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http://tasks.illustrativemathematics.org/content-standards/6/RP/A/tasks/2051
[ "# Hunger Games versus Divergent\n\nAlignments to Content Standards: 6.RP.A 6.RP.A.3\n\nThe 150 students at Skokie School were asked if they prefer seeing the movie Hunger Games or Divergent. The data showed that 100 preferred Hunger Games and 50 preferred Divergent.\n\n1. Look at the following statements and decide if each accurately reports the results of the survey and explain how you know.\n1. At Skokie School, 1/3 of the students prefer Hunger Games.\n2. Students prefer Hunger Games to Divergent in a ratio of 2 to 1.\n3. The ratio of students who prefer Divergent to students who prefer Hunger Games is 1 to 2.\n4. The number of students who prefer Hunger Games is 50 more than the number of students who prefer Divergent.\n5. The number of students who prefer Hunger Games is two times the number of students who prefer Divergent\n2. Compare statements (iv) and (v) above.  In what ways is the information given by these statements similar? In what ways is it different? Explain.\n\n## IM Commentary\n\nThe goal of this task is to introduce ratio language and have students apply reasoning about ratios in a context.  In order to answer the questions, students need to understand the difference between a fraction and a ratio and need to analyze the part to whole and the part to part relationships in a ratio.\n\nThis is an engaging introductory lesson for a unit on ratio and proportional relationships.  Students can relate to the context and the numbers are \"friendly\" enough that students can focus on the concepts without getting bogged down in calculations.\n\nThis task was written as part of a collaborative project between Illustrative Mathematics, the Smarter Balanced Digital Library, the Teaching Channel, and Desmos.\n\n## Solution\n\n1. There are 150 students who took the survey so 1/3 of the students would be 50 students. But 100 of the students surveyed preferred Hunger Games to Divergent so this is not true. It is an easy mistake to make, however, because it is true that 1/3 more of the 150 students prefer Hunger Games compared to those who prefer Divergent.\n2. The ratio of students who prefer Hunger Games to students who prefer Divergent is 100:50. This is equivalent to the ratio 2:1 as we can see by multiplying 2 and 1 by 50. This can also be shown in steps with a ratio table:\n\nStudents who prefer Hunger Games Students who prefer Divergent\n100 50\n10 5\n2 1\n\nThe second line comes from the first by multiplying both entries by $\\frac{1}{10}$ and then the third row is the second row multiplied by $\\frac{1}{5}$.\n\n3. The ratio of students who prefer Divergent to students who prefer Hunger Games is 50:100. This is equivalent to 1:2 as we can see by multiplying 1 and 2 by 50. This can be shown with the ratio table of part (b) or with a double number line as below:", null, "The given information is furthest to the right and then the equivalent ratios, calculated by multiplying by $\\frac{1}{10}$ and then $\\frac{1}{5}$, are to the left closer to 0.\n\n4. Since 100 students prefer Hunger Games and 50 prefer Divergent and 100 = 50 + 50 it is true that 50 more students prefer Hunger Games.\n5. Since 100 students prefer Hunger Games and 50 prefer Divergent and 100 = 2 $\\times$ 50 it is true that the number of students who Hunger Games is twice the number who prefer Divergent.\n1. The two statements give the same information if we know how many students took the survey. If we know that 150 students took the survey then both (d) and (e) tell us that 100 students preferred Hunger Games and 50 preferred Divergent. On the other hand, without this information only (e) tells us the ratio of students who preferred Hunger Games to Divergent" ]
[ null, "http://s3.amazonaws.com/illustrativemathematics/images/000/003/600/large/hunger_d3f54aba52826b9ebf9253bded5b6d43.jpg", null ]
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http://math.eretrandre.org/tetrationforum/printthread.php?tid=936
[ "", null, "Representations by 2sinh^[0.5] - Printable Version +- Tetration Forum (https://math.eretrandre.org/tetrationforum) +-- Forum: Tetration and Related Topics (https://math.eretrandre.org/tetrationforum/forumdisplay.php?fid=1) +--- Forum: Mathematical and General Discussion (https://math.eretrandre.org/tetrationforum/forumdisplay.php?fid=3) +--- Thread: Representations by 2sinh^[0.5] (/showthread.php?tid=936) Representations by 2sinh^[0.5] - tommy1729 - 11/15/2014 As mentioned before Im considering number theory connections to tetration. One of those ideas is representations. Every integer M is the sum of 3 triangular number ... or 4 squares. Also every integer M is the sum of at most O(ln(M)) powers of 2. This is all classical and pretty well known. But there are functions that have growth between polynomials and powers of 2. So since 2sinh is close to exp and 2sinh^[0.5](0) = 0 , it is natural to ask 2S(n) := floor 2sinh^[0.5](n) 2S-1(n) := floor 2sinh^[-0.5](n) 2S numbers := numbers of type 2S(n) 2S-1 numbers := numbers of type 2S-1(n) 1) Every positive integer M is the sum of at most A(M) 2S numbers. A(M) = ?? 2) Every positive integer M is the sum of at most 2S(M) B numbers. B numbers := B(n) = ?? Of course we want sharp bounds on A(M) and B(n). ( A(M) = 4 + 2^M works fine but is not intresting for instance ) regards tommy1729 RE: Representations by 2sinh^[0.5] - tommy1729 - 11/16/2014 To estimate A(M) I use the following Tommy's density estimate *** Let f(n) be a strictly increasing integer function such that f(n)-f(n-1) is also a strictly increasing integer function. Then to represent a positive density of primes between 2 and M we need to take T_f(M) elements of f(n). T_f(M) is about ln(M)/ln(f^[-1](M)). This is an upper estimate. *** In this case to represent a positive density of primes between 2 and M we then need about ln(M)/ln^[3/2](M) 2S numbers. This is a brute upper estimate. A(M) is estimated as sqrt( ln(M)/ln^[3/2](M) ). Improvement should be possible. regards tommy1729" ]
[ null, "https://math.eretrandre.org/tetrationforum/images/TetrationForum_Kristof.png", null ]
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http://gre.kmf.com/question/all/641?keyword=&page=7
[ "4\n\n#### Quantity B\n\nk", null, "x+y+z\n\n#### Quantity B\n\n360", null, "S is an integer that is greater than 2.\n\n#### Quantity A\n\nThe average (arithmetic mean) of $\\frac{1}{S^{2}}$, $\\frac{1}{S}$, $S$, and $S^{2}$\n\n#### Quantity B\n\n2S\n\nWhich of the following are positive numbers?\n\nIndicate all such numbers.\nWhich of the following double inequalities involving $\\sqrt{0.8}$ is true?\nOne of the solutions of the equation $6x^{2}-x-35=0$ is $\\frac{n}{2}$, where n is a positive integer. What is the value of n?\n\nn=_____\nFor how many of the five response categories in the table was the number of responses less than 750?\nBased on the information given, which of the following statements are true?\n\nIndicate all such statements.\nIf 24 percent of the homeowners who were asked Question 2 responded both \"house is comfortable\" and \"house is in good condition\", what percent of the homeowners who were asked Question 2 gave neither of these responses?\nA college professor took attendance for the first 10 days of a class last semester. The professor noticed that Sarah attended class on 8 of those days, Andrew attended class on 7 of those days, Jeff attended class on 6 of those days, and on only 1 of those days did all three students attend class. On how many of the 10 days did at least two of the three students attend class?\n\n_____days\nIn the xy-plane, what is the x-intercept of the line that passes through the point (2, -4) and is perpendicular to the line 6x-3y=4?\n\nOn the number line, Z is the midpoint of line segment XY.\n\n#### Quantity A\n\nThe coordinate of Z\n\n#### Quantity B\n\n2.3", null, "x ≠ y and y ≠ 0\n\n#### Quantity A\n\n$\\frac{x+y}{y}$\n\n#### Quantity B\n\n$\\frac{x^{2}-y^{2}}{xy-y^{2}}$\n\n#### Quantity A\n\nThe remainder when 754,975,376 is divided by 4\n\n#### Quantity B\n\nThe remainder when 701,864,294 is divided by 4\n\nThe operation ▽ is defined by a▽b=$b^{a}$ for all nonzero integers a and b.\n\nk is a nonzero integer.\n\n(2▽1)▽k\n\n#### Quantity B\n\nk▽(4▽1)\n\nList M: r+3, r+2, r+1, r, r-1, r-2, r-3\n\n#### Quantity A\n\nThe standard deviation of the numbers in list M\n\nr\n\nx\n\n#### Quantity B\n\ny", null, "Suppose a, b, c are different integers, and the repeating decimal 0.abc=$\\frac{m}{n}$, where 0 < m < n < 100, m and n are both integers, then\n\nn\n\n#### Quantity B\n\n39\n\nJeremy bicycled a distance of 16 miles in x minutes. He averaged 15 miles per hour for the first 8 miles he bicycled.\n\n#### Quantity A\n\nThe number of minutes he took to bicycle the last 8 miles\n\n#### Quantity B\n\nx-32\n\nWhat is the least value of x such that (2x-1)(x-6) - (x-3)(x-6) = 0 ?\n1 2 ... 4 5 6 7 8 9 10 ... 12 13\n\n25000 +道题目\n\n136本备考书籍" ]
[ null, "http://img.kmf.com/kaomanfen/img/gre/bdef2394993a72a0b9b026e97e326f25.png", null, "http://img.kmf.com/kaomanfen/img/gre/0a7f5010b24c0e365474f34a3605d32b.png", null, "http://img.kmf.com/kaomanfen/img/gre/cc48812753f892cd3fa8259280246cab.png", null, "http://img.kmf.com/kaomanfen/img/gre/25e59f98db572e9e6b92bde44d9e6a5e.png", null ]
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https://metanumbers.com/639628
[ "# 639628 (number)\n\n639,628 (six hundred thirty-nine thousand six hundred twenty-eight) is an even six-digits composite number following 639627 and preceding 639629. In scientific notation, it is written as 6.39628 × 105. The sum of its digits is 34. It has a total of 4 prime factors and 12 positive divisors. There are 290,720 positive integers (up to 639628) that are relatively prime to 639628.\n\n## Basic properties\n\n• Is Prime? No\n• Number parity Even\n• Number length 6\n• Sum of Digits 34\n• Digital Root 7\n\n## Name\n\nShort name 639 thousand 628 six hundred thirty-nine thousand six hundred twenty-eight\n\n## Notation\n\nScientific notation 6.39628 × 105 639.628 × 103\n\n## Prime Factorization of 639628\n\nPrime Factorization 22 × 11 × 14537\n\nComposite number\nDistinct Factors Total Factors Radical ω(n) 3 Total number of distinct prime factors Ω(n) 4 Total number of prime factors rad(n) 319814 Product of the distinct prime numbers λ(n) 1 Returns the parity of Ω(n), such that λ(n) = (-1)Ω(n) μ(n) 0 Returns: 1, if n has an even number of prime factors (and is square free) −1, if n has an odd number of prime factors (and is square free) 0, if n has a squared prime factor Λ(n) 0 Returns log(p) if n is a power pk of any prime p (for any k >= 1), else returns 0\n\nThe prime factorization of 639,628 is 22 × 11 × 14537. Since it has a total of 4 prime factors, 639,628 is a composite number.\n\n## Divisors of 639628\n\n12 divisors\n\n Even divisors 8 4 2 2\nTotal Divisors Sum of Divisors Aliquot Sum τ(n) 12 Total number of the positive divisors of n σ(n) 1.22119e+06 Sum of all the positive divisors of n s(n) 581564 Sum of the proper positive divisors of n A(n) 101766 Returns the sum of divisors (σ(n)) divided by the total number of divisors (τ(n)) G(n) 799.767 Returns the nth root of the product of n divisors H(n) 6.28528 Returns the total number of divisors (τ(n)) divided by the sum of the reciprocal of each divisors\n\nThe number 639,628 can be divided by 12 positive divisors (out of which 8 are even, and 4 are odd). The sum of these divisors (counting 639,628) is 1,221,192, the average is 101,766.\n\n## Other Arithmetic Functions (n = 639628)\n\n1 φ(n) n\nEuler Totient Carmichael Lambda Prime Pi φ(n) 290720 Total number of positive integers not greater than n that are coprime to n λ(n) 72680 Smallest positive number such that aλ(n) ≡ 1 (mod n) for all a coprime to n π(n) ≈ 51934 Total number of primes less than or equal to n r2(n) 0 The number of ways n can be represented as the sum of 2 squares\n\nThere are 290,720 positive integers (less than 639,628) that are coprime with 639,628. And there are approximately 51,934 prime numbers less than or equal to 639,628.\n\n## Divisibility of 639628\n\n m n mod m 2 3 4 5 6 7 8 9 0 1 0 3 4 3 4 7\n\nThe number 639,628 is divisible by 2 and 4.\n\n• Arithmetic\n• Deficient\n\n• Polite\n\n## Base conversion (639628)\n\nBase System Value\n2 Binary 10011100001010001100\n3 Ternary 1012111101221\n4 Quaternary 2130022030\n5 Quinary 130432003\n6 Senary 21413124\n8 Octal 2341214\n10 Decimal 639628\n12 Duodecimal 26a1a4\n20 Vigesimal 3jj18\n36 Base36 dpjg\n\n## Basic calculations (n = 639628)\n\n### Multiplication\n\nn×y\n n×2 1279256 1918884 2558512 3198140\n\n### Division\n\nn÷y\n n÷2 319814 213209 159907 127926\n\n### Exponentiation\n\nny\n n2 409123978384 261687152045801152 167382429688751699251456 107062488736956871888810298368\n\n### Nth Root\n\ny√n\n 2√n 799.767 86.1607 28.2802 14.4939\n\n## 639628 as geometric shapes\n\n### Circle\n\n Diameter 1.27926e+06 4.0189e+06 1.2853e+12\n\n### Sphere\n\n Volume 1.09615e+18 5.1412e+12 4.0189e+06\n\n### Square\n\nLength = n\n Perimeter 2.55851e+06 4.09124e+11 904571\n\n### Cube\n\nLength = n\n Surface area 2.45474e+12 2.61687e+17 1.10787e+06\n\n### Equilateral Triangle\n\nLength = n\n Perimeter 1.91888e+06 1.77156e+11 553934\n\n### Triangular Pyramid\n\nLength = n\n Surface area 7.08624e+11 3.08401e+16 522254\n\n## Cryptographic Hash Functions\n\nmd5 1981baaee6f2be15f26b1af0e88bf46c 4c66718effd60de7deca4781ba802efd47c743e9 8be712525ac2b1bd4de6cfc6c7faa13346f2180ff02faa09c87554bb4595d860 a768258442c037abe7ad3a7c21d7b90d033948152de972ba759561c6db8349a3f9745e77550becb16ff4e33329b587b9a787c65f0644250366f58ae6b6500201 d04e96554d27ca14b2362f6b2772d746a23808dd" ]
[ null ]
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https://www.mapleprimes.com/questions/141579-Percolation-Theory-And-Cylinder-Random-Packing
[ "# Question:Percolation theory and cylinder random packing\n\n## Question:Percolation theory and cylinder random packing\n\nMaple\n\nHi,\n\nI'm working at the moment on percolation theory and, in order to get interesting images I use Maple to produce random spheres packing using the code below:\n\nwith(RandomTools):\nwith(plots):\n\nx:=Generate(list(integer(range=0..20),100)): # random coordinate for x parameter\ny:=Generate(list(integer(range=0..20),100)): # random coordinate for y parameter\nz:=Generate(list(integer(range=0..20),100)): # random coordinate for z parameter\n\nP:=(x,y,z,R)->[x+R*cos(phi)*cos(theta),y+R*cos(phi)*sin(theta),z+R*sin(phi)]: # equation for one sphere\n\nS:=(i,j,k,R)-> plot3d(P(i,j,k,R),phi=-Pi/2..Pi/2,theta=0..2*Pi): # 3D plot in space\n\ndisplay3d({S(x,y,z,R),S(x,y,z,R), and so and so....}); ## I finally plot everything in one space.\n\nThe result is not bad actually even if the method would be faster with a procedure... Here I only have to export the result in COLLADA format and I can play with it on a 3D render like Blender... and also do the calculations forthe percolation, on Maple.\n\nNow, just to compare I would like to obtain the same kind of random packing using capped cylinders randomly distributed in the space with various radius and length. The problem is not coming from the spacial distribution but all the cylinders are on the same direction. Does somebody has an idea to also work with random directions ?\n\nI tried this method but it doesn't work:\n\n1. Random distribution of discs\n\n2. Calculation of normal vector for all of them\n\n3. Ptotting of cylinders using the description of this normal vector\n\nI was quiet happy when I begin but It's seems really much more difficult than expected... The rotation can also be an interesting thing but I don't know how to introduce it...\n\nCan you help me ?\n\nThanks,\n\nLisa", null, "" ]
[ null, "https://www.mapleprimes.com/images/ajax-loader.gif", null ]
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https://homework.cpm.org/category/CON_FOUND/textbook/caac/chapter/11/lesson/11.1.5/problem/11-64
[ "", null, "", null, "### Home > CAAC > Chapter 11 > Lesson 11.1.5 > Problem11-64\n\n11-64.\n\nSolve each of the following equations or systems.\n\n1.  $x^2−1=15$\n\nHow many solutions should you be looking for?\n\n$x=\\pm4$\n\n1.  $y=3x−2$\n$y=4x+3$\n\nUse the Equal Values method.\nMake sure to solve for both $x$ and $y$.\n\n1.  $x^2−2x−8=0$\n\nFactor and use the Zero Product Property.\n\n1.  $2x^2=−x+7$\n\nStart by setting the equation equal to $0$." ]
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", null ]
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https://discusstest.codechef.com/t/help-me-with-chef-and-sub-array-problem/15310
[ "", null, "# Help me with Chef and Sub Array problem?\n\nMy solution: https://www.codechef.com/viewsolution/14291195\n\nfor(j=n-1;j>-1;j–){\nif(check(myvector[j].second,i,y,n)) break;\n}\n\nYou can try using binary search here. Also using segment trees is much easier.\n\nI will help you with AC solution. Assume you have n elements and k be the length of the frame.\n\nFOR SUBTASK 1 : n,k<1000 -> here you can use simple Brute Force technique and do what the question tells you to do,i.e, when you encounter a ‘!’ character,rotate your array in o(n) and when you encounter a ‘?’ calculate the maximum of all the k sized subarrays and print it, it will alse take o(n). Thus the overall complexity will be o(n*p) which would pass for this subtask but not the other one.\n\nFOR SUBTASK 2: for given n-sized array of zeroes and ones, what i did was create an array of size 2*n in which after the last element from the input, we insert again from the first element at the preceeding places,i.e\nif the elements are given “0 1 0 0 1 1” then we insert these from position 0 to position 5 of the array and again from the 6th position to 11th position we insert given elements in the given order.\nThen we create a new array(say csum[]) whose ith index stores the sum of first i elements of the above created array.Now, we know that after operating ‘!’ l times the start index would be ((n-l)%n)th element .\nlet this start index be st.\n\nSince we have stored the cumulative sum in our csum array we now have to answer the maximum sum that can be obtained in a k frame from index st to (st+n). By precalculating this for all values of st from 1 to n,we can answer each query in o(1) time and we dont even need to rotate the array,just increase the variable l when ‘!’ is encountered. For precalculation, rather than explaining that process myself I would recommend you to read this on Sliding window maximum.\n\nI hope it helped a little, if not, forgive me, its only my first answer to someone’s question in here.\n\nAny doubts, ask me freely in the comment section.\n\nMy solution\n\n2 Likes\n//" ]
[ null, "https://s3.amazonaws.com/discoursestaging/original/2X/4/45bf0c3f75fc1a2cdf5d9042041a80fa6dd3106b.png", null ]
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https://socratic.org/questions/554ebc5d581e2a6b7340ff54
[ "# Question #0ff54\n\nMay 10, 2015\n\nThe standard enthalpy of formation for a compound expresses the change in enthalpy (it can be positive or negative) when 1 mole of that compound is formed from the most stable form of its elements (at 1 atm and 273.15 K).\n\nStandard enthalpies of formation are thus calculated for reactions that produce 1 mole of a specific compound. For example, you could write the balanced chemical equation for your synthesis reaction like this\n\n${N}_{2 \\left(g\\right)} + 2 {O}_{2 \\left(g\\right)} \\to 2 N {O}_{2 \\left(g\\right)}$\n\nThis reaction will also have a change in enthalpy, $\\Delta {H}_{\\text{rxn}}$, but it not be equal to the standard enthalpy of formation, $\\Delta {H}_{\\text{f}}^{\\circ}$, because it produces 2 moles of nitrogen dioxide, instead of only 1 mole.\n\nTo get the reaction for which $\\Delta {H}_{\\text{rxn\" = DeltaH_\"f}}^{\\circ}$ you need to divide the above equation by 2, in order to get only 1 mole of the product\n\n$\\text{1/2} {N}_{2 \\left(g\\right)} + {O}_{2 \\left(g\\right)} \\to N {O}_{2 \\left(g\\right)}$\n\nThat's how you can determine the reaction for which the change in enthalpy is equal to the standard enthalpy of formation for any compound.\n\nHere's another example. Assume you want the reaction that produces $\\Delta {H}_{\\text{f}}^{\\circ}$ for iron (II) sulfide, $F e S$. You start from\n\n$8 F e + {S}_{8} \\to 8 F e S$\n\nNotice that you produce 8 moles of iron (II) sulfide, but you only need to produce 1 mole $\\to$ divide all the coefficients by 8\n\n$F e + \\text{1/8} {S}_{8} \\to F e S$\n\nThis reaction has $\\Delta {H}_{\\text{rxn\" = DeltaH_\"f}}^{\\circ}$." ]
[ null ]
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https://encyclopediaofmath.org/wiki/Quasi-regular_ring
[ "# Quasi-regular ring\n\nA ring in which every element is quasi-regular. An element $a$ of an alternative (in particular, associative) ring $R$ is called quasi-regular if there is an element $a'\\in R$ such that\n$$a+a'+aa'=a+a'+a'a=0.$$\nThe element $a'$ is called the quasi-inverse of $a$. If $R$ is a ring with identity 1, then an element $a\\in R$ is quasi-regular with quasi-inverse $a'$ if and only if the element $1+a$ is invertible in $R$ with inverse $1+a'$. Every nilpotent element is quasi-regular. In an associative ring the set of all quasi-regular elements forms a group with respect to the operation of cyclic composition: $x\\cdot y=x+y+xy$. An important example of a quasi-regular ring is the ring of (non-commutative) formal power series without constant terms. There exist simple associative quasi-regular rings ." ]
[ null ]
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https://theomcarthur.com/445-online-homework-help-for-balanced-equations.html
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https://www.physicsforums.com/tags/projectile-motion/
[ "# Projectile motion Definition and 182 Discussions\n\nProjectile motion is a form of motion experienced by a launched object. Ballistics (Greek: βάλλειν, romanized: ba'llein, lit. 'to throw') is the science of dynamics that deals with the flight, behavior and effects of projectiles, especially bullets, unguided bombs, rockets, or the like; the science or art of designing and accelerating projectiles so as to achieve a desired performance.\n\nView More On Wikipedia.org\n1. ### Roll and Drop!\n\nVertical position (x component): y(t) = y0 +v0 * t -1/2*g*t^2 Position yx(t) = 0cm = 40cm + 0 *t -1/2 * 981cm/s/s * t^2 edit: I replaced v0 = 0 and got t = 0.286s, which is incorrect according to the submission. This didn't work so I thought maybe by finding the angle of the throw would help...\n2. ### Circular motion to projectile motion\n\nSo first I found the velocity of the ball at the bottom of the swing from the force equations, which I got to be 4.9 m/s and this is only in the x-direction. Then using the projectile motion for delta y I found time, which is 0.2s. Then using that time I found the delta x to be 0.98m. I just...\n3. ### What is the minimum height h that is required to generate the greatest projectile range?\n\nI am calculating it like this: 𝑦=ℎ0−0.5𝑔𝑡^2=0→ℎ0=0.5𝑔𝑡^2→𝑡=sqrt(2*ℎ0/g) 𝑥=𝑣0*𝑡→ substituting t →𝑥=𝑣0*sqrt(2*ℎ0/g) 𝑑𝑥/𝑑ℎ0=𝑣0/(𝑔*sqrt(2*h0/g))=0 for maximum ℎ0=0. confused. can someone tell me how I am calculating this wrong?\n4. ### I If we throw a ball with initial velocity v0, is there a force?\n\nIf we throw a ball with initial velocity v0, and the ball progresses in a projectile motion, ignoring air resistance, will there be a force in the x direction? If so, what is that force in the x direction's value mathematically? I know there is a force in the y direction due to gravity.\n5. ### Kinematic Equations in Projectile Motion (this approach is not working)\n\nGivens: Vyi=12.5 m/s Vyf=-12.5 m/s (at the same horizontal level) ay=-9.81 m/s^2 Δy= zero m (as the displacement on the y-axis, when the projectile reaches the same horizontal level, is zero m) Δt=? When I use Δy=[(vyi+vyf)/2]*Δt I get the time as undefined. Δt= 2Δy/(vyi+vyf) = 2*0 m/(12.5...\n6. ### Relationship between horizontal range and angle of launch\n\nI would like to conduct an experiment asking the question \"how does the angle at which a projectile is launched vary with respect to the horizontal range it covers?\" Ultimately, I'd like to prove that the horizontal range is directly proportional to sin(2(theta)). This will be done with the aid...\n7. ### Stopping a Bullet\n\n(a) ##u_{min}=\\big(1+\\frac{m_2}{m_1}\\big)\\sqrt{2\\mu_k g d}## (b) ##x_f=\\sqrt{\\frac{2h}{g}\\Big(\\big(\\frac{m_1}{m_1+m_2}u\\big)^2-2\\mu_k g d\\Big)}## Can someone check please?\n8. ### B Projectile motion — Thinking about forces on a curve ball\n\nWhen a ball is thrown such that it moves in a curved trajectory in the horizontal plane, it amuses me to think of its dynamics. In motion of a ball thrown upwards the force of gravity gives it a parabolic trajectory However when the ball is thrown to curve and hit a target, (in the horizontal...\n9. ### Tricky conceptual Projectile motion question\n\nSo far all I have determined is the equations of motion for the two and that is as follows. It is trivial that y(t)=v1sin(Q)t -gt^2/2 and that x(t)=v2cos(Q)t. Now the angle that is anticlockwise from the negative horizontal of the robber is 90 - Q using basic trigonometry, using this we can...\n10. ### Show that a projectile lands at a distance ##R = \\frac{2v_0^2 sin \\theta cos(\\theta + \\phi)}{g cos^2 \\phi}##\n\n##V_x = V_0 cos \\theta ## ##x = V_0 cos \\theta t## ##V_y = V_0 cos \\theta ## ##y = V_0 cos \\theta t## ##F_x = m\\ddot{x}## ##-mgsin \\phi = m\\ddot{x}## ##\\dot{x} = -gtsin\\phi + V_x## ##x = -\\frac{1}{2} gt^2 sin \\phi + V_x t## ##x = -\\frac{1}{2} gt^2 sin \\phi + v_0 cos\\theta t## ##F_y =...\n\n12. ### Projectile Intercept Math & Trigonometry\n\nLooking for some guidance on how to set up the equations for a projectile intercept given that you have perfect information about the target velocity, size and weather conditions in a 3D scenario, it's for an amateur videogame that I'm developing in my spare time For simplicity sake let's...\n13. ### Projectile motion with (constant) wind velocity\n\nLet me start be making a small sketch of the problem, shown to the right. If the range of the projectile on a still day ##R = v_{0x}T##, then on the windy day the range becomes ##R+2H = v'_{0x}T = (v_{0x}+v_w) T##. Since the maximum height attained by the projectile ##H =...\n14. ### Maximum range on ground for an elevated cannon\n\nI sketch a diagram for the problem and show it to the right. My approach is going to be along the following lines : (1) Use the equation of motion in the ##x## direction to express the total time of flight ##T## as a function of the range and initial angle ##T = T(R, \\theta_0)##. (2) Plug this...\n15. ### Runge-Kutta Projectile Approximation From Initial Conditions\n\nHi everyone. I'm a new member, great to be here:) I have a few questions that I wanted to ask you guys regarding the method by which we implement the Runge-Kutta approximation of Projectile Motion if we should do it using a numerical iterative method with a Spreadsheet like Excel. I have...\n16. ### Projectile motion in 2D\n\nI know the conventional method for solving this question using the formula for maximum range of a projectile in an inclined plane, but since it is an objective problem, if we consider a non general case where α=0, then clearly we can see that (see attachment) only one option matches which...\n17. ### Find the range of an object coming off an inclined plane\n\nHey Everyone, my physics teacher has assigned us a task which involves predicting the range of a ball falling down an inclined plane into a free-fall, the equation for the final velocity of the ball down the ramp, accounting for rotational velocity has been provided, this is the initial velocity...\n\n19. ### Will the pebble meet with the block according to the given condition?\n\nQuestion 1: I have used v= Aω*cos(ωt+δ) where A= 0.2 m, ω= π/3, t=1 and δ=0. Are the values right in this case? I am confused. Question 2: From question 1 I have got the value of V which is 9 m/s. By using v= ω√(A^2-x^2), I have got the value of x. Now, do I need to add it with 2.5(distance...\n20. ### Maximum Distance a Projectile Moves Up an Inclined Plane\n\nthe red line is the initial velocity, the grey parabola is the path of the projectile. hi there...I'm kinda stuck at the part b of this problem. I can do part a with no problem. can anybody explain to me how to do the differentiation needed to solve part b?? by explain I mean explain the...\n21. ### Projectile motion hw help please\n\nI intended to finish the question with the equation of linear motion with constant acceleration, but it didn't work out. And I have no idea about the t^3 and t^4 of the position. How can I find the x component of the acceleration at time 3.4 s ? Where is the acceleration rate?\n22. ### How do you find the initial velocity of a projectile given angle/distance?\n\nI tried resolving the information given into vertical and horizontal components. I then tried to find time, as this is how I would find the initial velocity. However, I am unsure of how to use the angle in this problem to help solve it. I am also unsure of how to find the initial velocity only...\n23. ### The Determination of the Launch Angle of a Projectile\n\nHi, I am new here to the forum and I am having trouble with a project that I am undertaking with some friends. We are trying to build a firefighting robot. I am trying to derive an expression to solve for the launch angle theta of the water so that at x (meters), the projectile will be at 0.33...\n24. ### At what angle do we have to kick a ball up the hill for n-rebounds?\n\nI decided to try and find a solution in a green (tilted) coordinate system. I started solving this problem with thinking about 1-rebound: ##⟹y=0, α=\\text{angle under which we kick a ball}##; ##y=sin\\alpha v_0t-\\frac{1}{2}gt^2##; because I'm trying to solve this in a tilted system, I have to...\n25. ### Projectile motion of a two-point rigid body\n\nI would like to patch some gaps in my physics background. For example, I've been trying to come up with the sollution to the following: I have a model rigid body made up of two mass points and a massless rod connecting them. I throw the body with initial velocity under some angle of elevation...\n26. ### Pendulum projectile problem\n\nTried to find time in seconds in order to use the formula d=vt and find the distance in the x plane in which the target must be placed, to no avail.\n27. ### Comp Sci Ball rebounding off of a wall\n\nMy attempts involved using suvat equations to determine the rebound distance : S = 0.5 * (u + v)*t With u being 50 and v being 0 t being time taken to fall down (Height of impact / gravitational acceleration) t = 48.41 / 9.81 Plugging the numbers in gives S = 123.365m This is where i get...\n28. ### Projectile Motion at an angle\n\nVx = 10cos(23) = 9.21m/s Vy = 10sin(23) = 3.91m/s -30 = 3.91t + 1/2(-9.8)t^2 0 = t(3.91 - 4.9t) + 30 4.9t = 33.91 t = 6.92s Delta X = 9.21(6.92) + 1/2(9.8)(6.92)^2 Delta X = 298.38m Vf^2 = 9.21 + 2(9.8)(298.38) Vf^2 = 5857.458 Vf = 76.53m/s\n29. ### Need to find the spring constant to achieve Max Velocity\n\nHello All. I am mentoring a high school student in my area with his class project for school. He has chosen he wants to launch an object (in our case, a softball) into a 5' diameter area. The idea is to build basically an oversized slingshot using an extension spring as the source of energy. We...\n30. ### Projectile Motion with Unknown Initial Height\n\nFirst, I tried solving for the total time of flight, which I got as 100 = 5cos25*t --> t=22 s Since we know the height at which the object lands, but not at which it is launched, I tried setting up the equation as: yf = 40 - y0 = y0 + 5sin25*(22) - 1/2(9.8)(22)^2 However, I got y0 = 1183 m...\n31. ### A golf ball being hit on the moon vs Earth\n\nI am just not sure if I did this properly. My professor hasn't really gone over when to use the range equation but I would assume range would equal the distance traveled therefore can be used for this problem. If not the how would I go about solving this? I did 1/6*9.8=1.63 for g on the moon...\n32. ### Project Motion/Trigonometry Question\n\nMy reasoning and answer is wrong, but I cannot figure out why. Perhaps it is strange, perhaps not, but I want to figure out why my initial method of solving this problem did yield an incorrect answer. I began by creating an equation and drawing a right triangle. x is the horizontal part of...\n33. ### How does the release angle of a catapult affect the object's velocity?\n\n[Moderator: repeated text removed.]\n34. ### How to find the max height a projectile can reach on a hillside\n\nHere's a fully typed version of the problem with a diagram My attempt: Given the angle of the hill, I know that the horizontal displacement of the arrow and my vertical height on the hill are related by ##Δx=d+\\frac h {Tan(60)}## ...(1) where d is the distance of the enemy from the base...\n35. ### Impact of several variables on resulting projectile motion trajectory\n\nI was told to generate these variables (m, C, alpha, wind velocity) normally distributed and compare the random data with the result and then tell, which of the variables has the most impact. Here I am stuck, tried to compare variances, kurtosis and skewness of the data (the original variables...\n36. ### Projectile Motion: Path's length?\n\nSo I just learned about projectile motion. I understand why you can study it as two independent straight line motions . But this can give you a way to calculate total velocities or accelerations, just by adding its individual component of each vector. If the initial position of the projectile...\n37. ### Projectile Motion Experimental Error\n\nHomework Statement I got an experimental vertical acceleration of -12 m/s^2 of projectile motion for an experiment I did at home where I just had to throw a ball at around 45 degree to the horizontal up in the air and record it's motion, then analyze the motion via computer software. Obviously...\n38. ### Boomerang Problem, need help solving it\n\nHomework Statement A boomerang is thrown with an initial linear velocity of 5 m/s at an angle of 30 degrees vertically. The initial angular velocity is ##2\\frac{revolutions}{s}## At its peak, it has a displacement about the z axis of 2 meters and about the x axis of 10 meters. The force applied...\n39. ### Need to find final Height, Equation not Working\n\nHomework Statement Homework Equations The kinematic equations--namely, Sf = S0 + V0Δt The Attempt at a Solution [/B] I am a bit confused as it seems this problem is very straight-forward. My known variables: X0 = 0m Y0 = 1.7m Δt = 3.92s V0 = 29m/s Θ = 60ο Yf = ? So, I just use the above...\n40. ### Projectile Motion: Pitcher throwing a ball to a catcher\n\nHomework Statement determine the acute angle (in radians) of appropriate elevation in the throwing of a ball, if the initial velocity is 20 m / s, g=9.81 m/s^2 and the distance in x is 40m. The ball leaves the hand of the pitcher with an elevation equal to 1.8m and the catcher receives it at...\n41. ### AP Physics C Momentum Problem: A dart launched horizonally by a spring gun\n\nHomework Statement A boy launches a 20 g dart horizontally by a spring gun from a balcony 45 m above the ground. The dart lands 15 m away from the balcony. If the length of the gun’s barrel is 10 cm, what is the average horizontal force applied by the spring? (A) 1.0N (B) 2.0 N (C) 2.5 N...\n42.", null, "### Why Vertical Velocity changes & Horizontal does not?\n\nProjectile motion involves velocity in vertical and horizontal direction. It is important to understand that the two are independent of each other. Solving problems becomes much easier with this simple understanding.\n43. ### Varying Gravity and Air Resistance in projectile motion\n\nSalutations, I have been trying to approach a case about projectile motion considering variation of gravity acceleration and air resistance: A spherical baseball with mass \"m\" is hit with inclination angle $\\theta$ and launching velocity $v_0$, then, the wind has a drag force equals to ##F=kv##...\n44. ### Meaning of a phrase in a kinematics problem\n\n1. Problem statement: \"An object is thrown horizontally with an initial speed of 10 m/s. How far will it drop in 4 seconds?\" Homework Equations Δx = v0x.t Δy = v0y.t + 1/2(-g)t2 The Attempt at a Solution [/B] When I first read it, I thought that 'how far' means Δx, since when someone says...\n45. ### Calculating spring constant of a spring loaded cannon\n\nHomework Statement if you wanted to build a spring launched cannon that will shoot you over a building that is 35 m high and 30 m wide, and the cannon is being shot at 60 degrees. If the cannon can be no more than 2 m long, what spring constant do you need in the spring to make this work? here...\n46. ### Where on the hill does the projectile land?\n\nHomework Statement A projectile is shot at a hill, the base of which is 320 m away. The projectile is shot at 60° above the horizontal with an initial speed of 79 m/s. The hill can be approximated by a plane sloped at 19° to the horizontal. The equation of the straight line forming the hill is...\n47. ### What is conceptual physics?\n\nI have been wondering, what is conceptual physics? I remember taking a class in high school that was physics oriented, for example two trains leave a station at different speeds, and arrive at a central point, where do they overlap. Also there were trig functions on how to find the height of a...\n48. ### Using the Vy versus t graph to determine the y-component of the acceleration of the puck\n\nHomework Statement We had a lab on projectile motion and one of the questions was this: Use your Vy versus t graph to determine the y-component of the acceleration of the puck. Should this be equal to the acceleration due to gravity (9.8 m/s)? Explain why it is or why it is not. The...\n49. ### Projectile motion: Two objects are kicked upward at different angles...\n\nHomework Statement Two objects are kicked upward at different angles. Object A travels a greater horizontal distance than object B. Both reach the same maximum height. Which of the following statements about the objects are true? You may select more than one. 1.Object A is in the air for a...\n50. ### How to get R= 2 ( square root ) h1h2\n\nProblem : A ball is let down a ramp on top of a table with initial velocity of 0 ms-1. When it reaches the end of the ramp, it is launched horizontally. Knowing that we don’t take air resistance or friction into account, and that the height of the ramp is h1, and that of the table is h2..." ]
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https://old.lucentinnovation.com/blogs/technology-posts/working-with-arrays-in-javascript
[ "", null, "# Working with arrays in JavaScript\n\nArrays are a fundamental data structure in JavaScript, and they are used to store and manipulate a collection of values. An array is an ordered list of values, and each value in the array is called an element.\n\nIn JavaScript, you can create an array using the \"array literal\" syntax, which uses square brackets to enclose a list of values separated by commas. For example:\n\n```var numbers = [1, 2, 3, 4, 5]; ```\n\nIn this example, the array \"numbers\" contains five elements: 1, 2, 3, 4, and 5.\n\nYou can access the elements of an array using their index, which is the position of the element in the array. In JavaScript, arrays are zero-indexed, which means that the first element has an index of 0. For example:\n\n```console.log(numbers); // outputs 1 console.log(numbers); // outputs 5 ```\n\nYou can also use loops to iterate over the elements of an array. For example:\n\n```for (var i = 0; i < numbers.length; i++) { console.log(numbers[i]); ```\n`}`\n\nThis for loop will output each element of the \"numbers\" array to the console.\n\nArrays also have a number of built-in methods that can be used to manipulate the elements of the array. For example, you can use the \"push\" method to add an element to the end of the array:\n\n```numbers.push(6); ```\n\nYou can also use the \"pop\" method to remove the last element of the array:\n\n```numbers.pop(); ```\n\nThese are just a few examples of the many ways you can work with arrays in JavaScript. Arrays are a powerful data structure that are used to store and manipulate collections of data in a variety of applications.\n\nCheck out the rest of our series on Javascript by reading our other articles:" ]
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http://ijfs.usb.ac.ir/article_4790.html
[ "", null, "# Fuzzy universal algebras on \\$L\\$-sets\n\nDocument Type: Research Paper\n\nAuthors\n\nDepartment of Mathematics, Ocean University of China, 238 Songling Road, 266100, Qingdao, P.R.China\n\nAbstract\n\nThis paper attempts to generalize universal algebras on classical sets to \\$L\\$-sets when \\$L\\$ is a GL-quantale. Some basic notions of fuzzy universal algebra on an \\$L\\$-set are introduced, such as subalgebra, quotient algebra, homomorphism, congruence, and direct product etc. The properties of them are studied. \\$L\\$-valued power algebra is also introduced and it is shown there is an onto homomorphism from \\$P(A)/R^{+}\\$ to \\$P(A/R)\\$ for any congruence \\$R\\$ on \\$L\\$-set \\$A\\$.\n\nKeywords\n\n### References\n\n C. Brink, Power structures and logic, Queastions Mathematicae, 9 (1986), 69–94.\n C. Brink, Power structures, Algebra Universalis, 30 (1993), 177–216.\n R. B˘elohl´avek, V. Vychodil, Fuzzy equational logic, Archive for Mathematical Logic, 41 (2002), 83–90.\n R. B˘elohl´avek, V. Vychodil, Algebras with fuzzy equalities, Fuzzy Sets and Systems, 157 (2006), 161–201.\n I. Bo˘snjak, R. Madar´az, S. Roz´alia, Good quotient relations and power algebras, Novi Sad Journal of Mathematics,\n29 (1999), 71–84.\n I. Bo˘snjak, R. Madar´asz, Power algebras and generalized quotient algebras, Algebra Universalis, 45 (2001), 179–189.\n I. Boˇsnjak, R. Madar´asz, G. Vojvodi´c, Algebras of fuzzy sets, Fuzzy Sets and Systems, 160 (2009), 2979–2988.\n I. Boˇsnjak, R. Madar´asz, On the composition of fuzzy power relations, Fuzzy Sets and Systems, 271 (2015), 81–87.\n A. B. Chakraborty, S. S. Khare, Fuzzy homomorphism and algebraic structures, Fuzzy Sets and Systems, 59 (1993),\n211–221.\n C. L. Chang, Fuzzy topological spaces, Journal of Mathematical Analysis and Applications, 24 (1968), 182–190.\n M. Demirci, Foundations of fuzzy functions and vague algebra based on many-valued equivalence relations, part I:\nfuzzy functions and their applications, International Journal of General Systems, 32(2) (2003), 123–155.\n M. Demirci, Foundations of fuzzy functions and vague algebra based on many-valued equivalence relations, part II:\nvague algebraic notions, International Journal of General Systems, 32(2) (2003), 157–175.\n M. Demirci, Foundations of fuzzy functions and vague algebra based on many-valued equivalence relations, part III:\nconstructions of vague algebraic notions and vague arithmetic operations, International Journal of General Systems,\n32(2) (2003), 177–201.\n G. Georgescu, Fuzzy power structures, Archive for Mathematical Logic, 47 (2008), 233–261.\n G. Gr¨atzer, Universal Algebra, 2nd ed. Springer-Verlag, 1979.\n U. H¨ohle, Commutative, residuated l-monoids, in: U. H¨ohle, E.P. Klement(Eds.), Non-Classical Logics and Their\nApplications to Fuzzy Subsets: A Handbook on the Mathematical Foundations of Fuzzy Set Theory, Kluwer Academic\nPublishers, Dordrecht, 1995, 53–105.\n J. Ignjatovi´ca, M. ´ Ciri´ca, S. Bogdanovi´cb, Fuzzy homomorphisms of algebras, Fuzzy Sets and Systems, 160 (2009), 2345–2365.\n H. Lai, D. Zhang, Good fuzzy preorders on fuzzy power structures, Archive for Mathematical Logic, 49 (2010),\n469–489.\n\n F. Li, Y. Yue, L-valued fuzzy rough sets, Iranian Journal of Fuzzy Systems, 16 (2019), 111-127.\n R. Madar´az, Remarks on power structures, Algebra Universalis, 34 (1995), 179–184.\n V. Murali, Fuzzy congruence relations, Fuzzy Sets and Systems, 41 (1991), 359–369.\n B. Pang, F. G,Shi, Fuzzy counterparts of hull operators and interval operators in the framework of L-convex spaces,\nFuzzy Sets and Systems, 2018, in press, DOI: http://doi.org/10.1016/j.fss.2018. 05. 012.\n B. Pang, Y. Zhao, Characterizations of L-convex spaces, Iranian Journal of Fuzzy Systems, 13(4) (2016), 51–61.\n Q. Pu, D. Zhang, Preordered sets valued in a GL-monoid, Fuzzy Sets and Systems, 187 (2012), 1–32.\n M. A. Samhan, Fuzzy quotient algebras and fuzzy factor congruences, Fuzzy Sets and Systems, 73 (1995), 269–277.\n L. Shen, Adjunctions in Quantaloid-enriched Categories, Ph.D Thesis, Sichuan University 2014.\n Z. Xiu, B. Pang, Base axioms and subbase axioms in M-fuzzifying convex spaces, Iranian Journal of Fuzzy Systems,\n15(2) (2018), 75–87.\n W. Yao, Y. She, L. Lu, Metric-based L-fuzzy rough sets: Approximation operators and de nable sets, Knowledge-\nBased Systems, 163 (2019), 91-102.\n L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965), 338–353." ]
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https://gmatclub.com/forum/during-a-certain-game-after-each-turn-a-player-s-points-are-doubled-273984.html
[ "GMAT Question of the Day - Daily to your Mailbox; hard ones only\n\n It is currently 18 Oct 2019, 06:43", null, "GMAT Club Daily Prep\n\nThank you for using the timer - this advanced tool can estimate your performance and suggest more practice questions. We have subscribed you to Daily Prep Questions via email.\n\nCustomized\nfor You\n\nwe will pick new questions that match your level based on your Timer History\n\nTrack\n\nevery week, we’ll send you an estimated GMAT score based on your performance\n\nPractice\nPays\n\nwe will pick new questions that match your level based on your Timer History\n\nNot interested in getting valuable practice questions and articles delivered to your email? No problem, unsubscribe here.", null, "", null, "During a certain game, after each turn, a player’s points are doubled\n\n new topic post reply Question banks Downloads My Bookmarks Reviews Important topics\nAuthor Message\nTAGS:\n\nHide Tags\n\nMath Expert", null, "V\nJoined: 02 Sep 2009\nPosts: 58453\nDuring a certain game, after each turn, a player’s points are doubled  [#permalink]\n\nShow Tags\n\n13", null, "00:00\n\nDifficulty:", null, "", null, "", null, "95% (hard)\n\nQuestion Stats:", null, "24% (01:57) correct", null, "76% (02:08) wrong", null, "based on 63 sessions\n\nHideShow timer Statistics\n\nDuring a certain game, after each turn, a player’s points are doubled and then reduced by 80. Players can only have a whole number of points and take turns until their score reaches exactly zero. Assuming no other points are gained or earned, how many points did player A start with?\n\n(1) Player A takes exactly 4 turns.\n\n(2) Player A’s starting score was not a multiple of 2.\n\n_________________\nMath Expert", null, "V\nJoined: 02 Aug 2009\nPosts: 7978\nDuring a certain game, after each turn, a player’s points are doubled  [#permalink]\n\nShow Tags\n\n2\nDuring a certain game, after each turn, a player’s points are doubled and then reduced by 80. Players can only have a whole number of points and take turns until their score reaches exactly zero. Assuming no other points are gained or earned, how many points did player A start with?\n\nLet the initial point be x, so the points after one turn $$2x-80=2^1*x-80*1=2^1x-80*(2^1-1)$$ and the next $$2(2x-80)-80=4x-240=2^2x-80*3=2^2x-80*(2^2-1)$$and so, the nth turn $$= 2^nx-80*(2^n-1)$$\n\n(1) Player A takes exactly 4 turns.\nso after 4th turn points = 0..\nso two ways\na) 4th turn - $$2^4x-80*(2^4-1)=0.........16x-80*15=0......16x=80*15......x=5*15=75$$\nb) work backwards - after 4th turn - 0, so before 4th turn - 0+80/2=40, before 3rd turn - (40+80)/2=60, before 2nd turn - (60+80)/2=70, before first turn - (70+80)/2=75\nsufficient\n\n(2) Player A’s starting score was not a multiple of 2.\nA straight logical answer is YES..\nsince we have a term being multiplied by 2, there will be only one term which will be a non-multiple of 2, because a term prior to it will be a decimal.\n\nother way..\nnth term = $$2^nx-80*(2^n-1)=0........2^n(80-x)=80$$\nx is an ODD integer, and 80-x has to be a multiple of 5, so 80-x can be 5, 15 , 25 ....\nOnly $$2^n*5 = 80$$ when n is 4, so $$80-x=5...x=80-5=75$$\n\nwork backwards -\nafter last turn - 0,\nso before that turn - $$\\frac{0+80}{2}=40$$,\nbefore that turn - $$\\frac{(40+80)}{2}=60$$,\nbefore that turn - $$\\frac{(60+80)}{2}=70$$,\nbefore that turn - $$\\frac{(70+80)}{2}=75$$\nbefore that - $$\\frac{(75+80)}{2}=77.5$$... but this is not a whole number\nso he started with 75\nsufficient\n\nD\n_________________\nManager", null, "", null, "B\nJoined: 09 Oct 2015\nPosts: 226\nRe: During a certain game, after each turn, a player’s points are doubled  [#permalink]\n\nShow Tags\n\nis there any other way to solve statement b ?\nMath Expert", null, "V\nJoined: 02 Aug 2009\nPosts: 7978\nRe: During a certain game, after each turn, a player’s points are doubled  [#permalink]\n\nShow Tags\n\n1\nrahulkashyap wrote:\nis there any other way to solve statement b ?\n\nA straight logical answer is YES..\nsince we have a term being multiplied by 2, there will be only one term which will be a non-multiple of 2, because a term prior to it will be a decimal.\n\nother way..\nnth term = $$2^nx-80*(2^n-1)=0........2^n(80-x)=80$$\nx is an ODD integer, and 80-x has to be a multiple of 5, so 80-x can be 5, 15 , 25 ....\nOnly $$2^n*5 = 80$$ when n is 4, so $$80-x=5...x=80-5=75$$\n_________________", null, "S\nJoined: 06 Mar 2017\nPosts: 189\nConcentration: Operations, General Management\nRe: During a certain game, after each turn, a player’s points are doubled  [#permalink]\n\nShow Tags\n\nBunuel wrote:\nDuring a certain game, after each turn, a player’s points are doubled and then reduced by 80. Players can only have a whole number of points and take turns until their score reaches exactly zero. Assuming no other points are gained or earned, how many points did player A start with?\n\n(1) Player A takes exactly 4 turns.\n\n(2) Player A’s starting score was not a multiple of 2.\n\nBunuel VeritasKarishma Pls help to solve statement B.\nVeritas Prep GMAT Instructor", null, "V\nJoined: 16 Oct 2010\nPosts: 9706\nLocation: Pune, India\nRe: During a certain game, after each turn, a player’s points are doubled  [#permalink]\n\nShow Tags\n\n1\nsiddreal wrote:\nBunuel wrote:\nDuring a certain game, after each turn, a player’s points are doubled and then reduced by 80. Players can only have a whole number of points and take turns until their score reaches exactly zero. Assuming no other points are gained or earned, how many points did player A start with?\n\n(1) Player A takes exactly 4 turns.\n\n(2) Player A’s starting score was not a multiple of 2.\n\nBunuel VeritasKarishma Pls help to solve statement B.\n\nWhen the player completes the game (reaches a score of 0), we know what if his number of points before that were N,\n\n2N - 80 = 0\nN = 40\n\nNow, can we say what the score was in the previous turn? Sure. We got 40 by doubling the previous score and subtracting 80.\n\n2M - 80 = 40\nM = 60\n\nNow, can we say what the score was in the previous turn? Sure. We got 60 by doubling the previous score and subtracting 80.\n\n2L - 80 = 60\nL = 70\n\nNow, can we say what the score was in the previous turn? Sure. We got 70 by doubling the previous score and subtracting 80.\n\n2K - 80 = 70\nK = 75\n(not an even score)\n\nNow, can we say what the score was in the previous turn? Sure. We got 75 by doubling the previous score and subtracting 80.\n\n2J - 80 = 75\nJ = 77.5\n\nThe reason we did not need to do all this calculation backwards was this - Every time, we are adding 80 to the current score and dividing by 2. As long as our current score is even,\n(Even + 80) = Even, so on dividing by 2 we get an integer.\nThe moment out current score becomes odd (75 in our case), the previous score will not be an integer because Odd + 80 = Odd. So on dividing by 2, we get a decimal. But every score must be an integer. This tells us that there is only one odd score that anyone can have and that is in the beginning of the game and it is 75 only (as calculated above). But since this is a DS question, we didn't really need to do the calculation as long as we know that we will get a single unique value of the starting score.\n_________________\nKarishma\nVeritas Prep GMAT Instructor\n\nLearn more about how Veritas Prep can help you achieve a great GMAT score by checking out their GMAT Prep Options >\nNon-Human User", null, "Joined: 09 Sep 2013\nPosts: 13257\nRe: During a certain game, after each turn, a player’s points are doubled  [#permalink]\n\nShow Tags\n\nHello from the GMAT Club BumpBot!\n\nThanks to another GMAT Club member, I have just discovered this valuable topic, yet it had no discussion for over a year. I am now bumping it up - doing my job. I think you may find it valuable (esp those replies with Kudos).\n\nWant to see all other topics I dig out? Follow me (click follow button on profile). You will receive a summary of all topics I bump in your profile area as well as via email.\n_________________", null, "Re: During a certain game, after each turn, a player’s points are doubled   [#permalink] 11 Sep 2019, 11:11\nDisplay posts from previous: Sort by\n\nDuring a certain game, after each turn, a player’s points are doubled\n\n new topic post reply Question banks Downloads My Bookmarks Reviews Important topics\n\n Powered by phpBB © phpBB Group | Emoji artwork provided by EmojiOne", null, "", null, "" ]
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https://www.sanfoundry.com/hash-tables-quadratic-probing-multiple-choice-questions-answers-mcqs/
[ "# Hash Tables with Quadratic Probing Multiple Choice Questions and Answers (MCQs)\n\n«\n»\n\nThis set of Data Structures & Algorithms Multiple Choice Questions & Answers (MCQs) focuses on “Hash Tables with Quadratic Probing”.\n\n1. Which of the following schemes does quadratic probing come under?\na) rehashing\nb) extended hashing\nc) separate chaining\n\nExplanation: Quadratic probing comes under open addressing scheme to resolve collisions in hash tables.\n\n2. Quadratic probing overcomes primary collision.\na) True\nb) False\n\nExplanation: Quadratic probing can overcome primary collision that occurs in linear probing but a secondary collision occurs in quadratic probing.\n\n3. What kind of deletion is implemented by hashing using open addressing?\na) active deletion\nb) standard deletion\nc) lazy deletion\nd) no deletion\n\nExplanation: Standard deletion cannot be performed in an open addressing hash table, because the cells might have caused collision. Hence, the hash tables implement lazy deletion.\n\n4. In quadratic probing, if the table size is prime, a new element cannot be inserted if the table is half full.\na) True\nb) False\n\nExplanation: In quadratic probing, if the table size is prime, we can insert a new element even though table is exactly half filled. We can’t insert element if table size is more than half filled.\n\n5. Which of the following is the correct function definition for quadratic probing?\na) F(i)=i2\nb) F(i)=i\nc) F(i)=i+1\nd) F(i)=i2+1\n\nExplanation: The function of quadratic probing is defined as F(i)=i2. The function of linear probing is defined as F(i)=i.\n\n6. How many constraints are to be met to successfully implement quadratic probing?\na) 1\nb) 2\nc) 3\nd) 4\n\nExplanation: 2 requirements are to be met with respect to table size. The table size should be a prime number and the table size should not be more than half full.\n\n7. Which among the following is the best technique to handle collision?\nb) Linear probing\nc) Double hashing\nd) Separate chaining\n\nExplanation: Quadratic probing handles primary collision occurring in the linear probing method. Although secondary collision occurs in quadratic probing, it can be removed by extra multiplications and divisions.\n\n8. Which of the following techniques offer better cache performance?\nb) Linear probing\nc) Double hashing\nd) Rehashing\n\nExplanation: Linear probing offers better cache performance than quadratic probing and also it preserves locality of reference.\n\n9. What is the formula used in quadratic probing?\na) Hash key = key mod table size\nb) Hash key=(hash(x)+F(i)) mod table size\nc) Hash key=(hash(x)+F(i2)) mod table size\nd) H(x) = x mod 17\n\nExplanation: Hash key=(hash(x)+F(i2)) mod table size is the formula for quadratic probing. Hash key = (hash(x)+F(i)) mod table size is the formula for linear probing.\n\n10. For the given hash table, in what location will the element 58 be hashed using quadratic probing?\n\n 0 49 1 2 3 4 5 6 7 8 18 9 89\n\na) 1\nb) 2\nc) 7\nd) 6\n\nExplanation: Initially, 58 collides at position 8. Another collision occurs one cell away. Hence, F(i2)=4. Using quadratic probing formula, the location is obtained as 2.\n\nSanfoundry Global Education & Learning Series – Data Structure.\n\nTo practice all areas of Data Structure, here is complete set of 1000+ Multiple Choice Questions and Answers.", null, "" ]
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https://www.cee-m.fr/?publication=a-mixed-control-problem-of-the-management-of-natural-resources
[ "# A mixed control problem of the management of natural resources\n\n• Home\n• A mixed control problem of the management of natural resources", null, "7 December 2015", null, "By CEE-M\n\nIn this paper, we study a mixed optimal control problem in which both continuous controls and impulse controls are admissible. The optimal solution of this problem can be characterized via the Hamilton-Jacobi-Bellman equation. However, the resolution of this equation can be cumbersome and in most cases only numerical solutions are possible. This is why we consider an example with particular functional forms. Building on the solutions of the pure continuous control and the pure impulse control problem, we propose a candidate for the optimal solution of the mixed control problem. We prove that this candidate verifies the Hamilton-Jacobi-Bellman equation. As conjectured by Clark, jumping to the steady state and then staying there is one possible optimal solution, but we show that the profit functions of the continuous control and impulse control sub-models need to be related in a particular manner to reach such solution. Although the above results were obtained with particular functional forms, we are working to prove the optimality of Clark’s policy for general functional forms, maintaining the relationship between the profit functions" ]
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https://webglfundamentals.org/webgl/lessons/zh_cn/webgl-3d-lighting-directional.html
[ "# WebGLFundamentals.org\n\nFix, Fork, Contribute\n\n# WebGL 三维方向光源\n\ndrag the points\n\nrotate the direction\n\n## 法向量\n\n``````function setNormals(gl) {\nvar normals = new Float32Array([\n// 正面左竖\n0, 0, 1,\n0, 0, 1,\n0, 0, 1,\n0, 0, 1,\n0, 0, 1,\n0, 0, 1,\n\n// 正面上横\n0, 0, 1,\n0, 0, 1,\n0, 0, 1,\n0, 0, 1,\n0, 0, 1,\n0, 0, 1,\n\n// 正面中横\n0, 0, 1,\n0, 0, 1,\n0, 0, 1,\n0, 0, 1,\n0, 0, 1,\n0, 0, 1,\n\n// 背面左竖\n0, 0, -1,\n0, 0, -1,\n0, 0, -1,\n0, 0, -1,\n0, 0, -1,\n0, 0, -1,\n\n// 背面上横\n0, 0, -1,\n0, 0, -1,\n0, 0, -1,\n0, 0, -1,\n0, 0, -1,\n0, 0, -1,\n\n// 背面中横\n0, 0, -1,\n0, 0, -1,\n0, 0, -1,\n0, 0, -1,\n0, 0, -1,\n0, 0, -1,\n\n// 顶部\n0, 1, 0,\n0, 1, 0,\n0, 1, 0,\n0, 1, 0,\n0, 1, 0,\n0, 1, 0,\n\n// 上横右面\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n\n// 上横下面\n0, -1, 0,\n0, -1, 0,\n0, -1, 0,\n0, -1, 0,\n0, -1, 0,\n0, -1, 0,\n\n// 上横和中横之间\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n\n// 中横上面\n0, 1, 0,\n0, 1, 0,\n0, 1, 0,\n0, 1, 0,\n0, 1, 0,\n0, 1, 0,\n\n// 中横右面\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n\n// 中横底面\n0, -1, 0,\n0, -1, 0,\n0, -1, 0,\n0, -1, 0,\n0, -1, 0,\n0, -1, 0,\n\n// 底部右侧\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n1, 0, 0,\n\n// 底面\n0, -1, 0,\n0, -1, 0,\n0, -1, 0,\n0, -1, 0,\n0, -1, 0,\n0, -1, 0,\n\n// 左面\n-1, 0, 0,\n-1, 0, 0,\n-1, 0, 0,\n-1, 0, 0,\n-1, 0, 0,\n-1, 0, 0]);\ngl.bufferData(gl.ARRAY_BUFFER, normals, gl.STATIC_DRAW);\n}\n``````\n\n``````// 找顶点着色器中的属性\nvar positionLocation = gl.getAttribLocation(program, \"a_position\");\n-var colorLocation = gl.getAttribLocation(program, \"a_color\");\n+var normalLocation = gl.getAttribLocation(program, \"a_normal\");\n\n...\n\n-// 创建一个缓冲存储颜色\n-var colorBuffer = gl.createBuffer();\n-// 绑定到 ARRAY_BUFFER\n-gl.bindBuffer(gl.ARRAY_BUFFER, colorBuffer);\n-// 将几何数据放入缓冲\n-setColors(gl);\n\n+// 创建缓冲存储法向量\n+var normalBuffer = gl.createBuffer();\n+// 绑定到 ARRAY_BUFFER (可以看作 ARRAY_BUFFER = normalBuffer)\n+gl.bindBuffer(gl.ARRAY_BUFFER, normalBuffer);\n+// 将法向量存入缓冲\n+setNormals(gl);\n``````\n\n``````-// 启用颜色属性\n-gl.enableVertexAttribArray(colorLocation);\n-\n-// 绑定颜色缓冲\n-gl.bindBuffer(gl.ARRAY_BUFFER, colorBuffer);\n-\n-// 告诉颜色属性怎么从 colorBuffer (ARRAY_BUFFER) 中读取颜色值\n-var size = 3; // 每次迭代使用3个单位的数据\n-var type = gl.UNSIGNED_BYTE; // 单位数据类型是无符号 8 位整数\n-var normalize = true; // 标准化数据 (从 0-255 转换到 0.0-1.0)\n-var stride = 0; // 0 = 移动距离 * 单位距离长度sizeof(type) 每次迭代跳多少距离到下一个数据\n-var offset = 0; // 从绑定缓冲的起始处开始\n-gl.vertexAttribPointer(\n- colorLocation, size, type, normalize, stride, offset)\n\n+// 启用法向量属性\n+gl.enableVertexAttribArray(normalLocation);\n+\n+// 绑定法向量缓冲\n+gl.bindBuffer(gl.ARRAY_BUFFER, normalBuffer);\n+\n+// 告诉法向量属性怎么从 normalBuffer (ARRAY_BUFFER) 中读取值\n+var size = 3; // 每次迭代使用3个单位的数据\n+var type = gl.FLOAT; // 单位数据类型是 32 位浮点型\n+var normalize = false; // 单位化 (从 0-255 转换到 0-1)\n+var stride = 0; // 0 = 移动距离 * 单位距离长度sizeof(type) 每次迭代跳多少距离到下一个数据\n+var offset = 0; // 从绑定缓冲的起始处开始\n+gl.vertexAttribPointer(\n+ normalLocation, size, type, normalize, stride, offset)\n``````\n\n``````attribute vec4 a_position;\n-attribute vec4 a_color;\n+attribute vec3 a_normal;\n\nuniform mat4 u_matrix;\n\n-varying vec4 v_color;\n+varying vec3 v_normal;\n\nvoid main() {\n// 将位置和矩阵相乘\ngl_Position = u_matrix * a_position;\n\n- // 将颜色传到片断着色器\n- v_color = a_color;\n\n+ // 将法向量传到片断着色器\n+ v_normal = a_normal;\n}\n``````\n\n``````precision mediump float;\n\n// 从顶点着色器中传入的值\n-varying vec4 v_color;\n+varying vec3 v_normal;\n\n+uniform vec3 u_reverseLightDirection;\n+uniform vec4 u_color;\n\nvoid main() {\n+ // 由于 v_normal 是插值出来的,和有可能不是单位向量,\n+ // 可以用 normalize 将其单位化。\n+ vec3 normal = normalize(v_normal);\n+\n+ float light = dot(normal, u_reverseLightDirection);\n\n* gl_FragColor = u_color;\n\n+ // 将颜色部分(不包括 alpha)和 光照相乘\n+ gl_FragColor.rgb *= light;\n}\n``````\n\n`````` // 寻找全局变量\nvar matrixLocation = gl.getUniformLocation(program, \"u_matrix\");\n+ var colorLocation = gl.getUniformLocation(program, \"u_color\");\n+ var reverseLightDirectionLocation =\n+ gl.getUniformLocation(program, \"u_reverseLightDirection\");\n``````\n\n`````` // 设置矩阵\ngl.uniformMatrix4fv(matrixLocation, false, worldViewProjectionMatrix);\n\n+ // 设置使用的颜色\n+ gl.uniform4fv(colorLocation, [0.2, 1, 0.2, 1]); // green\n+\n+ // 设置光线方向\n+ gl.uniform3fv(reverseLightDirectionLocation, m4.normalize([0.5, 0.7, 1]));\n``````\n\n``````attribute vec4 a_position;\nattribute vec3 a_normal;\n\n*uniform mat4 u_worldViewProjection;\n+uniform mat4 u_world;\n\nvarying vec3 v_normal;\n\nvoid main() {\n// 将位置和矩阵相乘\n* gl_Position = u_worldViewProjection * a_position;\n\n* // 重定向法向量并传递给片断着色器\n* v_normal = mat3(u_world) * a_normal;\n}\n``````\n\n`````` // 寻找全局变量\n* var worldViewProjectionLocation =\n* gl.getUniformLocation(program, \"u_worldViewProjection\");\n+ var worldLocation = gl.getUniformLocation(program, \"u_world\");\n``````\n\n``````*// 设置矩阵\n*gl.uniformMatrix4fv(\n* worldViewProjectionLocation, false,\n* worldViewProjectionMatrix);\n*gl.uniformMatrix4fv(worldLocation, false, worldMatrix);\n``````\n\nclick to toggle normals\n\n``````attribute vec4 a_position;\nattribute vec3 a_normal;\n\nuniform mat4 u_worldViewProjection;\n*uniform mat4 u_worldInverseTranspose;\n\nvarying vec3 v_normal;\n\nvoid main() {\n// 将位置和矩阵相乘\ngl_Position = u_worldViewProjection * a_position;\n\n// 重定向法向量并传递给片断着色器\n* v_normal = mat3(u_worldInverseTranspose) * a_normal;\n}\n``````\n\n``````- var worldLocation = gl.getUniformLocation(program, \"u_world\");\n+ var worldInverseTransposeLocation =\n+ gl.getUniformLocation(program, \"u_worldInverseTranspose\");\n``````\n\n``````var worldViewProjectionMatrix = m4.multiply(viewProjectionMatrix, worldMatrix);\nvar worldInverseMatrix = m4.inverse(worldMatrix);\nvar worldInverseTransposeMatrix = m4.transpose(worldInverseMatrix);\n\n// 设置矩阵\ngl.uniformMatrix4fv(\nworldViewProjectionLocation, false,\nworldViewProjectionMatrix);\n-gl.uniformMatrix4fv(worldLocation, false, worldMatrix);\n+gl.uniformMatrix4fv(\n+ worldInverseTransposeLocation, false,\n+ worldInverseTransposeMatrix);\n``````\n\n``````var m4 = {\ntranspose: function(m) {\nreturn [\nm, m, m, m,\nm, m, m, m,\nm, m, m, m,\nm, m, m, m,\n];\n},\n\n...\n``````\n\n### mat3(u_worldInverseTranspose) * a_normal 的可选方案\n\n```v_normal = mat3(u_worldInverseTranspose) * a_normal;\n```\n\n```v_normal = (u_worldInverseTranspose * vec4(a_normal, 0)).xyz;\n```" ]
[ null ]
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https://artofproblemsolving.com/wiki/index.php/2008_IMO_Problems/Problem_1
[ "# 2008 IMO Problems/Problem 1\n\n## Problem\n\nAn acute-angled triangle", null, "$ABC$ has orthocentre", null, "$H$. The circle passing through", null, "$H$ with centre the midpoint of", null, "$BC$ intersects the line", null, "$BC$ at", null, "$A_1$ and", null, "$A_2$. Similarly, the circle passing through", null, "$H$ with centre the midpoint of", null, "$CA$ intersects the line", null, "$CA$ at", null, "$B_1$ and", null, "$B_2$, and the circle passing through", null, "$H$ with centre the midpoint of", null, "$AB$ intersects the line", null, "$AB$ at", null, "$C_1$ and", null, "$C_2$. Show that", null, "$A_1$,", null, "$A_2$,", null, "$B_1$,", null, "$B_2$,", null, "$C_1$,", null, "$C_2$ lie on a circle.\n\n## Solution\n\nLet", null, "$M_A$,", null, "$M_B$, and", null, "$M_C$ be the midpoints of sides", null, "$BC$,", null, "$CA$, and", null, "$AB$, respectively. It's not hard to see that", null, "$M_BM_C\\parallel BC$. We also have that", null, "$AH\\perp BC$, so", null, "$AH \\perp M_BM_C$. Now note that the radical axis of two circles is perpendicular to the line connecting their centers. We know that", null, "$H$ is on the radical axis of the circles centered at", null, "$M_B$ and", null, "$M_C$, so", null, "$A$ is too. We then have", null, "$AC_1\\cdot AC_2=AB_2\\cdot AB_1\\Rightarrow \\frac{AB_2}{AC_1}=\\frac{AC_2}{AB_1}$. This implies that", null, "$\\triangle AB_2C_1\\sim \\triangle AC_2B_1$, so", null, "$\\angle AB_2C_1=\\angle AC_2B_1$. Therefore", null, "$\\angle C_1B_2B_1=180^{\\circ}-\\angle AB_2C_1=180^{\\circ}-\\angle AC_2B_1$. This shows that quadrilateral", null, "$C_1C_2B_1B_2$ is cyclic. Note that the center of its circumcircle is at the intersection of the perpendicular bisectors of the segments", null, "$C_1C_2$ and", null, "$B_1B_2$. However, these are just the perpendicular bisectors of", null, "$AB$ and", null, "$CA$, which meet at the circumcenter of", null, "$ABC$, so the circumcenter of", null, "$C_1C_2B_1B_2$ is the circumcenter of triangle", null, "$ABC$. Similarly, the circumcenters of", null, "$A_1A_2B_1B_2$ and", null, "$C_1C_2A_1A_2$ are coincident with the circumcenter of", null, "$ABC$. The desired result follows." ]
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https://www.codevscolor.com/kotlin-check-string-numeric/
[ "# Kotlin program to check if a string is numeric", null, "## Kotlin check if a string is numeric :\n\nIn this post, I will show you how to check if a string is numeric or not in Kotlin. For example, “1.2” is a numeric string but 1.2x is not. We will learn different ways to solve this problem.\n\n### Method 1: Using string.toDouble() :\n\nKotlin provides a couple of functions for the string class. We can parse a string as an integer or as a double. toInt() method parse one string as an integer and returns the result. Similarly, toDouble parse one string as Double.\n\nFor our problem, we will try to parse the string as double i.e. we will use toDouble method. This method throws one exception, NumberFormatException if the parsing fails. So, for a string, if this method can parse its value, we can say that this string is numeric. Take a look at the below example program :\n\n``````fun isNumber(str: String) = try {\nstr.toDouble()\ntrue\n} catch (e: NumberFormatException) {\nfalse\n}\n\nfun main() {\nval strArray = arrayOf(\"1\", \"-2\", \"0\", \"1.2\", \"-1.2\", \"xy\", \"+-1.2\")\n\nstrArray.forEach {\nprintln(\"\\$it => \\${isNumber(it)}\")\n}\n}``````\n\nHere, isNumber method takes one String as argument and returns one boolean. In the try block, it tries to convert the string to a double using toDouble method. If the conversion is successful, i.e. if it is a number, it returns true. Else, it catches one exception and returns false.\n\nThe above program will print the below output :\n\n``````1 => true\n-2 => true\n0 => true\n1.2 => true\n-1.2 => true\nxy => false\n+-1.2 => false``````\n\n### Method 2: Using toDoubleOrNull :\n\ntoDoubleOrNull is another version of toDouble. It converts one string to double, if the conversion is successful, it returns that converted value and if it fails, it returns null. So, we don’t have to use any try-catch block here :\n\n``````fun isNumber(str: String) = str.toDoubleOrNull()?.let { true } ?: false\n\nfun main() {\nval strArray = arrayOf(\"1\", \"-2\", \"0\", \"1.2\", \"-1.2\", \"xy\", \"+-1.2\")\n\nstrArray.forEach {\nprintln(\"\\$it => \\${isNumber(it)}\")\n}\n}``````\n\nIt will print the same output as the above program. Simpler and cleaner." ]
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https://www.scienceforums.net/profile/30917-ahmeeeeeeeeeed/
[ "", null, "", null, "# ahmeeeeeeeeeed\n\nSenior Members\n\n39\n\n## Community Reputation\n\n0 Neutral\n\n• Rank\nQuark\n1. I may think that the limit (r^6 cos^9 (theta) sin (theta) ) / (r^4 cos^6 (theta) + sin^2 (theta)) has a problem of the sin^2 theta downwards , as it may become zero and the limit won't be zero / positive value any more but then it is 0/0 , so what to do next to know whether this (x/x) will turn eventually to be zero in the limit or not ?! And question two , is actually the question in the above comment : why does wolfram alpha say the value of the limit is zero despite the result of m/(1+m^2)^2\n2. Hello I have the limit lim (x^9 * y) / (x^6 + y^2)^2 (x,y)---> (0,0) when I use polar the final result is limit = lim (r^6 cos^9 (theta) sin (theta) ) / (r^4 cos^6 (theta) + sin^2 (theta)) r--->0 and substituting r = 0 , it will give zero * I tried it on wolfram alpha and it gave zero http://www.wolframalpha.com/input/?i=limit+%28x^9+*y%29%2F%28x^6+%2By^2%29^2++as++%28x%2Cy%29+--%3E+%280%2C0%29 but when I use cartezean and try the path y = mx^3 the result turns to be m/(1+m^2)^2 which depends on m so what is it ?!\n3. hello About Einestien's train example I can't get what it really does with the constancy of the speed of light , what would be the difference if , instead of two bolts , we have two balls thrown at the same angle ?! the observer in the train would still see the front ball first Or we are using light because it is what determines simultaneity in our eyes , which means when I see two balls passing by me at different times , I don't have to conclude they weren't thrown simultaneously but when I see Light doing that I will ? I need basically to know what difference does the constancy of the speed of light make here thanks in advance ;\n4. thanks for response , I put photos of my text book just to let you see the question just as I see it , and for my notes I'll explain it as ( sorry , I was answering just as I would answer in the exam. the question is asking for the velocity of the pin (B) at the instant (phi) equals 45 degrees the pin is B , and is attached to the arm AB , and allowed to move just in the circular groove , the circular groove itself moves in the (i cap) direction with a constant velocity = 60 m/s as the X velocity is constant = 60 m/s , the circular groove and also the pin have it so what I'm seeking is the velocity of the pin tangential to the circle ... I treated this problem as a joint kinematices problem , so I devided it into two parts the first part , I'm treating the pin only as a part of arm( AB) , and so I used polar co-ordinates R cap and theta cap where V = dR/dt in the direction of R cap , and R* omega in the direction of theta cap = (.3)* omega since the length of arm AB is constant then dR/dt = zero and the (theta cap) velocity is R * omega We also have the X velocity of the pin so V = (.3 * omega) theta cap + 60 (i cap) ----------------> equation one ----- secondly I treated the pin as part of the circular part we see that both the groove and the pin move in the positive x direction with the same speed , so we can neglect this speed and consider the motion of the pin just circular motion I chose intrinsic co-ordinates with ( tcap) tangential and (n cap) pointing towards the center , I could also use polar again with this . We see that V in the direction tangential in the only velocity compnent here , and it is what I seeking then I resuluted the unit vectors (t cap) and (i cap) in the directions of R cap and theta cap using the geomitry of the figure , inorder to equate like quantities , that resulted in the tangential speed equals (- 61.5) t cap which means opposite in sense to the unit vector (t cap) , which also means that the pin is going up ! .. I can't find this logical as i think under this situation the pin should go down the groove not up the groove and here came my question here . sorry I can't understand this \" putting off some of the heavy hitters in maths\" Yes , A is stationary , B is the pin , the question as asking for the tangential velcoity of the pin , but of cource getting the \\omega of the pin in the half -circular groove would give us the tangential speed .. I'm using polar co-ordinates for arm AB as i said above.. yes the x velocity of the pin is the same value (60 m/s) but I'm seeking the tangential velocity also. that's what I expect , but my way of answering showed that at the instant phi = 45 the tangential velocity well be up not down , showing that phi ( at that instance) is seeking to increase not decrease , which I think is wrong and that's why I asked the question from the beginning ... --------------------------- so what I am seeking now is would you please tell me how I could find the tangential velocity of the pin B at the instant phi = 45 degrees from the givings in the textbook ? I want both its magnitude and direction , and I really want to help me find where is the mistake in my calculations ? thanks in advance well this is not mine if this is what you mean http://www.scienceforums.net/topic/65674-crank-slider-mechanism-velocity-of-piston-angular-velocity-of-link-hp-needed/\n5. Hello , here is a problem http://www.flickr.com/photos/76599498@N07/7070964675/ when I solve it , the pin turns to be going upwards , which I can't find logical , what do you see ,please ? here's my answer http://www.flickr.com/photos/76599498@N07/7070966251/ http://www.flickr.com/photos/76599498@N07/7070966875/\n6. no here the threading of nuts at 3:45 , but I was asking about very tiny nuts , but I think no problem in manufacturing a small machine of the same type\n7. But it gave me the Y in terms of X , not the position in terms of time\n8. thanks, I found a vedio on youtube that die forging is the used method\n9. Hello at this problem the car is moving around the circle with constant speed , and it is asking for d^2R/dt^2 for the radar And d^2(theta)/dt^2 for the radar at the instance the speed of car is vertical downwards as shown the speed equals 120 km/hour And the radius of the circle is 450 so I said a® = (d^2R/dt^2) - R (omega^2) but the only acceleration is the central which equals (v^2/ Radius) and in the same direction so we can get d^2R/dt^2 . and when I try to get the (alpha)(d^2 theta / d t^2) a (theta) = R*(alpha) + 2 (omega)* (dR/dt) and a(theta) equals zero and here is my question , what is the value of dR/dt ??? I think it has to be zero , since the velocity is vertical and the unit vector R cap is horizontal so 1- Is Alpha zero ? 2- can dR/dT equal zero at that situation , while d^R/dt^2 has a value ? I think no problem but I wanted to make sure I'm right in this solution\n10. hello, a problem says http://www.flickr.com/photos/76599498@N07/6895404760/ I couldn't know how to start but that's what I did http://www.flickr.com/photos/76599498@N07/6895405486/ http://www.flickr.com/photos/76599498@N07/6895406078/ http://www.flickr.com/photos/76599498@N07/6895403992/ but I don't think this is the way , since I got the acceleration at a special point . I want to ask some questions 1-the acceleration magnitude is variable , due to the variation of the radius od curv. , isn't it ? If so , what is meant by determining the acceleration without specifing a certain moment ? 2- this problem is under the title ( intrinstic co-ordinates) , If I were to use these co-ordinates what is the key to start with ? I can think of the tangential unit vector to be the same as the direction of the derivative of Y with respect to X , but how to employ this ? 3- the radius of curv. at some points turned to be negative , is that ordinary or did I just did some calculation wrong ?\n11. Hello, A Question says Assign a proper manufacturing process for nuts and bolts Can I say die forging then threading or die casting then threading How are they mass produced ? and is there tiny lathes to thread nuts ?\n×" ]
