dr. Ettore Aldrovandi Outside Committee Member ______________________________ Dr. J. Piers Rawling Committee Member Abstract: It is inarguable that mathematics serves a quintessential role in the natural sciences and that ZFC — extended by large cardinal axioms — provides a foundation for vast swaths of contemporary mathematics. As such, it is understandable that the naturalistic philosopher may inquire into the ontological status of mathematical entities and sets. Many have argued that the indispensability of mathematics from scientific enterprise warrants belief in mathematical platonism, but it is unclear how knowledge of entities that exist independently of the natural world is possible. Furthermore, indispensability arguments are notoriously antithetical to mathematical practice: mathematicians typically do not refer to scientific
    [Show full text]
  • Guide to Set Theory Proofs
    CS103 Handout 07 Summer 2019 June 28, 2019 Guide to Proofs on Sets Richard Feynman, one of the greatest physicists of the twentieth century, gave a famous series of lectures on physics while a professor at Caltech. Those lectures have been recorded for posterity and are legendary for their blend of intuitive and mathematical reasoning. One of my favorite quotes from these lectures comes early on, when Feynman talks about the atomic theory of matter. Here’s a relevant excerpt: If, in some cataclysm, all of scientific knowledge were to be destroyed, and only one sen- tence passed on to the next generations of creatures, what statement would contain the most information in the fewest words? I believe it is the atomic hypothesis (or the atomic fact, or whatever you wish to call it) that all things are made of atoms [. ...] In that one sentence, you will see, there is an enormous amount of information about the world, if just a little imagination and thinking are applied. This idea argues for a serious shift in perspective about how to interpret properties of objects in the physical world. Think about how, for example, you might try to understand why steel is so much stronger and tougher than charcoal and iron. If you don’t have atomic theory, you’d proba- bly think about what sorts of “inherent qualities” charcoal and iron each possess, and how those qualities interacting with one another would give rise to steel’s strength. That’s how you’d think about things if you were an alchemist.
    [Show full text]