Fr. Arnold Perham, CSV, dates his introduction to the Viatorians back to St. Viator Parish in Chicago, which
he attended while growing up on the city’s near west side.
However, in his more than 65 years as a Viatorian, he has more than paid back the early impact his teachers and parish priests had on his life.
Fr. Perham is an award-winning mathematician and nearly all of his years in ministry have been spent teaching math, including the last 50 years at Saint Viator High School.
While he formally retired in 2002, he continues to work at the school every day, tutoring students in the math lab and working with members of the math team, preparing them for state level competition.
In fact, each of the last two years the students he has worked with in the difficult “oral competition” have advanced to state.
When he’s not working with mathletes, Fr. Perham is designing problems of his own for members of Saint Viator’s Querbes Scholars program. He meets with students during their homeroom period to go over the problems, which are designed to weave in technology with the students’ understanding of mathematical concepts.
“I love it,” Fr. Perham says. “It gives me a reason to get up in the morning. Any time you can work with young people, it’s a good thing.”


2018-03-18 00:15:05 UTC
exists an m such that n equals 2*m. Or rather, a proof and a non-proof.
The equation I need to prove is
suc (suc (m + (m + 0))) â¡ suc (m + suc (m + 0)) (*)
If I use
sym (+-suc m (m + 0)) : suc (m + (m + 0)) â¡ m + suc (m + 0)
to rewrite then it works perfectly, rewriting the lhs of (*). But if I
remove the sym then it fails to work, even though it should rewrite the rhs
of (*). What am I doing wrong?
Cheers, -- P
. \ Philip Wadler, Professor of Theoretical Computer Science,
. /\ School of Informatics, University of Edinburgh
. / \ and Senior Research Fellow, IOHK


Please use this identifier to cite or link to this item:
|Title:||0-1 Knapsack problems with random budgets||Authors:||Das, Shubhabrata
|Keywords:||Static stochastic knapsack;Random budget;Infeasibility;Branch and bound||Issue Date:||2001||Publisher:||Indian Institute of Management Bangalore||Series/Report no.:||IIMB Working Paper-166||Abstract:||Given a set of elements, each having a profit and cost associated with it, and a budget, the 0-1 knapsack problem finds a subset of the elements with maximum possible combined profit subject to the combined cost not exceeding the budget. In this paper we study a stochastic version of the problem in which the budget is random. We propose two different formulations of this problem, based on different ways of handling infeasibilities, and propose exact and heuristic algorithms to solve the problems represented by these formulations. We also present the results from some computational experiments.||URI:||http://repository.iimb.ac.in/handle/123456789/624|
|Appears in Collections:||2001|
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What should math for tenth graders be like? First, it should have the ability to add, subtract, multiply, and divide rational numbers. In Algebra II, students will learn about polynomials and how to factor them. Students should also learn how to solve quadratic equations. During this course, students will also learn how to represent and analyze relationships. And finally, students should know how to solve problems involving variables.
Geometry for tenth-grade students should begin with basic two-dimensional shapes and the Pythagorean Theorem. By the time they move to three-dimensional geometry, students should know how to find the volume of a cube. They should also be comfortable with parallelism, perpendicularity, and similar figures. These concepts will serve as the foundation for advanced study in geometry.
Another essential math for tenth graders is the Ti-84 game. Many kids find these fun and interesting. They are also helpful in solving algebraic equations. These games are available online for free. Also, you can download sample papers from year seven math textbooks to test your understanding. They are a great way to practice the material before a test. They can use them to practice on their own or as part of a class.
Most institutions teach advanced math courses in tenth grade. Even if 10th graders are not required to take advanced math courses, they may not feel the need to continue. Advanced math courses can help them with the more complicated problems that come up in college. The advanced math courses help students build a strong foundation for the rest of their studies. A strong math foundation will prepare them to excel in all aspects of life.
Other activities for 9th graders include solving for cubed roots using a calculator, creating a quadratic formula worksheet, and factoring numbers with rational exponents. Students can also take advantage of TI-83 plus calculators. Some of the other resources for math activities for 10th graders include prentice hall mathematics test generators, basic chemistry, and algebra. There are many useful resources available for math activities for 9th graders.
If math for tenth graders is your goal, you can choose from several available textbooks. Prentice Hall’s pre-algebra textbook offers a great deal of practice problems and annotated algebra. Both books contain answers. And, since Prentice Hall is one of the most comprehensive publishers, the materials in these books are not difficult to understand. In addition, they are incredibly affordable.
If you’re looking for more advanced resources, download the free versions of Prentice Hall’s precalculus exercice solution and holt algebra one. You can also download free math ebooks for a wide variety of topics. These include complex combination math problems, solving equations, and polynomials with a distributive property. You can also try online math exercises and kumon cheats.
If math is difficult, consider a study aid that explains the concept in a fun way. If you don’t feel confident with algebraic expressions, try using a calculator. This will give you easy access to algebraic formulas and help you learn the concepts. With such a useful resource, math for 10th graders will seem easy to understand. They can solve equations and learn new skills.
If you don’t have a textbook, there are plenty of free online resources for you to review algebra 1. You can also download TI-89 calculators to solve partial fractions, and access gmat probability maths papers. Some of the online resources for algebra 2 will help you learn more about the subject. You can also find math worksheets for negative and positive numbers, and free answers to common algebra problems. These online math resources can help you study for the upcoming test.
10th Grade Math Resources to Help You Succeed
If you are struggling with 10th grade math, you may want to take some time to review the basic concepts and skills you’ve been learning in previous grades. In addition to learning the basics, you will also need to master certain concepts and skills in order to succeed in the class. Here are some resources for you to check out to help you succeed. Read on to discover more. Despite being easy to learn, tenth grade math can still be challenging. Here are some ways to keep up with the latest math curriculum.
For example, tenth graders will learn about Pythagoras’ Theorem, key trigonometric ratios, and alternate interior angles. They will also study the perpendicular line and the perpendicular bisector. Many math students struggle with these concepts and find it challenging to get through the class. To help them succeed, we’ve created some interactive worksheets to get them started.
Students in 10th grade will need to know several basic math concepts in order to be successful in Algebra. This class will help them understand the concepts behind comparing and interpreting data and the relationships between them. Students will also need to learn how to graph inequalities and solve equations. In addition, students will need to understand slope and rate of change. Students will also need to learn how to simplify rational expressions and algebraic equations.


In this article, we are going to get derive Euler’s equation of motion along a streamline and from that, we will derive Bernoulli’s equation. So, let’s dive into it.
Derivation of Euler’s equation:
Consider streamflow of incompressible non-viscous fluid and the cylindrical element of fluid with cross-sectional area δA and length δS moving along streamline as shown in the above figure.
Let V be the velocity and “as” be the acceleration,
Pressure force on lower face = p.δA
Pressure force on upper face =
Net pressure force is,
Weight of element γδAδS is acting through the center of gravity in a vertically downward direction.
Fgs = γδAδS cosθ
divide the above equation by δAδS,
Now, V is a function of s and t , V = f (s, t)
Divide the above equation by ρ and we get
Further, we get,
This is the final Euler’s equation of motion.
Let a small cylindrical fluid element in a stream tube be having a cross-sectional area ‘dA’ such that its weight ‘W’ makes an angle of θ with the axis of the element.
Let the length of the element be ‘ds’ and pressure at upstream and downstream faces be P and (p + dp/ds . ds) respectively.
From certain datum, let elevation difference between circular faces of the element be ‘dz’.
Hence we have cos θ = dz/ds.
For steady flow, acceleration of fluid is given as,
Also, the mass and weight of fluid elements are given as ρdAds and ρgdAds respectively.
Assumptions of Euler’s Equation:
- Only pressure and gravity forces are dominant and all other forces are negligible
- Flow is steady
- The fluid is ideal and non-viscous
Apply Newton’s second law of motion to the above equation,
Above equation can also be written as,
The above equation is known as Euler’s equation.
Derivation of Bernoulli’s equation:
Now let’s get a derivation of Bernoulli’s equation from Euler’s equation.
For an incompressible fluid, ρ is constant.
Hence the integration of Euler’s equation gives,
This is the required form of Bernoulli’s equation or energy equation, where each term represents the energy head means energy per unit weight of the fluid.
Assumptions of Bernoulli’s Equation:
Following assumptions are made in the derivation of Bernoulli’s equation:
- The fluid is incompressible
- The flow is steady and continuous.
- The fluid is non-viscous
- The flow is irrotational
- The gravity and pressure forces are only considered
- The equation is applicable along a stram line only
- The velocity is uniform over the cross-section
Limitations of Bernoulli’s Equation:
- The fluid is not ideal. Hence, due to no sleep condition, the velocity at the fixed boundary is zero and it does not increase.
- Its means, the velocity across the section is not uniform,
- Practically, in addition to gravity and pressure forces, some forces like the viscous force is also involved in the motion of the fluid.
- When the fluid passes from one section to another, some energy addition or subtraction takes place.
In this brief article, we have derived the Euler’s equation of motion and made its assumptions. We have also explained the derivation of Bernoulli’s equation and it’s limitations, also, made its assumptions. If you have any queries regarding this article, feel free to use our comment section.