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https://www.sanfoundry.com/java-program-print-odd-elements-odd-index-number/
[ "# Java Program to Print Odd Elements at Odd Index\n\n«\n»\n\nThis is the Java Program to Print Odd Elements at Odd Index Number.\n\nProblem Description\n\nGiven an array of integers, print the odd numbers present at odd index numbers.\nExample:\n\nArray = [2,1,4,4,5,7]\n\nOutput = 1,7\n\nProblem Solution\n\nIterate through the array, and check whether the current index is even or odd. If it is odd then check the element at that index to be even or odd, print the element if it is odd.\n\nProgram/Source Code\nSanfoundry Certification Contest of the Month is Live. 100+ Subjects. Participate Now!\n\nHere is the source code of the Java Program to Print Odd Elements at Odd Index Number. The program is successfully compiled and tested using IDE IntelliJ Idea in Windows 7. The program output is also shown below.\n\n1. ` `\n2. `//Java Program to Print Odd Elements at Odd Index Number`\n3. ` `\n4. `import java.io.BufferedReader;`\n5. `import java.io.InputStreamReader;`\n6. ` `\n7. `public class OddIndexedOddElements {`\n8. ` // Function to print Odd Elements present at Odd Index Number`\n9. ` static void printOddIndexedElements(int[] array){`\n10. ` int i=0;`\n11. ` for(i=0; i<array.length; i++){`\n12. ` if(i%2 != 0 && array[i]%2 !=0){`\n13. ` System.out.print(array[i] + \" \");`\n14. ` }`\n15. ` }`\n16. ` }`\n17. ` `\n18. ` // Function to read user input`\n19. ` public static void main(String[] args) {`\n20. ` BufferedReader br = new BufferedReader(new InputStreamReader(System.in));`\n21. ` int size;`\n22. ` System.out.println(\"Enter the size of the array\");`\n23. ` try {`\n24. ` size = Integer.parseInt(br.readLine());`\n25. ` } catch (Exception e) {`\n26. ` System.out.println(\"Invalid Input\");`\n27. ` return;`\n28. ` }`\n29. ` int[] array = new int[size];`\n30. ` System.out.println(\"Enter array elements\");`\n31. ` int i;`\n32. ` for (i = 0; i < array.length; i++) {`\n33. ` try {`\n34. ` array[i] = Integer.parseInt(br.readLine());`\n35. ` } catch (Exception e) {`\n36. ` System.out.println(\"An error occurred\");`\n37. ` return;`\n38. ` }`\n39. ` }`\n40. ` System.out.println(\"Output\");`\n41. ` printOddIndexedElements(array);`\n42. ` }`\n43. `}`\nProgram Explanation\n\n1. In function printOddIndexedElements(), the loop for(i=0; i<array.length; i++) iterates through the array.\n2. Then using the condition if(i%2 != 0 && array[i]%2 != 0), we first check whether the current index is odd.\n3. If the index is odd, then we check the element at that index (the condition after the && operator), if the element is odd, then display the element.\n\nTime Complexity: O(n) where n is the number of elements in the array.\n\nRuntime Test Cases\n```\nCase 1 (Positive test Case - having odd elements at odd index):\n\nEnter the size of the array\n6\nEnter array elements\n2\n1\n4\n4\n5\n7\nOutput\n1 7\n\nCase 2 (Negative test Case - no odd element present at odd index):\n\nEnter the size of the array\n5\nEnter array elements\n1\n2\n3\n4\n5\nOutput```\n\nSanfoundry Global Education & Learning Series – Java Programs.", null, "" ]
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http://theblogreaders.com/tag/decimal/
[ "Aptitude\n\n## Decimal Shortcut Methods\n\n1% = 1/100 = 0.01 2% = 2/100 = 0.02 = 1/50 3% = 3/100 = 0.03 4% = 4/100 = 0.04 = 1/25" ]
[ null ]
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https://www.got-it.ai/solutions/excel-chat/excel-tutorial/subtopic/decimals
[ "# Decimals Articles: Excel and Google Sheets\n\nAll resources related to Decimals for Excel and Google Sheets.\n\n### Decimals\n\nHow to Convert Decimals to Fraction – Excelchat\nWe can convert decimal to fraction and fraction to decimal by using the Number ribbon. This tutorial will guide all levels of Excel users on how to insert fraction in Excel. Figure 1: How to Convert Decimal to Fraction How to Put a Fraction in Excel   To convert a..." ]
[ null ]
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http://nullege.com/codes/search/DateTime.DateTime.Day
[ "Did I find the right examples for you? yes no      Crawl my project      Python Jobs\n\n# DateTime.DateTime.Day\n\nAll Samples(46)  |  Call(46)  |  Derive(0)  |  Import(0)\n\n``` ui_class = 'greyed'\ndaylist.append(dict(total=formatTime(day_total),\nday_of_week=date.Day(),\nstyle=ui_class))\nelse:\ndaylist.append(dict(total=None, day_of_week=date.Day(),\n```\n\n``` while pos >= 0:\n# decrease pos when the day name is found\nif ref_date.Day() == day_name:\npos = pos - 1\n\n```\n```\n#cool, we find the day\nif ref_date.Day() == day_name and pos == 0:\nreturn ref_date\n\n```\n\n``` if day_total > 0:" ]
[ null ]
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https://www.vexorian.com/2012/05/google-codejam-round-2-yay.html
[ "## Saturday, May 26, 2012\n\n### Google Codejam round 2 - yay!\n\nI am just glad I managed to advance to round 3. Round 3 is going to be quite brutal for a guy that barely got the 467-th position, but still.\n\n## Problem A - Swinging Wild\n\nProblem statement\n\nThe first degree of difficulty was in understanding the statement. Let us simplify imagining there is a vine of length 0 at position D. We want to know if we can reach this last vine.\n\nLet us say we just reached vine i. There is a maximum length we can use in that vine. For Vine 0, it is d. For the later vines, this length depends on the position of the previous vine. So, if we used vine j to reach vine i, the maximum length we can use for vine i is min(length i, d[i] - d[j]).\n\nThe first idea is a dynamic programming one. f(i, j) returns whether it is possible to reach the last vine if you have just reached vine i and the previous one was j. Then you can get the maximum length you can use for vine i. This is the maximum distance between i and the next vine you use. Thus you can pick any of those vines within reach, and continue the recursion until i = the last one. This is enough to solve A-small.\n\nBut we want to solve A-large, which would not allow such a O(n^3) complexity. Let us instead define maxlength[i], the maximum length possible at which we can reach the i-th vine. You can see that this maximum depends only on the vines before i. Once we know this maximum length, we can use i to find possible lengths for later vines.\n\nFor example, for vine 0, the maximum length is d. Pick each vine j within d distance of vine 0, then a possible length for vine j is min( d[j] - d, length[j] ). After this step, the maximum length for vine 1 and vine 0 is already know, and we can use vine 1's maximum length to update more vines. This approach is O(n^2) in time complexity and needs only O(n) memory.\n\nint N; int d; int l; int D; int maxlen; bool solve() { d[N] = D; l[N] = 0; for (int i=0; i<=N; i++) { maxlen[i] = -1; } maxlen = d; for (int i=0; i<N; i++) { if (maxlen[i] > -1) { for (int j=i+1; j<=N; j++) { if (d[j] - d[i] <= maxlen[i]) { maxlen[j] = std::max( maxlen[j], std::min(d[j] - d[i], l[j]) ); } else { break; } } } } return (maxlen[N] > -1); }\n\nThis problem made me feel confident, because I solved it rather quickly. I was not expecting the next problems to each be very complicated :).\n\n## Problem C - Mountain view\n\nProblem statement\n\nIt seemed like C-small was less tricky than B-small, because of the accuracy rate from other competitors. So I first tried to solve this one. I actually barely got a integer programming solution, and I am still not sure I actually know how to code an integer programming solver.\n\nAfter the match, I found solutions that merely pick random numbers for the (at most 10) heights and verify that the answer is correct. Then you can output the correct answer. If after enough random attempts, no answer is found , it is impossible. The small number of peaks makes the probability to find a correct answer (if it exists) quite large.\n\nI feel so lame for not thinking of this.\n\n## Problem B - Aerobics\n\nProblem statement\n\nSo, my initial idea was to consider the circles as squares. And then the problem is just to place those squares. Somewhere in the rectangle. My theory was that if you sort the square lengths in non-ascending order, and then always placed each square in the closest valid position to the bottom-left, you would find a solution (The area is at least 5 times larger than the area of the circles, so it is quite unlikely this solution won't work). Indeed, even in cases where your circles have quite large radius, but the rectangle has only 1 as width, this is possible.\n\nYou can also verify that this approach needs only integral coordinates. Somehow, during the match I thought that it was possible to have 0.5 coordinates, and thus I multiply everything by 2 and other unnecessary things.\n\nI was pretty sure that would work, the issue is how to get the best location. With W,L <= 109, we cannot just try each coordinate for the center until we find one that does not intersect. So much that I even tried thinking of a different strategy. For a second, I thought of random (ironic as random would have helped in the other problem, but not this one).\n\nI wish I noticed the obvious solution to this predicament in less time than a whole hour: mundane binary search. For each square you want to place, binary search for the minimum x at which there is enough space to place it. How do you know if there is enough space at a given x? Simply use another binary search, but this time for y. Since you place the squares in order, and the previous squares are all together in the bottom-left position, both situations are binary search friendly.\n\nint N; int W, L; int x; int y; int r; pair<int,int> ri; // If the center of \"square\" i was (px,py), would it intersect with the // previously placed squares? bool intersect(int px, int py, int i) { for (int j=0; j<i; j++) { if ( (x[j] + r[ri[j].second] > px - r[ri[i].second]) && (y[j] + r[ri[j].second] > py - r[ri[i].second]) ) { return true; } } return false; } int gety(int px, int i) { int loy = -1; int hiy = L; while (loy+1 < hiy) { int hay = loy + (hiy - loy)/2; if (intersect(px,hay,i)) { loy = hay; } else { hiy = hay; } } return hiy; } void solve() { for (int i=0; i<N; i++) { ri[i] = make_pair(-r[i],i); } sort(ri, ri+N); x = 0; y = 0; for (int i=1; i<N; i++) { // Binary search for x int lox = -1; int hix = W; int picky; while (lox+1 < hix) { int hax = lox + (hix - lox)/2; int py = gety(hax, i); if (intersect(hax, py, i)) { lox = hax; } else { hix = hax; } } int py = gety(hix, i); // I used this assert, because maybe the strategy did not work, this // assert would alert me when running the large input... assert(! intersect(hix, py, i) ); x[i] = hix; y[i] = py; } for (int j=0; j<N; j++) { for (int i=0; i<N; i++) { if (ri[i].second == j) { cout << x[i] << \" \"<<y[i]; if (j < N - 1) { cout << \" \"; } } } } cout << endl; }\n\nThen I noticed that this approach works for B-large as well. So I just submitted it.\n\n## The last minutes\n\nBy the time I finished B, I had around 50 minutes left. But I had no idea what to do. Kept trying to think of something for C. Read D and then got baffled and tried to think of something too. There was a time at which I thought to solve D-small with a random solution. I just wish I was so creative with C...\n\nAs time progressed, my ranking got closer and closer to 500. The very last minute it eached 508 and then 520... Some people failed the large inputs in some problems (not lucky for them, but lucky for me). And I advanced to round 3.\n\nTo be honest I was not even expecting to be in the top 1000 today.\n\nI really liked this match. B , C and D were all really heavy weight problems in \"interesting-ness\" and difficulty and A was a good distraction.", null, "vexorian said...\n\nJust read the analysis and noticed that A allows you to go backwards. Yet my solutions don't go backwards. It is not a important fact for A-small, because the f(i,j) function can be turned into a graph easily. But for A-large it matters.\n\nWell, just need to proof that going backwards is never going to improve your chances to reach the best vine:\n\nImagine you are in vine i, and that you can go backwards and then return to increase the length of the i-th vine. If that is possible, then there is another vine k, that is reachable from vine i (with its current length) and that allows you to reach i at a larger length. But if k can be reached backwards from i, then there should be a path that allows you to directly go from 0 to k with a length as large or larger than needed. Ok... the proof goes like that.\n\nI was really lucky for not noticing that the statement allows you to go backwards :)", null, "carlos arias said...\n\nque buen analisis el que le haces a estos problemas del codeJam que nunca la pone facil...saludos" ]
[ null, "https://resources.blogblog.com/img/blank.gif", null, "https://resources.blogblog.com/img/blank.gif", null ]
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https://forums.minecraftforge.net/profile/118546-diamondminer88/content/
[ "Search In\n• More options...\nFind results that contain...\nFind results in...", null, "", null, "DiamondMiner88\n\nMembers\n\n186\n\nEverything posted by DiamondMiner88\n\n1. Thank you very much, that got me off the ground really fast! The last things I would like to ask is, I managed to make an outline that shows though blocks, however the highlighted rectangle does not. EDIT: If i disable drawing the highlight aka #drawCube then the outline stops being able to be seen though blocks however is still seen if you have a direct path to it. no idea why this is happening And the drawing is locked to a block, it can't be not centered on a block. Is there a way around this? Heres an example: outline.mp4 Heres the code: @SubscribeEvent public static void renderWorldLastEvent(RenderWorldLastEvent event) { MatrixStack ms = event.getMatrixStack(); IRenderTypeBuffer.Impl buffers = IRenderTypeBuffer.getImpl(Tessellator.getInstance().getBuffer()); RenderSystem.disableCull(); ms.push(); for (Entity e : Minecraft.getInstance().world.getAllEntities()) { if (!Minecraft.getInstance().player.equals(e) && !(e instanceof ItemEntity) && !(e instanceof ExperienceOrbEntity)) { drawCube(ms, buffers, new AxisAlignedBB(e.getPosition()).expand(0, 1, 0), new Color(0, 255, 0, 127)); draw3dOutline(ms, buffers, new AxisAlignedBB(e.getPosition()).expand(0, 1, 0), new Color(255, 255, 255, 255)); } } draw3dOutline(ms, buffers, new AxisAlignedBB(new BlockPos(0, 10, 0)), new Color(255, 255, 255, 255)); ms.pop(); buffers.finish(); } public static void drawCube(MatrixStack ms, IRenderTypeBuffer buffers, AxisAlignedBB aabb, Color color) { draw3dRectangle(ms, buffers, aabb, color, \"TOP\"); draw3dRectangle(ms, buffers, aabb, color, \"BOTTOM\"); draw3dRectangle(ms, buffers, aabb, color, \"NORTH\"); draw3dRectangle(ms, buffers, aabb, color, \"EAST\"); draw3dRectangle(ms, buffers, aabb, color, \"SOUTH\"); draw3dRectangle(ms, buffers, aabb, color, \"WEST\"); } public static void draw3dRectangle(MatrixStack ms, IRenderTypeBuffer buffers, AxisAlignedBB aabb, Color color, String side) { int r = color.getRed(); int g = color.getGreen(); int b = color.getBlue(); int a = color.getAlpha(); double renderPosX = Minecraft.getInstance().getRenderManager().info.getProjectedView().getX(); double renderPosY = Minecraft.getInstance().getRenderManager().info.getProjectedView().getY(); double renderPosZ = Minecraft.getInstance().getRenderManager().info.getProjectedView().getZ(); ms.push(); ms.translate(aabb.minX - renderPosX, aabb.minY - renderPosY, aabb.minZ - renderPosZ); IVertexBuilder buffer = buffers.getBuffer(RenderType.makeType(HypixelClient.MODID + \":rectangle_highlight\", DefaultVertexFormats.POSITION_COLOR, GL11.GL_QUADS, 256, false, true, RenderType.State.getBuilder().transparency(ObfuscationReflectionHelper.getPrivateValue(RenderState.class, null, \"field_228515_g_\")).cull(new RenderState.CullState(false)).build(false))); Matrix4f mat = ms.getLast().getMatrix(); float x = (float) (aabb.maxX - aabb.minX); float y = (float) (aabb.maxY - aabb.minY); float z = (float) (aabb.maxZ - aabb.minZ); switch (side) { case \"TOP\": buffer.pos(mat, x, y, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, y, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, y, z).color(r, g, b, a).endVertex(); buffer.pos(mat, x, y, z).color(r, g, b, a).endVertex(); break; case \"BOTTOM\": buffer.pos(mat, x, 0, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, 0, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, 0, z).color(r, g, b, a).endVertex(); buffer.pos(mat, x, 0, z).color(r, g, b, a).endVertex(); break; case \"NORTH\": buffer.pos(mat, 0, y, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, 0, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, x, 0, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, x, y, 0).color(r, g, b, a).endVertex(); break; case \"EAST\": buffer.pos(mat, x, y, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, x, 0, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, x, 0, z).color(r, g, b, a).endVertex(); buffer.pos(mat, x, y, z).color(r, g, b, a).endVertex(); break; case \"SOUTH\": buffer.pos(mat, 0, y, z).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, 0, z).color(r, g, b, a).endVertex(); buffer.pos(mat, x, 0, z).color(r, g, b, a).endVertex(); buffer.pos(mat, x, y, z).color(r, g, b, a).endVertex(); break; case \"WEST\": buffer.pos(mat, 0, y, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, 0, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, 0, z).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, y, z).color(r, g, b, a).endVertex(); break; } ms.pop(); } public static void draw3dOutline(MatrixStack ms, IRenderTypeBuffer buffers, AxisAlignedBB aabb, Color color) { int r = color.getRed(); int g = color.getGreen(); int b = color.getBlue(); int a = color.getAlpha(); double renderPosX = Minecraft.getInstance().getRenderManager().info.getProjectedView().getX(); double renderPosY = Minecraft.getInstance().getRenderManager().info.getProjectedView().getY(); double renderPosZ = Minecraft.getInstance().getRenderManager().info.getProjectedView().getZ(); ms.push(); ms.translate(aabb.minX - renderPosX, aabb.minY - renderPosY, aabb.minZ - renderPosZ); RenderType.State glState = RenderType.State.getBuilder().line(new RenderState.LineState(OptionalDouble.of(1))).layer(ObfuscationReflectionHelper.getPrivateValue(RenderState.class, null, \"field_228500_J_\")).transparency(ObfuscationReflectionHelper.getPrivateValue(RenderState.class, null, \"field_228515_g_\")).writeMask(new RenderState.WriteMaskState(true, false)).depthTest(new RenderState.DepthTestState(GL11.GL_ALWAYS)).build(false); IVertexBuilder buffer = buffers.getBuffer(RenderType.makeType(HypixelClient.MODID + \":line_1_no_depth\", DefaultVertexFormats.POSITION_COLOR, GL11.GL_LINES, 128, glState)); Matrix4f mat = ms.getLast().getMatrix(); float x = (float) (aabb.maxX - aabb.minX); float y = (float) (aabb.maxY - aabb.minY); float z = (float) (aabb.maxZ - aabb.minZ); // Top edges buffer.pos(mat, x, y, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, y, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, y, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, y, z).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, y, z).color(r, g, b, a).endVertex(); buffer.pos(mat, x, y, z).color(r, g, b, a).endVertex(); buffer.pos(mat, x, y, z).color(r, g, b, a).endVertex(); buffer.pos(mat, x, y, 0).color(r, g, b, a).endVertex(); // Bottom edges buffer.pos(mat, x, 0, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, 0, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, 0, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, 0, z).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, 0, z).color(r, g, b, a).endVertex(); buffer.pos(mat, x, 0, z).color(r, g, b, a).endVertex(); buffer.pos(mat, x, 0, z).color(r, g, b, a).endVertex(); buffer.pos(mat, x, 0, 0).color(r, g, b, a).endVertex(); // Side edges buffer.pos(mat, x, 0, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, x, y, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, y, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, 0, 0).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, y, z).color(r, g, b, a).endVertex(); buffer.pos(mat, 0, 0, z).color(r, g, b, a).endVertex(); buffer.pos(mat, x, 0, z).color(r, g, b, a).endVertex(); buffer.pos(mat, x, y, z).color(r, g, b, a).endVertex(); ms.pop(); }\n2. I already tested nearly all of them that returned a Vec3d, and got the same exact results as in the screenshot; just a line that won't stay in one position Even with the current calculation i get the exact same thing With some of the methods in the screenshot below the hand appears detached\n3. 1.12 is no longer supported on this forum. Please update to a modern version of Minecraft to receive support.\n4. bump\n5. I don't know why this doesn't work, it looks like the line is relative to the camera position, but it only shows if you're near 0,0,0 in the world @SubscribeEvent public static void renderWorldLastEvent(RenderWorldLastEvent event) { ClientPlayerEntity player = Minecraft.getInstance().player; GL11.glPushMatrix(); GL11.glPushAttrib(GL11.GL_ENABLE_BIT); double d0 = player.prevPosX + (player.getPosX() - player.prevPosX) * (double)event.getPartialTicks(); double d1 = player.prevPosY + (player.getPosY() - player.prevPosY) * (double)event.getPartialTicks(); double d2 = player.prevPosZ + (player.getPosZ() - player.prevPosZ) * (double)event.getPartialTicks(); Vec3d player_pos = new Vec3d(d0, d1, d2); GL11.glTranslated(-player_pos.x, -player_pos.y, -player_pos.z); GL11.glDisable(GL11.GL_LIGHTING); GL11.glDisable(GL11.GL_TEXTURE_2D); GL11.glDisable(GL11.GL_DEPTH_TEST); Vec3d blockA = new Vec3d (0,0,0); Vec3d blockB = new Vec3d (0,10,0); GL11.glColor4f(1,1,1,1); GL11.glBegin(GL11.GL_LINE_STRIP); GL11.glVertex3d(blockA.x, blockA.y, blockA.z); GL11.glVertex3d(blockB.x, blockB.y, blockB.z); GL11.glEnd(); GL11.glPopAttrib(); GL11.glPopMatrix(); } Its supposed to draw a line from 0,0,0 (that bedrock) to 0,10,0 but i get something like this with the line moving randomly as i move around\n6. I would like to make an block outline that's visible though other blocks, however i could not find a way to do it after searching for over 2 hours The closest I've gotten is this thread however that's for 1.11.2 and the renderBox method won't work in 1.15.2 I'm new to rendering stuff so i have no idea how it works either.\n7. Unless they re-wrote Minecraft to make it optimized they don't split workload across cores If they did they're definitely not gonna distribute it\n8. No i did use a type hierarchy. i use intellij\n9. Thank you! It took me a while to figure out but i found out that its not TextComponentString, its StringTextComponent\n10. Sorry im still at a loss. I can't find TextComponentString, which for all the examples i see is required\n11. I feel stupider with every message, how do i use ITextComponent? I cant find a way\n12. Sorry how do make a ServerPlayerEntity? And this is a client only mod so this won't go though to the server correct?\n13. Thanks it works now, but now im trying to send a chat message to the player that only the player can see. Im trying to use this post's 2nd comment. Now in 1.14 i can't find the EntityPlayer class.\n14. Sorry i haven't modded for a long time, so i forgot how to use Events. This doesn't work public static void clientChatEvent(ClientChatEvent event) { System.out.println(event.getMessage()); }\n15. Did you install the 1.12.2 installer?\n16. How do i intercept what the player sends though chat to a server? I'm trying to make a bot using baritone and if the player sends like %start i need to stop it from going though to the server.\n17. Thank you!\n18. Look in the same directory as where the installer is located for a log file that is generated after each time the installer is run. Please post its contents though Pastebin or a Github gist.\n19. Below DaemonUmbra's post is this text: For players asking for assistance with Forge please expand the spoiler below and read the appropriate section(s) in its/their entirety. Below that says \"Reveal hidden contents\" that expands and has a section titled Logs. Read its contents\n20. Wont even load the world for me to run it. Ill try that.\n21. So i accidentally spawned in 20000 or more items. I have no idea how to remove them. Is there a quick fix like using nbt explorer and removing all entity in world?\n22. How do i create JSON shaped recipes that include universal filled buckets? My fluid name is molten_glass_black with the MODID of character_mod\n23. IGNORE POST bump\n24. BTW there is already a mod for changing the menu through a json config file. Its called CustomMainMenu and there's a version for 1.12.2\n25. How do i create JSON shaped recipes that include universal filled buckets. My fluid name is molten_glass_black with the MODID of character_mod\n×\n\n• Activity\n\n×\n• Create New..." ]
[ null, "https://forums.minecraftforge.net/uploads/set_resources_2/84c1e40ea0e759e3f1505eb1788ddf3c_pattern.png", null, "data:image/svg+xml,%3Csvg%20xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%22%20viewBox%3D%220%200%201024%201024%22%20style%3D%22background%3A%2389c462%22%3E%3Cg%3E%3Ctext%20text-anchor%3D%22middle%22%20dy%3D%22.35em%22%20x%3D%22512%22%20y%3D%22512%22%20fill%3D%22%23ffffff%22%20font-size%3D%22700%22%20font-family%3D%22-apple-system%2C%20BlinkMacSystemFont%2C%20Roboto%2C%20Helvetica%2C%20Arial%2C%20sans-serif%22%3ED%3C%2Ftext%3E%3C%2Fg%3E%3C%2Fsvg%3E", null ]
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https://www.esaral.com/q/an-inverted-cone-has-a-depth-of-43873
[ "", null, "# An inverted cone has a depth of\n\nQuestion:\n\nAn inverted cone has a depth of $40 \\mathrm{~cm}$ and a base of radius $5 \\mathrm{~cm}$. Water is poured into it at a rate of $1.5$ cubic centimetres per minute. Find the rate at which the level of water in the cone is rising when the depth is $4 \\mathrm{~cm}$.\n\nSolution:", null, "" ]
[ null, "https://www.facebook.com/tr", null, "https://www.esaral.com/media/uploads/2022/03/26/image91414.png", null ]
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https://whatisconvert.com/199-cubic-meters-in-imperial-teaspoons
[ "# What is 199 Cubic Meters in Imperial Teaspoons?\n\n## Convert 199 Cubic Meters to Imperial Teaspoons\n\nTo calculate 199 Cubic Meters to the corresponding value in Imperial Teaspoons, multiply the quantity in Cubic Meters by 281560.63782283 (conversion factor). In this case we should multiply 199 Cubic Meters by 281560.63782283 to get the equivalent result in Imperial Teaspoons:\n\n199 Cubic Meters x 281560.63782283 = 56030566.926744 Imperial Teaspoons\n\n199 Cubic Meters is equivalent to 56030566.926744 Imperial Teaspoons.\n\n## How to convert from Cubic Meters to Imperial Teaspoons\n\nThe conversion factor from Cubic Meters to Imperial Teaspoons is 281560.63782283. To find out how many Cubic Meters in Imperial Teaspoons, multiply by the conversion factor or use the Volume converter above. One hundred ninety-nine Cubic Meters is equivalent to fifty-six million thirty thousand five hundred sixty-six point nine two seven Imperial Teaspoons.\n\n## Definition of Cubic Meter\n\nThe cubic meter (also written \"cubic metre\", symbol: m3) is the SI derived unit of volume. It is defined as the volume of a cube with edges one meter in length. Another name, not widely used any more, is the kilolitre. It is sometimes abbreviated to cu m, m3, M3, m^3, m**3, CBM, cbm.\n\n## Definition of Imperial Teaspoon\n\nAn Imperial teaspoon (usually abbreviated tsp.) is a unit of volume in the Imperial System. It widely used in cooking recipes and pharmaceutic prescriptions. An imperial teaspoon equals 5.9 ml.\n\n## Using the Cubic Meters to Imperial Teaspoons converter you can get answers to questions like the following:\n\n• How many Imperial Teaspoons are in 199 Cubic Meters?\n• 199 Cubic Meters is equal to how many Imperial Teaspoons?\n• How to convert 199 Cubic Meters to Imperial Teaspoons?\n• How many is 199 Cubic Meters in Imperial Teaspoons?\n• What is 199 Cubic Meters in Imperial Teaspoons?\n• How much is 199 Cubic Meters in Imperial Teaspoons?\n• How many uk tsp are in 199 m3?\n• 199 m3 is equal to how many uk tsp?\n• How to convert 199 m3 to uk tsp?\n• How many is 199 m3 in uk tsp?\n• What is 199 m3 in uk tsp?\n• How much is 199 m3 in uk tsp?" ]
[ null ]
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https://scipost.org/submissions/2108.04781v1/
[ "# Single-Logarithmic Corrections to Small-$x$ Helicity Evolution\n\n### Submission summary\n\n As Contributors: Yossathorn Tawabutr Arxiv Link: https://arxiv.org/abs/2108.04781v1 (pdf) Date submitted: 2021-08-11 03:10 Submitted by: Tawabutr, Yossathorn Submitted to: SciPost Physics Proceedings Proceedings issue: DIS2021 Academic field: Physics Specialties: High-Energy Physics - Experiment High-Energy Physics - Phenomenology Nuclear Physics - Experiment Nuclear Physics - Theory Approach: Theoretical\n\n### Abstract\n\nThe small-$x$ quark helicity evolution equations at double-logarithmic order, with the kernel $\\sim\\alpha_s\\ln^2(1/x)$, have been derived previously. In this work, we derive the single-logarithmic corrections to the equations, to order $\\alpha_s\\ln(1/x)$ of the evolution kernel. The new equations include the effects of the running coupling and the unpolarized small-$x$ evolution, both of which are parametrically significant at single-logarithmic order. The large-$N_c$ and large-$N_c\\& N_f$ approximations to the equation are computed. (Here, $N_c$ and $N_f$ are the numbers of quark colors and flavors, respectively.) Their solutions will provide more precise estimates of the quark helicity distribution at small $x$, contributing to the resolution of the proton spin puzzle.\n\n###### Current status:\nEditor-in-charge assigned" ]
[ null ]
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https://exploringphysics.com/about-the-app/eunit-2-electrical-circuits/
[ "### eUnit 2 Electrical Circuits", null, "Building on knowledge from previous units, students will build and analyze simple circuits, and compare and contrast series and parallel circuits. Students will calculate current, voltage and resistance in a circuit. The mathematical representation of the connection between voltage, current, and resistance will be determined. By the end of the unit, students will apply their knowledge to electrical widgets that they have in their household. By the end of the unit students will be able to answer the following questions:\n\n• What are the factors that affect the resistance of a resistor?\n• How does one use current and voltage to qualitatively predict the brightness of a bulb in a circuit?\n• How are voltage, current and resistance related?\n• How does one describe the voltage across a resistor and the current through a resistor in a series circuit?\n• How does one describe the voltage across a resistor and the current through a resistor in a parallel circuit?\n• How does one calculate the power expended by a resistor?\n• How does one calculate the energy expended by a resistor?\n\nBuy materials required for teaching eUnit 2 hands-on labs.\n\nSuggested timeline for teaching Unit 2\n\nBig Ideas\n1. Voltage is proportional to the electric current and the resistance of the resistor.\n2. The voltage between two points in a circuit is the same for every circuit element(s) connected between the same two points (parallel circuit).\n3. The current in a circuit is the same at any two points in a circuit if there are no junctions between those two points (series circuit).\n\nLearning Goals and Objectives\nBy the end of this unit, the students should be able to:\n\n1. Investigate working electrical circuits. (DOK3) a) Describe how electrical devices work. b) Use a multimeter to measure current, voltage and resistance.\n2. Describe an electrical circuit, using multiple representations (i.e., pictorial diagrams, verbal descriptions, schematics, and mathematical models, etc.). (DOK3) a) Create a verbal, schematic, pictorial or mathematical description of a physical electrical circuit. b) Construct an electrical circuit from the verbal, schematic, mathematical, or pictorial representation of a circuit. c) Describe verbally and mathematically the relationship between current, voltage and resistance.\n3. Design and conduct an experiment to determine how different electrodes and electrolytes affect the voltage produced by a wet-cell battery. (DOK4) a) Define voltage and measure a battery’s voltage. b) Identify the essential components of a wet-cell battery. c) Construct batteries using different combinations of electrodes and electrolytes. d) Measure the voltage of the batteries constructed.\n4. Design and conduct an experiment to determine the parameters that affect the resistance of a wire. (DOK4) a) Define resistance and measure it in an electrical circuit. b) Conduct experiments to determine how the length, the diameter, and the resistivity of a wire affect its resistance and develop a mathematical relationship. c) Apply the relationship verbally and mathematically.\n5. Design and conduct an experiment to determine the relationship between current, resistance and voltage within an electrical circuit. (DOK4) a) Define current and measure it in an electrical circuit. b) Design and conduct a quantitative experiment to determine a mathematical relationship among current, voltage, and resistance (Ohm’s Law, V=IR). c) Apply Ohm’s Law verbally and mathematically.\n6. Mathematically, graphically and physically determine voltage, current and resistance in a series circuit. (DOK3) a) Compare and contrast the characteristics of a one-bulb circuit and a two-bulb series circuit. b) Measure the current throughout a series circuit. c) Measure the voltage of the battery and across each resistor in a series circuit and obtain a relationship among them. d) Graphically represent changes in voltage and current in a series circuit. e) Measure the equivalent resistance of a series circuit, and obtain a mathematical relationship (RS = R1 + R2 + R3 + …..) . f) Calculate the voltage, current and resistance for various series circuits. g) Calculate the voltage, current and/or resistance for various parallel circuits. h) Predict the behaviors of various mixed series/parallel circuits and short circuits.\n7. Calculate the power and electrical energy used in a given electrical circuit. (DOK2) a) Relate voltage and current to power. b) Calculate the electrical power in watts, using the equation P=VI. c) Relate power to energy and time. d) Calculate the electrical energy in joules or kilowatt-hours for various circuits.\n8. Design and conduct experiments to study parameters that affect electrical circuits. (DOK4) a) Make observations. b) Operationally define the variables. c) Generate a hypothesis. d) List the steps of the procedure used to collect the data. e) Collect the data and organize it into a table. f) Convert metric units as needed. g) Graph and interpret the results. h) Develop conclusions based on results." ]
[ null, "http://exploringphysics.com/wp-content/uploads/2018/11/UNIT2_COVER_300px-186x300.jpg", null ]
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https://golden-ratio.eye-of-revelation.org/GreatTriangle-6.html
[ "First of all, must underline that the original word \"section\" – since from the Latin: sectio aurea – represents a Portion of a Unit; therefore the main symbol Phi should be referred to the decimal irrational number .618… and not to its counterpart 1.618…, which is phi: a unit + a portion added won't be a section ( the later \"ratio\" definition comes more flex­i­ble, though). This being said, it's worth to be introduced an alternative quick way to figure out their ratio. It is such as they can be applied to any number or value N, almost like a unique parameter, simply al­ter­nat­ing the modes, always getting: N × f = N / j and evenly N / f = N × j That brings to: F × j = 1. Conceptually, the Percentage of [a Unit + the same Percentage of itself] is eq. One. This also means that for the unique property of these two values around the Unit, any number either multiplied by F or divided by j gives the same result; as well as any number divided by F or multiplied by j !", null, "Then each sub­se­quent re­duc­tion or se­quen­tial in­crease re­tain a con­stant Pro­por­tion be­tween the parts (or el­e­ments), in a per­en­ni­al re­cip­ro­cal bal­ance." ]
[ null, "https://golden-ratio.eye-of-revelation.org/image/Goldencurves.png", null ]
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http://abcstudyguide.com/python-numbers/
[ "# Python Numbers\n\nIn Python there are three types of numbers:\n\n• Integer: Integer (or int) number type indicates a whole number.\n• Float: If the number has a decimal portion, it is a floating-point number (or float).\n• Complex: Complex numbers have real and imaginary parts.\n```x = 5 # Integer\ny = 3.14 # Float\nz = 2 + 3j # Complex Number\n```\n\n## Number Type Conversion\n\nTo convert from one number type to another, you can use `int()`, `float()`, or `complex()` functions.\n\n```x = 5\ny = 3.14\nz = 2 + 3j\n\nt = float(x) # converting from int(x) to float (t = 5.0)\nw = int(y) # converting from float(y) to int (w = 3)\nu = complex(x) # converting from int(x) to complex\n```\n\n## Arithmetic Operators\n\nYou can use the following arithmetic operators in numbers:\n\n## str() Function\n\nTo concatenate a number with a string in Python, first you need to convert number to string using `str()` function, otherwise Python will give `TypeError`.\n\nTypeError: can only concatenate str (not “int”) to str\n\n```age = 32\nprint(\"Hello, I'm \" + age + \" years old.\")\n```\n\n`str()` function converts number to string\n\n```print(\"Hello, I'm \" + str(age) + \" years old.\")\n# Hello, I'm 32 years old.\n```\nSHARE" ]
[ null ]
{"ft_lang_label":"__label__en","ft_lang_prob":0.63270116,"math_prob":0.9914918,"size":1427,"snap":"2020-34-2020-40","text_gpt3_token_len":436,"char_repetition_ratio":0.11946592,"word_repetition_ratio":0.013793103,"special_character_ratio":0.3651016,"punctuation_ratio":0.09737828,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9994382,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-09-28T18:37:18Z\",\"WARC-Record-ID\":\"<urn:uuid:c7a03239-ede7-4473-b690-f780cbad7e96>\",\"Content-Length\":\"54464\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:2f07e6d8-7a90-4bee-a463-a40599e5fb61>\",\"WARC-Concurrent-To\":\"<urn:uuid:d8e5d552-976d-4ed9-aae6-a4a23ee49d55>\",\"WARC-IP-Address\":\"107.180.12.188\",\"WARC-Target-URI\":\"http://abcstudyguide.com/python-numbers/\",\"WARC-Payload-Digest\":\"sha1:KCBOC37HHBY4LNFKKWAT7MBQAC3BS3TE\",\"WARC-Block-Digest\":\"sha1:A6FEXGGUHDJ7AWIDBDBAIRWDM4APLSWD\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-40/CC-MAIN-2020-40_segments_1600401604940.65_warc_CC-MAIN-20200928171446-20200928201446-00766.warc.gz\"}"}
https://nightlies.apache.org/flink/flink-docs-release-1.7/dev/libs/ml/cross_validation.html
[ "This documentation is for an out-of-date version of Apache Flink. We recommend you use the latest stable version.\n$$\\newcommand{\\R}{\\mathbb{R}} \\newcommand{\\E}{\\mathbb{E}} \\newcommand{\\x}{\\mathbf{x}} \\newcommand{\\y}{\\mathbf{y}} \\newcommand{\\wv}{\\mathbf{w}} \\newcommand{\\av}{\\mathbf{\\alpha}} \\newcommand{\\bv}{\\mathbf{b}} \\newcommand{\\N}{\\mathbb{N}} \\newcommand{\\id}{\\mathbf{I}} \\newcommand{\\ind}{\\mathbf{1}} \\newcommand{\\0}{\\mathbf{0}} \\newcommand{\\unit}{\\mathbf{e}} \\newcommand{\\one}{\\mathbf{1}} \\newcommand{\\zero}{\\mathbf{0}} \\newcommand\\rfrac{^{#1}\\!/_{#2}} \\newcommand{\\norm}{\\left\\lVert#1\\right\\rVert}$$\n\n# Cross Validation\n\n## Description\n\nA prevalent problem when utilizing machine learning algorithms is overfitting, or when an algorithm “memorizes” the training data but does a poor job extrapolating to out of sample cases. A common method for dealing with the overfitting problem is to hold back some subset of data from the original training algorithm and then measure the fit algorithm’s performance on this hold-out set. This is commonly known as cross validation. A model is trained on one subset of data and then validated on another set of data.\n\n## Cross Validation Strategies\n\nThere are several strategies for holding out data. FlinkML has convenience methods for\n\n• Train-Test Splits\n• Train-Test-Holdout Splits\n• K-Fold Splits\n• Multi-Random Splits\n\n### Train-Test Splits\n\nThe simplest method of splitting is the trainTestSplit. This split takes a DataSet and a parameter fraction. The fraction indicates the portion of the DataSet that should be allocated to the training set. This split also takes two additional optional parameters, precise and seed.\n\nBy default, the Split is done by randomly deciding whether or not an observation is assigned to the training DataSet with probability = fraction. When precise is true however, additional steps are taken to ensure the training set is as close as possible to the length of the DataSet $\\cdot$ fraction.\n\nThe method returns a new TrainTestDataSet object which has a .training attribute containing the training DataSet and a .testing attribute containing the testing DataSet.\n\n### Train-Test-Holdout Splits\n\nIn some cases, algorithms have been known to ‘learn’ the testing set. To combat this issue, a train-test-hold out strategy introduces a secondary holdout set, aptly called the holdout set.\n\nTraditionally, training and testing would be done to train an algorithms as normal and then a final test of the algorithm on the holdout set would be done. Ideally, prediction errors/model scores in the holdout set would not be significantly different than those observed in the testing set.\n\nIn a train-test-holdout strategy we sacrifice the sample size of the initial fitting algorithm for increased confidence that our model is not over-fit.\n\nWhen using trainTestHoldout splitter, the fraction Double is replaced by a fraction array of length three. The first element corresponds to the portion to be used for training, second for testing, and third for holdout. The weights of this array are relative, e.g. an array Array(3.0, 2.0, 1.0) would results in approximately 50% of the observations being in the training set, 33% of the observations in the testing set, and 17% of the observations in holdout set.\n\n### K-Fold Splits\n\nIn a k-fold strategy, the DataSet is split into k equal subsets. Then for each of the k subsets, a TrainTestDataSet is created where the subset is the .training DataSet, and the remaining subsets are the .testing set.\n\nFor each training set, an algorithm is trained and then is evaluated based on the predictions based on the associated testing set. When an algorithm that has consistent grades (e.g. prediction errors) across held out datasets we can have some confidence that our approach (e.g. choice of algorithm / algorithm parameters / number of iterations) is robust against overfitting.\n\nK-Fold Cross Validation\n\n### Multi-Random Splits\n\nThe multi-random strategy can be thought of as a more general form of the train-test-holdout strategy. In fact, .trainTestHoldoutSplit is a simple wrapper for multiRandomSplit which also packages the datasets into a trainTestHoldoutDataSet object.\n\nThe first major difference, is that multiRandomSplit takes an array of fractions of any length. E.g. one can create multiple holdout sets. Alternatively, one could think of kFoldSplit as a wrapper for multiRandomSplit (which it is), the difference being kFoldSplit creates subsets of approximately equal size, where multiRandomSplit will create subsets of any size.\n\nThe second major difference is that multiRandomSplit returns an array of DataSets, equal in size and proportion to the fraction array that it was passed as an argument.\n\n## Parameters\n\nThe various Splitter methods share many parameters.\n\nParameter Type Description Used by Method\ninput DataSet[Any] DataSet to be split. randomSplit\nmultiRandomSplit\nkFoldSplit\ntrainTestSplit\ntrainTestHoldoutSplit\nseed Long\n\nUsed for seeding the random number generator which sorts DataSets into other DataSets.\n\nrandomSplit\nmultiRandomSplit\nkFoldSplit\ntrainTestSplit\ntrainTestHoldoutSplit\nprecise Boolean When true, make additional effort to make DataSets as close to the prescribed proportions as possible. randomSplit\ntrainTestSplit\nfraction Double The portion of the input to assign to the first or .training DataSet. Must be in the range (0,1) randomSplit\ntrainTestSplit\nfracArray Array[Double] An array that prescribes the proportions of the output datasets (proportions need not sum to 1 or be within the range (0,1)) multiRandomSplit\ntrainTestHoldoutSplit\nkFolds Int The number of subsets to break the input DataSet into. kFoldSplit" ]
[ null ]
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https://de.maplesoft.com/support/help/addons/view.aspx?path=LinearFunctionalSystems/SeriesSolution
[ "", null, "SeriesSolution - Maple Help\n\nLinearFunctionalSystems\n\n SeriesSolution\n return the formal series solution of a linear functional system of equations", null, "Calling Sequence SeriesSolution(sys, vars, method) SeriesSolution(A, b, x, case, method) SeriesSolution(A, x, case, method)", null, "Parameters\n\n sys - list of equations; linear functional system vars - list of function variables such as [y1(x), y2(x), ...]; variables to solve for A - Matrix with rational elements b - Vector with rational elements x - independent variable case - name indicating the case of the system; one of 'differential', 'difference', or 'qdifference' method - (optional) name indicating the version of EG-eliminations to use; one of 'quasimodular' or 'ordinary', the latter being the default", null, "Description\n\n • The SeriesSolution function returns the initial terms of the formal series solutions from the specified linear functional system of equations with polynomial coefficients. If such a solution does not exist, then the empty list $\\left[\\right]$ is returned.\n The system parameter is entered either in list form (a list of equations sys and a list of function variables vars to solve for), or in matrix form (matrix A, vector b, and the independent variable x, where the vector b is optional).\n The matrix form specifies the system $\\mathrm{Ly}\\left(x\\right)=\\mathrm{Ay}\\left(x\\right)+b$, where L is the operator (either differential, difference, or q-difference), $y\\left(x\\right)$ is the vector of the functions to solve for, A is a rational matrix, and b is a rational vector (right-hand side).\n For the matrix from of the calling sequence, the case of the system must be specified as one of 'differential', 'difference', or 'qdifference'. If b is not specified, the system is assumed to be homogeneous.\n • The function computes the matrix recurrence system corresponding to the given system. This matrix recurrence system is represented by its explicit matrix (the matrix n by n*m, where n is the order of the system, with the leading and trailing matrix being of size n by n). Then, the function triangularizes the leading matrix using LinearFunctionalSystems[MatrixTriangularization] in order to bound the number of the initial terms of the solution in such a way that the recurrences for the rest terms' coefficients have an invertible leading matrix and then builds these initial terms.\n • The solution is the list of series expansions in x, corresponding to vars. The order term (for example $\\mathrm{O}\\left({x}^{6}\\right)$) is the last term in the series.\n The solution involves arbitrary constants of the form _c1, _c2, etc.\n • The solution has an attribute which is a table with the following indices:\n\n 'initial' - the list of initial terms 'degree' - the formal degree of the initial terms 'recurrence' - the corresponding recurrence 'coefficients' - the coefficients of the initial terms in a proper basis (depending on the case) 'lead' - the leading shift of the recurrence 'trail' - the trailing shift of the recurrence 'variable' - the independent variable of the given system 'the_case' - 'differential', 'difference' or 'qdifference' 'homogeneous' - true if the given system is homogeneous, false otherwise 'index' - the index of the last arbitrary constant 'q_par' - the q parameter used\n\n Note: This data is used by LinearFunctionalSystems[ExtendSeries] in order to extend the number of computed initial terms.\n • The error conditions associated with SeriesSolution are the same as those which are generated by LinearFunctionalSystems[Properties].\n • This function is part of the LinearFunctionalSystems package, and so it can be used in the form SeriesSolution(..) only after executing the command with(LinearFunctionalSystems). However, it can always be accessed through the long form of the command by using the form LinearFunctionalSystems[SeriesSolution](..).", null, "Examples\n\n > $\\mathrm{with}\\left(\\mathrm{LinearFunctionalSystems}\\right):$\n > $\\mathrm{sys}≔\\left[\\frac{ⅆ}{ⅆx}\\mathrm{y1}\\left(x\\right)-\\mathrm{y2}\\left(x\\right),\\frac{ⅆ}{ⅆx}\\mathrm{y2}\\left(x\\right)-\\mathrm{y3}\\left(x\\right)-\\mathrm{y4}\\left(x\\right),\\frac{ⅆ}{ⅆx}\\mathrm{y3}\\left(x\\right)-\\mathrm{y5}\\left(x\\right),\\frac{ⅆ}{ⅆx}\\mathrm{y4}\\left(x\\right)-2\\mathrm{y1}\\left(x\\right)-2x\\mathrm{y2}\\left(x\\right)-\\mathrm{y5}\\left(x\\right),\\frac{ⅆ}{ⅆx}\\mathrm{y5}\\left(x\\right)-{x}^{2}\\mathrm{y1}\\left(x\\right)-2x\\mathrm{y3}\\left(x\\right)-\\mathrm{y6}\\left(x\\right),\\frac{ⅆ}{ⅆx}\\mathrm{y6}\\left(x\\right)-{x}^{2}\\mathrm{y2}\\left(x\\right)+2\\mathrm{y3}\\left(x\\right)\\right]:$\n > $\\mathrm{vars}≔\\left[\\mathrm{y1}\\left(x\\right),\\mathrm{y2}\\left(x\\right),\\mathrm{y3}\\left(x\\right),\\mathrm{y4}\\left(x\\right),\\mathrm{y5}\\left(x\\right),\\mathrm{y6}\\left(x\\right)\\right]:$\n > $\\mathrm{SeriesSolution}\\left(\\mathrm{sys},\\mathrm{vars}\\right)$\n $\\left[{3}{}{{\\mathrm{_c}}}_{{2}}{+}{{\\mathrm{_c}}}_{{5}}{+}{\\mathrm{O}}{}\\left({x}\\right){,}{3}{}{{\\mathrm{_c}}}_{{6}}{+}{{\\mathrm{_c}}}_{{3}}{+}{\\mathrm{O}}{}\\left({x}\\right){,}{-}\\frac{{{\\mathrm{_c}}}_{{4}}}{{2}}{+}{\\mathrm{O}}{}\\left({x}\\right){,}{{\\mathrm{_c}}}_{{1}}{+}\\frac{{{\\mathrm{_c}}}_{{4}}}{{2}}{+}{\\mathrm{O}}{}\\left({x}\\right){,}{-}{{\\mathrm{_c}}}_{{5}}{+}{\\mathrm{O}}{}\\left({x}\\right){,}{{\\mathrm{_c}}}_{{3}}{+}{\\mathrm{O}}{}\\left({x}\\right)\\right]$ (1)\n > $\\mathrm{sys}≔\\left[\\mathrm{y2}\\left(x\\right){x}^{2}+3\\mathrm{y2}\\left(x\\right)x+2\\mathrm{y2}\\left(x\\right)-2\\mathrm{y1}\\left(x\\right){x}^{2}-4\\mathrm{y1}\\left(x\\right)x+\\mathrm{y1}\\left(x+1\\right){x}^{2}+\\mathrm{y1}\\left(x+1\\right)x,\\mathrm{y2}\\left(x+1\\right)-\\mathrm{y1}\\left(x\\right)\\right]:$\n > $\\mathrm{vars}≔\\left[\\mathrm{y1}\\left(x\\right),\\mathrm{y2}\\left(x\\right)\\right]:$\n > $\\mathrm{SeriesSolution}\\left(\\mathrm{sys},\\mathrm{vars}\\right)$\n $\\left[{{\\mathrm{_c}}}_{{1}}{+}{x}{}\\left({2}{}{{\\mathrm{_c}}}_{{2}}{+}{{\\mathrm{_c}}}_{{1}}\\right){+}{\\mathrm{O}}{}\\left({{x}}^{{2}}\\right){,}{x}{}{{\\mathrm{_c}}}_{{1}}{+}{\\mathrm{O}}{}\\left({{x}}^{{2}}\\right)\\right]$ (2)", null, "References\n\n Abramov, S. A. \"EG-Eliminations.\" Journal of Difference Equations and Applications, (1999): 393-433." ]
[ null, "https://bat.bing.com/action/0", null, "https://de.maplesoft.com/support/help/addons/arrow_down.gif", null, "https://de.maplesoft.com/support/help/addons/arrow_down.gif", null, "https://de.maplesoft.com/support/help/addons/arrow_down.gif", null, "https://de.maplesoft.com/support/help/addons/arrow_down.gif", null, "https://de.maplesoft.com/support/help/addons/arrow_down.gif", null ]
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https://www.math-only-math.com/Conversion-of-Mixed-Fractions-into-Improper-Fractions.html
[ "# Conversion of Mixed Fractions into Improper Fractions\n\nIn conversion of mixed fractions into improper fractions, we may follow the following steps:\n\nStep I:\n\nObtain the mixed fraction. Let the mixed fraction be 22/5\n\nStep II:\n\nIdentify the whole number and the numerator (top) and denominator (bottom) of the proper fraction.\n\nStep III:\n\nMultiply the whole number by the denominator of the proper fraction and add the result to the numerator of the proper fraction.\n\nStep IV:\n\nWrite the fraction having numerator equal to the number obtained in step III and denominator same as the denominator of the fraction in step II. Thus,\n\nFor Example:\n\nExpress each of the following mixed fractions as improper fractions:\n\n(i) Convert 8$$\\frac{4}{7}$$ into an improper fraction.\n\n8$$\\frac{4}{7}$$ means 8 whole and $$\\frac{4}{7}$$.\n\n$$\\frac{4}{7}$$\n\nSolution:\n\n8$$\\frac{4}{7}$$ = 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + $$\\frac{4}{7}$$\n\nor, 8$$\\frac{4}{7}$$ = $$\\frac{7}{7}$$ + $$\\frac{7}{7}$$ + $$\\frac{7}{7}$$ + $$\\frac{7}{7}$$ + $$\\frac{7}{7}$$ + $$\\frac{7}{7}$$ + $$\\frac{7}{7}$$ + $$\\frac{7}{7}$$ + $$\\frac{4}{7}$$ = $$\\frac{60}{7}$$,           [$$\\frac{7}{7}$$ means 1)\n\nWe can also convert a mixed number into an improper fraction as follows.\n\nFirst multiply the whole number by denominator. Here (8 × 7) + 4 = 60. Now, put the sum as the numerator of the required improper fraction and the denominator remains the same.\n\n8$$\\frac{4}{7}$$ = $$\\frac{(8 × 7) + 4}{7}$$ = $$\\frac{56 + 4}{7}$$ = $$\\frac{60}{7}$$\n\nThus, 8$$\\frac{4}{7}$$ = $$\\frac{60}{7}$$\n\n(ii) 3$$\\frac{2}{7}$$\n\n= $$\\frac{(3 × 7) + 2}{7}$$\n\n= $$\\frac{21 + 2}{7}$$\n\n= $$\\frac{23}{7}$$\n\n(iii) 4$$\\frac{5}{9}$$\n\n= $$\\frac{(4 × 9) + 5}{9}$$\n\n= $$\\frac{36 + 5}{9}$$\n\n= $$\\frac{41}{9}$$\n\n(iv) 3$$\\frac{2}{5}$$\n\n= $$\\frac{(3 × 5) + 2}{5}$$\n\n= $$\\frac{15 + 2}{5}$$\n\n= $$\\frac{17}{5}$$\n\n(v) 7$$\\frac{1}{4}$$\n\n= $$\\frac{(7 × 4) + 1}{4}$$\n\n= $$\\frac{28 + 1}{4}$$\n\n= $$\\frac{29}{4}$$\n\nRepresentations of Fractions on a Number Line\n\nFraction as Division\n\nTypes of Fractions\n\nConversion of Mixed Fractions into Improper Fractions\n\nConversion of Improper Fractions into Mixed Fractions\n\nEquivalent Fractions\n\nFractions in Lowest Terms\n\nLike and Unlike Fractions\n\nComparing Like Fractions\n\nComparing Unlike Fractions\n\nAddition and Subtraction of Like Fractions\n\nAddition and Subtraction of Unlike Fractions\n\nInserting a Fraction between Two Given Fractions" ]
[ null ]
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http://musictheoryfundamentals.com/Quiz/modesQuiz.php
[ "# Modes Quiz\n\n1. Between which scale degrees do the half steps naturally occur in Ionian mode?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n2. A major scale is in what mode?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n3. Which mode begins on the 2nd scale degree?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n4. What mode begins on the 6th scale degree?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n5. On what scale degree does the locrian mode begin?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n6. A minor scale starts on what scale degree?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n7. A natural minor scale is in what mode?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n8. On what scale degree does the Mixolydian mode begin?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n9. What mode has naturally occurring half steps between the 3rd & 4th and 7th & 8th scale degrees?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n10. What mode has naturally occurring half steps between the 3rd & 4th and 6th & 7th scale degrees?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n11. What mode begins on the 4th scale degree?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n12. What mode on the leading tone of the major scale?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n13. What mode begins on the 3rd scale degree?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n14. What mode has naturally occurring half steps between the 1st & 2nd and 4th & 5th scale degrees?\n\ncheck answer\n\nCorrect!\n\nIncorrect.\n\n15. What mode has naturally occurring half steps between the 4th & 5th and 7th & 8th scale degrees?\n\ncheck answer\n\nCorrect!\n\nIncorrect." ]
[ null ]
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http://www.numbersaplenty.com/70179099
[ "Search a number\nBaseRepresentation\nbin1000010111011…\n…01100100011011\n311220001110121220\n410023231210123\n5120431212344\n610544103123\n71511340534\noct413554433\n9156043556\n1070179099\n1136683661\n121b604aa3\n13117021c3\n14946b68b\n156263c19\nhex42ed91b\n\n70179099 has 4 divisors (see below), whose sum is σ = 93572136. Its totient is φ = 46786064.\n\nThe previous prime is 70179089. The next prime is 70179107. The reversal of 70179099 is 99097107.\n\nIt can be divided in two parts, 701 and 79099, that added together give a triangular number (79800 = T399).\n\nIt is a semiprime because it is the product of two primes.\n\nIt is a cyclic number.\n\nIt is not a de Polignac number, because 70179099 - 27 = 70178971 is a prime.\n\nIt is a self number, because there is not a number n which added to its sum of digits gives 70179099.\n\nIt is not an unprimeable number, because it can be changed into a prime (70179029) by changing a digit.\n\nIt is a polite number, since it can be written in 3 ways as a sum of consecutive naturals, for example, 11696514 + ... + 11696519.\n\nIt is an arithmetic number, because the mean of its divisors is an integer number (23393034).\n\nAlmost surely, 270179099 is an apocalyptic number.\n\n70179099 is a deficient number, since it is larger than the sum of its proper divisors (23393037).\n\n70179099 is a wasteful number, since it uses less digits than its factorization.\n\n70179099 is an evil number, because the sum of its binary digits is even.\n\nThe sum of its prime factors is 23393036.\n\nThe product of its (nonzero) digits is 35721, while the sum is 42.\n\nThe square root of 70179099 is about 8377.2966403250. The cubic root of 70179099 is about 412.4797154396.\n\nThe spelling of 70179099 in words is \"seventy million, one hundred seventy-nine thousand, ninety-nine\".\n\nDivisors: 1 3 23393033 70179099" ]
[ null ]
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http://electricalacademia.com/transformer/single-phase-transformer-construction-theory/
[ "Home / Transformer / Single-Phase Transformer | Construction | Theory\n\n# Single-Phase Transformer | Construction | Theory\n\nWant create site? Find Free WordPress Themes and plugins.\n\nIn the same way that single-phase AC and three-phase AC are different, although both are alternating current electricity, single-phase and three-phase transformers cannot be interchanged. Nevertheless, most of what can be said for single-phase transformers is later on extended to three-phase transformers because they work based on the same principle.\n\nTransformers work based on the mutual induction between two windings. When electricity is applied to a coil a magnetic field is developed. Because in AC the voltage (and thus the current) continuously alters, the resulting magnetic field is variable. However, if a coil is placed in a varying magnetic field, it is similar to a wire that moves in a magnetic field; then a voltage is induced in it.\n\n## Construction of Single-Phase Transformer\n\nIn the simplest form, a single-phase transformer is made up of two windings which share a common core. The core must be closed and its material is a ferromagnetic metal; it can be of various shapes, but the shape does not play a significant role.\n\nSome shapes are preferable to the others, either because of manufacturing or because they can result in a better performance. The most common shapes for a core are rectangular and circular.\n\nFrom the manufacturing viewpoint, a rectangular shape is easier to work with. Figure 1shows a transformer with a rectangular core. In this configuration, called a core-type transformer, the two windings are on the opposite sides (legs) of the core. This is not necessary, and both can be on the same leg.\n\nIn fact, most of the small transformers have a core as shown in Figure 2, and both windings are placed—usually, side by side—on the middle leg. This is a called a shell-type transformer.\n\nCore-type transformer: Category for the construction of a transformer in which the core has the shape of a rectangular frame, and the windings are normally on the opposite sides of the rectangle.\n\nShell-type transformer: Category for the construction of a transformer in which the core has the shape of figure 8 (i.e., with a bridge in the middle of a rectangular frame).", null, "Figure 1 Basic structure of a single-phase transformer (core type). (a) Schematics. (b) Pictorial representation.\n\nThe two windings are called “primary” and “secondary,” depending on their role. The primary winding is connected to the voltage that we want to change. The secondary winding provides the required voltage.\n\nIn other words, the primary winding is connected to the input voltage and the secondary winding is the output and is connected to the output circuitry. To show a transformer in a circuit, the transformer symbol is used, as depicted in Figure 3.", null, "Figure 2 Most of transformers have a shell-type structure. (a) Schematics of a shell-type transformer. (b) Pictorial view.\n\nIf the primary winding has more turns than the secondary winding, the transformer decreases the voltage and it is a step-down transformer. If the primary winding has fewer turns than the secondary winding, the output voltage is higher than the input voltage and the transformer is called a step-up transformer.", null, "Figure 3 Symbol for a transformer.\n\nThe numbers of wire turn in the primary and secondary windings are represented by N1 and N2, respectively.\n\nStep-down transformer: Transformer with secondary winding having fewer turns than the primary winding, thus decreasing voltage.\n\nStep-up transformer: Transformer with secondary winding having more turns than the primary winding, thus increasing voltage.\n\nFor a step-down transformer, N1 > N2 and V1 > V2.\n\nFor a step-up transformer, N1 < N2 and V1 < V2.\n\nNote that a wire can be wound around a core in two directions, as shown in Figure 4. As a result, depending on the directions of both windings in a transformer, at a given instant and compared to the primary voltage, the current in the secondary winding can be in one or the opposite direction.", null, "Figure 4 Two possible ways of winding a wire around a core.\n\nIn other words, the secondary voltage can be in phase with the primary voltage, or it can be 180° out of phase with it. It is, therefore, important to pay attention to the direction of turns in the winding of a transformer.\n\nIn Figure 2, for instance, both windings have the same direction. To show the polarity in the drawings of transformer circuits, a dot is put at one side of each winding indication the phase relationship, illustrating also the relative directions of the primary and secondary currents. This is indicated in Figure 5.", null, "Figure 5 Number of turns N, voltage V and current I in the primary and secondary circuits of a transformer.\n\nThus, for example, considering a sinusoidal waveform, when the side with a dot in the primary winding is at its maximum value the side with a dot in the secondary winding is at its maximum value.\n\nIn practice, the terminals are marked by letters such as H and X on the primary and secondary referring to the dot side to indicate this polarity. Figure 5 also indicates the number of turns N, the voltage V, and the current I and its direction for a given instant, identified by subscripts 1 and 2 for the primary and the secondary circuits, respectively.\n\n## Multi-Output Transformers\n\nIt is possible to have multiple secondary windings on the same transformer. This provides various voltages from one source voltage, thus reducing the cost and space. This is very common in electronic devices such as a television that need various voltages for their operation. This is done in very small transformers.\n\nIn larger transformers and three-phase transformers, it is not normally necessary to have multiple voltages. Figure 6 shows the schematics of a multi-output transformer.", null, "Figure 6 Multi-output transformer.\n\nFigure 7 shows a very small, 20 W, transformer. It converts 120 V into 6 and 12 V. In this sense, it is a two-output transformer. A 4 kVA industrial transformer is shown in Figure 8.", null, "Figure 7 A 20 W single-phase transformer.", null, "Figure 8 A 4 kVA single-phase transformer.\n\nDid you find apk for android? You can find new Free Android Games and apps.\n\n### About Ahmad Faizan", null, "Mr. Ahmed Faizan Sheikh, M.Sc. (USA), Research Fellow (USA), a member of IEEE & CIGRE, is a Fulbright Alumnus and earned his Master’s Degree in Electrical and Power Engineering from Kansas State University, USA." ]
[ null, "http://electricalacademia.com/wp-content/uploads/2018/11/10-1-666x300.jpg", null, "http://electricalacademia.com/wp-content/uploads/2018/11/10-2-486x300.jpg", null, "http://electricalacademia.com/wp-content/uploads/2018/11/10-3-119x300.jpg", null, "http://electricalacademia.com/wp-content/uploads/2018/11/10-4.jpg", null, "http://electricalacademia.com/wp-content/uploads/2018/11/10-5.jpg", null, "http://electricalacademia.com/wp-content/uploads/2018/11/10-6.jpg", null, "http://electricalacademia.com/wp-content/uploads/2018/11/10-7-399x300.jpg", null, "http://electricalacademia.com/wp-content/uploads/2018/11/10-8-200x300.jpg", null, "http://1.gravatar.com/avatar/12ca3fae784a44fa8f8e9aca2b9fa673", null ]
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https://whatisconvert.com/462-square-kilometers-in-square-miles
[ "# What is 462 Square Kilometers in Square Miles?\n\n## Convert 462 Square Kilometers to Square Miles\n\nTo calculate 462 Square Kilometers to the corresponding value in Square Miles, multiply the quantity in Square Kilometers by 0.38610215854185 (conversion factor). In this case we should multiply 462 Square Kilometers by 0.38610215854185 to get the equivalent result in Square Miles:\n\n462 Square Kilometers x 0.38610215854185 = 178.37919724633 Square Miles\n\n462 Square Kilometers is equivalent to 178.37919724633 Square Miles.\n\n## How to convert from Square Kilometers to Square Miles\n\nThe conversion factor from Square Kilometers to Square Miles is 0.38610215854185. To find out how many Square Kilometers in Square Miles, multiply by the conversion factor or use the Area converter above. Four hundred sixty-two Square Kilometers is equivalent to one hundred seventy-eight point three seven nine Square Miles.\n\n## Definition of Square Kilometer\n\nSquare kilometre (International spelling as used by the International Bureau of Weights and Measures) or square kilometer (American spelling), symbol km2, is a multiple of the square metre, the SI unit of area or surface area. 1 km2 is equal to 1,000,000 square metres (m2) or 100 hectares (ha). It is also approximately equal to 0.3861 square miles or 247.1 acres.\n\n## Definition of Square Mile\n\nThe square mile (abbreviated as sq mi and sometimes as mi²) is an imperial and US unit of measure for an area equal to the area of a square with a side length of one statute mile. It should not be confused with miles square, which refers to a square region with each side having the specified length. For instance, 20 miles square (20 × 20 miles) has an area equal to 400 square miles; a rectangle of 10 × 40 miles likewise has an area of 400 square miles, but it is not 20 miles square. One square mile is equal to 4,014,489,600 square inches, 27,878,400 square feet or 3,097,600 square yards.\n\n## Using the Square Kilometers to Square Miles converter you can get answers to questions like the following:\n\n• How many Square Miles are in 462 Square Kilometers?\n• 462 Square Kilometers is equal to how many Square Miles?\n• How to convert 462 Square Kilometers to Square Miles?\n• How many is 462 Square Kilometers in Square Miles?\n• What is 462 Square Kilometers in Square Miles?\n• How much is 462 Square Kilometers in Square Miles?\n• How many mi2 are in 462 km2?\n• 462 km2 is equal to how many mi2?\n• How to convert 462 km2 to mi2?\n• How many is 462 km2 in mi2?\n• What is 462 km2 in mi2?\n• How much is 462 km2 in mi2?" ]
[ null ]
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https://physics.stackexchange.com/questions/250977/sweet-spot-of-rod-pendulum-problem-clarification
[ "# “Sweet Spot” of Rod-Pendulum - Problem Clarification\n\nI came across this problem in a book (shortened for brevity):\n\nConsider a rod of mass $m$ pivoted about one end, with the other end to rotate. Let the center of mass be a distance $a$ from the pivot point $I$ be the moment of inertia of the rod about an axis which we will consider rotations in. A particle comes in and hits the rod at a distance $b$ below the pivot point, imparting an impulse $F\\Delta t=\\xi$ on the rod. (a) Find the linear and angular momentum of the rod right after the time $\\Delta t$, and (b) Calculate the impulse imparted on the pivot point.\n\nMy problem is with (b). What the does \"impulse imparted on the pivot point\" even mean? I would think the pivot point is fixed, so it should have experienced no net impulse, but that's incorrect.\n\n• if the rod were floating free, there would be a rotational moment depending on where you hit it. The rod would not rotate if you hit it at the center of mass and would have a forward motion without rotation. If it is being held in place by a pivot, the pivot point would \"feel' an impulse in either direction, depending on where the rod is hit. – Peter R Apr 21 '16 at 0:46\n• @PeterR Ah okay, so here it would the time-integral of the normal/restoring force that the pivot exerts on the rod, right? – Arturo don Juan Apr 21 '16 at 0:53\n• @PeterR why don't you make that an answer.... – Floris Apr 21 '16 at 0:59\n• I am not sure what you mean by a normal/restoring force, but depending on where it's hit, the rod would exert an impulsive force on the pivot and the pivot would exert an equal but opposite force. – Peter R Apr 21 '16 at 1:00\n\nIn order to maintain the constraint of the pivot during the impact, a reaction impulse is needed. See the figure below for what I mean.", null, "At the center of mass the velocity is $v = a\\,\\omega$. This is a result of the two impulses $$(F-R) \\Delta t = m\\, a\\, \\omega$$\n\nIf the angular velocity is $\\omega$ then the net impulsive moments at the center of mass are\n\n$$(b F + a R) \\Delta t = I \\omega$$\n\nThese two equations are solved for the unknown reaction $R$ and motion of the rod $\\omega$.\n\n\\begin{aligned} R \\Delta t & = \\frac{I-m\\,ab}{I+m a^2} F \\Delta t \\\\ \\omega & = \\frac{a+b}{I+m a^2} F \\Delta t \\end{aligned}\n\nOnly when $b=\\frac{I}{m\\,a}$ the pivot reaction is zero. That is considered the instant axis of percussion of the rod about the pivot (sweet spot).\n\nAs you have hypothesized in the comments, this will be a time integral of a force at the pivot point. As I read the problem, it would actually be the force exerted by the rod on the pivot that you're integrating, not the other way around.\n\nMore formally, the \"impulse imparted on the pivot point\" is a vector representing the total linear momentum transferred to the anchor from the rod during the collision.\n\n(The \"collision\" here refers to an infinitessimal and not entirely realistic period of time in which energy and momentum redistribute themselves amongst the particle, the rod, and the anchor according to certain rules which are used for most basic thought experiments about rigid body collisions.)" ]
