Introduction to Combinatorics University of Toronto Scarborough Lecture Notes
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LNCS 7215, Pp
ACoreCalculusforProvenance Umut A. Acar1,AmalAhmed2,JamesCheney3,andRolyPerera1 1 Max Planck Institute for Software Systems umut,rolyp @mpi-sws.org { 2 Indiana} University [email protected] 3 University of Edinburgh [email protected] Abstract. Provenance is an increasing concern due to the revolution in sharing and processing scientific data on the Web and in other computer systems. It is proposed that many computer systems will need to become provenance-aware in order to provide satisfactory accountability, reproducibility,andtrustforscien- tific or other high-value data. To date, there is not a consensus concerning ap- propriate formal models or security properties for provenance. In previous work, we introduced a formal framework for provenance security and proposed formal definitions of properties called disclosure and obfuscation. This paper develops a core calculus for provenance in programming languages. Whereas previous models of provenance have focused on special-purpose languages such as workflows and database queries, we consider a higher-order, functional language with sums, products, and recursive types and functions. We explore the ramifications of using traces based on operational derivations for the purpose of comparing other forms of provenance. We design a rich class of prove- nance views over traces. Finally, we prove relationships among provenance views and develop some solutions to the disclosure and obfuscation problems. 1Introduction Provenance, or meta-information about the origin, history, or derivation of an object, is now recognized as a central challenge in establishing trust and providing security in computer systems, particularly on the Web. Essentially, provenance management in- volves instrumenting a system with detailed monitoring or logging of auditable records that help explain how results depend on inputs or other (sometimes untrustworthy) sources. -
11 Practice Lesso N 4 R O U N D W H Ole Nu M Bers U N It 1
CC04MM RPPSTG TEXT.indb 11 © Practice and Problem Solving Curriculum Associates, LLC Copying isnotpermitted. Practice Lesson 4 Round Whole Numbers Whole 4Round Lesson Practice Lesson 4 Round Whole Numbers Name: Solve. M 3 Round each number. Prerequisite: Round Three-Digit Numbers a. 689 rounded to the nearest ten is 690 . Study the example showing how to round a b. 68 rounded to the nearest hundred is 100 . three-digit number. Then solve problems 1–6. c. 945 rounded to the nearest ten is 950 . Example d. 945 rounded to the nearest hundred is 900 . Round 154 to the nearest ten. M 4 Rachel earned $164 babysitting last month. She earned $95 this month. Rachel rounded each 150 151 152 153 154 155 156 157 158 159 160 amount to the nearest $10 to estimate how much 154 is between 150 and 160. It is closer to 150. she earned. What is each amount rounded to the 154 rounded to the nearest ten is 150. nearest $10? Show your work. Round 154 to the nearest hundred. Student work will vary. Students might draw number lines, a hundreds chart, or explain in words. 100 110 120 130 140 150 160 170 180 190 200 Solution: ___________________________________$160 and $100 154 is between 100 and 200. It is closer to 200. C 5 Use the digits in the tiles to create a number that 154 rounded to the nearest hundred is 200. makes each statement true. Use each digit only once. B 1 Round 236 to the nearest ten. 1 2 3 4 5 6 7 8 9 Which tens is 236 between? Possible answer shown. -
Nieuw Archief Voor Wiskunde
Nieuw Archief voor Wiskunde Boekbespreking Kevin Broughan Equivalents of the Riemann Hypothesis Volume 1: Arithmetic Equivalents Cambridge University Press, 2017 xx + 325 p., prijs £ 99.99 ISBN 9781107197046 Kevin Broughan Equivalents of the Riemann Hypothesis Volume 2: Analytic Equivalents Cambridge University Press, 2017 xix + 491 p., prijs £ 120.00 ISBN 9781107197121 Reviewed by Pieter Moree These two volumes give a survey of conjectures equivalent to the ber theorem says that r()x asymptotically behaves as xx/log . That Riemann Hypothesis (RH). The first volume deals largely with state- is a much weaker statement and is equivalent with there being no ments of an arithmetic nature, while the second part considers zeta zeros on the line v = 1. That there are no zeros with v > 1 is more analytic equivalents. a consequence of the prime product identity for g()s . The Riemann zeta function, is defined by It would go too far here to discuss all chapters and I will limit 3 myself to some chapters that are either close to my mathematical 1 (1) g()s = / s , expertise or those discussing some of the most famous RH equiv- n = 1 n alences. Most of the criteria have their own chapter devoted to with si=+v t a complex number having real part v > 1 . It is easily them, Chapter 10 has various criteria that are discussed more brief- seen to converge for such s. By analytic continuation the Riemann ly. A nice example is Redheffer’s criterion. It states that RH holds zeta function can be uniquely defined for all s ! 1. -