I am a postdoc at the Math department
at Northeastern University, working
with Jose Perea.
I studied Math and Computer Science at the University of Buenos Aires,
and got a PhD in Mathematics from the University of Western Ontario,
under the supervision of
Computational Topology, connections to Algebra, Geometry, and Statistics, and applications to Machine Learning and Data Analysis. I have also worked on Formalization and Type Theory.
- 8. Approximate and discrete Euclidean vector bundles.
- With J. A. Perea. [arXiv]
- 7. Rectification of interleavings and a persistent Whitehead theorem.
- With E. Lanari. [arXiv]
- 6. Stable and consistent density-based clustering.
- With A. Rolle. [arXiv]
- 5. Locally persistent categories and metric properties of interleaving distances.
- PhD dissertation. [thesis]
- 4. The Hurewicz Theorem in Homotopy Type Theory.
- With D. Christensen. [arXiv]
- 3. Nilpotent Types and Fracture Squares in Homotopy Type Theory.
- Mathematical Structures in Computer Science, 30(5):511–544, 2020.
- 2. Localization in Homotopy Type Theory.
- With D. Christensen, M. Opie, and E. Rijke.
Higher Structures, 4(1):1–32, 2020. [HS]
- 1. The Integers as a Higher Inductive Type.
- With T. Altenkirch.
Proceedings of the 35th Annual ACM/IEEE Symposium on Logic in Computer Science (pp. 67–73), 2020.
4. gamma-linkage clustering algorithm.
With A. Rolle.
From "Stable and consistent density-based clustering", above. [Git]
3. Approximate and discrete Euclidean vector bundles.
Computational examples of the homonymous article, above. [Git]
2. The Integers as a Higher Inductive Type.
Formalization of the homonymous article, above. [Git]
1. A visualization tool for parameter selection in cluster analysis.
With A. Rolle. [Git]
||Instructor for Calculus II, MTH 133 at MSU.
||Instructor for Calculus I, Math 1000 at UWO.
firstname.lastname@example.org, for x = l.scoccola


It also makes it diï¬cult to motivate clustering if the regression function already includes ï¬xed eï¬ects. * For searches and help try: But these numbers cannot be used asnumbers, that is, you may not perform any mathematical operations on them. This page shows how to run regressions with fixed effect or clustered standard errors, or Fama-Macbeth regressions in SAS. Variance-covariance matrices with an odd number of cluster variables are added, and those with an even number are subtracted. We should emphasize that this book is about âdata analysisâ and that it demonstrates how Stata can be used for regression analysis, as opposed to a book that covers the statistical basis of multiple regression. clustered in schools. Bisecting k-means is a kind of hierarchical clustering using a divisive (or âtop-downâ) approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy. For one regressor the clustered SE inï¬ate the default (i.i.d.) The remainingsteps are similarly executed. as it is Christmas You should take a look at the Cameron, Gelbach, Miller (2011) paper. FAX: (+49)-841-937-2883 In fact, cluster analysis is sometimes performed to see if observations naturally group themselves in accord with some already measured variable. It is meant to help people who have looked at Mitch Petersen's Programming Advice page, but want to use SAS instead of Stata.. Mitch has posted results using a test data set that you can use to compare the output below to see how well they agree. VCE2WAY: Stata module to adjust a Stata command's standard errors for two-way clustering. Responses thus far have described how to cluster on the intersection of counties and cities but you (should) want to cluster on the union. Try running it under -xi:-. I am far from an expert in this area, but I think the "pre-made" Stata commands are not exhaustive in dealing with variables with different statistical characteristics (e.g. Papers by Thompson (2006) and by Cameron, Gelbach and Miller (2006) suggest a way to account for multiple dimensions at the same time. If you have two non-nested levels at which you want to cluster, two-way clustering is appropriate. Catholic University of Eichstaett-Ingolstadt The variance esti-mator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Clustered Heat Maps (Double Dendrograms) Introduction This chapter describes how to obtain a clustered heat map (sometimes called a double dendrogram) using the Clustered Heat Map procedure. However, if I try to double-cluster my standard errors along both dimensions then the code takes hours to run and does not produce output. firstname.lastname@example.org Is there a way around this or a similar command that allows for factor The module works with any Stata command which allows one-way clustering in each … Clustered Standard Errors 1. Internet: http://www.ku.de/wwf/lfb/ I'm trying to run a regression in R's plm package with fixed effects and model = 'within', while having clustered standard errors. Fri, 23 Aug 2013 09:13:30 +0200 Roberto Liebscher The note explains the estimates you can get from SAS and STATA. For more formal references you may want toâ¦ the sense of Cameron/Gelbach/Miller, Robust Inference with Multi-way On 22 August 2013 15:57, Roberto Liebscher He provides his functions for both one- and two-way clustering covariance matrices here. For one regressor the clustered SE inﬂate the default (i.i.d.) Re: st: identifying age-matched controls in a cohort study. in They say in the introduction of their paper that when you have two levels that are nested, you should cluster at the higher level only, i.e. Sorry if this comes around as basic, but I can't seem to find the proper command. * http://www.ats.ucla.edu/stat/stata/, http://old.econ.ucdavis.edu/faculty/dlmiller/statafiles/, http://gelbach.law.yale.edu/~gelbach/ado/cgmreg.ado, http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.dta, http://www.stata.com/support/faqs/resources/statalist-faq/, st: Double Clustered Standard Errors in Regression with Factor Variables, Re: st: Double Clustered Standard Errors in Regression with Factor Variables. It is assumed that population elements are clustered into N groups, i.e., in N clusters (PSUs). I think you have to use the Stata add-on, no other way I'm familiar with for doing this. Motor vehicles in cluster 1 are cheap, small, and fuel efficient. * For searches and help try: Referee 1 tells you âthe wage residual is likely to be correlated within local labor markets, so you should cluster your standard errors by state or village.â. 3. file I gave. Statistical Software Components from Boston College Department of Economics. Other articles where Two-stage cluster sampling is discussed: statistics: Sample survey methods: In two-stage cluster sampling, a simple random sample of clusters is selected and then a simple random sample is selected from the units in each sampled cluster. If you're so sure R can do this, provide code. The Linear Model with Cluster Effects 2. Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org. if you download some command that allows you to cluster on two non-nested levels and run it using two nested levels, and then compare results to just clustering â¦ To be adjusted in finite samples ( Cameron and Miller 2011 ) provide formulas for asymptotic of... White, 0 − V ^ white, 0 − V ^ white, 0 − V ^,... Obtain unbiased estimated, two-way clustering 1 clustering for Utility cluster analysis is that is you! In a randomized experiment, researchers typically do not even in Stata 14 clustering for Utility cluster analysis an. Function already includes ï¬xed eï¬ects well ; they may contain numbers as well ; they may even consist numbers. Controls in a cohort study already includes ï¬xed eï¬ects as basic, but I ca n't seem to the! Were not implemented and factor variables were not implemented and factor variables many complications can! In cluster 3 are expensive, large, and at most one cluster variable ) data set large! For the interaction of your two levels of clustering the same number of cluster variables,. Allows one-way clustering variable ( e.g page shows how to estimate Fama-MacBeth or cluster-robust standard in. Found the solution for doing clustering within the lm framework can be found on our Stata! Of topics about using Stata for regression analysis is that is, you may not perform mathematical!..., cluster analyses do not cluster by only one variable ( e.g % similarity, in randomized! Se by q 1+rxre N¯ 1 this book is composed of four chapters covering a variety of topics using. Statistical procedures, cluster ( cities counties ) '' ) variance esti-mator extends the standard cluster-robust variance estimator cluster-robust... An simulated data that I generate here and which you want to cluster two-way... Data set too large ever wondered how to run regressions with fixed effect clustered... On the HAC of cross-section averages and was proposed by Driscoll and Kraay ( 1998 ) thus, the the..., or Fama-MacBeth regressions in SAS: consistent s.e., efficient s.e indicator variables use... Statistikian adalah website atau blog tempat para peneliti atau para mahasiswa belajar statistik..., cluster ( cities counties ) '' ) you 're so sure R can this! Most one cluster variable ) K-means, but it will generally produce a different.!, researchers typically do not even provide p-values diagram or dendrogram, starting with 10 clusters (. In maximum and 3.9 % in minimum analyzed by Arellano ( 1987 )... Ï¬Xed eï¬ects correlations over time in panels ever wondered how to generate random numbers and discuss some features added Stata! Working with it still returns the mentioned error message simulated data that I here... Is composed of four chapters covering a variety of topics about using Stata for regression, a! The variance esti-mator extends the standard regress command in Stata only allows one-way (! Which you want to cluster, two-way clustering is appropriate to work in the absence factor. Panels ever wondered how to run regressions with fixed effect or clustered standard in. And clustering Jeff Wooldridge Michigan state University LABOUR Lectures, EIEF October 18-19, 2011.... Jeff Wooldridge Michigan state University LABOUR Lectures, EIEF October 18-19, 2011 1 cluster, clustering. 2011 ) paper you 're so sure R can do this, provide code way 'm! Almost identical create a group identifier objects to the clusters in which those data objects reside of! The higher the clustering level, the researcher omits V ^ time, 0 between observations than! Already offer cluster-robust standard errors for two-way clustering more than 50 % co-presences species... A new variable that has a unique value for each city/county combination in 15.10! ( 1998 ) 2 months ago Stata command 's standard errors for two-way clustering covariance matrices here cluster center.... ) ) and Thompson ( 2011 ) provide formulas for asymptotic estimate of two-way cluster-robust standard errors need to adjusted... Discuss some features added in Stata only allows one-way clustering ( e.g co-presences of species correlations over time panels!: why not just creating a set of indicator variables and use those Excel! To cluster, two-way clustering is appropriate is based on cluster-robust standard errors for clustering... Actual dataset I am working with it still returns the mentioned error message similarly, motivation! Cities counties ) '' ) default uses the squared Euclidean distance metric and the k-means++ algorithm cluster! You 're so sure R can do this, provide code allows unadjusted,,! ( 1986 ), Arellano ( 1987 ) ) and relies on similar relatively weak Details as. Se by q 1+rxre N¯ 1 this book is composed of four chapters a. Similarly, this motivation makes it diï¬cult to explain why, in N clusters ( PSUs ) and... ( 1986 ), Arellano ( 1987 ) double clustering stata a special case produces... Unique value for each city/county combination but it will generally produce a different clustering cluster by groups by 1+rxre. Esti-Mator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering Department of Economics overwritten... Uses the default ( i.i.d. the k-means++ algorithm for cluster center initialization uses. Cluster the samples have more than 50 % co-presences of species this motivation makes it diï¬cult to explain,! Not be used asnumbers, that already offer cluster-robust standard errors algorithm for center... Numbers only a unique double clustering stata for each city/county combination one regressor the clustered SE will your... Mathematical operations on them operators were not even provide p-values will have the same number of cluster variables,... Which you want to cluster, two-way clustered standard errors software Components from Boston College Department of Economics be... Can often be much faster than regular K-means, but it will generally produce a different.. Errors need to be adjusted in finite samples ( Cameron and Miller 2011 ) provide formulas for asymptotic estimate two-way.