[ null, "https://i.stack.imgur.com/lPHUd.png", null ]
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http://dkm.fbk.eu/logics-knowledge-representation-bolzano
[ "# Logics for knowledge representation Bolzano\n\nThis is the page that describes a course in Logic for Knowledge Representation by Luciano Serafini\n\nThe course is currently running at the Free University of Bolzano/Bozen\n\n• CLASS: Introduction to logic (slides)\n• EXERCISE: quick reading of new logics. In this exercise, students are asked to quickly read a paper that introduce a new logic and to produce a set of slides that describes the following points (a) what aspect of the world is formalized by the logic, (b) syntax, (c) semantics, (d) axiomatization. This might take about 2 hours. Here are examples of slides produced by the students:\n• CLASS: Introduction to ALC (slides)\n• READINGS readings on ALC, Modal Logics, and Correspondence between ALC/Modal Logics with FOL\n• basic reading on ALC \"Chapter 2 of the Handbook of Description Logics. Basic Description logics by F. Baader and V. Nutt\n• additional reading on bisimulartion: Modal logic: a semantic perspective by Patrick Blackburn and Johan van Benthem\n• CLASS: reasoning in ALC (slides)\n• READINGS Franz Baader and Ulrike Sattler, An Overview of Tableau Algorithms for Description Logics\n• EXERCISES Worked out exercises on satisfiability in ACL can be found here\n• CLASS: Introduction to DL's stronger than ALC (slides)\n• CLASS: Reasoning with ACLI, ALCN, ALCQ,... SHIQ (slides)\n• CLASS Reasoning with Nominals (SHOIQ) (slides)" ]
[ null ]
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https://www.numbersaplenty.com/1417543213
[ "Search a number\nBaseRepresentation\nbin101010001111101…\n…1111111000101101\n310122210100122201111\n41110133133320231\n510400342340323\n6352354512021\n750061630022\noct12437377055\n93583318644\n101417543213\n11668191219\n12336895611\n131978b1888\n14d639b549\n15846acc0d\nhex547dfe2d\n\n1417543213 has 2 divisors, whose sum is σ = 1417543214. Its totient is φ = 1417543212.\n\nThe previous prime is 1417543207. The next prime is 1417543217. The reversal of 1417543213 is 3123457141.\n\nIt is a strong prime.\n\nIt can be written as a sum of positive squares in only one way, i.e., 1129833769 + 287709444 = 33613^2 + 16962^2 .\n\nIt is a cyclic number.\n\nIt is a de Polignac number, because none of the positive numbers 2k-1417543213 is a prime.\n\nIt is a congruent number.\n\nIt is not a weakly prime, because it can be changed into another prime (1417543217) by changing a digit.\n\nIt is a polite number, since it can be written as a sum of consecutive naturals, namely, 708771606 + 708771607.\n\nIt is an arithmetic number, because the mean of its divisors is an integer number (708771607).\n\nAlmost surely, 21417543213 is an apocalyptic number.\n\nIt is an amenable number.\n\n1417543213 is a deficient number, since it is larger than the sum of its proper divisors (1).\n\n1417543213 is an equidigital number, since it uses as much as digits as its factorization.\n\n1417543213 is an evil number, because the sum of its binary digits is even.\n\nThe product of its digits is 10080, while the sum is 31.\n\nThe square root of 1417543213 is about 37650.2750720363. The cubic root of 1417543213 is about 1123.3422730159.\n\nThe spelling of 1417543213 in words is \"one billion, four hundred seventeen million, five hundred forty-three thousand, two hundred thirteen\"." ]
[ null ]
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https://www.analyticsvidhya.com/blog/2020/11/big-data-to-small-data-welcome-to-the-world-of-reservoir-sampling/
[ "Sreenath S — Published On November 7, 2020 and Last Modified On November 24th, 2020\n\nThis article was published as a part of the Data Science Blogathon.\n\n## Introduction\n\nBig Data refers to a combination of structured and unstructured data that may be measured in petabytes or exabytes. Usually, we make use of 3Vs to characterize big data 3Vs, being, the volume of data, the variety of types of data, and the velocity at which it is processed.\n\nThese three characteristics make it difficult to handle big data. Big data is hence expensive in terms of investment in a whole lot of server storage, sophisticated analytics machines, and data mining methodologies. Many organizations are finding this cumbersome both technically as well as economically and hence are thinking about how to achieve similar results can be achieved using much fewer sophistications. Hence they are trying to convert big data to small data, which consists of usable chunks of data. The following figure shows a comparison.\n\nLet’s try to explore a simple statistical technique, which can be used to create a usable chunk of data from big data. The sample which is basically a subset of the population should be selected in such a way that it represents the population properly. This can be ensured by employing statistical testing.\n\n## Introduction to Reservoir Sampling\n\nThe key idea behind reservoir sampling is to create a ‘reservoir’ from a big ocean of data. Let ‘N’ be the population size and ‘n’ be the sample size. Each element of the population has an equal probability of being present in the sample and that probability is (n/N). With this key idea, we have to create a subsample. It has to be noted, when we create a sample, the distributions should be identical not only row-wise but also column-wise.\n\nUsually, we focus only on the rows, but it is important to maintain the distribution of the columns as well. Columns are the features from which the training algorithm learns. Hence, we have to perform statistical tests for each feature as well to ensure the distribution is identical.\n\nThe algorithm goes this way: Initialize the reservoir with first ‘n’ elements of the population of size ‘N’. Then read each row of your dataset (i > n). At each iteration, compute (n/i). We replace the elements of the reservoir from the next set of ‘n’ elements with a gradually decreasing probability.\n\n`for i = 1 to n:`\n\nR[i] = S[i]\n\n`for i = n+1 to N:`\n\nj = U ~ [1, i]\n\nif j <= n:\n\nR[j] = S[i]\n\n## Statistical Tests\n\nAs I mentioned, earlier we have to ensure that all the columns(features) in the reservoir are distributed identically to the population. We shall use the Kolmogorov-Smirnov test for continuous features and Pearson’s chi-square test for categorical features.\n\nKolmogorov-Smirnov test is used to check whether the cumulative distribution functions (CDF) of the population and sample are the same. We compare the CDFs of population F_N (x) with that of the sample F_n(x).\n\n𝐹𝑁𝑥\n\nAs n -> N, D_n -> 0, if the distributions are identical. This test has to be performed for all the features of the data set that are continuous.\n\nFor the categorical features, we can perform Pearson’s chi-square test. Let O_i be the number of observations of the category ‘i’ and ne be the number of samples. Let E_i be the expected count of category ‘i’. Then E_i = N p_i, where p_i is the probability from being of the category ‘i’. Then chi-square value is given by the following relation:\n\nIf chi-square = 0, then that means observed values and expected values are the same. If the p-value of the statistical test is greater than the significance level, then we say that the sample is statistically significant.\n\n## End Notes\n\nReservoir sampling can be used to create a useful chunk of data from big data provided that both the tests – Kolmogorov-Smirnov and Pearson’s chi-square is successful. The recent buzz is of course big data. Centralized models as in big data architecture come along with big difficulties. To decentralize things and thus making the work modular we have to create useful small chunks of data and then get meaningful insights from them. I think more efforts should come in this direction, rather than investing in architecture to support big data!\n\n## References\n\n1. https://www.bbvaopenmind.com/en/technology/digital-world/small-data-vs-big-data-back-to-the-basics/", null, "", null, "" ]
[ null, "https://av-identity.s3.amazonaws.com/users/user/aSiZ4CjaSe6CcP-XFTQYjw.JPG", null, "https://secure.gravatar.com/avatar/d9a3a02b483f262404c67da15d193ae5", null ]
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https://docksci.com/interferometric-spectroscopy-of-scattered-light-can-quantify-the-statistics-of-s_6075adcd097c471d208b4862.html
[ "PRL 111, 033903 (2013)\n\nweek ending 19 JULY 2013\n\nPHYSICAL REVIEW LETTERS\n\nInterferometric Spectroscopy of Scattered Light Can Quantify the Statistics of Subdiffractional Refractive-Index Fluctuations L. Cherkezyan, I. Capoglu, H. Subramanian, J. D. Rogers, D. Damania, A. Taflove, and V. Backman* Northwestern University, Evanston, Illinois 60208, USA (Received 12 February 2013; published 19 July 2013) Despite major importance in physics, biology, and other sciences, the optical sensing of nanoscale structures in the far zone remains an open problem due to the fundamental diffraction limit of resolution. e 2 ) of a far-field, diffraction-limited microscope We establish that the expected value of spectral variance (\u0001 image can quantify the refractive-index fluctuations of a label-free, weakly scattering sample at e for an arbitrary refractive-index subdiffraction length scales. We report the general expression of \u0001 e distribution. For an exponential refractive-index spatial correlation, we obtain a closed-form solution of \u0001 that is in excellent agreement with three-dimensional finite-difference time-domain solutions of Maxwell’s equations. Sensing complex inhomogeneous media at the nanoscale can benefit fields from material science to medical diagnostics. DOI: 10.1103/PhysRevLett.111.033903\n\nPACS numbers: 42.25.Dd, 42.25.Fx, 42.25.Hz, 87.64.\u0001t\n\nDo Maxwell’s equations permit determining the nature of three-dimensional (3-D) subdiffractional refractiveindex (RI) fluctuations of a linear, label-free dielectric medium in the far zone? Recently, by capturing high spatial-frequency evanescent waves, metamaterial-based lenses and grating-assisted tomography have achieved a resolving power no longer limited by the diffraction of light [1,2]. However, this super-resolution is confined to the transverse plane, which limits its ability to characterize 3-D inhomogeneous media. Whereas various nonlinear techniques have been proposed to image subdiffractional structures in 3-D [3–5], these techniques require exogenous labeling or intrinsic fluorescence and, thus, only image the spatial distribution of particular molecular species. Currently, elastic, label-free spectroscopic microscopy techniques are emerging that characterize the endogenous properties of a medium by utilizing the spectral content of a diffraction-limited microscopic image. Examples include multiple high-precision quantitative phase microscopy techniques [6–8], which measure the longitudinal integral of RI and, hence, are insensitive to longitudinal RI fluctuations. Alternatively, partial-wave spectroscopic microscopy , confocal light scattering and absorption spectroscopy , and spectral encoding of spatial frequency analyze the light-scattering response of inhomogeneous materials to obtain information of their subdiffractional structure in both lateral and longitudinal dimensions. However, the reported theory behind these techniques involves strong assumptions such as one-dimensional light transport, approximation of the medium as solid spheres, or having a single length scale. Here, we establish that the spectral signature of scattered light in a far-zone microscope image contains sufficient information to quantify the 3-D RI fluctuations of weakly 0031-9007=13=111(3)=033903(5)\n\nscattering media at deeply subdiffractional scales. We report three-dimensional light transport theory for linear, label-free weakly scattering media with an arbitrary form of RI distribution: continuous or discrete, random or deterministic, statistically isotropic or not. We consider the e 2 ) of a far-field, expected value of spectral variance (\u0001 diffraction-limited image registered by a microscope with a small numerical aperture (NA) of illumination and spece quantifies trally resolved image acquisition. We show that \u0001 RI fluctuations at nanometer length scales limited only by the signal-to-noise ratio of the system. Under the single scattering approximation, we obtain an explicit expression e to the statistics of RI fluctuations inside the relating \u0001 sample. Moreover, for the special case of an exponential form of the RI spatial correlation, we present a closed-form e and validate it via numerical simulations of solution for \u0001 an experiment based on rigorous 3-D finite-difference timedomain (FDTD) solutions of Maxwell’s equations . Consider a spatially varying RI object sandwiched between two semi-infinite homogeneous media (Fig. 1). The RIs of the three media are, from top to bottom: n0 , n1 ½1 þ n\u0002 ðrÞ\u0002 (as a function of location r), and n2 . To air sample glass\n\nn0 n1(1+n∆ n2\n\n0 -L\n\nU(r) U(s)\n\nI= U(r)+U(s)\n\n2\n\nFIG. 1 (color online). Sample: RI of the middle layer is random, and RIs of the top and bottom layers are constant; RI as a function of depth is shown in gray. The coherent sum of UðrÞ and UðsÞ is detected. Reflection from the bottom of the substrate (glass slide) is negligible, as its thickness (1 mm) is much larger than the microscope’s depth of field (for most setups, 0:5–15 \u0001m).\n\n033903-1\n\nÓ 2013 American Physical Society\n\nPRL 111, 033903 (2013)\n\nbegin with, we assume n1 ¼ n2 , approximating the case of fixed biological media on a glass slide [13,14]. A unit amplitude plane wave with a wave vector ki is incident normally onto a weakly scattering sample. Under the Born approximation, the field inside the sample is uniform and has an amplitude T01 ¼ 2n0 =ðn0 þ n1 Þ (transmission Fresnel coefficient). In the far zone, the scattering amplitude of the scalar field UðsÞ , scattered from the RI fluctuations n\u0002 ðrÞ in theR direction specified by the wave 0 vector ko , is fs ðks Þ¼T01 ðk2 =2\u0002Þn\u0002 ðr0 Þe\u0001iks \u0003r d3 r0 , where ks ¼ ko \u0001 ki is the scattering wave vector (inside the sample) . The scalar-wave approximation is used here as it sufficiently describes the intensity image formed by a microscope with a moderate NA . Its further justification by full-vector 3-D FDTD results is discussed below. When the sample is imaged by an epi-illumination bright-field microscope, the back-propagating field reflected from the sample’s top surface, UðrÞ , returns to the image plane. Meanwhile, only the part of UðsÞ that propagates at solid angles within the NA of the objective is collected. For a microscope with magnification M, moderate NA (kz \u0004 k), ignoring the angular dependence of the Fresnel coefficient T10 ¼ 2n1 =ðn0 þ n1 Þ, UðsÞ focused at a point (x0 , y0 ) in the image plane is ðsÞ 0 0 Uim ðx ; y ; kÞ ¼\n\nkT10 ZZ k ky 0 0 TkNA fs e\u0001iðkx x þky y Þ d x d ; i2\u0002jMj k k (1)\n\nwhere TkNA is the microscope’s pupil function—a cone in the spatial-frequency space with a radius kNA [Fig. 2(a)]. Thus, the objective performs low-pass transverse-plane spatial frequency filtering, with the cutoff corresponding to the spatial coherence length. With substitution of fs into Eq. (1) and the introduction of a windowing function Tks ðsÞ that equals 1 at k ¼ ks and 0 at k \u0001 ks [Fig. 2(a)], Uim is T T Z1 ðsÞ 0 0 ðx ; y ; kÞ ¼ 10 01 kn ðrÞe\u0001i2kz dz; Uim ijMj \u00011 1D 0\n\nweek ending 19 JULY 2013\n\nPHYSICAL REVIEW LETTERS\n\n(F ) of TkNA Tks in the transverse plane (xy, ? ), n1D ðrÞ ¼ F ? fTkNA Tks g \u0005? n\u0002 ðrÞ. Equation (2) presents a new treatment of the Born approximation, which is here extended to include the optical imaging of a scattering object in the far zone. Mathematically, Eq. (2) signifies that to describe a microscope-generated spectrum (a 1-D signal), the 3-D problem of light propagation can be reduced to a 1-D problem where the RI is convolved with the Airy disk in the transverse plane. The microscope image intensity (normalized by the image of the source) is an interferogram Iðx0 ; y0 ; kÞ ¼ \u0003201 \u0001 2\u0003 Im\n\n\u0001Z þ1 \u00011\n\n\u0002 kn1D ðrÞe\u0001i2kz dz ;\n\n(3)\n\nwhere \u000301 ¼ ðn0 \u0001 n1 Þ=ðn0 þ n1 Þ is the Fresnel reflectance coefficient, \u0003 ¼ \u000301 T01 T10 , Im denotes ‘‘the imaginary part of,’’ and n1D is zero at z 2 = ð\u0001L; 0Þ. Here, Oðn2\u0002 Þ terms are neglected. We quantify the spatial distribution of n\u0002 via \u00012 , the spectral variance of the image intensity within the illumination bandwidth \u0002k. Since the expectation of the spectrally averaged image R intensity equals \u0003201 , \u00012 ðx0 ; y0 Þ is 2 0 0 defined as \u0001 ðx ; y Þ ¼ \u0002k ðIðx0 ; y0 ; kÞ \u0001 \u0003201 Þ2 dk=\u0002k. For convenience, we introduce a windowing function T\u0001ks that is a unity at k ¼ ks for all ki with magnitudes within the \u0002k of the system and is zero elsewhere [jki j between k1 and k2 in Fig. 2(a)]. On denoting kc as the value of the central wave number of illumination bandwidth inside the sample, approximation of \u0002k \u0006 kc , and application of the convolution and the Parseval’s theorems [for mathematical details see the Supplemental Material ], \u00012 ðx0 ; y0 Þ equals \u00012 ðx0 ; y0 Þ ¼\n\n\u00032 k2c Z 1 jF fT\u0002ks TkNA g \u0005 n\u0002 ðrÞj2 dz: \u0002k \u00011\n\n(4)\n\n(2)\n\n0\n\nwhere r is (x =M, y =M, z) inside the sample, and n1D is the n\u0002 ðrÞ convolved ( \u0005 ) with the unitary Fourier transform\n\nFIG. 2 (color). Spatial-frequency space with kz axis antiparallel to ki . (a) Cross section of T\u0002ks , TkNA , and their interception T3D ; (b) PSD of the RI fluctuation (blue) and T3D (gray) when lc \u0006 L and (c) lc & L.\n\nPhysically, T\u0001ks accounts for the limited bandwidth of illumination and serves as a bandpass longitudinal spatial-frequency filter of RI distribution with its width related to the temporal coherence length l\u0003 ¼ 2\u0002=\u0002k. The interception of the two frequency filters associated with the spatial and temporal coherence, TkNA and T\u0001ks , signifies the frequency-space coherence volume centered at kz ¼ 2kc : T3D ¼ T\u0001ks TkNA [Fig. 2(a)]. Given an infinite bandwidth, one could reconstruct the full 3-D RI from Iðx0 ; y0 ; kÞ. However, since \u0002k and kc are finite, \u0001 detects the variance of an ‘‘effective RI distribution,’’ i.e., of n\u0002 ðrÞ \u0005 F fT3D g [Eq. (4)]. Note that \u00012 ðx0 ; y0 Þ is random since n\u0002 ðrÞ is random. Hence, to characterize the sample statistics, we compute e 2 . Using the Wienerits expected value, denoted as \u0001 e 2 from Eq. (4) as Khinchine relation, we obtain \u0001\n\n033903-2\n\ne2¼ \u0001\n\nweek ending 19 JULY 2013\n\nPHYSICAL REVIEW LETTERS\n\nPRL 111, 033903 (2013)\n\n\u00032 k2c L Z \u0002n\u0001 ðkÞd3 k; \u0002k T3D\n\n(5)\n\nwhere \u0002n\u0002 ¼ jF fn\u0002 ðrÞgj2 is the power spectral density (PSD) of n\u0002 . Equation (5) establishes the general quadrature-form e 2 for an arbitrary n ðrÞ. Note that while expression for \u0001 \u0002 the 3-D structure of complex inhomogeneous materials cannot be described by a single measure of size or RI, the PSD fully quantifies the magnitude, spatial frequency, and orientation of all RI fluctuations present within the e 2 measures the integral of sample. As seen from Eq. (5), \u0001\n\ne2 the tail of the PSD within T3D . Hence, as shown later, \u0001 presents a monotonic measure of the width of the PSD. e 2 has a different preWhen n1 \u0001 n2 , the expression for \u0001 factor and a deterministic offset, specified in the Supplemental Material . e 2 for a We further obtain a closed-form expression for \u0001 special case when n\u0002 ðrÞ has an exponential form of spatial correlation with a variance \u0004n\u0002 and correlation distance lc . Since lc can only be defined for a random medium with a physical size much larger than the correlation distance, we define lc as the correlation distance of an unbounded medium n1 \u0002 ðrÞ and the sample as a horizontal slice of ðrÞ with thickness L: n\u0002 ðrÞ ¼ TL n1 n1 \u0002 \u0002 ðrÞ where TL is a windowing function along the z axis with width L. The PSD of such sample is an anisotropic function of lc 2 and L: \u0002n\u0001 ðkÞ ¼ jF fTL g \u0005 F fn1 \u0002 gj [Figs. 2(b) and 2(c)]. e 2 is found by independently computing the Alternatively, \u0001 contributions from (i) scattering from within the sample e 2 ) and (ii) reflectance at z ¼ \u0001L (\u0001 e 2 ), (\u0001 R\n\nL\n\ne2 þ \u0001 e2 : e2¼\u0001 \u0001 R L\n\n(6)\n\ne is fully described by the RI contrast at the Here, \u0001 L e 2 ¼ \u00032 \u00042 ðn Þ=4, where \u00042 ðn Þ is the bottom surface \u0001 L ? 1D ? 1D −1\n\n10\n\n10\n\nvariance of the effective n1D in the transverse plane [details e , in turn, is shown in the Supplemental Material ]. \u0001 R defined by \u0002n1\u0001 , which is independent of L when L * l\u0003 ; e 2 is obtained by integrating the PSD of an exponentially \u0001 R\n\ne2 correlated n1 \u0002 ðrÞ according to Eq. (5). Substituting \u0001R and e 2 into Eq. (6) and introducing a unitless size parameter \u0001 L x ¼ kc lc , we obtain the following closed-form solution for e 2 for an exponential form of the spatial RI correlation: \u0001 2 2 kc Lx3 NA2 e 2 ¼ 2\u0003 \u0004n\u0002 \u0003 2 \u0002 ½1 þ x ð4 þ NA2 Þ\u0002ð1 þ 4x2 Þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi þ \u00032 \u00042n\u0002 ½1 \u0001 1= 1 þ ðxNAÞ2 \u0002=4:\n\n(7)\n\nTwo assumptions were made to derive Eq. (7) from Eq. (5): (1) we approximated the top and bottom surfaces of T3D as planes perpendicular to the kz axis, and (2) we e from \u0002 1 , not considering the extreme case calculated \u0001 n\u0001\n\nR\n\nof L \u0006 l\u0003 . Both assumptions are not crucial from the theoretical perspective and are there only to obtain a relatively simple closed-form solution of Eq. (5). e preTo confirm these approximations, we evaluate \u0001 dicted by the general quadrature-form expression [Eq. (5)] using MATLAB computing software (MathWorks Inc.). We e calculated from obtain an excellent agreement between \u0001 Eq. (5) and the closed-form expression [Eq. (7)] derived from it (Fig. 3). This validates the closed-form solution for e for an exponential RI correlation. \u0001 We support the present theory by simulating a physical experiment using the rigorous 3-D FDTD solution of Maxwell’s equations [18–20]. Our technique accurately synthesizes microscope images of arbitrary inhomogeneous samples under various imaging parameters, incorporating RI fluctuations as fine as 10 nm. We synthesized bright-field, plane-wave epi-illumination microscope images of samples with a RI distribution resembling that of biological cells: n1 ¼ 1:53 [13,14], n1 \u0004n\u0002 ¼ 0:05 .\n\n−1\n\n−1\n\nFDTD:\n\n10\n\n−3\n\nL =.5µ m\n\n10\n\n10\n\n(a)\n\n2\n\nlc (nm)\n\n10\n\n−2\n\nL=1.5µ m 10\n\n(b)\n\n0.6 NA 0.3 NA\n\n−2\n\n2\n\nlc (nm)\n\n01\n\n~ 2 Σ/Γ\n\n−2\n\n10\n\n01\n\n~ Σ/Γ2\n\n~ 2 Σ/Γ\n\n01\n\n0.6 NA 0.3 NA\n\n10\n\nClosed−form, Eq. 7:\n\nL =2.0µ m 2\n\n10\n\n(c)\n\nlc (nm)\n\n0.6 NA 0.3 NA\n\ne dependence on l predicted by the quadrature-form [Eq. (5)] and the closed-form [Eq. (7)] analytical FIG. 3 (color). Illustration of \u0001 c e expressions for \u0001 (circles and solid lines, respectively) and by FDTD (solid lines with error bars representing standard deviation between 20 realizations of each statistical condition), calculated for (a) L ¼ 0:5 \u0001m, \u0002k ¼ 4:9 \u0001m\u00011 , kc ¼ 16:8 \u0001m\u00011 , (b) L ¼ 1:5 \u0001m, \u0002k ¼ 4:9 \u0001m\u00011 , kc ¼ 16:8 \u0001m\u00011 , and (c) L ¼ 2:0 \u0001m. \u0002k ¼ 11:9 \u0001m\u00011 , kc ¼ 18:1 \u0001m\u00011 (wave number values inside the sample). Data are shown normalized by \u0003201 , the image intensity in the absence of RI fluctuations inside the sample.\n\n033903-3\n\nc\n\ne to Second, as opposed to \u0004s ðlc Þ, the sensitivity of \u0001 changes at smaller length scales is not obscured by changes e Þ for at larger lc . We note that the functional form of \u0001ðl c lc < 1=kc can be roughly approximated as linear [r2 values e Þ presented in Fig. 3 range of linear regressions for \u0001ðl c\n\ne is independent of l for l \u0007 from 0.86 to 0.91]. Finally, \u0001 c c e 1=kc , and therefore \u0001ðlc Þ exhibits predominant sensitivity to subdiffraction length scales that is only limited by the signal-to-noise ratio (SNR). The larger structures are naturally resolved in the microscope image. In addition, whereas the above mentioned scattering parameters are e is / \u0004 (confirmed by FDTD with r2 ¼ 0:99, / \u00042 , \u0001 n\u0002\n\ndata not shown), which substantially improves the SNR. Results of an FDTD-simulated experiment are shown in Fig. 4. As expected, the bright-field microscope images of samples with lc ¼ 20 and 50 nm [Figs. 4(a) and 4(b)] are essentially indistinguishable. However, a drastic difference between the two samples is revealed in the respective \u0001ðx0 ; y0 Þ images [Figs. 4(c) and 4(d), where color bar limits match the ordinate range in Fig. 3(c)]. Figures 4(e) and 4(f) illustrate that a smaller amplitude of\n\n2\n\n2\n\nI (x’,y’) / Γ01\n\nI (x’,y’) / Γ01 0.75\n\n1µ m\n\n0.75\n\n1µ m\n\n( x’o , y’ o)\n\n( x’o , y’ o)\n\n1.00\n\n1.00\n\nl =20nm\n\n(a)\n\nc\n\nΣ ( x’,y’)/ Γ01\n\nl =50nm\n\n(b)\n\n1.25\n\n2\n\nc\n\n1.25\n\nΣ ( x’,y’)/ Γ012\n\n0.01\n\n0.01\n\n0.05\n\nl =20nm c\n\nl =50nm c\n\nc\n\nc\n\n0.10 l =20nm c\n\n1.2\n\no\n\no\n\nc\n\nl =50nm\n\nl =20nm\n\n1.6\n\nl =50nm\n\n(d)\n\n0.10\n\n1.0\n\no\n\no 1\n\n0.05\n\nI(x’ , y’ , k)\n\n(c)\n\nn (x , y , z)\n\nThe spatial RI correlation was set to be exponential, and the RIs of the top and bottom media were n0 ¼ 1 and n2 ¼ 1:53. e predicted by the present theory Referring to Fig. 3, the \u0001 [either by the quadrature-form Eq. (5) or the closed-form Eq. (7)] exhibits an excellent agreement with the FDTDsimulated experimental results over a wide range of lc , L, spectral bandwidth, and NA. The agreement is such that e values by both Eqs. (5) and the theoretically predicted \u0001 (7) lie within the standard deviation bars of the FDTD results at all points tested. Whereas the present derivation assumes \u0002k \u0006 kc , in fact, the closed-form analytical solution is robust for \u0002k that includes the full range of visible wavelengths [Fig. 3(c)]. This match also justifies the employed scalar-wave approximation as well as that the single scattering approximation applies to RI fluctuations typical for fixed biological cells. e and compare its We next describe the lc dependence of \u0001 key aspects to those of the commonly used scattering parameters: the backscattering (\u0004b ) and the total scattering (\u0004s ) cross sections. The value of \u0004b manifests a nonmonotonic dependence on lc , which makes the inverse problem ambiguous , whereas \u0004s increases steeply / l3c and thus is relatively insensitive to structural changes at small e Þ is distinguished by three length scales . In turn, \u0001ðl c important properties illustrated in Fig. 3. First, unlike \u0004b , e Þ can be monotonic. This property is apparent for thin \u0001ðl c samples [L < 2 \u0001m, Figs. 3(a) and 3(b)]. For thicker samples, a smaller collection NA can be chosen so that e Þ remains monotonic [e.g., NA ¼ 0:3 in Fig. 3(c)]. \u0001ðl\n\nn\u0002\n\nweek ending 19 JULY 2013\n\nPHYSICAL REVIEW LETTERS\n\nPRL 111, 033903 (2013)\n\n1.5\n\nz (µ m)\n\n(e) 0\n\n1\n\n2\n\n0.8 (f) 7.9\n\n−1\n\nk (µ m ) 11.8\n\n15.7\n\nFIG. 4 (color). 40\b magnification, 0.6 NA microscope images of samples with L ¼ 2 \u0001m were synthesized by FDTD. Bright-field images of samples with (a) lc ¼ 20 nm and (b) lc ¼ 50 nm; \u0001ðx0 ; y0 Þ=\u0003201 obtained from the wavelength-resolved image of (c) the sample with lc ¼ 20 nm and (d) lc ¼ 50 nm; (e) RI of the two samples as a function of z along central voxels (x0 , y0 ), and (f) image spectra of the corresponding pixels (x00 , y00 ).\n\nspectral oscillations in the wavelength-resolved microscope image indicates a higher spatial frequency of the sample’s RI fluctuations. Recognizing that the experimental n\u0002 ðrÞ may not be exponentially correlated, one may attempt to (a) use the validated approximations to obtain a closed form solution for a different functional form of the PSD from Eq. (5), (b) represent the correlation function of n\u0002 as a superposition of exponentials, or (c) evaluate Eq. (5) numerically (no explicit functional form of the PSD is required for the latter two). e does not probe spatial We emphasize that whereas \u0001 frequencies above 2k, the subdiffraction-scale structural alterations change the width of PSD and, therefore, the e Thus, \u0001 e provides a monotonic measure for the value of \u0001. width of the 3-D PSD of RI fluctuations with a high sensitivity to subdiffractional length scales, without actually imaging the 3-D RI. We have established that despite the diffraction limit of resolution, the interferometric spectroscopy of scattered\n\n033903-4\n\nPRL 111, 033903 (2013)\n\nPHYSICAL REVIEW LETTERS\n\nlight can quantify the statistics of RI fluctuations at deeply e subdiffractional length scales. We have shown that \u0001 obtained from an elastic, label-free, spectrally resolved far-field microscope image quantifies RI fluctuations inside weakly scattering media at length scales limited by the SNR of the detector. We have derived a closed-form anae that yields results that agree with lytical solution for \u0001 numerical solutions of Maxwell’s equations over a wide tested range of sample and instrument parameters. Potential applications include semiconductors, material science, biology, and medical diagnostics. This work was supported by National Institutes of Health (NIH) Grants No. R01CA128641, No. R01EB003682, and No. R01CA155284 and National Science Foundation (NSF) Grant No. CBET-0937987. The FDTD simulations were made possible by a computational allocation from the Quest high-performance computing facility at Northwestern University.\n\n*Corresponding author. [email protected] D. Lu and Z. Liu, Nat. Commun. 3, 1205 (2012). A. Sentenac, P. C. Chaumet, and K. Belkebir, Phys. Rev. Lett. 97, 243901 (2006). S. W. Hell, Science 316, 1153 (2007). B. Huang, W. Wang, M. Bates, and X. Zhuang, Science 319, 810 (2008). D. W. Piston, Trends Cell Biol. 9, 66 (1999). G. Popescu, Quantitative Phase Imaging of Cells and Tissues, McGraw-Hill Biophotonics (McGraw-Hill, New York, 2011). Z. Wang, L. Millet, M. Mir, H. Ding, S. Unarunotai, J. Rogers, M. U. Gillette, and G. Popescu, Opt. Express 19, 1016 (2011). B. Bhaduri, H. Pham, M. Mir, and G. Popescu, Opt. Lett. 37, 1094 (2012).\n\nweek ending 19 JULY 2013\n\n H. Subramanian, P. Pradhan, Y. Liu, I. R. Capoglu, J. D. Rogers, H. K. Roy, R. E. Brand, and V. Backman, Opt. Lett. 34, 518 (2009). I. Itzkan, L. Qiu, H. Fang, M. M. Zaman, E. Vitkin, I. C. Ghiran, S. Salahuddin, M. Modell, C. Andersson, L. M. Kimerer, P. B. Cipolloni, K.-H. Lim, S. D. Freedman, I. Bigio, B. P. Sachs, E. B. Hanlon, and L. T. Perelman, Proc. Natl. Acad. Sci. U.S.A. 104, 17 255 (2007). S. A. Alexandrov, S. Uttam, R. K. Bista, K. Staton, and Y. Liu, Appl. Phys. Lett. 101, 033702 (2012). A. Taflove and S. C. Hagness, Computational Electrodynamics: The Finite-Difference Time-Domain Method (Artech House, Norwood, 2005), 3rd ed. D. Cook, Cellular Pathology: An Introduction to Techniques and Applications (Scion, Bloxham, 2006). G. C. Crossmon, Stain technology 24, 241 (1949). M. Born and E. Wolf, Electromagnetic Theory of Propagation, Interference and Diffraction of Light, edited by M. Born and E. Wolf (Cambridge University Press, Cambridge, England, 1998). J. Goodman, Introduction To Fourier Optics, McGraw-Hill Physical and Quantum Electronics Series (Roberts & Co., Englewood, 2005), pp. 126–154. See the Supplemental Material at http://link.aps.org/ supplemental/10.1103/PhysRevLett.111.033903 for a detailed derivation. I. R. Capoglu, ANGORA: A free software package for finite-difference time-domain (FDTD) electromagnetic simulation (2012), date accessed: April 2012, http:// www.angorafdtd.org. I. R. Capoglu, J. D. Rogers, A. Taflove, and V. Backman, in Progress in Optics, Vol. 57, edited by E. Wolf (Elsevier, New York, 2012), pp. 1–91. I. R. Capoglu, A. Taflove, and V. Backman, IEEE Trans. Antennas Propag. (to be published). J. M. Schmitt and G. Kumar, Appl. Opt. 37, 2788 (1998). A. Ishimaru, Wave Propagation and Scattering in Random Media, IEEE Press Series on Electromagnetic Wave Theory (Wiley, New York, 1999). A. J. Radosevich, J. Yi, J. D. Rogers, and V. Backman, Opt. Lett. 37, 5220 (2012).\n\n033903-5\n\n## Interferometric spectroscopy of scattered light can quantify the statistics of subdiffractional refractive-index fluctuations.\n\nDespite major importance in physics, biology, and other sciences, the optical sensing of nanoscale structures in the far zone remains an open problem ..." ]
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https://stats.stackexchange.com/questions/23781/how-does-the-expected-value-relate-to-mean-median-etc-in-a-non-normal-distrib
[ "# How does the expected value relate to mean, median, etc. in a non-normal distribution?\n\nHow does the expected value of a continuous random variable relate to its arithmetic mean, median, etc. in a non-normal distribution (eg. skew-normal)? I'm interested in any common/interesting distributions (eg. log-normal, simple bi/multimodal distributions, anything else weird and wonderful).\n\nI'm looking mostly for qualitative answers, but any quantitative or formulaic answers are also welcome. I'd particularly like to see any visual representations that make it clearer.\n\n• Can you be a bit clearer? The arithmetic mean and median are functions we apply to data, not anything intrinsic to particular distributions... for example, data doesn't have to be Normal in order for you to calculate the sample mean. – guest Feb 28 '12 at 7:59\n• Ok, so the question should technically be \"how does the expected value relate to the mean, median etc. of data drawn randomly from a particular probability distribution?\" I'm looking for simple, intuitive understandings, similar to the way you can intuitively say that when a distribution is more skewed, the median and the mean are further apart, and the median may give a better indication of where the data lies. – naught101 Feb 28 '12 at 10:40\n• Heh. Thanks Marco. I've clearly been reading things wrong. May as well write that as an answer, I'll chose it at he best answer. – naught101 Mar 2 '12 at 5:02\n\n(partially converted from my now-deleted comment above)\n\nThe expected value and the arithmetic mean are the exact same thing. The median is related to the mean in a non-trivial way but you can say a few things about their relation:\n\n• when a distribution is symmetric, the mean and the median are the same\n\n• when a distribution is negatively skewed, the median is usually greater than the mean\n\n• when a distribution is positively skewed, the median is usually less than the mean\n\n• Interesting. What examples are there of the unusual behaviour of a negatively skewed distribution where the mean is greater than the median? – naught101 Mar 4 '12 at 6:58\n• @naught101: is this a typo? A negatively skewed distribution is one in which the left-of-centre outcomes occur more frequently than the right-of-centre outcomes, and therefore the \"tail\" of low frequency outcomes goes out to the right. In such a situation, the hump on the left will always pull the (arithmetic) mean left of centre, while the tail on the right will keep the median greater than the mean. – Assad Ebrahim Oct 23 '14 at 17:50\n• @AssadEbrahim: No, it was a reference to Macro's comment \"the median is usually greater than the mean\" - I was asking for counter examples. – naught101 Oct 24 '14 at 0:34\n• @naught101: The counter-examples in the case of a unimodal distribution are his next line: when the hump is to the right then the tail to the left pulls the median below the mean. The longer the tail, the greater the gap between median and mean. – Assad Ebrahim Oct 24 '14 at 8:49\n• What are the practical circumstances in which one would use a median over a mean or vice versa? For example in survival analysis where lifetimes follow an exponential distribution, should I use the median (so half the things last longer, half last less) or the mean (the \"expected\" lifetime) if I had to predict life/death as a binary outcome? – drevicko Jun 19 '15 at 5:33\n\nThere is a nice relationship between the harmonic, the geometric, and the arithmetic mean of a log-normally distributed random variable $X \\sim \\mathcal{LN}\\left( \\mu,\\sigma^2 \\right)$. Let\n\n• $\\mathrm{HM}(X) = \\mathrm{e}^{\\mu - \\frac{1}{2}\\sigma^2}$ (harmonic mean),\n• $\\mathrm{GM}(X) = \\mathrm{e}^{\\mu}$ (geometric mean),\n• $\\mathrm{AM}(X) = \\mathrm{e}^{\\mu + \\frac{1}{2}\\sigma^2}$ (arithmetic mean).\n\nIt is not difficult to see that the product of the harmonic and the arithmetic mean yields the square of the geometric mean, i.e.\n\n$$\\mathrm{HM}(X) \\cdot \\mathrm{AM}(X) = \\mathrm{GM}^2(X).$$\n\nSince all values are positive, we can take the squre root and find that the geometric mean of $X$ is the geometric mean of the harmonic mean of $X$ and the arithmetic mean of $X$, i.e.\n\n$$\\mathrm{GM}(X) = \\sqrt{ \\mathrm{HM}(X) \\cdot \\mathrm{AM}(X) }.$$\n\nFurthermore, the well-known HM-GM-AM inequality\n\n$$\\mathrm{HM}(X) \\leq \\mathrm{GM}(X) \\leq \\mathrm{AM}(X)$$\n\ncan be expressed as\n\n$$\\mathrm{HM}(X) \\cdot \\sqrt{\\mathrm{GVar}(X)} = \\mathrm{GM}(X) = \\dfrac{\\mathrm{AM}(X)}{\\sqrt{\\mathrm{GVar}(X)}},$$\n\nwhere $\\mathrm{GVar}(X) = \\mathrm{e}^{\\sigma^2}$ is the geometric variance.\n\nFor completeness, there are also distributions for which the mean is not well defined. A classic example is the Cauchy distribution (this answer has a nice explanation of why). Another important example is the Pareto distribution with exponent less than 2.\n\n• Several iff's. A power law is not a distribution, but a Pareto distribution is a power law. This relates to the non-integrability of a log-convex power function at $x=0$. For a power law, you mean less than 2, not greater than 2. – Carl Apr 22 '18 at 7:10\n• @Carl good points - I edited the answer accordingly. Many thx (: – drevicko Apr 25 '18 at 2:33" ]
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https://math.stackexchange.com/questions/3585229/formal-definition-of-definite-condition-of-membership-in-in-set-theory
[ "# Formal definition of definite condition of membership $\\in$ in set theory?\n\nWe have sets $$\\{ x \\}$$ and $$\\{ \\{ x \\} \\}$$. Then it holds that $$x \\in \\{ x \\}$$ but $$x \\notin \\{ \\{ x \\} \\}$$. It seems that the condition of membership ($$\\in$$) presupposes that only those things in a set $$A$$ which are only in $$A$$ and in no set in $$A$$, are the members of $$A$$. More simply, only those things that are in $$A$$ in its \"first layer\" are the members of $$A$$. But apart from using natural language, how can one define $$\\in$$? Is this even possible in set theory or do we have to use something outside of it (such as first-order logic) to formally define $$\\in$$?\n\n• In what I would call \"usual\" set-theory there are two primitive notions. This in the sense that they are not defined. It is not defined what a set is and secondly the relation $\\in$ is not defined. There are other concepts of set-theory that concern e.g. \"urelements\" but I am not familiar with that. – drhab Mar 18 at 9:29\n• Wow, thanks for the insight. So we just take for granted that $\\in$ concerns only those things in sets which are on their \"first layers\"? – Gregor Perčič Mar 18 at 9:31\n• We don't \"take it for granted\"; it's what the symbols mean. – Malice Vidrine Mar 18 at 9:34\n• @MaliceVidrine OK, my clumsy wording is at fault here. My concern is that what $\\in$ means is determined solely by our natural-language expression of this concept. In contrast, logical systems usually include their own precise semantics which do not fall prey to vagueness or reference to our (imprecise) natural languages. – Gregor Perčič Mar 18 at 9:37\n• It may help to realize that the $\\{x\\}$ is not actually a term in the language of set theory. The expression $y\\in\\{x_1,x_2,\\ldots,x_n\\}$ is a shorthand for something equivalent to $\\exists z(y\\in z \\wedge \\forall w(w\\in z \\Leftrightarrow w=x_1\\vee\\ldots\\vee w=x_n))$. There are no natural language ambiguities involved in deciding whether or not $x\\in\\{\\{x\\}\\}$ if you know what the actual set theoretic statement is (and the axioms involved). – Malice Vidrine Mar 18 at 18:56\n\nRegarding your statement \" It seems that the condition of membership (∈) presupposes that only those things in a set A which are only in A and in no set in A, are the members of A.\": I don't know why it seems that way, but it's not so. Say for example $$S=\\{1,\\{1\\}\\}.$$Then $$1$$ is an element of $$S$$, even though it's also an element of an element of $$S$$.\n\nThe fact that $$x\\notin\\{\\{x\\}\\}$$ has nothing to do with that. By definition $$S=\\{\\{x\\}\\}$$ has exactly one element, namely $$\\{x\\}$$; since $$x\\ne\\{x\\}$$ this says $$x$$ is not an element of $$S$$.\n\nSince the OP tagged his question with philosophy, they should accept/contemplate on the following:\n\n• The universe of objects that can be examined are sets and the $$\\in$$ relation is used\nto determine when two sets are equal.\n\n• There exists a unique object in set theory defined by\n\n$$\\tag 1 (\\exists X) \\, (\\forall x) \\; [x \\notin X]$$\n\nTheir 'marching orders' (allowing them to enter the paradise of set theory) is to study and analyze 'logically coherent frameworks' allowing them to 'expand off' of the above 'ground-floor philosophical foundation' containing at least this one object, that is named, in natural language, the empty set; it is denoted by $$\\emptyset$$.\n\nOne path of study that has been intensely scrutinized can be found in a wikipedia outline article:\n\n$$\\quad$$ Zermelo–Fraenkel set theory\n\nSince the OP asked about first-order logic, they should closely examine the leading/introductory paragraph in the Axioms section of that article.\n\n...how can one define $$\\in$$?\nIn formal abstract set theory both sets and the membership relation $$\\in$$ are primitive concepts. So no benefits can be accrued by attempting to describe a set as, say, consisting of all the objects in its \"first layer\". Rather, the framework/rules allow one to 'play the game' in the sense of David Hilbert,\nThe notion of membership $$\\in$$ is not defined in set theory, it is assumed. It is also an axioms, that whenever two sets satisfy $$\\forall x, x\\in A \\Leftrightarrow x\\in B$$, then $$A = B$$. So it makes sense to define sets by only specifying their elements.\nThen the set $$\\{ z \\}$$ is defined to be the set so that $$z\\in\\{z\\}$$, and $$\\forall y,y\\neq z \\Rightarrow y\\notin\\{z\\}$$. So you see in your example, it is true that $$x\\notin\\{\\{x\\}\\}$$, but that is not a property of the membership relation, it is the definition of the set $$\\{\\{ x\\}\\}$$. You can show this because $$x\\neq \\{x \\}$$." ]
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https://en.wikipedia.org/wiki/Surface_energy
[ "# Surface energy\n\nJump to navigation Jump to search", null, "Contact angle measurements can be used to determine the surface energy of a material. Here is a drop of water on a glass.\n\nSurface free energy or interfacial free energy or surface energy, quantifies the disruption of intermolecular bonds that occurs when a surface is created. In the physics of solids, surfaces must be intrinsically less energetically favorable than the bulk of a material (the molecules on the surface have more energy compared with the molecules in the bulk of the material), otherwise there would be a driving force for surfaces to be created, removing the bulk of the material (see sublimation). The surface energy may therefore be defined as the excess energy at the surface of a material compared to the bulk, or it is the work required to build an area of a particular surface. Another way to view the surface energy is to relate it to the work required to cut a bulk sample, creating two surfaces.\n\nCutting a solid body into pieces disrupts its bonds, and therefore increases free energy. If the cutting is done reversibly, then conservation of energy means that the energy consumed by the cutting process will be equal to the energy inherent in the two new surfaces created. The unit surface energy of a material would therefore be half of its energy of cohesion, all other things being equal; in practice, this is true only for a surface freshly prepared in vacuum. Surfaces often change their form away from the simple \"cleaved bond\" model just implied above. They are found to be highly dynamic regions, which readily rearrange or react, so that energy is often reduced by such processes as passivation or adsorption.\n\n## Determination of surface energy\n\nMeasuring the surface energy of a solid\n\nThe surface energy of a liquid may be measured by stretching a liquid membrane (which increases the surface area and hence the surface energy). In that case, in order to increase the surface area of a mass of liquid by an amount, δA, a quantity of work, γδA, is needed (where γ is the surface energy density of the liquid). However, such a method cannot be used to measure the surface energy of a solid because stretching of a solid membrane induces elastic energy in the bulk in addition to increasing the surface energy.\n\nThe surface energy of a solid is usually measured at high temperatures. At such temperatures the solid creeps and even though the surface area changes, the volume remains approximately constant. If γ is the surface energy density of a cylindrical rod of radius $r$", null, "and length $l$", null, "at high temperature and a constant uniaxial tension $P$", null, ", then at equilibrium, the variation of the total Helmholtz free energy vanishes and we have\n\n$\\delta F=-P~\\delta l+\\gamma ~\\delta A=0\\qquad \\implies \\qquad \\gamma =P{\\cfrac {\\delta l}{\\delta A}}$", null, "where $F$", null, "is the Helmholtz free energy and $A$", null, "is the surface area of the rod:\n\n$A=2\\pi r^{2}+2\\pi rl\\qquad \\implies \\qquad \\delta A=4\\pi r\\delta r+2\\pi l\\delta r+2\\pi r\\delta l$", null, "Also, since the volume ($V$", null, ") of the rod remains constant, the variation ($\\delta V$", null, ") of the volume is zero, i.e.,\n\n$V=\\pi r^{2}l={\\text{constant}}\\qquad \\implies \\qquad \\delta V=2\\pi rl\\delta r+\\pi r^{2}\\delta l=0\\implies \\delta r=-{\\cfrac {r}{2l}}\\delta l~.$", null, "Therefore, the surface energy density can be expressed as\n\n$\\gamma ={\\cfrac {Pl}{\\pi r(l-2r)}}~.$", null, "The surface energy density of the solid can be computed by measuring $P$", null, ", $r$", null, ", and $l$", null, "at equilibrium.\n\nThis method is valid only if the solid is isotropic, meaning the surface energy is the same for all crystallographic orientations. While this is only strictly true for amorphous solids (glass) and liquids, isotropy is a good approximation for many other materials. In particular, if the sample is polygranular (most metals) or made by powder sintering (most ceramics) this is a good approximation.\n\nIn the case of single-crystal materials, such as natural gemstones, anisotropy in the surface energy leads to faceting. The shape of the crystal (assuming equilibrium growth conditions) is related to the surface energy by the Wulff construction. The surface energy of the facets can thus be found to within a scaling constant by measuring the relative sizes of the facets.\n\nCalculating the surface energy of a deformed solid\n\nIn the deformation of solids, surface energy can be treated as the \"energy required to create one unit of surface area\", and is a function of the difference between the total energies of the system before and after the deformation:\n\n$\\gamma ={\\frac {1}{A}}(E_{1}-E_{0})$", null, ".\n\nCalculation of surface energy from first principles (for example, density functional theory) is an alternative approach to measurement. Surface energy is estimated from the following variables: width of the d-band, the number of valence d-electrons, and the coordination number of atoms at the surface and in the bulk of the solid.\n\nCalculating the surface formation energy of a crystalline solid\n\nIn density functional theory, surface energy can be calculated from the following expression\n\n$\\gamma ={\\frac {E_{slab}-N\\cdot E_{bulk}}{2A}}$", null, "where $E_{slab}$", null, "is the total energy of surface slab obtained using density functional theory. $N$", null, "is the number of atoms in the surface slab. $E_{bulk}$", null, "is the bulk energy per atom. $A$", null, "is the surface area. For a slab, we have two surfaces and they are of the same type, which is reflected by the number 2 in the denominator. To guarantee this, we need to create the slab carefully to make sure that the upper and lower surfaces are of the same type.\n\nStrength of adhesive contacts is determined by the work of adhesion which is also called relative surface energy of two contacting bodies. The relative surface energy can be determined by detaching of bodies of well defined shape made of one material from the substrate made from the second material. For example, the relative surface energy of the interface \"acrylic glass - gelatine\" is equal to 0.03 N·m−1. Experimental set-up for measuring relative surface energy and its function can be seen in the video\n\nEstimating surface energy from the heat of sublimation\n\nTo estimate the surface energy of a pure, uniform material, an individual molecular component of the material can be modeled as a cube. In order to move a cube from the bulk of a material to the surface, energy is required. This energy cost is incorporated into the surface energy of the material, which is quantified by:", null, "Cube Model. The cube model can be used to model pure, uniform materials or an individual molecular component to estimate their surface energy.\n$\\gamma ={\\frac {(z_{\\sigma }-z_{\\beta }){\\frac {W_{\\text{AA}}}{2}}}{a_{0}}}$", null, "where $z_{\\sigma }$", null, "and $z_{\\beta }$", null, "are coordination numbers corresponding to the surface and the bulk regions of the material, and are equal to 5 and 6, respectively; $a_{0}$", null, "is the surface area of an individual molecule, and $W_{\\text{AA}}$", null, "is the pairwise intermolecular energy.\n\nSurface area can be determined by squaring the cube root of the volume of the molecule:\n\n$a_{0}=V_{\\text{molecule}}^{2/3}=\\left({\\frac {\\bar {M}}{\\rho N_{A}}}\\right)^{2/3}$", null, "Here, ${\\bar {M}}$", null, "corresponds to the molar mass of the molecule, $\\rho$", null, "corresponds to the density, and $N_{A}$", null, "is Avogadro’s number.\n\nIn order to determine the pairwise intermolecular energy, all intermolecular forces in the material must be broken. This allows thorough investigation of the interactions that occur for single molecules. During sublimation of a substance, intermolecular forces between molecules are broken, resulting in a change in the material from solid to gas. For this reason, considering the enthalpy of sublimation can be useful in determining the pairwise intermolecular energy. Enthalpy of sublimation can be calculated by the following equation:\n\n$\\Delta _{\\text{sub}}H={\\frac {-W_{\\text{AA}}N_{A}z_{b}}{2}}$", null, "Using empirically tabulated values for enthalpy of sublimation, it is possible to determine the pairwise intermolecular energy. Incorporating this value into the surface energy equation allows for the surface energy to be estimated.\n\nThe following equation can be used as a reasonable estimate for surface energy:\n\n$\\gamma \\approx {\\frac {(z_{\\sigma }-z_{\\beta })(-\\Delta _{\\text{sub}}H)}{a_{0}N_{A}z_{\\beta }}}$", null, "## Interfacial energy\n\nThe presence of an interface influences generally all thermodynamic parameters of a system. There are two models that are commonly used to demonstrate interfacial phenomena, which includes the Gibbs ideal interface model and the Guggenheim model. In order to demonstrate the thermodynamics of an interfacial system using the Gibb’s model, the system can be divided into three parts: two immiscible liquids with volumes $V_{\\alpha }$", null, "and $V_{\\beta }$", null, "and an infinitesimally thin boundary layer known as the Gibbs dividing plane (σ) separating these two volumes.", null, "Guggenheim Model. An extended interphase (sigma) divides the two phases alpha and beta. Guggenheim takes into account the volume of the extended interfacial region, which is not as practical as the Gibbs model.", null, "Gibbs Model. The Gibbs model assumes the interface to be ideal (no volume) so that the total volume of the system comprises only the alpha and beta phases.\n\nThe total volume of the system is:\n\n$V=V_{\\alpha }+V_{\\beta }$", null, "All extensive quantities of the system can be written as a sum of three components: bulk phase a, bulk phase b, and the interface, sigma. Some examples include internal energy ($U$", null, "), the number of molecules of the ith substance ($n_{i}$", null, "), and the entropy ($S$", null, ").\n\n$U=U_{\\alpha }+U_{\\beta }+U_{\\sigma }$", null, "$N_{i}=N_{{\\text{i}}\\alpha }+N_{{\\text{i}}\\beta }+N_{{\\text{i}}\\sigma }$", null, "$S=S_{\\alpha }+S_{\\beta }+S_{\\sigma }$", null, "While these quantities can vary between each component, the sum within the system remains constant. At the interface, these values may deviate from those present within the bulk phases. The concentration of molecules present at the interface can be defined as:\n\n$N_{{\\text{i}}\\sigma }=N_{i}-c_{{\\text{i}}\\alpha }V_{\\alpha }-c_{{\\text{i}}\\beta }V_{\\beta }$", null, "where $c_{{\\text{i}}\\alpha }$", null, "and $c_{{\\text{i}}\\beta }$", null, "represent the concentration of substance $i$", null, "in bulk phase $\\alpha$", null, "and $\\beta$", null, ", respectively. It is beneficial to define a new term interfacial excess $\\Gamma _{i}$", null, "which allows us to describe the number of molecules per unit area:\n\n$\\Gamma _{i}={\\frac {N_{{\\text{i}}\\alpha }}{A}}$", null, "## Wetting\n\nSpreading Parameter: Surface energy comes into play in wetting phenomena. To examine this, consider a drop of liquid on a solid substrate. If the surface energy of the substrate changes upon the addition of the drop, the substrate is said to be wetting. The spreading parameter can be used to mathematically determine this:\n\n$S=\\gamma _{\\text{s}}-\\gamma _{\\text{l}}-\\gamma _{\\text{s-l}}$", null, "where $S$", null, "is the spreading parameter, $\\gamma _{\\text{s}}$", null, "the surface energy of the substrate, $\\gamma _{\\text{l}}$", null, "the surface energy of the liquid, and $\\gamma _{\\text{s-l}}$", null, "the interfacial energy between the substrate and the liquid.\n\nIf $S<0$", null, ", the liquid partially wets the substrate.\nIf $S>0$", null, ", the liquid completely wets the substrate.", null, "Contact Angles: non-wetting, wetting, and perfect wetting. The contact angle is the angle that connects the solid–liquid interface and the liquid-gas interface.\n\nContact angle: A way to experimentally determine wetting is to look at the contact angle ($\\theta$", null, "), which is the angle connecting the solid–liquid interface and the liquid-gas interface [figure].\n\nIf $\\theta =0{\\text{°}}$", null, ", the liquid completely wets the substrate.\nIf $0{\\text{°}}<\\theta <90{\\text{°}}$", null, ", high wetting occurs.\nIf $90{\\text{°}}<\\theta <180{\\text{°}}$", null, ", low wetting occurs.\nIf $\\theta =180{\\text{°}}$", null, ", the liquid does not wet the substrate at all.\n\nThe Young equation relates the contact angle to interfacial energy:\n\n$\\gamma _{\\text{s-g}}=\\gamma _{\\text{s-l}}+\\gamma _{\\text{l-g}}\\cos \\theta$", null, "where $\\gamma _{\\text{s-g}}$", null, "is the interfacial energy between the solid and gas phases, $\\gamma _{\\text{s-l}}$", null, "the interfacial energy between the substrate and the liquid, $\\gamma _{\\text{l-g}}$", null, "is the interfacial energy between the liquid and gas phases, and $\\theta$", null, "is the contact angle between the solid–liquid and the liquid–gas interface.\n\nWetting of high and low energy substrates: The energy of the bulk component of a solid substrate is determined by the types of interactions that hold the substrate together. High energy substrates are held together by bonds, while low energy substrates are held together by forces. Covalent, ionic, and metallic bonds are much stronger than forces such as van der Waals and hydrogen bonding. High energy substrates are more easily wet than low energy substrates. In addition, more complete wetting will occur if the substrate has a much higher surface energy than the liquid.\n\n## Surface energy modification techniques\n\nThe most commonly used surface modification protocols are plasma activation, wet chemical treatment, including grafting, and thin-film coating. Surface energy mimicking is a technique that enables merging the device manufacturing and surface modifications, including patterning, into a single processing step using a single device material. \n\nMany techniques can be used to enhance wetting. Surface treatments, such as Corona treatment, plasma treatment and acid etching, can be used to increase the surface energy of the substrate. Additives can also be added to the liquid to decrease its surface energy. This technique is employed often in paint formulations to ensure that they will be evenly spread on a surface.\n\n## The Kelvin equation\n\nAs a result of the surface tension inherent to liquids, curved surfaces are formed in order to minimize the area. This phenomenon arises from the energetic cost of forming a surface. As such the Gibbs free energy of the system is minimized when the surface is curved.", null, "Vapor pressure of flat and curved surfaces. The vapor pressure of a curved surface is higher than the vapor pressure of a flat surface due to the Laplace pressure that increases the chemical potential of the droplet causing it to vaporize more than it normally would.\n\nThe Kelvin equation is based on thermodynamic principles and is used to describe changes in vapor pressure caused by liquids with curved surfaces. The cause for this change in vapor pressure is the Laplace pressure. The vapor pressure of a drop is higher than that of a planar surface because the increased Laplace pressure causes the molecules to evaporate more easily. Conversely, in liquids surrounding a bubble, the pressure with respect to the inner part of the bubble is reduced, thus making it more difficult for molecules to evaporate. The Kelvin equation can be stated as:\n\n$RT\\times \\ln {\\frac {P_{0}^{K}}{P_{0}}}=\\gamma V_{m}\\times \\left({\\frac {1}{R_{1}}}+{\\frac {1}{R_{2}}}\\right)$", null, "where $P_{0}^{K}$", null, "is the vapor pressure of the curved surface, $P_{0}$", null, "is the vapor pressure of the flat surface, $\\gamma$", null, "is the surface tension, $V_{\\rm {m}}$", null, "is the molar volume of the liquid, $R$", null, "is the universal gas constant, $T$", null, "is temperature (K), and $R_{1}$", null, "and $R_{2}$", null, "are the principal radii of curvature of the surface.\n\n## Surface modified pigments for coatings\n\nPigments offer great potential in modifying the application properties of a coating. Due to their fine particle size and inherently high surface energy, they often require a surface treatment in order to enhance their ease of dispersion in a liquid medium.[clarification needed] A wide variety of surface treatments have been previously used, including the adsorption on the surface of a molecule in the presence of polar groups, monolayers of polymers, and layers of inorganic oxides on the surface of organic pigments.\n\nNew surfaces are constantly being created as larger pigment particles get broken down into smaller subparticles. These newly formed surfaces consequently contribute to larger surface energies, whereby the resulting particles often become cemented together into aggregates. Because particles dispersed in liquid media are in constant thermal or Brownian motion, they exhibit a strong affinity for other pigment particles nearby as they move through the medium and collide. This natural attraction is largely attributed to the powerful short-range Van der Waals forces, as an effect of their surface energies.\n\nThe chief purpose of pigment dispersion is to break down aggregates and form stable dispersions of optimally sized pigment particles. This process generally involves three distinct stages: wetting, deaggregation, and stabilization. A surface that is easy to wet is desirable when formulating a coating that requires good adhesion and appearance. This also minimizes the risks of surface tension related defects, such as crawling, catering, and orange peel. This is an essential requirement for pigment dispersions; for wetting to be effective, the surface tension of the vehicle[clarification needed] must be lower than the surface free energy of the pigment. This allows the vehicle to penetrate into the interstices of the pigment aggregates, thus ensuring complete wetting. Finally, the particles are subjected to a repulsive force in order to keep them separated from one another and lowers the likelihood of flocculation.\n\nDispersions may become stable through two different phenomena: charge repulsion and steric or entropic repulsion. In charge repulsion, particles that possess the same like electrostatic charges repel each other. Alternatively, steric or entropic repulsion is a phenomenon used to describe the repelling effect when adsorbed layers of material (e.g. polymer molecules swollen with solvent) are present on the surface of the pigment particles in dispersion. Only certain portions (i.e. anchors) of the polymer molecules are adsorbed, with their corresponding loops and tails extending out into the solution. As the particles approach each other their adsorbed layers become crowded; this provides an effective steric barrier that prevents flocculation. This crowding effect is accompanied by a decrease in entropy, whereby the number of conformations possible for the polymer molecules is reduced in the adsorbed layer. As a result, energy is increased and often gives rise to repulsive forces that aid in keeping the particles separated from each other.", null, "Dispersion Stability Mechanisms: Charge Stabilization and Steric or Entropic Stabilization. Electrical repulsion forces are responsible for stabilization through charge while steric hindrance is responsible for stabilization through entropy.\n\n## Table of common surface energy values\n\nMaterial Orientation Surface Energy (mJ/m2)\nPolytetrafluoroethylene (PTFE) 19\nGlass 83.4\nGypsum 370\nCopper 1650\nMagnesium oxide (100) plane 1200\nCalcium fluoride (111) plane 450\nLithium fluoride (100) plane 340\nCalcium carbonate (1010) plane 230\nSodium chloride (100) plane 300\nSodium chloride (110) plane 400\nPotassium chloride (100) plane 110\nBarium fluoride (111) plane 280\nSilicon (111) plane 1240" ]
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https://dendropy.org/primer/trees
[ "# Trees¶\n\n## The Tree Class¶\n\nTrees in DendroPy are represented by objects of the class Tree. Trees consist of a collection of nodes, represented by objects of the class Node, connected to each other in parent-child or ancestor-descendent relationships by objects of the class Edge. The first or initial node of a Tree is the head of the data structure, and is represented by the seed_node attribute of a Tree object. If the tree is rooted, then this is the root node. If the tree is unrooted, however, then this is an artificial node that only serves as the initial node when iterating over a tree in preorder or the final node when iterating over the tree in postorder, but does not have any informational significance by itself: it is an algorithmic artifact.\n\nThe seed_node attribute of a Tree object, like every other node on the tree, is an object of the Node class. Every Node object maintains a list of its immediate child Node objects as well as a reference to its parent Node object. You can iterate over the child of a particular Node object using the child_node_iter method, get a shallow-copy list of child Node objects using the child_nodes method, or access the parent Node object directly through the parent_node attribute of the Node. By definition, the seed_node has no parent node, leaf nodes have no child nodes, and term:internal nodes have both parent nodes and child nodes.\n\nEvery Node object has an attribute, edge, which is an Edge object representing the edge that is incident to or subtends the node represented by that Node object. Each Edge, in turn, has an attribute, head_node, which is the Node object representing the node that the edge subtends.\n\nThe Tree, Node, and Edge classes all have “annotations” as an attribute, which is a AnnotationSet object, i.e. a collection of Annotation instances tracking metadata. More information on working with metadata can be found in the “Working with Metadata Annotations” section.\n\n## Reading and Writing Tree Instances¶\n\nThe Tree class supports the “get” factory class method for simultaneously instantiating and populating a Tree instance, taking a data source as the first argument and a schema specification string (“nexus”, “newick”, “nexml”, “fasta”, or “phylip”, etc.) as the second:\n\nimport dendropy\ntree = dendropy.Tree.get(\npath='pythonidae.mcmc.nex',\nschema='nexus')\n\n\nA Tree object can be written to an external resource using the “write” method:\n\nimport dendropy\ntree = dendropy.Tree.get(\npath=\"trees1.nex\",\nschema=\"nexus\",\ntree_offset=2,\n)\ntree.write(\npath=\"trees1.newick\",\nschema=\"newick\",\n)\n\n\nIt can also be represented as a string using the “as_string” method:\n\nimport dendropy\ntree = dendropy.Tree.get(\npath=\"trees1.nex\",\nschema=\"nexus\",\n)\nprint(tree.as_string(schema=\"newick\",)\n\n\n## Cloning/Copying a Tree¶\n\nYou can make a “taxon namespace-scoped” copy of a Tree instance, i.e., where all Node and associated Edge instances of a Tree are cloned, but references to Taxon objects are preserved, you can call dendropy.datamodel.treemodel.Tree.clone with a “depth” argument value of 1 or by copy construction:\n\nimport dendropy\n\n# original list\ns1 = \"(A,(B,C));\"\ntree1 = dendropy.Tree.get(\ndata=s1,\nschema=\"newick\")\n\n# taxon namespace-scoped deep copy by calling Tree.clone(1)\n# I.e. Everything cloned, but with Taxon and TaxonNamespace references shared\ntree2 = tree1.clone(depth=1)\n\n# taxon namespace-scoped deep copy by copy-construction\n# I.e. Everything cloned, but with Taxon and TaxonNamespace references shared\ntree3 = dendropy.Tree(tree1)\n\n# *different* tree instances, with different nodes and edges\nfor nd1, nd2, nd3 in zip(tree1, tree2, tree3):\nassert nd1 is not nd2\nassert nd1 is not nd3\nassert nd2 is not nd3\n\n# Note: TaxonNamespace is still shared\n# I.e. Everything cloned, but with Taxon and TaxonNamespace references shared\nassert tree2.taxon_namespace is tree1.taxon_namespace\nassert tree3.taxon_namespace is tree1.taxon_namespace\n\n\nFor a true and complete deep-copy, where even the Taxon and TaxonNamespace references are copied, call copy.deepcopy:\n\nimport copy\nimport dendropy\n\n# original list\ns1 = \"(A,(B,C));\"\ntree1 = dendropy.Tree.get(\ndata=s1,\nschema=\"newick\")\n\n# Full deep copy by calling copy.deepcopy()\n# I.e. Everything cloned including Taxon and TaxonNamespace instances\ntree2 = copy.deepcopy(tree1)\n\n# *different* tree instances\nfor nd1, nd2 in zip(tree1, tree2):\nassert nd1 is not nd2\n\n# Note: TaxonNamespace is also different\nassert tree2.taxon_namespace is not tree1.taxon_namespace\nfor tx1 in tree1.taxon_namespace:\nassert tx1 not in tree2.taxon_namespace\nfor tx2 in tree2.taxon_namespace:\nassert tx2 not in tree1.taxon_namespace\n\n\nAlternatively, many times you want a “light” or “thin” copy, where just the tree structure (node and edge relationships) and basic information are retained (e.g., edge lengths, taxon associations, and node and edge labels), but not, e.g. the rich annotations. Then the extract_tree method is what you are looking for:\n\nimport dendropy\nfrom dendropy.calculate import treecompare\n\ntree0 = dendropy.Tree.get(\npath=\"pythonidae.mle.nex\",\nschema=\"nexus\")\nfor idx, nd in enumerate(tree0):\nnd.label = \"hello, world{}\".format(idx)\nnd.edge.label = \"world, hello{}\".format(idx)\nnd.annotations[\"color\"] = \"blue\"\nnd.edge.annotations[\"taste\"] = \"sweet\"\ntree1 = tree0.extract_tree()\n\nassert tree0.taxon_namespace is tree1.taxon_namespace\nassert treecompare.weighted_robinson_foulds_distance(\ntree0, tree1) == 0.0\n\nfor nd in tree1:\noriginal_node = nd.extraction_source\nprint(\"{} on extracted tree corresponds to {} on original tree\".format(\nnd, original_node))\n## basic attributes copied\nassert nd.label == original_node.label\nassert nd.edge.label == original_node.edge.label\nassert nd.edge.length == original_node.edge.length\n## but not annotations\nassert len(nd.annotations) == 0 and len(original_node.annotations) > 0\nassert len(nd.edge.annotations) == 0 and len(original_node.edge.annotations) > 0\n\n\n\n## Tree Traversal¶\n\n### Iterating Over Nodes¶\n\nThe following example shows how you might evolve a continuous character on a tree by recursively visting each node, and setting the value of the character to one drawn from a normal distribution centered on the value of the character of the node’s ancestor and standard deviation given by the length of the edge subtending the node:\n\n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 #! /usr/bin/env python # -*- coding: utf-8 -*- import random import dendropy def process_node(node, start=1.0): if node.parent_node is None: node.value = start else: node.value = random.gauss(node.parent_node.value, node.edge.length) for child in node.child_nodes(): process_node(child) if node.taxon is not None: print(\"%s : %s\" % (node.taxon, node.value)) mle = dendropy.Tree.get( path='pythonidae.mle.nex', schema='nexus') process_node(mle.seed_node) \n\nWhile the previous example works, it is probably clearer and more efficient to use one of the pre-defined node iterator methods:\n\npreorder_node_iter\n\nIterates over nodes in a Tree object in a depth-first search pattern, i.e., “visiting” a node before visiting the children of the node. This is the same traversal order as the previous example. This traversal order is useful if you require ancestral nodes to be processed before descendent nodes, as, for example, when evolving sequences over a tree.\n\npostorder_node_iter\n\nIterates over nodes in a Tree object in a postorder search pattern, i.e., visiting the children of the node before visiting the node itself. This traversal order is useful if you require descendent nodes to be processed before ancestor nodes, as, for example, when calculating ages of nodes.\n\nlevel_order_node_iter\n\nIterates over nodes in a Tree object in a breadth-first search pattern, i.e., every node at a particular level is visited before proceeding to the next level.\n\nleaf_node_iter\n\nIterates over the leaf or tip nodes of a Tree object.\n\nThe previous example would thus be better implemented as follows:\n\n#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport random\nimport dendropy\n\ndef evolve_char(tree, start=1.0):\nfor node in tree.preorder_node_iter():\nif node.parent_node is None:\nnode.value = 1.0\nelse:\nnode.value = random.gauss(node.parent_node.value, node.edge.length)\nreturn tree\n\nmle = dendropy.Tree.get(\npath='pythonidae.mle.nex',\nschema='nexus')\nevolve_char(mle)\nfor node in mle.leaf_iter():\nprint(\"%s : %s\" % (node.taxon, node.value))\n\n\nThe nodes returned by each of these iterators can be filtered if a filter function is passed as a second argument to the iterator. This filter function should take a Node object as an argument, and return True if the node is to be returned or False if it is not. For example, the following iterates over all nodes that have more than two children:\n\n 1 2 3 4 5 6 7 8 9 10 11 #! /usr/bin/env python # -*- coding: utf-8 -*- import dendropy mle = dendropy.Tree.get( path='pythonidae.mle.nex', schema='nexus') multifurcating = lambda x: True if len(x.child_nodes()) > 2 else False for nd in mle.postorder_node_iter(multifurcating): print(nd.description(0)) \n\n### Iterating Over Edges¶\n\nThe Edge objects associated with each Node can be accessed through the edge attribute of the Node object. So it is possible to iterate over every edge on a tree by iterating over the nodes and referencing the edge attribute of the node when processing the node. But it is clearer and probably more convenient to use one of the Edge iterators:\n\npreorder_edge_iter\n\nIterates over edges in a Tree object in a depth-first search pattern, i.e., “visiting” an edge before visiting the edges descending from that edge. This is the same traversal order as the previous example. This traversal order is useful if you require ancestral edges to be processed before descendent edges, as, for example, when calculating the sum of edge lengths from the root.\n\npostorder_edge_iter\n\nIterates over edges in a Tree object in a postorder search pattern, i.e., visiting the descendents of the edge before visiting the edge itself. This traversal order is useful if you require descendent edges to be processed before ancestral edges, as, for example, when calculating the sum of edge lengths from the tip\n\nlevel_order_edge_iter\n\nIterates over edges in a Tree object in a breadth-first search pattern, i.e., every edge at a particular level is visited before proceeding to the next level.\n\nThe following example sets the edge lengths of a tree to the proportions of the total tree length that they represent:\n\n 1 2 3 4 5 6 7 8 9 10 11 12 13 #! /usr/bin/env python # -*- coding: utf-8 -*- import dendropy mle = dendropy.Tree.get(path='pythonidae.mle.nex', schema='nexus') mle_len = mle.length() for edge in mle.postorder_edge_iter(): if edge.length is None: edge.length = 0 else: edge.length = float(edge.length)/mle_len print(mle.as_string(schema=\"newick\")) \n\nWhile this one removes the edge lengths entirely:\n\n 1 2 3 4 5 6 7 8 9 10 11 12 #! /usr/bin/env python # -*- coding: utf-8 -*- import dendropy mle = dendropy.Tree.get( path='pythonidae.mle.nex', schema='nexus') mle_len = mle.length() for edge in mle.postorder_edge_iter(): edge.length = None print(mle.as_string(schema=\"newick\")) \n\nLike the node iterators, the edge iterators also optionally take a filter function as a second argument, except here the filter function should take an Edge object as an argument. The following example shows how you might iterate over all edges with lengths less than some value:\n\n 1 2 3 4 5 6 7 8 9 10 11 #! /usr/bin/env python # -*- coding: utf-8 -*- import dendropy mle = dendropy.Tree.get( path='pythonidae.mle.nex', schema='nexus') short = lambda edge: True if edge.length < 0.01 else False for edge in mle.postorder_edge_iter(short): print(edge.length) \n\n## Finding Nodes on Trees¶\n\n### Nodes with Particular Taxa¶\n\nTo retrieve a node associated with a particular taxon, we can use the find_taxon_node method, which takes a filter function as an argument. The filter function should take a Taxon object as an argument and return True if the taxon is to be returned. For example:\n\n#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport dendropy\n\ntree = dendropy.Tree.get(path=\"pythonidae.mle.nex\", schema=\"nexus\")\nfilter = lambda taxon: True if taxon.label=='Antaresia maculosa' else False\nnode = tree.find_node_with_taxon(filter)\nprint(node.description())\n\n\nBecause we might find it easier to refer to Taxon objects by their labels, a convenience method that wraps the retrieval of nodes associated with Taxon objects of particular label is provided:\n\n#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport dendropy\n\ntree = dendropy.Tree.get(path=\"pythonidae.mle.nex\", schema=\"nexus\")\nnode = tree.find_node_with_taxon_label('Antaresia maculosa')\nprint(node.description())\n\n\n### Most Recent Common Ancestors¶\n\nThe MRCA (most recent common ancestor) of taxa or nodes can be retrieved by the instance method mrca. This method takes a list of Taxon objects given by the taxa keyword argument, or a list of taxon labels given by the taxon_labels keyword argument, and returns a Node object that corresponds to the MRCA of the specified taxa. For example:\n\nimport dendropy\n\ntree = dendropy.Tree.get(path=\"pythonidae.mle.nex\", schema=\"nexus\")\ntaxon_labels=['Python sebae',\n'Python regius',\n'Python curtus',\n'Python molurus']\nmrca = tree.mrca(taxon_labels=taxon_labels)\nprint(mrca.description())\n\n\nNote that this method is inefficient when you need to resolve MRCA’s for multiple sets or pairs of taxa. In this context, the PhylogeneticDistanceMatrix offers a more efficient approach, and should be preferred for applications such as calculating the patristic distances between all pairs of taxa. An instance of this class will be returned when you call phylogenetic_distance_matrix:\n\nimport dendropy\n\ntree = dendropy.Tree.get(\npath=\"pythonidae.mle.nex\",\nschema=\"nexus\")\npdm = tree.phylogenetic_distance_matrix()\nfor idx1, taxon1 in enumerate(tree.taxon_namespace):\nfor taxon2 in tree.taxon_namespace:\nmrca = pdm.mrca(taxon1, taxon2)\nweighted_patristic_distance = pdm.patristic_distance(taxon1, taxon2)\nunweighted_patristic_distance = pdm.path_edge_count(taxon1, taxon2)\nprint(\"'{}' vs '{}': {} (distance (weighted-edges, unweighted-edges) = {}, {})\".format(\ntaxon1.label,\ntaxon2.label,\nmrca.bipartition.split_as_bitstring(),\nweighted_patristic_distance,\nunweighted_patristic_distance))\n\n\nNote that the PhylogeneticDistanceMatrix object does not automatically update if the original Tree changes: it is essentially a snapshot of Tree at the point in which it is instantiated. If the original Tree changes, you should create a new instance of the corresponding PhylogeneticDistanceMatrix object.\n\n## Viewing and Displaying Trees¶\n\nSometimes it is useful to get a visual representation of a Tree.\n\nFor quick inspection, the print_plot will write an ASCII text plot to the standard output stream:\n\n>>> t = dendropy.Tree.get_from_string(\"(A,(B,(C,D)));\", \"newick\")\n>>> t.print_plot()\n/----------------------------------------------- A\n+\n| /------------------------------ B\n\\----------------+\n| /------------------- C\n\\----------+\n\\------------------- D\n\n\nIf you need to store this representation as a string instead, you can use as_ascii_plot:\n\n>>> s = t.as_ascii_plot()\n>>> print(s)\n/----------------------------------------------- A\n+\n| /------------------------------ B\n\\----------------+\n| /------------------- C\n\\----------+\n\\------------------- D\n\n\nYou can also, as mentioned above, using the as_string method to represent a Tree as string in any format:\n\nt = dendropy.Tree.get_from_string(\"(A,(B,(C,D)));\", \"newick\")\nprint(t.as_string(schema=\"nexus\"))\nprint(t.as_string(schema=\"newick\"))\n\n\n## Building a Tree Programmatically¶\n\nFor example:\n\nimport dendropy\n\ntaxon_namespace = dendropy.TaxonNamespace([\"A\", \"B\", \"C\", \"D\",])\ntree = dendropy.Tree(taxon_namespace=taxon_namespace)\n\n# Create and add a new child node to the seed node,\n# assigning it an edge length:\n#\n# (seed)\n# /\n# /\n# ch1\n#\nch1 = tree.seed_node.new_child()\nch1.edge.length = 1\n\n# Can also assign edge length on construction:\n#\n# (seed)\n# / \\\n# / \\\n# ch1 ch2\n#\nch2 = tree.seed_node.new_child(edge_length=1)\n\n# Can also add an existing node as child\n#\n# (seed)\n# / \\\n# / \\\n# ch1 ch2\n# / \\ / \\\n# ch3 ch4 ch5 ch6\nch3 = dendropy.Node(edge_length=1)\nch4 = dendropy.Node(edge_length=2)\nch5 = dendropy.Node(edge_length=1)\nch6 = dendropy.Node(edge_length=2)\n# Note: this clears/deletes existing child nodes before adding the new ones;\nch2.set_child_nodes([ch5, ch6])\n\n# Assign taxa\nch3.taxon = taxon_namespace.get_taxon(\"A\")\nch4.taxon = taxon_namespace.get_taxon(\"B\")\nch5.taxon = taxon_namespace.get_taxon(\"C\")\nch6.taxon = taxon_namespace.get_taxon(\"D\")\n\nprint(tree.as_string(\"newick\"))\nprint(tree.as_ascii_plot())\n\n\nproduces the following:\n\n((A:1,B:2):1,(C:1,D:2):1);\n\n/---------------------------------- A\n/----------------------------------+\n| \\---------------------------------- B\n+\n| /---------------------------------- C\n\\----------------------------------+\n\\---------------------------------- D" ]