The Modal Logic of Potential Infinity, with an Application to Free Choice
The Modal Logic of Potential Infinity, With an Application to Free Choice Sequences Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Ethan Brauer, B.A. ∼6 6 Graduate Program in Philosophy The Ohio State University 2020 Dissertation Committee: Professor Stewart Shapiro, Co-adviser Professor Neil Tennant, Co-adviser Professor Chris Miller Professor Chris Pincock c Ethan Brauer, 2020 Abstract This dissertation is a study of potential infinity in mathematics and its contrast with actual infinity. Roughly, an actual infinity is a completed infinite totality. By contrast, a collection is potentially infinite when it is possible to expand it beyond any finite limit, despite not being a completed, actual infinite totality. The concept of potential infinity thus involves a notion of possibility. On this basis, recent progress has been made in giving an account of potential infinity using the resources of modal logic. Part I of this dissertation studies what the right modal logic is for reasoning about potential infinity. I begin Part I by rehearsing an argument|which is due to Linnebo and which I partially endorse|that the right modal logic is S4.2. Under this assumption, Linnebo has shown that a natural translation of non-modal first-order logic into modal first- order logic is sound and faithful. I argue that for the philosophical purposes at stake, the modal logic in question should be free and extend Linnebo's result to this setting. I then identify a limitation to the argument for S4.2 being the right modal logic for potential infinity. -
Sabermetrics: the Past, the Present, and the Future
Sabermetrics: The Past, the Present, and the Future Jim Albert February 12, 2010 Abstract This article provides an overview of sabermetrics, the science of learn- ing about baseball through objective evidence. Statistics and baseball have always had a strong kinship, as many famous players are known by their famous statistical accomplishments such as Joe Dimaggio’s 56-game hitting streak and Ted Williams’ .406 batting average in the 1941 baseball season. We give an overview of how one measures performance in batting, pitching, and fielding. In baseball, the traditional measures are batting av- erage, slugging percentage, and on-base percentage, but modern measures such as OPS (on-base percentage plus slugging percentage) are better in predicting the number of runs a team will score in a game. Pitching is a harder aspect of performance to measure, since traditional measures such as winning percentage and earned run average are confounded by the abilities of the pitcher teammates. Modern measures of pitching such as DIPS (defense independent pitching statistics) are helpful in isolating the contributions of a pitcher that do not involve his teammates. It is also challenging to measure the quality of a player’s fielding ability, since the standard measure of fielding, the fielding percentage, is not helpful in understanding the range of a player in moving towards a batted ball. New measures of fielding have been developed that are useful in measuring a player’s fielding range. Major League Baseball is measuring the game in new ways, and sabermetrics is using this new data to find better mea- sures of player performance. -
Chapter 1. Combinatorial Theory 1.3: No More Counting Please 1.3.1
Chapter 1. Combinatorial Theory 1.3: No More Counting Please Slides (Google Drive) Alex Tsun Video (YouTube) In this section, we don't really have a nice successive ordering where one topic leads to the next as we did earlier. This section serves as a place to put all the final miscellaneous but useful concepts in counting. 1.3.1 Binomial Theorem n We talked last time about binomial coefficients of the form k . Today, we'll see how they are used to prove the binomial theorem, which we'll use more later on. For now, we'll see how they can be used to expand (possibly large) exponents below. You may have learned this technique of FOIL (first, outer, inner, last) for expanding (x + y)2. We then combine like-terms (xy and yx). (x + y)2 = (x + y)(x + y) = xx + xy + yx + yy [FOIL] = x2 + 2xy + y2 But, let's say that we wanted to do this for a binomial raised to some higher power, say (x + y)4. There would be a lot more terms, but we could use a similar approach. 1 2 Probability & Statistics with Applications to Computing 1.3 (x + y)4 = (x + y)(x + y)(x + y)(x + y) = xxxx + yyyy + xyxy + yxyy + ::: But what are the terms exactly that are included in this expression? And how could we combine the like-terms though? Notice that each term will be a mixture of x's and y's. In fact, each term will be in the form xkyn−k (in this case n = 4). -
Useful Relations in Permutations and Combination 1. Useful Relations