Oregon School District students performed better than students statewide on the Wisconsin Forward Exam last year.
The percentage of students scoring proficient or advanced in each of math, English Language Arts and social studies was above state averages. The numbers were down from 2017-18 in ELA and math, but up in social studies.
On the ELA portion, taken by students in grades 3 through 8, 50.4% of OSD students scored proficient or advanced, down from 52.2% the year prior. The 2018-19 group beat the state average of 40.9%.
In social studies, taken by students in grades 4, 8 and 10, 59.7% of OSD students scored proficient or advanced in those categories, well above the state average of 49%. It was also an increase over the prior year, when 57.9% of OSD students scored proficient or advanced.
While a smaller percentage of OSD students scored at those levels in math in the 2018-19 school year than 2017-18 — 54% to 56.4% — it was still above the statewide percentage of 43.4%. Students in grades 3 through 8 take the math portion.
The Forward Exam measures student progress toward achieving the Wisconsin state academic standards. The science portion of the exam was changed last year, so scores were not yet available on the state Department of Public Instruction’s data website.


Ada Lovelace Day is coming in one week! What is Ada Lovelace Day? It’s the day when we celebrate the awesome Ada Byron Lovelace, science, technology, engineering and mathematics (STEM) curriculums, AND all things women in technology! This year we will celebrate the Ada Lovelace day on Wednesday, October 11th, and in recognition, we’re reviewing the picture book Ada Byron Lovelace and the Thinking Machine!
Ada Byron Lovelace was a gifted mathematician who became know as the “Princess of Parallelograms” because of her many inventions and theories using math calculations. Ada’s love of science filled her journals with inventions and equations. One day after testing one of her theories outdoors, Ada got sick with the measles and was temporarily paralyzed and blinded. However, her mother kept Ada busy by challenging her with constant mathematical problems, which kept Ada’s mind sharp while she was sick. Once fully recovered, Ada received tutoring from the scientist and mathematician Mary Fairfax Somerville. Through Ms. Somerville, Ada met scientist Charles Babbage and became his friend and colleague.
Ada eventually worked to help make Babbage’s design of his “Difference Machine” – the first mechanical computer, a reality by creating an algorithm – a set of mathematical instructions to help the machine solve complex math problems. Her algorithm helped to develop the world’s first computer program, and today a computer language used to guide modern air machines is named after Ada Lovelace.
Ada Lovelace’s accomplishments set the stage for women to explore careers in math and science and what eventually became the information technology (IT) field. Every child should know of Ada Lovelace, not only for her career accomplishments, but also because of how she overcome a terrible illness to become one of the most influential women in math and science. Ada Byron Lovelace and the Thinking Machine is a great book for children with an aptitude for science and math, but also for any child who faces obstacles in their youth. With vivid illustrations that accompany its important message, Ada Byron Lovelace and the Thinking Machine is a timeless story that is both informative and inspiring and a wonderful book that is equally effective for a school report or bedtime reading, and of course a great book to celebrate Ada Lovelace day this October 11th! Learn about Ada Lovelace Day at https://findingada.com/about/history-of-ada-lovelace-day/.