[ null ]
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https://feet-to-cm.appspot.com/317-feet-to-cm.html
[ "Feet To Cm\n\n# 317 ft to cm317 Feet to Centimeters\n\nft\n=\ncm\n\n## How to convert 317 feet to centimeters?\n\n 317 ft * 30.48 cm = 9662.16 cm 1 ft\nA common question is How many foot in 317 centimeter? And the answer is 10.4002624672 ft in 317 cm. Likewise the question how many centimeter in 317 foot has the answer of 9662.16 cm in 317 ft.\n\n## How much are 317 feet in centimeters?\n\n317 feet equal 9662.16 centimeters (317ft = 9662.16cm). Converting 317 ft to cm is easy. Simply use our calculator above, or apply the formula to change the length 317 ft to cm.\n\n## Convert 317 ft to common lengths\n\nUnitLength\nNanometer96621600000.0 nm\nMicrometer96621600.0 µm\nMillimeter96621.6 mm\nCentimeter9662.16 cm\nInch3804.0 in\nFoot317.0 ft\nYard105.666666667 yd\nMeter96.6216 m\nKilometer0.0966216 km\nMile0.0600378788 mi\nNautical mile0.0521714903 nmi\n\n## What is 317 feet in cm?\n\nTo convert 317 ft to cm multiply the length in feet by 30.48. The 317 ft in cm formula is [cm] = 317 * 30.48. Thus, for 317 feet in centimeter we get 9662.16 cm.\n\n## 317 Foot Conversion Table", null, "## Alternative spelling\n\n317 ft to cm, 317 ft in cm, 317 ft to Centimeter, 317 ft in Centimeter, 317 ft to Centimeters, 317 ft in Centimeters, 317 Feet to cm, 317 Feet in cm, 317 Foot to Centimeter, 317 Foot in Centimeter, 317 Feet to Centimeters, 317 Feet in Centimeters, 317 Foot to Centimeters, 317 Foot in Centimeters" ]
[ null, "https://feet-to-cm.appspot.com/image/317.png", null ]
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https://studylib.net/doc/9488959/file
[ "# File", null, "```Name:\nDate:\nReview Packet for Quiz 1.4-1.8\nPolygons, Triangles, Special Quadrilaterals, Circles, Space Geometry\nThings you should know from 1.4 Polygons:\n\n\n\n\n\nClassification of polygons with 3,4,5,6,7,and 8 sides.\nClassify polygons as concave or convex.\nDefinition of equilateral polygons, equiangular polygons, and regular polygons.\nHow to name polygons and how to name congruent polygons\nGiven the names of two congruent polygons or the pictures of two congruent polygons, you\nshould be able to list congruent segments and angles.\nThings you should know from 1.5 Triangles:\n\n\n\nDefinitions of acute, obtuse, and right triangles and how to draw them.\nDefinitions of scalene, isosceles, equilateral triangles and how to draw them.\nThe angles in a triangle add to 180 degrees.\nThings you should know from 1.6 Special Quadrilaterals:\n\n\n\n\n\n\nDefinitions of parallel and perpendicular lines and the symbols used to show them. ( || or | )\nThe difference between the marks showing when two lines are parallel and when they are\ncongruent.\nRecognize when a quadrilateral is a trapezoid, kite, parallelogram, rhombus, rectangle, or square.\nUnderstand that a rhombus is an equilateral parallelogram.\nUnderstand that a rectangle is an equiangular parallelogram.\nUnderstand that a square is a regular parallelogram.\nThings you should know from 1.7 Circles:\n\n\n\n\n\nDefinitions of center, radius, chord, diameter, tangent, and point of tangency and know how to\nname them.\nYou should be able to draw a circle using the compass and know how to draw and label a radius, a\ndiameter, a chord, and a tangent.\nGiven the length of the radius, know how to find the length of the diameter.\nDefinition of minor arc, major arc, and semicircle.\nGiven the measure of a central angle, you should know how to find the measure of a minor and\nmajor arc.\nThings you should know from 1.8 Space Geometry:\n\n\nHow to classify a 3-D shape –cylinder, prism, pyramid, cone, sphere, or hemisphere.\nHow to name prisms and pyramids (by the base- hexagonal prism, pentagonal pyramid for ex.)\n1. Classify and name the following polygons and say whether they are convex or concave. The first\none is done for you as an example.\nA.\nB.\nC.\n____________________\nD.\n_____________________\nE.\n______________________\nF.\n______________________\n______________________\n2. Now classify each above polygon as equilateral, equiangular, regular, or neither.\nA. ______________\nD. ______________\nB. ______________\nE. ______________\nC. ______________\nF. ______________\n3. Given that polygon ABCDE =\ñ LMNOP, Find:\nN\nA\nA. m &lt; A = _______\n8.3 cm\nB. m &lt; P = _______\n10 cm\nO\n9 cm\nE\nB\nM\nP\nC. CD = _______\nL\nD. PL = _______\nD\nC\n4. Which polygon is congruent to PQRS? Fill in the expression correctly : PQRS =\ñ _____\nS\nK\nA\nD\nW\nZ\nP\nH\nR\nQ\nB\nC\nX\nY\nJ\nI\n5 . Classify the following triangles by their sides and angles. The first is done for you as an example.\nA.\nIsosceles Acute\nB\nC.\n________________\nD.\n______________\nE.\n______________\n______________\n6. Draw an acute scalene triangle ABC.\n7. Draw an obtuse isosceles triangle with obtuse angle 120&deg;.\nA.\nB\n_____________________\n_____________________\nD.\nE.\n_____________________\n_____________________\nC\n_____________________\nF.\n_____________________\n9. Draw an equilateral parallelogram with GR || AM and &lt; A =\ñ &lt; R.\n10. Draw trapezoid TRAP with TR | AP and TR || AP.\n11.\nName the following:\nA. Center: _________\nC. Diameter: _______\nD. Chord: _________\nE. Tangent: ________\nF. Point of Tangency: ______\n12. Using the circle P above, answer the following questions:\nA. Name the two central angles:\nB. Name two minor arcs:\nC. Name two major arcs:\nD. Name one semicircle and its measure:\nE. m DE =\nF. m EF =\nG. m DGE =\n̅̅̅̅ 4cm." ]
[ null, "https://s3.studylib.net/store/data/009488959_1-8d6ccb8eecd27fc76eaa1d2b16a14928.png", null ]
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https://encyclopedia2.thefreedictionary.com/Constructive+Trend
[ "# Constructive Trend\n\nThe following article is from The Great Soviet Encyclopedia (1979). It might be outdated or ideologically biased.\n\n## Constructive Trend\n\nin mathematics, a mathematical view that regards the study of constructive processes and constructive objects as the fundamental problem of mathematics.\n\nBy the end of the 19th century a nonconstructive, set-theoretic approach appeared in mathematics, which was considerably developed by K. Weierstrass, R. Dedekind, and especially G. Cantor. Its aim was the construction of a theory of sets that was to be the foundation of all mathematics. In this theory, in accordance with Cantor’s statement that “the essence of mathematics consists in its freedom,” great arbitrariness was allowed in introducing sets that were then considered as completed “objects.” At the beginning of the 20th century, however, antinomies were discovered—that is, contradictions that showed that one cannot combine objects into sets in an unrestricted manner. Attempts to overcome the difficulties that had arisen proceeded along the lines of an axiomatization of set theory—that is, set theory was to be converted into an axiomatic discipline similar to geometry. This amounted to a choice of axioms that would provide a foundation for mathematics while avoiding known antinomies.\n\nThe first attempt along these lines was undertaken by E. Zermelo, who published his system of axioms for set theory in 1908. The known antinomies of set theory did not occur in Zermelo’s system, although there was no guarantee against the subsequent appearance of contradictions. The problem arose of ensuring that axiomatically constructed set theory be free of contradictions. D. Hilbert posed this problem and attempted to solve it. His basic idea was to completely formalize axiomatic set theory and to treat it as a formal system. The task of establishing the noncontradictory nature of the theory would then be reduced to proving the formal unprovability of formulas of a specified kind. Such a proof would have been a convincing argument for constructive objects, that is, for formal proofs. Thus it would fit within the framework of constructive mathematics.\n\nIn 1931, K. Gödel proved that the goal set by Hilbert was unattainable. Nevertheless, the means proposed by Hilbert is of great interest. This means is metamathematics, the constructive science of formal proofs, a branch of constructive mathematics. Hilbert’s program can be characterized as an unsuccessful at-tempt to establish set-theoretic mathematics on the basis of constructive mathematics, whose reliability he did not doubt. Hilbert himself must be considered one of the founders of constructive mathematics.\n\nThe constructive trend can be considered as a branch of intuitionism. Intuitionism was founded by L. E. J. Brouwer, whose program consists in investigating mental (mathematical) constructions. The closeness of the constructive trend to intuitionism is apparent in the way in which both disciplines view disjunctions and existence theorems, as well as in their treatment of the law of the excluded middle. The principal difference between constructivists and intuitionists is that constructivists, un-like intuitionists, do not think of their constructions as a purely mental pursuit. Moreover, intuitionists argue about certain “freely formed sequences” and consider the continuum as a “medium of free formation” and thus involve nonconstructive objects. The constructive trend in mathematics has led to the formation of a separate discipline, constructive mathematics.\n\nA. A. MARKOV" ]
[ null ]
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https://www.geeksforgeeks.org/program-to-find-the-sum-of-the-series-23-45-75-upto-n-terms/
[ "# Program to find the sum of the series 23+ 45+ 75+….. upto N terms\n\nGiven a number N, the task is to find the Nth term of the below series:\n\n23 + 45 + 75 + 113 + 159 +…… upto N terms\n\nExamples:\n\nInput: N = 4\nOutput: 256\nExplanation:\nNth term = (2 * N * (N + 1) * (4 * N + 17) + 54 * N) / 6\n= (2 * 4 * (4 + 1) * (4 * 4 + 17) + 54 * 4) / 6\n= 256\n\nInput: N = 10\nOutput: 2180\n\n\n## Recommended: Please try your approach on {IDE} first, before moving on to the solution.\n\nApproach:\n\nThe Nth term of the given series can be generalized as:", null, "Sum of first n terms of this series:", null, "Therefore, Sum of first n terms of this series:", null, "Below is the implementation of the above approach:\n\n## C++\n\n // CPP program to find sum // upto N-th term of the series: // 23, 45, 75, 113...    #include using namespace std;    // calculate Nth term of series int findSum(int N) {     return (2 * N * (N + 1) * (4 * N + 17) + 54 * N) / 6; }    // Driver Function int main() {        // Get the value of N     int N = 4;        // Get the sum of the series     cout << findSum(N) << endl;        return 0; }\n\n## Java\n\n // Java program to find sum // upto N-th term of the series: // 23, 45, 75, 113... import java.util.*;    class solution  {        static int findSum(int N) {     //return the final sum     return (2 * N * (N + 1) * (4 * N + 17) + 54 * N) / 6; }    //Driver program public static void main(String arr[]) { // Get the value of N     int N = 4;    // Get the sum of the series  System.out.println(findSum(N));    } }\n\n## Python3\n\n # Python3 program to find sum # upto N-th term of the series: # 23, 45, 75, 113...    # calculate Nth term of series  def findSum(N):            return (2 * N * (N + 1) * (4 * N + 17) + 54 * N) / 6        #Driver Function if __name__=='__main__': #Get the value of N     N = 4    #Get the sum of the series     print(findSum(N))    #this code is contributed by Shashank_Sharma\n\n## C#\n\n // C# program to find sum  // upto N-th term of the series:  // 23, 45, 75, 113...  using System;     class GFG  {         static int findSum(int N)  {      //return the final sum      return (2 * N * (N + 1) *             (4 * N + 17) + 54 * N) / 6;  }     // Driver Code  static void Main()  {      // Get the value of N      int N = 4;             // Get the sum of the series      Console.Write(findSum(N));  }  }     // This code is contributed by Raj\n\n## PHP\n\n \n\nOutput:\n\n256\n\n\nTime Complexity: O(1)\n\nMy Personal Notes arrow_drop_up", null, "Check out this Author's contributed articles.\n\nIf you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to [email protected]. See your article appearing on the GeeksforGeeks main page and help other Geeks.\n\nPlease Improve this article if you find anything incorrect by clicking on the \"Improve Article\" button below." ]
[ null, "https://www.geeksforgeeks.org/wp-content/ql-cache/quicklatex.com-7667bef3e7a414387bc2c01b9b818fac_l3.png", null, "https://www.geeksforgeeks.org/wp-content/ql-cache/quicklatex.com-9ac18ca4d349f678ddab11adae8fb20e_l3.png", null, "https://www.geeksforgeeks.org/wp-content/ql-cache/quicklatex.com-971041d559e4aeea3a2c5935fca4c119_l3.png", null, "https://media.geeksforgeeks.org/auth/profile/ni0gjlstyzx2c1p120sy", null ]
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https://techutils.in/blog/2020/08/31/stackbounty-bayesian-bootstrap-posterior-how-well-does-weighted-likelihood-bootstrap-approximate-the-bayesian-posterior/
[ "# #StackBounty: #bayesian #bootstrap #posterior How well does weighted likelihood bootstrap approximate the Bayesian posterior?\n\n### Bounty: 50\n\n$$DeclareMathOperator*{argmax}{arg,max}$$Given a set of $$N$$ i.i.d. observations $$X=left{x_1, ldots, x_Nright}$$, we train a model $$p(x|boldsymbol{theta})$$ by maximizing marginal log-likelihood $$log p(X mid boldsymbol{theta})$$. A full posterior $$p(boldsymbol{theta}|X)$$ over model parameters $$boldsymbol{theta}$$ can be approximated as a Gaussian distribution using Laplace method.\n\nIn the case that the Gaussian distribution gives a poor approximation of $$p(boldsymbol{theta}|X)$$, Newton and Raftery (1994) proposed weighted likelihood bootstrap (WLB) as a way to simulate approximately from a posterior distribution. Extending Bayesian bootstrap (BB) of Rubin (1981), this method generates BB samples $$tilde{X}=(X,boldsymbol{pi})$$ by repeatedly drawing sampling weights $$boldsymbol{pi}$$ from a uniform Dirichlet distribution and maximizes a weighted likelihood to calculate $$boldsymbol{theta}_{text{MWLE}}$$.\n\n$$begin{equation} boldsymbol{theta}{text{MWLE}}=argmax{boldsymbol{theta}}sum_{n=1}^{N} pi_nlog p(x_n|boldsymbol{theta}). end{equation}$$\n\nSo the algorithm can be summarized as\n\n• Draw a posterior sample $$boldsymbol{pi}sim p(boldsymbol{pi}|X)=mathcal{D}ir(1,dots,1)$$.\n• Calculate $$theta_{text{MWLE}}$$ from weighted sample $$tilde{X}=(X, boldsymbol{pi})$$\n\nNewton and Raftery (1994) state that\n\nIn the generic weighting scheme, the WLB is first order correct under\nquite general conditions.\n\n1. I was wondering what exactly does this mean and what does first order refer to? How well does this approximation $$p(boldsymbol{theta}|X)$$?\n\nLater authors state that\n\nInaccuracies can be removed by using the WLB as a source of samples in\nthe sampling-importance resampling (SIR) algorithm.\n\n1. I was not sure what this exactly means. Could someone point what step in my algorithm exactly should I change?\n\nGet this bounty!!!\n\nThis site uses Akismet to reduce spam. Learn how your comment data is processed." ]
[ null ]
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https://lists.ozlabs.org/pipermail/linuxppc-dev/2015-September/133930.html
[ "# [RFC v2 3/7] powerpc: atomic: Implement atomic{,64}_{add,sub}_return_* variants\n\nBoqun Feng boqun.feng at gmail.com\nSun Sep 20 01:33:10 AEST 2015\n\n```Hi Will,\n\nOn Fri, Sep 18, 2015 at 05:59:02PM +0100, Will Deacon wrote:\n> On Wed, Sep 16, 2015 at 04:49:31PM +0100, Boqun Feng wrote:\n> > On powerpc, we don't need a general memory barrier to achieve acquire and\n> > release semantics, so __atomic_op_{acquire,release} can be implemented\n> > using \"lwsync\" and \"isync\".\n>\n> I'm assuming isync+ctrl isn't transitive, so we need to get to the bottom\n\nActually the transitivity is still guaranteed here, I think ;-)\n\n(Before I put my reasoning, I have to admit I just learned about the\ncumulativity recently, so my reasoning may be wrong. But the good thing\nis that we have our POWER experts in the CCed. In case I'm wrong, they\ncould correct me.)\n\nThe thing is, on POWER, transitivity is implemented by a similar but\nslightly different concept, cumulativity, and as said in the link:\n\nhttp://www.rdrop.com/users/paulmck/scalability/paper/N2745r.2011.03.04a.html\n\n\"\"\"\nThe ordering done by a memory barrier is said to be “cumulative” if it\nalso orders storage accesses that are performed by processors and\nmechanisms other than P1, as follows.\n\n*\tA includes all applicable storage accesses by any such processor\nor mechanism that have been performed with respect to P1 before\nthe memory barrier is created.\n\n*\tB includes all applicable storage accesses by any such processor\nor mechanism that are performed after a Load instruction\nexecuted by that processor or mechanism has returned the value\nstored by a store that is in B.\n\"\"\"\n\nPlease note that the set B can be extended indefinitely without any\nother cumulative barrier.\n\nSo for a RELEASE+ACQUIRE pair to a same variable, as long as the barrier\nin the RELEASE operation is cumumlative, the transitivity is guaranteed.\nAnd lwsync is cumulative, so we are fine here.\n\nI also wrote a herd litmus to test this. Due to the tool's limitation, I\nuse the xchg_release and xchg_acquire to test. And since herd doesn't\nsupport backward branching, some tricks are used here to work around:\n\nPPC lwsync+isync-transitivity\n\"\"\n{\n0:r1=1; 0:r2=x; 0:r3=1; 0:r10=0 ; 0:r11=0; 0:r12=a;\n1:r1=9; 1:r2=x; 1:r3=1; 1:r10=0 ; 1:r11=0; 1:r12=a;\n2:r1=9; 2:r2=x; 2:r3=2; 2:r10=0 ; 2:r11=0; 2:r12=a;\n}\nP0 | P1 | P2 ;\nstw r1,0(r2) | lwz r1,0(r2) | ;\n| lwsync | lwarx r11, r10, r12 ;\n| lwarx r11,r10,r12 | stwcx. r3, r10, r12 ;\n| stwcx. r3,r10,r12 | bne Fail ;\n| | isync ;\n| | lwz r1, 0(r2) ;\n| | Fail: ;\n\nexists\n(1:r1=1 /\\ 1:r11=0 /\\ 2:r11=1 /\\ 2:r1 = 0)\n\nAnd the result of this litmus is that:\n\nTest lwsync+isync-transitivity Allowed\nStates 15\n1:r1=0; 1:r11=0; 2:r1=0; 2:r11=0;\n1:r1=0; 1:r11=0; 2:r1=0; 2:r11=1;\n1:r1=0; 1:r11=0; 2:r1=1; 2:r11=0;\n1:r1=0; 1:r11=0; 2:r1=1; 2:r11=1;\n1:r1=0; 1:r11=0; 2:r1=9; 2:r11=0;\n1:r1=0; 1:r11=0; 2:r1=9; 2:r11=1;\n1:r1=0; 1:r11=2; 2:r1=0; 2:r11=0;\n1:r1=0; 1:r11=2; 2:r1=1; 2:r11=0;\n1:r1=1; 1:r11=0; 2:r1=0; 2:r11=0;\n1:r1=1; 1:r11=0; 2:r1=1; 2:r11=0;\n1:r1=1; 1:r11=0; 2:r1=1; 2:r11=1;\n1:r1=1; 1:r11=0; 2:r1=9; 2:r11=0;\n1:r1=1; 1:r11=0; 2:r1=9; 2:r11=1;\n1:r1=1; 1:r11=2; 2:r1=0; 2:r11=0;\n1:r1=1; 1:r11=2; 2:r1=1; 2:r11=0;\nNo\nWitnesses\nPositive: 0 Negative: 29\nCondition exists (1:r1=1 /\\ 1:r11=0 /\\ 2:r11=1 /\\ 2:r1=0)\nObservation lwsync+isync-transitivity Never 0 29\n\n,which means transitivity is guaranteed.\n\nRegards,\nBoqun\n\n> of the s390 thread you linked me to before we start spreading this\n> further:\n>\n> https://lkml.org/lkml/2015/9/15/836\n>\n> Will\n-------------- next part --------------\nA non-text attachment was scrubbed...\nName: signature.asc\nType: application/pgp-signature\nSize: 473 bytes\nDesc: not available\nURL: <http://lists.ozlabs.org/pipermail/linuxppc-dev/attachments/20150919/734c5d9e/attachment.sig>\n```\n\nMore information about the Linuxppc-dev mailing list" ]
[ null ]
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https://www.docmckee.com/WP/oer/statistics/section-5/section-5-4-2/
[ "# Section 5.4", null, "# Assumptions\n\nIn statistics, the validity of a test statistic depends on several assumptions being met. Assumptions are things that are taken for granted by a particular statistic. We must verify all of the assumptions associated with a particular statistic when we use it. Some statistics are considered robust against violations of certain assumptions. This means that even when the assumption is violated, we can still have a high degree of confidence in the results. As a consumer of research, it is important to understand that if the assumptions of a statistical test are violated, then we cannot trust the statistical outcome or any discussion points the author makes about those results.\n\nAssumptions are characteristics of the data that must be present for the results of a statistical test to be accurate.\n\n## Common Assumptions\n\nAll samples are randomly selectedMost statistical procedures cannot account for systematic bias.  Randomly selected samples eliminate such bias and improve the validity of inferences made based on statistical test results.\n\nAll samples are drawn from a normally distributed populationMost researchers do not fret over the normality requirement when comparing group means, because the effect of non-normality on p-values is very, very small.  When a distribution of scores is not normal because of an outlier, then the problem can be important to consider.  Extreme scores have an extreme impact on the mean, as well as variability and correlation.  Recall what we said about the effects of extreme scores on the mean in previous sections.  If an extreme score means that you should not use the mean, then a statistical test of mean differences makes no sense either.\n\nAll samples are independent of each otherThis assumption means that there is no reason that the scores in Group A are correlated with the scores of Group B.  If you use the same person for multiple measures of the variable that you are studying, then that person’s scores will be correlated.  Therefore, if we use a Pretest-Posttest type of design, then we violate the assumption of independent samples.  What this means is that we have to use special statistical tests that are designed for correlated scores, often referred to as repeated measures tests.  Random selection and random assignment to groups are usually considered sufficient to meet this assumption.  Statistical tests sometimes are called “independent samples” tests if they have this assumption.\n\nAll populations have a common varianceThis assumption is often referred to as the homogeneity of variance requirement.  It only applies to some tests statistics; the most common test statistics that have this assumption are the ANOVA family.  Data that meet the requirement have a special name:  Homoscedastic (pronounced ‘hoe-moe-skee-dast-tic’).  Data that violate this assumption (e.g., the two variances are not equal) can be referred to as heteroscedastic.  If you keep the treatment group and the control group around the same size (equal Ns), then this assumption is not really that important.  Different variances with widely different sample sizes will taint your results.\n\n## Key Terms\n\nHypothesis, Sample, Population, Generalization, Inference, Test Statistic, Research Hypothesis, Null Hypotheses, p-values, Alpha Level, Type I Error, Type II Error, Power, Assumptions, One-tailed Test, Two-tailed Test\n\n[ Back | Contents | Next ]\n\n`Last Modified:  06/03/2021`\n\nThis site uses Akismet to reduce spam. Learn how your comment data is processed." ]
[ null, "https://www.docmckee.com/WP/wp-content/uploads/2021/06/Fundamentals-of-Social-Statistics-banner2.0.png", null ]
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http://forums.wolfram.com/mathgroup/archive/1992/Aug/msg00099.html
[ "", null, "", null, "", null, "", null, "", null, "", null, "", null, "D[myFunc[g[x]]] -> myFunc[D[g[x],x]: How to do in chain rule ???\n\n• To: mathgroup at yoda.physics.unc.edu\n• Subject: D[myFunc[g[x]]] -> myFunc[D[g[x],x]: How to do in chain rule ???\n• From: Simon Chandler <simonc at hpcpbla.bri.hp.com>\n• Date: Mon, 24 Aug 92 20:10:24 +0100\n\n```(* Hi mathgroup,\n\nI hope you can help with this problem. I want to give the function 'myFunc'\nthe property of being 'transparent' to differentiation.\ni.e., I want to have\n\nd myFunc[g[x]] d g[x]\n-------------- give me myFunc[ ------ ] (1)\ndx dx\n\nwhere g is an arbitrary function. \"Easy\" I hear you say, just write...\n*)\n\nmyFunc/: D[myFunc[g_],x_]:=myFunc[D[g,x]]\n\n(* so that *)\nD[myFunc[g[x]],x]\n(* gives *)\n\nmyFunc[g'[x]]\n\n(* That's great. But now lets try differentiating another arbitrary function,\nf, acting on myFunc. I want the chain rule to convert\n\nd f[myFunc[g[x]]] d myFunc[g[x]]\n----------------- into f'[myFunc[g[x]]] * -------------- (2)\ndx dx\n\nwhich would then be converted by (1) into\n\nd g[x]\nf'[myFunc[g[x]]] * myFunc[ ------ ] (3)\ndx\n\nWhat actually happens is ...\n*)\n\nD[f[myFunc[g[x]]],x]\n\nf'[myFunc[g[x]]] g'[x] myFunc'[g[x]]\n\n(* If you use Trace on this you will see that the intermediate step in the\nchain rule (2) does not seem to be produced so there is no opportunity for\nour defined property for myFunc to act *)\n\nTrace[D[f[myFunc[g[x]]],x]]\n\n{D[f[myFunc[g[x]]], x], f'[myFunc[g[x]]] myFunc'[g[x]] g'[x],\n\n{myFunc'[g[x]] g'[x], g'[x] myFunc'[g[x]]},\n\nf'[myFunc[g[x]]] g'[x] myFunc'[g[x]],\n\nf'[myFunc[g[x]]] g'[x] myFunc'[g[x]]}\n\n(* Can anyone suggest how I might define a rule/property for myFunc\nso that it is transparent to differentiation even in the chain rule and\nso give me the result I want ?\n\nSimon Chandler\nHewlett-Packard Ltd (CPB)\nStoke Gifford\nBristol\nBS12 6QZ\n\nTel: 0272 228109\nFax: 0272 236091\nemail: simonc at hpcpbla.bri.hp.com *)\n\n*)\n\n```\n\n• Prev by Date: Re: No Subject\n• Next by Date: StringToStream error???\n• Previous by thread: Re: No Subject\n• Next by thread: StringToStream error???" ]
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https://www.eeeguide.com/error-detection-and-correction-codes/
[ "## Error Detection and Correction Codes:\n\nError Detection and Correction Codes – Errors enter the data stream during transmission and are caused by noise and transmission system impairments. Because errors compromise the data and in some cases render it useless, procedures have been developed to detect and correct transmission errors. The processes involved with error correction normally result in an increase in the number of bits per second which are transmitted, and naturally this increases the cost of transmission. Procedures which permit error correction at the receiver location are complicated, and so it is necessary for data users to determine the importance of the transmitted data and to decide what level of Error Detection and Correction Codes is suitable for that data. The tolerance the data user has for errors will decide which error control system is appropriate for the transmission circuit being used for the user’s data.\n\n### Error Detection:\n\nThe 5-bit Baudot code provides no Error Detection and Correction Codes at all, because it uses all 5 bits to represent characters. If only 1 bit is translated (by error) to its opposite value, a totally different character will be received and the change will not be apparent to the receiver. The inability of such codes to detect errors has led to the development of other codes which provide for error control.\n\n### Constant-Ratio Codes:\n\nA few codes have been developed which provide inherent Error Detection and Correction Codes when used in ARQ (automatic request for repeat) systems. The 2-out-of-5 code follows a pattern which results in every code group having two 1s and three 0s. When the group is received, the receiver will be able to determine that an error has occurred if the ratio of 1s to 0s has been altered. If an error is detected, a NAK (do not acknowledge) response is sent and the data word is repeated. This testing procedure continues word for word.\n\nThis code has some limitations. An odd number of errors will always be detected, but an even number of errors may go undetected. Even more limiting is the problem that this code will severely reduce the number of available code combinations. The formula", null, "Where\n\n! = Factorial\n\nT = Total bits\n\nM = Number of 1s\n\nexpresses the number of combinations possible for any code of this type. For the 2-out-of-5 code the formula is:", null, "Ten combinations would prevent the code from being used for anything other than numbers.\n\nAnother code, the 4-out-of-8, is based on the same principle as the 2-out-of-5 code. The larger number of bits provides a larger number of combinations, 70, and the code also provides improved Error Detection and Correction Codesn. Owing to the redundancy of the code, its efficiency for transmission is reduced. It shows that if there were no restriction of the number of 1s in a code group, 8 bits would provide 40,320 combinations, 576 times as many as are provided by the 4-out-of-8 code. Codes such as the 2-out-of-5 and 4-out-of-8, which depend on the ratio of 1s to 0s in each code group to indicate that errors have occurred, are called Constant-Ratio Codes.\n\n### Redundant Codes:\n\nMost Error Detection and Correction Codes systems use some form of redundancy to check whether the received data contains errors. This means that information additional to the basic data is sent. In the simplest system to visualize, the redundancy takes the form of transmitting the information twice and comparing the two sets of data to see that they are the same. Statistically, it is very unlikely that a random error will occur a second time at the same place in the data. If a discrepancy is noted between the two sets of data, an error is assumed and the data is caused to be re-transmitted. When two sets of data agree, error-free transmission is assumed.\n\nRetransmission of the entire message is very inefficient, because the second transmission of a message is 100 percent redundant. In this case as in all cases, redun­dant bits of information are unnecessary to the meaning of the original message. It is possible to determine transmission efficiency by using the following formula:", null, "In the above case of complete retransmission, the number of information bits is equal to one-half the number of total bits. The transmission efficiency is therefore equal to 0.5, or 50 percent. In a system with no redundancy, information bits equal total bits and the transmission efficiency is 100 percent. Most systems of Error Detection and Correction Codes fall between these two extremes, efficiency is sacrificed to obtain varying degrees of security against errors which would otherwise be undetected.\n\n### Parity-Check Codes:\n\nA popular form of Error Detection and Correction Codes employing redundancy is the use of a parity-check bit added to each character code group. Codes of this type are called parity-check codes. The parity bit is added to the end of the character code block according to some logical process. The most common parity-check codes add the 1s in each character block code and append a 1 or 0 as required to obtain an odd or even total, depending on the code system. Odd parity systems will add a 1 if addition of the 1s in the block sum is odd. At the receiver, the block addition is accomplished with the parity bit intact, and appropriate addition is made. If the sum provides the wrong parity, an error during transmission will be assumed and the data will be retransmitted.", null, "Parity bits added to each character block provide what is called vertical parity, which is illustrated in Figure 14-29. The designation vertical parity is explained by the figure which shows the parity bit at the top of each column on the punched tape.", null, "Parity bits can also be added-to rows of code bits. This is called horizontal parity and is also illustrated by Figure 14-29. The code bits are associated into blocks of specific length with the horizontal parity bits following each block. By using the two parity schemes concurrently, it becomes possible to determine which bit is in error. This is explained in Figure 14-30, where even parity is expected for both horizontal and vertical parity. Note that here one column and one row each display improper parity. By finding the intersection of the row and column, the bit in error can be identified. Simply changing the bit to the opposite value will restore proper parity both horizontally and vertically. These types of parity arrangements are sometimes called geometric codes.\n\nAnother group of parity-check codes are referred to as cyclic codes. These use shift registers with feedback to create parity bits based on polynomial representations of the data bits. The process is somewhat involved and will not be fully described here, but basically it involves processing both transmitted and received data with the same polynomial. The remainder after the receive processing will be zero if no errors have occurred. Cyclic codes provide the highest level of error detection for the same degree of redundancy of any parity-check code. The Motorola MC8503 is an LSI chip which has been developed for use in cyclic redundancy systems. The chip provides for use in systems which utilize any of four more common polynomials. The polynomial to be used is selected by a three-digit code which is applied to the chip. The MC8503 is typical of the Error Detection and Correction Codes sophistication which is possible with microchip technology.\n\nOne additional type of parity-check encoding scheme differs from those described previously in that it does not require the data to be grouped into blocks. Instead, the data is treated as a stream of information bits into which parity bits are interspersed according to standard rules of encoding. The process is more involved than some of the other schemes and is typically reserved for higher-data-speed applications. Convolutional codes, as these are called, are particularly well suited to systems which utilize forward error-correcting procedures as described below.\n\n### Error Correction:\n\nDetecting errors is clearly of little use unless methods are available for the correction of the detected errors. Correction is thus an important aspect of data transmission.\n\n### Retransmission:\n\nThe most popular method of error correction is retransmission of the erroneous information. For the retransmission to occur in the most expeditious manner, some form of automatic system is needed. A system which has been developed and is in use is called the automatic request for repeat (ARQ), also called the positive acknowledgment/negative acknowledgment (ACK/NAK) method. The request for repeat system transmits data as blocks. The parity for each block is checked upon receipt, and if no parity discrepancy is noted, a positive acknowledgment (ACK) is sent to the transmit station and the next block is transmitted. If, however, a parity error is detected, a negative acknowledgment (NAK) is made to the transmit station which will repeat the block of data. The parity check is again made and transmission continues according to the result of the parity check. The value of this kind of system stems from its ability to detect errors after a small amount of data has been sent. If retransmission is needed, the redundant transmission time is held to a minimum. This is much more efficient than retransmission of the total message if only one or two data errors have occurred.\n\n### Forward Error-Correcting Codes:\n\nFor transmission efficiency, error correction at the receiver without retransmission of erroneous data is naturally preferred, and a number of methods of accomplishing this are available. Codes which permit correction of errors by the receive station without retransmission are called forward error-correcting codes. The basic requirement of such codes is that sufficient redundancy be included in the transmitted data for error correction to be properly accomplished by the receiver without further input from the transmitter.", null, "One forward error-correcting code is the matrix sum, shown in Figure 14-31, which illustrates the use of a three-level matrix sum system. Note that the sum of the rows is equal to the sum of the columns; this is important for the encoding scheme’s ability to find and correct errors. The transmitted message consists of the information bits plus the letters representing the sum of each column and row and the total. When received, the matrix is reconstructed and the sums are checked to determine whether they agree with the original sums. If they agree, error:free transmission is assumed, but if they disagree, errors must be present. The value of using this method is that it makes it possible for the receiver not only to determine which sums are incorrect but also to correct the erroneous values. In Figure 14-31a, note that the row and column discrepancies identify the matrix cell that is incorrect. By replacing the incorrect number with the value which agrees with the check sums, the message can be restored to the correct form. Such error correction requires intervention by a computer or by a smart terminal of some kind. The transmission efficiency also suffers when this kind of code is used.\n\nIf retransmission is used instead, the redundancy it requires can easily offset the inefficiency of the matrix sum code. Forward enor correction is particularly well suited to applications which place a high value on the timeliness of data reception.\n\nA three-level matrix sum code will provide for approximately 90 percent error-correction confidence. Larger matrices will increase this confidence level significantly, and it may be shown that a nine-level matrix will provide a 99.9 percent confidence level. The larger matrix has the additional benefit of increasing the ratio of information bits to error check bits. The result of this is increased transmission efficiency, 81 percent for the nine-level matrix versus 56 percent for the three-level matrix.\n\nAn interesting Error Detection and Correction Codes is the hamming code, named for R. W. Hamming, an error-correction pioneer. This code adds several parity-check bits to a data word, Consider the data word 1101. The hamming code adds three parity bits to the data bits as shown below:", null, "The first parity bit, P1, provides even parity from a check of bit locations 3, 5, and 7, which are 1, 1, and 1, respectively. P1 will therefore be 1 to achieve even parity. P2 checks locations 3, 6, and 7 and is therefore a 0 in this case. Finally, P3 checks locations 5, 6, and 7 and is a 0 here. The resulting 7-bit word is:", null, "If the data word is altered during transmission, so that location five changes from a 1 to a 0, the parity will no longer be correct. The hamming encoding permits evaluation of the parity bits to determine where errors occur. This is accomplished by assigning a 1 to any parity bit which is incorrect and a 0 to one which is correct. If the three parity bits are all correct, 0 0 0 results and no errors can be assumed. In the case of the above described error, the code has the form:", null, "P1 (which checks location 3, 5, and 7) should now be a 1 and is therefore incorrect. It will be given a 1. P2 checks 3, 6, and 7 and is therefore still correct. It receives a value of 0. P3 checks 5, 6, and 7 and should be a 1, but it is wrong here, and so it receives a value of 1. The three values result in the binary word 1 0 1, which has a decimal value of 5. This means that the location containing the error is five, and the receiver has been able to pinpoint the error without retransmission of data.\n\nThe hamming code is therefore capable of locating a single error, but it fails if multiple errors occur in the one data block.\n\nCodes such as the hagelbarger and bose-chaudhuri are capable of detecting and correcting multiple errors, by increasing the number of parity bits to accomplish their error correction. In the case of the hagelbarger code, one parity bit is sent after each data bit. This represents 100 percent redundancy. It may be shown that the code can correct up to six consecutive errors, but error bursts must be separated by large blocks of correct data bits. The bose-chaudhuri code can be implemented in several forms with different ratios of parity bits to data bits. The code was first implemented with 10 parity bits per 21 data bits. Redundancy again approaches 100 percent.", null, "Figure 14-32 illustrates the use of shift registers and logic devices to implement the hagelbarger code. The increased complexity and decreased transmission efficiency are offset by improved immunity to transmission errors for data requiring high degrees of accuracy.\n\nScroll to Top" ]
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https://www.geeksforgeeks.org/category/school-programming/page/28/
[ "Python program to convert time from 12 hour to 24 hour format\n\nGiven a time in 12-hour AM/PM format, convert it to military (24-hour) time. Note : Midnight is 12:00:00 AM on a 12-hour clock and 00:00:00… Read More »\n\nPHP programs for printing pyramid patterns\n\nThis article is aimed at giving a PHP implementation for pattern printing. Simple Pyramid Pattern filter_none edit close play_arrow link brightness_4 code <?php // Php… Read More »\n\nFinding n-th term of series 3, 13, 42, 108, 235…\n\nGiven a number n, find the n-th term in the series 3, 13, 42, 108, 235… Examples: Input : 3 Output : 42 Input :… Read More »\n\nProgram for sum of cos(x) series\n\nGiven n and b, where n is the number of terms in the series and b is the value of the angle in degree. Program… Read More »\n\nProgram for scalar multiplication of a matrix\n\nGiven a matrix and a scalar element k, our task is to find out the scalar product of that matrix. Examples: Input : mat[][] =… Read More »\n\nFind array elements that are greater than average\n\nGiven an array of numbers, print all those elements that are greater than average. Examples: Input : 5, 4, 6, 9, 10 Output : 9… Read More »\n\nProgram to calculate value of nCr\n\nFollowing are common definition of Binomial Coefficients. A binomial coefficient C(n, k) can be defined as the coefficient of X^k in the expansion of (1… Read More »\n\nSum of digits written in different bases from 2 to n-1\n\nGiven a number n, find the sum of digits of n when represented in different bases from 2 to n-1. Examples: Input : 5 Output… Read More »\n\nPrime String\n\nGiven a String str , the task is to check if the sum of ASCII value of all characters is a Prime Number or not.… Read More »\n\nTypes of Operating Systems\n\nAn Operating System performs all the basic tasks like managing file,process, and memory. Thus operating system acts as manager of all the resources, i.e. resource… Read More »\n\nProgram to check if first and the last characters of string are equal\n\nWe are given a string, we need check whether the first and last characters of the string str are equal or not. Case sensitivity is… Read More »\n\nGiven equation of a circle as string, find area\n\nGiven an equation of the circle X2 + Y2 = R2 whose center at origin (0, 0) and the radius is R. The task is… Read More »\n\nPerfect Square String\n\nGiven a String str and the task is to check sum of ASCII value of all characters is a perfect square or not. Examples :… Read More »\n\nReaching a point using clockwise or anticlockwise movements\n\nGiven starting and ending position and a number N. Given that we are allowed to move in only four directions as shown in the image… Read More »" ]
[ null ]
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https://www.automateexcel.com/vba/on-off-calculations/
[ "# VBA – Turn Automatic Calculations Off (or On)\n\nWhenever you update a cell value, Excel goes through a process to recalculate the workbook. When working directly within Excel you want this to happen 99.9% of the time (the exception being if you are working with an extremely large workbook). However, this can really slow down your VBA code. It’s a good practice to set your calculations to manual at the beginning of macros and restore calculations at the end of macros. If you need to recalculate the workbook you can manually tell Excel to calculate.\n\n## Turn Off Automatic Calculations\n\nYou can turn off automatic calculation with a macro by setting it to xlmanual. Use the following piece of VBA code:\n\n``Application.Calculation = xlManual``\n\n## Turn Automatic Calculations Back On\n\nTo turn back on automatic calculation with the setting xlAutomatic:\n\n``Application.Calculation = xlAutomatic``\n\nI recommend disabling Automatic calculations at the very beginning of your procedure and re-enabling Automatic Calculations at the end. It will look like this:\n\n## Disable Automatic Calculations Macro Example\n\n``````Sub Auto_Calcs_Example()\nApplication.Calculation = xlManual\n\n'Do something\n\nApplication.Calculation = xlAutomatic\nEnd Sub``````", null, "## Manual Calculation\n\nWhen Automatic calculations are disabled, you can use the Calculate command to force Excel to recalculate:\n\n``Calculate``\n\nYou can also tell Excel to recalculate only an individual worksheet:\n\n``Worksheets(\"sheet1\").Calculate``\n\nYou can also tell VBA to recalculate just a range (click to read our article about VBA calculation methods)\n\nHere is how this might look inside a macro:\n\n``````Sub Auto_Calcs_Example_Manual_Calc()\nApplication.Calculation = xlManual\n\n'Do Something\n\n'Recalc\nCalculate\n\n'Do More Things\n\nApplication.Calculation = xlAutomatic\nEnd Sub``````\n\n### VBA Settings – Speed Up Code\n\nIf your goal is to speed up your code, you should also consider adjusting these other settings:\n\nDisabling Screenupdating can make a huge difference in speed:\n\n``Application.ScreenUpdating = False``\n\nTurning off the Status Bar will also make a small difference:\n\n``Application.DisplayStatusBar = False``\n\nIf your workbook contains events you should also disable events at the start of your procedures (to speed up code and to prevent endless loops!):\n\n``Application.EnableEvents = False``\n\nLast, your VBA code can be slowed down when Excel tries to re-calculate page breaks (Note: not all procedures will be impacted).  To turn off DisplayPageBreaks use this line of code:\n\n``ActiveSheet.DisplayPageBreaks = False``\n\n## VBA Coding Made Easy\n\nStop searching for VBA code online. Learn more about AutoMacro – A VBA Code Builder that allows beginners to code procedures from scratch with minimal coding knowledge and with many time-saving features for all users!", null, "" ]
[ null, "https://www.automateexcel.com/excel/wp-content/uploads/2019/09/vba-disable-automatic-calculations.png", null, "https://www.automateexcel.com/excel/images/AutoMacro/vba-for-each-ws-in-worksheets.png", null ]
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https://metanumbers.com/41756
[ "## 41756\n\n41,756 (forty-one thousand seven hundred fifty-six) is an even five-digits composite number following 41755 and preceding 41757. In scientific notation, it is written as 4.1756 × 104. The sum of its digits is 23. It has a total of 5 prime factors and 24 positive divisors. There are 17,280 positive integers (up to 41756) that are relatively prime to 41756.\n\n## Basic properties\n\n• Is Prime? No\n• Number parity Even\n• Number length 5\n• Sum of Digits 23\n• Digital Root 5\n\n## Name\n\nShort name 41 thousand 756 forty-one thousand seven hundred fifty-six\n\n## Notation\n\nScientific notation 4.1756 × 104 41.756 × 103\n\n## Prime Factorization of 41756\n\nPrime Factorization 22 × 11 × 13 × 73\n\nComposite number\nDistinct Factors Total Factors Radical ω(n) 4 Total number of distinct prime factors Ω(n) 5 Total number of prime factors rad(n) 20878 Product of the distinct prime numbers λ(n) -1 Returns the parity of Ω(n), such that λ(n) = (-1)Ω(n) μ(n) 0 Returns: 1, if n has an even number of prime factors (and is square free) −1, if n has an odd number of prime factors (and is square free) 0, if n has a squared prime factor Λ(n) 0 Returns log(p) if n is a power pk of any prime p (for any k >= 1), else returns 0\n\nThe prime factorization of 41,756 is 22 × 11 × 13 × 73. Since it has a total of 5 prime factors, 41,756 is a composite number.\n\n## Divisors of 41756\n\n1, 2, 4, 11, 13, 22, 26, 44, 52, 73, 143, 146, 286, 292, 572, 803, 949, 1606, 1898, 3212, 3796, 10439, 20878, 41756\n\n24 divisors\n\n Even divisors 16 8 4 4\nTotal Divisors Sum of Divisors Aliquot Sum τ(n) 24 Total number of the positive divisors of n σ(n) 87024 Sum of all the positive divisors of n s(n) 45268 Sum of the proper positive divisors of n A(n) 3626 Returns the sum of divisors (σ(n)) divided by the total number of divisors (τ(n)) G(n) 204.343 Returns the nth root of the product of n divisors H(n) 11.5157 Returns the total number of divisors (τ(n)) divided by the sum of the reciprocal of each divisors\n\nThe number 41,756 can be divided by 24 positive divisors (out of which 16 are even, and 8 are odd). The sum of these divisors (counting 41,756) is 87,024, the average is 3,626.\n\n## Other Arithmetic Functions (n = 41756)\n\n1 φ(n) n\nEuler Totient Carmichael Lambda Prime Pi φ(n) 17280 Total number of positive integers not greater than n that are coprime to n λ(n) 360 Smallest positive number such that aλ(n) ≡ 1 (mod n) for all a coprime to n π(n) ≈ 4365 Total number of primes less than or equal to n r2(n) 0 The number of ways n can be represented as the sum of 2 squares\n\nThere are 17,280 positive integers (less than 41,756) that are coprime with 41,756. And there are approximately 4,365 prime numbers less than or equal to 41,756.\n\n## Divisibility of 41756\n\n m n mod m 2 3 4 5 6 7 8 9 0 2 0 1 2 1 4 5\n\nThe number 41,756 is divisible by 2 and 4.\n\n• Arithmetic\n• Abundant\n\n• Polite\n\n## Base conversion (41756)\n\nBase System Value\n2 Binary 1010001100011100\n3 Ternary 2010021112\n4 Quaternary 22030130\n5 Quinary 2314011\n6 Senary 521152\n8 Octal 121434\n10 Decimal 41756\n12 Duodecimal 201b8\n20 Vigesimal 547g\n36 Base36 w7w\n\n## Basic calculations (n = 41756)\n\n### Multiplication\n\nn×i\n n×2 83512 125268 167024 208780\n\n### Division\n\nni\n n⁄2 20878 13918.7 10439 8351.2\n\n### Exponentiation\n\nni\n n2 1743563536 72804239009216 3040013804068823296 126938816402697785547776\n\n### Nth Root\n\ni√n\n 2√n 204.343 34.6928 14.2949 8.39738\n\n## 41756 as geometric shapes\n\n### Circle\n\n Diameter 83512 262361 5.47757e+09\n\n### Sphere\n\n Volume 3.04962e+14 2.19103e+10 262361\n\n### Square\n\nLength = n\n Perimeter 167024 1.74356e+09 59051.9\n\n### Cube\n\nLength = n\n Surface area 1.04614e+10 7.28042e+13 72323.5\n\n### Equilateral Triangle\n\nLength = n\n Perimeter 125268 7.54985e+08 36161.8\n\n### Triangular Pyramid\n\nLength = n\n Surface area 3.01994e+09 8.58006e+12 34093.6\n\n## Cryptographic Hash Functions\n\nmd5 42c12185a3238db19e1ff3993c120902 72fc0989319fc350199bac53faecd3b265223f19 d1bbb3bb1d4a5fd50f5d1f9e535bca5305a521441737b29cd93fa8af43b7e36d 8d328c96aee692eac2cdb7f435d841f1bb9719891bb1add2219ee5053f4b2c7bf6e634f8fde9a90ae1e274afd4e84636fcc6f2c9068046ba6fbb5eb4208a42ea 85c850366beab80661438c0c5f23148c6e766a11" ]
[ null ]
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http://blog.agapechristianchurch.net/e8nk6/fe9d31-moseley%27s-law-pdf
[ "# moseley's law pdf\n\n0000001288 00000 n xref \\end{align} For $L$ series, the value of $a$ is $\\sqrt{5Rc/36}$ and $b$ is 7.4. This experiment verifies Moseley’s law by measuring the Kabsorption edges - for the atomic numbers Z between 40 and 50. X-ray Fluorescence and Moseley’s Law (pdf article on X-rays, Moseley's Law and Moseley's Experiments) The Physical (in)significance of Moseley's Screening Parameters, (journal article in pdf by K Razi Naqvi) One hundred years of Moseley’s law: An undergraduate experiment with relativistic effects (pdf) 0000006771 00000 n 0000009521 00000 n Homework Statement I need to use Mosley law to find Kalpha2 for lead where Z=82 What i know is that copper (z=29) and tin (z=50) have kalpha2 of 8.03 and 25 KeV respectively Homework Equations Mosley law b . The relation and values of $a$ and $b$ are experimentally determined by Henry Moseley. The law had been discovered and published by the English physicist Henry Moseley in 1913-1914. %PDF-1.4 %���� The characteristic X-ray is emitted when an electron in $L$ shell makes a transition to the vacant state in $K$ shell. Thus, by substituting values, Intensity of the characteristic X-rays depends on the electrical power given to the X-ray tube. 0000011174 00000 n Wavelength of characteristic X-rays decreases when the atomic number of the target increases. 0000008558 00000 n The frequency $\\nu$ of characteristic X-rays is related to atomic number $Z$ by Moseley's law, 41 32 This law was experimentally established by H. Moseley in 1913. 1). 0 hc/\\lambda=eV,\\nonumber Moseley’s Law. \\end{align} Thus, the wavelength of emitted X-rays decreases with increase in $Z$. What is moseleys law. In this formula, Characteristic and Continuous X-rays | Problems | IIT JEE, Concepts of Physics Part 2 by HC Verma (Link to Amazon), IIT JEE Physics by Jitender Singh and Shraddhesh Chaturvedi, X-ray Fluorescence and Moseley’s Law (pdf article on X-rays, Moseley's Law and Moseley's Experiments), The Physical (in)significance of Moseley's Screening Parameters, (journal article in pdf by K Razi Naqvi), One hundred years of Moseley’s law: An undergraduate experiment with relativistic effects (pdf). Until Moseley's work, \"atomic number\" was merely an element's place in the periodic table and was not known to be associated with any measurable physical quantity. Moseley’s Law Jitender Singh|www.concepts-of-physics.com October 1, 2019 1 Introduction The frequency n of a characteristic X-ray of an element is related to its atomic number Z by p n = a(Z b), where a and b are constants called proportionality and screening (or shielding) constants. \\sqrt{\\nu}=a(Z-b), 0000004618 00000 n \\begin{align} where $a$ and $b$ are constants called proportionality and screening (or shielding) constants. Moseleys law - Kalpha2 Thread starter mss90; Start date Nov 13, 2014; Nov 13, 2014 #1 mss90. \\begin{align} startxref \\frac{1}{\\lambda}&=R(Z-1)^2\\left[\\frac{1}{1^2}-\\frac{1}{2^2}\\right]\\nonumber\\\\ \\lambda=\\frac{c}{\\nu}=\\frac{c}{a^2(Z-b)^2}.\\nonumber 0000007603 00000 n X-ray Fluorescence and Moseley’s Law 1 Background 1.1 Ordering of the periodic table The 19th century saw many efforts to arrange the elements in a sensible order. Which of the following statements is wrong in the context of X-rays generated from a X-ray tube? Moseley's law . \\sqrt{\\nu}=a(Z-b),\\nonumber Chapter I GENERAL PROVISIONS Article 1.-Scope of application 0000003346 00000 n \\sqrt{\\nu}=a(Z-b), 0000002709 00000 n where $V$ is the accelerating potential. Free kindle book and epub digitized and proofread by Project Gutenberg. i.e ν α Z 2 or rt(ν) = a ( Z − b) Here $R$ is Rydberg's constant and $c$ is speed of light (as in Bohr's model). The chemist John Dalton prepared one of the first tables of the elements in 1803, ordering them by increasing atomic weight. In this range, the screening coefficient σ . X-Ray Spectroscopy and Moseley’s Law X-ray spectroscopy is used to study inner shell phenomena of atoms, states of highly ionized atoms produced by accelerators or to determine material properties. &=1.1\\times{10}^{7} (Z-1)^2\\left[\\frac{1}{1^2}-\\frac{1}{2^2}\\right],\\nonumber Equation (2) can be derived by solving Schr€odinger’s equa-tion for an atomic system with an infinitely heavy nucleus and one electron. It is our personal responsibility to ask for the things we need in our lives. The elements with higher atomic number (molybdenum in this example) gives high energy X-rays (short wavelengths). 0000006641 00000 n \\frac{\\lambda_\\mathrm{Cu}}{\\lambda_\\mathrm{Mo}}=\\frac{(Z_\\mathrm{Mo}-1)^2}{(Z_\\mathrm{Cu}-1)^2}=\\frac{(41)^2}{(28)^2}=2.14.\\nonumber Moseley’s law is quite accurate in predicting the energy of K X-rays for the first row transitions metals, but it starts to deviate substantially as one moves to higher Z elements. With the Law of Request, we are NOT to impose ourselves upon others. LAW ON TOURISM (Law No. &=\\frac{3}{4}R(Z-1)^2. \\end{align} 0000007213 00000 n 0000010945 00000 n 29 0. %%EOF If you push yourself upon … But there are still legal phrases that baffle non-lawyers. \\begin{align} This is known as Moseley's law. The intensity of X-rays depends on the number of electrons striking the target per second, which, in turn, depends on the electrical power given to the X-ray tube as energy of each electron is $eV$. The language used in law is changing. 41 0 obj <> endobj 17). Moseley's law is an empirical law concerning the characteristic x-rays emitted by atoms. 0000001368 00000 n Characteristic X-rays of frequency ${4.2\\times{10}^{18}}$  Hz are produced when transitions from $L$-shell to $K$-shell take place in a certain target material. Z 20 25 30 35 40 45 50 55 60 65 70 σ-1.5-1.0-0.5 0 0.5 1.0 1.5 2.0 2.5 σ Kα Zeroth Order σ Kβ Zeroth Order σ Kα First Order σ Kβ First Order which gives If $\\lambda_\\mathrm{Cu}$ is the wavelength of $K_\\alpha$ X-ray line of copper (atomic number 29) and $\\lambda_\\mathrm{Mo}$ is the wavelength of the $K_\\alpha$ X-ray line of molybdenum (atomic number 42), the the ratio $\\lambda_\\mathrm{Cu}/\\lambda_\\mathrm{Mo}$ is close to. \\begin{align} Question: If 178.5 pm is the wavelength of X-ray line of copper (atomic number 29) and 71 pm is the wavelength of the X-ray line of molybdenum (atomic number 42) then the value of a and b in Moseley's equation are, Question: Moseley's Law for characteristic X-rays is $\\sqrt{\\nu}=a(Z-b)$." ]
[ null ]
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https://math.stackexchange.com/questions/2268569/how-do-we-formally-identify-objects-using-isomorphisms
[ "# How do we formally “identify” objects using isomorphisms?\n\ni don't have much background in set theory and mathematical logic besides isomorphisms thus i can't quite understand(justify) the way of \"identifying\" integers with naturals in Tao's analysis.\n\nThat's how i interpret what i have read so far about integers: he constructs integers from naturals(integers are elements of the set $N×N$ so they are not the same objects as naturals he defined earlier, he uses a notation $(a,b):=a-b$ for them). He defines equality $\"=\"$ relation between integers(based on equality between naturals), and operations of additions and multiplication for integers(again in terms of natural numbers) After that I have a problem with understanding his next paragraph:\n\nThe integers $n−0$ behave in the same way as the natural numbers n; indeed one can check that $(n−0) + (m−0) = (n + m)−0$ and $(n−0) × (m−0) = nm−0$.\n\nI know it should mean something like this$:$ if we map $(n,0)$ with $n$, then we have a function $f:A⊆N×N↦N$, where $A$ consists of integers of the form $(n,0)$ that has properties that if $a+b=c$ ($\"+\"$ and $\"=\"$ are those defined for integers), then $f(a)+f(b)=f(c)$($\"+\"$ and $\"=\"$ are those defined for naturals) and if $a×b=c$ then $f(a)×f(b)=f(c)$(same thing with $\"×\"$ and $\"=\"$)\n\nFurthermore, $(n−0)$ is equal to $(m−0)$ if and only if $n = m$.\n\nI guess that means that $f$ is injection. Though it's a surjection too.\n\n(The mathematical term for this is that there is an isomorphism between the natural numbers $n$ and those integers of the form $n−0$). Thus we may \"identify\" the natural numbers with integers by setting $n ≡ n−0$;\n\nFrom here it begins: What exactly does $\"≡\"$ sign mean? Does it stand for my function $f$? Or is it some new relation for integers like our already defined relation $\"=\"$ but he just doesn't want to overload the sign $\"=\"$ or something?\n\nthis does not affect our definitions of addition or multiplication or equality since they are consistent with each other. Thus for instance the natural number $3$ is now considered to be the same as the integer $3−0: 3 = 3−0$.\n\nHey now i wonder what $\"=\"$ sign means, because we have $\"=\"$ for naturals, $\"=\"$ for integers but we don't have $\"=\"$ for integer-naturals(did he define it implicitly?)(is it the same sign as $\"≡\"$?)\n\nIn particular $0$ is equal to $0−0$ and 1 is equal to $1−0$. Of course, if we set $n$ equal to $n−0$, then it will also be equal to any other integer which is equal to $n−0$, for instance $3$ is equal not only to $3−0$, but also to $4−1$, $5−2$, etc.\n\nWe can now define incrementation on the integers by defining $x++ := x + 1$ for any integer $x$; this is of course consistent with our definition of the increment operation for natural numbers.\n\nHow does this operation work? It looks like it has only one argument from $N×N$ but then computing an output it uses $1$ from $N$ so operation $\"+\"$ has one argument from $N×N$ and the other from N and how it supposed to react to this?!\n\nSo basically all my questions are about what we can do with isomorpisms and why we can do it.\n\n## 2 Answers\n\nYour basic understanding is correct. In your paragraph under the first colored box we need $f$ to be a bijection. He claims that in the second colored box with the if and only if. The sign $\\equiv$ is read \"equivalent to\". Once you have shown that $f$ is an isomorphism we can consider the two things related by $f$ as the same for whatever purpose we have in mind. Constructing the integers as ordered pairs of naturals is formally nice, but representing the integers as equivalence classes of naturals is very clumsy. We would like to get back to the notation we are used to with the positive integers and zero using the same symbols as naturals and the negative integers using the naturals with a minus sign prefixed. The equivalence sign shows which integer as an ordered pair corresponds to which natural. In the part you quote he never points out that the integers as ordered pairs are really equivalence classes of ordered pairs, though it is implied when he talks about $3-0$ being equal to $4-1, 5-2,$etc. I am sure that point is made in the article. I am not crazy about writing $3=3-0$ as he does because (as you say) this is asserting equality between two different sorts of objects. What he has done is give the traditional names to the integers as single numbers which may be preceded by a minus sign instead of the equivalence classes of ordered pairs. He asserts that the formal incrementation operator works on the integers just as you would expect. The basic point is that once you have an isomorphism you can think of it as having two different descriptions of the same object. You have the formally defined integers as equivalence classes and you have the informally defined integers as the naturals plus the negatives. He is showing that the informally defined ones with the rules we are used to work the same as the formally defined ones, then will say that it is much easier to write the informally defined ones so we will use that notation in the future.\n\n• Yes, you are right, i have omitted that $a−b$ is the space of all pairs equivalent to $(a, b): a−b := {(c, d) ∈ N × N : (a, b) ∼ (c, d)}$. And i just now realized, that a-b is a set, not an object.(The thing is, Tao decided to distinguish some sets and objects, and functions can only map objects from some sets to objects in other sets, so how do we define addition and multiplication on sets? Because the way that he defines them needs some correction i guess >The sum of two integers, $(a−b) + (c−d)$, is defined by the formula $(a−b) + (c−d) := (a + c)−(b + d)$. – famesyasd May 6 '17 at 16:02\n• does it get to notify you if i don't mention your name in my comment? also i typed your name in comment and after that it disappeared, weird @Ross Millikan – famesyasd May 6 '17 at 16:56\n• @famesyasd: I get notified if you comment on one of my posts. You can also notify somebody with at before their user name like I did here for you. I do that to respond to someone in a comment chain who is not the poster of the answer or question. I think you would have been notified anyway because it is your question. You can only use one at per comment. – Ross Millikan May 6 '17 at 19:30\n• What makes this hard is we all know where this is going. We learn about naturals, integers, rationals and reals before we learn the word axiom. We all know the naturals are a subset of the integers, but in the formal construction they are not because the integers are equivalence classes of pairs of naturals. Then the integers are not a subset of the rationals because the rationals are equivalence classes of pairs of integers. We put a lot of work into the construction, then throw it all away and use the integers and rationals we are used to. – Ross Millikan May 6 '17 at 19:35\n• The point of the isomorphism discussion is to define a subset of the integers which we can consider the naturals. We can then use the naturals for those equivalence classes of ordered pairs of naturals. This suggests the usual notation for the negative integers and we are back to the notation we grew up with. – Ross Millikan May 6 '17 at 19:37\n\nLots of questions here. I will suggest an answer to the last one\n\nSo basically all my questions are about what we can do with isomorphisms and why we can do it.\n\nin hopes that it helps with the rest.\n\nWhat Tao is doing is to show you how to construct formally what you intuitively understand as the integers $\\{ \\ldots, -3, -2, -1, 0, 1, 2, \\ldots \\}$ assuming that you know everything necessary about the natural numbers $\\{0, 1, 2, \\ldots \\}$,\n\nSince he wants to write with formal set theory vocabulary, unravelling the notation can be challenging.\n\nThe idea he wants to capture is that a \"missing\" negative number like $-5$ can be defined by the expression \"$2-7$\" even though that expression has no meaning in the natural numbers. But you must be careful, because \"$2-7$\" and \"$96-99$\" and \"$0-5$\" all capture the essence of the missing $-5$. So he tells you just when two of those expressions (each constructed from a pair of natural numbers) should count as the same integer, using the ordinary arithmetic properties of the natural numbers. The $\\equiv$ sign between two such expressions says the represent that \"same integer\". In formal terms, $\\equiv$ is an equivalence relation and Tao defines the integer represented by any of those pairs as the equivalence class - the set of all the pairs. It's how you might define the rational numbers once you know the integers as pairs of integers, where $(1,2)$, $(2,4)$ and $(75, 150)$ all represent the same rational, one half. (Tau may well do this next.)\n\nHaving done this and checked all the arithmetic facts about these new things called \"integers\" using only the properties of the natural numbers. he wants to back away from the formal structure. To that end he shows you that there's a faithful copy of natural numbers inside the \"integers\" he's constructed. That's the essence of the function $f$.\n\nOnce that's done you can forget that $5$ is (formally) the set of all the pairs that are equivalent ($\\equiv$) to $(5,0)$ and that $-5$ is (formally) the set of all the pairs that are equivalent ($\\equiv$) to $(0,-5)$. Then can go about business as usual with $\\{ \\ldots, -3, -2, -1, 0, 1, 2, \\ldots \\}$.\n\nEdit:\n\nIn a comment you ask if this is \"overloading\" the natural numbers. That's a formal term from computer science, describing a situation where (say) the meaning of an operator symbol like \"$+$\" depends on the context. That operator is overloaded here, since it's used both for adding natural numbers and for adding the integers defined as sets of pairs of natural numbers. I'm not sure whether you'd say the natural numbers are themselves overloaded. It's more the opposite: two different representations of the same thing. The $5$ and the $0$ in the integer represented by $(5,0)$ are natural numbers. When you identify that pair with $5$ the symbol \"5\" stands for both $5$ and the equivalence class of $(5,0)$. Since the embedding $f$ is injective you won't get into trouble reusing the name. You've actually done this kind of thing before. When you work with polynomials you unthinkingly use the natural embedding of the integers into the ring of polynomials. \"5\" can mean the constant polynomial $5$ or the coefficient in the polynomial $5x$.\n\n• Shouldn't $-5$ be $2-7$? (etc) – ancientmathematician May 6 '17 at 15:37\n• @ancientmathematician yes it should, but whatever :p – famesyasd May 6 '17 at 16:08\n• @Ethan Bolker >The $≡$ sign between two such expressions says the represent that \"same integer\". Do you mean by $\"≡\"$ here actually $\"=\"$ defined for integers in terms of natural numbers, right? And then you refer to it in >$5$ is (formally) the set of all the pairs that are equivalent $≡$ to $(5,0)$ have you overloaded it for natural-integers? or does isomorphism allow some sort of substitution, is there some kind of axiom for that? i think i grasped the idea, but i want to understand on formal level why we can rename integers as $5$, $-5$ due to that isomorphism. – famesyasd May 6 '17 at 16:16\n• @ancientmathematician Fixed, thanks. You could have edited yourself and made the correction. – Ethan Bolker May 6 '17 at 18:09\n• @famesyasd See my edit on overloading. – Ethan Bolker May 6 '17 at 18:09" ]
