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A Combinatorial Game Theoretic Analysis of Chess Endgames
A COMBINATORIAL GAME THEORETIC ANALYSIS OF CHESS ENDGAMES QINGYUN WU, FRANK YU,¨ MICHAEL LANDRY 1. Abstract In this paper, we attempt to analyze Chess endgames using combinatorial game theory. This is a challenge, because much of combinatorial game theory applies only to games under normal play, in which players move according to a set of rules that define the game, and the last player to move wins. A game of Chess ends either in a draw (as in the game above) or when one of the players achieves checkmate. As such, the game of chess does not immediately lend itself to this type of analysis. However, we found that when we redefined certain aspects of chess, there were useful applications of the theory. (Note: We assume the reader has a knowledge of the rules of Chess prior to reading. Also, we will associate Left with white and Right with black). We first look at positions of chess involving only pawns and no kings. We treat these as combinatorial games under normal play, but with the modification that creating a passed pawn is also a win; the assumption is that promoting a pawn will ultimately lead to checkmate. Just using pawns, we have found chess positions that are equal to the games 0, 1, 2, ?, ", #, and Tiny 1. Next, we bring kings onto the chessboard and construct positions that act as game sums of the numbers and infinitesimals we found. The point is that these carefully constructed positions are games of chess played according to the rules of chess that act like sums of combinatorial games under normal play. -
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. -
Appendix B. Random Number Tables
Appendix B. Random Number Tables Reproduced from Million Random Digits, used with permission of the Rand Corporation, Copyright, 1955, The Free Press. The publication is available for free on the Internet at http://www.rand.org/publications/classics/randomdigits. All of the sampling plans presented in this handbook are based on the assumption that the packages constituting the sample are chosen at random from the inspection lot. Randomness in this instance means that every package in the lot has an equal chance of being selected as part of the sample. It does not matter what other packages have already been chosen, what the package net contents are, or where the package is located in the lot. To obtain a random sample, two steps are necessary. First it is necessary to identify each package in the lot of packages with a specific number whether on the shelf, in the warehouse, or coming off the packaging line. Then it is necessary to obtain a series of random numbers. These random numbers indicate exactly which packages in the lot shall be taken for the sample. The Random Number Table The random number tables in Appendix B are composed of the digits from 0 through 9, with approximately equal frequency of occurrence. This appendix consists of 8 pages. On each page digits are printed in blocks of five columns and blocks of five rows. The printing of the table in blocks is intended only to make it easier to locate specific columns and rows. Random Starting Place Starting Page. The Random Digit pages numbered B-2 through B-8. -
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. -
Effects of a Prescribed Fire on Oak Woodland Stand Structure1
Effects of a Prescribed Fire on Oak Woodland Stand Structure1 Danny L. Fry2 Abstract Fire damage and tree characteristics of mixed deciduous oak woodlands were recorded after a prescription burn in the summer of 1999 on Mt. Hamilton Range, Santa Clara County, California. Trees were tagged and monitored to determine the effects of fire intensity on damage, recovery and survivorship. Fire-caused mortality was low; 2-year post-burn survey indicates that only three oaks have died from the low intensity ground fire. Using ANOVA, there was an overall significant difference for percent tree crown scorched and bole char height between plots, but not between tree-size classes. Using logistic regression, tree diameter and aspect predicted crown resprouting. Crown damage was also a significant predictor of resprouting with the likelihood increasing with percent scorched. Both valley and blue oaks produced crown resprouts on trees with 100 percent of their crown scorched. Although overall tree damage was low, crown resprouts developed on 80 percent of the trees and were found as shortly as two weeks after the fire. Stand structural characteristics have not been altered substantially by the event. Long term monitoring of fire effects will provide information on what changes fire causes to stand structure, its possible usefulness as a management tool, and how it should be applied to the landscape to achieve management objectives. Introduction Numerous studies have focused on the effects of human land use practices on oak woodland stand structure and regeneration. Studies examining stand structure in oak woodlands have shown either persistence or strong recruitment following fire (McClaran and Bartolome 1989, Mensing 1992). -