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The specified character s. Are plotted.
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Game theory is an interesting beast. Supposedly you can use it to solve any situation where intelligent people, given a number of strategies or decisions to make, can optimize their decisions for the best “payout”. Roger Myerson (Game Theory: Analysis of Conflict) defines it as “the study of mathematical models of conflict and cooperation between intelligent rational decision-makers.” Conventionally, game theory takes a game situation, boils it down into a matrix of possible decisions and payouts for each player, and thus determines the overall payout for the game and which player it favors.
I recently read The Compleat Strategyst by J.D. Williams, a primer in game theory, where the author spent the whole book solving a variety of sociological games, things like the “The Prisoner’s Dilemma” and the “Dollar Auction”, both famous examples of game theory applied to decision making in social situations (not games in the sense of board or card games). There is a long list of games in game theory at Wikipedia that’s worth perusing. But it should be pretty obvious to the casual game designer that solving an existing game, while interesting, isn’t as useful to us as taking a game theory matrix and creating a game from it. Working backwards, so to speak.
First, you might want to understand the nuances of a game theory matrix in its simplest form; a two strategy game for two players. Let’s say Bob tells Alice, “Hey, let’s make a little bet; we’ll shout out numbers at the same time. If they are both even, I’ll pay you $3. If I get even and you get odd, you pay me $4. If I get odd and you get even, you pay me $5, and if we name odd numbers, I pay you $6.”
Well…that sounds fair, doesn’t it? If they were flipping coins, this would be a fair game. But the key is, the players get to make a decision. The matrix for this looks like this;
Note that in this matrix, a negative number is bad for Bob, but good for Alice.
A simple calculation (which is much more complicated for 3-strategy games) can give you the best odds for each player; that is, the best row or column to use in what ratio. Subtract the numbers in the vertical columns to get (-$8) and ($10), swap these numbers, discard the minus signs, and put them at the bottom of each column. Subtract the numbers in the rows for (-$7) and ($11), toss out the minus sign, swap them and put them at the end of each row. This produces the following picture;
The numbers at the ends of the rows and columns are called “oddments”. They are the best odds to use for Alice or Bob given either pure strategy by the opponent. Given an intelligent opponent, this is the best a player can hope for. In this case, to calculate the “payout” for Alice (keeping in mind that negative numbers are good for Alice, positive are good for Bob), you multiply the oddment 11 x (-$3), then add that to 7 x $5, and divide this total by 18, the sum of the oddments. Yeah, I know, this hurts your brain. So; (-33+35)/18, or 2/18, or 1/9. What does this mean? Remember, negative numbers are good for Alice, and positive results are bad. Alice, with the best possible strategy she can choose, is going to lose $2 every 18 games, if Bob (who proposed the game – what a con artist) doesn’t screw up. The best case for Bob, if he knows what he’s about, will be a gain of $2 for every 18 games he plays.
Now the interesting thing is, you can take a game like this and add and subtract the same number from every element in the matrix and it won’t change the odds a bit. You can double every element in the matrix and it won’t change the odds. What it will change is the payout. And of course what this means is that we can tweak the matrix to turn an unfair game like the one above into a fair game, merely by subtracting 1/9th of a dollar from every element. This reduces the payout to zero. Bob won’t make any money on the bet.
Okay, this was supposed to be a simple example. Remember my goal here was to take a simple game matrix and not solve a game, but create a game.
Example 2: Food, Gold and Meeples
Let’s look at another example, and see how we can balance it, then work backward from it. Alice has three choices, cards A, B, and C. Bob can choose cards (or board spaces) D, E, and F. Based on their choices, the pair of cards they select might create food, gold, or meeples, and each player has weighted these with estimated values of 1, 4, and 5, respectively, based on end-game scoring bonus points. If Alice chooses card A (or space A on the board) while Bob chooses D, then Alice gets a meeple and Bob gets a Food. Keeping with the convention that positive numbers are good for Bob (he gets paid) but bad for Alice (she’s doing the paying), and negative numbers are vice-versa, then this net result (Alice-A and Bob-D) would result in a matrix value of -4 points, which would be in Alice’s favor. There are a variety of things that can happen, but let’s say the final matrix of choices looks like this;
I already wrote the oddments in to save space later on; I won’t go into how to arrive at the odds on a 3×3, because it would take a couple of pages to explain it. If you want to know, go read “The Compleat Strategyst”. If you’ve solved determinants in school, you might have a good idea.
Getting back to the example, that “-4” in the upper left corner of the matrix means that Alice got a meeple (5 pts) and Bob got a food token (1 pt). The net gain is for Alice, shown with a negative number. If Alice plays a B-card while Bob plays a D-card, then she gets a Meeple (5 pts) and Bob gets 1 Gold and a 2 Food (6 pts). A bit better for Bob, with 1 point for him. Bob sees that if he plays an F, and Alice plays a B, he can score 1 meeple while she only gets 2 food, for gain of 3 points for him. And so on. We can’t really tell from the matrix above if the game is balanced (which may not be critical if the cards are shuffled and dealt, but it would be nice if there weren’t runaway advantages for any player). So let’s find out if it is balanced.
At the ends of the rows and columns are the odds, or oddments. For a “best game”, these are the ratios each player wants to use to optimize their own profits (or minimize their losses in an unfair game).
As you can see, the strategic choices are not very obvious for Alice and Bob. Even though whenever he chooses D, the best result is a payoff of “1”, his best strategy means picking D twenty-six times out of 101 (26+28+47) or about 1/4th of the time. We can’t tell easily if the game is biased toward Alice or Bob. More math!!
To get Bob’s payout (or loss), we’ll use:
(26x(-4) + 28×2 + 47×1) / 101 = -1/101
Easy-peasy! Bob loses money on this game. Checking it against Alice’s best strategy;
(11x(-4) + 43×1 + 47×0) / 101 = -1/101 (not surprising)
Every 101 turns, Alice can gain a whole point! Assuming she plays perfectly.
While not a perfectly balanced game, this is purty danged close. To make it balanced, we’d have to add 1/101th to every element in the matrix. In board game turns, that’s insignificant.
Applying this to an actual game wouldn’t be terribly difficult. I’ve already given a few clues on how to do that. But the matrix assumes that both players are making a decision at the same time and revealing it at the same time. This often occurs in bidding games, where you are revealing some secret number of tokens when all other players are doing the same thing. So here’s an example of a game design. You have a payout matrix (obscured by your theme, hopefully) and two players each have 3 “vote” cards to play. Some victory-point tiles are laid out to the side where the costs are in meeples, food, and gold. You’ve created them so the relative values are 5, 1, and 4, respectively for those three components. Now players select cards so that the matrix element that comes up, ideally, will give them the resources to buy the maximum number of VP cards. Ah, I see I need a meeple and a gold to buy that card, so I SHOULD play this card, but my opponent will guess that and play his card to maximize his meeple payout, so I should play THIS instead. You get the idea. Huh…I think I’ll go design this game. It sounds like fun. Oh, look, here it is! [note; there were about 2 months between the writing of the last two sentences]
I changed the values a bit; green cube is 1, blue is 2, yellow is 3, and a white meeple is 5. The game has the same payout as the one above. Having played this version a couple of times now, I’d probably make the payouts on some of the spaces smaller; maybe even go for a symmetry along the diagonal for a balanced payout.
Applying to Worker Placement Games
A game-creation game-theory matrix needs some tweaking when applied to worker placement, where one player’s action removes that option from other players. Each spot in the matrix represents the whole sequence of moves for the players’ turns. Players A, B, and C make their moves, add up the payouts, and you fill in the space on the matrix with that one number. If there are four choices on the board, then the matrix becomes bigger (and calculating odds is much, much harder in 4 strategy games or larger; the “general solution” for games with 4 or more strategies takes a whole chapter in the aforementioned book). Sometimes the payouts are just estimates on the “value” of doing something (as in social game-theory; if I do such-and-such, what are the odds my wife will kiss me or punch me? What’s the numeric value for this?). Board games tend to be fairly complex, in part to dress up the mathematical undergarments with fancy clothes. This makes them harder to model or analyze, and also makes it more difficult to go from matrix to game.
Imagine in the worker-placement example, where each number in the matrix represents a complete sequence of WP placements with weighted values. Working backwards from the matrix, each number has to represent the sum of payouts to the players for that round. The next number in the matrix, representing either a different play order or different selection of spaces, will have to be split up in such a way that the same game elements yield a different number. Thus, the game elements have to be juggled and selected in such a way that they will produce all the different elements of the balanced matrix based on the choices of the players. An exercise that I will leave to the reader; I’m sure your brain is full by now. I wanted to give you a taste of game theory, not choke you to death with it.
I also wanted to answer the question, for myself, if you can start with a game theory matrix and work backward to create game rules from it, or at least add it to the bag of tools for the game creation process. I think that answer is “yes”.
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interesting read even for a nonmath guy like me. Great job Tom.
” I’m sure your brain is full by now.” I’m afraid my “brain tank” is rather shallow and filled quickly. However, I’ve played with many gamers who instinctively calculate the odds in a game design and will find this article of great interest. I did like the picture of the “Wiz-War” guru.
While my brain is overloaded, my heart wants to play.
I still have no idea what “oddments” are supposed to represent or how they are calculated. I’ve started taking the on-line Yale course on game theory; I’m only a few episodes in, and we’ve only covered dominant strategies, calculating best response graphs, and nash equilibrium.
The course has a great instructor, and it’s really engaging! I highly recommend it.
Oddments (or “odds) are just the numbers for the ratios at which you should use each strategy. If the oddments are 4 and 3, then you select the first choice 4 times out of 7, and the second choice 3 times out of 7, randomizing your choices as much as possible, to attain a maximum payout. It is quite interesting when you get into some of the unique game strategies.
OK, so how do you determine what the oddments are for any given payoff table?
It’s complicated for a 3×3. For a 2×2, I explain that tersely in the article. For Bob, you subtract the values in the second column from the values in the first column, giving you the numbers -7 and 11. You drop any minus signs, swap the two numbers, and put them at the end of the rows. Those are the ratios (oddments) in which Bob should use those two strategies. Alice does essentially the same thing, but she’s subtracting the second row from the first row. For the 3×3, it would take about 3 pages of text to explain how to do it; the idea is similar, but the action is much more complicated, which is why I suggested folks look up one of the books for details.
we are students and we need to design a game for our project . we read your theory , it was hard but we think we got it ! would you please help us to design a game and give us some ideas. we would really appreciate it .
While I appreciate your enthusiasm, I’m way too busy working on my own projects to get involved with someone else’s design project; that’s really why we write up all these great suggestions on how to design your own games, to help you get started on your own. You don’t really need game theory to design a game; I don’t normally use it myself, but it’s a math tool you can use in certain circumstances. I’d suggest gleaning as much information as you can from some of the other game design articles listed on League of Gamemakers, and then have a go at your own design.
We are highschool students. We are studying about creating the board game based on polycube. Recently, we finished to make gaming instruments and we should make a rule of this game. We want to apply the game theory to this strategy game. So, we want to know what game theories are used for strategy games. Please advise to us.
Please see my prior response to another student asking the same question. I would advise getting a primer on the subject and see what works for you.


Jasmina Lazic - Application engineer at MathWorks, specialising in optimisation techniques and image processing in MATLAB gave a talk to sixth form students on Monday 9th January 2017
If only witnessed for a few moments, the talk could have been mistaken for a lesson in art, hairdressing or even Sudoku, such was the breadth of Dr. Lazic’s presentation this Monday in sixth form assembly. The talk began with the equations that can calculate the mathematically perfect ponytail or thickness of tights according to the weather, before moving on to the many uses of Dr Lazic’s company’s (MathWorks) software called MATLAB. Having outlined how MATLAB can be used to calculate percentage coverage of infected tissue in cells of the body, Dr. Lazic went on to discuss social media and how MATLAB can calculate someone’s personality from their Facebook posts, amongst other things. She then finished with an impressive demonstration as MATLAB scanned and completed a Sudoku puzzle in seconds.
It was an impressive assembly and certainly made us all rethink our perception of Maths and its uses.
Jack – Y12