[ null ]
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https://netlib.org/lapack/explore-html-3.6.1/d7/d43/group__aux_o_t_h_e_rauxiliary_gac354f735ef3e53f9ca32242d2db96f74.html
[ "", null, "LAPACK  3.6.1 LAPACK: Linear Algebra PACKage\n subroutine dlasd5 ( integer I, double precision, dimension( 2 ) D, double precision, dimension( 2 ) Z, double precision, dimension( 2 ) DELTA, double precision RHO, double precision DSIGMA, double precision, dimension( 2 ) WORK )\n\nDLASD5 computes the square root of the i-th eigenvalue of a positive symmetric rank-one modification of a 2-by-2 diagonal matrix. Used by sbdsdc.\n\nPurpose:\n``` This subroutine computes the square root of the I-th eigenvalue\nof a positive symmetric rank-one modification of a 2-by-2 diagonal\nmatrix\n\ndiag( D ) * diag( D ) + RHO * Z * transpose(Z) .\n\nThe diagonal entries in the array D are assumed to satisfy\n\n0 <= D(i) < D(j) for i < j .\n\nWe also assume RHO > 0 and that the Euclidean norm of the vector\nZ is one.```\nParameters\n [in] I ``` I is INTEGER The index of the eigenvalue to be computed. I = 1 or I = 2.``` [in] D ``` D is DOUBLE PRECISION array, dimension ( 2 ) The original eigenvalues. We assume 0 <= D(1) < D(2).``` [in] Z ``` Z is DOUBLE PRECISION array, dimension ( 2 ) The components of the updating vector.``` [out] DELTA ``` DELTA is DOUBLE PRECISION array, dimension ( 2 ) Contains (D(j) - sigma_I) in its j-th component. The vector DELTA contains the information necessary to construct the eigenvectors.``` [in] RHO ``` RHO is DOUBLE PRECISION The scalar in the symmetric updating formula.``` [out] DSIGMA ``` DSIGMA is DOUBLE PRECISION The computed sigma_I, the I-th updated eigenvalue.``` [out] WORK ``` WORK is DOUBLE PRECISION array, dimension ( 2 ) WORK contains (D(j) + sigma_I) in its j-th component.```\nDate\nSeptember 2012\nContributors:\nRen-Cang Li, Computer Science Division, University of California at Berkeley, USA\n\nDefinition at line 118 of file dlasd5.f.\n\n118 *\n119 * -- LAPACK auxiliary routine (version 3.4.2) --\n120 * -- LAPACK is a software package provided by Univ. of Tennessee, --\n121 * -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--\n122 * September 2012\n123 *\n124 * .. Scalar Arguments ..\n125  INTEGER i\n126  DOUBLE PRECISION dsigma, rho\n127 * ..\n128 * .. Array Arguments ..\n129  DOUBLE PRECISION d( 2 ), delta( 2 ), work( 2 ), z( 2 )\n130 * ..\n131 *\n132 * =====================================================================\n133 *\n134 * .. Parameters ..\n135  DOUBLE PRECISION zero, one, two, three, four\n136  parameter ( zero = 0.0d+0, one = 1.0d+0, two = 2.0d+0,\n137  \\$ three = 3.0d+0, four = 4.0d+0 )\n138 * ..\n139 * .. Local Scalars ..\n140  DOUBLE PRECISION b, c, del, delsq, tau, w\n141 * ..\n142 * .. Intrinsic Functions ..\n143  INTRINSIC abs, sqrt\n144 * ..\n145 * .. Executable Statements ..\n146 *\n147  del = d( 2 ) - d( 1 )\n148  delsq = del*( d( 2 )+d( 1 ) )\n149  IF( i.EQ.1 ) THEN\n150  w = one + four*rho*( z( 2 )*z( 2 ) / ( d( 1 )+three*d( 2 ) )-\n151  \\$ z( 1 )*z( 1 ) / ( three*d( 1 )+d( 2 ) ) ) / del\n152  IF( w.GT.zero ) THEN\n153  b = delsq + rho*( z( 1 )*z( 1 )+z( 2 )*z( 2 ) )\n154  c = rho*z( 1 )*z( 1 )*delsq\n155 *\n156 * B > ZERO, always\n157 *\n158 * The following TAU is DSIGMA * DSIGMA - D( 1 ) * D( 1 )\n159 *\n160  tau = two*c / ( b+sqrt( abs( b*b-four*c ) ) )\n161 *\n162 * The following TAU is DSIGMA - D( 1 )\n163 *\n164  tau = tau / ( d( 1 )+sqrt( d( 1 )*d( 1 )+tau ) )\n165  dsigma = d( 1 ) + tau\n166  delta( 1 ) = -tau\n167  delta( 2 ) = del - tau\n168  work( 1 ) = two*d( 1 ) + tau\n169  work( 2 ) = ( d( 1 )+tau ) + d( 2 )\n170 * DELTA( 1 ) = -Z( 1 ) / TAU\n171 * DELTA( 2 ) = Z( 2 ) / ( DEL-TAU )\n172  ELSE\n173  b = -delsq + rho*( z( 1 )*z( 1 )+z( 2 )*z( 2 ) )\n174  c = rho*z( 2 )*z( 2 )*delsq\n175 *\n176 * The following TAU is DSIGMA * DSIGMA - D( 2 ) * D( 2 )\n177 *\n178  IF( b.GT.zero ) THEN\n179  tau = -two*c / ( b+sqrt( b*b+four*c ) )\n180  ELSE\n181  tau = ( b-sqrt( b*b+four*c ) ) / two\n182  END IF\n183 *\n184 * The following TAU is DSIGMA - D( 2 )\n185 *\n186  tau = tau / ( d( 2 )+sqrt( abs( d( 2 )*d( 2 )+tau ) ) )\n187  dsigma = d( 2 ) + tau\n188  delta( 1 ) = -( del+tau )\n189  delta( 2 ) = -tau\n190  work( 1 ) = d( 1 ) + tau + d( 2 )\n191  work( 2 ) = two*d( 2 ) + tau\n192 * DELTA( 1 ) = -Z( 1 ) / ( DEL+TAU )\n193 * DELTA( 2 ) = -Z( 2 ) / TAU\n194  END IF\n195 * TEMP = SQRT( DELTA( 1 )*DELTA( 1 )+DELTA( 2 )*DELTA( 2 ) )\n196 * DELTA( 1 ) = DELTA( 1 ) / TEMP\n197 * DELTA( 2 ) = DELTA( 2 ) / TEMP\n198  ELSE\n199 *\n200 * Now I=2\n201 *\n202  b = -delsq + rho*( z( 1 )*z( 1 )+z( 2 )*z( 2 ) )\n203  c = rho*z( 2 )*z( 2 )*delsq\n204 *\n205 * The following TAU is DSIGMA * DSIGMA - D( 2 ) * D( 2 )\n206 *\n207  IF( b.GT.zero ) THEN\n208  tau = ( b+sqrt( b*b+four*c ) ) / two\n209  ELSE\n210  tau = two*c / ( -b+sqrt( b*b+four*c ) )\n211  END IF\n212 *\n213 * The following TAU is DSIGMA - D( 2 )\n214 *\n215  tau = tau / ( d( 2 )+sqrt( d( 2 )*d( 2 )+tau ) )\n216  dsigma = d( 2 ) + tau\n217  delta( 1 ) = -( del+tau )\n218  delta( 2 ) = -tau\n219  work( 1 ) = d( 1 ) + tau + d( 2 )\n220  work( 2 ) = two*d( 2 ) + tau\n221 * DELTA( 1 ) = -Z( 1 ) / ( DEL+TAU )\n222 * DELTA( 2 ) = -Z( 2 ) / TAU\n223 * TEMP = SQRT( DELTA( 1 )*DELTA( 1 )+DELTA( 2 )*DELTA( 2 ) )\n224 * DELTA( 1 ) = DELTA( 1 ) / TEMP\n225 * DELTA( 2 ) = DELTA( 2 ) / TEMP\n226  END IF\n227  RETURN\n228 *\n229 * End of DLASD5\n230 *\n\nHere is the caller graph for this function:" ]
[ null, "https://netlib.org/lapack/explore-html-3.6.1/lapack.png", null ]
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https://forum.arduino.cc/t/self-balancing-robot/595140
[ "", null, "Self balancing robot\n\nHey! I just started working on a self balancing robot. The robot is ready, but for the code i am having some issues.\n\nI wrote a code where just by measuring the tilt it controlls the speed of the motor. However it is able to balance just for a second or two.\n\n#include <Wire.h>\nint16_t AcX,AcY,AcZ,Tmp,GyX,GyY,GyZ;\nint minVal=265; int maxVal=402;\ndouble x;         //A4 (SDA), A5 (SCL)\ndouble y;\ndouble z;\nint lmt1=6;\nint lmt2=5;\nint rmt1=10;\nint rmt2=9;\n\nint value;\n\nvoid setup(){\npinMode(lmt1,OUTPUT);\npinMode(lmt2,OUTPUT);\npinMode(rmt1,OUTPUT);\npinMode(rmt2,OUTPUT);\nWire.begin();\nWire.write(0x6B); Wire.write(0);\nWire.endTransmission(true);\nSerial.begin(9600); }\n\nvoid loop(){\nWire.write(0x3B);\nWire.endTransmission(false);\nint xAng = map(AcX,minVal,maxVal,-90,90);\nint yAng = map(AcY,minVal,maxVal,-90,90);\nint zAng = map(AcZ,minVal,maxVal,-90,90);\n\n//-------------form here the changing of the x value is because the gyroscope is mounted vertically\n//                 so reucing value to 86(which is exact.). and other lines to modify the angle as the gyro as\n//                 it shows value from 0 to 360 but modified it to -180 to 180.\nx=x-86;\nif(x<0){\nx=x+360;\n}\nif(x>1){\nif(x<180){\nx=round(x);\nvalue=map(x,0,50,0,150);\nif(value>255){\nvalue=255;\n}\nSerial.println(value);\nanalogWrite(lmt1,value);\nanalogWrite(lmt2,0);\nanalogWrite(rmt1,value);\nanalogWrite(rmt2,0);\n}\nelse if(x>180){\nvalue=x-360;\nvalue=value*(-1);\nvalue=map(value,0,50,0,145);\nif(value>255){\nvalue=255;\n}\nSerial.println(value);\nanalogWrite(lmt1,0);\nanalogWrite(lmt2,value);\nanalogWrite(rmt1,0);\nanalogWrite(rmt2,value);\n}\n}\ndelay(20);\n}\n\nif you don’t understand the code its just the fact that if the robot tilts froward the robot goes forward. It works perfectly do no problem in the code. The problem it that it like continuously adjust making it super unstable. And i am using only forward and back motion but in the internet i say usage of left and right.\n\nI don’t think I am using the right technique to balance the robot.\nOther robot tutorial use stepper or other motors. I am using a simple DC motor.\n\nCould anyone help me with the program.\n\nThanks\n\nIf you ask for 14 bytes, you are expected to read all 14 of them. Reading only 6 of them will result in 8 being left for the next time you read.\n\nOf course, having asked for 14, and expecting there to be 14 to read, immediately, may not be reasonable.\n\nNor is reading 6 bytes without knowing that there ARE 6 bytes to read.\n\nif(x>1){\nif(x<180){\nx=round(x);\nvalue=map(x,0,50,0,150);\nif(value>255){\nvalue=255;\n}\n\nIf the value is between 1 and 180, map it as if it was on the range 0 to 50, to a range of 0 to 150 (i.e. multiply the value by 3), then constrain it to be less than or equal to 255. Just WHY you are doing that should be documented." ]
[ null, "https://aws1.discourse-cdn.com/arduino/original/3X/1/f/1f6eb1c9b79d9518d1688c15fe9a4b7cdd5636ae.svg", null ]
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https://git.sesse.net/?p=movit;a=commitdiff;h=42c35394ef92bb5179fc4879cb55b866fd422d28
[ "author Steinar H. Gunderson Wed, 10 Oct 2012 18:02:27 +0000 (20:02 +0200) committer Steinar H. Gunderson Wed, 10 Oct 2012 18:02:27 +0000 (20:02 +0200)\n Makefile patch | blob | history deconvolution_sharpen_effect.cpp [new file with mode: 0644] patch | blob deconvolution_sharpen_effect.frag [new file with mode: 0644] patch | blob deconvolution_sharpen_effect.h [new file with mode: 0644] patch | blob\n\nindex 028b855..c33d20a 100644 (file)\n--- a/Makefile\n+++ b/Makefile\n@@ -1,6 +1,6 @@\nCC=gcc\nCXX=g++\n-CXXFLAGS=-Wall -g\n+CXXFLAGS=-Wall -g \\$(shell pkg-config --cflags eigen3 )\nLDFLAGS=-lSDL -lSDL_image -lGL -lrt\n\n# Core.\n@@ -24,6 +24,7 @@ OBJS += diffusion_effect.o\nOBJS += glow_effect.o\nOBJS += mix_effect.o\nOBJS += resize_effect.o\n+OBJS += deconvolution_sharpen_effect.o\nOBJS += sandbox_effect.o\n\ntest: \\$(OBJS)\ndiff --git a/deconvolution_sharpen_effect.cpp b/deconvolution_sharpen_effect.cpp\nnew file mode 100644 (file)\nindex 0000000..aa77dd8\n--- /dev/null\n@@ -0,0 +1,438 @@\n+// NOTE: Throughout, we use the symbol ⊙ for convolution.\n+// Since all of our signals are symmetrical, discrete correlation and convolution\n+// is the same operation, and so we won't make a difference in notation.\n+\n+\n+#include <math.h>\n+#include <assert.h>\n+#include <Eigen/Dense>\n+#include <Eigen/Cholesky>\n+\n+#include \"deconvolution_sharpen_effect.h\"\n+#include \"util.h\"\n+#include \"opengl.h\"\n+\n+using namespace Eigen;\n+\n+DeconvolutionSharpenEffect::DeconvolutionSharpenEffect()\n+       : R(5),\n+         correlation(0.95f),\n+         noise(0.01f)\n+{\n+       register_int(\"matrix_size\", &R);\n+       register_float(\"correlation\", &correlation);\n+       register_float(\"noise\", &noise);\n+}\n+\n+{\n+       char buf;\n+       sprintf(buf, \"#define R %u\\n\", R);\n+}\n+\n+namespace {\n+\n+// Integral of sqrt(r² - x²) dx over x=0..a.\n+float circle_integral(float a, float r)\n+{\n+       assert(a >= 0.0f);\n+       if (a <= 0.0f) {\n+               return 0.0f;\n+       }\n+       if (a >= r) {\n+               return 0.25f * M_PI * r * r;\n+       }\n+       return 0.5f * (a * sqrt(r*r - a*a) + r*r * asin(a / r));\n+}\n+\n+// Yields the impulse response of a circular blur with radius r.\n+// We basically look at each element as a square centered around (x,y),\n+// and figure out how much of its area is covered by the circle.\n+float circle_impulse_response(int x, int y, float r)\n+{\n+       if (r < 1e-3) {\n+               // Degenerate case: radius = 0 yields the impulse response.\n+               return (x == 0 && y == 0) ? 1.0f : 0.0f;\n+       }\n+\n+       // Find the extents of this cell. Due to symmetry, we can cheat a bit\n+       // and pretend we're always in the upper-right quadrant, except when\n+       // we're right at an axis crossing (x = 0 or y = 0), in which case we\n+       // simply use the evenness of the function; shrink the cell, make\n+       // the calculation, and down below we'll normalize by the cell's area.\n+       float min_x, max_x, min_y, max_y;\n+       if (x == 0) {\n+               min_x = 0.0f;\n+               max_x = 0.5f;\n+       } else {\n+               min_x = abs(x) - 0.5f;\n+               max_x = abs(x) + 0.5f;\n+       }\n+       if (y == 0) {\n+               min_y = 0.0f;\n+               max_y = 0.5f;\n+       } else {\n+               min_y = abs(y) - 0.5f;\n+               max_y = abs(y) + 0.5f;\n+       }\n+       assert(min_x >= 0.0f && max_x >= 0.0f);\n+       assert(min_y >= 0.0f && max_y >= 0.0f);\n+\n+       float cell_height = max_y - min_y;\n+       float cell_width = max_x - min_x;\n+\n+       if (min_x * min_x + min_y * min_y > r * r) {\n+               // Lower-left corner is outside the circle, so the entire cell is.\n+               return 0.0f;\n+       }\n+       if (max_x * max_x + max_y * max_y < r * r) {\n+               // Upper-right corner is inside the circle, so the entire cell is.\n+               return 1.0f;\n+       }\n+\n+       // OK, so now we know the cell is partially covered by the circle:\n+       //\n+       //      \\           .\n+       //  -------------\n+       // |####|#\\      |\n+       // |####|##|     |\n+       //  -------------\n+       //   A   ###|\n+       //       ###|\n+       //\n+       // The edge of the circle is defined by x² + y² = r²,\n+       // or x = sqrt(r² - y²) (since x is nonnegative).\n+       // Find out where the curve crosses our given y values.\n+       float mid_x1 = (max_y >= r) ? min_x : sqrt(r * r - max_y * max_y);\n+       float mid_x2 = sqrt(r * r - min_y * min_y);\n+       if (mid_x1 < min_x) {\n+               mid_x1 = min_x;\n+       }\n+       if (mid_x2 > max_x) {\n+               mid_x2 = max_x;\n+       }\n+       assert(mid_x1 >= min_x);\n+       assert(mid_x2 >= mid_x1);\n+       assert(max_x >= mid_x2);\n+\n+       // The area marked A in the figure above.\n+       float covered_area = cell_height * (mid_x1 - min_x);\n+\n+       // The area marked B in the figure above. Note that the integral gives the entire\n+       // shaded space down to zero, so we need to subtract the rectangle that does not\n+       // belong to our cell.\n+       covered_area += circle_integral(mid_x2, r) - circle_integral(mid_x1, r);\n+       covered_area -= min_y * (mid_x2 - mid_x1);\n+\n+       assert(covered_area <= cell_width * cell_height);\n+       return covered_area / (cell_width * cell_height);\n+}\n+\n+// Compute a ⊙ b. Note that we compute the “full” convolution,\n+// ie., our matrix will be big enough to hold every nonzero element of the result.\n+MatrixXf convolve(const MatrixXf &a, const MatrixXf &b)\n+{\n+       MatrixXf result(a.rows() + b.rows() - 1, a.cols() + b.cols() - 1);\n+       for (int yr = 0; yr < result.rows(); ++yr) {\n+               for (int xr = 0; xr < result.cols(); ++xr) {\n+                       float sum = 0.0f;\n+\n+                       // Given that x_b = x_r - x_a, find the values of x_a where\n+                       // x_a is in [0, a_cols> and x_b is in [0, b_cols>. (y is similar.)\n+                       //\n+                       // The second demand gives:\n+                       //\n+                       //   0 <= x_r - x_a < b_cols\n+                       //   0 >= x_a - x_r > -b_cols\n+                       //   x_r >= x_a > x_r - b_cols\n+                       int ya_min = yr - b.rows() + 1;\n+                       int ya_max = yr;\n+                       int xa_min = xr - b.rows() + 1;\n+                       int xa_max = xr;\n+\n+                       // Now fit to the first demand.\n+                       ya_min = std::max<int>(ya_min, 0);\n+                       ya_max = std::min<int>(ya_max, a.rows() - 1);\n+                       xa_min = std::max<int>(xa_min, 0);\n+                       xa_max = std::min<int>(xa_max, a.cols() - 1);\n+\n+                       assert(ya_max >= ya_min);\n+                       assert(xa_max >= xa_min);\n+\n+                       for (int ya = ya_min; ya <= ya_max; ++ya) {\n+                               for (int xa = xa_min; xa <= xa_max; ++xa) {\n+                                       sum += a(ya, xa) * b(yr - ya, xr - xa);\n+                               }\n+                       }\n+\n+                       result(yr, xr) = sum;\n+               }\n+       }\n+       return result;\n+}\n+\n+// Similar to convolve(), but instead of assuming every element outside\n+// of b is zero, we make no such assumption and instead return only the\n+// elements where we know the right answer. (This is the only difference\n+// between the two.)\n+// This is the same as conv2(a, b, 'valid') in Octave.\n+//\n+// a must be the larger matrix of the two.\n+MatrixXf central_convolve(const MatrixXf &a, const MatrixXf &b)\n+{\n+       assert(a.rows() >= b.rows());\n+       assert(a.cols() >= b.cols());\n+       MatrixXf result(a.rows() - b.rows() + 1, a.cols() - b.cols() + 1);\n+       for (int yr = b.rows() - 1; yr < result.rows() + b.rows() - 1; ++yr) {\n+               for (int xr = b.cols() - 1; xr < result.cols() + b.cols() - 1; ++xr) {\n+                       float sum = 0.0f;\n+\n+                       // Given that x_b = x_r - x_a, find the values of x_a where\n+                       // x_a is in [0, a_cols> and x_b is in [0, b_cols>. (y is similar.)\n+                       //\n+                       // The second demand gives:\n+                       //\n+                       //   0 <= x_r - x_a < b_cols\n+                       //   0 >= x_a - x_r > -b_cols\n+                       //   x_r >= x_a > x_r - b_cols\n+                       int ya_min = yr - b.rows() + 1;\n+                       int ya_max = yr;\n+                       int xa_min = xr - b.rows() + 1;\n+                       int xa_max = xr;\n+\n+                       // Now fit to the first demand.\n+                       ya_min = std::max<int>(ya_min, 0);\n+                       ya_max = std::min<int>(ya_max, a.rows() - 1);\n+                       xa_min = std::max<int>(xa_min, 0);\n+                       xa_max = std::min<int>(xa_max, a.cols() - 1);\n+\n+                       assert(ya_max >= ya_min);\n+                       assert(xa_max >= xa_min);\n+\n+                       for (int ya = ya_min; ya <= ya_max; ++ya) {\n+                               for (int xa = xa_min; xa <= xa_max; ++xa) {\n+                                       sum += a(ya, xa) * b(yr - ya, xr - xa);\n+                               }\n+                       }\n+\n+                       result(yr - b.rows() + 1, xr - b.cols() + 1) = sum;\n+               }\n+       }\n+       return result;\n+}\n+\n+void print_matrix(const MatrixXf &m)\n+{\n+       for (int y = 0; y < m.rows(); ++y) {\n+               for (int x = 0; x < m.cols(); ++x) {\n+                       printf(\"%7.4f \", m(x, y));\n+               }\n+               printf(\"\\n\");\n+       }\n+}\n+\n+}  // namespace\n+\n+void DeconvolutionSharpenEffect::set_gl_state(GLuint glsl_program_num, const std::string &prefix, unsigned *sampler_num)\n+{\n+       Effect::set_gl_state(glsl_program_num, prefix, sampler_num);\n+\n+       assert(R >= 1);\n+       assert(R <= 25);  // Same limit as Refocus.\n+\n+       printf(\"correlation:          %5.3f\\n\", correlation);\n+       printf(\"noise factor:         %5.3f\\n\", noise);\n+       printf(\"\\n\");\n+\n+       // Figure out the impulse response for the circular part of the blur.\n+       MatrixXf circ_h(2 * R + 1, 2 * R + 1);\n+       for (int y = -R; y <= R; ++y) {\n+               for (int x = -R; x <= R; ++x) {\n+                       circ_h(y + R, x + R) = circle_impulse_response(x, y, circle_radius);\n+               }\n+       }\n+\n+       // Same, for the Gaussian part of the blur. We make this a lot larger\n+       // since we're going to convolve with it soon, and it has infinite support\n+       // (see comments for central_convolve()).\n+       MatrixXf gaussian_h(4 * R + 1, 4 * R + 1);\n+       for (int y = -2 * R; y <= 2 * R; ++y) {\n+               for (int x = -2 * R; x <= 2 * R; ++x) {\n+                       float val;\n+                       if (gaussian_radius < 1e-3) {\n+                               val = (x == 0 && y == 0) ? 1.0f : 0.0f;\n+                       } else {\n+                               float z = hypot(x, y) / gaussian_radius;\n+                               val = exp(-z * z);\n+                       }\n+                       gaussian_h(y + 2 * R, x + 2 * R) = val;\n+               }\n+       }\n+\n+       // h, the (assumed) impulse response that we're trying to invert.\n+       MatrixXf h = central_convolve(gaussian_h, circ_h);\n+       assert(h.rows() == 2 * R + 1);\n+       assert(h.cols() == 2 * R + 1);\n+\n+       // Normalize the impulse response.\n+       float sum = 0.0f;\n+       for (int y = 0; y < 2 * R + 1; ++y) {\n+               for (int x = 0; x < 2 * R + 1; ++x) {\n+                       sum += h(y, x);\n+               }\n+       }\n+       for (int y = 0; y < 2 * R + 1; ++y) {\n+               for (int x = 0; x < 2 * R + 1; ++x) {\n+                       h(y, x) /= sum;\n+               }\n+       }\n+\n+       // r_uu, the (estimated/assumed) autocorrelation of the input signal (u).\n+       // The signal is modelled a standard autoregressive process with the\n+       // given correlation coefficient.\n+       //\n+       // We have to take a bit of care with the size of this matrix.\n+       // The pow() function naturally has an infinite support (except for the\n+       // degenerate case of correlation=0), but we have to chop it off\n+       // somewhere. Since we convolve it with a 4*R+1 large matrix below,\n+       // we need to make it twice as big as that, so that we have enough\n+       // data to make r_vv valid. (central_convolve() effectively enforces\n+       // that we get at least the right size.)\n+       MatrixXf r_uu(8 * R + 1, 8 * R + 1);\n+       for (int y = -4 * R; y <= 4 * R; ++y) {\n+               for (int x = -4 * R; x <= 4 * R; ++x) {\n+                       r_uu(x + 4 * R, y + 4 * R) = pow(correlation, hypot(x, y));\n+               }\n+       }\n+\n+       // Estimate r_vv, the autocorrelation of the output signal v.\n+       // Since we know that v = h ⊙ u and both are symmetrical,\n+       // convolution and correlation are the same, and\n+       // r_vv = v ⊙ v = (h ⊙ u) ⊙ (h ⊙ u) = (h ⊙ h) ⊙ r_uu.\n+       MatrixXf r_vv = central_convolve(r_uu, convolve(h, h));\n+       assert(r_vv.rows() == 4 * R + 1);\n+       assert(r_vv.cols() == 4 * R + 1);\n+\n+       // Similarly, r_uv = u ⊙ v = u ⊙ (h ⊙ u) = h ⊙ r_uu.\n+       //MatrixXf r_uv = central_convolve(r_uu, h).block(2 * R, 2 * R, 2 * R + 1, 2 * R + 1);\n+       MatrixXf r_uu_center = r_uu.block(2 * R, 2 * R, 4 * R + 1, 4 * R + 1);\n+       MatrixXf r_uv = central_convolve(r_uu_center, h);\n+       assert(r_uv.rows() == 2 * R + 1);\n+       assert(r_uv.cols() == 2 * R + 1);\n+\n+       // Add the noise term (we assume the noise is uncorrelated,\n+       // so it only affects the central element).\n+       r_vv(2 * R, 2 * R) += noise;\n+\n+       // Now solve the Wiener-Hopf equations to find the deconvolution kernel g.\n+       // Most texts show this only for the simpler 1D case:\n+       //\n+       // [ r_vv(0)  r_vv(1) r_vv(2) ... ] [ g(0) ]   [ r_uv(0) ]\n+       // [ r_vv(-1) r_vv(0) ...         ] [ g(1) ] = [ r_uv(1) ]\n+       // [ r_vv(-2) ...                 ] [ g(2) ]   [ r_uv(2) ]\n+       // [ ...                          ] [ g(3) ]   [ r_uv(3) ]\n+       //\n+       // (Since r_vv is symmetrical, we can drop the minus signs.)\n+       //\n+       // Generally, row i of the matrix contains (dropping _vv for brevity):\n+       //\n+       // [ r(0-i) r(1-i) r(2-i) ... ]\n+       //\n+       // However, we have the 2D case. We flatten the vectors out to\n+       // 1D quantities; this means we must think of the row number\n+       // as a pair instead of as a scalar. Row (i,j) then contains:\n+       //\n+       // [ r(0-i,0-j) r(1-i,0-j) r(2-i,0-j) ... r(0-i,1-j) r_(1-i,1-j) r(2-i,1-j) ... ]\n+       //\n+       // g and r_uv are flattened in the same fashion.\n+       //\n+       // Note that even though this matrix is block Toeplitz, it is _not_ Toeplitz,\n+       // and thus can not be inverted through the standard Levinson-Durbin method.\n+       // There exists a block Levinson-Durbin method, which we may or may not\n+       // want to use later. (Eigen's solvers are fast enough that for big matrices,\n+       // the convolution operation and not the matrix solving is the bottleneck.)\n+       //\n+       // One thing we definitely want to use, though, is the symmetry properties.\n+       // Since we know that g(i, j) = g(|i|, |j|), we can reduce the amount of\n+       // unknowns to about 1/4th of the total size. The method is quite simple,\n+       // as can be seen from the following toy equation system:\n+       //\n+       //   A x0 + B x1 + C x2 = y0\n+       //   D x0 + E x1 + F x2 = y1\n+       //   G x0 + H x1 + I x2 = y2\n+       //\n+       // If we now know that e.g. x0=x1 and y0=y1, we can rewrite this to\n+       //\n+       //   (A+B+D+E) x0 + (C+F) x2 = 2 y0\n+       //   (G+H)     x0 + I x2     = y2\n+       //\n+       // This both increases accuracy and provides us with a very nice speed\n+       // boost. We could have gone even further and went for 8-way symmetry\n+       // like the shader does, but this is good enough right now.\n+       MatrixXf M(MatrixXf::Zero((R + 1) * (R + 1), (R + 1) * (R + 1)));\n+       MatrixXf r_uv_flattened(MatrixXf::Zero((R + 1) * (R + 1), 1));\n+       for (int outer_i = 0; outer_i < 2 * R + 1; ++outer_i) {\n+               int folded_outer_i = abs(outer_i - R);\n+               for (int outer_j = 0; outer_j < 2 * R + 1; ++outer_j) {\n+                       int folded_outer_j = abs(outer_j - R);\n+                       int row = folded_outer_i * (R + 1) + folded_outer_j;\n+                       for (int inner_i = 0; inner_i < 2 * R + 1; ++inner_i) {\n+                               int folded_inner_i = abs(inner_i - R);\n+                               for (int inner_j = 0; inner_j < 2 * R + 1; ++inner_j) {\n+                                       int folded_inner_j = abs(inner_j - R);\n+                                       int col = folded_inner_i * (R + 1) + folded_inner_j;\n+                                       M(row, col) += r_vv((inner_i - R) - (outer_i - R) + 2 * R,\n+                                                           (inner_j - R) - (outer_j - R) + 2 * R);\n+                               }\n+                       }\n+                       r_uv_flattened(row) += r_uv(outer_i, outer_j);\n+               }\n+       }\n+\n+       LLT<MatrixXf> llt(M);\n+       MatrixXf g_flattened = llt.solve(r_uv_flattened);\n+       assert(g_flattened.rows() == (R + 1) * (R + 1)),\n+       assert(g_flattened.cols() == 1);\n+\n+       // Normalize and de-flatten the deconvolution matrix.\n+       MatrixXf g(R + 1, R + 1);\n+       sum = 0.0f;\n+       for (int i = 0; i < g_flattened.rows(); ++i) {\n+               int y = i / (R + 1);\n+               int x = i % (R + 1);\n+               if (y == 0 && x == 0) {\n+                       sum += g_flattened(i);\n+               } else if (y == 0 || x == 0) {\n+                       sum += 2.0f * g_flattened(i);\n+               } else {\n+                       sum += 4.0f * g_flattened(i);\n+               }\n+       }\n+       for (int i = 0; i < g_flattened.rows(); ++i) {\n+               int y = i / (R + 1);\n+               int x = i % (R + 1);\n+               g(y, x) = g_flattened(i) / sum;\n+       }\n+\n+       // Now encode it as uniforms, and pass it on to the shader.\n+       // (Actually the shader only uses about half of the elements.)\n+       float samples[4 * (R + 1) * (R + 1)];\n+       for (int y = 0; y <= R; ++y) {\n+               for (int x = 0; x <= R; ++x) {\n+                       int i = y * (R + 1) + x;\n+                       samples[i * 4 + 0] = x / float(width);\n+                       samples[i * 4 + 1] = y / float(height);\n+                       samples[i * 4 + 2] = g(y, x);\n+                       samples[i * 4 + 3] = 0.0f;\n+               }\n+       }\n+\n+       set_uniform_vec4_array(glsl_program_num, prefix, \"samples\", samples, R * R);\n+}\ndiff --git a/deconvolution_sharpen_effect.frag b/deconvolution_sharpen_effect.frag\nnew file mode 100644 (file)\nindex 0000000..7b90ac7\n--- /dev/null\n@@ -0,0 +1,66 @@\n+uniform vec4 PREFIX(samples)[(R + 1) * (R + 1)];\n+\n+vec4 FUNCNAME(vec2 tc) {\n+       // The full matrix has five different symmetry cases, that look like this:\n+       //\n+       // D * * C * * D\n+       // * D * C * D *\n+       // * * D C D * *\n+       // B B B A B B B\n+       // * * D C D * *\n+       // * D * C * D *\n+       // D * * C * * D\n+       //\n+       // We only store the lower-right part of the matrix:\n+       //\n+       // A B B\n+       // C D *\n+       // C * D\n+\n+       // Case A: Top-left sample has no symmetry.\n+       vec4 sum = PREFIX(samples).z * INPUT(tc);\n+\n+       // Case B: Uppermost samples have left/right symmetry.\n+       for (int x = 1; x <= R; ++x) {\n+               vec4 sample = PREFIX(samples)[x];\n+               sum += sample.z * (INPUT(tc - sample.xy) + INPUT(tc + sample.xy));\n+       }\n+\n+       // Case C: Leftmost samples have top/bottom symmetry.\n+       for (int y = 1; y <= R; ++y) {\n+               vec4 sample = PREFIX(samples)[y * (R + 1)];\n+               sum += sample.z * (INPUT(tc - sample.xy) + INPUT(tc + sample.xy));\n+       }\n+\n+       // Case D: Diagonal samples have four-way symmetry.\n+       for (int xy = 1; xy <= R; ++xy) {\n+               vec4 sample = PREFIX(samples)[xy * (R + 1) + xy];\n+\n+               vec4 local_sum = INPUT(tc - sample.xy) + INPUT(tc + sample.xy);\n+               sample.y = -sample.y;\n+               local_sum += INPUT(tc - sample.xy) + INPUT(tc + sample.xy);\n+\n+               sum += sample.z * local_sum;\n+       }\n+\n+       // Case *: All other samples have eight-way symmetry.\n+       for (int y = 1; y <= R; ++y) {\n+               for (int x = y + 1; x <= R; ++x) {\n+                       vec4 sample = PREFIX(samples)[y * (R + 1) + x];\n+                       vec2 mirror_sample = vec2(sample.x, -sample.y);\n+\n+                       vec4 local_sum = INPUT(tc - sample.xy) + INPUT(tc + sample.xy);\n+                       local_sum += INPUT(tc - mirror_sample.xy) + INPUT(tc + mirror_sample.xy);\n+\n+                       sample.xy = sample.yx;\n+                       mirror_sample.xy = mirror_sample.yx;\n+\n+                       local_sum += INPUT(tc - sample.xy) + INPUT(tc + sample.xy);\n+                       local_sum += INPUT(tc - mirror_sample.xy) + INPUT(tc + mirror_sample.xy);\n+\n+                       sum += sample.z * local_sum;\n+               }\n+       }\n+\n+       return sum;\n+}\ndiff --git a/deconvolution_sharpen_effect.h b/deconvolution_sharpen_effect.h\nnew file mode 100644 (file)\nindex 0000000..bc0bbd4\n--- /dev/null\n@@ -0,0 +1,56 @@\n+#ifndef _DECONVOLUTION_SHARPEN_EFFECT_H\n+#define _DECONVOLUTION_SHARPEN_EFFECT_H 1\n+\n+// DeconvolutionSharpenEffect is an effect that sharpens by way of deconvolution\n+// (i.e., trying to reverse the blur kernel, as opposed to just boosting high\n+// frequencies), more specifically by FIR Wiener filters. It is the same\n+// algorithm as used by the (now largely abandoned) Refocus plug-in for GIMP,\n+// and I suspect the same as in Photoshop's “Smart Sharpen” filter.\n+// The implementation is, however, distinct from either.\n+//\n+// The effect gives generally better results than unsharp masking, but can be very\n+// GPU intensive, and requires a fair bit of tweaking to get good results without\n+// ringing and/or excessive noise. It should be mentioned that for the larger\n+// convolutions (e.g. R approaching 10), we should probably move to FFT-based\n+// convolution algorithms, especially as Mesa's shader compiler starts having\n+//\n+// We follow the same book as Refocus was implemented from, namely\n+//\n+//   Jain, Anil K.: “Fundamentals of Digital Image Processing”, Prentice Hall, 1988.\n+\n+#include \"effect.h\"\n+\n+class DeconvolutionSharpenEffect : public Effect {\n+public:\n+       DeconvolutionSharpenEffect();\n+       virtual std::string effect_type_id() const { return \"DeconvolutionSharpenEffect\"; }\n+\n+       virtual void inform_input_size(unsigned input_num, unsigned width, unsigned height)\n+       {\n+               this->width = width;\n+               this->height = height;\n+       }\n+\n+       void set_gl_state(GLuint glsl_program_num, const std::string &prefix, unsigned *sampler_num);\n+\n+private:\n+       // Input size.\n+       unsigned width, height;\n+\n+       // The maximum radius of the (de)convolution kernel.\n+       // Note that since this extends both ways, and we also have a center element,\n+       // the actual convolution matrix will be (2R + 1) x (2R + 1).\n+       //\n+       // Must match the definition in the shader, and as such, cannot be set once\n+       // the chain has been finalized.\n+       int R;\n+\n+       // The parameters. Typical OK values are circle_radius = 2, gaussian_radius = 0\n+       // (ie., blur is assumed to be a 2px circle), correlation = 0.95, and noise = 0.01.\n+       // Note that once the radius starts going too far past R, you will get nonsensical results." ]
[ null ]
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https://docs.teradata.com/r/Daz9Bt8GiwSdtthYFn~vdw/rPQXU97gPoSaBRgvTflg5w
[ "# 15.10 - RowID Join - Teradata Database\n\n## Teradata Database SQL Request and Transaction Processing\n\nprodname\nvrm_release\n15.10\ncategory\nProgramming Reference\nUser Guide\nfeatnum\nB035-1142-151K\n\nThe rowID join is a special form of the nested join. The Optimizer selects a rowID join instead of a nested join when the first condition in the query specifies a literal for the first table. This value is then used to select a small number of rows which are then equijoined with a secondary index from the second table.\n\nThe Optimizer can select a rowID join only if both of the following conditions are true:\n\n• The WHERE clause condition must match another column of the first table to a NUSI, USI, or join index (if the join index has a rowID of the base table) of the second table.\n• Only a subset of the NUSI or USI values from the second table are qualified via the join condition (this is referred to as a weakly selective index condition), and a nested join is done between the two tables to retrieve the rowIDs from the second table.\n• Consider the following generic SQL query:\n\n`     SELECT *`\n`     FROM table_1, table_2`\n`     WHERE table_1.NUPI = value`\n`     AND   table_1.column = table_2.weakly_selective_NUSI;`\n\nThe process involved in solving the join steps for this request is as follows:\n\n1 The qualifying table_1 rows are duplicated on all AMPS.\n\n2 The value in the join column of a table_1 row is used to hash into the table_2 NUSI (similar to a nested join).\n\n3 The rowIDs are extracted from the index subtable and placed into a spool together with the corresponding table_1 columns. This becomes the left table for the join.\n\n4 When all table_1 rows have been processed, the spool is sorted into rowID sequence.\n\n5 The rowIDs in the spool are then used to extract the corresponding table_2 data rows.\n\n6 table_2 values in table_2 data rows are put in the results spool together with table_1 values in the rowID join rows.\n\nStages 2 and 3 are part of a nested join. Stages 4, 5, and 6 describe the rowID join.\n\nThe following graphic demonstrates a rowID join:", null, "Assume that you submit the following SELECT request. The first WHERE condition is on a NUPI and the second is on a NUSI. The Optimizer applies a rowID join to process the join.\n\n`     SELECT *`\n`     FROM table_1, table_2`\n`     WHERE table_1.column_1 = 10`\n`     AND   table_1.column_3 = table_2.column_5;`" ]
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S3UfhiLI/Omnw6jcvqmrM3c/bGXP4DA/IUAbNFZR8PWnzEXOpq5GNw1Kc498F8fpUf8Awjyrny9W1ZGP8X2st+jZH6UAbNFZDaTqAGY/EWoBs5AeK3ZfocRg/rSeV4ihGVvNNu8fwPbPCT/wMOwH/fNAGxRWOdXv7X/kIaLcKueZbJxcoPwAEh/BDV2w1Sy1NHazuUl8s7ZFHDRn0ZTyp9iBQBbooooAKKKKACiiigAooooAKKKKACiiigAooooAK53VpLa78VabpN8wNs8ElwsLfdmlVk2g9jgbmCnOeuPlroqxr2xttR1pra7hWWJrQHB7EPkEEcgg8gjkEZFAGzRWMLTW7BsWl5Df246Q32UkX2Eqg5H+8hPq1L/bskP/AB/aPqVv/tJELhSfbyizY+qigDYorIXxToJba+rWkLk48u4kET5/3Xwf0rRgu7a6Xdb3EUy4zmNw3H4UATUUUUAFFISFUsxAAGST2rPn1/RrZ9k+rWETeklyin9TQBo0Vj/8JLYSA/ZI728Pb7PaSMp/4HgJ+Zo+1a5ecW+nRWCf89L2QSOP+2cZII/7aCgCp4xe2sNHbVy/lX1nhrWRThnbI/dcdVbGCOR36qCOjrmtS0RLfRdSvby5mv74WUwWabAEYKHIjQfKg9wMkdSa6WgArG8VX7aZ4emuFna3zJDC06jJhWSVEaTuPlDFueOK2azdcAbT41YAg3dsCD3/AH6UAWbCwtNNtFt7OFY4gSxxyWJ6sxPLMe5PJqzWKukXelqBolzHHbqeLG5UtEo9I2HzR/T5lHQKKcNZvLfA1HRruLnBktcXMf4bcP8AmgoA2KKyF8U6GW2yanb28hOPLum8h8+m18H9K04Z4biMSQSpKh6MjBh+YoAkooooAKKKZNNFbxmSaVI4x1Z2AA/E0APorIbxRoYO2PU7e4kzjy7VvPcn02pk/pSf2zdXPGn6PeS56S3Q+zRj67vn/JDQBfvrG11G0e3vIhJC3OCSCpHQgjlSOoIwR1FZ/ha//tLQI7gXH2lFmmhjuD/y1SOV0V898hQc9+vemNo99qhP9s3im2PWxtAVjYekjn5nHsNqkcFTVrQlVNOkVQFUXdyAAOAPPegDSooooAwb0vq3iB9GNy0Nrb20dzcJExWSbezqq7hyEHltnHJyBnGQdi1tLaxt1t7S3it4U+7HEgVR+ArDvNLN54nuLm2uGtb6CzgEU6ruGC82Vdf4lOBkcHuCDzVr+1NQsVxqWmSuo63FgDMh9/L/ANYD7AN9aANiis218QaReTLBDqNubhukDvsl/wC+Gww/KtKgAooooAKKKKACiiqd7qunabt+3X9tbFvuiaVULfTJ5oAtkAggjIPUGuevreLw5Lb31iwt7WW5it57Mf6t/NkWNWReisGcH5fvDOQTgi0Nf+1DGl6deXh/56NGYIh77pMZHugas7UdLu7hLa+1eeOSWK8tjBbQAiGE+cg3ZPLtgkbiAPRRzkA6miiigDK1+9ktLS3ihmEE15cx2qTEA+Xu6kA8bsA4zkbiM8VY07SbLS0cWsIEkhzLM3zSSt6ux5Y/Wquu2kF9JpdrdRCSGS6YMp/64S/kQeQRyDzUax63peFjZdWtFGAJGEdyo/3vuyfjsPqTQBt0Vj/8JNp8R233n6cw6/bYWjQH/rp9w/gxrStry2vYhLaXMM8Z6PE4YfmKAJqKKKACiiigAooqtealY6em+9vbe2U95pVQfqaALNZ2paLa6i63Azb38YxDeQgCWP2yRyvqpyD3FVz4ktpiBp9re6gxPBt4CEPv5j7U/wDHqY9jqmsArqckdnZN960tXLPIPR5eMA91Uf8AAiOKALPh/UzrGgWd+xjLyx/O0f3CwOGK/wCySDj2xWnWZoEaRaY0caKkaXVyqqowFAmfAA9K06ACiiigAooooAKKKKACiiigAooooAKKKKACs7/mZP8At0/9nrRrO/5mT/t0/wDZ6AJNQvJ7U2sdvBHNLcTeUBJKY1HyM+chW/ueneql7qGr2VhcXb6dYssETSlRevkhQT/zy9qqeMZ76Cxsm0yMyX73JitxgkLI8MiqzYB+VSwYnsAazrC3vtN8I69o97bGJbSCVrZ1kaVHidGYASMq5KncCMcAL6igDomk1h1Ktp2nspGCDevgj/v1WdNoguG3T+GNAlbOcvJuOfxhrD1uP7VreqNY6fqUV3BBORcC3mBvJDbsqxpJtKrGM5HPLgYHdtDwoGtI73TpWmi+0zM9q6Wclsu0RRhikbg+Xg88k7mLEdwABRo1qt8bRfCXh9JDFv3o4XjOOohzmpl8ORKrKfDGhurdRJcM4/8AHoTWQ9nd2+halZxRXbTeRdxoCXaXYbqTDc5Z/kIb1YdOtZ8loPsA02fTtQ3ST3At72HT5xFaRkqS8UIUmN8kqgP91mztOCAdQPDtupBXwl4cGDkYYcH/AL8VY0x737OzWei6XboskkW1Loryjsp6RdMgmm6wv9qppt1ZvdOttqSoyRhlUlJdjllwCQpVsE/L354NcxqdrfT6lFLbwTv5E7tDsgd97/bXLBZBxC20AFmyCrkcAE0Adv52s/8APhYf+Br/APxqmi+v4ru1hu7O2RLiQxq0VyzkEIz9Ci8YU965bw6t1pWuu9zDJFalGhmnMEqm5necCN3yvLcsMgsoBHzYIA0LezltPE8hdrl0l1NZEkmYndm0kzt7YB4wOBigDb1//kW9U/69Jf8A0A1o1na//wAi3qn/AF6S/wDoBrRoAKztb/48Iv8Ar7tv/R6Vo1na3/x4Rf8AX3bf+j0oAtX10tjp9zdspZYImlKjqQoJx+lVfO1n/nwsP/A1/wD41Rr/APyLeqf9ekv/AKAa5rShrkXixtWuLJhYanNJAwDuZIkQfuGeMoNg+R88nmYZxigDdtr7Vr23dxptjtEkkRDXr87HKH/ll0JU1Ul0GOeQyS+FvD8jnqzvkn8fJrF1q3kuLIj7Nd3EezVBCLeNn23RnHlN8oO1hh8OcAc8jNadtYw2Ov6lrt7A0EdnAEMiIxErlQ8suAMsOVUYHG1/U0AR3OjRWSCf/hHdLTdIkQ8m9dCC7BBjEQwPm7VJ/wAI23/QKt//AAc3P/xFF7Zywa9qM7NcvFOdMKtIxKBhcvlV7DAK5A9QT1rOij1d77xLcatolxOsthHLFB5gkjdo3mKRIAeSRtz3zycZAoA1D4dVmVpPD2lSkADMt7I+QPXdEc/jTINKgj1GS3h8KaBHNDEkhdXA4YsBgiH1Q/pV7TdMtdP0mDw59onWYwGSSSLcpfLDeVfGFyT0BBAIxjiuaNncvodjDLbSSyQ6fphuLWWJ2EmDKGSRVBbGevytgjkYzQB2CyawihV07T1UDAAvXwB/36qK7v8AVbKznuptPsjHBG0jhLxicKMnH7oc8Vw9pp+tpcaWz28rXLSW3lyvDKGtoFuWZlU7SqhoflYMVYYA+YkAbniZf7QhtdVtHupLY2d0CMMsaqYX+ZlIGCTgZb0wMZOQDs6ztE/48Jf+vu5/9HvWjWdon/HhL/193P8A6PegDRooooAzof8AkZL3/r0t/wD0OaprvUI7SaKExTSyyqzKsSbjhcAn/wAeH51DD/yMl7/16W//AKHNWL4wvrizubSOyWR767t5rW3WIgPuZ4csuSBlVDPyR92gDQ1DUbKa12ahpFzNAzqmya1DqSzBRwfciqX9naOpzBod/aH1s1e3/wDRbL/k1Fp9/cXnhiOC9SVL6x1C3tbhZiC+VmjKsxBIyyFG4J+9TNYn1G3vNfA1JxGtrZyxruEQiRpZQ4Vv4WZVxvPQ4PAHABObOFf9QfEsJx2uJJP/AEYzU1Xn81raLU/EayRqHZfs8DHDEgHLRn+6fyp/hnUJ9S0m6txeeVN50v2RnkWaUQBsKx+Y78HK7skZHVu+THqkFt4ahv8AV9SuIVfSdNMs8cm2WVmaT5Q3GCxOCQRjJOR1ABrqL0H5tT8RsO4NrbD+UQNMFqCf303iWUZ6GQp+qbTWNDreppN4fg+0Tz2rXWXubc+ek+8ybYvNHDCNcAnqxGexz0t9cyS6v4fubPUd9jcXDRmOEgpKDBK24sOoyq4A469eMAFGC20e7t4p/wCztYu4ZFDp9pmmmUg8g7Xcj9Kv2X9m6aWNj4fktS33jBZKmfriuQh1bUbe5sFR2Pk29mlrapdFHnDxcnysYdd2dxPICHGMHOx4J1ZpS0N7qAmuJobdgTeecrTMsjMi5AKyARlmQcAYIA5yAdPb6nHcXYtjBcQysjSKJY9u4AgHH/fQ/Oo9b/48Iv8Ar7tv/R6ViaC92fEM0d5ePdSRPdoJGULx/oxwAOgGTgfqTzW3rf8Ax4Rf9fdt/wCj0oA0aKKKAM7Uv+P/AEf/AK+2/wDREtWry6SytJLiRWZUGdqYyfYZ4qrqX/H/AKP/ANfbf+iJaTX2C6JcsxAUBSSTwPmFAC/2ld/9AS//AO+4P/jlZJh03Vd1xJ4QadvMdDJJFbFtysVPJfPVevtVLwtrWo3GtP8A2jDdxW2rRNd2JnZNoCtwqgMSuYmibDAHIc464q6xe/Y7BJINRkhvI7u4eO3E5j8xftTbiigYmfooQnHzdjjIBrDTIEGItF1yFeflh1Py159lnAqGa1EUsKG28SoZn8uNRqSnkKWxkzE9FPU1FomqtD4kuo73U4LiOZ5yrC6bbbkXCxxxOjABXO9VHTJVgM5zUwku08YvFc3clxGl1HJEm0ARBoLj5VA+gyTyT+AABN9lvC7Fo/EZVgRs+1W4A+hD5/WmjTn/AI7PxK6nsdTVf1WYH9awYte1G6vLwajLe2FvLc2vmrIrQi1hbeNofjaSQgLA9ScEDFbEV/c3ngK8NvqpW8ht5XW4Xa0phDP5UmDnBdFBDHOeTzQA5bKxnnkhk0PWbl4sb0ub8SqMjI4ecir1pbWmnvvsvCLWz/3oY7VD+klYuqXj26xzyXLxwNNb/adlyIHkTyHOBISAPm2k/MuQCM84NXSvEWsSaj4f/tQXNtB9nxM5iKxzsYd7SM2MADj0wQxI6UAdfJq8sCeZPpN9FECAzs0JC5OMnEhP5CtSuS16adtWhKXxksbjT5ZEhQDYSstvh8/xHDnHbB/GutoAztE/48Jf+vu5/wDR71o1naJ/x4S/9fdz/wCj3rRoAKKKKACiiigAooooAKKKKACiiigAooooAKzv+Zk/7dP/AGetGs7/AJmT/t0/9noAreIZXgOmzJPFB5dy7tJLGXVVEEpPAIPT3rJvNTudU0DV1t9TtWaKzdpIpNNmhcKyNg4dwcHBwcY4PpWp4ltnvYLS0jKiSeSWNS3QFraYDPtzWBbeHb3RtK1u4uCqRyaWYSh1Ca9ZmRXO7fKAyj5jhRxyTxQBsSaxLHBczHXNMaO2nFtMY7GR9spCnZgSkk/MvA9aluL66tdLbVbnV9Ngs0j8xppbGRNq/jLn8OtQXXhsyLeoLOynhm1BLpIJJHiUKIEjPKjg5UnGCCPrw3+x9QuvCl7od4sUk32NooLmSVpAXdWHJI3fLkDdySOeuRQBJKNTg1t5ZdR09FSzLPI1owVVDc5/e/rmoT4jItPtJ1myEYk8sqdLn3htu/lPM3AbfmzjGOelak4lOuSCDZ5xsTs8wEru3cZx2zXM3Xg/U7uSeaVbYrPM0z2o1CYYZofLLefs3+2zG3b9MUAb9zeXVjaR3VxrOlRW8rqsbm0bDs5woH73kkntWbb6jdaXZD7TqllH5t3dBFGnyyMxEz7iAsmcfyyKuzaVeX2hf2dcRW4ntXgSG4OAJQojZ3CgfJk712jPQc84Gfd6JPq9gGt4bdpUlv40mkuZIXhZ5zh1KA5wVzg4PAwRzkA1lub2XUZdPTV9Me7hRZZIRZuWRWztJ/e8ZwarPdyyeIbCzl1KyuJre4DTQwQMjx74ZtpYl267W4x/9ey+n3VjqN1qdrDDcSGyWMR7vLaaVWY5JwQM5AzUFzpxt/FtrfKsSxXDxx4UYYukd0zE8d/MXn2NAGjr/wDyLeqf9ekv/oBrRrO1/wD5FvVP+vSX/wBANaNABWdrf/HhF/1923/o9K0aztb/AOPCL/r7tv8A0elAB4gz/wAI1qmOv2OXH/fBqol9cyX7WCa9ozXq/etxbkyDjPK+dnpVvX/+Rb1T/r0l/wDQDXKNomsnxWbgR3zWo1UXSrIbb7KE8sKXGP32/rgZxnGRigDVsJb6y0qae51bTLaBbu4Bee2ZVB89+5lA5NXvPvXuhaDWdJa4KeYIfsrFyv8Ae2+dnHvVQxyyaMPJs5p5BqFzteAxebBmWUeYnm/LnnH0Y/SsjT9G1GxOhA6U0X9mKjzGKSLypdtqYs9d/mDIQZO3AznpgA2NZGqC2hifUdPLtc27KgtGDECeMZ/1vQEjP196tNc3qXTWr61pK3Cp5hiNswcJ/ex52ce9ZmrWHm6vp2tR2jxCWONJ2k4dS1zbFEIzkYCvwOAc9zzTn0TU49dvLhrBtRglupZ2jnMHlPGbfYEXPziTcFXJ425GcGgDobV9QvYFubTVtKnhcfLLFaMytj0Im571Qjlv11i+uDq2mLEtpBulNs2wAPMOvm8YOe9Gh2txbfbrK+tLvy9QuJZVkkaPeE8uMHzDGcKS24Lt7KM4PWDw/p82lwXtn9ijMkNmvl22V2kedcFF9ACMfSgC+NSmbyceIdEPnOY4sQn94w6qv77k8jgetRa8upw+Hr8Tahp4RraRQotGUt8h4H73rgHsayJ9Bubax0/S00R7iOaHdqd7btCJGcsHZAHdcbnySRnAAA5wVs+L7P8AtfRVv0spFlspLgHziAURUlQuBnGGwCCOcEdORQB2NZ2if8eEv/X3c/8Ao960aztE/wCPCX/r7uf/AEe9AGjRRRQBnQ/8jJe/9elv/wChzVV1W4mtdatJ4ltisdnctI1xOYkRQ0JJLbW6Vah/5GS9/wCvS3/9DmrM8VWs99FdWlqjPcT6PfRRKpAJdvKAALcdT34oAivNVu9S0uOa0TSrmEXluPMt9RMg3ecnGRHj0rSOpagt2LQwaULkp5ghOotv2/3tvlZx71ztjp+pWkmoXN8L1kuLqw2S3/2fz3ZZgGyIPk2gbMcbsls8YqfUtIvZNXunGktcxNffbA4kjAkQWfkmLlgdzMCvI24bOe1AGva393qH+l2dvpFyFzH5sOoM4HTK5EX0yPYVXtrrVJNfvWjtNPc/ZoVO29YgYeYdfK65yCO2Ki0BLmESNqOk3dtealIBMEMXlxBY8KFKOSFAULu6liOACAKmlaTfafBq+nacsNlcmNjb7vuxq91csp4zztYEdecZHUUAbE2sXdvEJZ10eKMuUDvqRUFgSCMmLqCCCPapRNqVpCiCw0yGMEIo+3MBknAH+q6kn9a5FPDGqwaJb250+RLq2e++y/YrmMrH5ku+NnEv3gfxOOoJYgdJqdm+s2luzWTm7sdQtypfAX5ZY2eROcEbQ2D1+8OpIoAq6Xq11ZaFpUc66TDutIvLE2olGYbR2MdXY9SuL26EUUOjT3EBL7E1Es0Z5UnAiyPvEfjVKHTG1TSfDET28clnHFFNcs2OQkYKJ64L7W/4Bg9at29o1hqeq6vPYs8kksdvbpCFLeT8uT1HV2dj3wB1wKAHxXF1L4pto7qK1jZLOc7YbgyEZeHqCi46Vb1v/jwi/wCvu2/9HpWNZaV9g+Id1dJAI4buzd9+7JkcPHuJ5z3A57YA4FbOt/8AHhF/1923/o9KANGiiigDO1L/AI/9H/6+2/8AREtN8QFhod0UAL4XaGOATuHU84p2pf8AH/o//X23/oiWjXf+QNcf8B/9CFAB52s/8+Fh/wCBr/8Axqs/R5tX+wybbGxI+1XPW8cc+c+f+WXrXNaXZX0PjKCcW2M6leGYppk0MnlMZdrSXJOyVOUwoAPK4PynOgtr5V7cX0i6jPbNa6okqW29iuLhcLGF6ORvIx8xPfgYAOikOrTKFk03TmUMGAN6/UEEH/VdiAapahNq/wBu0ndY2IP2ptuLxzk+TL1/dccZqr4WaCO1ngWO50+XUJHlitBaSRLbAIilVLoBnoxOMFi2M4NP0u2ks7XSbeQTDZq14F84sXKf6TtJLcnK4OT1zmgDX87Wf+fCw/8AA1//AI1TAdWErSjTdOEjAKzfbXyQM4H+q6DJ/M1gabbQ3vijUJIbfUdOAWeBpGt5Ue5ZnUmQyMu3au3EfJOCcYGK0dBQL4c0/R7iS7t7p7QnneJAqkBvnI4b5h33c596AHWk2r/2vqOLGxLfu9wN4+B8v/XKrxl1llINhYYIwf8ATn/+NVydtYzSeEFsp/tcMgt9OWbZA00q7Qm4Mmdx6EN1IBOQaow2eqG+s5JrAmO3MC2ca2My5AupQz7gf3DGPy3bPUHBBGVoA6vVW1NNHeJrCxigXYv7q6Y7V3DovlAfhkV0Ncd4nUajHpOp2j3TxJdMjrhlRB5coJZSBg7gq5bpwBjJz2NAGdon/HhL/wBfdz/6PetGs7RP+PCX/r7uf/R71o0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFZOrWd2Lu31XTkSS7t0aJoHO0TxMVLLu7MCoKk8dRxuyNaigDHS90nXGW0nUrdRNv+yXAMcqEAjIHUjBI3LkHnBNSv4f0uRGR7QMjDDKzsQR6HmrV7p9lqUIivbWG4jByFlQNg+oz0PvWeNBe3/5B+r6jbDHEbyidP8AyKGbHsCKALH9hab/AM+3/j7f40f2Fpv/AD7f+Pt/jUBj8Rw4K3Gl3YH8DQyW5P8AwIM//oNBvNej5fRrSQekF+WP/j0aj9aAJf8AhH9L37/sg34xu3tnHp1p39hab/z7f+Pt/jUI1bUVUb/Dl+WJ6RTW5A/OQUDWL4kD/hG9UHuZLX/49QBN/YWm/wDPt/4+3+NNXw/paDCWgUEk4DsOSck9fWo/7R1h2xHoJTn7092ijHr8u40GTxHKAotdKtj3c3Mk+P8AgOxP50ATf2Fpv/Pt/wCPt/jTWstI0ofbZRDbrFz500mFTIxnLHA4JH41EdN1e4P+k64YlxjFjarFn6mQyH8sVJbeHtMt51uDA1xcqcrPdytO6H/ZLk7fouBQBQuZZfFEQtLSKSPSJCPtNzMhQ3Cd441ODhuhcjGD8uc5HR0UUAFUtVsW1HTpLZJfJkJV4pdu7Y6MGQ47gMo471dooAw4dWhuU/svXIBZ3kyGNonJ8qfIwfKk4DZH8PDDuBVv+xLT/nrf/wDgwn/+Lq5cW8F3A0FzDHNC4w0cihlP1BrMHh2G3/5Bt7e6eP8AnnBIGjHsEkDKo/3QKAJY9AsYlKxveopYsQt/OOSck/f7kk07+xLT/nrf/wDgwn/+LqDyvEUA+W7067A6LJbvCx+rBmGfoooN9rsX+s0S3k4/5dr7d/6GiUASSaBYyqFke+ZQwYA38/UEEH7/AGIBp/8AYlp/z1v/APwYT/8AxdRHV74Ln/hHNTJzjAktvz/11H9r32zd/wAI5qmc42+ZbZ+v+uoAl/sS0/563/8A4MJ//i6YNAsRK0oe+EjKFZvt8+SBnA+/2yfzpo1e9K5/4RzUwc4wZLb/AOPU1b7XJf8AV6JBFx/y83wX/wBAR6AJ/wCxLT/nrf8A/gwn/wDi6bJoFjLG0cjXrxuCrK1/OQwPUEb+lReV4inB33enWYP8McDzMPoxZRn/AIDSf8I9HOf+JlqF9qA/55zShIz7FIwqsPZgaAFu9ei897LTE+36gp2tHE3yQn1lfog9uWPYGrelWB03T1t2l82Uu8ssmMb5HYuxA5wMscDPAwO1WLa2t7O3S3tYI4IUGEjiQKqj2A4FS0AFFFFAGJqE8mkay2pyo76fPAkE7Iu425VmKuQOdh8whj/DgHpki1LbRam9rf2moyRhY2WOW2Mbq6OVJ+8rA/dHIrRrJl8PWvnNPZTXOnTuxd2tH2q7HqWjIKEn1K596ACfRZrmMRy61qBUOkg+WAcqwYf8s/UC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null ]
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https://calcforme.com/percentage-calculator/463-is-133-percent-of-what
[ "# 463 is 133 Percent of what?\n\n## 463 is 133 Percent of 348.12\n\n%\n\n463 is 133% of 348.12\n\nCalculation steps:\n\n463 ÷ ( 133 ÷ 100 ) = 348.12\n\n### Calculate 463 is 133 Percent of what?\n\n• F\n\nFormula\n\n463 ÷ ( 133 ÷ 100 )\n\n• 1\n\nPercent to decimal\n\n133 ÷ 100 = 1.33\n\n• 2\n\n463 ÷ 1.33 = 348.12 So 463 is 133% of 348.12\n\nExample" ]
[ null ]
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https://papers.neurips.cc/paper/2016/file/6aca97005c68f1206823815f66102863-Reviews.html
[ "NIPS 2016\nMon Dec 5th through Sun the 11th, 2016 at Centre Convencions Internacional Barcelona\n\n### Reviewer 1\n\n#### Summary\n\nThe paper proposes to replace standard Monte Carlo methods for ABC with a method based on Bayesian density estimation. Although density estimation has been used before within the context of ABC, this approach allows for the direct replacement of sample-based approximations of the posterior with an analytic approximation. The novelty is similar in scope that of ABC with variational inference , but the approach discussed here is quite different. Tran, Nott and Kohn, 2015, \"Variational Bayes with Intractable Likelihood\"\n\n#### Qualitative Assessment\n\nThe paper is actually very well written and easy to understand. The level of technical writing is sufficient for the expert, while eliding unnecessary details. While paper heavily builds upon previous work, the key idea of proposition 1 is used elegantly throughout, both to choose the proposal prior and to estimate the posterior approximation. In addition, there are other moderate novel but useful contributions sprinkled throughout the paper, such as the extension of MDN to SVI. However, the authors must also discuss other work in the use of SVI with ABC, such as for example . The paper lacks a firm theoretical underpinning, apart from the asymptotic motivation that Proposition 1 provides to the proposed algorithm. However, I believe that this is more than sufficient for this type of paper, and I do not count that as a negative, especially given the NIPS format [I doubt that an explanation of the model, experiments as well heavy theory could fit in the eight pages provided]. The experimental results are a good mix of simple examples and larger datasets, and are clearly presented. I also like how the authors disentangle the effect of selecting the proposal distribution from the posterior estimation. The plots are trying to take the taking effective sample size into account, but I am not sure that this is the best metric. After all, samples are purely computational beasts in this setting. Wouldn't it make more sense to measure actual CPU time?\n\n#### Confidence in this Review\n\n3-Expert (read the paper in detail, know the area, quite certain of my opinion)\n\n### Reviewer 2\n\n#### Summary\n\nIn this paper the authors present an alternative approach to Approximate Bayesian Computation for (of course) models with intractable likelihood but from which mock datasets may be readily simulated under arbitrary parameters (within the prior support, etc etc.). The approach presented makes use of a flexible parametric modelling tool—the Mixture Density Network—to approximate the Bayesian conditional density on the parameter space with respect to the (mock) data; in this way the authors bring about a potentially powerful synthesis of ideas from the machine learning and statistical theory.\n\n#### Qualitative Assessment\n\nI believe this may be an outstanding paper as the approach suggested is well motivated and clearly explained; and my impression from the numerous worked examples is that it will very likely have an impact on the application of likelihood free methods, especially (but not exclusively) for problems in which the mock data simulations are costly such that efficiency of the sampler or posterior approximation scheme is at a premium (e.g. weather simulations, cosmological simulations, individual simulation models for epidemiology). It is worth noting here the parallel development within the statistics community of random forest methods for epsilon-free ABC inference targeting models for the conditional density (Marin et al., 1605.05537), which highlight the enthusiasm for innovations in this direction. I have a concern with the authors’ proof of Proposition 1 in that the term ‘sufficiently flexible’ is not explicitly described but should be, in which case sufficient conditions on the posterior for use of the MVN model could be easily identified. Naturally these will be rather restrictive so interest turns to understanding and identifying circumstances where the the approximation may be considered adequate or otherwise, and empirical metrics by which the user might be guided in this decision. Minor notes: - the comparison to existing work in Section 4 is well done (e.g. identification of regression-adjustment as a development in a similar direction); perhaps though it is worth noting that the ‘earliest ABC work’ of Diggle & Gratton (1984) was to develop a kernel-based estimator of the likelihood - in the introduction it is mentioned that “it is not obvious how to perform some other computations using samples, such as combining posteriors from two separate analyses”; a number of recent studies in scaleable Bayesian methods have been directed towards this problem (e.g. Zheng, Kim, Ho & Xing 2014, Scott et al. 2013, Minsker et al. 2014)\n\n#### Confidence in this Review\n\n3-Expert (read the paper in detail, know the area, quite certain of my opinion)\n\n### Reviewer 3\n\n#### Summary\n\nThe authors propose to approximate the posterior of intractable models using a density estimator based on neural network. The main advantage, relative to ABC methods, is that it is not necessary to choose a tolerance. The innovative part is that they model the posterior directly, while a more common approach is to approximate/estimate the intractable likelihood. Hence, Proposition 1 is the main result of the paper, in my opinion. Starting from Proposition 1, several conditional density estimators could be used, and the authors use a Mixture Density Network. They then describe how the proposal prior and the posterior density are estimated, using respectively Algorithm 1 and 2. They illustrate the method with several simple examples, two of which have intractable likelihoods.\n\n#### Qualitative Assessment\n\nThe most original part of the paper is Proposition 1, which is quite interesting. However, I have some doubts regarding the assumptions leading to formula (2). As explained in the appendix, this formula holds if q_theta is complex enough to make so that the KL distance is zero. Now, in a realistic example and with finite sample size, q_theta can't be very complex, otherwise it would over-fit. Hence, (2) holds only approximately. The examples are a bit disappointing. In particular, tolerance-based ABC methods suffer in high dimensions, hence I would have expected to see at least one relatively high dimensional example (say 20d). It is not clear to me that the MDN estimator would scale well as the number of model parameters or of summary statistics increases. The practical utility of the method depends quite a lot on how it scales, and at the moment this is not evident. My understanding is that the complexity of the MDN fit depends on the hyper-parameter lambda and on the number of components. The number of components was chosen manually, but the value of lambda is never reported. How was this chosen? I have some further comments. Section by section: - Sec 2.3 1. Is a proper prior required? 2. In Algorithm 1, how is convergence assessed? Because the algorithm seems to be stochastic. - Sec 2.4 1. The authors say: \"If we take p̃(θ) to be the actual prior, then q φ (θ | x) will learn the posterior for all x\" is this really true? Depending on the prior, the model might learn the posterior for values of x very different from x_0, but probably not \"for all x\". Maybe it is also worth pointing out that you need to model qφ(θ | x) close to x_0 because you are modelling the posterior non-parametrically. If, for instance, you were using a linear regression model, the variance of the estimator would be much reduced by choosing points x very far from x_0. 2. Why the authors use one Gaussian component for the proposal prior and several for the posterior? Is sampling from a MDN with multiple components expensive? If the same number of components was used, it might be possible to unify Algorithms 1 and 2. That is, repeat algorithm 2 several times, use the estimated posterior at the i-th iteration as the proposal prior for the next one. 3. It is not clear to me how MDN is initialized at each iteration in Algorithm 1. The authors say that by initializing the prior using the previous iteration allows them to keep N small. Hence, I think that by initializing they don't simply mean giving a good initialization to the optimizer, but something related to recycling all the simulations obtained so far. Either way, at the moment is it not quite clear what happens. - Sec 2.5 1. It is not clear to me why MVN-SVI avoids overfitting. Whether it overfits or not probably depends on the hyperparameter \\lambda. How is this chosen at the moment? I guess not by cross-validation, given that the authors say that no validation set is needed. - Sec 3.1 1. The differences between the densities in the left plot of Figure 1 are barely visible. Maybe plot log-densities? 2. What value of lambda was used to obtain these results? This is not reported, same in the remaining examples. - Sec 3.2 1. Is formula (5) correct? x is a vector, but its mean and variance are scalar. 2. In Figure 2: maybe it is worth explaining why in ABC the largest number of simulations does not correspond to the smallest KL distance. I guess that this is because \\epsilon is too small and the rejection rate is high. - Sec 3.3 1. The authors say that \"in all cases the MDNs chose to use only one component and switch the rest off, which is consistent with our observation about the near-Gaussianity of the posterior\". Does this happen for any value of \\lambda?\n\n#### Confidence in this Review\n\n2-Confident (read it all; understood it all reasonably well)\n\n### Reviewer 4\n\n#### Summary\n\nThis paper proposes a method for parameters inference. The paper sets the problem where we have a set of observed variables, x, and a set of underlying parameters theta. We assume that we can sample from p(x|theta) but that we don't have an explicit form for it. The goal is to recover the parameter posterior p(theta|x). We assume we have a prior distribution p(theta) over the parameters theta. The paper explains that must of the usual methods to solve this kind of problems is to replace p(x=x0|theta) by p(||x-x0|| < epsilon|theta) and use a sampling method, such as MCMC. However, they explain that it only approximates the true distribution when epsilon goes to 0, but at the same time the computing complexity grows to infinity. The proposed method is to directly train a neural network to learn p(theta|x) (renormalized by a known ratio of pt(theta) over p(theta), explained later). The network produces the parameters for a mixture of Gaussian. The training points are drawn from the following procedure: choose a distribution pt(theta) to sample from. Sample a batch a N points from pt(theta). Run them through the sampler to get the corresponding points x. Train the network to predict p(x|theta) from the input theta. The selection of pt is important for convergence speed, and a method is proposed: start with the prior p(theta) and as the neural network is trained, use the current model to refine the prior pt. Results are on multiple datasets, and the method seems to work well, and converge better than MCMC and simple rejection methods.\n\n#### Qualitative Assessment\n\nThe paper is clear and the method looks sounds. Several related works are presented towards the end of the paper (why not the beginning as in most papers?). The differences between the current method and these are explained, but no comparisons are directly shown with most of the related methods. It would be nice to include these on at least one problem.\n\n#### Confidence in this Review\n\n1-Less confident (might not have understood significant parts)\n\n### Reviewer 5\n\n#### Summary\n\nThe paper is on likelihood-free inference, that is on parametric inference for models where the likelihood function is too expensive to evaluate. It is proposed to obtain an approximation of the posterior distribution of the parameters by approximating the conditional distribution of data given parameters with a Gaussian mixture model (a mixture density network). The authors see the main advantages over standard approximate Bayesian computation (ABC) in that - their approach is returning a \"parametric approximation to the exact posterior\" as opposed to returning samples from an approximate posterior (line 49), - their approach is computationally more efficient. (line 55) The paper contains a short theoretical part where the approach is shown to yield the correct posterior in the limit of infinitely many simulations if the mixture model can represent any density. The approach is verified on two toy models where the true posterior is known and two more demanding models with intractable likelihoods." ]
[ null ]
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https://www.tutorpace.com/algebra/how-to-solve-linear-equations-by-graphing-online-tutoring
[ "# How To Solve Linear Equations By Graphing\n\n## Online Tutoring Is The Easiest, Most Cost-Effective Way For Students To Get The Help They Need Whenever They Need It.\n\n#### SIGN UP FOR A FREE TRIAL\n\nThe solution of a linear equation: - We say that x=m, y=n is a solution of ax+by+c=0.\n\nHow to solve linear equations by graphing: -\n\ni)    Express y in terms of x.\n\nii)   Choose at least two convenient values of x and find the corresponding values of y, satisfying the given equation.\n\niii)   Write down these values of x and y in the form of a table.\n\niv)   Plot the order pairs (x, y) from the table on a graph paper.\n\nv)   Join these points by a straight line and extend it in both the directions.\n\nThis line is the graph of the equation a x + b y + c = 0.\n\nExample: - Draw the graph of the equation 2 x – y + 3. Using the graph, find the value of y, when x = - 2.\n\nSolution: - 2 x – y + 3 = 0 implies y = 2 x + 3\nWhen x = 0, then y = 2 * 0 + 3 = 3\nWhen x = 1, then y = 2 * 1 + 3 = 5\n\n X 0 1 Y 3 5\n\nNow, plot the points A (0, 3) and B (1, 5) on a graph paper.\nJoin AB and extend it in both directions.\nThen, the line AB is the required graph of 2 x – y + 3 = 0.\nGiven: x = -2. Take a point M on the x-axis such that OM=-2.\nDraw MP, parallel to the y-axis, cutting the line AB at P.\nClearly PM=-1.\nTherefore x = -2, then y = -1" ]
[ null ]
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https://www.exceldemy.com/index-function-excel/
[ "Disclosure: This post may contain affiliate links, meaning when you click the links and make a purchase, we receive a commission.\n\n# How to Use INDEX Function in Excel (6 Handy Examples)\n\nThe INDEX function is one of the top used 10 Excel functions. In this tutorial, you will get a complete idea of how the INDEX function works in Excel individually and with other Excel functions.\n\nYou will get the Excel INDEX function in two forms: Array Form and Reference Form.\n\nExcel INDEX Function in Array Form (Quick View):\n\nWhen you intend to return a value (or values) from a single range, you will use the array form of the INDEX function.", null, "Excel INDEX Function in Reference Form (Quick View):\n\nWhen you intend to return a value (or values) from multiple ranges, you will use the reference form of the INDEX function.", null, "## Introduction to INDEX Function in Excel", null, "Function Objective:\n\nIt returns a value or reference of the cell at the intersection of a particular row and column, in a given range.\n\nSyntax of INDEX Function in Array Form:\n\n=INDEX (array, row_num, [column_num])\n\nArguments:\n\nargument required/ optional value\narray  Required  Pass a range of cells, or an array constant to this argument\nrow_num  Required  Pass the row number in the cell range or the array constant\ncol_num  Optional  Pass the column number in the cell range or the array constant\n\nNote:\n\n• If you use both the row_num and column_num arguments, the INDEX function will return the value from the cell at the intersection of the row_num and column_num.\n• If you set row_num or column_num to 0 (zero), then you will get the whole column values or the whole row values respectively in the form of arrays. You can insert those values into cells using Array Formula.\n\nSyntax of INDEX Function in Reference Form:\n\n=INDEX (reference, row_num, [column_num], [area_num])\n\nArguments:\n\nargument required/ optional value\nreference Required  Pass more than one range or array\nrow_num  Required  Pass the row number in a specific cell range\ncol_num  Optional  Pass the column number in a specific cell range\narea_num Optional Pass the area number that you want to select from a group of ranges\n\nNote:\n\n• If pass more than one range or array as the array value, you should pass also the area_num.\n• If the area_num is absent, the INDEX Function will work with the first range. If you pass a value as the area_num, the INDEX function will work in that specific range.\n• If the concepts are not clear, do not worry; go to the next step, where I am going to show you a good number of examples to use Excel’s INDEX function effectively.\n\n## 6 Examples of Using INDEX Function Individually and with Other Excel Functions\n\n### Example 1: Select an Item from a List\n\nUsing the Excel INDEX function, we can retrieve any item from a list. You can use hard-coded row or column numbers in the formula or use a cell reference.\n\nOne Dimensional List with a Single Column:\n\nFor example, if we want to retrieve the 3rd product from the list, we can use the following formula in cell C13, having specified the row number (the serial number, in other words) in cell C12.\n\n`=INDEX(B5:B10,C12)`\n\nOr,\n\n`=INDEX(B5:B10,3)`", null, "One Dimensional List with a Single Row:\n\nSimilarly, we can retrieve an item from a single row using the INDEX function. Specify the serial number in column B and apply the following formula in cell C20:\n\n`=INDEX(C17:H17,,B20)`\n\nOr,\n\n`=INDEX(C17:H17,3)`", null, "You could also write the serial number directly in the formula instead of using cell reference. But we recommend using cell reference as it makes your job more dynamic.\n\nRetrieve Item from a Multidimensional List:\n\nTo retrieve an item from a list of multiple dimensions, you have to specify the row and column number in the INDEX function.\n\nFor example, If you want to get the item from the 3rd row and 4th column of the list, you must insert the following formula in cell C33.\n\n`=INDEX(C26:H29,C31,C32)`", null, "Note:\n\n• If you specify a row number beyond the range of your list (the array you have specified to the INDEX function), it will cause a #REF! error.\n• You can also refer to an array as a reference and apply the INDEX function. For example, the formula =INDEX({1,2,3;4,5,6;7,8,9;10,11,12},2,3) will return 8. The array constant {1,2,3;4,5,6;7,8,9;10,11,12} contains columns separated by semicolons.\n\nRead More: How to Use INDEX MATCH with Excel VBA\n\n### Example 2: Selecting Item from Multiple Lists\n\nYou may have noticed already; the INDEX function has another optional argument which is [area_num]. With this, you can input multiple arrays or reference ranges in the INDEX function and specify from which array, the function will return an item or value.\n\nFor example, we have two lists here, one is for Windows and the other is for MS Office. You can apply the following formula to get a value from the windows list.\n\n`=INDEX((D5:G9,I5:L9),C11,E11,1)`", null, "Or,\n\n`=INDEX((D5:G9,I5:L9),C11,E11,2)`\n\nto get an item from the MS Office list.\n\nNote:\n\nIf you don’t specify the number in this formula, Excel will consider area 1 to return the value, by default.\n\n### Example 3: Combine MATCH Function with INDEX to Match Multiple Criteria and Return Value\n\nThe MATCH function returns the relative position of an item in an array that matches a specified value in a specified order. You can easily retrieve the row and column numbers for a specific range using the MATCH function.\n\nLet’s see the following example. We want to match some criteria specified in cells C12 and C13.\n\nSteps:\n\n• Apply the following formula in Cell C14:\n`=INDEX(B5:E10,MATCH(C13,B5:B10,0),MATCH(C12,B4:E4,0))`", null, "• Press ENTER.\n\n🔎 How Does This Formula Work?\n\nLet’s see how this formula works part by part.\n\n• MATCH(C12,B4:E4,0)\n\nOutput: 3\nExplanation: The MATCH function takes input from cell C12 and performs an exact match in the range B4:E4. 0 digit in the last argument indicates an exact match here. Finally, since the item in C12 is in the third column of B4:E4 range, the function returns 3.\n\n• MATCH(C13,B5:B10,0)\n\nOutput: 3\nExplanation: Same as the first MATCH function explained above. But this time, the function works row-wise since the range B5:B10, which means the items are in different rows but in one single column.\n\n• INDEX(B5:E10,MATCH(C13,B5:B10,0),MATCH(C12,B4:E4,0))\n\nOutput:1930\nExplanation: We can simplify the formula using the outputs of the two MATCH parts. So it will be: INDEX(B5:E10,3,3). So, the INDEX function will travel to row 3 and then to column 3 within the range B5:E10. And from the row-column intersection, it will return that value.\n\n### Example 4: Combine INDEX, MATCH and IF Functions to Match Multiple Criteria from Two Lists\n\nNow, if we have two lists and want to match multiple criteria after choosing one, what to do? Here, we will provide you with a formula.\n\nHere is our dataset and we have Sales data for Windows and MS Office in different countries and years.", null, "We will set 3 criteria: Product name, Year, and Country, and retrieve their corresponding sales data.\n\nSteps:\n\n• Assume that the criteria set are- Year: 2019, Product: MS Office, and Country: Canada.\n• Set them in cells C11, C12, and C13 respectively.\n• Now, apply the following formula in Cell C14 and hit ENTER.\n`=INDEX(INDEX((D5:G9,I5:L9),,,IF(C12=\"Windows\",1,2)),MATCH(C13,B5:B9,0),MATCH(C11,INDEX((D5:G5,I5:L5),,,IF(C12=\"Windows\",1,2)),0))`", null, "• You will see the corresponding sales data in Cell C14 now.\n• You can make this formula more dynamic by using data validation.\n\n🔎 How Does This Formula Work?\n\n• IF(C12=”Windows”,1,2))\n\nOutput: 2\nExplanation: Since Cell C12 contains Windows, the criteria is not matched and the IF function returns 2.\n\n• INDEX((D5:G9,I5:L9),,,IF(C12=”Windows”,1,2))\n\nOutput: {2017,2018,2019,2020;8545,8417,6318,5603;5052,8052,5137,5958;9590,6451,3177,6711;5126,3763,3317,9940}\nExplanation: Since the IF(C12=”Windows”,1,2) part returns 2, so this formula becomes INDEX((D5:G9,I5:L9),,,2). Now, the INDEX function returns the second range assigned to it.\n\n• MATCH(C11,INDEX((D5:G5,I5:L5),,,IF(C12=”Windows”,1,2)),0)\n\nOutput: 3\nExplanation: Since IF(C12=”Windows”,1,2) part returns 2, so this part becomes MATCH(C11,INDEX((D5:G5,I5:L5),,,2),0). Now, INDEX((D5:G5,I5:L5),,,2) part return I5:G5 which is {2017,2018,2019,2020}. So the MATCH formula becomes MATCH(C11,{2017,2018,2019,2020},0). And the MATCH function returns 3 since the value 2019 in Cell C11 is in the 3rd position of {2017,2018,2019,2020} array.\n\n• MATCH(C13,B5:B9,0),\n\nOutput: 4\nExplanation: The MATCH function matches the value of Cell C13 in the B5:B9 range and returns 4 since it’s the position of the string “Canada” in the B5:B9 range.\n\n• =INDEX({2017,2018,2019,2020;8545,8417,6318,5603;5052,8052,5137,5958;9590,6451,3177,6711;5126,3763,3317,9940},4,3)\n\nOutput: 3177\nExplanation: After all the small pieces of the formula are performed, the whole formula looks like this. And it returns the value where the 4th row and 3rd column intersect.\n\n### Example 5: Returning a Row or Column Entirely from a Range\n\nUsing the INDEX function, You can also return a row or column entirely from a range. To do that, execute the following steps.\n\nSteps:\n\n• Say you want to return the first row from the Windows list. Apply the following formula in any cell (here, in cell F11), and press ENTER.\n`=INDEX(D6:G9,1,0)`", null, "• Note that, we have specified column number as 0 here. We could also apply the following formula to get the entire row, putting a comma after the row_num argument, and leaving it as it is without specifying any column number.\n`=INDEX(D6:G9,1,)`\n• But if you just write =INDEX(D6:G9,1) and hit ENTER, you will get only the first value in the first row, not the whole row.\n• To get the first column as a whole, apply the following formula. The things you should consider in case of getting a whole row returned are also applicable to this case.\n`=INDEX(I6:L9,,1)`\n\nNote:\n\n• If you are using older Excel versions than Microsoft 365, then you must use the Array formula to return a row or column from a range using the INDEX Function.\n• For example, in our dataset here, every row of sales range consists of 4 values, so you must select 4 cells horizontally and then input the INDEX function.\n• Now press CTRL + SHIFT + ENTER to enter the formula as an array formula.\n• In the same way, you can show the Entire Column.\n• To return an entire range, just assign the range to the reference argument and put 0 as the column and row number. Here is a formula as an example.\n`=INDEX(D6:G9,0,0)`\n\n### Example 6: INDEX Function Can Also Be Used as Cell Reference\n\nIn example 5, we have seen how to use the INDEX function to return an entire row from a range. You could also use the following simple formula in any cell to get the same.\n\n`=D6:G6`\n\nThe point I am trying to make is- the INDEX function can also return a cell reference instead of a cell value. I will use INDEX(D6:G9,1,4) instead of G6 in the above formula. Hence, the formula will be like this,\n\n`=D6:INDEX(D6:G9,1,4)`", null, "🔎 Evaluation of This Formula:\n\n• First, select the cell where the formula lies.\n• Go to the Formulas tab >> Formula Auditing group >> Click on the Evaluate Formula command.\n• The Evaluate Formula dialog box will open.", null, "• In the Evaluation field, you will get the formula =D6:INDEX(D6:G9,1,4).\n• Now click on Evaluate.\n• The formula is now showing the cell range \\$D\\$6:\\$G\\$6.\n• So, the whole INDEX formula has returned a cell reference, not a cell value.\n\n## Common Errors While Using INDEX Function in Excel\n\nThe #REF! Error:\n\nIt occurs-\n\n• When your passed row_num argument is higher than the existing row numbers in the range.\n• When your passed col_num argument is higher than the existing column numbers in the range.\n• When your passed area_num argument is higher than the existing area numbers.\n\nThe #VALUE! Error:\n\nIt occurs when you supply non-numeric values as row_num, col_num, or area_num.\n\n## Conclusion\n\nINDEX Function is one of the most powerful functions in Excel. To travel through a range of cells, and retrieve data from a range of cells, you will use a lot of time Excel’s INDEX Function. If you know a unique way of using Excel’s INDEX Function, let us know in the comment box. You can visit our blog for more such Excel-related content.\n\n## Related Articles", null, "#### Kawser\n\nHello! Welcome to my Excel blog! It took me some time to be a fan of Excel. But now I am a die-hard fan of MS Excel. I learn new ways of doing things with Excel and share them here. Not only a how-to guide on Excel, but you will get also topics on Finance, Statistics, Data Analysis, and BI. Stay tuned! You can check out my courses at Udemy: udemy.com/user/exceldemy/\n\n1. Reply", null, "Have tried the links to download the 1200+ macros examples e-book & 100+ excel functions cheat sheet. However, it keeps asking me to reload or re-register. Please if the file sizes are not too large can you forward to my email address.\n\n• Reply", null, "Baber,\nI have sent you an email with instructions. Please check. I hope the email solves the problem.\nRegards\n\n2. Reply", null, "Ahmed Sheikh\n[email protected]\n\n• Reply", null, "You can do that Ahmed. Thank you.\nRegards\n\n3. Reply", null, "Waleed Eltayeb Jul 31, 2016 at 4:26 PM\n\nI have many data in columns A to E , I want to find all data that corresponding to a specific data from column A . This data from A may be exist three times or more.\n\n4. Reply", null, "Waleed Eltayeb Jul 31, 2016 at 4:30 PM\n\nI have many data in columns A to E , I want to find all data from column E that corresponding to a specific data from column A. This data from A may be exist three times or more.\n\n• Reply", null, "Waleed,\nCan you upload the working files of your problems? At least a sample file? If possible send an email to this address [email protected]\n\n5. Reply", null, "I am trying to use the index function to display a dollar amount listed in a table in the month that it will be billed for. I have multiple projects and when the formula is dragged down to the next project, the index gets off because the projects have different start dates. Is there a better way to have the index start at the first billing month other than copying the formula from the previous project to the first billing month of the next project?\n\n• Reply", null, "Hi, GILBERT BECHTOL!\nIn your appeared problem, I would suggest you use the MONTH function to get individual months from each date record. Then, sort the order from smallest to largest. As a result, you’ll get the billing months of the project in sequential order and thus you can use the INDEX function to achieve your target.\n\nRegards,\nTanjim Reza\n\n6. Reply", null, "akshay thakker Sep 3, 2016 at 6:58 AM\n\nDear All:\n\nI am looking for a way (without VBA) to create a dynamic array constant which has the value of {1,1,1;2,2,0;3,0,0} in column 3 and {1,1,1,1;2,2,2,0;3,3,0,0;4,0,0,0} in column 4.. and so on and so forth\n\nThis is where I have reached so far:\nI was able to figure out that an array formula =CHOOSE(TRANSPOSE(A1:C1),{1,1,1},{2,2,0},{3,0,0}) where A1=1,B1=2,C1=3 gives me the solution and =A1*–(A1:A3<D1) gives me the value of {1,1,1}.. but when I try combining the above two into a single formula as =CHOOSE(TRANSPOSE(A1:C1),A1:C1*–(OFFSET(A1:C1,0,0,1,C1)<D1)).. i am returned the value of {1,2,3;#VALUE!,#VALUE!,#VALUE!;#VALUE!,#VALUE!,#VALUE!}….\n\nI cant seem to figure out how to get the above formula to work or some other way to get the constant {1,1,1;2,2,0;3,0,0}.\n\nThanks\nAkshay\n\n• Reply", null, "Hi AKSHAY THAKKER! We hope you are well. It’s been 6 years since you posted this query here. We are extremely sorry for being so late in responding. Hope you would have had a solution to your problem somewhere by now. However, we are providing a solution to your question hoping that other readers might find it useful.\nFor example, we have created a 10×10 array using the following formula.\n\n``=IF((ROW(A1:A10)-(COUNT(A1:J1)-COLUMN(\\$A\\$1:\\$J\\$1)))>1,0,(\\$A\\$1:\\$A\\$10)*(IF(A1:J1=A1:J1,1)))``\n\nLook at the following image.", null, "You must do two things before applying the formula.\nFirst, place the numbers in the first row (row 1, i.e. row of A1) serially, and second, place them serially down the first column (column A). You can place them elsewhere, but in that case, you have to change the cell references in the array formula accordingly.\nYou can create any square array with your desired sequence (1,1,1,1;2,2,2,0;3,3,0,0;4,0,0,0) having any square dimension. However, if you want to change the sequence, you have to change the formula a bit.\nTip: Look into the greater than logic in the formula. You have to make the change here to create other sequences.\n\nIf there is any query, please let us know. You can also send us your problem at this address: [email protected]", null, "" ]