Useful Relations in Permutations and Combination 1. Useful Relations - Factorial n! = n.(n-1)! 2. n퐶푟= n푃푟/r! n n-1 3. Pr = n( Pr-1) 4. Useful Relations - Combinations n n 1. Cr = C(n - r) Example 8 8 C6 = C2 = 8×72×1 = 28 n 2. Cn = 1 n 3. C0 = 1 n n n n n 4. C0 + C1 + C2 + ... + Cn = 2 Example 4 4 4 4 4 4 C0 + C1 + C2 + C3+ C4 = (1 + 4 + 6 + 4 + 1) = 16 = 2 n n (n+1) Cr-1 + Cr = Cr (Pascal's Law) n퐶푟 =n/퐶푟−1=n-r+1/r n n If Cx = Cy then either x = y or (n-x) = y. 5. Selection from identical objects: Some Basic Facts The number of selections of r objects out of n identical objects is 1. Total number of selections of zero or more objects from n identical objects is n+1. 6. Permutations of Objects when All Objects are Not Distinct The number of ways in which n things can be arranged taking them all at a time, when st nd 푃1 of the things are exactly alike of 1 type, 푃2 of them are exactly alike of a 2 type, and th 푃푟of them are exactly alike of r type and the rest of all are distinct is n!/ 푃1! 푃2! ... 푃푟! 1 Example: how many ways can you arrange the letters in the word THESE? 5!/2!=120/2=60 Example: how many ways can you arrange the letters in the word REFERENCE? 9!/2!.4!=362880/2*24=7560 7.Circular Permutations: Case 1: when clockwise and anticlockwise arrangements are different Number of circular permutations (arrangements) of n different things is (n-1)! 1. -
Molecular Symmetry
Molecular Symmetry Symmetry helps us understand molecular structure, some chemical properties, and characteristics of physical properties (spectroscopy) – used with group theory to predict vibrational spectra for the identification of molecular shape, and as a tool for understanding electronic structure and bonding. Symmetrical : implies the species possesses a number of indistinguishable configurations. 1 Group Theory : mathematical treatment of symmetry. symmetry operation – an operation performed on an object which leaves it in a configuration that is indistinguishable from, and superimposable on, the original configuration. symmetry elements – the points, lines, or planes to which a symmetry operation is carried out. Element Operation Symbol Identity Identity E Symmetry plane Reflection in the plane σ Inversion center Inversion of a point x,y,z to -x,-y,-z i Proper axis Rotation by (360/n)° Cn 1. Rotation by (360/n)° Improper axis S 2. Reflection in plane perpendicular to rotation axis n Proper axes of rotation (C n) Rotation with respect to a line (axis of rotation). •Cn is a rotation of (360/n)°. •C2 = 180° rotation, C 3 = 120° rotation, C 4 = 90° rotation, C 5 = 72° rotation, C 6 = 60° rotation… •Each rotation brings you to an indistinguishable state from the original. However, rotation by 90° about the same axis does not give back the identical molecule. XeF 4 is square planar. Therefore H 2O does NOT possess It has four different C 2 axes. a C 4 symmetry axis. A C 4 axis out of the page is called the principle axis because it has the largest n . By convention, the principle axis is in the z-direction 2 3 Reflection through a planes of symmetry (mirror plane) If reflection of all parts of a molecule through a plane produced an indistinguishable configuration, the symmetry element is called a mirror plane or plane of symmetry . -
Combinatorics
Combinatorics Problem: How to count without counting. I How do you figure out how many things there are with a certain property without actually enumerating all of them. Sometimes this requires a lot of cleverness and deep mathematical insights. But there are some standard techniques. I That's what we'll be studying. We sometimes use the bijection rule without even realizing it: I count how many people voted are in favor of something by counting the number of hands raised: I I'm hoping that there's a bijection between the people in favor and the hands raised! Bijection Rule The Bijection Rule: If f : A ! B is a bijection, then jAj = jBj. I We used this rule in defining cardinality for infinite sets. I Now we'll focus on finite sets. Bijection Rule The Bijection Rule: If f : A ! B is a bijection, then jAj = jBj. I We used this rule in defining cardinality for infinite sets. I Now we'll focus on finite sets. We sometimes use the bijection rule without even realizing it: I count how many people voted are in favor of something by counting the number of hands raised: I I'm hoping that there's a bijection between the people in favor and the hands raised! Answer: 26 choices for the first letter, 26 for the second, 10 choices for the first number, the second number, and the third number: 262 × 103 = 676; 000 Example 2: A traveling salesman wants to do a tour of all 50 state capitals. How many ways can he do this? Answer: 50 choices for the first place to visit, 49 for the second, . -
Inclusion‒Exclusion Principle