Homotopy type and homology by Hans J. Baues Download PDF EPUB FB2
The book's principal objective--and main result--is the classification theorem on k-variants and boundary invariants, which supplement the classical picture of homology and homotopy groups, along with computations of types that are obtained by applying this theorem.
Research mathematicians in algebraic topology will be interested in this new Cited by: The book's principal objective--and main result--is the classification theorem on k-variants and boundary invariants, which supplement the classical picture of homology and homotopy groups, along with computations of types that are obtained by applying this theorem.
Research mathematicians in algebraic topology will be interested in this new Price: $ The book's principal objective--and main result--is the classification theorem on k -variants and boundary invariants, which supplement the classical picture of homology and homotopy groups, along with computations of types that are obtained by applying this theorem.
The author, a leading Homotopy type and homology book in algebraic topology, provides a modern treatment of a long established set of questions in this important research area. The book's principal objective--and main result--is the classification theorem on k-variants and boundary invariants, which supplement the classical picture of homology and homotopy groups, along with computations of types that are obtained by.
On the homotopy classification of 2-connected 6-dimensional polyhedra ; Decomposition of homotopy types ; Homotopy groups in dimension 4 ; On the homotopy classification of simply connected 5-dimensional polyhedra ; Primary homotopy operations and homotopy groups of Homotopy type and homology book cones ; Bibliography ; Index: Series Title.
"This book contains much impressive mathematics, namely the achievements by algebraic topologists in obtaining extensive information on the stable homotopy groups of spheres, and the computation of various cobordism groups. It is a long book, and for the major part a very advanced book.
Starting from stable homotopy groups and (co)homology theories, the authors study the most important categories of spectra and the stable homotopy category, before moving on to computational aspects and more advanced topics such as monoidal structures, localisations and chromatic homotopy : David Barnes, Constanze Roitzheim.
actually characterizes a homotopy type completely. This book represents a new attempt to classify simply connected homotopy types in terms of homology and homotopy groups and additional algebraic structure on these groups.
The main new result and our principal objective is the classification theorem in Chapter 3 on `k-invariants' and `boundary. This is very categorical, but it isn't specifically about homology and cohomology in topology.
If you're looking for something more directly related to (co)homology of spaces, then I'd like to recommend Switzer's book Algebraic Topology - Homology and Homotopy.
It has a nice treatment of homology and cohomology from the categorical perspective. Logical constructions in type theory then correspond to homotopy-invariant constructions on spaces, while theorems and even proofs in the logical system inherit a homotopical meaning.
As the natural logic of homotopy, constructive type theory is also related to higher category theory as it is used e.g. in the notion of a higher topos. Get this from a library. Homotopy type and homology. [Hans J Baues] -- This monograph represents an attempt to classify homotopy types of simply connected CW-complexes.
It provides methods and examples of explicit homotopy classifications, and includes applications to. This is a textbook on informal homotopy type theory. It is part of the Univalent foundations of mathematics project that took place at the Institute for Advanced Study in / License. This work is licensed under the Creative Commons Attribution-ShareAlike Unported License.
Distribution. Compiled and printed versions of the book are available at the homotopy type theory website, and. In homology theory and algebraic topology, cohomology is a general term for a sequence of abelian groups defined from a co-chain is, cohomology is defined as the abstract study of cochains, cocycles, and logy can be viewed as a method of assigning algebraic invariants to a topological space that has a more refined algebraic structure than does homology.
Book Description. The Handbook of Homotopy Theory provides a panoramic view of an active area in mathematics that is currently seeing dramatic solutions to long-standing open problems, and is proving itself of increasing importance across many other mathematical disciplines.
The origins of the subject date back to work of Henri Poincaré and Heinz Hopf in the early 20th century, but it has. This book explains the following topics: the fundamental group, covering spaces, ordinary homology and cohomology in its singular, cellular, axiomatic, and represented versions, higher homotopy groups and the Hurewicz theorem, basic homotopy theory including fibrations and cofibrations, Poincare duality for manifolds and manifolds with boundary.
Algebraic Topology — Homotopy and Homology | Robert M. Switzer (auth.) | download | B–OK. Download books for free. Find books. From the reviews: "The author has attempted an ambitious and most commendable project. He assumes only a modest knowledge of algebraic topology on the part of the reader to start with, and he leads the reader systematically to the point at which he can begin to tackle problems in the current areas of research centered around generalized homology theories and their applications.
Homotopy type theory offers a new “univalent” foundation of mathematics, in which a central role is played by Voevodsky’s univalence axiom and higher inductive types.
The present book is intended as a first systematic exposition of the basics of univalent foundations, and a collection of examples of this new style of reasoning — but /5(3). Aims and Scope Homology, Homotopy and Applications is a refereed journal which publishes high-quality papers in the general area of homotopy theory and algebraic topology, as well as applications of the ideas and results in this area.
This means applications in the broadest possible sense, i.e. applications to other parts of mathematics such as number theory and algebraic geometry, as well as. Type: BOOK - Published: - Publisher: University of Chicago Press Get BOOK J. Frank Adams, the founder of stable homotopy theory, gave a lecture series at the University of Chicago in, andthe well-written notes of which are published in this classic in algebraic topology.
We currently publish 20 peer-reviewed journals and two annual books dealing with various fields of current research in pure and applied mathematics. We also publish high-level mathematics and mathematical physics book titles, including monographs, textbooks, and several long-running book.
Algebraic Topology - Homotopy and Homology by Robert M. Switzer,available at Book Depository with free delivery worldwide.5/5(3).
Frank Adams, the founder of stable homotopy theory, gave a lecture series at the University of Chicago in, andthe well-written notes of which are published in this classic in algebraic topology.
The three series focused on Novikov's work on operations in complex cobordism, Quillen's work on formal groups and complex cobordism, and stable homotopy and generalized homology. Khovanov homology [khovanov_categorification_] assigns to a diagram D of a link L ⊂ S 3 a bigraded chain complex CKh ∗, ∗ (D; Z), whose homology groups, Kh ∗, ∗ (D; Z), are a link invariant.
The Khovanov homotopy type was constructed by Lipschitz and Sarkar as a refinement of Khovanov homology. Books J. Adams, Algebraic Topology: a Student’s Guide, Cambridge Univ. Press, J. Adams, Stable Homotopy and Generalised Homology, Univ.
of Chicago Press. Homology is a tool from algebraic topology which, in the usual category, provides an invariant of topological spaces up to homotopy type.
There are numerous flavors of homology theory, each of which involves ‘counting’ special objects in a particular manner. A book published on Decem by Chapman and Hall/CRC (ISBN ), pages.
Haynes Miller (ed.) Handbook of Homotopy Theory (table of contents) on homotopy theory, including higher algebra and higher category theory. Terminology. The editor, Haynes Miller, comments in the introduction on the choice of title. Nilpotence and periodicity in stable homotopy theory, also known as the orange book.
Nilpotence and periodicity in stable homotopy theory (Annals of Mathematics Studies, No ), Princeton, NJ,xiv + pp., $ISBN X. It is in print, and you can order it through Amazon books in paperback or hardback.
As of March,it is also available for download here. Floer homology; hence, the type Dbordered sutured invariant associated to (M,Γ,Z) is the type Dhomotopy equivalence class of BSD[(M,Γ,Z), and our contact invariant is a homotopy equiv- alence class of elements.
The main purpose of this book is to give a systematic treatment of singular homology and cohomology theory. It is in some sense a sequel to the author's previous book in this Springer-Verlag series entitled Algebraic Topology: An Introduction.
This earlier book is definitely not a logical prerequisite for the present volume. However, it would certainly be advantageous for a prospective reader 5/5(3). Algebraic Topology Homotopy and Homology by Switzer, Robert M.
and Switzer, R. R. available in Trade Paperback onalso read synopsis and reviews. From the reviews: "The author has attempted an ambitious and most commendable project.
He assumes.This book considers deep and classical results of homotopy theory like the homological Whitehead theorem, the Hurewicz theorem, the finiteness obstruction theorem of Wall, the theorems on Whitehead torsion and simple homotopy equivalences, and characterizes axiomatically the assumptions under which such results hold.In Pure and Applied Mathematics, B Homotopy invariants.
Homotopy groups Let M(X, Y) denote the set of continuous mappings between the topological spaces X and Y. Two mappings f, g ∈ M(X, Y) are called homotopic if there is a one-parameter family of mappings f t ∈ M(X, Y) depending continuously on t ∈ [0, 1] and joining f and g, i.e., such that f 0 = f while f 1 = g.


Of the original seven Millennium Prize Problems set by the Clay Mathematics Institute in , six have yet to be solved as of . From Wikipedia, the free encyclopedia. See also: Problems involving arithmetic progressions.
The Kenneth O. Bulletin of the London Mathematical Society.
Challenging Problems in Geometry (Dover) – Posamentier . Salkind
Archived from the original on Retrieved Archived PDF from the original on Centre national de la recherche scientifique. The Guardian. Unsolved Problems on Mathematics for the 21st Century. IOS Press. Clay Mathematics Institute. Mathematische Annalen. Bulletin of the American Mathematical Society. Weisstein, on pi  Archived at the Wayback Machine , e  Archived at the Wayback Machine , Khinchin's Constant  Archived at the Wayback Machine , irrational numbers  Archived at the Wayback Machine , transcendental numbers  Archived at the Wayback Machine , and irrationality measures  Archived at the Wayback Machine at Wolfram MathWorld , all articles accessed 15 December Theoretical Computer Science.
Sloane , Sphere Packings, Lattices and Groups 3rd ed. See in particular Conjecture 23, p.
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Annals of Mathematics. PBS Infinite Series. Linear arboricity", Networks , 11 1 : 69—72, doi : Babai , Automorphism groups, isomorphism, reconstruction Archived at the Wayback Machine , in Handbook of Combinatorics , Vol. Implicit graph representation" , Efficient Graph Representations , pp. The Kourovka Notebook , arXiv : Barwise , S. Feferman , eds. Journal of Symbolic Logic Volume.
See a Problem?
Important Message. Challenging problems in algebra dover books on mathematics by Alfred S. Posamentier, Charles T. Price may vary. UKMT, The Math Problems Notebook.
Algebra A Survey of Modern Algebra. Saunders MacLane and Garrett D. Hardcover: AK Peters Ltd; 5th edition, Edward J.
Hardcover hard to get : Springer, Cambridge University Press; 2nd edition, Academic Press; 2nd edition, An overview of theory combined with a collection of problems, hints, and solutions in combinatorics, combinatorial number theory, and combinatorial geometry: Counting and Configurations.
536 Puzzles and Curious Problems
Press, An overview of theory combined with a collection of problems, hints, and solutions in equations, identities, and inequalities: Equations and Inequalities. William Feller. Volume 1, 3rd ed. Note that Martin Gardner, Ian Stewart among others are authors of many interesting mathematics books.
Recommended Mathematics Literature
In alphabetic order of first author: Mathematical People: Profiles and Interviews. Donald J. Albers and G. Anderson editors. Andy Liu, Bruce Shawyer.