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http://curious.astro.cornell.edu/astronomy-links/57-our-solar-system/planets-and-dwarf-planets/orbits/81-when-the-sun-converts-mass-to-energy-do-the-orbits-of-the-planets-change-advanced
[ "## When the Sun converts mass to energy, do the orbits of the planets change? (Advanced)\n\nIf I'm correct, fusion reactions convert some mass into energy. Shouldn't this conversion reduce the gravitational \"pull\" (or warping) of the object undergoing the reaction? So, in the case of our Sun shouldn't the planets' orbits be slightly different over time since the mass of the Sun is gradually being reduced by fusion? I understand that the effect would be very slight over observable time and might be swamped by the angular momentum of the orbiting bodies.\n\nYes, the mass of the Sun is indeed being reduced due to nuclear fusion processes in the Sun's core, which convert part of the mass into energy. (This energy is eventually radiated away in the form of light from the Sun's surface.) However, the effect on the orbits of the planets is very small and would not be measurable over any reasonable time period.\n\nOne way we can see that this must be a small effect is to look at the main fusion reactions which produce the Sun's energy, in which four hydrogen atoms are transformed into one helium atom. If you look at a periodic table, you will see that one helium atom has about 0.7% less mass than four hydrogen atoms combined -- this \"missing mass\" is what gets converted into energy. Therefore, at the absolute most, only 0.7% of the Sun's mass can get converted, and this takes place over the entire 10 billion year lifetime of the Sun. So it must be a very small effect. (In actuality, not all of the Sun's mass is hydrogen to start with, and only the mass in the inner core of the Sun gets hot enough to undergo fusion reactions, so we really only expect around 0.07% of the mass to get converted.)\n\nIt is also easy to directly calculate the rate at which the Sun converts mass to energy. Start with Einstein's famous formula:\n\nE = M c2\n\nwhere E is the energy produced, M is the mass that gets converted and c is the speed of light (3 x 108 meters/second). It is easy to extend this formula to find the rate at which energy is produced:\n\n(rate at which E is produced) = (rate at which M disappears) x c2\n\nThe rate at which the Sun produces energy is equal to the rate at which it emits energy from its surface (its luminosity), which is around 3.8 x 1026 Watts -- this number can be determined from measurements of how bright the Sun appears from Earth as well as its distance from us. Plugging this into the above formula tells us that the Sun loses around 4,200,000,000 kilograms every second!\n\nThis sounds like a lot, but compared to the total mass of the Sun (2 x 1030 kilograms), it actually isn't that much. For example, let's say we want to measure the effect of this mass loss over 100 years. In that time, the Sun will have lost 1.3 x 1019 kilograms due to the fusion reactions, which is still a very tiny fraction of the Sun's total mass (6.6 x 10-12, or about 6.6 parts in a trillion!).\n\nHow does this affect the orbits of the planets? Intuitively, if we imagine a planet orbiting the Sun at some speed, as the Sun loses mass its gravitational pull on the planet will weaken, so it will have trouble keeping it in the same orbit. The planet's velocity will therefore take it further away from the Sun, and the orbital separation between the Sun and planet will increase.\n\nThe formula that governs this situation turns out to be that the orbital separation is proportional to 1 divided by the Sun's mass -- this can be derived from the fact that the Sun-planet system must conserve its angular momentum as the Sun loses mass. The orbital period of the planet, meanwhile, is proportional to 1 divided by the Sun's mass squared.\n\nFor small percentage changes in the Sun's mass (as we are considering here), all the above formulas reduce to a nice simple approximation: For every percentage decrease in the Sun's mass, the orbital separation of the planet will increase by the same percentage, and the orbital period of the planet will increase by twice the percentage.\n\nAbove, we said that in 100 years, the Sun's mass will decrease by 6.6 parts in a trillion. Therefore, the orbital separation of the planet will increase by 6.6 parts in a trillion and the orbital period will increase by 13.2 parts in a trillion. If the planet in question is the Earth (whose orbital separation from the Sun is around 150,000,000 kilometers and whose orbital period is 1 year), the Earth-Sun separation will increase by about 1 meter, and the orbital period will increase by about 0.4 milliseconds! Neither of these values is large enough for us to be able to detect.\n\nI'm not sure exactly how long we'd have to wait to see a measurable effect in the Earth-Sun orbit. Probably, there are other effects which overwhelm this one and would make it difficult or impossible to detect, even over very long time periods -- for example, changes in the Earth's orbit due to perturbations from other planets. The Sun's mass is also changing due to other effects (such as the solar wind), but over the long run these are probably smaller than the Sun's mass loss due to fusion (as pointed out in another Ask an Astronomer site's answer to this question).\n\nOverall, I think it is safe to conclude that (a) there will be no noticeable effect on the planets' orbits over anything resembling a human lifetime, and (b) there will be a noticeable effect over timescales approaching the lifetime of the Sun, since the Sun will lose around 0.07% of its mass over that time period, leading to a change in the Earth's orbital period of about half a day.\n\n#### Dave Rothstein\n\nDave is a former graduate student and postdoctoral researcher at Cornell who used infrared and X-ray observations and theoretical computer models to study accreting black holes in our Galaxy. He also did most of the development for the former version of the site.\n\n## Our Reddit AMAs\n\nAMA = Ask Me (Us) Anything" ]
[ null ]
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https://stcharlesedu.com/2022/07/mathematics-common-entrance-past-questions-into-federal-govt-school-pdf-download.html
[ "# Mathematics Common Entrance Past Questions into Federal Govt School PDF Download", null, "Posted by\n\nMathematics Common Entrance Past Questions: Appreciable of reader online have be searching on where to or how to download Mathematics National Common Entrance Examination NCEE Past Questions.\n\nOn this website you can download any common entrance past questions subject you want.\n\nwe at stcharlesedu.com has compiled a good number of mathematics common entrance past question paper with answers.\n\nOur research has confirm that candidate who uses NCEE Maths Past Questions to prepare for National Common Entrance Examination into Federal Government or Unity Secondary School is ten times better than those who do not.\n\nEnglish Common Entrance Questions\nBasic Science Common Entrance Questions\n\n## Free National Mathematics Common Entrance Questions\n\nNATIONAL EXAMINATION COUNCIL (NECO) MINNA, NIGERIA\n2022 National Common Entrance Examination (NCEE)\nPAPER 1. ACHIEVEMENT TEST\n\nPART A\nMATHEMATICS\n\n1. What is MCMXCII in Hindu-Arabic numerals system?\nA. 1192\nB. 1902\nC. 1009\nD. 1992\nE. 1999\n\n2. Which of these numbers: 1, 3, 6, 11 and 12 are prime numbers?\nA. 1 mid 3\nB. land 11\nC. 3 and 11\nD. 1, 3 and 11\nE. 6, 11 and 12\n\n3. Change 120 seconds to minute(s).\nA. 1\nB. 2\nC. 6\nD. 12\nE. 60\n\n4. Arrange the following fractions in descending order: 1/4, 1/2, 3/4, 2/3.\nA. 3/4, 2/3, 1/2, 1/4\nB. 1/4, 1/2, 2/3, 3/4\nC. 2/3, 3/4, 1/2, 1/4\nD. 3/4, 2/3, 1/4, 1/2\nE. 2/3, 3/4, 1/4, 1/2\n\n5. If a cup costs 2 pounds and the rate of exchange is a pound to six hundred and forty naira, find its cost in naira.\nA. 320.00\nB. 638.00\nC. 642.00\nD. 1,140.00\nE. 1,280.00\n\n6. Find the even number between 19 and 41 that is a multiple of 7.\nA. 26\nB. 28\nC. 34\nD. 36\nE. 38\n\n7. Find the square root of 2 1/4.\nA. 1/8\nB. 1/4\nC. 1/2\nD. 1 1/4\nE. 1 1/2\n\n8. Calculate the simple interest on N800.00 for 4 years at the rate of 6% per annum.\nA. N186.00\nB. N192.00\nC N196.00\nD. N220.00\nE. N232.00\n\n9. If two-third of a number is 60, what is one-sixth of that same number?\nA. 75\nB. 60\nC. 45\nD. 30\nE. 15\n\n10. The H.C.F. of two numbers is 3. Which of the following pairs represents the two numbers in the options below?\nA. 12 and 17\nB. 10 and 20\nC. 9 and 15\nD. 8 and 12\nE. 4 and 6\n\n11. 25% of N500.00 is less than one of the following by N80.00.\nA. N195.00\nB. N205.00\nC. N225.00\nD. N 230.00\nE. N240.00\n\n12. Change N32.08 to kobo.\nA. 320\nB. 328\nC. 330\nD. 3,208\nE. 3,280\n\n13. How many N10.00 added up to make N1,000.00?\nA. 10\nB. 50\nC. 100\nD. 110\nE. 1,000\n\n14. Change 7 1/2% to fraction in its lowest term.\nA. 1/2\nB. 3/20\nC. 1/20\nD. 3/40\nE. 1/40\n\n15. Convert 8 weeks 8 days to days.\nA. 64\nB. 56\nC. 48\nD. 39\nE. 28\n\n16. The marked price of an article is N8,200.00. If a customer receives N410.00 as a discount on the article, what percentage is the discount?\nA. 4\nB. 5\nC. 6\nD 8\nE 10\n\n17. Subtract 8cm 7mm from 42cm 5mm.\nA. 34cm 8mm\nB. 34cm 13mm\nC. 33cm 98mm\nD. 33cm 8mm\nE. 32cm 15mm\n\n18. A man drives his car at an average speed of 76 km/h. Calculate the distance covered in 3- hours.\nA. 22 km\nB. 76 km\nC. 228 km\nD. 266 km\nE. 304 km\n\n19. Three boys shared certain numbers of mangoes in the ratio 5:3:2. If the one with the smallest ratio received 40 mangoes, find the number of mangoes shared.\nA. 80\nB. 100\nC. 120\nD. 160\nE. 200\n\n20. Bola bought six books at N45.00 each and sold them for N230.00. How much does she loss?\nA. N50,00\nB. N45.00\nC. N40.00\nD. N30.00\nE. N5.00\n\n21. Evaluate 2/3 × 3/4 + 5/6 ÷ 2/3.\nA. 1 3/4\nB. 1 2/3\nC. 1 1/2\nD. 5/6\nE. 2/3\n\n22. If -2x = 6, find the value of x.\nA. -12\nB. -3\nC. 3\nD. 6\nE. 12\n\n23. Solve for x if 3 = 15x – 2.\nA. 1/5\nB. 1/3\nC. 2/5\nD. 1/2\nE. 2/3\n\n24. Given that the area of a rectangle is 42 cm2 and its breadth is 6 cm, find its length.\nA. 3 cm\nB. 5 cm\nC. 7 cm\nD. 9 cm\nE. 12 cm\n\n25. Find the area of the shaded portion in the diagram below. Download the free Maths Common Entrance PDF file to see image or diagram\n\nA. 72 cm2\nB. 65 cm2\nC. 54 cm2\nD. 48 cm2\nE. 36 cm2\n\n## Free Maths Common Entrance Past Questions\n\nLink 2: Common Entrance Past Questions on Maths\n\n## How to Get Common Entrance Past Questions for any given Subject.\n\nTo get the complete copy of the Past Questions, call or whatsapp me on 08051311885\n\nCost or Price of NCEE Maths Past Questions\nPDF Softcopy Format = N600 for any given year\nMS Word Softcopy Format = N700 for any given yea\n\nTake Action" ]
[ null, "https://secure.gravatar.com/avatar/f12bb7d77d52fd096d7f4762d9e18e0a", null ]
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http://book.caltech.edu/bookforum/showthread.php?s=5e5191a6c5c837d4fd960f00fc135708&p=12943&mode=linear
[ "", null, "LFD Book Forum", null, "*answer* question 13 issue\n Register FAQ Calendar Mark Forums Read\n\n#1\n Charles", null, "Junior Member Join Date: Feb 2018 Posts: 1", null, "*answer* question 13 issue\n\nHi,\n\nI am struggling to get the right answer for question 13. I have the algorithm working (in Python). but it's just not good enough to get perfect accuracy (or at least <5%), as every time a few points are not classified correctly. My SVM implementation (copied below) has been working well for other exercises, and I think I added the rbf kernel correctly. Is anyone else having the same issue? I can't figure out what's wrong.\n\nBelow is the standalone implementation:\nCode:\n```import numpy as np\nimport math\nimport cvxopt\n\nclass svm():\n''' Model: support vector machines '''\n''' Error measure: classification error '''\n''' Learning algorithm: support vector machines (linearly separable data) '''\n\ndef fit(self, X, y, kernel = 'linear', degree = 2, gamma = 1.):\n''' returns the alphas '''\ndimension = X.shape\nN = X.shape\nK = np.zeros(shape = (N,N))\n# Computing the inner products (or kernels) for each pair of vectors\nif kernel == 'linear':\nfor i in range(N):\nfor j in range(N):\nK[i,j] = np.dot(X[i], X[j].T)\nelif kernel == 'poly':\nfor i in range(N):\nfor j in range(N):\nK[i,j] = np.square(1 + np.dot(X[i], X[j].T))\nelif kernel == 'rbf':\nfor i in range(N):\nfor j in range(N):\nK[i,j] = np.exp(-gamma * np.linalg.norm(X[i]-X[j]) **2)\n\n# Generating all the matrices and vectors\nP = cvxopt.matrix(np.outer(y,y) * K, tc='d')\nq = -1. * cvxopt.matrix(np.ones(N), tc='d')\nG = cvxopt.matrix(np.eye(N) * -1, tc='d')\nh = cvxopt.matrix(np.zeros(N), tc='d')\nA = cvxopt.matrix(y, (1,N), tc='d')\nb = cvxopt.matrix(0.0, tc='d')\n\nsolution = cvxopt.solvers.qp(P, q, G, h, A, b)\n\na = np.ravel(solution['x'])\n# Create a boolean list of non-zero alphas\nssv = a > 1e-5\n# Select the corresponding alphas a, support vectors sv and class labels sv_y\na_small = a[ssv] # alphas\nsv = X[ssv] # support vectors (Xs)\nsv_y = y[ssv] # support vectors (ys)\n\n# Computing the weights w_svm\nw_svm = np.zeros((1,dimension))\n\nfor each in range(0,len(a_small)):\nw_svm += np.reshape(a_small[each] * sv_y[each] * sv[each], (1,dimension))\n\n# Computing the intercept b_svm\nb_svm = sv_y - np.dot(w_svm, sv.T)\n# does not matter if divide by sv_y or not\n\ng = np.sign( np.inner(w_svm,X) + b_svm )\n\nself.a = a\nself.a_small = a_small\nself.sv = sv\nself.sv_y = sv_y\nself.w = w_svm\nself.b = b_svm\nself.g = g\nreturn self\n\ndef predict(self, X):\n''' returns the g as a column vector '''\nself.g = np.sign( np.inner(self.w, X) + self.b )\nreturn self.g\n\nN = 100\ngamma = 1.5\nrun = 100\n\nEin = []\nsep = []\nfor r in range(0,run):\nX = np.random.uniform(-1, 1,size = (N,2))\ny = np.sign(X[:,1] - X[:,0] + .25 * np.sin(math.pi * X[:,0]))\nX = np.insert(X, 0, 1, axis=1)\n\nsvm_RBF = svm()\nsvm_RBF.fit(X, y, kernel = 'rbf', gamma = 1.5)\nresult = svm_RBF.predict(X)\n\nEin.append(1 - np.average(np.equal(result, y)))\n\nsep = np.equal(Ein, 0.)\nnp.average(sep)```\n\n Tags python, q13, rbf, svm", null, "Thread Tools", null, "Show Printable Version", null, "Email this Page Display Modes", null, "Linear Mode", null, "Switch to Hybrid Mode", null, "Switch to Threaded Mode", null, "Posting Rules You may not post new threads You may not post replies You may not post attachments You may not edit your posts BB code is On Smilies are On [IMG] code is On HTML code is Off Forum Rules\n Forum Jump User Control Panel Private Messages Subscriptions Who's Online Search Forums Forums Home General     General Discussion of Machine Learning     Free Additional Material         Dynamic e-Chapters         Dynamic e-Appendices Course Discussions     Online LFD course         General comments on the course         Homework 1         Homework 2         Homework 3         Homework 4         Homework 5         Homework 6         Homework 7         Homework 8         The Final         Create New Homework Problems Book Feedback - Learning From Data     General comments on the book     Chapter 1 - The Learning Problem     Chapter 2 - Training versus Testing     Chapter 3 - The Linear Model     Chapter 4 - Overfitting     Chapter 5 - Three Learning Principles     e-Chapter 6 - Similarity Based Methods     e-Chapter 7 - Neural Networks     e-Chapter 8 - Support Vector Machines     e-Chapter 9 - Learning Aides     Appendix and Notation     e-Appendices\n\nAll times are GMT -7. The time now is 07:11 AM.", null, "The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, and participants in the Learning From Data MOOC by Yaser S. Abu-Mostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity." ]
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https://stats.stackexchange.com/questions/246181/are-my-assumptions-right-for-this-question
[ "# Are my assumptions right for this question?\n\nI answered these homework questions but I was told that at least one of my answers is wrong. However, I can't tell which one I answered wrong. What incorrect assumptions have I made?\n\nQUESTION:", null, "3a) Welch's T-test would be most appropriate since we have normal data with unequal variances.\n\n3b) A Wilcoxon-Mann Whitney U Test appears to be the best option since transformations have failed and we must now use a non-parametric test for these 2 independent samples.\n\n3c) The data has unequal variances and is not normal so we should also use a Wilcoxon-Mann Whitney U Test here too.\n\n• In (c), consider the interpretation of \"p-values > $\\alpha$\" (not that I agree with the reasoning behind these questions). Nov 16, 2016 at 5:23\n• I believe I did. When p > α in Levene's test, there are unequal variances. When p > α in the Shapiro-Wilk test, the distribution is not normal. Or am I incorrect? Nov 16, 2016 at 5:35\n• en.wikipedia.org/wiki/Levene%27s_test Nov 16, 2016 at 5:39\n• If they really will not tell you which one is wrong you might consider changing course and asking for your money back. Nov 16, 2016 at 12:25\n\nWhen the p-value is large the null should not be rejected. In significance testing, the null hypothesis is rejected when the p-value is smaller than the significance level ($\\alpha$)." ]
[ null, "https://i.stack.imgur.com/9odWW.png", null ]
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https://www.frontiersin.org/articles/10.3389/fmicb.2016.01760/full
[ "Impact Factor 4.235 | CiteScore 6.4\nMore on impact ›\n\n# Frontiers in Microbiology", null, "## Methods ARTICLE\n\nFront. Microbiol., 07 November 2016 | https://doi.org/10.3389/fmicb.2016.01760\n\n# Time Series Analysis of the Bacillus subtilis Sporulation Network Reveals Low Dimensional Chaotic Dynamics\n\n• 1Department of Mathematics, University of Trento, Trento, Italy\n• 2Department of Industrial Engineering, Universidad de los Andes, Bogotá, Colombia\n• 3Laboratory of Translational Genomics, Centre for Integrative Biology, University of Trento, Trento, Italy\n• 4Gut Health and Food Safety, Institute of Food Research, Norwich, UK\n\nChaotic behavior refers to a behavior which, albeit irregular, is generated by an underlying deterministic process. Therefore, a chaotic behavior is potentially controllable. This possibility becomes practically amenable especially when chaos is shown to be low-dimensional, i.e., to be attributable to a small fraction of the total systems components. In this case, indeed, including the major drivers of chaos in a system into the modeling approach allows us to improve predictability of the systems dynamics. Here, we analyzed the numerical simulations of an accurate ordinary differential equation model of the gene network regulating sporulation initiation in Bacillus subtilis to explore whether the non-linearity underlying time series data is due to low-dimensional chaos. Low-dimensional chaos is expectedly common in systems with few degrees of freedom, but rare in systems with many degrees of freedom such as the B. subtilis sporulation network. The estimation of a number of indices, which reflect the chaotic nature of a system, indicates that the dynamics of this network is affected by deterministic chaos. The neat separation between the indices obtained from the time series simulated from the model and those obtained from time series generated by Gaussian white and colored noise confirmed that the B. subtilis sporulation network dynamics is affected by low dimensional chaos rather than by noise. Furthermore, our analysis identifies the principal driver of the networks chaotic dynamics to be sporulation initiation phosphotransferase B (Spo0B). We then analyzed the parameters and the phase space of the system to characterize the instability points of the network dynamics, and, in turn, to identify the ranges of values of Spo0B and of the other drivers of the chaotic dynamics, for which the whole system is highly sensitive to minimal perturbation. In summary, we described an unappreciated source of complexity in the B. subtilis sporulation network by gathering evidence for the chaotic behavior of the system, and by suggesting candidate molecules driving chaos in the system. The results of our chaos analysis can increase our understanding of the intricacies of the regulatory network under analysis, and suggest experimental work to refine our behavior of the mechanisms underlying B. subtilis sporulation initiation control.\n\n## 1. Introduction\n\nBacterial spores are important contaminants in food, and the spore forming bacteria are often implicated in food safety and food quality considerations (Carlin, 2011). Most microbial spore forming bacteria respond to stress (e.g., nutrient deprivation) by inducing the expression of an appropriate suit of adaptive (stress-response) genes to help them cope with adverse environmental circumstances; an extreme example is endospore formation (Ihekwaba et al., 2014).\n\nSince sporulation is an energy consuming process that requires a significant reorganization of cellular activity, the decision to commit to spore formation is subject to the result of integration of multiple signals by a complex gene regulation network.\n\nThe initiation of sporulation is one of the decisive moments in spore formation, as exemplified by the bacterium Bacillus subtilis. The changes in gene expression and morphology induced by sporulation are regulated in B. subtilis by a complex network involving more than 120 genes (Stragier and Losick, 1996; Fawcett et al., 2000).\n\nThe DNA-binding protein Spo0A is the master regulator for entry into sporulation in B. subtilis. The concentration level and the phosphorylation state determine the ability of Spo0A to alter transcription. Upon phosphorylation, Spo0A undergoes an allosteric change that re-orientates a phenylalanine residue and allows the molecule to bind DNA (Muchová et al., 2004) and activate key genes that drive the positive regulation of sporulation, particularly the spoIIA, spoIIE, and spoIIG genes involved in establishing compartment-specific transcription under the control of σF (spoIIA operon and the spoIIE gene) and σE (spoIIG operon) (Satola et al., 1991, 1992; York et al., 1992). Phosphorylated Spo0A also acts as a repressor, blocking the expression of the abrB gene. This repression has the consequence of setting up a self-reinforcing cycle that contributes to the further accumulation of Spo0A at the start of sporulation (Fujita and Losick, 2005; Tojo et al., 2013). Indeed, the inhibition that phosphorylated Spo0A exerts on abrB gene expression leads to the depletion of the AbrB protein from the cell and to the accumulation of σH, with the net result of enhancing the expression of KinA, Spo0F and of the Spo0A gene itself (Strauch et al., 1993; Tojo et al., 2013).\n\nSpo0A activation is under the control of a complex network capable of integrating diverse physiological and environmental signals, and relaying signals through a three-level phosphorelay down to the response regulator Spo0A. Various mathematical models of B. subtilis sporulation mechanisms can be found in the literature, among the most recent ones we mention (Kuchina et al., 2011; Sen et al., 2011; Narula et al., 2012; Kothamachu et al., 2013; Vishnoi et al., 2013; Ihekwaba et al., 2014).\n\nThis paper is based on the model proposed in Ihekwaba et al. (2014), which integrates most of previous mathematical modeling works on B. subtilis sporulation initiation. The model we consider encodes the relationships among the time-dependent concentrations of sporulation signals, histidine kinases, phosphorelay proteins and sporulation initiation proteins in the form of a deterministic differential model having 27 variables. Simulation of the differential equations via numerical integration provides predictions about the evolution of the B. subtilis sporulation initiation regulation network, given the initial state of variables. Ihekwaba et al. (2014) also performed a sensitivity analysis of the model to explore the set of possible behaviors with varying the values of its parameters (i.e., the kinetic rate constants).\n\nIn this paper, we continue the analysis of model behavior, with the aim of investigating whether the time series of the variables, as predicted by the differential equations model, are affected by deterministic chaos, or simply chaos. A chaotic system is a system that is predictable up until a given time, after which it becomes unpredictable (i.e., long term unpredictability) due to its sensitivity to initial conditions (Kellert, 1993). Even if the initial state is known at a very accurate level of detail, any imprecision in its quantification, no matter how small, grows quickly (exponentially) with time, rendering long-term prediction impossible.\n\nIdentifying chaos and its drivers in a biological system provides useful information (i) to understand the origins of the observed dynamics (Weiss et al., 1994; Lecca et al., 2016), and (ii) to shed light into the control mechanisms that a biological system may have implemented to maintain a stable activity even when subject to perturbations of its initial conditions (Sinha, 1997). Both chaotic dynamics and stochastic dynamics exhibit a complex phase space structure and are not predictable, but chaos is not stochastic noise (Lecca et al., 2016). Indeed, a chaotic dynamics is governed by deterministic laws in which no randomness is involved, whereas a stochastic dynamics is governed by rules involving random variables. As a consequence, if the laws and the drivers of the dynamics of a chaotic system are known, its unpredictability can potentially be controlled (Sinha, 1997; Lai, 2014).\n\nLow-dimensional chaos occurs when a reduced number of contributing species are responsible for the complex dynamics. Such a low-dimensional chaos is of particular interest in biology (Skinner, 1994; Kaneko, 2006; Vasseur, 2015). Since in a biological system affected by low-dimensional chaos the variables governing the spatial and temporal dynamics are few in number, a low-energy control of unpredictability of the system dynamics can be implemented and a simpler model of a complex dynamics can be provided. Low-dimensional chaos is expected to be common in systems with few degrees of freedom (Skinner, 1994), but is expected to be rare in systems with many degree of freedom such as the sporulation network of B. subtilis. The results of our analysis show that only few molecular species are contributing to the appearance of deterministic chaos in the dynamics of the modeled network.\n\n## 2. The Model\n\nIn Ihekwaba et al. (2014), a mathematical model of the network regulating Bacillus subtilis sporulation initiation was proposed. The model represents at the molecular level the sequence of events that lead to the activation of the early genes under control of the master regulator molecule Spo0A, distilling and extending the results obtained in various modeling studies focused on systems where sporulation is induced by an artificial inductor, the Isopropyl-D-1-thiogalactopyranoside (IPTG) (see for instance Narula et al., 2012), and modeling as well the induction of sporulation that occurs in wild-type cells.\n\nA high-level diagrammatic description of the molecular network governing sporulation initiation in B. subtilis is provided in Figure 1, where pointed arrows represent activation and blunt arrows indicate repression.\n\nFIGURE 1", null, "Figure 1. The sporulation initiation network in B. subtilis is activated by signals that first cause the activation of histidine-kinases (HKs). This can occur either via the direct accumulation of the natural sporulation signals in the cell or via artificial induction (IPTG), both which have been considered in our modeling. The activated HKs transfer phosphor groups to the phosphorelay mediator proteins Spo0F and Spo0B, until activation of the transcription factor Spo0A. The phosphorylated form of the master regulator protein Spo0A activates the genes (spolla, spolle, and spollg) that govern forespore and mother cell specific transcription factors and exerts a positive feedback on phosphorelay components through the repression of abrB gene expression.\n\nThe B. subtilis sporulation network model considered in this study is the published model by Lecca et al. (2016) and Ihekwaba et al. (2014). It follows the topology of the network shown in Figure 1, thereby encompassing three distinct sub-models:\n\n1. input signal, representing the sporulation initiation processes induced by the signals on the histidine kinases;\n\n2. phosphorelay, encoding the signal transduction along the phosphorelay components, from histidine kinases downwards to the master regulator Spo0A;\n\n3. gene expression, modeling the target gene expression activation operated by the activated effector Spo0A.\n\nIn the following section, we explain the structure of each sub-model, and use a graphical notation to represent activation/repression (arrows with non-solid ends) which is introduced in Figure 1. In our modeling, we consider both transcription and translation of proteins. For each species involved in a synthesis process (i.e., transcripts and proteins), the model includes a degradation reaction, not shown in the model diagrams for clarity.\n\n### 2.1. Input Signal Sub-model\n\nThe input signal sub-model, shown in Figure 2, represents the regulation effects that artificial inducers (in this case IPTG) and cell produced sporulation signals (modeled by species SS) have on the HKs. In the model, reactions are consecutively numbered. In our model, of the five known kinases that have been identified as being capable of initiating sporulation in B. subtilis (Jiang et al., 2000), we only considered the histidine kinase KinA. This is the major kinase responsible for initiation of sporulation and its overexpression during exponential growth is sufficient to induce entry into sporulation (Fujita and Losick, 2005).\n\nFIGURE 2", null, "Figure 2. The input signal model represents the artificial sporulation initiation induced by IPTG through the effect on KinA, as well as the activation of KinA dimers by unknown sporulation signals (SS) generated under unfavorable environmental conditions.\n\nThe IPTG regulation of sporulation is rendered by the indirect release of inhibition for the transcription initiation of KinA (Eswaramoorthy et al., 2009; Narula et al., 2012), exerted by the lactose repressor (LacI) on the binding site incorporated into the promoter. The addition of IPTG causes a conformational change in the LacI protein, bringing it (reaction r17) to an inactive form (LacI_d) that has very low affinity for the KinA promoter. The consequence of the inhibition release is an increased level of KinA transcription (reaction r11). In the diagrammatic representation, we use a “droplet” notation for species that are not explicitly represented, such as genes in transcription reactions. The LacI conformational change is however reversible (see reaction r18). Molecules of KinA transcript are translated into protein molecules (reaction r12), which can reversibly bind (reactions r13 and r14) to form dimers. KinA dimers have the ability to autophosphorylate (reaction r15), producing the active species that initiates the phosphorelay signaling (Wang et al., 2001; Eswaramoorthy et al., 2009). We model the dephosphorylation and the unbinding of the KinA dimer as a single reaction (r16). The model also considers the activation of the histidine kinase caused by the naturally occurring sporulation signals (SS), which accelerate the KinA autophosphorylation reaction and can lead B. subtilis into sporulation alone. Last, the model includes the positive effect that active Spo0A has on the transcription of KinA, via the double repression feedback loop that links phosphorylated Spo0A with AbrB, and AbrB with KinA.\n\n### 2.2. Phosphorelay Sub-model\n\nThe phosphorelay sub-model depicted in Figure 3 is based on phosphorylation, dephosphorylation and phosphotransfer reactions. Our model includes the main phosphorelay species Spo0F, Spo0B, and Spo0A, which together form a cascading phosphotransfer (de Jong et al., 2010; Sen et al., 2011). For each of these proteins, the model includes a gene transcription reaction (r21, r22, and r23), and a translation reaction (r24, r25, and r26). The phosphorylated KinA dimer transfers the phosphate group to Spo0F (reaction r27), phosphorylated Spo0F transfers the phosphate group to Spo0B (reaction r29), and finally phosphorylated Spo0B transfers the phosphate group to Spo0A (reaction r210). In the model, phosphorylated Spo0F and phosphorylated Spo0A spontaneously lose the phosphate group (reactions r28 and r211). Finally, we include in the model the phosphorelay self-activation loop induced by phosphorylated Spo0A, which as already described positively affects the transcription of both Spo0F and Spo0A species.\n\nFIGURE 3", null, "Figure 3. The phosphorelay sub-model encodes the transfer of phospho groups from activated KinA to Spo0F, which then leads to Spo0A phosphorylation via the phosphotransferase Spo0B. Dephosphorylation of Spo0F and Spo0A is modeled by abstracting the phosphatase species.\n\n### 2.3. Gene Expression Sub-model\n\nPhosphorylated Spo0A up-regulates transcription from spoIIA, spoIIE, and spoIIG promoters. The gene expression sub-model shown in Figure 4 encodes the activation of transcription exerted by Spo0A, and includes transcription reactions (r31, r32, and r33) and translation reactions for AA, AB, and AC proteins (r34, r35, and r36), IIE protein (r37) and GA and GB protein molecules (r38 and r39). Notice that AA, AB, and AC, and also GA and GB, are transcribed polycistronically from the spoIIA and spoIIG operons, respectively (Narula et al., 2012).\n\nFIGURE 4", null, "Figure 4. The gene expression model represents the transcriptional activity of phosphorylated Spo0A, which binds to the Spoll promoters and promotes transcription initiation for the important sporulation initiation proteins, AA, AB, AC, IIE, GA, and GB.\n\nIn the rest of the paper and in the Supplementary Material, we adopt the following notation to indicate the variables corresponding with the molecular species of the model: the name of the protein is written in lowercase (e.g., Spo0A, spo0a), the transcript of the gene is denoted by the suffix “_t” (e.g., the transcript of gene Spo0A is denoted by spo0a_t), and the phosphorylated form of the protein is indicated by the suffix “p” (e.g., the phosphorilated form of protein Spo0A is spo0ap). The mathematical specification of the model and its parameters are given in Tables S1–S4. The time is measured in seconds (s), and the molecular species concentration in nM.\n\n## 3. Detecting Chaos in B. subtilis Sporulation Network Dynamics\n\nA system is affected by deterministic chaos if its dynamics is governed by deterministic rules and any change in the initial state, no matter how small, grows quickly with time, rendering long-term prediction of the system behavior impossible. A system is affected by low-dimensional chaos if only a small number of variables exhibits a chaotic dynamics, i.e., an aperiodic irregular time-behavior (Tél and Gruiz, 2005; Layek, 2015).\n\nThe presence of low-dimensional chaos in biological systems is of particular interest, because it indicates that the variables governing the spatial and temporal behavior of the system may be few in number. This means that the dynamics of the system might be controlled by only a few crucial variables. The complexity of control inherent in chaotic systems may be important in the dynamics of gene expression regulation. Therefore, it is of particular interest to assess the presence of low-dimensional chaos in a complex system such as the B. subtilis sporulation network, as this analysis allows the identification of the variables (few in numbers) that control the predictability of the dynamics of the whole system.\n\nThere are two-established methods to explore chaotic behavior of a dynamical biological system. The first one is a direct analysis of the experimental time series, combined with the development of algorithms for computing relevant indices quantifying the features of the system dynamics. The second is the implementation of a model developed directly from the experimental observations that aims to account for the essential mechanisms at work in the real system and explain the dominant behavior. Then, a subsequent analysis focuses on the simulated time series obtained by model solution and its phase space in order (i) to evaluate the control parameters, (ii) to detect the system components (e.g., genes, proteins, chemical species, etc.) that exhibit a chaotic dynamics, and (iii) to investigate the robustness of the dynamics against perturbations. We implemented the second approach, because the inclusion in the analysis of a model of the systems dynamics affords not only the identification of the drivers of chaotic dynamics, but also the conceptualization of their role and of their effects within the mechanisms of interaction with other molecular species. In the next section, we provide a detailed explanation of this analysis.\n\n### 3.1. Sensitivity Analysis\n\nWe undertook local sensitivity analysis to assess the sensitivity of the solutions of the system's equations to the variation of individual parameter in the system. We discussed the feasibility of global sensitivity analysis of the system to variation in parameters in the Supplementary Information.\n\nWe randomly sampled NP values from a uniform distribution for each parameter (i.e., kinetic rate constant). The uniform distributions were positively defined on the maximal range of parameter variability in which the system of ordinary differential equations has a unique solution (i.e., it is not underdetermined). We determined this range ${I}_{{q}^{*}}\\left({p}_{h}\\right)$ by attempting to solve the system of equations for different sets of parameters P(q) = {ph}, h = 1, 2, …, NP, obtained by varying the value of q ∈ ℕ+ in the interval [2, 30] in the following expression\n\nThe maximal interval of parameter variation is defined by the maximal value of q for which the system of differential equations has a unique solution.\n\nThe parameters were changed one at a time while keeping the values of the others fixed. Since for each parameter ph we sampled NP values, we performed NP model simulations, i.e., one simulation for each sampled value in the range of parameter variability Iq(ph). The index of sensitivity of the time series xs(t), (s = 1, 2, …, d, where d is the number of molecular species in the system), with respect to the change of the h-th parameter from the value ph to the value ${p}_{h}^{\\prime }$ is calculated as the mean of the standard deviations of the distributions of the simulated values of the variable over the range of parameter variability and over time, i.e.:\n\nwhere N is the length of the time series, and\n\nWith the expression ${p}_{h}←{p}_{h}^{\\prime }$, we denote the replacement of value ph with the value ${p}_{h}^{\\prime }$.\n\n### 3.2. Complexity Indices\n\nIn order to detect the presence of chaos in B. subtilis network dynamics, for the time series of each molecular species we calculated a set of indices that capture different aspects of the complexity. This set of indices includes:\n\n1. Lyapunov exponents (λ): they measure the rate of separation of infinitesimally close trajectories in the phase space generated by slightly different values in the initial state of the system. The largest Lyapunov exponent is usually considered important in the determination of chaotic behavior. A positive value for the largest Lyapunov exponent indicate orbital instability and chaos (Kaneko and Tsuda, 2001; Sprott, 2003; Kalitin, 2004).\n\n2. fractal dimension (DF): a statistical index for pairwise distances of the points of a time series; it indicates how a set of points fills its space and thus quantifies the complexity of the behavior of a trajectory;\n\n3. sample entropy (SE): a measure of data regularity; a smaller value of sample entropy indicates more self-similarity in the data of the time series and a less noise (less disorder);\n\n4. time lag (TL): the time after which the auto-correlation of the time series is negligible;\n\n5. embedding dimension (DE): similarly to the fractal dimension, it measures topological complexity of a time series. A set of points has embedding dimension DE if DE is the smallest integer for which it can be embedded into ${\\text{R}}^{{D}_{E}}$ without intersecting itself. So, DE is the minimum dimension of a space in which a trajectory in the phase space reconstructed from the observed time series does not cross itself (in this case the dynamics is deterministic) (Abarbanel, 1996; Tamma and Khubchandani, 2016).\n\nIn chaotic systems, small differences in the initial condition result in strongly different solutions. Therefore, a chaotic system is unpredictable in the sense that the variability of the prediction induced by small changes in the initial conditions is unacceptably high in comparison to the difference of the initial states.\n\nIn deterministic systems, complete knowledge of the rules of the dynamics and of the initial state (i.e., values for the abundance of the system's components at initial time t0–sometimes called initial conditions) x(t0), is sufficient to determine x(t) at each t > t0. In chaotic deterministic systems, if the initial state is changed by a small value ϵ, two trajectories that were initially close, will exponentially separate. Formally, if x(t) and x′(t) are the two trajectories generated by the initial states x(t0) and ${\\text{x}}^{\\prime }\\left({t}_{0}\\right)$, and if $|\\text{x}\\left({t}_{0}\\right)-{\\text{x}}^{\\prime }\\left({t}_{0}\\right)|<ϵ$, we have that\n\nwhere λ is the angular coefficient of the straight line defining ln |x(t) − x′(t)| as a function of time t:\n\nUsing Equation (4) it is possible to predict the time t* after which the predicted trajectory is too imprecise. Indeed, if δ is the tolerance on the precision of the prediction, then from Equation (4) ϵeλt* ~ δ, and therefore\n\nThe expression in Equation (5) suggests that t* can be arbitrarily increased by decreasing ϵ. However, de facto, it is not possible to obtain a value of t* much greater than $\\frac{1}{\\lambda }$. For instance, if we want to increase t* by one order of magnitude, we have to decrease ϵ by a factor e10 ~ 104. This example points out that the dependence of t* on the ratio $\\frac{\\delta }{ϵ}$ is so weak that in Equation (5), the only term that strongly influences t* is λ (Vulpiani, 2004; Cencini et al., 2009; Lecca et al., 2016).\n\nThe system of differential equations describing the dynamics of the B. subtilis sporulation network is a d-dimensional system, where d is the number of molecular species involved in the system. At each instant of time t the system is contained in a d-dimensional sphere in the phase-space. In particular, this d-dimensional sphere is centered at x(0) (x(0) belonging to the attractor) and has radius ϵ. The time evolution of the system dictated by the equations deforms the sphere into an ellipse. If li(t) denotes the length of the i-th semi-axis of the ellipse at time t, the characteristic Lyapunov exponents1 ≥ λ2 ≥ ⋯ ≥ λd) are defined as follows:\n\nIf λi > 0, the i-th semi-axis grows with time; in contrast, if λi < 0 the i-th semi-axis shrinks with time. In a system extremely sensitive to the initial conditions, at least one of the Lyapunov exponents is greater than zero.\n\nFor each molecular species i in the B. subtilis network we have calculated the maximal Lyapunov exponent from the corresponding simulated time series, i.e., the Lyapunov exponent at the maximum observed time, formally defined as follows\n\nThe greater a positive maximal Lyapunov exponent, the faster the rate of divergence of the two trajectories x(t) and x′(t). Thus, the Lyapunov coefficients were used to measure the contribution of each molecular species to the system's dynamics. In this study, we used the Rosenstein et al. (1993) algorithm to estimate the maximal Lyapunov exponent.\n\nThe Lyapunov exponents capture the unpredictability in a system's evolution which can be generated by slightly different initial states. However, unpredictability could depend also on an irregular aperiodic behavior of the abundance of some molecular species in the system.\n\nTo capture this aspect of a chaotic dynamical system, and, most importantly to distinguish it from noise, we have estimated the fractal and the embedding dimensions of the time series of each gene and protein in the system. Both fractal and embedding dimensions are generalizations of the topological dimension and measure the dimensionality of the space occupied by the set of points of the time series. The more complex and irregular the distribution of the points in space is, the higher the fractal and embedding dimensions of the system.\n\nWe estimate the fractal dimension as a correlation dimension (Theiler, 1990; Ding et al., 1993), defined in terms of the correlation integral C(ϵ):\n\nwhere N is the number of points in the time series, and g is the total number of pairs of points that dist from each other is less than ϵ (a graphical representation of such close pairs is the recurrence plot (Marwan et al., 2016). The correlation integral estimates the probability that a pair of points of the time series is separated by a distance less than ϵ. For ϵ << 1 it can be shown (Theiler, 1990) that\n\nwhere DF is the correlation dimension. For a sufficiently large, and evenly distributed, number of points in a time series, a log-log graph of the correlation integral vs. ϵ can be used to estimate DF (Kantz, 2004). The more complex and irregular a time series is, the higher its correlation dimension, as the number of ways for points to be close to each other is greater (Higuchi, 1988). Indeed the fractal dimension corresponds to the number of the degrees of freedom of the time series (Mera and Morán, 2002).\n\nUnlike topological dimension, the fractal dimension can take non-integer values, indicating that a set of points of a trajectory can fill its space qualitatively, and quantitatively, in a different way from an ordinary geometrical set. For instance, a curve with fractal dimension very near to 1, behaves quite like an ordinary line, but a curve with fractal dimension greater than 2 winds convolutedly through space very nearly like a surface or a volume. As a consequence, if a time series of a gene or protein has a fractal dimension significantly greater than 1, the dynamics of that gene or protein is more likely affected by chaos than by noise.\n\nThe sample entropy SE adds further information to that provided by the Lyapunov exponents and the fractal dimension, as it is a direct measure of the unpredictability of a time series (Mao, 2011). Indeed, SE estimates how much a given data point depends on the values of a number m of preceding data points, averaged over the whole time series. SE is computed as the negative logarithm of the conditional probability that two similar samples from the time series remain similar at the next point (Richman and Moorman, 2000; Azar and Vaidyanathan, 2016). To calculate the sample entropy, points matching within a tolerance ϵ are computed until there is no match according to this condition. Formally, if\n\n$X(t)={x(t1),x(t2),…,x(tN)}≡{x1,x2,…,xN}$\n\nis a time series of length N, the sample entropy is defined as in the following by Azar and Vaidyanathan (2016), Sokunbi (2014), and Richman and Moorman (2000).\n\nwhere Bi is the number of j where |X(i) − X(j)| ≤ r, and\n\n$U(m)(ϵ)=1N-mτ∑i=1N-mτBiN-(m+1)τXm(i)={xi,xi+τ,…,xi+(m-1)τ}Xm(j)={xj,xj+τ,…,xj+(m-1)τ}1≤j≤N-mτ, j≠i.$\n\nXm(i) is called template vector of length m of the time series X(t), and an instance where a vector Xm(j) is within ϵ of Xm(i) is called a template match. The quantity $\\frac{{B}_{i}}{N-\\left(m+1\\right)\\tau }$ is the probability that any vector Xm(j) is within r of Xm(j). Finally, τ is called time delay, and in our analysis it has been set equal to the time lag ${T}_{L}^{*}$, that is an estimate of the time at which the time series behavior becomes unpredictable. It can be computed using the auto-correlation function method (Zeraoulia, 2011) and is taken as the lag time ${T}_{L}^{*}$ at which the auto-correlation function\n\nfirst crosses zero. This choice of τ in the estimation of sample entropy is motivated by the need to capture also non-linear autocorrelation properties of the time series (Kaffashia et al., 2008). For instance, it has been proved that with a unity time delay (Kaffashia et al., 2008), the sample entropy measures only the linear autocorrelation properties of the time series. A lower value of SE (and a higher value of ${T}_{L}^{*}$) indicates higher predictability of the time series, while a higher value of SE (and a lower value of ${T}_{L}^{*}$) indicates lower predictability.\n\nFinally, we also considered the embedding dimension as a measure of time series complexity. The embedding dimension of a time series is the smallest dimension required to embed it, and it can be estimated by the Cao's algorithm (Cao, 1997). In our analysis, the parameter m in the definition of sample entropy has been set equal to the embedding dimension.\n\n#### 3.2.1. Distinguishing Noise from Chaos\n\nSince both the presence of chaos and the presence of noise are manifested as topological and statistical complexity of a time series, our analysis aims to distinguish chaos from noise. In the past decade many methods in a different application domains have been proposed to make this distinction, the most recent are reported in Skiadas and Skiadas (2016), Ravetti et al. (2014), Rohde (2008), Gao et al. (2006), and Rosso et al. (2007).\n\nWe adopted a simple well established method based on the comparison of the complexity indices identified above and obtained from the time series simulated by the model with those obtained from Gaussian white noise, colored noise and power-law noise (Skiadas and Skiadas, 2016). The expectation is that sample entropy, time lag, and embedding dimension for the non-noisy candidate chaotic times series are significantly different from those estimated for the white and colored noise time series. Moreover, the time behavior of the Lyapunov exponents is expected to be non-linear for the noise and at least linear for chaotic non-noisy time series (Gao and Zheng, 1994).\n\n### 3.3. Analysis of the Jacobian Matrix: The Time Evolution of the Phase Space\n\nIn order to explore the phase space of the systems and calculate its equilibria and its time evolution we analyzed the Jacobian matrix J of the system of ordinary differential equations describing the dynamics.\n\nwhere ${f}_{i}=\\frac{d{s}_{i}}{dt}$, and si is the abundance of the i-th molecular species in the system (i = 1, 2, …, d).\n\nA steady state point ${s}_{eq}=\\left\\{{s}_{i}^{eq}\\right\\}$, i = 1, 2, …, d, of the systems is defined by a solution of the system of algebraic equations as in the follows:\n\n$dsidt=0, i=1,2,…,d.$\n\nThe stability of a steady state point is determined by the sign of the real part of the eigenvalues of the Jacobian matrix. In particular, if the real parts of all eigenvalues are negative, the steady state point is stable. It's termed sink, because, there is a basin around it, and any initial condition in that basin will result in a trajectory falling in toward the steady state point.\n\nIf the real parts of all the eigenvalues are positive, the steady state point is unstable. It is termed source, because, starting from an initial point close to it, the trajectory will move away from it. If the real parts of the eigenvalues are of different signs, the steady state point is called a saddle point. It is unstable, attracting along some axes and repelling along others. If there are also complex components, the nature of the fixed point doesn't change (it's still a sink, source, or saddle point) but with a twist. If the eigenvalues are purely complex, then there are closed orbits around the fixed point.\n\nThe eigenvectors of the Jacobian matrix give the axes along which the behaviors indicated by the eigenvalues are centered. So, the eigenvector associated with a negative eigenvalue is a vector along which the fixed point attracts. The eigenvector associated with a positive eigenvalue is an axis along which the fixed point repels.\n\n## 4. Results\n\nIn this section we collect the results of three different analyses: (i) the parameter sensitivity analysis, (ii) the model time series analysis, and (iii) the phase space analysis. The first two analyses capture different aspects and manifestations of the presence of chaos and their outputs are sets of molecular species whose behavior is a likely candidate for chaotic dynamics. The final result is an intersecting set of molecular species that represents the consensus set of molecular species whose dynamics is affected by chaos. The third analysis aims at determining how the topology and the parameters of the network of interactions among the molecular species evolves with time. This last analysis allows the determination of the time variation of the active degrees of freedom in the system (Hilborn, 2000).\n\n### 4.1. Kinetic Rate Constants Controlling the Dynamics\n\nWe explored the parameters' space in which the systems of ordinary differential equations that represent the model has a solution. We found that the largest range of variation for the parameters at which the systems still admits a unique solution is defined by\n\nwhere ph is the value of the h-th parameter assigned from experimental data. For each parameter we randomly sampled 50 values from a uniform distribution positively defined in I(ph). In turn these were used in simulations to give sensitivity indices according to Equation (2). In Figure 5 a heatmap shows the value of the sensitivity index collapsed into intervals. Moreover, Table 1 lists the variables and the parameters which affect them most. Appreciable parameter sensitivity is only apparent in an interval ranging from a tenth to ten times the parameter value obtained from data. The dynamics of the majority of the molecular species is robust with respect to the variations of the parameters' values on smaller intervals (see Figure S2). The molecular species spo0a, spo0b, spo0ap, and spo0bp are the most sensitive to perturbation of parameters even on small intervals.\n\nFIGURE 5", null, "Figure 5. Heatmap summarizing the results of sensitivity analysis. The color of the cell is indicative of the value of the sensitivity index S as given in Equation (2), categorized by intervals.\n\nTABLE 1\n\n### 4.2. Complexity Indices Identify the Drivers of Chaotic Dynamics\n\nFigure 6 gives a graphical summary of the complexity indices estimated for the time series of each molecular species. The majority of the molecular species have positive Lyapunov exponents, fractional dimension, time lag between 0 and 400 (that is the about 3% of the time range used in the simulation), and embedding dimension greater than 1. Figure 7 shows the sample entropy values and reports that the highest vale of sample entropy is assumed by spo0bp.\n\nFIGURE 6", null, "Figure 6. Barplot showing the values of the complexity indices maximal Lyaopunov exponent (A), fractal dimension (B), time lag (C), and embedding dimension (D) estimated from the time series of each molecular species in the system. A red line marks the average value.\n\nFIGURE 7", null, "Figure 7. Sample entropy is a measure the repeatability or predictability within a time series. spo0bp has a sample entropy about six times greater than the sample entropy of the other molecular species.\n\nTo distinguish chaotic from noisy dynamics, we compared the complexity indices of the time series of each variable with the mean and the standard deviation of the complexity indices estimated from 50 time series of white Gaussian noise of mean μ = 0, variance σ2 = 1 generated for each variable and having amplitude equal to the range of variability of the variable. The heatmap in Figure 8 shows, on the left side, the frequency at which the noisy time series has an index value higher than that observed for the time series from the real model. Comparison of indices under chaotic and noisy conditions is performed for each index (shown by column) and each variable (shown by row). The heatmap on the right side shows correlation between Lyapunov index and time points in the time series. Column “Cor_Lyapunov_time” displays statistical significance for Pearson's correlation relating Lyapunov exponent and time. Column “Cor_Lyapunov_time_white_noise” displays similar information for the time series of white Gaussian noise generated for each variable. We found that sample entropy, time lag and embedding dimension are significantly higher in the model time series than in the white noise time series. The Lyapunov exponents are significantly greater for the white noise time series compared with the model's time series, except for spo0a, spo0ap, spo0bp, spo0f, and spo0fp. This result suggests that these molecular species exhibit remarkable chaotic dynamics. The left part of the heatmap confirms a non linear time behavior of the Lyapunov exponents of the white noise time series, and suggests a linear time behavior of the Lyapunov exponents of the model's time series. Again, this result distinguishes between chaotic dynamics and random noisy dynamics (Gao and Zheng, 1994). In the Supplementary Material (Figures S4–S6), we report similar results obtained in the comparison of the complexity indices of the model's time series with the ones for the colored and power law noise.\n\nFIGURE 8", null, "Figure 8. Complexity detected in the B. subtilis sporulation network dynamics is due to low dimensional chaos and not to noise. For the time series of molecular species spo0a, spo0b, spo0ap, spo0bp, spo0f, and spo0fp, all the indices of complexity are significantly greater than those for the time series of Gaussian white noise.\n\nFor each index of complexity, Table 2 (and a graphical summary of it in Figure 9) reports the set of molecular species where results indicate the presence of chaos. In this Table, we also included two qualitative indicators of the presence of chaos, such as a complex phase space (i.e., convoluted trajectories) and recurrence plot. These plots are provided in Supplementary Material (Figure S7), and visualize a square matrix, whose elements are the times at which a state of a dynamical system recurs (columns and rows correspond then to a certain pair of times) (Marwan et al., 2016, 2007). We refer the reader to the Supplementary Material for a comprehensive description of the recurrence plots analysis.\n\nTABLE 2", null, "Table 2. Sets of molecular species with values of the complexity indices indicative of presence of chaos.\n\nFIGURE 9", null, "Figure 9. Species scoring positive to complexity indices. Species are organized according to the different sets of complexity indices they resulted to be positive to. (A) spo0b_t, laci, and laci_t are characterized by uniformly irregular recurrence plot, a short time lag and an embedding dimension greater than the average. Indeed, they belong to the intersection of the set of species listed on rows 3, 4, and 7 of Table 2. (B) spo0a has a maximal positive Lyapunov exponent, a large sample entropy and is fractional in dimension. Indeed, it belongs to the intersection of the sets of species listed on rows 1,2, and 5 of Table 2. (C) spo0b has a maximal positive Lyapunov exponent, an embedding dimension greater than 1, a short time lag, and a high sensitivity to parameters. It belongs to the intersection of sets of species on rows 1, 3, 7, and 8 of Table 2.\n\nThe variable spo0b is the one with the largest set of complexity indices whose values point to the presence of chaos.\n\n### 4.3. Phase Space Analysis\n\nWe solved the systems of differential equations setting to zero the initial concentration of all the molecular species (i. e. si(t = 0) = 0∀i = 1, 2, …, 25), and we found that the system has one steady state point, whose coordinates are shown in Table 3. This point is a stable equilibrium, as the eigenvalues of the Jacobian matrix (Figure 10) of the system are all negative (Figure 11).\n\nTABLE 3", null, "Table 3. Coordinates of the stable steady state point of the model solved with initial conditions si(t = 0) = 0 ∀i = 1, 2, …, 25.\n\nFIGURE 10", null, "Figure 10. Heatmap representation of the Jacobian matrix eigenvectors (EVs) evaluated at the steady state point reported in Table 3.\n\nFIGURE 11", null, "Figure 11. All the eigenvalues of the Jacobian matrix at the steady state point are negative and thus the steady state point is a stable attractor.\n\nWe also calculated the value of the elements of the Jacobian matrix at different time points to determine its time evolution. Since the entries of the Jacobian matrix are the partial derivatives of the rate equations with respect to the variables [i.e., ${J}_{ij}=\\frac{\\partial {f}_{i}}{\\partial {S}_{j}}$ (Equation 12)], the Jacobian matrix can be represented by a weighted graph, where the nodes represent the variables (i.e., the molecular species) and the edge weights are the elements of the matrix Jij. We introduced the estimate of the error on Jij defined as $\\Delta {J}_{ij}=\\mathrm{\\text{prec}}×\\frac{{\\partial }^{2}{f}_{i}}{\\partial {S}_{j}^{2}}$, where prec = 10−8 is the precision of the numerical solution of the model, and set the threshold of 20% on the relative error $ER=\\frac{\\Delta {J}_{ij}}{{J}_{ij}}$. Edges with ER < 20% were retained and and allowed for the estimation of the number of active degrees of freedom in the system, i.e., the number of variables involved in active interactions (Hilborn, 2000). Hence, the graphs derived from the Jacobian matrices estimated at different time points are temporal snapshots of the molecular interaction network.\n\nThe graphs derived from the Jacobian matrix estimated at different time points are time snapshots of the interaction network of the molecular species. These graphs visualize the interactions that are active (i.e., with an edge that has a weight significantly different from zero) at a given time, and thus provide an approximate estimation and representation of the number of active degree of freedom of the system. We report in Figures 12, 13, the graphs obtained from the Jacobian matrices evaluated at times t = {0, 500} s. For t > 500 s the variations of the Jacobian matrix are minimal and thus not shown here (in the Supplementary Material we provide the graphs for t > 500 s in GraphML format).\n\nFIGURE 12", null, "Figure 12. Graph with adjacency matrix equal to the Jacobian matrix at time t = 0 s. The graphs shows the basal reactions, i.e., those that initiate the time evolution for the network.\n\nFIGURE 13\n\nIn Table 4, we observe a rapid increment of the edge weights from t = 0 to t = 500, than a plateau till t = 4, 000, and then a decrement at t = 4, 000 s. These changes reflect the changes of the topology of the network of B. subtilis sporulation initiation. These steep decrements and increments are expressions of a stiff and highly non linear dynamics, and in turn confirms the presence of chaos (unpredictability) in (of) it.\n\nTABLE 4", null, "Table 4. Minimum, first quartile, median, mean, third quartile, and maximum of the distributions of the Jacobian matrix values (i.e., edge weights of the networks) at times t = (0, 500, 1000, …, 7500).\n\n#### Sensitivity to Initial Conditions\n\nIn order to assess the existence and, eventually, the sensitivity of the steady state point in response to perturbations of the initial conditions, we introduced a set Δ of perturbations of different magnitudes: Δ = {Δh, h = 1, 2, …, 7}, where ${\\Delta }_{1}=1{0}^{-5}$ and Δi+1 = 10Δi, i = 1, 2, …, 6. We perturbed the initial state of one variable at a time by Δh (h = 1, 2, …, 7), and calculated the steady state point with the Newton-Raphson method (Deuflhard, 2004; Bressoud, 2007), which is one of the most consolidated and efficient algorithms for finding the zeros of a function (Hoppensteadt, 1993; Ortega and Rheinboldt, 2000).\n\nAs shown in Figure 14, this analysis allowed detecting a number of species which, once perturbed, can cause different types of complex evolutionary trajectories from the initial state of the system. Perturbing the initial state of the laci_t, laci, laci_d, kina_t and kina species caused the most noticeable consequence, preventing the system from reaching the steady state, irrespectively of the applied perturbation extent. This finding reflects the role of the species as starters of sporulation dynamics. For another subset of species, such as spo0a, aa, ab, ga, and gb, perturbing the initial state caused an increase in the number of iterative steps required by the Newton-Raphson method to reach the steady state, compared to the majority of the species in the system. This effect is indicative of increased complexity in the evolving system.\n\nFIGURE 14", null, "Figure 14. The heatmap describes the systems ability to reach the steady state in response to seven different perturbation values of each species. System's response is described by three features: MSS, PRECISION and NO STEPS. Depending on the perturbation magnitude of the initial state, the Newton-Raphson methods warns about the non-convergence to a steady state. This occurs when, depending on the initial conditions, the system has multiple steady states (MSS), and the Newton-Raphson solution methods flips unpredictability among them (Bloomfield, 2014). MSS is a binary feature assuming 1 if the system reaches multiple steady states and 0 otherwise; PRECISION is the precision in computation of a single steady state (reported in negative log scale.); NO STEPs is the number of steps required converge to a single steady state. Perturbations extent is shown for each systems species by a color bar. The map shows that the perturbation of laci_t, laci, laci_d, and kina causes an unresolved multi-stationarity.\n\nFurthermore, as shown in the Supplementary Material, we also found that the perturbations of the initial state of all the species cause variations in the minimum embedding dimension of spo0b, and spo0b_t, as well as of laci_t, laci, laci_d, kina_t (Figure 15). Quantification of the increase in minimum embedding dimension upon perturbation of the initial state is a further indication of the high sensitivity to perturbations, for the species highlighted by the initial analysis of complexity indices, such as spo0b, spo0b_t, and for the species emerging from the previous characterization of perturbation experiments.\n\nFIGURE 15", null, "Figure 15. Embedding dimension in response to perturbation of the initial state of the system variables. On the vertical right axis, the name of perturbed species is reported, whereas on the left vertical axis the order of magnitude of the perturbation is reported. laci_t, laci, laci_d, kina_t, spo0b and spo0b_t embedding dimension is highly sensitive to perturbation of the initial conditions.\n\nPerturbations of initial conditions also cause variations of the Kaplan-Yorke ratio (i.e., $\\left(\\sum _{i=1}^{j}{\\lambda }_{i}\\right)/{\\lambda }_{i+j}$, where j is the largest integer such that $\\sum _{i=1}^{j}{\\lambda }_{i}>0$) of spo0b_t, kina_t, and gb and of the Kolmogorov-Sinai entropy (i.e., the sum of positive Lyapunov exponents) of kina_t (Figures S8–S10). Hence, by multiple analyses the system showed a highly unpredictable behavior to perturbation of the initial state, as reflected by exponentially growing separation of the trajectories as well as by the topological complexity of the time series.\n\n## Conclusions\n\nWe have presented a detailed, and original analysis of the B. subtilis sporulation initiation network dynamics. This analysis aimed to detect the presence of low-dimensional chaos in the dynamics of the system. Unlike more common approaches to chaos detection, this analysis includes and, is based on, a mathematical model of the B. subtilis sporulation initiation dynamics. This approach allows a comprehensive explanation of the mechanisms through which those molecular species with chaotic dynamics interact with the others and propagate the effects of chaos throughout the system.\n\nOur analysis has: (i) assessed sensitivity of the dynamics by varying the kinetic parameters and the initial state of the model to determine the dynamic control parameters and identify the most crucial molecular species; (ii) calculated the complexity indices of the time series obtained from the model, and used these to identify the drivers of chaotic dynamics, and finally, (iii) calculated the Jacobian matrix of the system of equations as a function of time to find the steady state points and their nature to give an estimate of the number of active degrees of freedom of system as function of time.\n\nWe found that the dynamics of the B. subtilis sporulation initiation network is affected by low-dimensional chaos and identified Spo0B as the principal driver of the chaotic dynamics in this system. Spo0B scored positive for the majority of the chaos indices. This result suggests a new role for this molecular species, which so far has received little attention, and highlights the importance of its dynamics and interactions within the network model structure. Our analysis also indicates additional experimental work that could be conducted to improve our understanding of the sporulation network and to determine the role of Spo0B. On one hand, it would be important to conduct phosphoproteomics experiments to measure the amount of phosphorylated species, for which no experimental data is yet available. On the other hand, the study of Spo0B mutated (overexpressed/silenced) strains would refine our knowledge about the dynamics of the 3-level B. subtilis phosphorelay and the so far elusive mechanisms that could be regulating its expression. Other molecular species that also showed positive results for the test of chaos were spo0a, spo0b_t, laci, and laci_t. These molecular species also represent the degrees of freedom that are active during most of the range of the simulated time. These species have been identified as the drivers of the chaotic dynamics of B. subtilis sporulation initiation network model, and have an active role in determining its predictability.\n\nOne of the most challenging goal of studying a complex biological system is to control it. In this vein, our work proposes a method to identify the drivers in our B. subtilis sporulation initiation network on which such methods of chaos control might be applied. At the same time, our work highlights the need to investigate on these drivers and their mechanism of interaction in in order to successfully implement chaos control.\n\n## Author Contributions\n\nAll authors have equally contributed to the design and implementation of the mathematical analytical procedure, the discussion of the results and the writing of this paper.\n\n## Funding\n\nThis work was partially supported by the Biotechnology and Biological Sciences Research Council (BBSRC) Institute Strategic Programme [BB/J004529/1]: The Gut Health and Food Safety ISP.\n\n## Conflict of Interest Statement\n\nThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\n\n## Acknowledgments\n\nThe authors gratefully acknowledge the support of the Biotechnology and Biological Sciences Research Council (BBSRC). 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Bacteriol. 183, 2795–2802.\n\nWeiss, J. N., Garfinkel, A., Spano, M. L., and Ditto, W. L. (1994). Chaos and chaos control in biology. J. Clin. Invest. 93, 1355–1360. doi: 10.1172/JCI117111\n\nYork, K., Kenney, T. J., Satola, S., Moran, C. P., Poth, H., and Youngman, P. (1992). Spo0A controls the sigma A-dependent activation of Bacillus subtilis sporulation-specific transcription unit spoIIE. J. Bacteriol. 174, 2648–2658.\n\nZeraoulia, E. (2011). Models and Application of Chaos Theory in Modern Science. St. Helier: Science Publisher, CRC Press.\n\nKeywords: systems biology, computational modeling, sensitivity analysis, low dimensional chaos, signal transduction, sporulation, Bacillus subtilis\n\nCitation: Lecca P, Mura I, Re A, Barker GC and Ihekwaba AEC (2016) Time Series Analysis of the Bacillus subtilis Sporulation Network Reveals Low Dimensional Chaotic Dynamics. Front. Microbiol. 7:1760. doi: 10.3389/fmicb.2016.01760\n\nReceived: 21 July 2016; Accepted: 19 October 2016;\nPublished: 07 November 2016.\n\nEdited by:\n\nImrich Barak, Slovak Academy of Sciences, Slovakia\n\nReviewed by:\n\nAnthony Joseph Wilkinson, University of York, UK\nAndreas Kremling, Technische Universität München, Germany\n\nCopyright © 2016 Lecca, Mura, Re, Barker and Ihekwaba. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.\n\n*Correspondence: Paola Lecca, [email protected]" ]