Call Numbers
Call numbers: It is our recommendation that libraries NOT put J, +, E, Ref, etc. in the call number field in front of the Dewey or other call number. Use the Home Location field to indicate the collection for the item. It is difficult if not impossible to sort lists if the call number fields aren’t entered systematically. Dewey Call Numbers for Non-Fiction Each library follows its own practice for how long a Dewey number they use and what letters are used for the author’s name. Some libraries use a number (Cutter number from a table) after the letters for the author’s name. Other just use letters for the author’s name. Call Numbers for Fiction For fiction, the call number is usually the author’s Last Name, First Name. (Use a comma between last and first name.) It is usually already in the call number field when you barcode. Call Numbers for Paperbacks Each library follows its own practice. Just be consistent for easier handling of the weeding lists. WCTS libraries should follow the format used by WCTS for the items processed by them for your library. Most call numbers are either the author’s name or just the first letters of the author’s last name. Please DO catalog your paperbacks so they can be shared with other libraries. Call Numbers for Magazines To get the call numbers to display in the correct order by date, the call number needs to begin with the date of the issue in a number format, followed by the issue in alphanumeric format. -
Multiplication As Comparison Problems
Multiplication as Comparison Problems Draw a model and write an equation for the following: a) 3 times as many as 9 is 27 b) 40 is 4 times as many as 10 c) 21 is 3 times as many as 7 A Write equations for the following: a) three times as many as four is twelve b) twice as many as nine is eighteen c) thirty-two is four times as many as eight B Write a comparison statement for each equation: a) 3 x 7 = 21 b) 8 x 3 = 24 c) 5 x 4 = 20 ___ times as many as ___ is ___. C Write a comparison statement for each equation a) 45 = 9 x 5 b) 24 = 6 x 4 c) 18 = 2 x 9 ___ is ___ times as many as ___. ©K-5MathTeachingResources.com D Draw a model and write an equation for the following: a) 18 is 3 times as many as 6 b) 20 is 5 times as many as 4 c) 80 is 4 times as many as 20 E Write equations for the following: a) five times as many as seven is thirty-five b) twice as many as twelve is twenty-four c) four times as many as nine is thirty-six F Write a comparison statement for each equation: a) 6 x 8 = 48 b) 9 x 6 = 54 c) 8 x 7 = 56 ___ times as many as ___ is ___. G Write a comparison statement for each equation: a) 72 = 9 x 8 b) 81 = 9 x 9 c) 36 = 4 x 9 ___ is ___ times as many as ___. -
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. -
Season 6, Episode 4: Airstream Caravan Tukufu Zuberi
Season 6, Episode 4: Airstream Caravan Tukufu Zuberi: Our next story investigates the exotic travels of this vintage piece of Americana. In the years following World War II, Americans took to the open road in unprecedented numbers. A pioneering entrepreneur named Wally Byam seized on this wanderlust. He believed his aluminium-skinned Airstream trailers could be vehicles for change, transporting Americans to far away destinations, and to a new understanding of their place in the world. In 1959, he dreamt up an outlandish scheme: to ship 41 Airstreams half way around the globe for a 14,000-mile caravan from Cape Town to the pyramids of Egypt. Nearly 50 years later, Doug and Suzy Carr of Long Beach, California, think these fading numbers and decal may mean their vintage Airstream was part of this modern day wagon train. Suzy: We're hoping that it's one of forty-one Airstreams that went on a safari in 1959 and was photographed in front of the pyramids. Tukufu: I’m Tukufu Zuberi, and I’ve come to Long Beach to find out just how mobile this home once was. Doug: Hi, how ya doing? Tukufu: I'm fine. How are you? I’m Tukufu Zuberi. Doug: All right. I'm Doug Carr. This is my wife Suzy. Suzy: Hey, great to meet you. Welcome to Grover Beach. Tukufu: How you doing? You know, this is a real funky cool pad. Suzy: It's about as funky as it can be. Tukufu: What do you have for me? Suzy: Well, it all started with a neighbor, and he called over and said, “I believe you have a really famous trailer.” He believed that ours was one of a very few that in 1959 had gone on a safari with Wally Byam. -
Fluid-Structure Interaction in Coronary Stents: a Discrete Multiphysics Approach