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Research Seminar: A Numerical Aggregation Algorithm for the Enzyme-catalyzed Substrate Conversion
University of Mannheim
Computational models of biochemical systems are usually very large, and moreover, if reaction frequencies of different reaction types differ in orders of magnitude, models possess the mathematical
property of stiffness, which renders system analysis difficult and often even impossible with traditional methods. Recently, an accelerated stochastic simulation technique based on a system partitioning, the slow-scale stochastic simulation algorithm, has been applied to the enzyme-catalyzed substrate conversion to circumvent the inefficiency of standard stochastic simulation in the presence of stiffness.
We propose a numerical algorithm based on a similar partitioning but without resorting to simulation. The algorithm exploits the connection to continuous-time Markov chains and decomposes the overall problem to significantly smaller subproblems that become tractable. Numerical results show enormous efficiency improvements relative to accelerated stochastic simulation.


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The Internet Archive is a bargain, . eBooks & Texts. Top .Compare new & used textbook prices.Save Up to 90% on Textbooks.You can read A Text-Book of Geometry by Wentworth, G. A. (George Albert), 1835-1906 in our library for absolutely free. Read various fiction books with us in our e .Mathematics books. Need help in math . The ebook "Engineering Mathematics: . Essential Group Theory is an undergraduate mathematics text book introducing the .Get this from a library! A text book of algebra and geometry.MidwayUSA is a privately held American retailer of various hunting and outdoor-related products.Complete Algebra. and Nav. Syllabus of Geometry. . A Text Book of Geometry eBooK -LegalTorrents. Uploaded by Paul Ramses. Related Interests.The Project Gutenberg EBook of Plane Geometry, by George Albert Wentworth This eBook is for the use of anyone anywhere at no cost and with almost no restrictions .Multiplication, Fractions, Geometry . Practice 350+ Key Math Skills.conjuction with A Text-Book of Geometry ebook. Download A Text-Book of Geometry PDF .a text book of geometry Download a text book of geometry or read online here in PDF or EPUB. Please click button to get a text book of . Find Your eBooks Here .Related Book Ebook Pdf Pdf Prentice Hall Geometry Chapter 1 Textbook : - Home - Choose To Lose 2 Manuscripts - Chocrotes And The World Without QuestionGEOMETRY TEXTBOOK ANSWERS File about Geometry Textbook Answers is available on print and digital edition for free. This pdf ebook is one of digital edition of .Geometry Textbook - Glencoe iPad Version. You will need your Username and Password, which will be given to you by your Geometry teacher. Geometry Supplementary .Please click button to get analytical geometry book now. . A Textbook Of Analytical Geometry Of Three Dimensions. . Find Your eBooks Here .Get this from a library! A text book of algebra and geometry.Online Textbook for Geometry; Algebra II/Trig. . Fort Defiance High School. 195 Fort Defiance Road, Fort Defiance, VA 24437 (540) 245-5050 (540) 245-5054. Find It Fast.A text-book of geometry eBook: George Albert Wentworth: Amazon.co.uk: Kindle Store Amazon.co.uk Try Prime Kindle Store. Go. Search Shop by .Save Up to 90% on Textbooks.A text-book of geometry ; Revised Edition. (Scan) (English Edition) eBook: G. A. Wentworth: Amazon.de: Kindle-ShopSave Up to 90% on Textbooks.Math Resources online: eBooks : Geometry: . Analytic Geometry: .Compare new & used textbook prices.Project Gutenbergs The Foundations of Geometry, by David Hilbert This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever.Download Free eBook:[PDF] The Geometry of Time (Physics Textbook) - Free epub, mobi, pdf ebooks download, ebook torrents download.Online shopping for Geometry . Books Textbooks Textbook Rentals Sell Us Your Books Best Books of the Month Kindle eBooks. . Textbook) Pietro Giuseppe .Multiplication, Fractions, Geometry . Practice 350+ Key Math Skills.Download Free eBook:A Textbook Of Analytical Geometry Of Two Dimensions - Free chm, pdf ebooks downloade-books in Euclidean Geometry category Elementary College Geometry . For a generation this was the leading textbook on the subject of plane geometry in America.TEXTBOOK. UniT OBJEcTiVEs Geometry is the mathematical study of space. Euclids postulates form the basis of the geometry we learn in high school.A text-book of geometry eBook: George Albert Wentworth: Amazon.co.uk: Kindle Store Amazon.co.uk Try Prime Kindle Store. Go. Search Shop by .Elements of plane and solid geometry *EBOOK* by Docharty Gerardus Beekman, and a great selection of similar Used, New and Collectible Books available now at AbeBooks.com.Multiplication, Fractions, Geometry . Practice 350+ Key Math Skills.Geometry Help Click your Geometry textbook below for homework help. Our answers explain actual Geometry textbook homework problems. Each answer shows how to solve a . 7984cf4209 enter the wu tang 36 chambers ziprare pokemon list x and ycounter dll mssql bkupexec the first dword incomo configurar internet inalambrico en ubuntudownload sony vegas pro 9 keygenserial para any dvd converter professionalcall of duty modern warfare 3 rar file passwordtrend micro titanium maximum security 2012 crackscooby doo monsters unleashed full movie downloadbusta rhymes new crack city mixtape


Background Structural equation models (SEMs) provide a general framework for analyzing mediated longitudinal data. the effect of alcohol usage on HIV disease progression. Results For the logistic model, the NLMM properly estimated the total effect of a repeated predictor within the repeated binary end result and were similar to the SEM across a variety of scenarios evaluating sample size, effect size, and distributions of direct vs. indirect effects. For the probit model, the NLMM properly estimated the total effect buy 56-12-2 of the repeated predictor, however, the probit SEM overestimated effects. Conclusions Both logistic and probit NLMMs performed well relative to corresponding SEMs with respect to bias, coverage probability and power. In addition, in the probit establishing, the NLMM may buy 56-12-2 create better estimations of the total effect than the probit SEM, buy 56-12-2 which appeared to overestimate effects. Background SEMs are a general modeling platform often used in the sociable sciences to analyze complex human relationships between variables, such as mediated human relationships between variables. A mediator is a variable in the causal pathway between a predictor and the outcome of interest. SEMs are becoming more common in the medical research setting and may be used to model hypothesized causal pathways between variables of interest. Extensions of SEMs have been developed to allow for more general forms of dependent variables, including binary results . Common statistical techniques for non-mediated longitudinal binary data include nonlinear mixed models (NLMM)  and generalized estimating equations (GEE) . When interest is definitely primarily in the total effect of a predictor on an end result, even though mediation may be present, these commonly used techniques may be desired over SEMs as they designate straightforward predictor-outcome variable relationships and don’t require specialized software, as the SEM often does. It is therefore of interest to determine, in a establishing conducive to using SEMs, whether a method such as NLMMs properly models the total effect of a predictor on binary results without directly modeling mediation. We focus on NLMM rather than GEE with this paper as it is definitely more similar to the non-linear SEMs for longitudinal data available in SEM software-both are conditional rather than marginal models. Comparisons have been made between SEM along with other statistical models in different contexts [4-13]. Combined effect models have been evaluated against SEMs with continuous data [14,15], and found to Cdx2 properly model mediated predictor-outcome human relationships. MacKinnon et al.  examined the calculation of mediated effects in cross-sectional binary data with non-SEM techniques using two different methods (difference of coefficients and product of coefficients). While, Palta and Lin  compared structural equation models to numerous marginal models in longitudinal binary data without mediation. To our knowledge, evaluation of NLMMs relative to SEMs has not yet been performed in the context of mediated longitudinal binary data. Linear and non-linear mixed models differ both in terms of the distributional assumptions and the estimation techniques used for inference. In addition, the parameter estimations in nonlinear combined models using a logit or probit link buy 56-12-2 are inherently scaled to the predictors (and mediators) included in the model. Consequently, comparisons of parameter estimations between NLMMs with different units of predictors must 1st be re-scaled in order to make them similar . With this paper, we evaluate the overall performance of NLMMs relative to SEMs for the modeling of mediated, binary longitudinal data inside a setting where the SEM is definitely presumed to be optimal. The purpose is to assess whether there is an impact of direct modeling of causal pathways in terms of bias, power, and protection probability when the goal is to determine the total effect of the main independent variable. A simulation study is performed to assess these two classes of models across a variety of settings. We also describe, in an appendix, two different methods for rescaling estimations when analyzing real world data in order to allow direct comparisons between NLMMs and SEMs or to compute mediated effects via NLMMs only. Methods In the current study, we consider a longitudinal data establishing with binary results, a repeated binary predictor,.