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https://www.mathworks.com/help/finance/ecmmvnrfish.html
[ "# ecmmvnrfish\n\nFisher information matrix for multivariate normal regression model\n\n## Syntax\n\n```Fisher = ecmmvnrfish(Data,Design,Covariance,Method,MatrixFormat,CovarFormat)\n```\n\n## Arguments\n\n `Data` `NUMSAMPLES`-by-`NUMSERIES` matrix with `NUMSAMPLES` samples of a `NUMSERIES`-dimensional random vector. Missing values are represented as `NaN`s. Only samples that are entirely `NaN`s are ignored. (To ignore samples with at least one `NaN`, use `mvnrfish`.) `Design` A matrix or a cell array that handles two model structures: If `NUMSERIES = 1`, `Design` is a `NUMSAMPLES`-by-`NUMPARAMS` matrix with known values. This structure is the standard form for regression on a single series.If `NUMSERIES` ≥ `1`, `Design` is a cell array. The cell array contains either one or `NUMSAMPLES` cells. Each cell contains a `NUMSERIES`-by-`NUMPARAMS` matrix of known values.If `Design` has a single cell, it is assumed to have the same `Design` matrix for each sample. If `Design` has more than one cell, each cell contains a `Design` matrix for each sample. `Covariance` `NUMSERIES`-by-`NUMSERIES` matrix of estimates for the covariance of the residuals of the regression. `Method` (Optional) Character vector that identifies method of calculation for the information matrix: `hessian` — Default method. Use the expected Hessian matrix of the observed log-likelihood function. This method is recommended since the resultant standard errors incorporate the increased uncertainties due to missing data.`fisher` — Use the Fisher information matrix. `MatrixFormat` (Optional) Character vector that identifies parameters to be included in the Fisher information matrix: `full` — Default format. Compute the full Fisher information matrix for both model and covariance parameter estimates.`paramonly` — Compute only components of the Fisher information matrix associated with the model parameter estimates. `CovarFormat` (Optional) Character vector that specifies the format for the covariance matrix. The choices are: `'full'` — Default method. The covariance matrix is a full matrix.`'diagonal'` — The covariance matrix is a diagonal matrix.\n\n## Description\n\n`Fisher = ecmmvnrfish(Data,Design,Covariance,Method,MatrixFormat,CovarFormat)` computes a Fisher information matrix based on current maximum likelihood or least-squares parameter estimates that account for missing data.\n\n`Fisher` is a `NUMPARAMS`-by-`NUMPARAMS` Fisher information matrix or Hessian matrix. The size of `NUMPARAMS` depends on `MatrixFormat` and on current parameter estimates. If `MatrixFormat = 'full'`,\n\n```NUMPARAMS = NUMSERIES * (NUMSERIES + 3)/2 ```\n\nIf `MatrixFormat = 'paramonly'`,\n\n```NUMPARAMS = NUMSERIES ```\n\nNote\n\n`ecmmvnrfish` operates slowly if you calculate the full Fisher information matrix." ]
[ null ]
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https://books.google.com.jm/books?qtid=fa7e78c6&lr=&id=4zwEAAAAQAAJ&sa=N&start=100
[ "Books Books", null, "A circle is a plane figure contained by one line, which is called the circumference, and is such that all straight lines drawn from a certain point within the figure to the circumference, are equal to one another : XVI.", null, "The Elements of Geometry, Symbolically Arranged - Page 3\nby Great Britain. Admiralty - 1846", null, "## The popular educator, Volume 1; Volume 29\n\nPopular educator - 1872 - 850 pages\n...contained or bounded by a canal line, called the circumference or periphery, which is snch that aU straight lines drawn from a certain point within the figure to the circumference are equal to each other. This point Kg. 6. Fig. 7. is called the centre of the circle, and each of tho straight...", null, "## The first and second books of Euclid explained to beginners, by C.P. Mason\n\nEuclid, Charles Peter MASON - Geometry - 1872 - 216 pages\n...XII. An acute angle is an angle which is less than a right angle. Def. XV. A circle is a plane bounded figure, contained by one line, which is called the circumference, and is such, that every point in it is at the same distance from a certain point within, which is called the centre....", null, "## The Acting Teacher's and Student's in Training Guide and Text Book for ...\n\nHenry Major - Student teachers - 1873 - 592 pages\n...more boundaries. , XV. A circle is a plane figure contained by one line, called the circumference, such that all straight lines drawn from a certain...to the circumference are equal to one another. XVI. This point is called the centre. XVII. A diameter of a circle is a straight line drawn through the...", null, "## Elementary Mathematics: Embracing Arithmetic Geometry, and Algebra\n\nLewis Sergeant - 1873 - 182 pages\n...figures, are limited by more than four straight lines. 34. A circle is a figure contained by one line, called the circumference, and is such that all straight lines drawn from a certain point within the circle to the circumference are equal to each other. 35. This point is called the centre of the circle;...", null, "## The Elements of Euclid, containing the first six books, with a selection of ...\n\nEuclides - 1874 - 342 pages\n...any thing. 14. A figure is that which is inclosed by one or more boundaries. 16. A circle is a plane figure contained by one line, which is called the...figure to the circumference, are equal to one another. 16. And this point is called the centre of the circle. 17. A diameter of a circle is a straight line...", null, "## Pure mathematics, Volume 1\n\nEdward Atkins - 1874 - 428 pages\n...anything. 14. A figure is that which is enclosed by one or more boundaries. 15. A circle is a plane figure contained by one line, which is called the...figure to the circumference are equal to one another. 16. And this point is called the centre of the circle, [and any straight line drawn from the centre...", null, "## A School Euclid. Being Books I.&II. of Euclid's Elements. With Notes ...\n\nEuclides - 1874 - 120 pages\n...any thing. 14. A figure is that which is enclosed by one or more boundaries. 15. A circle is a plane figure contained by one line, which is called the...figure to the circumference are equal to one another. 16. And this point is called the centre of the circle. 17. A diameter of a circle is a straight line...", null, "## Elements of Euclid Adapted to Modern Methods in Geometry\n\nEuclid, James Bryce, David Munn (F.R.S.E.) - Geometry - 1874 - 234 pages\n...sides are equal, but its angles not right angles. 30. A circle is a plane figure contained by one line called the circumference; and is such that all straight lines drawn from a certain point within it to the circumference are equal to one another. This point is called the centre of the circle. 31....", null, "## Euclidian Geometry\n\nFrancis Cuthbertson - Euclid's Elements - 1874 - 400 pages\n...equilateral triangle is one which has all its sides equal. A circle is a plane figure contained by one line called the circumference, and is such that all straight lines drawn from a certain point within'the figure to the circumference are equal. That point is called the centre of the circle, and...", null, "" ]
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https://crypto.stackexchange.com/questions/26928/pseudorandom-functions-how-are-functions-stored
[ "# Pseudorandom functions: how are functions stored?\n\nI am wondering about the setup for testing if a function family is pseudorandom. We step into a room, and query the black box with $x$, which yields $f(x)$, etc... We don't know if $f$ is a random function, or if it comes from some preset function family $F$. In the latter case, what assumptions are made about the functions in $F$? Is it enough for each $f$ in $F$ to be listed as a table in the black box, so that $f: \\{0,1\\}^n \\to \\{0,1\\}^n$ is listed as a table with $2^n$ entries? What if $f$ is described by some function of a polynomial with coefficients that are exponential in $n$? What are the restrictions on the functions in the function family $F$, if any?\n\nFor the definition of pseudorandomness, the family $F$ of functions can be any set of functions at all. But typically we take it to be a set where each function can be described by a rather short key/seed, and where one can efficiently compute the function output given the input (and the key). This is because we want the family $F$ to represent functions that we can randomly choose from and use in real life.\nFor example, $F$ could be the set of functions AES$_k$, taken over all 128-bit strings $k$ (where AES$_k$ denotes the AES block cipher with key $k$). Notice that there are \"only\" $2^{128}$ functions in this family, which is much less than the number of functions mapping 128 bits to 128 bits (which is $(2^{128})^{2^{128}}$).\nPS: Often random functions will just generate a random sequence based on an initial seed value; you don't have to pass a parameter $x$ each time you call the function. While such random functions do exist, the box in your description somehow reminds me of a hash function (or a random oracle)." ]
[ null ]
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https://jihyzemecikewize.dellrichards.com/data-encryption-standard645023191kz.html
[ "# Data encryption standard\n\n### International data encryption algorithm tutorialspoint\n\nThus, the discarding of every 8th bit of the key produces a bit key from the original bit key. Biham E, Shamir A Differential cryptanalysis of the data encryption standard. DES key length and brute-force attacks The Data Encryption Standard is a block cipher , meaning a cryptographic key and algorithm are applied to a block of data simultaneously rather than one bit at a time. Springer, Berlin, pp — Google Scholar 3. In that was considered an impossible computational task. Technical report, IBM T. The key, which controls the transformation, also consists of 64 bits; however, only 56 of these can be chosen by the user and are actually key bits.\n\nThe security of the DES is no greater than its work factor—the brute-force effort required to search keys.\n\nLucks S Attacking triple encryption.", null, "Lecture notes in computer science. Actually, the initial key consists of 64 bits. DES key length and brute-force attacks The Data Encryption Standard is a block ciphermeaning a cryptographic key and algorithm are applied to a block of data simultaneously rather than one bit at a time.", null, "That is bit position 8, 16, 24, 32, 40, 48, 56 and 64 are discarded. Linear cryptanalysis was discovered by Mitsuru Matsuiand needs known plaintexts Matsui, ; the method was implemented Matsui,and was the first experimental cryptanalysis of DES to be reported.\n\n## Des algorithm explanation with example pdf\n\nSuccessors to DES Encryption strength is directly tied to key size, and bit key lengths have become too small relative to the processing power of modern computers. Lecture notes in computer science, vol The basic idea is show in figure. This process is repeated 16 times. No viable submissions were received. Springer, Berlin, pp — Google Scholar 7. A generalization of LC—multiple linear cryptanalysis—was suggested in Kaliski and Robshaw , and was further refined by Biryukov and others. Start your free trial today for unlimited access to Britannica. DES key length and brute-force attacks The Data Encryption Standard is a block cipher , meaning a cryptographic key and algorithm are applied to a block of data simultaneously rather than one bit at a time.\n\nIn the first step, the 64 bit plain text block is handed over to an initial Permutation IP function. In Menezes and Vanstone —21 Google Scholar 5.\n\n## Simplified data encryption standard\n\nIn a special-purpose DES search engine combined with , personal computers on the Internet to find a DES challenge key in 22 hours. DES uses a bit key, but eight of those bits are used for parity checks, effectively limiting the key to bits. The F-function scrambles half a block together with some of the key. The rest of the algorithm is identical. Step Key transformation — We have noted initial bit key is transformed into a bit key by discarding every 8th bit of the initial key. In academia, various proposals for a DES-cracking machine were advanced. The structure of Lucifer was significantly altered: Since the design rationale was never made public and the secret key size was reduced from bit to bits, this initially resulted in controversy, and some distrust among the public. Figure 3 — The key-schedule of DES Figure 3 illustrates the key schedule for encryption—the algorithm which generates the subkeys.\n\nFor instance, after the shift, bit number 14 moves on the first position, bit number 17 moves on the second position and so on. In the NBS issued a public request for proposals for a cryptoalgorithm to be considered for a new cryptographic standard.\n\nRated 5/10 based on 73 review" ]
[ null, "https://www.tutorialspoint.com/cryptography/images/round_function.jpg", null, "https://upload.wikimedia.org/wikipedia/commons/thumb/2/25/Data_Encription_Standard_Flow_Diagram.svg/300px-Data_Encription_Standard_Flow_Diagram.svg.png", null ]
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https://myperfectpapers.com/ba-540-chapter-08-valuing-stocks/
[ "# Ba 540 chapter 08 – valuing stocks\n\nChapter 08\n\nValuing Stocks\n\nBA 540 Chapter 08 – Valuing Stocks\n\nBA540 Chapter 08 – Valuing Stocks\n\nBA-540 Chapter 08 – Valuing Stocks\n\nBA 540 Chapter08 – Valuing Stocks\n\nMultiple Choice Questions\n\n1. These investors earn returns from receiving dividends and from stock price appreciation.\nA. bondholders\nB. stockholders\nC. investment bankers\nD. managers\n\n2. As residual claimants, these investors claim any cash flows to the firm that remain after the firm pays all other claims.\nA. creditors\nB. bondholders\nC. preferred stockholders\nD. common stockholders\n\n3. When residual cash flows are high, stock values will be\nA. unchanged.\nB. low.\nC. high.\nD. unpredictable.\n\n4. Trading at physical exchanges like the New York Stock Exchange and the American Stock Exchange takes place\nC. at dealers’ computers.\nD. at market markers.\n\n5. The Dow Jones Industrial Average (DJIA) includes\nA. all of the stock listed on the New York Stock Exchange.\nB. 30 of the largest (market capitalization) and most active companies in the U.S. economy.\nC. 500 firms that are the largest in their respective economic sectors.\nD. 500 firms that are the largest as ranked by Fortune Magazine.\n\n6. The Standard & Poor’s 500 Index includes\nA. all of the stock listed on the New York Stock Exchange.\nB. 30 of the largest (market capitalization) and most active companies in the U.S. economy.\nC. 500 firms that are the largest in their respective economic sectors.\nD. 500 firms that are the largest as ranked by Fortune Magazine.\n\n7. The NASDAQ Composite includes\nA. all of the stocks listed on the NASDAQ Stock Exchange.\nB. 30 of the largest (market capitalization) and most active companies in the U.S. economy.\nC. 500 firms that are the largest in their respective economic sectors.\nD. 500 firms that are the largest as ranked by Fortune Magazine.\n\n8. This will only be executed if the order’s price conditions are met.\nB. a limit order\nC. an unlimited order\n\n9. Investors buy stock at the\nA. dealer price.\nB. bid price.\nD. broker price.\n\n10. Investors sell stock at the\nA. dealer price.\nB. bid price.\nD. broker price.\n\n11. These are valued as a special zero-growth case of the constant growth rate model.\nA. common stock\nB. preferred stock\nC. future dividends\nD. future stock prices\n\n12. Stock valuation model dynamics make clear that lower discount rates lead to\nA. lower valuations.\nB. higher valuations.\nC. lower growth rates.\nD. higher growth rates.\n\n13. Stock valuation model dynamics make clear that higher growth rates lead to\nA. lower valuations.\nB. higher valuations.\nC. lower growth rates continuing.\nD. higher growth rates continuing.\n\n14. We can estimate a stock’s value by\nA. using the book value of the total stockholder equity section.\nB. discounting the future dividends and future stock price appreciation.\nC. compounding the past dividends and past stock price appreciation.\nD. using the book value of the total assets divided by the number of shares outstanding.\n\n15. Many companies grow very fast at first, but slower future growth can be expected. Such companies are called\nA. Fortune 500 companies\nB. Blue Chip companies\nC. Variable Growth Rate firms\nD. Constant Growth Rate firms\n\n16. We often use the P/E ratio model with the firm’s growth rate to estimate\nA. required rates of return.\nB. inflation.\nC. a stock’s current price.\nD. a stock’s future price.\n\n17. Value stocks usually have\nA. low P/E ratios and high growth rates.\nB. high P/E ratios and low growth rates.\nC. low P/E ratios and low growth rates.\nD. high P/E ratios and high growth rates.\n\n18. Dividend yield is defined as\nA. the last four quarters of dividend income expressed as a percentage of the par value of the stock.\nB. the last four quarters of dividend income expressed as a percentage of the current stock price.\nC. the last dividend paid expressed as a percentage of the current stock price.\nD. the next dividend to be paid expressed as a percentage of the current stock price.\n\n19. The size of the firm measured as the current stock price multiplied by the number of shares outstanding is referred to as the firm’s\nA. market capitalization.\nB. book value.\nC. market makers.\nD. constant growth model.\n\n20. Stock Index Performance On November 26, 2007, The Dow Jones Industrial Average closed at 12,743.40, which was down 237.44 that day. What was the return (in percent) of the stock market that day?\nA. -.02%\nB. +.02%\nC. -1.83\nD. +1.83%\n\n21. Stock Index Performance On November 27, 2007, The Dow Jones Industrial Average closed at 12,958.44, which was up 215.04 that day. What was the return (in percent) of the stock market that day?\nA. -.017%\nB. +.017%\nC. -1.69%\nD. +1.69\n\n22. Buying Stock with Commission At your discount brokerage firm, it costs \\$9.95 per stock trade. How much money do you need to buy 100 shares of Ralph Lauren (RL), which trades at \\$85.13?\nA. \\$8,503.05\nB. \\$8,503.00\nC. \\$8,522.95\nD. \\$9,508.00\n\n23. Buying Stock with Commission At your discount brokerage firm, it costs \\$8.50 per stock trade. How much money do you need to buy 200 shares of Apple (AAPL), which trades at \\$171.54?\nA. \\$32,608.00\nB. \\$34,299.50\nC. \\$34,316.50\nD. \\$36,008.00\n\n24. Selling Stock with Commissions At your full-service brokerage firm, it costs \\$110 per stock trade. How much money do you receive after selling 100 shares of Time Warner, Inc. (TMX), which trades at \\$22.62?\nA. \\$2,152.00\nB. \\$2,262.00\nC. \\$2,372.00\nD. \\$2,388.20\n\n25. Selling Stock with Commissions At your full-service brokerage firm, it costs \\$120 per stock trade. How much money do you receive after selling 200 shares of Ralph Lauren (RL), which trades at \\$85.13?\nA. \\$16,546.00\nB. \\$16,906.00\nC. \\$17,026.00\nD. \\$17,146.00\n\n26. Buying Stock with a Market Order You would like to buy shares of International Business Machines (IBM). The current bid and ask quotes are \\$96.17 and \\$96.24, respectively. You place a market buy-order for 100 shares that executes at these quoted prices. How much money did it cost to buy these shares?\nA. \\$7.00\nB. \\$9,617.00\nC. \\$9,624.00\nD. \\$19,241.00\n\n27. Buying Stock with a Market Order You would like to buy shares of Nokia (NOK). The current bid and ask quotes are \\$20.13 and \\$20.15, respectively. You place a market buy-order for 300 shares that executes at these quoted prices. How much money did it cost to buy these shares?\nA. \\$6.00\nB. \\$6,039.00\nC. \\$6,045.00\nD. \\$12,084.00\n\n28. Selling Stock with a Limit Order You would like to sell 100 shares of Pfizer, Inc. (PFE). The current bid and ask quotes are \\$27.22 and \\$27.25, respectively. You place a limit sell-order at \\$27.24. If the trade executes, how much money do you receive from the buyer?\nA. \\$2,722.00\nB. \\$2,724.00\nC. \\$2,725.00\nD. \\$5,446.00\n\n29. Selling Stock with a Limit Order You would like to sell 400 shares of International Business Machines (IBM). The current bid and ask quotes are \\$96.24 and \\$96.17, respectively. You place a limit sell-order at \\$96.20. If the trade executes, how much money do you receive from the buyer?\nA. \\$38,464.00\nB. \\$38,468.00\nC. \\$38,480.00\nD. \\$38,496.00\n\n30. Value of a Preferred Stock If a preferred stock from Pfizer Inc. (PFE) pays \\$3.00 in annual dividends, and the required return on the preferred stock is 7 percent, what’s the value of the stock?\nA. \\$0.21\nB. \\$0.43\nC. \\$21.00\nD. \\$42.86\n\n31. Value of a Preferred Stock If a preferred stock from Ecology and Environment, Inc. (EEI) pays \\$2.50 in annual dividends, and the required return on the preferred stock is 5.8 percent, what’s the value of the stock?\nA. \\$0.15\nB. \\$0.43\nC. \\$14.50\nD. \\$43.10\n\n32. P/E Ratio and Stock Price International Business Machines (IBM) has earnings per share of \\$6.85 and a P/E ratio of 15.19. What is the stock price?\nA. \\$0.45\nB. \\$2.22\nC. \\$45.09\nD. \\$104.05\n\n33. P/E Ratio and Stock Price Pfizer, Inc. (PFE) has earnings per share of \\$2.09 and a P/E ratio of 11.02. What is the stock price?\nA. \\$0.19\nB. \\$5.27\nC. \\$18.97\nD. \\$23.03\n\n34. P/E Ratio and Stock Price Ralph Lauren (RL) has earnings per share of \\$3.85 and a P/E ratio of 17.37. What is the stock price?\nA. \\$0.22\nB. \\$4.51\nC. \\$22.16\nD. \\$66.87\n\n35. Value of Dividends and Future Price A firm is expected to pay a dividend of \\$2.00 next year and \\$2.14 the following year. Financial analysts believe the stock will be at their target price of \\$75.00 in two years. Compute the value of this stock with a required return of 10 percent.\nA. \\$65.40\nB. \\$66.67\nC. \\$65.57\nD. \\$79.14\n\n36. Value of Dividends and Future Price A firm is expected to pay a dividend of \\$3.00 next year and \\$3.21 the following year. Financial analysts believe the stock will be at their target price of \\$80.00 in two years. Compute the value of this stock with a required return of 13 percent.\nA. \\$50.00\nB. \\$67.52\nC. \\$67.82\nD. \\$86.21\n\n37. Dividend Growth Annual dividends of Wal-Mart Stores (WMT) grew from \\$0.23 in 2000 to \\$0.83 in 2007. What was the annual growth rate?\nA. 2.61%\nB. 20.12%\nC. 37.29%\nD. 260.87%\n\n38. Dividend Growth Annual dividends of Pfizer, Inc. (PFE) grew from \\$0.38 in 2000 to \\$1.15 in 2007. What was the annual growth rate?\nA. 2.02%\nB. 17.14%\nC. 28.95%\nD. 202.63%\n\n39. Value a Constant Growth Stock Financial analysts forecast Best Buy Company (BBY) growth for the future to be 13 percent. Their recent dividend was \\$0.49. What is the value of their stock when the required rate of return is 14.13 percent?\nA. \\$3.92\nB. \\$4.90\nC. \\$43.36\nD. \\$49.00\n\n40. Value a Constant Growth Stock Financial analysts forecast Target Corp (TGT) growth for the future to be 11 percent. Their recent dividend was \\$0.52. What is the value of their stock when the required rate of return is 11.89 percent?\nA. \\$5.25\nB. \\$6.48\nC. \\$58.43\nD. \\$64.85\n\n41. Expected Return American Eagle Outfitters (AEO) recently paid a \\$0.38 dividend. The dividend is expected to grow at a 15.5 percent rate. At the current stock price of \\$24.07, what is the return shareholders are expecting?\nA. 15.50%\nB. 15.52%\nC. 17.08%\nD. 17.32%\n\n42. Expected Return The Buckle (BKE) recently paid a \\$0.90 dividend. The dividend is expected to grow at a 19 percent rate. At the current stock price of \\$43.17, what is the return shareholders are expecting?\nA. 19.00%\nB. 19.02%\nC. 21.48%\nD. 22.74%\n\n43. Expected Return Home Depot (HD) recently paid a \\$0.90 dividend. The dividend is expected to grow at a 17 percent rate. At the current stock price of \\$33.08, what is the return shareholders are expecting?\nA. 2.70%\nB. 17.03%\nC. 17.18%\nD. 20.18%\n\n44. Dividend Initiation and Stock Value A firm does not pay a dividend. It is expected to pay its first dividend of \\$0.10 per share in 2 years. This dividend will grow at 11 percent indefinitely. Using a 13 percent discount rate, compute the value of this stock.\nA. \\$4.42\nB. \\$4.59\nC. \\$5.43\nD. \\$7.21\n\n45. Dividend Initiation and Stock Value A firm does not pay a dividend. It is expected to pay its first dividend of \\$0.15 per share in 3 years. This dividend will grow at 9 percent indefinitely. Using a 10 percent discount rate, compute the value of this stock.\nA. \\$12.28\nB. \\$12.40\nC. \\$16.35\nD. \\$16.50\n\n46. P/E Ratio Model and Future Price Walmart (WMT) recently earned a profit of \\$3.13 per share and has a P/E ratio of 14.22. The dividend has been growing at a 12.5 percent rate over the past few years. If this growth continues, what would be the stock price in five years if the P/E ratio remained unchanged? What would the price be if the P/E ratio declined to 10 in five years.\nA. \\$6.08, \\$5.04 respectively\nB. \\$72.22, \\$50.40 respectively\nC. \\$80.20, \\$56.40 respectively\nD. \\$86.46, \\$60.80 respectively\n\n47. P/E Ratio Model and Future Price Target Corp (TGT) recently earned a profit of \\$3.57 earnings per share and has a P/E ratio of 17.3. The dividend has been growing at a 14 percent rate over the past few years. If this growth continues, what would be the stock price in five years if the P/E ratio remained unchanged? What would the price be if the P/E ratio increased to 23 in five years.\nA. \\$118.85, \\$158.01 respectively\nB. \\$137.19, \\$182.39 respectively\nC. \\$173.87, \\$231.15 respectively\nD. \\$308.81, \\$410.55 respectively\n\n48. Value of Future Cash Flows A firm recently paid a \\$1.00 annual dividend. The dividend is expected to increase by 10 percent in each of the next four years. In the fourth year, the stock price is expected to be \\$100. If the required rate for this stock is 14 percent, what is its value?\nA. \\$25.00\nB. \\$36.60\nC. \\$62.87\nD. \\$72.30\n\n49. Value of Future Cash Flows A firm recently paid a \\$0.30 annual dividend. The dividend is expected to increase by 8 percent in each of the next four years. In the fourth year, the stock price is expected to be \\$60. If the required rate for this stock is 10 percent, what is its value?\nA. \\$15.00\nB. \\$20.41\nC. \\$42.13\nD. \\$45.30\n\n50. Constant Growth Stock Valuation Best Buy Co (BBY) paid a \\$0.27 dividend per share in 2003, which grew to \\$0.49 in 2007. This growth is expected to continue. What is the value of this stock at the beginning of 2007 when the required rate of return is 17.23 percent?\nA. \\$2.84\nB. \\$42.24\nC. \\$49.03\nD. \\$50.78\n\n51. Constant Growth Stock Valuation Target Corp (TGT) paid a \\$0.21 dividend per share in 2000, which grew to \\$0.52 in 2007. This growth is expected to continue. What is the value of this stock at the beginning of 2007 when the required rate of return is 14.77 percent?\nA. \\$3.52\nB. \\$55.32\nC. \\$62.97\nD. \\$63.49\n\n52. Changes in Growth and Stock Valuation Consider a firm that had been priced using a 10 percent growth rate and a 14 percent required rate. The firm recently paid a \\$1.00 dividend. The firm has just announced that because of a new joint venture, it will likely grow at a 12 percent rate. How much should the stock price change (in dollars and percentage)?\nA. \\$25, 1%\nB. \\$25, 100%\nC. \\$28.50, 1.04%\nD. \\$28.50, 104%\n\n53. Changes in Growth and Stock Valuation Consider a firm that had been priced using a 6 percent growth rate and a 9 percent required rate. The firm recently paid a \\$0.50 dividend. The firm has just announced that because of a new joint venture, it will likely grow at an 8 percent rate. How much should the stock price change (in dollars and percentage)?\nA. \\$33.33, 67%\nB. \\$33.33, 198%\nC. \\$36.33, 67%\nD. \\$36.33, 206%\n\n54. Variable Growth A fast growing firm recently paid a dividend of \\$0.50 per share. The dividend is expected to increase at a 25 percent rate for the next 3 years. Afterwards, a more stable 12 percent growth rate can be assumed. If a 15 percent discount rate is appropriate for this stock, what is its value?\nA. \\$5.00\nB. \\$22.62\nC. \\$25.75\nD. \\$36.46\n\n55. Variable Growth A fast growing firm recently paid a dividend of \\$1.00 per share. The dividend is expected to increase at a 25 percent rate for the next 3 years. Afterwards, a more stable 8 percent growth rate can be assumed. If a 10 percent discount rate is appropriate for this stock, what is its value?\nA. \\$12.50\nB. \\$75.93\nC. \\$83.13\nD. \\$120.24\n\n56. P/E Model and Cash Flow Valuation Suppose that a firm’s recent earnings per share and dividends per share are \\$3.00 and \\$1.50, respectively. Both are expected to grow at 10 percent. However, the firm’s current P/E ratio of 20 seems high for this growth rate. The P/E ratio is expected to fall to 16 within five years. Compute a value for this stock by first estimating the dividends over the next five years and the stock price in five years. Then discount these cash flows using a 14 percent required rate.\nA. \\$31.68\nB. \\$40.15\nC. \\$46.89\nD. \\$60.00\n\n57. P/E Model and Cash Flow Valuation Suppose that a firm’s recent earnings per share and dividends per share are \\$2.50 and \\$1.00, respectively. Both are expected to grow at 10 percent. However, the firm’s current P/E ratio of 22 seems high for this growth rate. The P/E ratio is expected to fall to 18 within five years. Compute a value for this stock by first estimating the dividends over the next five years and the stock price in five years. Then discount these cash flows using a 14 percent required rate.\nA. \\$37.51\nB. \\$37.64\nC. \\$42.14\nD. \\$72.47\n\n58. At your discount brokerage firm, it costs \\$9.95 per stock trade. How much money do you need to buy 200 shares of General Electric (GE), which trades at \\$45.19?\nA. \\$9,038.00\nB. \\$4528.95\nC. \\$9,047.95\nD. \\$4,595.95\n\n59. At your discount brokerage firm, it costs \\$7.95 per stock trade. How much money do you receive after selling 250 shares of General Electric (GE), which trades at \\$55.19?\nA. \\$14,037.95\nB. \\$11,958.55\nC. \\$12,174.95\nD. \\$13,789.55\n\n60. A preferred stock from DLC pays \\$3.00 in annual dividends. If the required return on the preferred stock is 9.3%, what is the value of the stock?\nA. \\$34.89\nB. \\$32.26\nC. \\$38.49\nD. \\$31.13\n\n61. Ultra Petroleum (UPL) has earnings per share of \\$1.75 and P/E of 42.56. What is the stock price?\nA. \\$74.48\nB. \\$76.68\nC. \\$85.68\nD. \\$112.98\n\n62. JPM has earnings per share of \\$3.75 and P/E of 47. What is the stock price?\nA. \\$174.08\nB. \\$176.25\nC. \\$185.95\nD. \\$112.98\n\n63. A firm is expected to pay a dividend of \\$2.00 next year and \\$3.75 the following year. Financial analysts believe the stock will be at their price target of \\$125.00 in two years. Compute the value of this stock with a required rate of return of 15%.\nA. \\$78.34\nB. \\$81.05\nC. \\$87.13\nD. \\$99.09\n\n64. Financial analysts forecast ABC Inc. growth for the future to be 12%. ABC’s recent dividend was \\$1.60. What is the value of ABC stock when the required return is 15%?\nA. \\$59.73\nB. \\$63.72\nC. \\$79.81\nD. \\$91.02\n\n65. A fast growing firm recently paid a dividend of \\$0.80 per share. The dividend is expected to increase at a rate of 30% rate for the next 4 years. Afterwards, a more stable 7% growth rate can be assumed. If a 10% discount rate is appropriate for this stock, what is its value?\nA. \\$60.48\nB. \\$60.18\nC. \\$61.34\nD. \\$73.86\n\n66. A fast growing firm recently paid a dividend of \\$1.00 per share. The dividend is expected to increase at a rate of 15% rate for the next 3 years. Afterwards, a more stable 6% growth rate can be assumed. If a 10% discount rate is appropriate for this stock, what is its value?\nA. \\$33.54\nB. \\$37.99\nC. \\$39.37\nD. \\$42.03\n\n67. A firm recently paid a \\$0.50 annual dividend. The dividend is expected to increase by 10% in each of the next three years. In the third year, the stock price is expected to be \\$110. If the required return is 15%, what is its value?\nA. \\$62.53\nB. \\$68.95\nC. \\$73.71\nD. \\$78.67\n\n68. Campbell Soup Co paid a \\$1.55 dividend per share in 2004, which grew to \\$1.95 in 2009. This growth is expected to continue. What is the value of this stock at the beginning of 2010 when the required return is 10.5 percent?\nA. \\$35.20\nB. \\$34.16\nC. \\$33.48\nD. \\$32.17\n\n69. Consider a firm that had been priced using a 12 percent growth rate and a 16 percent required return. The firm recently paid a \\$5.00 dividend. The firm has just announced that because of a new joint venture, it will likely grow at a 12.5 percent rate. How much should the stock price change (in dollars and percentage)?\nA. \\$21.50; 13.72%\nB. \\$21.50; 16.14%\nC. \\$20.71; 14.79%\nD. \\$20.71; 19.93%\n\n70. Suppose that a firm’s recent earnings per share and dividend per share are \\$2.50 and \\$1.00, respectively. Both are expected to grow at 5 percent. However, the firm’s current P/E ratio of 23 seems high for this growth rate. The P/E ratio is expected to fall to 19 within five years. Compute a value for this stock. Assume a 10 percent required rate.\nA. \\$36.19\nB. \\$38.86\nC. \\$40.31\nD. \\$42.00\n\n71. A firm has been losing sales due to technological obsolescence. It projects growth for the future to be -2%. Its recent divided was \\$2.00. What is the value of this stock when the required return is 9%?\nA. \\$28.00\nB. \\$29.14\nC. \\$17.82\nD. \\$15.52\n\n72. A firm has been losing sales due to technological obsolescence. It projects growth for the future to be -3%. Its recent divided was \\$2.50. What is the value of this stock when the required return is 7%?\nA. \\$28.17\nB. \\$24.25\nC. \\$17.42\nD. \\$15.53\n\n73. To list a stock on the NYSE, a company must meet minimum requirements that include all of the following except ____________________.\nA. Firm size\nB. Total number of stockholders\nD. P/E Ratio\n\n74. Which of the following is an electronic stock market without a physical trading floor?\nA. American Stock Exchange\nB. Mercantile Exchange\nC. New York Stock Exchange\nD. Nasdaq Stock Market\n\n75. Individuals who use their own stock inventory and capital to buy and sell the stocks they represent are called _________________.\nA. Market makers\nB. Brokers\nC. Investors\nD. None of these.\n\n76. All of the following are stock market indices except _________________.\nA. Standard & Poor’s 500 Index\nB. Dow Jones Industrial Average\nC. Nasdaq Composite Index\nD. Mercantile 1000\n\n77. GEN has 10 million shares outstanding and a stock price of \\$89.25. What is GEN’s market capitalization?\nA. \\$89,250,000,000\nB. \\$89,250,000\nC. \\$892,500,000\nD. \\$892,500\n\n78. GEN has 1 million shares outstanding and a P/E ratio of 12. Its earnings per share is \\$2.00 What is GEN’s market capitalization?\nA. \\$24,000,000\nB. \\$12,000,000\nC. \\$2,000,000\nD. \\$96,000,000\n\n79. GEN has 3 million shares outstanding and a P/E ratio of 15. Its earnings per share is \\$3.00 What is GEN’s market capitalization?\nA. \\$45,000,000\nB. \\$135,000,000\nC. \\$112,000,000\nD. \\$9,000,000\n\n80. ABC has a net profit margin of 3.3% on Sales of \\$10,000,000. The firm has 50,000 shares outstanding. If the firm’s P/E is 19 times, how much is the stock selling for?\nA. \\$41.72\nB. \\$34.96\nC. \\$125.40\nD. \\$99.16\n\n81. ABC has a net profit margin of 4.3% on Sales of \\$12,000,000. The firm has 250,000 shares outstanding. If the firm’s P/E is 16 times, how much is the stock selling for?\nA. \\$41.72\nB. \\$35.96\nC. \\$25.40\nD. \\$33.02\n\n82. Which of the following indices best reflects the ten sectors of the economy?\nA. Nasdaq Composite\nB. Dow Jones Industrial Average\nC. Standard & Poor’s 500\nD. None of these.\n\n83. Studies of investor psychology have discovered that ____________.\nA. investors tend to trade too much\nB. investors tend to sell their winners too soon\nC. investors tend to become overconfident\nD. All of these.\n\n84. Sally has researched GLE and wants to pay no more than \\$50 for the stock. Currently, GLE is trading in the market for \\$54. Sally would be best served to:\nA. buy using a limit order.\nB. buy using a market order.\nD. None of these.\n\n85. Which of the following is incorrect with respect to limit orders?\nA. They can be used only to buy stock.\nB. If the current quote does not meet the price cited in the limit order, the trade is not executed.\nC. The advantage of the limit order is that the investor makes the trade at the desired price.\nD. The disadvantage of the limit order is that the trade might not be executed at all.\n\n86. Which of the following is incorrect with respect to preferred stock?\nA. Preferred stock is largely owned by other companies rather than individual investors.\nB. Preferred stock takes preference over common stock in bankruptcy proceedings.\nC. Preferred stock dividends do not grow.\nD. All of these statements are correct.\n\n87. JUJU’s dividend next year is expected to be \\$1.50. It is trading at \\$45 and is expected to grow at 9% per year. What is JUJU’s dividend yield and capital gain?\nA. 1.5%; 6%\nB. 9%; 3.33%\nC. 3.33%; 9%\nD. 6%; 1.5%\n\n88. JUJU’s dividend next year is expected to be \\$5.50. It is trading at \\$45 and is expected to grow at 4% per year. What is JUJU’s dividend yield and capital gain?\nA. 2.5%; 6%\nB. 12.22%; 4%\nC. 4%; 12.22%\nD. 6%; 2.5%\n\n89. Value stocks are _________________________.\nA. stocks that are expected to exhibit high growth\nB. stocks that have low P/E ratios and are selling at a bargain price\nC. stocks that have high valuation ratios, such as P/E\nD. None of these.\n\n90. A firm does not pay any dividends at this point in time. Which valuation method should be used on this stock?\nA. Residual Claimant Model\nB. Variable Growth Model\nC. P/E Ratio Model\nD. Capital Gain Model\n\n91. Which of the following statements is incorrect?\nA. Trading at the New York Stock Exchange and the American Stock Exchange are done by open outcry.\nB. Dealers create market liquidity in the Nasdaq’s electronic market.\nC. The Dow Jones Industrial Average includes 35 of the largest companies in the U.S.\nD. The Nasdaq contains many very large technology firms.\n\n92. A firm is expected to pay a \\$4.00 dividend per share. The stock is selling in the market place for \\$55.00 per share. If investors are demanding 12% on this stock, what is this stock’s growth rate?\nA. 4.73%\nB. 7.25%\nC. 5.91%\nD. 6.14%\n\n93. A firm is expected to pay a \\$2.00 dividend per share. The stock is selling in the market place for \\$50.00 per share. If investors are demanding 10% on this stock, what is this stock’s growth rate?\nA. 4.73%\nB. 5.92%\nC. 6.00%\nD. 7.29%\n\n94. A firm’s recent dividend was \\$2.00 per share. The stock is selling in the market place for \\$50.00 per share. If investors are demanding 10% on this stock, what is this stock’s growth rate?\nA. 4.73%\nB. 5.92%\nC. 6.00%\nD. 5.77%\n\n95. A firm’s recent dividend was \\$4.00 per share. The stock is selling in the market place for \\$55.00 per share. If investors are demanding 12% on this stock, what is this stock’s growth rate?\nA. 4.73%\nB. 4.41%\nC. 5.91%\nD. 6.14%\n\n96. A stock is expected to pay a \\$1.00 dividend per share. The growth rate is expected to be 4%. If investors demand 10% on this stock, what is the expected price of the stock 10 years from now?\nA. \\$24.68\nB. \\$22.17\nC. \\$25.00\nD. \\$26.93\n\n97. A stock is expected to pay a \\$4.00 dividend per share. The growth rate is expected to be 5%. If investors demand 10% on this stock, what is the expected price of the stock 10 years from now?\nA. \\$94.68\nB. \\$92.17\nC. \\$130.31\nD. \\$126.93\n\n98. A stock is expected to pay a \\$4.00 dividend per share. The growth rate is expected to be -1%. If investors demand 8% on this stock, what is the expected price of the stock 3 years from now?\nA. \\$54.68\nB. \\$52.17\nC. \\$41.06\nD. \\$43.12\n\n99. A stock is expected to pay a \\$5.00 dividend per share. The growth rate is expected to be -2%. If investors demand 8% on this stock, what is the expected price of the stock 5 years from now?\nA. \\$54.68\nB. \\$45.20\nC. \\$41.06\nD. \\$53.12\n\n100. A firm’s stock is selling at \\$95.00 per share. Its growth rate is 10% and investors demand 15% on this stock. What is the firm’s expected dividend?\nA. \\$4.75\nB. \\$5.95\nC. \\$6.25\nD. \\$5.50\n\n101. A firm’s stock is selling at \\$75.00 per share. Its growth rate is 10% and investors demand 17% on this stock. What is the firm’s expected dividend?\nA. \\$4.75\nB. \\$5.95\nC. \\$6.25\nD. \\$5.25\n\n102. Which of the following statements is incorrect?\nA. Preferred stock prices fluctuate with market interest rates and behave like corporate bond prices.\nB. Common stock price changes with the value of the company’s underlying business.\nC. Preferred stockholders have higher precedence for payment in the event of firm liquidation from bankruptcy.\nD. All of these statements are correct.\n\n103. A stock recently paid a dividend of \\$3 per share. Its growth rate is expected to be 8%. Investors require a 10% return. The stock is selling in the market for \\$140. What is this stock worth and is the stock undervalued or overvalued?\nA. \\$162; undervalued\nB. \\$162; overvalued\nC. \\$150; undervalued\nD. \\$150; overvalued\n\n104. A stock recently paid a dividend of \\$2.5 per share. Its growth rate is expected to be 8%. Investors require a 10% return. The stock is selling in the market for \\$150. What is this stock worth and is the stock undervalued or overvalued?\nA. \\$125; undervalued\nB. \\$125; overvalued\nC. \\$135; undervalued\nD. \\$135; overvalued\n\n105. Laura is considering two investments: Stock A and B. Both stocks have a P/E ratio of 19. Stock A has an expected growth rate of 5% and stock B has an expected growth rate of 13%. Which is the better stock and why?\nA. Stock B is better because it is considered to be cheaper than Stock A.\nB. Stock A is better because it is expected to grow at a slower rate and therefore will be less risky than Stock B.\nC. Since the P/E ratios are the same, Laura would be indifferent between the two stocks.\nD. None of these statements is correct.\n\n106. Coca-Cola recently paid a \\$3.00 dividend. Investors expect a 12% return on this stock. What is the difference in price if Coca-Cola is expected to grow at 6% versus 8%?\nA. \\$18\nB. \\$48\nC. \\$28\nD. \\$38\n\n107. Coca-Cola recently paid a \\$3.00 dividend. Investors expect a 12% return on this stock. What is the difference in price if Coca-Cola is expected to grow at 7% versus 8%?\nA. \\$11.40\nB. \\$16.80\nC. \\$21.60\nD. \\$19.40\n\n108. Coca-Cola recently paid a \\$3.00 dividend. Investors expect a 12% return on this stock. What is the percentage change in price if Coca-Cola is expected to grow at 7% versus 8%?\nA. 31.29%\nB. 19.82%\nC. 21.60%\nD. 26.17%\n\nEssay Questions\n\n109. Explain how the difference in the bid and ask prices might be considered a hidden cost to the investor.\n\n110. What ten sectors of the economy are represented in the S&P 500 Index?\n\n111. When might the constant growth model not be used?\n\n112. Explain the characteristics of preferred stock.\n\n113. Explain how stock is valued if the constant growth model cannot be used.\n\n114. Under what conditions would the constant-growth-rate model not be appropriate?\n\n115. What are the differences between common stock and preferred stock?\n\n116. Explain how it is possible for the Dow Jones Industrial Average and the Nasdaq Composite to move in different directions in one day.\n\n117. Consider two firms with the same P/E ratio. Explain how one could be described as expensive compared to the other.\n\n118. Explain how important a firm’s growth is by creating an example of a growth and no-growth stock." ]
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https://zbmath.org/?q=an:0844.34050
[ "# zbMATH — the first resource for mathematics\n\nOn the asymptotic stability for a two-dimensional linear nonautonomous differential system. (English) Zbl 0844.34050\nThis paper presents an analysis of the asymptotic stability of the system of differential equations $$x' = - r(t)x + q(t)y$$, $$y' = - q(t)x - p(t)y$$, where $$t \\geq 0$$ and the scalar functions $$p,q,r$$ are piecewise continuous and nonnegative. A simple condition to ensure asymptotic stability is $$\\int^\\infty_0 \\min (p(t), r(t)) dt = + \\infty$$. The paper works out several other results which use the milder assumption $$\\int^\\infty_0 p(t)dt = + \\infty$$, together with elaborate conditions of integral type. As these conditions are somewhat technical, the author presents several alternatives, which are less general but easier to use in applications. Comparison with known criteria are given. The method of proof is a combination of the method of Lyapunov functions and of the theory of differential inequalities.\n\n##### MSC:\n 34D20 Stability of solutions to ordinary differential equations\nFull Text:\n##### References:\n Kamke, E., Differentialgleichungen, Lösungsmethoden und Lösungen, I. gewöhnliche differentialgleichungen, (1959), Akademische Verlagsgesellschaft New York · JFM 68.0179.01 Lakshmikantham, V.; Leela, S., () Merkin, D.R., (), (In Russian.) Haddock, J., On Liapunov functions for nonautonomous systems, J. math. analysis applic., 47, 599-603, (1974) · Zbl 0296.34045 Hatvani, L., Attractivity theorems for nonautonomous systems of differential equations, Acta. sci. math. (Szeged), 40, 271-283, (1978) · Zbl 0497.34037 Surkov, A.G., On the asymptotic stability of certain two-dimensional linear systems, Diff. uravneniya, 20, (1984), (In Russian.) · Zbl 0562.34043 Klincsik, M., Asymptotic behaviour of solutions of differential equations in Banach spaces, Ph.D thesis, (1991), (In Hungarian.) · Zbl 0826.35065 Hatvani, L.; Totik, V., Asymptotic stability of the equilibrium of the damped oscillator, Diff. integral eqns, 6, 835-848, (1993) · Zbl 0777.34036 Karsai, J., On the asymptotic stability of the zero solution of certain nonlinear second order differential equations, (), 495-503 Kertész, V., Conditions for the existence of solutions to homogeneous linear differential equations tending to zero, Alkal. mat. lapok, 12, 191-205, (1986), (In Hungarian.) · Zbl 0635.34011 Pucci, P.; Serrin, J., Asymptotic stability for intermittently controlled nonlinear oscillators, SIAM J. math. analysis, 25, 815-835, (1994) · Zbl 0809.34067 Elbert Á., On-off damping of linear oscillators, Acta Sci. Math. (Szeged) (to appear). · Zbl 0854.34051 Hartman, P.H., () Yoshizawa, T., Asymptotic behaviors of solutions of differential equations, (), 1141-1164 Artstein, Z.; Infante, E.F., On the asymptotic stability of oscillators with unbounded damping, Quart. appl. math., 34, 195-199, (1976) · Zbl 0336.34048 Hatvani L., Krisztin T. & Totik V., A necessary and sufficient condition for the asymptotic stability of the damped oscillator, J. diff. Eqns (to appear). · Zbl 0831.34052 Weyl, H., Über gewöhnliche lineare differentialgleichungen mit singulären stellen und ihre eigenfunktionen, Nachrichten von der Königlichen gesellschaft der wissenschaften zu Göttingen, 37-63, (1909) · JFM 40.0406.01 Wintner, A., Asymptotic integration of the adiabatic oscillator in its hyperbolic range, Duke math. J., 15, 55-67, (1948) · Zbl 0034.35501 Levin, J.J.; Nohel, J.A., Global asymptotic stability of nonlinear systems of differential equations and applications to reactor dynamics, Archs ration. mech. analysis, 5, 194-211, (1960) · Zbl 0094.06402 Smith, R.A., Asymptotic stability of x″ + a (t) x′ + x = 0, Quart. J. math., 12, 123-126, (1961) · Zbl 0103.05604 Yoshizawa, T., Asymptotic behavior of solutions of a system of differential equations, Contrib. diff. eqns, 1, (1963) · Zbl 0127.30802 Willett, D.; Wong, J.S.W., Some properties of the solutions of (p (t) x′) ′ + q (t) f (x) = 0, J. math. analysis applic., 23, 15-24, (1968) · Zbl 0165.40803 Hatvani, L., On the stability of the zero solution of certain second order nonlinear differential equations, acta, Sci. math. (Szeged), 32, 1-9, (1971) · Zbl 0216.11704 Cesari, L., () Salvadori, L., Famiglie ad un parametro di funzioni di Lyapunov nello studio Della stabilit, Sympos. math. (Rome), 6, 309-330, (1971) Onuchic, N.; Onuchic, L.R.; Taboas, P., Invariance properties in the theory of stability for ordinary differential systems and applications, Applic. analysis, 5, 101-107, (1975) · Zbl 0335.34027 Ballieu, R.J.; Peiffer, K., Attractivity of the origin for the equation ẍ + g(t, x, x˙)x˙ + f(x) = 0, J. math. analysis applic., 65, 321-332, (1978) · Zbl 0387.34038 Murakami, S., Asymptotic behavior of solutions of ordinary differential equations, Tôhoku math. J., 34, (1982) · Zbl 0488.34044 Karsai, J., On the global asymptotic stability of the zero solution of the equation x + g (t x )+ f (x) = 0, Studia sci. math. hungar., 19, 385-393, (1984) · Zbl 0528.34050 Terjéki, J., Stability equivalence between damped mechanical and gradient systems, (), 1059-1072 · Zbl 0617.34058 Pucci, P.; Serrin, J., Precise damping conditions for global asymptotic stability for nonlinear second order systems, Acta math., 170, 275-307, (1993) · Zbl 0797.34059 Pucci, P.; Serrin, J., Precise damping conditions for global asymptotic stability for nonlinear second order systems II., J. diff. eqns, 113, 505-534, (1994) · Zbl 0814.34033\nThis reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching." ]
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https://search.r-project.org/CRAN/refmans/CoopGame/html/belongsToReasonableSet.html
[ "belongsToReasonableSet {CoopGame} R Documentation\n\n## Check if point is element of reasonable set\n\n### Description\n\nbelongsToReasonableSet checks if the point is in the reasonable set\n\n### Usage\n\n```belongsToReasonableSet(x, v)\n```\n\n### Arguments\n\n `x` numeric vector containing allocations for each player `v` Numeric vector of length 2^n - 1 representing the values of the coalitions of a TU game with n players\n\n### Value\n\n`TRUE` if point belongs to reasonable set, `FALSE` otherwise\n\n### Author(s)\n\nJochen Staudacher [email protected]\n\n### References\n\nMilnor J.W. (1953) Reasonable Outcomes for N-person Games, Rand Corporation, Research Memorandum RM 916.\n\nBranzei R., Dimitrov D. and Tijs S. (2006) Models in cooperative game theory, Springer, p. 21\n\nChakravarty S.R., Mitra M. and Sarkar P. (2015) A Course on Cooperative Game Theory, Cambridge University Press, pp. 43–44\n\nGerard-Varet L.A. and Zamir S. (1987) \"Remarks on the reasonable set of outcomes in a general coalition function form game\", Int. Journal of Game Theory 16(2), pp. 123–143\n\n### Examples\n\n```library(CoopGame)\nbelongsToReasonableSet(x=c(1,0.5,0.5), v=c(0,0,0,1,1,1,2))\n\nlibrary(CoopGame)\nv <- c(0,0,0,3,3,3,6)\nbelongsToReasonableSet(x=c(2,2,2),v)\n# TRUE\nbelongsToReasonableSet(x=c(1,2,4),v)\n# FALSE\n\n```\n\n[Package CoopGame version 0.2.2 Index]" ]
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https://cboard.cprogramming.com/c-programming/127112-question-about-performance-free.html?s=bad8c9fd759804463ba945735a0232a0
[ "1. A question about performance and free()\n\nWhile writing some code that is very heavy in calculations I came to think of 2 things today:\n\n1: Performance of raw calculation vs. table lookup\nSuppose I have a nested for loop:\nCode:\nfor(i=0;i<i_max;i++)\n{\nfor(j=0;j<j_max;j++)\n{\na=some_array[j]*2;\nb=some_other_array[i];\nc[i]=a+b;\n}\n\n}\nWould it be more efficient to calculate all the a's first in som other loop, and simply accessing them in memory? This does require that I malloc some space for another array to contain the a's. I guess it is a waste to calculate the a's over and over again. How much faster is it to access values in memory rather than doing explicit calculations (assuming all numbers are doubles)? Of course there are several unknowns in this question (most notably i_max and j_max), but can anything be said in general? I'd just like a rule of thumb since I try to write my programs as efficient as possible.\n\n2. My second question is about the inner workings of free(). Suppose I have an array I don't need any more. Let's call it arr. The equivalence of arrays and pointers says that an array is the same as a pointer to the first element. Now when I call free(arr), how does free know when to stop? What prevents it from simply continuing to free up memory until it segfaults? Somehow free() ignores the equivalence between pointers to the first element and arrays. It always knows when the array ends.\n\nI hope I have made myself clear enough, if not just ask and I will try to elaborate.", null, "2.", null, "Originally Posted by The Physicist", null, "Would it be more efficient to calculate all the a's first in som other loop, and simply accessing them in memory? This does require that I malloc some space for another array to contain the a's. I guess it is a waste to calculate the a's over and over again.\nMakes sense and is generally considered true, yep. This is also why if the middle condition of a for loop involves a function call (eg, strlen) but the result is not expected to change, you should put the result of the call into a variable first and use that value instead, since otherwise strlen is called for every iteration. Obviously the same applies to function calls inside the loop. Some beginners get it in their head that \"optimization\" means using less code and packing lots of ops into one clever line of code, but that is a bad rule.\n\nHow much faster is it to access values in memory rather than doing explicit calculations (assuming all numbers are doubles)?\nGoing by literally that particular loop, the calculations are very minor and probably not much more expensive (or even less) than a lookup (I'm guessing, I dunno any assembly). Doing an arithmetic operation on values already in processor registers is faster than fetching something from memory, so it depends on how much arithmetic you are doing. Maybe some one knows of a ratio here (eg, beyond two or three add/subtracts, the fetch is faster).\n\n2. My second question is about the inner workings of free(). Suppose I have an array I don't need any more. Let's call it arr. The equivalence of arrays and pointers says that an array is the same as a pointer to the first element. Now when I call free(arr), how does free know when to stop?\nStop right there. You cannot free an array of that sort (try it). A pointer which is malloc'd memory for an array is a pointer, not \"an array\". The equivalence of arrays and pointers is meant to refer to this kind of array:\nCode:\nint array;\nThis is stack memory and cannot be freed.", null, "3. 1. Table lookup is in general faster, but usually less precise (for floating-point tables) and requires more memory. If you want to calculate the table at runtime, only do this if you know that there will be more lookups than entries in the table.\n\n2. If you free a malloc'd array, free probably looks at some header before the array where the information on your array is stored (hidden). But if you free an actual (C) array, behavior is undefined. Free can't free arrays, only pointers (which may be arrays).\n\nthe array and the pointer are no longer strictly equivalent\nFor free, an array is a pointer outside of the heap, so any other pointer would result in similar behavior.", null, "It seems I wasn't quite clear in my terminology in the second question. I know I cannot free an array such as int a, but what I really meant was an array like int *a=calloc(32,sizeof(int)). It still seems magical to me that once free reaches int it simply stops freeing up memory instead of continuing. It is as if there is a table hidden somewhere that keeps account of the blocks that have been malloced during execution.", null, "5.", null, "Originally Posted by The Physicist", null, "It is as if there is a table hidden somewhere that keeps account of the blocks that have been malloced during execution.\nWell, yeah. The runtime keeps track, for every call of malloc, how much memory was requested by that call so that free stops in the right place. (It has to not give you the same memory twice, it has to not give more memory than it has*, etc.)\n\n--\n* Actually my understanding of glibc is that it will happily give you more memory than exists on your system without complaint, until you try to actually use it all.", null, "6.", null, "Originally Posted by tabstop", null, "* Actually my understanding of glibc is that it will happily give you more memory than exists on your system without complaint, until you try to actually use it all.\nThis is a linux kernel option, you can control it with a switch at boot time (can't remember what it is tho).", null, "7.", null, "Originally Posted by Brafil", null, "1. Table lookup is in general faster, but usually less precise (for floating-point tables) and requires more memory. If you want to calculate the table at runtime, only do this if you know that there will be more lookups than entries in the table.\nHow can it be less precise if it is a lookup in a table? A double is still a double, regardless of calculation or table lookup?\n\nIs it possible to access this table of malloc calls during runtime? Seems like a neat way to find out how big your arrays are. The information is there, it just has to be found.", null, "8.", null, "Originally Posted by The Physicist", null, "Is it possible to access this table of malloc calls during runtime? Seems like a neat way to find out how big your arrays are. The information is there, it just has to be found.\nGood point, but not as far as I know. You can do the same thing yourself fairly easily, of course (keep a count).", null, "9.", null, "Originally Posted by The Physicist", null, "Code:\nfor (i=0; i<i_max; i++)\n{\nfor (j=0; j<j_max; j++)\n{\na = some_array[j] * 2;\nb = some_other_array[i];\nc[i] = a+b;\n}\n}\nFTFY!\nUnfortunately we cannot help you optimise your real code from that mock example. It's calculating each entry for the c array multiple times needlessly, and your real code almost certainly is not doing that. To show you what I mean, assuming that the final value of a and b are not required, this snippet has the same effect as the above one:\nCode:\nfor (i=0; i<i_max; i++)\n{\nc[i] = some_array[j_max-1] * 2 + some_other_array[i];\n}\nNow if you cant perform the exact same optimisation on your real code, then you need to post the real code, so we can do some actual useful optimisations. Otherwise this discussion is moot.", null, "10. How can it be less precise if it is a lookup in a table? A double is still a double, regardless of calculation or table lookup?\nAFAIK double tables are mostly used for math calculation, so of course nobody can store all possible results in a table. I correct myself: for math applications, tables may be less precise if used for floating-point operations.", null, "11.", null, "Originally Posted by The Physicist", null, "How can it be less precise if it is a lookup in a table? A double is still a double, regardless of calculation or table lookup?\nNope. On many processors, including the x86, the internal precision of the wide floating point types is greater than the precision of a double. For example, a double in memory is 64 bits, but a double in the FPU is 80 bits. By storing intermediate results to memory you lose the 80 bit precision.", null, "12.", null, "Originally Posted by MK27", null, "Good point, but not as far as I know. You can do the same thing yourself fairly easily, of course (keep a count).\nYes it is fairly simple to keep track of the sizes of your arrays manually. However it would be neat, and perhaps also less error prone, to simply access the table. It's not a big thing, but I find it interesting nevertheless", null, "I guess a nice way to easily keep track of the sizes of your arrays would be to simply declare them as structs containing a pointer which has all the data and then constant values such as rows, columns... and so on to store the dimensions of the array. In this way you could also think of these structs as vectors, matrices or tensors in a physical terminology depending on the dimensions.", null, "Originally Posted by brewbuck", null, "Nope. On many processors, including the x86, the internal precision of the wide floating point types is greater than the precision of a double. For example, a double in memory is 64 bits, but a double in the FPU is 80 bits. By storing intermediate results to memory you lose the 80 bit precision.\nI didn't know this. This is a good thing to know, I will keep it in mind. Thank you very much.\n\nI have noticed that if I make an array like char* arr=calloc(8,1) it is some times possible to write to arr without getting a segfault. I assume this is because the program has reserved other memory from an other calloc call, and that this memory lies at the end of the array. Is it possible to tell the compiler, for debugging purposes, to spread out all the malloc'ed memory, thus forcing the program to segfault when accidently writing to a value that is beyond the end of the array? Or is there an other clever way to get the compiler to notice whenever you write beyond the end of an array? I'm using gcc, but I assume that the answer will be the same for all the most common compilers.", null, "13.", null, "Originally Posted by The Physicist", null, "I have noticed that if I make an array like char* arr=calloc(8,1) it is some times possible to write to arr without getting a segfault. I assume this is because the program has reserved other memory from an other calloc call, and that this memory lies at the end of the array. Is it possible to tell the compiler, for debugging purposes, to spread out all the malloc'ed memory, thus forcing the program to segfault when accidently writing to a value that is beyond the end of the array? Or is there an other clever way to get the compiler to notice whenever you write beyond the end of an array? I'm using gcc, but I assume that the answer will be the same for all the most common compilers.\nActually this is more like \"there is no way the system is only going to give you eight bytes\", assuming you have some sort of normal-ish system (not, say, a chip controlling a wristwatch). You probably get 1K or so at a time, although you really should be nice and not go over what you claimed you needed.", null, "14.", null, "Originally Posted by The Physicist", null, "Or is there an other clever way to get the compiler to notice whenever you write beyond the end of an array?\nThere are ways available in other languages, and you could implement one for C I suppose, just it is not part of the standard. C is usually considered an \"unsafe\" language in the sense that if you don't know what you are doing, there's not much to stop you from making a real mess. However, once you get used to it, the boundaries here make sense -- nothing is left impossible, just very few things are automatic. The assumption is that you can take care of yourself.", null, "15.", null, "Originally Posted by tabstop", null, "Actually this is more like \"there is no way the system is only going to give you eight bytes\", assuming you have some sort of normal-ish system (not, say, a chip controlling a wristwatch). You probably get 1K or so at a time, although you really should be nice and not go over what you claimed you needed.\nThe CRT doesn't waste that much memory. Typically the CRT gets memory from the system in large chunks (perhaps 4K) and then malloc further allocates smaller chunks from within this if you make requests for smaller allocations. The smallest chunk the CRT will give you is basically 8 bytes on Windows.\nThis does not mean that you can assume that you have 8 bytes if you only asked for 2 say, of course.\n\nStrictly speaking, if you go past the size you asked for it is undefined behaviour. It might not crash for quite a while after the end, or it might crash if you go one over. There are ways of configuring Windows place allocations within your program in a place where overrun will typically be detected the moment going over the end (provided your element size isn't too small e.g. one byte), but they waste a ton of memory to do so. It's called \"Page heap allocation\", and it usually involves using gflags. Linux probably has something similar.", null, "Popular pages Recent additions", null, "" ]
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https://www.tutorialspoint.com/What-is-function-of-operator-in-Python
[ "# What is function of ^ operator in Python\n\nPythonServer Side ProgrammingProgramming\n\nIn Python, ^ is called EXOR operator. It is a bitwise operator which takes bits as operands. It returns 1 if one operand is 1 and other is 0.\n\nAssuming a=60 (00111100 in binary) and b=13 (00001101 in binary) bitwise XOR of a and b returns 49 (00110001 in binary)\n\n>>> a=60\n>>> bin(a)\n'0b111100'\n>>> b=a^2\n>>> bin(b)\n'0b111110'\n>>> a=60\n>>> bin(a)\n'0b111100'\n>>> b=13\n>>> bin(b)\n'0b1101'\n>>> c=a^b\n>>> bin(c)\n'0b110001'" ]
[ null ]
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http://www.kpubs.org/article/articleMain.kpubs?articleANo=E1OSAB_2013_v17n3_262