chemengineering Article Fluid-Structure Interaction in Coronary Stents: A Discrete Multiphysics Approach Adamu Musa Mohammed 1,2,* , Mostapha Ariane 3 and Alessio Alexiadis 1,* 1 School of Chemical Engineering, University of Birmingham, Birmingham B15 2TT, UK 2 Department of Chemical Engineering, Faculty of Engineering and Engineering Technology, Abubakar Tafawa Balewa University, Bauchi 740272, Nigeria 3 Department of Materials and Engineering, Sayens—University of Burgundy, 21000 Dijon, France; [email protected] * Correspondence: [email protected] or [email protected] (A.M.M.); [email protected] (A.A.); Tel.: +44-(0)-776-717-3356 (A.M.M.); +44-(0)-121-414-5305 (A.A.) Abstract: Stenting is a common method for treating atherosclerosis. A metal or polymer stent is deployed to open the stenosed artery or vein. After the stent is deployed, the blood flow dynamics influence the mechanics by compressing and expanding the structure. If the stent does not respond properly to the resulting stress, vascular wall injury or re-stenosis can occur. In this work, a Discrete Multiphysics modelling approach is used to study the mechanical deformation of the coronary stent and its relationship with the blood flow dynamics. The major parameters responsible for deforming the stent are sorted in terms of dimensionless numbers and a relationship between the elastic forces in the stent and pressure forces in the fluid is established. The blood flow and the stiffness of the stent material contribute significantly to the stent deformation and affect its rate of deformation. The stress distribution in the stent is not uniform with the higher stresses occurring at the nodes of the structure. -
Basic Combinatorics
Basic Combinatorics Carl G. Wagner Department of Mathematics The University of Tennessee Knoxville, TN 37996-1300 Contents List of Figures iv List of Tables v 1 The Fibonacci Numbers From a Combinatorial Perspective 1 1.1 A Simple Counting Problem . 1 1.2 A Closed Form Expression for f(n) . 2 1.3 The Method of Generating Functions . 3 1.4 Approximation of f(n) . 4 2 Functions, Sequences, Words, and Distributions 5 2.1 Multisets and sets . 5 2.2 Functions . 6 2.3 Sequences and words . 7 2.4 Distributions . 7 2.5 The cardinality of a set . 8 2.6 The addition and multiplication rules . 9 2.7 Useful counting strategies . 11 2.8 The pigeonhole principle . 13 2.9 Functions with empty domain and/or codomain . 14 3 Subsets with Prescribed Cardinality 17 3.1 The power set of a set . 17 3.2 Binomial coefficients . 17 4 Sequences of Two Sorts of Things with Prescribed Frequency 23 4.1 A special sequence counting problem . 23 4.2 The binomial theorem . 24 4.3 Counting lattice paths in the plane . 26 5 Sequences of Integers with Prescribed Sum 28 5.1 Urn problems with indistinguishable balls . 28 5.2 The family of all compositions of n . 30 5.3 Upper bounds on the terms of sequences with prescribed sum . 31 i CONTENTS 6 Sequences of k Sorts of Things with Prescribed Frequency 33 6.1 Trinomial Coefficients . 33 6.2 The trinomial theorem . 35 6.3 Multinomial coefficients and the multinomial theorem . 37 7 Combinatorics and Probability 39 7.1 The Multinomial Distribution . -
Chapter 5 Measurement Operational Definitions Numbers and Precision
5 - 1 Chapter 5 Measurement Operational Definitions Numbers and Precision Scales of Measurement Nominal Scale Ordinal Scale Interval Scale Ratio Scale Validity of Measurement Content Validity Face Validity Concurrent Validity Predictive Validity Construct Validity Thinking Critically About Everyday Information Reliability of Measurement Test–Retest Reliability Alternate Form Reliability Split-Half Reliability Factors That Affect Reliability Case Analysis General Summary Detailed Summary Key Terms Review Questions/Exercises 5 - 2 Operational Definitions An essential component of an operational definition is measurement. A simple and accurate definition of measurement is the assignment of numbers to a variable in which we are interested. These numbers will provide the raw material for our statistical analysis. Measurement is so common and taken for granted that we seldom ask why we measure things or worry about the different forms that measurement may take. It is often not sufficient to describe a runner as “fast,” a basketball player as “tall,” a wrestler as “strong,” or a baseball hitter as “good.” If coaches recruited potential team members on the basis of these imprecise words, they would have difficulty holding down a job. Coaches want to know how fast the runner runs the 100-yard dash or the mile. They want to know exactly how tall the basketball player is, the strength of the wrestler, the batting average of the hitter. Measurement is a way of refining our ordinary observations so that we can assign numerical values to our observations. It allows us to go beyond simply describing the presence or absence of an event or thing to specifying how much, how long, or how intense it is.