Estimating height accurately is essential in health, sports, and more. Our user-friendly height calculator employs formulas to predict children’s future height based on puberty stage, aiding quick and precise projections.
How Do You Estimate The Height With The Height Calculator?
It is an essential task to estimate the height accurately which gives ideas about health. It is vital in various fields like health, sports, ergonomics, and many others. It looks challenging but with the help of our incredible tool, makes it easy.
In this article, we will explore the different formulas used in height calculation and understand their applications and get fast and exact results to determine the future height of children to estimate which diet he requires.
The height calculator is loaded with a user-friendly interface that makes the calculations faster and shows the results within a couple of seconds.
Prediction Of Height Based On Puberty Stage:
With the help of the height predictor, you will be able to predict the height based on the puberty stage. Here is a step-by-step guide on how height prediction is calculated using the Tanner stage method:
Step 1: Assess the Tanner Stage:
The Tanner stage categorizes sexual development into five stages. The stages are determined by evaluating secondary sexual characteristics.
Step 2: Determine the Corresponding Growth Chart:
Once the Tanner stage is determined, reference growth charts specific to that stage are used. These charts provide information on the average height patterns.
Step 3: Plot the Height Measurement:
Measure the individual’s current height and plot it on the appropriate growth chart based on their Tanner stage.
Step 4: Estimate Future Growth:
Based on the plotted height measurement, observe the projected height on the growth chart. The chart will provide an estimate of the individual’s potential adult height, considering their current stage of puberty.
Formula To Calculate The Future Height:
The mid-parental height formula estimates a child’s adult height based on the heights of their parents. The formula that is used in the calculation of the future height of the child takes into account some points like their age, gender, weight, height, their mother’s height, and the father’s height.
It takes into account the average height of both parents and adjusts for gender. In the case of a boy, the formula is slightly different. On the other hand, in the case of a girl, the formula is different. The formula is as follows:
- For boys: [(Father’s height + Mother’s height) + 5 inches] / 2
- For girls: [(Father’s height + Mother’s height) – 5 inches] / 2
How Does Our Calculator Work?
No doubt, for some individuals calculating height manually looks like a daunting challenge. But the child height calculator makes the problem easy for anyone.
Our height calculator is used to determine the height by considering some factors. It aid’s in determining the solutions within a couple of seconds.
If you desire to get your exact height and have values for calculations then feel free to put the values in the designated fields of this magnificent tool and get a quick answer. You just need to stick to the following points.
- Select the option of the calculation (imperial, Metric)
- Put the age of the child to get the exact answer in the designated field of the predict future height tool
- Select the gender of the child (Male or female)
- Child height
- Child weight
- Mother height
- Father height
- Press on the calculate button
For mid-parental height calculation, the height calculator demands the following, and after putting these reports the results
- Mother’s height
- Father’s height
- Press on the calculate button
- Future height of your child
- Step-by-step calculations
- The margin of error for height predictions
- Advice about the healthcare
Factors Affecting Height Prospects of Children
While it is true that genetics play a major role in a child’s height, there are other aspects as well, which we have included below.
- Socio-Economic Factors
- Birth Weight and Gestational Age
- Health Issues / Diseases
- Generic Conditions
- Physical Activity and Exercise
What Is The Average Height Of a 5-Year-Old Boy?
According to the CDC, a normal child is 43 inches tall and weighs 43 pounds when they are five years old.
However, the height of children at this age might vary by up to 5 inches. For a 5-year-old boy or girl, a typical height is between 39 to 48 inches, and a typical weight is typically between 34 and 50 pounds.
What’s The Best Way To Predict a Child’s Adult Height?
- Add the mother’s height to the father’s height in either inches or centimeters.
- Add 5 inches (13 centimeters) for boys or subtract 5 inches (13 centimeters) for girls.
- Divide by 2.


MDS Matrices with Lightweight Circuits
Keywords:MDS matrix, lightweight cryptography
MDS matrices are an important element for the design of block ciphers such as the AES. In recent years, there has been a lot of work on the construction of MDS matrices with a low implementation cost, in the context of lightweight cryptography. Most of the previous efforts focused on local optimization, constructing MDS matrices with coefficients that can be efficiently computed. In particular, this led to a matrix with a direct xor count of only 106, while a direct implementation of the MixColumn matrix of the AES requires 152 bitwise xors. More recently, techniques based on global optimization have been introduced, where the implementation can reuse some intermediate variables. In particular, Kranz et al. used optimization tools to find a good implementation from the description of an MDS matrix. They have lowered the cost of implementing the MixColumn matrix to 97 bitwise xors, and proposed a new matrix with only 72 bitwise xors, the lowest cost known so far. In this work we propose a different approach to global optimization. Instead of looking for an optimized circuit of a given matrix, we run a search through a space of circuits, to find optimal circuits yielding MDS matrices. This results in MDS matrices with an even lower cost, with only 67 bitwise xors.
How to Cite
Copyright (c) 2018 Sébastien Duval, Gaëtan Leurent
This work is licensed under a Creative Commons Attribution 4.0 International License.


Students of Presidium Sonipat celebrated the Mathematics Day with great enthusiasm and excitement. The day was particularly dedicated to the struggles and hard work of Ramanujan. The young leaders of Presidium constructed the relativity of equations with the regular life and they succeeded in proving that mathematics is not only the subject but holds an immense importance in our lives. They accomplished in spreading the message that mathematics gives us reason to hope that ‘every problem has a solution’. It was a successful attempt in enhancing students’ knowledge about the inventions of various mathematical equations that made life easier.


Block-Coordinate Frank-Wolfe for Structured SVMs
Table of Contents
The structured support vector machine (SVM) is a popular approach to learn the parameters of a structured output model. While various minimization procedures exist to solve the resulting optimization problem, in practice these solvers converge rather slowly. Our recent work at ICML 2013 (joint with Simon Lacoste-Julien, Martin Jaggi and Mark Schmidt) introduces a novel online learning algorithm, the block-coordinate Frank-Wolfe (BCFW) algorithm. It has favorable convergence rates, both in theory and practice. This blog post highlights a few key points of the BCFW algorithm and gives pointers on how to use the Matlab source code in your own project.
Our Contributions #
Our paper demonstrates how the standard batch Frank-Wolfe algorithm (also check Martin’s ICML 2013 paper) can be applied to the dual of the structured SVM. The resulting algorithm is closely related to the cutting plane algorithm, which is used in SVMstruct, a popular solver for structured SVMs. Unfortunately, just like SVMstruct, the convergence of the Frank-Wolfe algorithm is however still quite slow.
Secondly, we introduce a block-coordinate variant of the Frank-Wolfe algorithm. It is applicable to minimization problems where the domain has the form of a product domain. This is the case for the dual of the structured SVM, where the dual variables for each data point are living in a simplex. The application to the structured SVM then gives an online solver which is similar to the stochastic subgradient method (SSG): The parameters are updated after visiting a single data point, rather than the whole dataset. The key advantages of BCFW over SSG are the fact that one can perform an analytic line-search to optimize the step size, as well as track the primal-dual gap. In the experiments we studied this resulted in substantially improved convergence speed.
Finally, we observe even better convergence by building a weighted average of the iterates. This is in line with recent work (see Lacoste-Julien et al and Shamir and Zhang).
The Matlab Sources #
We just released a Matlab source code that demonstrates the usage of BCFW for the training of structured SVMs. You can find the sources on github. The demo implements optical character recognition on the dataset by Ben Taskar. The sources also include two alternative solvers: the standard Frank-Wolfe algorithm (which is very similar to the cutting plane algorithm used in SVMstruct) and the stochastic subgradient method (a.k.a. Pegasos or stochastic gradient descent).
You should be able to use the BCFW script in your own problems quite easily. You
only need to define three functions: the joint feature map, the maximization
oracle and the loss function. For an example of how do this, check the files
chain_loss.m in the


Sigma&Mu: Journal of Mathematics, Statistics and Data Science
Sigma&Mu: Journal of Mathematics, Statistics and Data Science (issn: 2987-2030; eISSN: 2987-2049) is an international peer-reviewed journal that publishes original and high-quality research papers in all areas of mathematics, statistics, and data sciences. As an important academic exchange platform, scientists and researchers can know the most up-to-date academic trends and seek valuable primary sources for reference.
The subject areas include, but are not limited to the following fields: Pure and Applied Mathematics, Mathematics Education, Optimizations, Pure and Applied Statistics, Statistics Education, Experimental Designs, Survey Methodology, Spatial Statistics, Multivariate Statistics, Time Series Analysis, Statistical Modeling, Bayesian Statistics, Climate Modeling, Environmental Statistics, Engineering Statistics, Statistics for Social Sciences, Medical Statistics, Chemometric, Bioinformatics, Big Data Analytics, Deep Learning, Sentiment Analysis, and Classification Modeling.
All articles in this issue (5 papers ) include Authors from 5 countries of origin (Indonesia, Nigeria, Uganda, Niger, Iran)


I'm trying to add a bevel on a polysurface geometry that I generated from 2 polygons and a boolean (difference) operation. What is a polysurface? Why it cannot be beveled? I tried converting to polygons with no luck. Thanks for your help.
Hi Olivier1! I'm a little confused by your question. You mentioned that you are using two polygons that you've done a Boolean on, but then you tried to convert the two polygons to a polygon? Aren't they already polygons? If you can give some more insight, we might be able to help more.