Inclusionexclusion principle 1 Inclusion–exclusion principle In combinatorics, the inclusion–exclusion principle (also known as the sieve principle) is an equation relating the sizes of two sets and their union. It states that if A and B are two (finite) sets, then The meaning of the statement is that the number of elements in the union of the two sets is the sum of the elements in each set, respectively, minus the number of elements that are in both. Similarly, for three sets A, B and C, This can be seen by counting how many times each region in the figure to the right is included in the right hand side. More generally, for finite sets A , ..., A , one has the identity 1 n This can be compactly written as The name comes from the idea that the principle is based on over-generous inclusion, followed by compensating exclusion. When n > 2 the exclusion of the pairwise intersections is (possibly) too severe, and the correct formula is as shown with alternating signs. This formula is attributed to Abraham de Moivre; it is sometimes also named for Daniel da Silva, Joseph Sylvester or Henri Poincaré. Inclusion–exclusion illustrated for three sets For the case of three sets A, B, C the inclusion–exclusion principle is illustrated in the graphic on the right. Proof Let A denote the union of the sets A , ..., A . To prove the 1 n Each term of the inclusion-exclusion formula inclusion–exclusion principle in general, we first have to verify the gradually corrects the count until finally each identity portion of the Venn Diagram is counted exactly once. -
Girls' Elite 2 0 2 0 - 2 1 S E a S O N by the Numbers
GIRLS' ELITE 2 0 2 0 - 2 1 S E A S O N BY THE NUMBERS COMPARING NORMAL SEASON TO 2020-21 NORMAL 2020-21 SEASON SEASON SEASON LENGTH SEASON LENGTH 6.5 Months; Dec - Jun 6.5 Months, Split Season The 2020-21 Season will be split into two segments running from mid-September through mid-February, taking a break for the IHSA season, and then returning May through mid- June. The season length is virtually the exact same amount of time as previous years. TRAINING PROGRAM TRAINING PROGRAM 25 Weeks; 157 Hours 25 Weeks; 156 Hours The training hours for the 2020-21 season are nearly exact to last season's plan. The training hours do not include 16 additional in-house scrimmage hours on the weekends Sep-Dec. Courtney DeBolt-Slinko returns as our Technical Director. 4 new courts this season. STRENGTH PROGRAM STRENGTH PROGRAM 3 Days/Week; 72 Hours 3 Days/Week; 76 Hours Similar to the Training Time, the 2020-21 schedule will actually allow for a 4 additional hours at Oak Strength in our Sparta Science Strength & Conditioning program. These hours are in addition to the volleyball-specific Training Time. Oak Strength is expanding by 8,800 sq. ft. RECRUITING SUPPORT RECRUITING SUPPORT Full Season Enhanced Full Season In response to the recruiting challenges created by the pandemic, we are ADDING livestreaming/recording of scrimmages and scheduled in-person visits from Lauren, Mikaela or Peter. This is in addition to our normal support services throughout the season. TOURNAMENT DATES TOURNAMENT DATES 24-28 Dates; 10-12 Events TBD Dates; TBD Events We are preparing for 15 Dates/6 Events Dec-Feb. -
STRUCTURE ENUMERATION and SAMPLING Chemical Structure Enumeration and Sampling Have Been Studied by Mathematicians, Computer
STRUCTURE ENUMERATION AND SAMPLING MARKUS MERINGER To appear in Handbook of Chemoinformatics Algorithms Chemical structure enumeration and sampling have been studied by 5 mathematicians, computer scientists and chemists for quite a long time. Given a molecular formula plus, optionally, a list of structural con- straints, the typical questions are: (1) How many isomers exist? (2) Which are they? And, especially if (2) cannot be answered completely: (3) How to get a sample? 10 In this chapter we describe algorithms for solving these problems. The techniques are based on the representation of chemical compounds as molecular graphs (see Chapter 2), i.e. they are mainly applied to constitutional isomers. The major problem is that in silico molecular graphs have to be represented as labeled structures, while in chemical 15 compounds, the atoms are not labeled. The mathematical concept for approaching this problem is to consider orbits of labeled molecular graphs under the operation of the symmetric group. We have to solve the so–called isomorphism problem. According to our introductory questions, we distinguish several dis- 20 ciplines: counting, enumerating and sampling isomers. While counting only delivers the number of isomers, the remaining disciplines refer to constructive methods. Enumeration typically encompasses exhaustive and non–redundant methods, while sampling typically lacks these char- acteristics. However, sampling methods are sometimes better suited to 25 solve real–world problems. There is a wide range of applications where counting, enumeration and sampling techniques are helpful or even essential. Some of these applications are closely linked to other chapters of this book. Counting techniques deliver pure chemical information, they can help to estimate 30 or even determine sizes of chemical databases or compound libraries obtained from combinatorial chemistry.