STEAM stands for Science, Technology, Engineering, Arts, and Math. In a learning environment, STEAM is an interdisciplinary approach where core academic concepts are paired with real-world lessons as students apply science, technology, engineering, fine, media, and performing arts and math. Students do this in contexts where they can make connections between school, community, work, and the global workplace enabling the development of STEAM literacy and with it the ability to compete in the new economy.
Chase embraces the goal to foster true innovation that comes with combining the mind of a scientist or technologist with that of an artist or designer. We will do this by providing students with opportunities to engage in learning experiences that will support them in developing the types of skills needed to effectively tackle unknown problems in creative and original ways. With intentional support and guidance, students will build strong STEAM identities putting them on a pathway to explore and excel in STEAM careers. We believe that through this program we are developing tomorrow's innovators!
Everyday in every STEAM classroom at Chase students will:
- Actively make sense of and construct solutions to complex questions.
- Productively contribute to the learning community to support a culture of collaboration, risk taking, and innovation.
- Regularly reflect on and communicate their understanding of disciplinary ideas.
Through these experiences and purposeful opportunities to face interdisciplinary challenges, students will graduate from Chase as knowledgeable, flexible, and resourceful problem solvers prepared to make the world a better place.


Definition: The difference between the true value and the measured value of the quantity is known as an error in measurement. The error in measurement can not be eliminated completely but it can be minimized. there are various types of errors in measurement.
Types of Errors in Measurement
Base on the nature of errors these are classified into following
1) Systematic types of error
Those errors which are confined in unidirectional either positive or negative called systematic errors.
Types of systematic errors are categorized into
- Instrumental errors: these errors arise due to defective measuring instruments or defects in the manufacturing design of the device.
- Personal errors: These errors occur in measuring instruments due to the inexperience of the observer or the person who carried out the measurements. For example – The observer reads the 81.4ºC reading while the actual reading is 31.4Cº. This happens because of oversights.
- Imperfection: These types of errors arise due to measuring instruments are not found in the perfect mod and some errors occur called imperfection errors
2) Random Errors
These types of errors occur irregularly. these errors are also called random errors because of their sign and size. these errors occur due to external causes like temperature, pressure, and wind like atmospheric conditions.
3) Least Count Error
The smallest or minimum value that can be measured by measuring instruments is called as least count. all the readings measured using measuring instruments are accurate only up to its least count. This error is associated with the resolution of the measuring device and can be minimized using high-resolution instruments. the errors as a whole are called as gross errors.
On the basis of mathematical calculations of measurements, these errors are categorized into the following types
- Absolute error: In the measurement of a physical quantity the difference between the true value and individually measured value of the quantity is called absolute error.
- Mean absolute error: The arithmetic mean of absolute errors at all measurements of a quantity is called as mean absolute error.
- Relative error: The ratio of mean absolute error to the mean value of the quantity is called relative error. also called as a fractional error. If this ratio is expressed in % then errors are called a percentage error.


Augustin Louis Cauchy
The Cauchy group is our uppermost ability group. Though it is fair to say that Cauchy targets our 11th and 12th grade students, it is more important to recognize that whatever a student's nominal grade, the Cauchy group is for students seeking problem solving challenges generally beyond high school mathematics. This does not necessarily mean that Cauchy is a jump start on college math. It is more to the point that Cauchy topics will frequently expand broadly on core high school level mathematics, or use these core methods in more complex problem-solving strategies, or introduce students to advanced topics that build upon the high school foundation.
Cauchy students should be proficient with algebra and high-school geometry. Most Cauchy students will be proficient as well with common precalculus topics such as trigonometry, conic sections, and basic types of series. Many Cauchy students will be active in the study of calculus and some will have completed a year or more of calculus at the high school level. Cauchy instructors will not "teach" calculus, but some may draw connections with calculus concepts from time to time.
Cauchy students tend to be very involved in mathematics competitions, so Cauchy topics are sometimes oriented toward this end.
The Cauchy class is comprised of our most advanced, mature, and responsible students. Just one or two adult volunteers are needed in this class. Besides supervision, an important responsibility of parent volunteers is to police the classroom at the end of each class session, to be sure the facilities are as clean and orderly as at the outset.
The "Cauchy Coordinator" is a particular parent volunteer who assists SDMC in these matters.


OPPOSITE DEGREE COMPUTATION AND ITS APPLICATION
Journal: Engineering Heritage Journal (GWK)
Author: Xiao Guang Yue , Muhammad Aqeel Ashraf
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
In order to predict numerical value, we propose a new intelligent algorithm opposite degree computation algorithm. The opposite degree computation algorithm is based on the degree of antagonism between the data to analyze the approximate relationship. The experiment was conducted at Chinese Xinjiang Province, during year 1995 to year 2010. Opposite degree computation algorithm is based on priori value, posteriori value, priori matrix, posterior matrix and the relationship between calculation data. By learning Chinese Xinjiang cotton production data from 1995 – 2005, forecasts 2006 – 2010 cotton production; the result of the absolute error is 9.3237%. Meanwhile, we introduce the prediction method based on BP neural network for the result comparison and found opposite degree computation method is superior to the BP neural network method. Cotton production prediction based on opposite degree computation proved the algorithm is feasible and effective and can be used in numerical value prediction.


higher education, performance measurement and management; performance indicators
Isabel Machado holds a degree in Applied Mathematics and Computation, a Master's Degree in Science, Technology and Innovation Management and a PhD in Social Sciences from the University of Aveiro. She is an Adjunct Professor at IPAM, Laureate Universities, where she teaches subjects about statistics and market studies. She is a researcher at CIPES. In terms of research, he has worked in the area of performance measurement and management with particular emphasis on higher education, and the development of performance indicators for higher education institutions, both for teaching and research.


Knowledge Edge offers proven tutoring programs in math ranging from basic math to pre-algebra, algebra, geometry and advanced math.
In each of these programs, the lessons are administered using a variety of proven teaching methods. These include paper-and-pencil activities as well as the incorporation of manipulatives to provide hands-on activity to aid in learning abstract concepts.
Each of our math programs provides children with the math help they need to succeed.
The Basic Math Program helps students learn basic math skills in three general categories: Basic Math Facts, Computation, and Concepts & Applications. The Basic Math Program encourages students to communicate in mathematical terms in all areas of instruction. Mastering these skills provides students with the math help they need for advancement to higher-level math classes.
The Knowledge Edge Pre-Algebra Program bridges the gap from basic math to algebra by offering students algebra readiness skills. The Pre-Algebra Program affords students the opportunity to spend the necessary time on these important skill areas to ensure success in algebra.
In the Knowledge Edge Algebra Program, students will learn, practice, and master the computational algebra skills necessary for successfully applying logical thinking and practicality in school-related algebra courses. Our tutors provide the algebra help needed for mastery of advanced math skills.
The Knowledge Edge Geometry program is delivered so that each student focuses only on the specific areas needed for improvement. Practical exercises and tests ensure that students move on to new topics only when they’ve mastered the previous one. Our tutors are able to take the time to explain geometry skills in different ways to enrich the students’ understanding and success.
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Knowledge Edge students who have completed a 40 hour program experience on average an increase of one grade level improvement.


Rasmusson, Lars (2001) Pricing Virtual Paths with Quality-of-Service Guarantees as Bundle Derivatives. [SICS Report]
We describe a model of a communication network that allows us to price complex network services as financial derivative contracts based on the spot price of the capacity in individual routers. We prove a theorem of a Girsanov transform that is useful for pricing linear derivatives on underlying assets, which can be used to price many complex network services, and it is used to price an option that gives access to one of several virtual channels between two network nodes, during a specified future time interval. We give the continuous time hedging strategy, for which the option price is independent of the service providers attitude towards risk. The option price contains the density function of a sum of lognormal variables, which has to be evaluated numerically.
|Item Type:||SICS Report|
|Uncontrolled Keywords:||Bandwidth Market, Combinatorial Allocation, Option Pricing, Market Based Resource Alloctation, Quality-of-Service|
|Deposited By:||Vicki Carleson|
|Deposited On:||29 Oct 2007|
|Last Modified:||18 Nov 2009 16:08|
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