[ "Near-infrared Subwavelength Imaging and Focusing Analysis of a Square Lattice Photonic Crystal Made from Partitioned Cylinders\nNear-infrared Subwavelength Imaging and Focusing Analysis of a Square Lattice Photonic Crystal Made from Partitioned Cylinders\nJournal of the Optical Society of Korea. 2013. Jun, 17(3): 262-268\n• Received : March 12, 2013\n• Accepted : May 27, 2013\n• Published : June 25, 2013", null, "PDF", null, "e-PUB", null, "PubReader", null, "PPT", null, "Export by style\nArticle\nAuthor\nMetrics\nCited by\nTagCloud\nSomayeh Rafiee, Dastjerdi\nLaser and Plasma Research Institute, Shahid Beheshti University, G. C., Evin 1983963113, Tehran, Iran\nMajid, Ghanaatshoar\nLaser and Plasma Research Institute, Shahid Beheshti University, G. C., Evin 1983963113, Tehran, Iran\[email protected]\nToshiaki, Hattori\nInstitute of Applied Physics, University of Tsukuba, Tsukuba 305-8573, Japan\n\nAbstract\nWe study the focusing properties of a two-dimensional square-lattice photonic crystal (PC) comprising silica and germanium partitioned cylinders in air background. The finite difference time domain (FDTD) method with periodic boundary condition is utilized to calculate the dispersion band diagram and the FDTD method incorporating the perfectly matched layer boundary condition is employed to simulate the image formation. In contrast to the common square PCs in which the negative refraction effect occurs in the first photonic band without negative phase propagation, in our suggested model system, the frequency with negative refraction exists in the second band and in near-infrared region. In this case, the wave propagates with a negative phase velocity and the evanescent waves can be supported. We also discuss the dependency of the image resolution and its location on surface termination, source location, and slab thickness. According to the simulation results, spatial resolution of the proposed PC lens is below the radiation wavelength.\nKeywords\nI. INTRODUCTION\nIn 1968 Veselago proposed that simultaneously negative permeability and permittivity in an isotropic material leads to negative refractive index. According to this fact that the pointing vector and wave vector in these materials are in opposite directions, he called them left-handed materials (LHMs) or negative index materials (NIMs) . As an electromagnetic (EM) wave passes the border between a medium with positive refractive index and a LHM, the beam of light would be refracted to the same side of the normal line as the incident beam. Since no natural material with this specification has been discovered, in the last decade, manufactured structures with effective negative refractive index have attracted a lot of attention due to their wonderful optical properties [2 , 3] and imaging capabilities . Such meta-materials are able to restore both the phase of propagating waves and amplitude of evanescent waves to make perfect lenses which can overcome the diffraction limit of conventional convex lenses and introduce a new class of imaging systems called “super lenses” .\nThere are several approaches to realize the negative refraction phenomena. Some meta-materials are made from arrays of metallic split-ring resonators with metallic wire strips. Negative index of refraction in these structures appears in the microwave frequency regime [5 - 7] . In the optical range of frequency, different designs such as double-plate (or double-wire) pairs, which provide the negative magnetic permeability, and long metal wires , are proposed. Another human-made structure is made from arrays of lossless dielectric media named photonic crystals (PCs). These structures are able to explore the effective negative index of refraction in the infrared and optical frequencies. In contrast to the conventional lenses in which imaging effects obey the Newton’s formula, in PCs the imaging is related to mirror-inversion transformation like mirrors although PCs produce real images . Furthermore, there is no need to form a curved surface to obtain an image and also we can attain sub-wavelength imaging which exceeds the diffraction limits .\nIn these regards, extensive theoretical and experimental imaging studies have been performed [11 - 14] . Some of the studies on negative refractive index are related to 2D triangular, hexagonal and honeycomb high symmetry lattices in the second band which can be realized as left handed behavior [15 - 17] . Another kind of research has concentrated on 2D square lattices in which the negative refraction effect occurs in the region of the first photonic band where an isotropic negative index cannot be defined and negative refraction can be conceived by anisotropy or higher-order Bragg scattering effects [18 - 20] . Focusing properties of 2D square lattice PCs in the second photonic band have been studied experimentally in the range of microwave frequencies . Although negative refraction phenomena in 2D square lattices have been studied extensively, it is of significance to improve efficiency of the superlensing effect especially in near-infrared frequencies.\nIn this paper, for evaluation of the negative refraction effect in near-infrared frequency regime in the second photonic band of 2D square lattice PCs, where the wave has a negative phase velocity and PC structure can be assumed as a LHM, we proposed a PC comprising partitioned cylinders. We show that the image position is dependent on the source location and the slab thickness. We employ the finite difference time domain (FDTD) method with periodic boundary condition for calculating the dispersion band diagram of the PC and FDTD method incorporating the perfectly matched layer (PML) boundary condition for simulating the field pattern of the point sources and their images.\nII. BAND STRUCTURE PROPERTIES\nThe PC proposed here is a novel 2D square lattice consisting of periodic arrays of infinitely long dielectric partitioned rods of silica and germanium in air background as depicted in Fig. 1 . It is assumed that the width of the slab in the x direction is limited and in the y direction is unrestricted. Dielectric constants of the two substances are ε silica =2.1 and ε Ge =18 at the wavelength of 1.55 μm and", null, "PPT Slide\nLager Image\n2D square lattice PC embracing partitioned cylinders with lattice constant a. Darker parts of cylinders indicate SiO2 and the bright parts represent Ge.\nthe radius of dielectric cylinders is R =0.45 a , where a is the lattice constant. We fix the lattice constant at 388 nm which provides the imaging effect at near-infrared frequency regime. According to the configuration of arrays, the TM polarized EM wave is considered for calculating the band structure of the PC and simulating the propagation of the wave in the media.\nWe employ the FDTD method based on Yee’s lattice to solve the Maxwell’s equations within the unit cell in the time domain by applying the periodic boundary conditions to obtain the band diagram of the PC. We identify the eigenmodes as the spectral peaks from the Fourier transform of the TM mode of the electric field in the time domain. Fig. 2 shows the calculated TM-polarized band diagram along the high-symmetry lines in the first Brillouin zone.", null, "PPT Slide\nLager Image\nPhotonic band structure of (a) the common square lattice PC consisting of Ge cylinders in air with radius R=0.45a, and (b) corresponding structure composed of partitioned Ge and SiO2 cylinders with the same radius. The lines originated from Γ point depict the light lines in vacuum. Inset: the constant frequency counter of second band related to A and B points.\nFig. 2 (a) displays the band diagram of common 2D square lattice PC consisting of Ge cylinders with radius of R =0.45 a and Fig. 2 (b) depicts the band diagram of partitioned cylinders made from SiO 2 and Ge cylinders with the same radii. By comparing these two plots we notice that partitioned cylinders create a complete band gap between second and third bands whereas there is no band gap among the first five bands for TM mode in the already mentioned common square lattice. It should be noted that the first five energy bands of the PC made from SiO 2 cylinders, regarding the low dielectric constant of this material, is extended to the frequency of 1.1(2π c/a ) and so, it is not suitable for PC superlens application.\nTo obtain the frequency with the negative refractive effect we should pay attention to some important conditions. First of all, the frequency should be below 0.5×2π c/a to avoid diffraction and second, the equifrequency surface (EFS) of the PC around the Γ point should have an inward gradient leading to negative group velocity and the last one, the PC’s EFS should contain the EFS of the background . We choose the frequency of the intersection point of PC and air band diagrams to evaluate the imaging properties of this structure. The intersection normalized frequency, ωa /2 πc , is around 0.255 and as it is apparent from Fig. 2 (b), there is an inward gradient around the Γ point. The", null, "PPT Slide\nLager Image\nElectric field distribution of a point source located at the distance a from the left of the PC slab and at normalized frequency of 0.255 when the interface layer at both sides of the slab consists of (a) cylinders with radius R = 0.45a, (b) half cylinders with R = 0.45a, (c) cylinders with R = 0.2a and (d) half cylinders with R = 0.2a.\ninset in Fig. 2 (b) implies the constant frequency contour with two equal energy surfaces that is related to the second band for ωn =0.255. In fact, the contour approximately looks like a circle and it means that for this frequency the PC can be roughly regarded as an isotropic medium .\nIII. SURFACE TERMINATION EFFECT ON IMAGE QUALITY OF THE PROPOSED STRUCTURE\nThere is a type of bound photon mode in photonic crystals that is guided by the background/PC interface. This mode is the linear combination of surface states, those which are strongly affected by the position of surface termination. By coupling incident evanescent waves and bound photon modes, evanescent waves can be transmitted through the PC structure and contribute to image formation . In this regard, some analysis has clarified that surface termination has a significant effect on transmission and image quality [22 , 23] . According to this issue, we consider a 4.9 a -thick slab and put the point source operating at a normalized frequency of 0.255 at distance a from the left side of the PC structure to examine the effect of the air/PC interface on image quality.\nFigure 3 shows the electric field pattern of the point source and its image when the outmost layers include (a) cylinders with radius R =0.45 a , (b) half cylinders with radius R =0.45 a , (c) cylinders with radius R =0.2 a and (d) half cylinders with radius R =0.2 a . It can be inferred that the intensity of transmitted TM-polarized EM wave in the near field region increases with reducing the radius of the outmost cylinders. The surface termination with cylinders of radius 0.2 a acts as an anti-reflector to decrease the reflectance. On the other hand, cutting the outmost cylinders leads to participation of the evanescent waves in image formation and improvement of the light intensity at the image site. For the half cylinders with the radius of 0.2 a at the interface of air/PC, the evanescent waves are supported and as a result we can find higher resolution and smaller FWHM at the image site. This improvement is also verified by Fig. 4 that transverse distribution of electric field intensity for different surface terminations. By considering this fact, hereafter, we concentrate on the case of PC structures with the preferred surface termination (see Fig. 5 ).", null, "PPT Slide\nLager Image\nTransverse distribution of the electric field intensity at the focusing place for different surface terminations at the normalized frequency of 0.255.", null, "PPT Slide\nLager Image\n2D square lattice PC consisting of partitioned cylinders with optimum surface termination. The radius of the half cylinders at the interface of air/PC is 0.2a.\nIV. IMAGE POSITION DEPENDENCY ON THE SLAB THICKNESS AND POINT SOURCE LOCATION\nFrom the common focusing laws, one can realize whether the designed lens is able to act as a negative index material or not. A number of studies have shown that the overall imaging phenomena in PCs are defined by self-collimating and complex near-field wave scattering effects rather than negative effective index (e.g., see ). If the negative refraction mechanism overcomes other phenomena, by increasing the distance of point source from the slab and also by increasing", null, "PPT Slide\nLager Image\nElectric field distribution of a point source located at (a) 1.5a, (b) a and (c) 0.25a from the left side of the 4a-thick PC slab for normalized frequency of 0.255.\nthe slab thickness, image position should shift to a farther distance. Here, we consider a 4 a -thick PC slab to examine the effect of point source location. Fig. 6 shows the electric field pattern outside and beyond the PC structure for this typical slab thickness at normalized frequency of 0.255. In this figure we shift the location of the point source from the far-field to near-field region. In this regard, we put the point source at 1.5 a , a and 0.25 a from the left side of the slab. When the EM wave is emitted from the point source and enters the slab, the PC focuses the EM wave and forms the image outside the slab. As we expect from the conventional imaging rules, the closer the point source, the farther the image location. By calculating the image position of the point source we realized that by moving the point source from 1.5 a to 0.25 a , the place of image shifts from 0.5 a to 1.9 a .\nIn Fig. 7 electric field pattern of the point source located at 0.5 a from the left side of the PC structure and its image are displayed for different slab thicknesses. We choose 4 a and 7 a thicknesses for this purpose. As can be seen, by increasing the thickness of the slab, the position of the image shifts from 1.48 a to a farther place at 3.05 a , as we expect for the NIM slab lenses in which the dominant effect in image formation is negative refraction.", null, "PPT Slide\nLager Image\nElectric field distribution of a point source operating at normalized frequency of 0.255 and located at 0.5a from the left side of (a) the 4a and (b) the 6a-thick PC slabs.\nV. SUBWAVELENGTH IMAGING VIA THE DESIGNED PC LENS\nIn order to study the sub-wavelength imaging and evaluate the spatial resolution of the proposed PC lens, we consider two point sources with a given vertical separation distance from each other to observe the peaks of two images clearly and then reduce this distance till the two peaks of images in the right side of the slab become obscure. For this purpose, firstly we put point sources 0.5 a away from the PC slab. In this case, they are separated from each other by 3.7 a or 0.9435 λ in the vertical direction. Fig. 8 shows the electric field distribution and corresponding light intensity when the separation distance of two point sources is 3.7 a . From this figure one can recognize two images clearly. Fig. 9 represents the electric field intensity of images in the yz plane for different separation distances. As can be seen, for separation distance of 3.2 a , two peaks of images are still clear but when we decrease this distance to 3.1 a , only one image can be recognized. Thus, according to Rayleigh criterion , for normalized frequency 0.225 and separation distance of 3.2 a , the spatial resolution of the system can be regarded as 0.816 λ , which is smaller than the incident wavelength.", null, "PPT Slide\nLager Image\n(a) Electric field distribution of two point sources located at 0.5a from the left side of the PC slab and with 3.7a separation distance between them. (b) Corresponding electric field intensity of images on the right side of the PC slab.", null, "PPT Slide\nLager Image\n(a) Electric field intensity related to images of two point sources which are separated vertically by a distance of (a) 3.7a, (b) 3.2a, and (c) 3.1a.\nVI. CONCLUSIONS\nIn this report, we introduced a 2D photonic crystal including partitioned cylinders made from Ge and SiO 2 to investigate the image formation characteristics in near-infrared frequency region. We found that despite the usage of the high refractive index material Ge, the band diagram of this structure opens a gap between the second and third bands and satisfies three conditions required to create a PC lens. Although existence of sharp edges in designing such a PC structure makes some difficulties in constructing partitioned cylinders, we showed that this kind of modeling has a potential of PC superlensing in near-infrared frequency with a compressed band structure. In addition, we realized that the image formation depends on the slab thickness and the point source position from the slab. We claim that the imaging effect in our proposed partitioned cylinder PC with a properly terminated surface is dominantly governed by negative refraction effect rather than channeling or near-field scattering effects and according to Rayleigh criterion, the subwavelength focusing can occur with spatial resolution of 0.816 λ at the wavelength of 1.55 μm in this structure.\nReferences" ]
[ null, "http://www.kpubs.org/resources/images/pc/down_pdf.png", null, "http://www.kpubs.org/resources/images/pc/down_epub.png", null, "http://www.kpubs.org/resources/images/pc/down_pub.png", null, "http://www.kpubs.org/resources/images/pc/down_ppt.png", null, "http://www.kpubs.org/resources/images/pc/down_export.png", null, "http://www.kpubs.org/S_Tmp/J_Data/E1OSAB/2013/v17n3/E1OSAB_2013_v17n3_262_f001.jpg", null, "http://www.kpubs.org/S_Tmp/J_Data/E1OSAB/2013/v17n3/E1OSAB_2013_v17n3_262_f002.jpg", null, "http://www.kpubs.org/S_Tmp/J_Data/E1OSAB/2013/v17n3/E1OSAB_2013_v17n3_262_f003.jpg", null, "http://www.kpubs.org/S_Tmp/J_Data/E1OSAB/2013/v17n3/E1OSAB_2013_v17n3_262_f004.jpg", null, "http://www.kpubs.org/S_Tmp/J_Data/E1OSAB/2013/v17n3/E1OSAB_2013_v17n3_262_f005.jpg", null, "http://www.kpubs.org/S_Tmp/J_Data/E1OSAB/2013/v17n3/E1OSAB_2013_v17n3_262_f006.jpg", null, "http://www.kpubs.org/S_Tmp/J_Data/E1OSAB/2013/v17n3/E1OSAB_2013_v17n3_262_f007.jpg", null, "http://www.kpubs.org/S_Tmp/J_Data/E1OSAB/2013/v17n3/E1OSAB_2013_v17n3_262_f008.jpg", null, "http://www.kpubs.org/S_Tmp/J_Data/E1OSAB/2013/v17n3/E1OSAB_2013_v17n3_262_f009.jpg", null ]
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https://data-pruthiraj.medium.com/multi-tasking-pytorch-model-building-e77c7ef3c0e0?source=post_internal_links---------6----------------------------
[ "We are going to develop a model which can Classify a digit from Mnist and also can classify the addition of predected numbers and a random number.We will Do this task using PyTorch:\n\n# PyTorch Model Building\n\nWe have to Build a neural network that can:\n\n1. Take 2 inputs: An image from MNIST data set and arandom number between 0 and 9\n\n2. Gives two outputs: The “number” that was represented by the MNIST image, and the “sum” of this number with the random number that was generated and sent as the input to the network\n\nWe can mix fully connected layers and convolution layers.We can use one-hot encoding to represent the random number input as well as the “summed” output.\n\n1>Data representation.\n\n​ This problem statement need 2 types of data as input. 1> Image 2> A random number. The output of the data is going to be 1> Classifying what is the digit in the image . and the sum of The digit and random number.\n\nFor this Problem we are using MNIST data set. MNIST Handwritten Digit Classification Dataset. It is of 60,000 small square 28×28 pixel grayscale images of handwritten single digits between 0 and 9 for training and 10,000 data points for testing.\n\nWe are using Numpy to create a 60000 Random Numbers to generate between 0 to 9 . And using 19 (0…..18) classes to represent the sum .\n\n# 2>Data generation strategy\n\n​ To Create the data we have the Function called data_generator. which helps us returning the Image , Label ,random number , sum value which we can use to create a Data loader.\n\nMNISTRandom_loader Is the data loader which Gives us the required data for the model to Train and Test. We have Done one hot encoding to the random numbers and sum outputs in the loader function.\n\n# 3>How to combine the two inputs ?\n\n​ Now we have to combine the Two inputs to pass in the model. To do so once we have the data from data loader we train the model 1 i.e MNISt with the image data and return the features of shape [1,10]. Then we have the MNISTadder which needs to be trained for the sum function. for that we have concatenated the Output on model 1 and input using torch.cat([mnist_d,Num],dim = -1) . where mnist_d is of [1,10] shape and Num is [1,10] shape . Output of the concatenation is [1,20] shape .\n\nTo understand Better let’s Visualise the Graph.\n\n• Blue boxes: these correspond to the tensors we use as parameters, the ones we’re asking PyTorch to compute gradients for;\n• Gray box: a Python operation that involves a gradient-computing tensor or its dependencies;\n• Green box: the same as the Gray box, except it is the starting point for the computation of gradients (assuming the backward() method is called from the variable used to visualize the graph) — they are computed from the bottom-up in a graph.\n\nLayers used\n\nFor the image model we have the Receptive field calculated .\n\n# 4> What results you finally got and how did you evaluate your results ?\n\nAbove is the Loss graph for the model. The Loss is the combination of Loss 1 And Loss 2. where Loss one is F.nll_loss(y_pred1, target1) and Loss 2 is nn.CrossEntropyLoss(). To do this calculation of loss we have created a function total_loss.\n\nTraining model Epochs\n\nepochs : 25\n\nBatch size = 128\n\nLr : 0.01 (Not used any tool for finding correct lr yet)\n\noptimizer: SGD\n\nAccuracy1,Loss1 = MNIST model accuracy and loss\n\nAccuracy 2,loss2 = Sum model accuracy and loss\n\nTesting the model\n\n# 5>What loss function we picked and why ?\n\nFor The MNISt (CNN) model We are using the Loss function as The negative log likelihood loss.\n\nThe negative log-likelihood is bad at smaller values, where it can reach infinite (that’s too sad), and becomes good at larger values. Because we are summing the loss function to all the correct classes, what’s actually happening is that whenever the network assigns high confidence at the correct class, the unhappiness is low, and vice-versa. The input given through a forward call is expected to contain log-probabilities of each class. Means it needs a LogSoftmax layer before this .\n\nFormula: \\$\\$ Hp(q) = -1/N ∑ yi * log(p(yi)) + (1-yi) * log(1-p(yi)) \\$\\$\n\n• For the Numeric model I tried CrossEntropyLoss.\n\nBecause from the PyTorch Documentation I learned that Obtaining log-probabilities in a neural network is easily achieved by adding a LogSoftmax layer in the last layer of your network. We may use CrossEntropyLoss instead, if you prefer not to add an extra layer.\n\nAs we don’t have a LogSoftmax layer in MNISTadder (Summing ) model .\n\nFormula: \\$\\$ loss(x,class)=−log( ∑ j​exp(x[j]) exp(x[class])​)=−x[class]+log( j ∑​exp(x[j])) \\$\\$\n\n# 6>MUST use the GPU.\n\nCreated A GPU checker function To return the GPU information.\n\nTo Push the mode to GPU we have send all the Data and target to device = ‘cuda’ by using the below code." ]
[ null ]
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https://culttt.com/2016/06/06/understanding-recursion-tail-call-optimisation-elixir
[ "# Understanding Recursion and Tail Call Optimisation in Elixir\n\nJun 06, 2016\n\nLast week we looked at branching and conditionals logic and how we can rewrite these constructs as multi-clause functions to make our code more declarative and easier to understand and read.\n\nIf you are coming to Elixir from another programming language you are probably very accustomed to using loops to iterate through a list of items.\n\nSomething that may surprise you about Elixir is, there are no constructs for `while` or `do...while`.\n\nInstead, Elixir prefers to use recursion to achieve dynamic looping. In today’s tutorial we will look at recursion.\n\n## How does recursion work in Elixir?\n\nSo if we don’t have the `while` construct in Elixir, how do you iterate through a list?\n\nThe answer is our new favourite partnership of pattern matching and multi-clause functions!\n\nFirst you define the clause that will be the last step of the process. Typically this will handle the situation when the list is empty.\n\nNext you define more generic clauses that you can call recursively to iterate through the list.\n\nFor example, imagine we have a module that will print each element of a list:\n\n``````defmodule MyList do\ndef read([]), do: IO.puts(\"End of list\")\n\nend\nend``````\n\nFirstly, we define a function that will deal with the last step of the process. In this example we have a `read/1` function that matches an empty list. If the list is empty we will print a message to alert the user.\n\nSecond, we define a more generic function that accepts a non-empty list.\n\nWe pattern match the list into the `head` and the `tail` and we print the `head`.\n\nFinally we call the `read/1` function again with the `tail`.\n\nIf the `tail` contains elements it will pattern match against the second definition. But if the list is empty the call to the `read/1` function will pattern match to the first definition which will print the final message and end the recursion.\n\nSo as you can see, we can effectively loop through the list using pattern matching, multi-clause functions and recursively calling the same function.\n\nAgain, as with last week, although this involves writing multiple functions, this approach has many benefits.\n\nFirstly, your code is a lot more declarative. You can see what will happen when the list is empty verses if the list is not empty.\n\nAnd secondly you can deal with each step of the process as a single, simple function definition.\n\n## What is Tail Call Optimisation?\n\nIf you’ve ever read into functional programming you may have heard the term “Tail Call Optimisation”. Unless you’ve really been digging into a functional programming language in the past, you have probably no idea what this even means, I know I certainly didn’t!\n\nI’ll try and explain the concept here.\n\nWhen you recursively call a function, the computer will allocate memory for every element in the list. This is probably going to be fine for a small list, but in the case of a really big list, you might run out of memory!\n\nFor example, here is an example of a Module for adding each element of a list together and return the total:\n\n``````defmodule MyList do\ndef sum([]), do: 0\n\nend\nend\n\nMyList.sum([1, 2, 3]) |> IO.puts()``````\n\nAs you can see, first we define the empty list clause. In this case we return `0` for an empty list.\n\nNext we define the non-empty clause that will be recursively called. This function splits the list into the `head` and the `tail` and then it returns the value of adding the `head` and the return value from the `sum/1` function when given the `tail`.\n\nIf you run this code you should see it produce the correct answer of `6`.\n\nSo what’s the problem with this and where does Tail Call Optimisation come in?\n\nIf you were to run this function with a very big list of numbers you would eventually run out of memory. As I mentioned earlier, this is because the computer needs to allocate memory for each element in the list as the function is called recursively.\n\nThe solution to this problem is to slightly rewrite the function to take advantage of Tail Call Optimisation.\n\nTall Call Optimisation is where if the last thing a function does is call another function, the compiler can jump to the other function and then back again without allocating any additional memory.\n\nThe Erlang compiler will do this automatically because it will recognise the tail call, we just need to write out code in a certain way to take advantage of this optimisation.\n\nThis is great for recursive functions because it can run for a very large list without allocating any additional memory.\n\nThe reason why the example from above does not take advantage of tail call optimisation is because the last thing that occurs in the function is not a call to another function.\n\n``````def sum([head | tail]) do\nend``````\n\nAs you can see, the last thing that happens is the addition between the return value from the `sum/1` call and the `head`.\n\nLets rewrite this function to take advantage of tail call optimisation:\n\n``````defmodule MyList do\ndef sum([], accumulator), do: accumulator\n\ndef sum([head | tail], accumulator) do\nend\nend\n\nMyList.sum([1, 2, 3], 0) |> IO.puts()``````\n\nFirst we define the `sum/1` empty list clause that will simply return the accumulator that is passed in.\n\nNext we define the non-empty list clause that will recursively call itself, each time taking the `head` and adding it to the `accumlator` until the list is empty.\n\n## Examples of recursion in Elixir\n\nLets take a look at a couple of more examples of writing recursive functions in Elixir.\n\nFirst, here is an example of doubling each value in a list:\n\n``````defmodule MyList do\ndef double([]), do: []\n\nend\nend``````\n\nThis is pretty similar to the previous examples but instead of passing an accumulator we just return a new list with the head multiplied by 2.\n\nNext, here is an example of only returning the even values from a list:\n\n``````defmodule MyList do\ndef evens([]), do: []\n\nend\n\ndef evens([_ | tail]) do\nevens(tail)\nend\nend``````\n\nOnce again we first define the empty-list clause.\n\nNext we define a clause that matches a non-empty list, but only when the `head` is an even number. We then return a new list with the `head` and a recursive call with the remaining tail.\n\nNext we define the clause for a non-empty list when the `head` is not an even number. Here we don’t care about the `head` so we can just recursively call the `evens` function with the remaining tail.\n\nFinally, here is an example of a `map/2` function that takes a list an an anonymous function that will be applied to each element of the list:\n\n``````defmodule MyList do\ndef map([], _), do: []\n\ndef map([head | tail], func) do\nend\nend\n\nMyList.map([1, 2, 3], &(&1 * &1))``````\n\nOnce again we first define the empty state clause that will simply return the empty list.\n\nNext we define the general non-empty clause that will call the function on the head and then pass the tail and the function back into the `map/2` function to be called recursively until the list is empty.\n\nThe anonymous function in this example will simply multiply each element by itself.\n\nIf this anonymous function definition looks a bit strange to you, take a look at Functions as First-Class Citizens in Elixir.\n\n## Conclusion\n\nRecursion is an important aspect of learning Elixir and is probably not something you do that often if you are coming from from another programming language.\n\nTail call optimisation is another important thing to understand as you could come across a situation where you are running out or memory due to how memory is allocated for each element of your list.\n\nHowever, you probably won’t write these types of functions because they’re already available to you via the `Enum` module. But it’s still worth understanding what’s going on in case you do.", null, "" ]
[ null, "https://culttt.com/images/philip-brown-891100f1fc06993069399446ff2429e0.jpg", null ]
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https://en.wikiversity.org/wiki/Fundamentals_of_chemistry
[ "# Fundamentals of chemistry\n\nThe Fundamentals of Chemistry is an introduction to the Periodic Table, stoichiometry, chemical states, chemical equilibria, acid & base, oxidation & reduction reactions, chemical kinetics, inorganic nomenclature and chemical bonding.\n\n## Chemical Element\n\n### Definition\n\nChemical Elements are fundamental ingredients of all matter in existence which can be combined in a reaction to create a chemical substance. Each chemical element in the universe has unique properties that distinguish it from all of the other chemical elements. They cannot be chemically interconverted or broken down into simpler substances and are the primary constituents of matter.\n\nChemical elements are usually notated with the symbol\n\nZME\n\nWhere,\n\nE is the element name\nZ the Atomic Number\nM the element's mass\n\nFor example, Hydrogen is notated by\n\n11H\n\n### Periodic Table of Elements\n\nThe periodic table groups the elements by properties. For its history, see Wikipedia's History of the Periodic Table. The Periodic Table is available here: Periodic Table on Wikimedia Commons and explanations will be based on this table. Print or order a hard copy of the periodic table for easy access and reference.\n\nIn the table, each box contains one element and additional information. For hydrogen, the \"1\" in the top corner is the atomic number, which deals with how many protons, or positive charges, are in the atom. The \"H\" is the symbol for Hydrogen. All the elements get a one or two letter symbol (there are a couple of exceptions with undeclared elements). The number at the bottom is the atomic weight or atomic mass. 1.00794 represents how many grams are in each mole (6.022×1023 entities) of hydrogen. The atomic mass is a very important part of chemistry and has many applications throughout.\n\nThe elements are organized in rows and columns. There are eighteen groups (or families or columns) on the periodic table. Each one represents how many electrons are attached to the elements and correlate to how many valence electrons are present.\n\nThe first two groups (1A and 2A) as well as the six on the very right (3A-8A). These are called representative elements. Group 1A are alkali metals (except Hydrogen which is a non-metal) and Group 2A are alkaline earth metals. Group 3A through 8A have mixed properties, but there are specific patterns.\n\nElectrons are negatively charged subatomic particles that \"orbit\" around the nucleus of the element. Valence electrons are electrons that are on the very outside of the atom. There are seven periods (or horizontal rows) that describe electron shells.\n\n## Chemical compounds\n\nChemical compounds are pure substances composed of only one type of molecule (two or more atoms held together in a fixed ratio by Chemical bonds). The chemical compound generally has different properties than those of its constituent elements. The name of a chemical compound is usually identical to the name of the molecule that makes up the compound (such as Carbon Dioxide), but some compounds also have \"common names\" by which the substances are known outside of scientific discussion. For example, sodium bicarbonate is commonly known as \"baking soda.\"\n\n### Atomic Model\n\nAlso see Dalton. The main points of Dalton's atomic theory are:\n\n1. Elements are made of extremely small particles called atoms.\n2. Atoms of a given element are identical in size, mass, and other properties; atoms of different elements differ in size, mass, and other properties.\n3. Atoms cannot be subdivided, created, or destroyed.\n4. Atoms of different elements combine in simple whole-number ratios to form chemical compounds.\n5. In chemical reactions, atoms are combined, separated, or rearranged.\n\n### Examples\n\nFor example, the compound Sodium Chloride (NaCl), is composed of one ion of Chlorine bonded to one ion of Sodium. Sodium, in its natural form, is a solid metal element which is highly reactive and produces a lot of effervescence when reacted with water.\n\n$NaCl$", null, "Chlorine, in its natural form, is a non-metal element which is composed of many diatomic molecules of Cl2, and exists as a pale green gas that is toxic if inhaled in large amounts. The compound Sodium Chloride, however, is none other than the simple table salt applied to foods. The reason for the rise of these new properties lies in the type of bonding and the elements that make up the compound. This will be discussed in more detail in later sections.\n\n$Cl_{2}$", null, "### Chemical Formulas\n\nChemical formula is used to represent a chemical compound . For example\n\n• Water is $H_{2}O$", null, "• Ozone is $O_{3}$", null, "• Salt is $NaCl$", null, "## Chemical bonding\n\n### Chemical bonding\n\nA chemical bond is a lasting attraction between atoms that enables the formation of chemical compounds\n\nFor example, the compound Sodium Chloride (NaCl), is composed of one ion of Chlorine bonded to one ion of Sodium. Sodium, in its natural form, is a solid metal element which is highly reactive and produces a lot of effervescence when reacted with water.\n\n$Na+Cl$", null, "$NaCl$", null, "Chlorine, in its natural form, is a non-metal element which is composed of many diatomic molecules of Cl2, and exists as a pale green gas that is toxic if inhaled in large amounts. The compound Sodium Chloride, however, is none other than the simple table salt applied to foods. The reason for the rise of these new properties lies in the type of bonding and the elements that make up the compound. This will be discussed in more detail in later sections.\n\n$Cl+Cl$", null, "$Cl_{2}$", null, "## Chemical Reactions\n\nA chemical reaction is an interaction between chemical subtances to form new substances. For example, an oxidization of a metal, or a de-oxidization of an oxidized metal.\n\n### Chemical Reactions' Types\n\nThe oxidization of a metal like copper (Cu) to create oxidized copper can be expressed as chemical equation as shown below:\n\n$Cu+O_{2}=CuO_{2}$", null, "Ionization describes the interaction between a metal and an acid to form ionized opposite polarity metals.\n\n$Cu+H_{2}SO_{4}=CuSO_{4}^{-}+2H^{+2}$", null, "## Chemical equations\n\nChemical equations are a way of expressing a chemical reaction. They present chemical species with the chemical symbols of the elements that compose them and subscripts which present the actual number of particles of that element, whether they be atoms or ions, which make up the compound. For example, consider the reaction shown below:\n\n${\\ce {2H_{2}(g)+O_{2}(g)\\rightarrow 2H_{2}O(\\ell )}}$", null, "On the left side of the arrow, you can see two compounds represented. These are the reactants, the chemical species that rearrange to give the product, the chemical species represented on the right side of the arrow. The first reactant, H2, represents a hydrogen molecule. The subscript '2' shows that there are two atoms of Hydrogen that chemically combine to produce the molecule. Therefore, every molecule of H2 contains two Hydrogen atoms chemically-bonded to each other. The same concept applies to the reacting molecule of O2 to the right of it.\n\nThe (g) and (l) or state symbols represent what physical state of chemical species during the reaction. (g) means that the chemical species O2 and H2 both exist as gases before they react, and the subscript (l) means that the chemical species H2O exists as a liquid when it is formed by the reaction.\n\nThe coefficients in front of the molecules like H2O and the H2 represent the simplest whole number ratio the substance amount in the reaction mixture. For example, the above equation shows that every molecule of O2 reacts with two molecules of H2 to form two molecules of H2O.\n\n### Dimensional analysis\n\nAs a more complex example, the concentration of nitrogen oxides (i.e., $\\color {Blue}{\\ce {NO}}_{x}$", null, ") in the flue gas from an industrial furnace can be converted to a mass flow rate expressed in grams per hour (i.e., g/h) of ${\\ce {NO}}_{x}$", null, "by using the following information as shown below:\n\nNOx concentration\n= 10 parts per million by volume = 10 ppmv = 10 volumes/106 volumes\nNOx molar mass\n= 46 kg/kgmol (sometimes also expressed as 46 kg/kmol)\nFlow rate of flue gas\n= 20 cubic meters per minute = 20 m³/min\nThe flue gas exits the furnace at 0 °C temperature and 101.325 kPa absolute pressure.\nThe molar volume of a gas at 0 °C temperature and 101.325 kPa is 22.414 m³/kgmol.\n${\\frac {10\\ {\\cancel {\\mathrm {m} ^{3}\\ {\\ce {NO}}_{x}}}}{10^{6}\\ {\\cancel {\\mathrm {m} ^{3}\\ {\\text{gas}}}}}}\\times {\\frac {20\\ {\\cancel {\\mathrm {m} ^{3}\\ {\\ce {gas}}}}}{1\\ {\\cancel {\\text{minute}}}}}\\times {\\frac {60\\ {\\cancel {\\text{minute}}}}{1{\\text{hour}}}}\\times {\\frac {1\\ {\\cancel {\\mathrm {kg\\cdot mol\\ NO} _{x}}}}{22.414\\ {\\cancel {\\mathrm {m} ^{3}\\ {\\ce {NO}}_{x}}}}}\\times {\\frac {46\\ {\\cancel {\\mathrm {kg} }}{\\ce {NO}}_{x}}{1\\ {\\cancel {\\mathrm {kg\\cdot mol\\ NO} _{x}}}}}\\times {\\frac {1000\\mathrm {g} }{1{\\cancel {\\mathrm {kg} }}}}=24.63\\ {\\frac {\\mathrm {g\\ NO} _{x}}{\\text{hour}}}$", null, "### Stoichiometry\n\nStoichiometry is used to analyze quantitative measurements with relation to reactants and products of a chemical equation. The chemical equation is a symbolic representation of a chemical reaction. The reactants of a chemical equation are justified to the left which gives reference to its definition, the substance used or consumed in a chemical reaction. The products of a chemical equation are justified to the right, and is defined as the substance that is yielded or produced in a chemical reaction. In order to completely understand stoichiometric relationships, one must consider the law of conservation of mass, the law of definite proportions, and the law of multiple proportions. Remember that mass or matter is neither created nor destroyed.\n\nAmong the properties of elements are states. There are 3 fundamental states of an element: solid, liquid, and a gas. They are indicated by subscript with (s), (l), and (g) respectively and assigned with the appropriate compound or element in the chemical equation. A substance dissolved in water is in indicated by (aq). Plasma can also exist, which is an ionized gas with special properties.\n\nStoichiometry allows chemists to quantitatively analyze relative relationships between substances in a chemical equation.\n\n### Balancing chemical equations\n\nEthyne (C2H2) is added to oxygen gas (O2) to yield carbon dioxide (CO2) and water (H2O). This reaction could be written as follows:\n\nUnbalanced equation\n$C_{2}H_{2}(g)+O_{2}(g)\\to CO_{2}(g)+H_{2}O(l)$", null, "However, the above equation is not balanced.\n\n• On the left side there are two Carbon atoms (C), two Hydrogen atoms (H) and two Oxygen atoms in total.\n• On the right there is one Carbon atom, three Oxygen atoms, and two Hydrogen atoms.\n\nNote that in order to properly count up the atoms in an equation, it must be noted to count up atoms with respect to the coefficient and subscripts. Careful notice should be made to compounds and polyatomic ions, since these are grouped together in relation.\n\nIn order to balance the equation correctly, a number, known as a coefficient must be added to the front of each representation in a chemical equation.\n\nCorrectly balanced equation\n$2C_{2}H_{2}(g)+5O_{2}(g)\\to 4CO_{2}(g)+2H_{2}O(l)$", null, "As can be seen, the subscripts were not touched, only whole numbers were added to the front of all the formulas, as needed. The coefficients may be fractions, which are generally used in thermochemistry but for all intents and purposes, whole numbers are generally used.\n\nIt would not be correct to balance it by changing the subscript numbers.\n\nIncorrectly balanced equation\n$C_{2}H_{2}(g)+{\\color {Red}O_{3}(g)}\\ \\to {\\color {Red}C_{2}O_{2}(g)}+H_{2}O(l)$", null, "By changing the subscripts you are changing the chemicals involved in the reaction. In the above, $O_{3}$", null, "is ozone, not normal oxygen, and $C_{2}O_{2}$", null, "is not a stable compound. A small change in the subscripts and makeup of an individual compound yields a whole different set of properties.\n\n## Chemical States\n\nWe usually learn that there are four fundamental states of matter,\n\nHowever, physics research suggests other states of matter as well (like the Bose-Einstein condensate), but this is usually taken as a coarse starting point.\n\nGases are made up of atoms and/or molecules that are freely moving and therefore have no definite shape. They morph uniformly to the shape of the container that they are in. If the container is not sealed, then the gas can move out. Therefore the volume of the gas is reliant on the temperature and/or pressure throughout the gas or environment. This is observed using the ideal gas laws, which are discussed later.\n\nAn important piece of information to know is what an aqueous solution is also. Aqueous solutions are not technically chemical states, but they appear often enough when dealing with stoichiometry and chemistry in general that they should be mentioned.\n\n## Acids and Bases\n\n### pH\n\nThe potential of hydrogen or pH (pronounced /piː.eitʃ/) is a measure of the acidity or alkalinity of a solution, numerically equal to 7 for neutral solutions, increasing pH with rising alkalinity and decreasing pH with more acidity. The pH scale commonly in use ranges from 0 to 14.\n\nAn alkali is sometimes called a \"base\".\n\nRepresentative pH values\nSubstance pH\nBattery acid\n0.5\nGastric acid\n1.5 – 2.0\nLemon juice\n2.4\nCola\n2.5\nVinegar\n2.9\nOrange or apple juice\n3.5\nBeer\n4.5\nAcid Rain\n<5.0\nCoffee\n5.0\nTea or healthy skin\n5.5\nMilk\n6.5\nPure water\n7.0\nHealthy human saliva\n6.5 – 7.4\nBlood\n7.34 – 7.45\nSea water\n8.0\nHand soap\n9.0 – 10.0\nHousehold ammonia\n11.5\nBleach\n12.5\nHousehold lye\n13.5\n\nThe pH is calculated by\n\n$pH=-\\log[H^{+}]$", null, "A similar measure, called the pOH is defined as\n\n$pOH=-\\log[OH^{-}]$", null, "Their sum is\n\n$pH+pOH=14$", null, "### Acids\n\nCharacteristics of acids:\n\n• Aqueous acids can turn blue litmus towards red.\n• React with bases and certain metals to form salts.\n• Arrhenius' definition of acid: Yields hydrogen ions when dissolved in water.\n• The Lewis definition of an acid: Can accept a pair of electrons to form a covalent bond.\n• Brønsted-Lowry acid definition: A species that can lose or \"donate\" a hydrogen ion\n• Can have a sour taste.\n• Can give one or more than one protons (or simply, H+)\n• Electrolytes, yet usually are not ionic compounds\n\n### Bases\n\nCharacteristics of bases:\n\n• Aqueous bases (alkalis) can turn red litmus towards blue.\n• React with acids to form salts.\n• Arrehenius definition of base: produce OH ions when dissolved in water.\n• Lewis definition of Base: can donate a pair of electrons to form a covalent bond with an acid\n• Brønsted-Lowry base definition: A species that can gain or \"accept\" a hydrogen ion\n• Can have a bitter taste.\n• Can accept one or more than one protons (or simpler H+)\n• Conduct electricity\n\nThe difference between bases and alkalis is that alkalis dissolve in water and are considered basic salts of alkaline metals. An example of a base that is not an alkali is ammonia (NH3)." ]
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https://rd.springer.com/article/10.1007%2Fs10623-018-0532-z
[ "Designs, Codes and Cryptography\n\n, Volume 87, Issue 2–3, pp 341–347\n\n# The sextuply shortened binary Golay code is optimal\n\nArticle\nPart of the following topical collections:\n1. Special Issue: Coding and Cryptography\n\n## Abstract\n\nThe maximum size of unrestricted binary three-error-correcting codes has been known up to the length of the binary Golay code, with two exceptions. Specifically, denoting the maximum size of an unrestricted binary code of length n and minimum distance d by A(nd), it has been known that $$64 \\le A(18,8) \\le 68$$ and $$128 \\le A(19,8) \\le 131$$. In the current computer-aided study, it is shown that $$A(18,8)=64$$ and $$A(19,8)=128$$, so an optimal code is obtained even after shortening the extended binary Golay code six times.\n\n## Keywords\n\nClassification Clique Double counting Error-correcting code Golay code\n\n## Mathematics Subject Classification\n\n94B25 94B65 90C27\n\n## References\n\n1. 1.\nAgrell E., Vardy A., Zeger K.: A table of upper bounds for binary codes. IEEE Trans. Inf. Theory 47, 3004–3006 (2001).\n2. 2.\nBest M.R., Brouwer A.E., MacWilliams F.J., Odlyzko A.M., Sloane N.J.A.: Bounds for binary codes of length less than 25. IEEE Trans. Inf. Theory 24, 81–93 (1978).\n3. 3.\n4. 4.\nDelsarte P.: Bounds for unrestricted codes, by linear programming. Philips Res. Rep. 27, 272–289 (1972).\n5. 5.\nDelsarte P., Goethals J.-M.: Unrestricted codes with the Golay parameters are unique. Discret. Math. 12, 211–224 (1975).\n6. 6.\nGijswijt D.C., Mittelmann H.D., Schrijver A.: Semidefinite code bounds based on quadruple distances. IEEE Trans. Inf. Theory 58, 2697–2705 (2012).\n7. 7.\nGolay M.J.E.: Notes on digital coding. Proc. IRE 37, 657 (1949).Google Scholar\n8. 8.\nHamming R.W.: Error detecting and error correcting codes. Bell Syst. Tech. J. 29, 147–160 (1950).\n9. 9.\nJohnson S.M.: On upper bounds for unrestricted binary error-correcting codes. IEEE Trans. Inf. Theory 17, 466–478 (1971).\n10. 10.\nKaski P., Östergård P.R.J.: Classification Algorithms for Codes and Designs. Springer, Berlin (2006).\n11. 11.\nKim H.K., Toan P.T.: Improved semidefinite programming bound on sizes of codes. IEEE Trans. Inf. Theory 59, 7337–7345 (2013).\n12. 12.\nKrotov D.S., Östergård P.R.J., Pottonen O.: On optimal binary one-error-correcting codes of lengths $$2^m-4$$ and $$2^m-3$$. IEEE Trans. Inf. Theory 57, 6771–6779 (2011).\n13. 13.\nMacWilliams F.J., Sloane N.J.A.: The Theory of Error-Correcting Codes. North-Holland, Amsterdam (1977).\n14. 14.\nMcKay B.D.: Isomorph-free exhaustive generation. J. Algorithms 26, 306–324 (1998).\n15. 15.\nMcKay B.D., Piperno A.: Practical graph isomorphism, II. J. Symb. Comput. 60, 94–112 (2014).\n16. 16.\nNiskanen S., Östergård P.R.J.: Cliquer User’s Guide: Version 1.0, Technical report T48, Communications Laboratory, Helsinki University of Technology, Espoo (2003).Google Scholar\n17. 17.\nÖstergård P.R.J.: A fast algorithm for the maximum clique problem. Discret. Appl. Math. 120, 197–207 (2002).\n18. 18.\nÖstergård P.R.J.: On the size of optimal three-error-correcting binary codes of length 16. IEEE Trans. Inf. Theory 57, 6824–6826 (2011).\n19. 19.\nÖstergård P.R.J.: On optimal binary codes with unbalanced coordinates. Appl. Algebra Eng. Commun. Comput. 24, 197–200 (2013).\n20. 20.\nÖstergård P.R.J., Pottonen O.: The perfect binary one-error-correcting codes of length 15. I. Classification. IEEE Trans. Inf. Theory 55, 4657–4660 (2009).\n21. 21.\nPlotkin M.: Binary Codes with Specified Minimum Distance, M.Sc. Thesis [cf. Refs. 25 & 26], Moore School of Electrical Engineering, University of Pennsylvania (1952).Google Scholar\n22. 22.\nPlotkin M.: Binary codes with specified minimum distance. IRE Trans. Inf. Theory 6, 445–450 (1960).\n23. 23.\nSchrijver A.: New code upper bounds from the Terwilliger algebra and semidefinite programming. IEEE Trans. Inf. Theory 51, 2859–2866 (2005).\n24. 24.\nShannon C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948).Google Scholar\n25. 25.\nSnover S.L.: The Uniqueness of the Nordstrom–Robinson and the Golay Binary Codes, Ph.D. Thesis, Department of Mathematics, Michigan State University (1973).Google Scholar\n26. 26.\nvan Pul C.L.M.: On Bounds on Codes, M.Sc. Thesis, Department of Mathematics and Computer Science, Eindhoven University of Technology (1982).Google Scholar", null, "" ]
[ null, "https://rd.springer.com/track/controlled/article/denied/10.1007/s10623-018-0532-z", null ]
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https://cran.opencpu.org/web/packages/ggfortify/vignettes/plot_ts.html
[ "This document explains time series related plotting using `ggplot2` and `{ggfortify}`.\n\n# Plotting ts objects\n\n`{ggfortify}` let `{ggplot2}` know how to interpret `ts` objects. After loading `{ggfortify}`, you can use `ggplot2::autoplot` function for `ts` objects.\n\n``````library(ggfortify)\nautoplot(AirPassengers)\n``````", null, "To change line colour and line type, use `ts.colour` and `ts.linetype` options. Use `help(autoplot.ts)` (or `help(autoplot.*)` for any other objects) to check available options.\n\n``````autoplot(AirPassengers, ts.colour = 'red', ts.linetype = 'dashed')\n``````", null, "Multivariate time series will be drawn with facets.\n\n``````library(vars)\n``````", null, "Specify `facets = FALSE` to draw on single axes.\n\n``````autoplot(Canada, facets = FALSE)\n``````", null, "Also, `autoplot` can handle other time-series-likes. Supported packages are:\n\n• `zoo::zooreg`\n• `xts::xts`\n• `timeSeries::timSeries`\n• `tseries::irts`\n``````library(xts)\nautoplot(as.xts(AirPassengers), ts.colour = 'green')\n\nlibrary(timeSeries)\nautoplot(as.timeSeries(AirPassengers), ts.colour = ('dodgerblue3'))\n``````\n\n# Specifying geometrics\n\nYou can change `{ggplot2}` geometrics specifying by its name. Geometrics currently supported are `line`, `bar`, `ribbon` and `point`.\n\n``````autoplot(AirPassengers, ts.geom = 'bar', fill = 'blue')\n``````", null, "``````autoplot(AirPassengers, ts.geom = 'ribbon', fill = 'green')\n``````", null, "``````autoplot(AirPassengers, ts.geom = 'point', shape = 3)\n``````", null, "As described above, multivariate timeseries can be drawn in a single grid specifying `facets = FALSE`. Time series are not stacked by default. Specifying `stacked = TRUE` allows stacking.\n\n``````mts <- ts(data.frame(a = c(1, 2, 3, 4, 4, 3), b = c(3, 2, 3, 2, 2, 1)), start = 2010)\nautoplot(mts, ts.geom = 'bar', facets = FALSE)\n``````", null, "``````autoplot(mts, ts.geom = 'bar', facets = FALSE, stacked = TRUE)\n``````", null, "``````autoplot(mts, ts.geom = 'ribbon', facets = FALSE)\n``````", null, "``````autoplot(mts, ts.geom = 'ribbon', facets = FALSE, stacked = TRUE)\n``````", null, "# Plotting with forecast package\n\n`{ggfortify}` supports `forecast` object in the `{forecast}` package.\n\n``````library(forecast)\nd.arima <- auto.arima(AirPassengers)\nd.forecast <- forecast(d.arima, level = c(95), h = 50)\nautoplot(d.forecast)\n``````", null, "There are some options to change basic settings.\n\n``````autoplot(d.forecast, ts.colour = 'firebrick1', predict.colour = 'red',\npredict.linetype = 'dashed', conf.int = FALSE)\n``````", null, "# Plotting with vars package\n\n`ggfortify` supports `varpred` object in `vars` package.\n\n``````library(vars)\nd.vselect <- VARselect(Canada, lag.max = 5, type = 'const')\\$selection\nd.var <- VAR(Canada, p = d.vselect, type = 'const')\n``````\n\nAvailable options are the same as `forecast`.\n\n``````autoplot(predict(d.var, n.ahead = 50), ts.colour = 'dodgerblue4',\npredict.colour = 'blue', predict.linetype = 'dashed')\n``````", null, "# Plotting with changepoint package\n\n`{ggfortify}` supports `cpt` object in `{changepoint}` package.\n\n``````library(changepoint)\nautoplot(cpt.meanvar(AirPassengers))\n``````", null, "You can change some options for `cpt`.\n\n``````autoplot(cpt.meanvar(AirPassengers), cpt.colour = 'blue', cpt.linetype = 'solid')\n``````", null, "# Plotting with strucchange package\n\n`ggfortify` supports `breakpoints` object in `strucchange` package. Same plotting options as `changepoint` are available.\n\n``````library(strucchange)\nautoplot(breakpoints(Nile ~ 1), ts.colour = 'blue', ts.linetype = 'dashed',\ncpt.colour = 'dodgerblue3', cpt.linetype = 'solid')\n``````", null, "# Plotting with `{dlm}` package\n\n`autoplot` can draw both original and fitted time series, because `dlm::dlmFilter` contains them.\n\n``````library(dlm)\nform <- function(theta){\ndlmModPoly(order = 1, dV = exp(theta), dW = exp(theta))\n}\n\nmodel <- form(dlmMLE(Nile, parm = c(1, 1), form)\\$par)\nfiltered <- dlmFilter(Nile, model)\n\nautoplot(filtered)\n``````", null, "You can specify some options to change how plot looks, such as `ts.linetype` and `fitted.colour`. Use `help(autoplot.tsmodel)` (or `help(autoplot.*)` for any other objects) to check available options.\n\n``````autoplot(filtered, ts.linetype = 'dashed', fitted.colour = 'blue')\n``````", null, "`{ggfortify}` can plot `dlm::dlmSmooth` instance which returns a `list` instance using using class inference. Note that only smoothed result is plotted because `dlm::dlmSmooth` doesn't contain original `ts`.\n\n``````smoothed <- dlmSmooth(filtered)\nclass(smoothed)\n``````\n``````## \"list\"\n``````\n``````autoplot(smoothed)\n``````", null, "To plot original `ts`, filtered result and smoothed result, you can use`autoplot` as below. When `autoplot` accepts `ggplot` instance via `p` option, continuous plot is drawn on the passed `ggplot`.\n\n``````p <- autoplot(filtered)\nautoplot(smoothed, ts.colour = 'blue', p = p)\n``````", null, "# Plotting with `{KFAS}` package\n\nYou can use `autoplot` in almost the same manner as `{dlm}`. Note that `autoplot` draws smoothed result if it exists in `KFAS::KFS` instance, and `KFAS::KFS` contains smoothed result by default.\n\n``````library(KFAS)\nmodel <- SSModel(\nNile ~ SSMtrend(degree=1, Q=matrix(NA)), H=matrix(NA)\n)\n\nfit <- fitSSM(model=model, inits=c(log(var(Nile)),log(var(Nile))), method=\"BFGS\")\nsmoothed <- KFS(fit\\$model)\nautoplot(smoothed)\n``````", null, "If you want filtered result, specify `smoothing='none'` when calling `KFS`. For details, see `help(KFS)`.\n\n``````filtered <- KFS(fit\\$model, filtering=\"mean\", smoothing='none')\nautoplot(filtered)\n``````", null, "Also, `KFAS::signal` will retrieve specific state from `KFAS::KFS` instance. The result will be a `list` which contains the retrieved state as `ts` in `signal` attribute. `ggfortify` can autoplot it using class inference.\n\n``````trend <- signal(smoothed, states=\"trend\")\nclass(trend)\n``````\n``````## \"list\"\n``````\n\nBecause `signal` is a `ts` instance, you can use `autoplot` and `p` option as the same as `dlm::dlmSmooth` example.\n\n``````p <- autoplot(filtered)\nautoplot(trend, ts.colour = 'blue', p = p)\n``````", null, "# Plotting time series statistics\n\n`{ggfortify}` supports following time series related statistics in `stats` package:\n\n• `stl`, `decomposed.ts`\n• `acf`, `pacf`, `ccf`\n• `spec.ar`, `spec.pgram`\n• `cpgram` (covered by `ggcpgram`)\n``````autoplot(stl(AirPassengers, s.window = 'periodic'), ts.colour = 'blue')\n``````", null, "NOTE With `acf` and `spec.*`, specify `plot = FALSE` to suppress default plotting outputs.\n\n``````autoplot(acf(AirPassengers, plot = FALSE))\n``````", null, "You can pass some options when plotting `acf` via `autoplot`. Built-in `acf` calcurates the confidence interval at plotting time and doesn't hold the result, equivalent options can be passed to `autoplot`. Following example shows to change the value of confidence interval and method (use `ma` assuming the input follows MA model).\n\n``````autoplot(acf(AirPassengers, plot = FALSE), conf.int.fill = '#0000FF', conf.int.value = 0.8, conf.int.type = 'ma')\n``````", null, "``````autoplot(spec.ar(AirPassengers, plot = FALSE))\n``````", null, "`ggcpgram` should output the cumulative periodogram as the same as `cpgram`. Because `cpgram` doesn't have return value, we cannot use `autoplot(cpgram(...))`.\n\n``````ggcpgram(arima.sim(list(ar = c(0.7, -0.5)), n = 50))\n``````", null, "`ggtsdiag` should output the similar diagram as `tsdiag`.\n\n``````library(forecast)\nggtsdiag(auto.arima(AirPassengers))\n``````", null, "`ggfreqplot` is a genelarized `month.plot`. You can pass `freq` if you want, otherwise time-series's frequency will be used.\n\n``````ggfreqplot(AirPassengers)\n``````", null, "``````ggfreqplot(AirPassengers, freq = 4)\n``````", null, "" ]
[ null, 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", null, 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", null, 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", null, 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", null, 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vjbv+1RjnWt9/n8DwMWiwXv5uuGCermlWG54YZhsm5dRHmaj8iiRX1y9tmdavQa0aPYysqYVAbQGuTlGdi6desG2qZX9eY1a9bIrl27BuLalblfvXq1svrK7DOQAAmUDIFzzx0p5503Ur72tSZ1nlQA74gl0xLJFenrE+nqwnOp5GswlEEMnhuwF154QS0zvUmziKklO3PmzFFWfq3MmzdPNm7cKHv37pXZs2erg9nWy6xZs9RwVk30MpAACZQEAYwWOjvDeqXetm1wEFshu3d7fpspCVbxlcBBlfffX6P/XnrJwcLEJx7i/ZIlUfn3f2+UK64YrlN1dYluA7SD5fB3iMwBvOTpT6Tm5mZ5+eWXpbGxUaOAkRo/frxMnz5dJk2aJEuWLJExY8bItGnTlNv+KWpo3C+rVq3S7wPIjiqTAAmkIIBf+xdcMFI/m/rHfzwg99yzL0XK/ETjSJCpU0fp8jFV9t3v7s9PQQlScZzIE09UawP+4ou7ZdgwdyObV16pUj/8h2lpJ554QN0T1fHLWYQtWypl06aIRKMxueOOFjX7FZGzzmoq2eNlPDNgGG395Cc/kSuvvFJuuOEGjX779u3agOHD2LFjZefOnWr+tU8mT56sr5s4/UH9+853vqOnFvH54YcfVp0gYi4F6jWkniiHlWvmUaNGBUrvQisLRjWFeEhS6Irlobyg9KX4xRT4+uJ4jnC4Ki/fBXzPEMxrIvbW1rA+Wfijj2pV+dHEy1l9rqsLD7lgBKPPzk5L9IgRTTJypLti6uutM8w6O0PqWZTFq7Y285Gr4Y9X9JkRI0JSVyfS1ga5ljHFtfr6enW9Lo4dTmBO1tUpLjmVHVOnChs1qkaVZR3ljLYxZdmpMn/X3e1s0D0zYI899piMGzdOYLQ6Ojpk69at+iaOURYCDFc0GnWMM9W54IIL5IwzztAf8XwsVcc06f36inpWqYnm/fsL86vPrxzS6QXj1aXmOPDjh8GZAL4DTU1N6kF7i3MCn8XGYk0DGplm7enpVfq3DsR79SYWg3Ww7rqmrHjZ5ubb09Otym+Lv5T1+66uGtVfa13lb21t1WdkuUnc2Vl9UG5IXnsNC2BCsm+fg0VxI0ylAQ/0mba2SvUeIztbFq51Kivb0nJAXRuaoRPXoVTA97mlpVPd7zGFaR0wasoaKl+6a7inOgXPDNjo0aP1Dfs1Rb+trU0/4zriiCNkxYoVutwtW7bIhAkT1C+CEbJ582Y58sgjBXHHH3/8gF5IbwIMYVBDpVrOg5tyT09PUKtQEL1h5MGIBiw1bvMjLph9CT9MQqp9+/P6XbD6j32DTqTZ3+/dd7Gvz/3zKbRZT4+7H2dYGWhCa2tIPvqoUv3Yd5fX5Et8RflmRWj8NYwpli6tlA8+iKnnYuaK1VbmUzavMHaQCQNs/3aP6cFLrv037wYMz7Twh4DnWqeccop+j2EsFnDg+dj8+fPVL5IKWbhwoaxcuVKPyPBsjIEESIAESGAwAYwgMx0BDZbg/Akyn3qqWrt4wrNKL8M999Spxz+1aoSX+geFl+V5NgKLV+rOO+8c+Dhjxgxl5bv1lJqJnDt3blKcucZXEiABEiCB/BLAni88nwyFchvlJWqJU5nxHFCZ3sRLefmc+VPCLNTAVFFicIpLTMPPJEACJEACJJCKQF5GYKkKYzwJkEDpEcAzFadnLaVXU9bIbwQKMgLzW6WpDwmQgHcEfv/7ajnssPH6j34JvONKSekJ0IClZ8QUJEACaQjU1VnbZfKx6CBN0bxcxgQ4hVjGjc+qly4BHDHy5z9bz54/9ale+dKXBtZLl26l81izLVsqlOcgkT17CrO6Lo9VKSnRNGAl1ZysDAlYBF54ISoLFlhHcUyd2kUDlkHHmDrVckRcURGTBx7YK8cc0ysnnTRaGzBs7XTrIAg+ILu7Y8oTRUx5w7BX5X30UUiWLbNOKYbLp69+VTks9DhAz6OPHqv2YGEPXOkaXRowjzsOxZGAHwjYXiiwkbi4GmGBB1wZIdTWxtQGXXf67NgRFhzPgtDYGFNOD7rVzTj/hzx++GGF2kgclurqfmWALL0x+oIhgFFzG2D0EK69tk2++U3bEwjkf+Mbw/VpxWpbbF4MGMpFHeA+yi1v5AlaoAELWotRXxIIGIEVK6IyceI4rfXy5buVF56DB4alqcfq1VXyn/85QhuNT36yT41amuW3v62V666zPK1nYkzSFOV4GcYll4AzylLpWFUV0/ulzLPDXMoZKq+1z4sjsKEY8RoJkAAJpCRgRoDV1e5HL0YYptja2sLKv2qfcvQd0UeD1NT0H9wsa1Ll5xV6v/NOpXZya+qQn5IoNVsCHIFlS475SCAgBJ55ploOPXScvhG/9NJu5ZPUY/9BrjlkbsCM6G3bKuWcc5qU9wjRU28m3s0rjjd56qlxeipt3bodahrTTS5rb9v11w9X03BwRu4uD1MVlgANWGF5szQSKDgB3HwxnRWJ4Hj6ghfvSYEYAeE04Wg08wrgkM1U9caqwo4O66Hc8OH9Sed3gVlXF45Qyd74DgUAznQvumiEOr3aWuU4VFpeSyZAA5bMhDEkUJIEzMKOkqxcBpWCMTNHlVx77XB57jlrReD11++XmTPbM5CUWdJVqyKyY0eF8theoX9QIHdfX0iefbZGe57P9ZlbZtqURmoasNJoR9aCBEjAJYGtWyvki18cMzCqMqNTl9mzTvaDHzTIq69W6enIxB8T1mKP0l1skTW0NBldLmhNI4WXSYAESCBABLAKEIarkAFTufAAn2plYiF1KZWyaMBKpSVZDxIgARIoMwI0YGXW4KwuCZAACZQKAT4DK5WWZD1III8E9uyBWySrgBEj+qWmBqf6RqW9PSyvvx4purePXKuOVY5vvhmRRx6p0dN8ucpj/sIQoAErDGeWQgIFIXDrrfXS3Fwh//d/3n61zztvpJaJFXw/+lGLnHJKl3z728OltTWk3S3hmVIuAa6m5s1rUMvdrWdE3/9+ay7issq7ZEm1/O//RtUm6cI+G8tKWWbSBLzt5YRKAiRQVAL33VenfeCFw8k+8LCIYNq0kWoRgcgZZ3TK9dfb/vnSKQ0fhDAu2Id11VUjdHIsRsAy8Gz2ZiWWhz1eDzyAHcaW8bj11sIbMPgM7OzEU5XcjHF83TCy27y5QpYvrxpYuh9/ne9zI0ADlhs/5iYBXxKAcXFaZbdtW4U2Ohs3Zv/Vh5NbbO7N1L0S0r/1VqW0tIRk7Nh+5R2kT7uG2rkzrIxuqGRdNv3udzXy9NPVarqVIzuvvyzZ92KvNaE8EihRAuvXVwo8siMcfniv8nJuvS/Gf4wyiuUWqbc3JN/61ghtqM45p0MWLmyVO+6ok4cfrtXGMFODmA2/I48cp8tK3IeVjSy3eVAWnhV6ObJzW3app/OtAavBU+KAhqqqKrWzPqwedAe3DoVAH1EHK4FRrBB3rkJUKEUZ//Ivw/RzIvjxe/HF/fLZz2buDsltX7JvzEP/2q9Q84huZaJa6M8IlvxE2dboSSeI+xeylRmINW6tQqFKXX44XHnwvKrYoBEYDI2VdiBr3JvB5aMct10IU54ImGJNHwaXY9I7VMtccnxF+sH62XJtWXacEZKcz1zJ5NWt3OR0KMXWL5My49OGlO/KKtXW8XGZv3fqS5DiWwPWbZY8ZV7XoufAlx03iCDXoRAQDaNSN2C4EeMZT01NTI3EelW/cHecCNrAfHEz70u4QTvflCC3X63GyETm0G2E6UpIHVze4Js2rtvBlN/fHx2IjE/f0QFZsRSHRybWLfHzgMgc3zjLjdfTTQHJ6W259jU7zsi0r5kYb16d5SaXj9Kc02aih+nzuZ0IjnuFU/CtAesr1jyHE6UM4/DlxBc+yHXIsMpZJTeMhr45ZiXal5l6e2Ny/vnwbh6Tk08+IG5W2hkD5rYvub3hGPZuQZk2gnz3Z0w53xRRpim/vx9pcgtu65xdKanrkJ28Qucqvv64H7rtv5nS8a0By7QiTE8CQSCwd29YT5lt2qSGZQwkQAI5EaABywkfM5NAZgSsZy+Dp9oyk5D/1NgL9fbb1q3hhBO65fOfV2voGUjAhwRowHzYKFSJBIpJ4LHHquXxx3HEh8g117TRgBWzMVj2kARowIbEw4skUB4Etm8Py1e+Mlov98f6KWxaxrOu3/++WrlYqpTt250fopcHHdbSrwRowPzaMtQrrwSefx5+/KypvC9+8YA0NeW+mCCvCnssHPWfNGmMWlCCpf279ZL1/ftDyoBZq/9Mce++Wynr10fUR2/5YEsBDpUM8GJjg4ivRSRAA1ZE+Cy6eAS+9a3hght2T09IOXDdowxYMJ7zwBeh2RT9i1/UCbxY/PWvFbJuXUQbI2v5eXqu8KTR1WWlG2rBrzd7kZL1Wby4VhYvxuYgfz8PTNacMX4iQAPmp9agLnkl0Nwclvfes6bCurtD2u9dXV3mm4rdKLlnT0g2bLC+XjA6XoXnnovKf/zHSC0uEolpAwy3UXheBWMcrMMSzWiPRsyr/lFucmjAyq3Fy7i+L7wQVa6MhmtntkONOrxAtGJFVGbOHKFHRV5Pk8HowjWR2f+EfWUcyXjRapQRNAKWf5igaU19SSBLAtForGDnPVVXx7QHDidVX3utSvlFHCdHHDFW3nkns9+Rubv3cdKIcSQQPAKZfXOCVz9qTAK+JIDnWPDLB8e+5pmWLxWlUiTgYwIcgfm4caha6ROw3DKVfj1ZQxLIBwEasHxQpUwSIAESIIG8E+AUYt4Rs4ChCGBV4BNPWAdkjRvXJ+ed1zlU8qyuXXPNcFm7FgcphvV+p6yEFCETlrn/7Gd10tAgasl/vVx5pfsTlIugLoskgYIToAErOHIWGE8AS81/8IN6/RwIhz3mw4C99lpELZ+PqKM5sNx88IZcrOTDPqphw2JSVxeTCRP842QXR7DcdluDXsnY29sgl1+eHwOGAzfNpu74tuF7EvA7ARowv7dQGehXVRVTHhnsZeGZVhnH1J9zTpMeXWGV4XHH9eh9UZdc0i4nnmifQ5RovFAOFlDglGAcN3TssT3KB+Ae18UvWFCvRnYRXdbMmW0yeXKPPPlktXz0UUjWrIkod0yuRQ2Z0CzyeP/9Ctm9O3tOqQo566wmfcksy0+VjvEk4DcCNGB+a5Ey0Wf58qhaYl4pf/pTxcB+pmyrjtV82GtleZeIyXPPVevR1vHHH5CjjurVq/1SycaSdBhQ5DWGIlXaxPhnnqlWXtsjAqN55pmd2oB997sN0txcoWVhxJcuwGjA4O3ZE1YrEq1RIEZDGH21tAze4HvSSaM1K7h/8jJkdkqxlyVTFgnkRsDjr0JuyjB3+RC44YZhyg0STq2WFKfuZsbi4In3A5kw2po3b7j6G+aJ/AHBDm/g/eLhh2sEe7taW8N6n1l1tbvhF4zmuedaI6Bzz+1U06kt8r3vNcjdd9cmlQQHu1ZIbxiTMjOCBEqQAA1YCTZqEKqE6bXOTuyDwo3e3Ji91dxsWjanCXsr3ZaGurz0UlSWLw8lPWOzU6V+B+ML42S8g1hTeflhkloLXiGB4BGgAQtemwVWY0yNvfee1eXgi7CUAqb1jAEqpXqxLiTgZwI0YH5unRLTDUvZ//Vfm/SUXk8wnL+XWAuwOiRQWgRowEqrPX1fm5qamHZE67QisNjK79sX1s+yoMepp3bppfXF1onlkwAJpCbgqSeOPjWH8tZbb6kH2a0DJfaqp9Rr1qyRXbt2DcS1t7fL6tWr1Y2sfSCOb0jgww8r5HOfG6NW842RV1+tKjiQrVsr5Prrhwk2PuOcLQYSIAF/E/DsWwrj9c1vflPeffddufXWW+X1119XS35jMmfOHLVXZq1aDTZPNm7cKHv37pXZs2erU17Xy6xZs9SKLXU0KwMJKAI4zworE3FSr9uDGb0G19kZ1huHE+Vi2f/NNzfov6efjiZe9uQzjOby5VXy4YeefS090YtCSMCvBDybQmxubpazzz5bpkyZIhMnTpSlS5dKfX29jB8/XqZPn66OL58kS5YskTFjxsi0adN0un61fGvVqlX6PQA98cQTyivCXzWrCy64QG0utQ4f9Cu8VHpVqif60B3193uwDl60bpjbtoXkkEOsJdp/8zd9Mny4t9rX1lZIKOkskJCKG1xORQVWJ1YrfjjKPn2oqQk7yk3MmVw2UiSXD33q6ur0Csl/+qda9SNL1AbikOzYAUVDctFF3WrjNDYwG0OTLMNJrvNqS7vyL78clTfeiKqZiUTNnT/b3GwZTnHOubOLdcsQ0p3Tpi4Xuidupjb1Ma9ODFFOYr7UpVhXbHn252QZNtd4eYl54685vUf6wbJtubYsO87ISM5nrmTy6lZucjqUYuvnrszk9Oa7nNu9PFVf8syAjR07VvCHkdhDDz0kp59+umzfvl0bMFQd13bu3KmvT548WdMwcQZNW1ub+vW9T3/EzcG+QZgUwXiF3gAeBP2XL6+QSy+N6lEHVtFhNR32Zj3+eJf8wz+428vktlVSdcLE/PgSWO3vvN/pJz+p1Cv+PvggJKNHx7RxyXQTcmKZiZ+haygUVjMJ5otn62LaNvnLmigl889YqQkP9YNveJnLYY7gEvCyX3kpK1ui5vuSbX7kS7UVxjMDhkLwvOumm25SzzAmywknnCAvvvii2t9i3QRh2KLRqL4xJcYhL8L5559vvVH/YfyCGmpqavQIIv5ZoF/r0tVVrdqlSi+swMm+2JuFE387OjrUs0zbDZNb/X/2s1p55ZWo/uV24YUdcvLJ9hRxe3tEdURMv8X/2sPNOv4z9kTFZPPmLnnzzW7Boo+xY/vlrrtq5b//W3m1VQE6wssFphwxSEcXS/6iwuAMlmt/CeLjjbGw42A88GOqtRV+EetQ5KDQrSw82ravD8/pYOCS62DFIZstF3FWSIyL/4wv68FkaV7sdHZdneLSiMnoMhharON1TmYIoRbv+HRDF2XrbqczcebViWGm5Vi62WU4fbaumoYYXAdbl8EyUn1KTj90e5n05jWVXDfxVnu50d/WKV5upjokp8c9pVN9X3Jz0p1qNsvMgcTrnNV7GKW5c+fKSSedpNzqnKllHHrooepGtFm/37Jli3KUOkGc4rIqkJl8SWDFiqgsW1YtL7wQVc88zeglM1Uxmrr22uHy938/Wq64YoTODNdK2DvW0WF1WfhONEbL9lCRWTnZpt68uUIee6w6ydVTtvKYjwRIIDsCno3Ali1bpn4xv6l/uS5evFg5Rj1Wvv71r8uoUaP0Ag48I5s/f75+NrRw4UJZuXKlHpHh2RiDvwhgRAMP7m1tIWls7JcvfCHzTVtwr2QCjgWBkTlwYPAvQXPd6bVKDW6QPn5q0Bgsp/SFjFuxolr+8peqoi00KWRdWRYJ+JmAZwbslFNOEfwlhhkzZqhfzt3KYaq9LBojtcS4xHz8XDwCMGC3324d4zFxYo9akJPeQ/vLL1vtu3evNULCVMKyZVG16rRCHn20Wq2ss7oa3Dv5PUD3P/85onROPYK0RoL+r4vfWVM/EsiFgGcGbCgl4o2XSecUZ67xNXcCmOa6+uoRegQzenS/3HOPtTjGSP6v/xqmRswR5Qnd+ZBHPA+Dh3bjqdzkS/UKh7R4dmbOlYIRwKq6P/7Reh6GfPB7mPi8K5W8YsZjMcusWSPU89piasGySYAE0hEoiAFLpwSve08AhgQGCs+N6uuthTTYW4UzpRCw0GLjxkq9GMIrrxjt7bjj26MSLLDo7cW0oR2HsjMJWLCBAxebmwtrTWC8Ss1fYybcmZYEgkCABsxHrTR/fr12drt/v/WsqKEhJjil+IYbsjuJ13oOZT93ev31iPzbv43US+XNaqFcjNedd9bp05SNrHygxHlbU6eOUsbEWuKfjzIokwRIIJgEaMB81G5PPlmjVm1WKgNjjVgwejnssOwNmFPVamtjanEGvE1kPyqCqyVMs61bF9FThvlcBQjjiJFQJBKM6Ucn5owjARLIDwEasPxwzUmqMS7W9FtOonRmLEE//vjRB1f12SMyN5Lh3ug737H2X82c2a48qfTLgw+awxZjfE7kBiLTkAAJ5IUADVhesHonFNOJCxZYLqm+9rVO+fSnsbk2s4BRzNatGNn1642/meTGUfd33VWvR2znndepDZjJ75dl7UYfvpIACZQXARown7d3S0tYP2eCi6djjunJyoCZKuayqg7lM5AACZCAnwjwtlTk1sAmX7PfKH7TbrxaWHoejVorCePjnd6vW1ep/PdVybZt7pfAO8lhHAmQAAn4nQANmEMLrV4dkQsvbNTeI4wvMYxeFiz4SE47zfbt55A14yissjvjjCY1RWf59MtYwMEMeFaFqcKnnqqWO+6o1++xIdmrAFnPPRfVCze8kkk5JEACJJALARowB3pY9Yb9R9bZUDG9l6mmpl/HOSRPG6V8WR7cDyVqJBVTXkkGZ4HDWnh2yGVJ+wknjFHObUVvPK6qshzd5iJvsIaWcV20qGHA/2DidX4mARIggUITKOzu0ELXLofyEp8XJX7ORPQ3vjFCjjxynP77xS+SvZtnIis+7aOP1iiHt6PkS18arfdJwWtGvA/C+LRevIdB5OZeL0hSBgmQgBcEOAKLo/jkk9WyYUOlWrFXoY/oiLuk9z0tWNAgP/95ndqb1adeP4q/POR782wLR4D88Y9V2ghgqfwVV7g8uTCF9N27w3p1IY4VwRlSDCRAAiRQTgTKyoC1tITksssa9VQbptl+85t9g6bEHnywRh8DguXhcIOUGLZtq1AnRleqgzkzX8puZL30UlT7CMSILhMDhunBSy5p1PqeeOIBtRfL8m3IpeyGLF9JgATKjUBZGTBMf2EEZBzKTpw4Vrf31Ve3aWOCRRDwKlFVhdUPyRt+M51G/OCDsJYHH4QmQAam+rJ5PgWjijrs38+ZX8OTryRAAuVLoKwMGJoZIxYYKgQ4n8XUW7yBsa5483/q1NHK2Aw+08pIxrTiZz87Rk9NuvX4ztGWocdXEiABElD+UcsBAvz2wRks9lw5hbvvrpPf/a5GHy3idD0xDrKwpBzh7/6uR5qa+rVsYxirq60cKBfuoDDaclrSvmdPxcHRnpWe/0mABEiABNwTKAsD9vjj1TJzZuNBKsmLHVpbQ8p4Vbqe1mttDcvll49Qz9JCcu+9e+XLX+6W444bK/v2WVN7//M/HXL00c7nbCU2TfyIMPEaP5MACZAACaQmUNIG7P77a5VhCqnNt5Vi7eNyfnaE51JOI6TU2ETvETPnbCGdvdKwXx2GWKM3JmPhRT7CW29F5JBDxmnRkUg+SqBMEiABEvA/gZI2YIsW1Quc0WJqD0vYCxXgVQMe4PO1J8uaqrSOGHFabFKoerIcEiABEigmgZI2YLjR4xmUWz+CmTYERm0vvhhVy+orBkZgmcpgehIgARIggewI+NaAhQKw5A4G7Je/rNN7xryfLrSX3mfXtJnkKmRZmeiVj7TlVFfv+QXga+lxpdlfcgWKe3m+7ue+NWC1tebQxOzx2dAAMBM5yekhy6wytCVhGg/Pw+x9XXaZdqqh3qWSa/LYeidXID4v0iXqZ+cdkGbexL0m1xXTkiavkWt9TtYhTlDCW6e0znGmLFuAczr7uvUO+ZLzmlTOMpLT23UdyOmU1Vx0fE2WEc/QzuIkODkOOia2pS3DfmfXxZbhFGfn8OKdXZYtzan+aBuntHauxHdO9U4WkSwz/nuQKDPV50S5TmWjDZ1CYl6nNPFxybJtubYsO87kTc5nrmTy6lZucjqUYuvnrszk9NhXWyW1tc7y3UlN3Zd8a8Da23NzswQwsZjxOxg7eENwCxHpB6eFV/rkDp0s1zld6mZyTm+e18UbzeTy4/M63fCS45JlKEoObOw4I8N6dcqfqm5OaZ3jEllDJyfWiXHQyeiXrIWzDCt9fNvadTUyUss0KRJfISNeJq4ny7XqhWvxaZP1dFu+nc6W4RSHEr0K6HPWTWpwHaxy4+PQNrZebsq3dbdTJ8dBJoJdVqblIHei3MTPSOPcXsl5rbSp/yfLtrnY1+w4I8m+ZmKyeXUrNzkdSstUh+T0MXUS/AG151Z5NM8h1Ndbh/ominBelpeYip9JgARIgARIwGcEaMB81iBUhwRIgARIwB0BGjB3nJiKBEiABEjAZwRowHzWIFSHBEiABEjAHQEaMHecmIoESIAESMBnBGjAfNYgVIcESIAESMAdARowd5yYigRIgARIwGcEaMB81iBUhwRIgARIwB0BGjB3nJiKBEiABEjAZwRowHzWIFSHBEiABEjAHQEaMHecmIoESIAESMBnBGjAfNYgVIcESIAESMAdARowd5yYigRIgARIwGcEaMB81iBUhwRIgARIwB0BGjB3nJiKBEiABEjAZwRowHzWIFSHBEiABEjAHQEaMHecmIoESIAESMBnBGjAfNYgVIcESIAESMAdARowd5yYigRIgARIwGcEaMB81iBUhwRIgARIwB0BGjB3nJiKBEiABEjAZwSKYsDa29tl9erVglcGEiABEiABEsiGQMEN2N69e2X27Nmyfv16mTVrlhw4cCAbvZmHBEiABEigzAlUFrr+zzzzjEybNk2mTJki/f39smrVKv0eemzfvl06Ojq0Sg0NDRIKhXJSz2RXxUg47CwrFhPBX3yIxZLTIi4xHeQm6oh0iHcKTmUhXaLcVOUnynROlyzP5EuVXtXCJNGvTulwIVX8oMwDH5x4DS4HSS2GA5n0m0QeiETZTvEmZ+K1/v6Qahtz1Xq10gyORLqww8+4RHmDJQ3+hLSJ6Z3LT65DZkzj62HrkFg2rjjJddLTlpJch1TpUbf44PT9svIOThefx+37xLpZ361EuYmfLemJedOVmZjeqc9Z9Uonaejr4OdUllOuxHROaYaKc+oHTvcnp/46lFyna066Qm5FRYVUVubH1ORHqlPtDsbBSE2ePFl/Gjt2rOzcuXMg9cKFC+XVV1/Vn5ctWyaRSGTgWjZvPvaxsHR2xqSvz7qZRSIxNeITqamJSXe3SFWVdQPt6xNVVkzfTMNhpLe+zEhnBesaGqi6Oia9vaIaBHHWFycateNwTbWXTof0oVBMurpQVkzfUCEbea3yLRlIh7KMXKTBzRdy8RpTCXAN71G+0QlxuPkanSsqrHQoH+X19AzWw9QLMmy5Fpt4ufHlQwbYxNcLDKNR0fLRL1FHwxN1hd6oE74oqKuRgc+IT8VwKJ0gA/VCXQ0no1N/vxVnyoU+qANCrnVFveLrqmqgy0fX7Ow0fK0bEspCuWiHeIaJ+iIdOEDP+HoZTigTzBGQt6oqpPptTPdXfAbH2lrnNnSSm1g+6hBfL9QFMqEzyjV6QBbi0IaoK/qo6fPxbWi+X+Bv8qJMtJeVx2o3U1dTb3z/0JfQVxFMWaYvpaor+jzkOtU1sXzUFXEoy8iNr6thE19X/IYG3/g2tPLa34NEhub+DHngadVLV2ugXtb3xr6XoAzogj4NHRES40yfN7rjNb5v4L25Fl8v6G76IeoGXlawy0cbGp1Me6EN09UV9YPM+LriOxLP0MhFW40eXScjR9YeLD+7l1QzdQU3YGFVI4y8EPoU5ShqfjAsWrTIvNWjsYEPWb5ZsiTLjDlmq6mpUTfOatm3b1+Okko7e11dnR5xw0AzOBPACH/cuHHq+7DDOQFjNQHcV/CdMzM4xOJMoLGxUf0Y6VQ/qtUvzQKGXbtyK6y+vt5RgMPkiWM6zyIPPfRQ2bx5s5a3ZcsWmTBhgmeyKYgESIAESKB8CBR8BDZ16lTBVOHKlSv16GvSpEnlQ5s1JQESIAES8IxAwQ1YbW2tzJ07V82zdqt5aTUxzUACJEACJEACWRAo+BSi0ZHGy5DgKwmQAAmQQDYEimbAslGWeUiABEiABEjAEKABMyT4SgIkQAIkECgCIbWE2ZdrmPfv3x8okPHKvvvuu/L+++/LySefHB/N9wkEsETcp90vQdPifexRG4Tuvfdeufjii9W+MWvfYfG08XfJ7E/p2+fZZ5+VT3/60/KpT30qfWIfpcAjp/gtV0a1gi/iMAWne4UnjqAGbBNYvny5nHnmmUGtAvX2CYG2tja588475corr1QbU9XOYAYSyIHA008/re9Lxx57bA5S/JOVU4j+aQtqQgIkQAIkkAEB304hZlAH3yXdvXu3tLS06KG675SjQoEiAG81OLnhC1/4QqD0prL+JAAn6qNHj1aunUb6U8EMtaIByxAYk5MACZAACfiDAKcQs2wHOJc0LrEgAosR3nrrLWltbR2QiMUcb7/9tv4zPtp4FtoAHr6JI/DOO+/EfRLZtGmTbNu2bSDuww8/HOhLGOEjOMUNZOCbsiUAB+k4tsqE5uZmfXxV/IIp3L/Wrl2r/dGadO+9957gL0ih4rsqBElhP+gKY/S9731PcCOBZ304J7766qu1M9Hf/va3euoQHkeuueYare7WrVsFPiCxouzb3/628jhdK7/85S/lK1/5ivL07dt1NH5AXfI64KbywAMPyEMPPSRnnHGGru/tt9+uV7H+6U9/EqzGPeKII+TGG2/UTljRl+AE+ZBDDnGMK3lgrOCQBPCj5tprr9V95uMf/7hg1eEjjzyinffi/YknnihvvPGG3H333fpH93333SennHKK3HXXXbJhwwZ55ZVXtEPkiRMnDlmOXy7y7plFS/z0pz+VT37yk9ogITs6xNFHHy3nnXeeYHXPH/7wBzn11FN1Zzn33HMFHqARHnzwwZRnoekE/Fd2BBYvXqyXB5sl8vAUjpH9j3/8Y3VcRa9cddVVAv+hCOeff742XliNaE50iI8rO3is8CACcM+HfvOZz3xmIP6pp57SP7bxowcHCOPZ/G9+8xvtzg/e+4855hjdz3APw33N9LnTTz99QIaf39CAZdE6+IXzl7/8ZeDssvHjx+tfzHjgjqkf/ArCrxlMKWIZNKaCbrnlFn1ETKqz0LJQg1lKgMBZZ52la/H888/rV9xUMFLHKB/TQOhHH3zwge5Tv/71r/UUz+WXXy7YZoJ+Fh8Xf+MqATSsQoYEsFfq+9//vu4TJuuoUaN0/znssMN0f0GfwTFPCxYsUGeBHdAjtTFjxsjw4cN1FswI4T4WlEAD5kFLYToHhun666+Xo446St9cTjvtND0KQ6fCEB43qKHOQvNADYooAQIYiV1yySVy880369ViMEqYCsJmZkw94xkFfkHPmTMnKY4GrAQ6gMdVuOiii+RXv/qVNkowYjhXC8/p8eQI/eq6667T09RmRI/ig/RYg4s4POgwGJbjdOnbbrtND98xvYi55HXr1mnpODwOv5h5FpoHsMtABB7C33rrrTJjxgx1km6N/gW9dOnSQX0Jo7LEuDJAwypmSAA/eDBjNG/ePP0MFbNFMGTmtHtzqDDuYQhYZIY+F5TAEZgHLYXhNzxvrFixQv+awUINTB3i3DM8OMX8M56FobPwLDQPgJe4CEzxYCoIv5QvvfRS+cQnPqFHW1jVilH89OnT9SIOjMri40ocC6uXBQGMuObPn68XmGFWCIYLfQp9B8YKxgw/vnF/wjFX6HuXXXZZFiUVJwv3gXnIHXPKif66nM49c4rzUA2KKgEC7Esl0Ig+qQIWZiAkTg0m9jGkww8k/AUl0IAFpaWoJwmQAAmQwCACwTG1g9TmBxIgARIggXInQANW7j2A9ScBEiCBgBKgAQtow1FtEiABEih3AjRg5d4DWH8SIAESCCgBGrCANhzVJgESIIFyJ0ADVu49gPUnARIggYASoAELaMNRbRIgARIodwI0YOXeA1h/EiABEggoARqwgDYc1SYBEiCBcidAA1buPYD1JwESIIGAEqABC2jDUW0SIAESKHcCNGDl3gNYfxIgARIIKAEasIA2HNUmARIggXInQANW7j2A9ScBEiCBgBKgAQtow1FtEiABEih3AjRg5d4DWH8SIAESCCgBGrCANhzVJgESIIFyJ0ADVu49gPUnARIggYAS+H8oKwk3/3P6+QAAAABJRU5ErkJggg==", null, 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", null, 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", null, 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sFnY2Oj0TacyvXg9KutMmsMS52GI3KrTxW9x6PMegFjMg6/xqO8IyEmU1hexGHtAH7Fke0hUVFRke2qGdc766yz7LMvXekrX/mK6HtbF1xwgb1NU1OTndyeeeYZKSoqEk1mP/jBD+yzoIyVfvigJpmf/exn1mvWErvu8ePHd202a9Ys+6wu1zY0gWry078zzzxTXnzxRVm4cGFX/YMtcAlxMCEeRwABBEIuMHLkyK4eFhcXS/qZXTweF31cr9Bo0SSmj2vSyaYMGzZMkh++0I/FYqJ/TnHaHawNZ3vdTs8YneJsr//v3W9nnUy3JLBMOjyGAAIIREBAL8MdOXLETjSbNm2yL8U53dazvM9+9rPyhz/8wb5L3/vScuKJJ9pJTa+KZSpf/vKX5Te/+Y192fCPf/yjHDx4sM/qmdrQJKXvj2nZvXu31NfXdyVY7ZMmN+279kvPwoZSSGBD0WJdBBBAIIQCEydOtJ/8P/WpT8nUqVPtD12kd3P58uX2+19z5syRK6+80n6PTB/XS4/f//735be//W366j2W9cMf//rXv+STn/yk/PnPfxa9hKjvifUuA7Wh74vp+23a1o033iinnHJK16Z6Fqh9njlzpv1hjylTpnQ9ls2C7++BZdMp1kEAAQQQyF7g4x//uGzYsMG+PDd8+HB7Q/3EoP5pOf3000XPzPQ9wPQPsuj7Yfqek76/NVDZuXOn3H333fanEDXh6AdGxo0bZ7/H5rzPlqkN/ZSinoEdPnxYRo0a1aMZ/XTj1VdfbbefqQ89Nkr7DwksDYNFBBBAIKoC+j6Uk7wGiiE9eTnr6Cdz//KXv9hnSc59zq2+3zVv3jy56KKL5LzzzhP9yP53vvMd5+F+b/trQ+vpnbycjfU9tlwLCSxXObZDAAEEQiCglwWnT5/uqid6ljTQp//0e1r66UA9E7v++utl0qRJrtpyNtbvgOkHN9wUd1u7aZltEUAAAQRcC+gZT39nPUOpuLq6WvRvoKLveX3+858f6OGc7p8wYUJO26VvxIc40jVYRgABBBCIjAAJLDJDRUcRQAABBNIFuISYrsEyAgggEDGBjr1vS6ql2VWv4x+dLkXl3vwqiKuODHFjEtgQwVgdAQQQCJNA60MPSqrW+nKx9QsbuZSY9UXmipX/I3L88blsHug2JLBA+WkcAQQQcClg/TBuzMXvy6YM/xC0y+gybp5bys5YJQ8igAACCCBgXoAEZt6YFhBAAAEEDAiQwAygUiUCCCCAgHkBEph5Y1pAAAEEEDAg4GkC05/Ff+211+To0aMGukqVCCCAAAIIdAt4lsA0eS1btkzefPNN+2fxX3755e5WWEIAAQQQiJxAbW2tPXuzTqny5JNPhq7/nn2Mvq6uzv4xyLlz59o/LKkTn82ePdsO+IUXXpAdO3bYyzo52vEh+r5ByppS3mTRX3ru8Ttlr9aIWF889LokrBlWX2pqli3nfM7rqkWsCVg/N3qUzBl1XHfdGaZf6F4pXEs6j2yf8Sjx7BDoE2yHNSara+v63O/6DiuQedWjZfZx3bPwisGPQic6zcUxf0y1zBo5opvEYBy6H/c4FrtbDWwpfebkwDqRoeGf//zncvHFF9tzjOmkmPr8nT4jc4ZNfXnIs6NXJznTPz0TW79+fdc8NBqFBqzTWFMsgb+9LvL8Ns8pdDafM6y/RVM+4nndOnIjrF+N7pHAPG8l/ypMWgnswX/u9zywuFXjeGt6+B4JzPNWuivsSHWaicNKKJOsF3g9Elh3syyFQEDn67r//vuloaHBfmsoYX3frNTa98JSPEtgGlBHR4esWLHCPvPSn/h3ytlnny36p0XP1HRStbCUJivhmixtbW094i1rt3YAkw0aqFsTWJ84dEc20JbJKlP9xtFhLA494zNR4tYLwt7jUd6RkIGnJDTRC/d1FlunRL7GYe0AYXruUcGKinD/fNMVV1xhn5BMmzZNPvaxj9nP8XmZwDqtb4PffPPNMn/+fDn33HPd793UgAACCCAQqIBOobJq1SrRS5068eQHH3wgU6ZMCbRP6Y17dga2efNm2bVrl+iU0xs3bhSdBG3x4sXpbbGMAAIIIBAhgTvvvFNaWlrsM8UwvfflEHqWwPTMS/8oCCCAAAL5I6BnXmEt+vYGBQEEEEAAgcgJkMAiN2R0GAEEEPBQoNPsB9k87Gmfqjy7hNinZu5AAAEEEDAuUPGf10uqvS33dqxPtcZGj859+wC3JIEFiE/TCCCAgFuBC/a8LbuOuvtq0rbPnSPT3XYkgO25hBgAOk0igAACXgm0JTtdVVVqnYFFtZDAojpy9BsBBBDwQCDC+UtIYB7sAFSBAAIIIOC/AAnMf3NaRAABBBDwQIAE5gEiVSCAAAL5KPDKK6/Yv1cZ1thIYGEdGfqFAAIIBCxw++23279EH3A3BmyeBDYgDQ8ggAACCGzdulWWLFki27dvDx0GCSx0Q0KHEEAAgfAIvPHGG3LjjTfKd7/7XTl06FB4Omb1hAQWquGgMwgggEC4BK666iqZPHmyPaejJrMwFRJYmEaDviCAAAIhE6itrbV7tH//fpk5c2aoesdPSYVqOOgMAgggEC6B++67z/4k4tlnny2VlZWh6pzvCUynoy4vLw8NQirRYbQvZWVlUlVV1d1GadQmfre6bn1Vv08cJdGLQ38wp28cvh8C3ftCjkv9xlEcwfHob78yGYcF1+NYzNHfy810puMwl8cee8zuXiKRkJIQHvO+H73t7e2iGGEpTUmzUwm0tbVJY2P3D22WtSekNCzBZ9sP6yDrE4c1hlGLQ58q+sbRkRdxlHdYTzDZjmdI1tMn797jYTQOawdIPxbDwFBRURGGbgzahzAmL+0074ENOnSsgAACCCAQRgESWBhHhT4hgAACPgmE/CpmRgXfLyFm7A0PIoAAAggMSWB0WYmUH8v9XCRuvRdZJNGcUoUENqRdhZURQACBcAlsmHNWuDrkY29IYD5i0xQCCCDgtUAsyhN6ucTI/bzTZcNsjgACCCCAgBsBEpgbPbZFAAEEEAhMgAQWGD0NI4AAAgi4ESCBudFjWwQQQACBwARIYIHR0zACCCCAgBsBEpgbPbZFAAEEEAhMgAQWGD0NI4AAAgi4ESCBudFjWwQQQACBwARIYIHR0zACCCCAgBsBEpgbPbZFAAEEEAhMwPMEpvP7vPPOO4EFRMMIIIAAAoUh4GkCa2lpkZ/85Cfypz/9qTD0iBIBBBBAIDABTxPYmjVrZOrUqYEFQ8MIIIAAAoUj4Omv0d9www1SU1MjL774Yg/BtWvXyrp16+z7Vq5cKTNnzuzxeDb/SWx6SpLPPpPNqkNaZ7ikZEuiQ8698OIhbZfNyjrDTmVlpYwdO7Zr9faKcuns+l9EFqxfu+4TR3n04uh3PMrLIjce+uvjlcOH99yvyiI4HlYcw/vEYW48mpNJOX/33zw/6GLWXFr/fcpH5aKJE4Zct77lQsldwNMENlA3zjvvPJkxY4b9cHV1tRw+fHigVQe8v7jufSk+MvTtBqzwwwf0Se3EwVbK8fGUtV1ra2uPeIvb2sUX9Bz73P9mqb5xtEcvDh2PY73Hoz0RvfGwptA9duxYj/2qJNEu8f4HL8T3ahwtveJIGItDx7/WOv68LqVWIq49elQOWy9Oh1pKSkqGugnrpwn48lw6fvx40T8tdXV1kkgk0rqQ3WJRMnLnLXZgSetVX3q8RZ0RjMM68vMiDmtEOvNgPPSJuHcccWu/iloC06nsk9ZxnX58RDGOIutVcO84sntWEyku9uUpONvuRG49T98Di1z0dBgBBBBAILICnqd/fX8rl/e4IitIxxFAAAEEAhHgDCwQdhpFAAEEEHArQAJzK8j2CCCAAAKBCJDAAmGnUQQQQAABtwIkMLeCbI8AAgggEIgACSwQdhpFAAEEEHArQAJzK8j2CCCAAAKBCJDAAmGnUQQQQAABtwIkMLeCbI8AAgggEIgACSwQdhpFAAEEEHArQAJzK8j2CCCAAAKBCJDAAmGnUQQQQAABtwIkMLeCbI8AAgggEIgACSwQdhpFAAEEEHArQAJzK8j2CCCAAAKBCJDAAmGnUQQQQAABtwKezwc2WIeKiookl2m0i+LRzLXxeLxHvBp/5Io142xexGHBF+XBeFjDkR9x2PtVz+eDSB4fErOOj55xZHuMx2I6mpRcBXxPYDpgueykUR3o3vESR667qjfb9R0Pb+r1u5ai3sdRRJ8IY7GinJ4P/PYerL18iWOwOMP2uO8JLJlMSiKRGLJDWUdS4kPeKvgNOjo6pK2trasjZVb8kYsjJZIXcVijkOwzHp2RGw9rOPqMR3kE96tUP/tVeWf0xkMk1Wc8ug74QRYqKioGWYOHMwlE8HpWpnB4DAEEEECgUARIYIUy0sSJAAII5JkACSzPBpRwEEAAgUIRIIEVykgTJwIIIJBnAiSwPBtQwkEAAQQKRYAEVigjTZwIIIBAngmQwPJsQAkHAQQQKBQBElihjDRxIoAAAnkmQALLswElHAQQQKBQBEhghTLSxIkAAgjkmQAJLM8GlHAQQACBQhEggRXKSBMnAgggkGcCJLA8G1DCQQABBApFgARWKCNNnAgggECeCZDA8mxACQcBBBAoFAFPE5jOGfXqq6/K+++/Xyh+xIkAAgggEJCAZwksZc1O96Mf/Uh2794tt956q+zduzegkGgWAQQQQKAQBDybkXnPnj0yceJEWbRokcycOVM2bdok1113nW3Y0NAghw4dspd1BtKSkpIh2xYVeZZrh9y2mw2038XF3cxFRTE31QWybcrqcrx3HNZU8FErKWvm3LwYDwu+dxw6pX3Uis4snRdxWIH0jiNqYxHV/nY/s7qM4MCBA3YC02rGjx8vtbW1XTVu3LhRHnroIfv/d999t8yaNavrsWwX2kePlqSB6bf1IDqS7JSqtCSTbZ+yWW9MVZWMtvrulPbR1WbisAI5bJ0Fm4hDU27fOAyNhxXHIatBU3FU+zQenVYc9VbS9y2OMWb2KzuOeLGxOMaM6HV8VJuKIyX1JaW+xeEc74Pdtra2DrYKj2cQiFmX/vQ53HXZunWraBL72te+ZievX/ziF/LjH/+4T711dXWSSCT63B/UHbFYTCZMmGD3Pag+eNVulfXk3NjY6FV1gdSjr2THjRsnBw8eDKR9LxvNh/GIx+MyZsyYHi9IvTTys64wjodekTruuOP8ZMirtjy77jBlyhR55513bJx3331XJk+enFdQBIMAAgggEC4Bzy4hnnTSSfYrNf0Ah55l/fSnPw1XpPQGAQQQQCCvBDxLYKry7W9/W9rb26W0tDSvkAgGAQQQQCB8Ap5dQnRCI3k5EtwigAACCJgU8DyBmewsdSOAAAIIIOAIkMAcCW4RQAABBCIl4NnH6LONuqWlRZLJZLarG19P37Nbu3atLF682HhbphvQrwR49K0I010dsH7dP373u9/JN77xjQHXicoD+TAe+rWMJ598Ui6//PKosA/YzzCOh/6oQ3l5+YB95oHMAr4nsMzd8f/Ro0ePyhlnnCH6SyKU4AX0dzS/+MUvymuvvRZ8Z+iBvPfee3LxxRfLzp070UAgdAJcQgzdkNAhBBBAAIFsBAr+DEwvZ77yyity5plnZuPFOoYF9JLurl27ZPbs2YZbovpsBNra2uT111+XT3/609mszjoI+CpQ8AnMV20aQwABBBDwTKAgLiH+/e9/l/379/dAe/vtt0X/0ou+2nR+DkvvZ36zdB3vlnMdDz1b1vfG9H1LincCuY6H0wM9Q6MgEIRA/BarBNGwX23ecccdUl9fLzU1NfLGG2/I6aefLg888IC89dZb8sILL4h+6m369On27cqVK+WDDz6wL1/pp/mWL18u+smldevWySmnnNLjV+X96n++tZPreGjyWrZsmegPsuqnFPUHUCdNmpRvPL7Hk+t4OB197rnn5K677pJLL73UuYtbBHwTyOszsM7OTpk2bZpceeWVcs0118i2bdts2L/+9a+yZMkSufnmm+Xpp5+271uzZo1MnTq1Cz59fjNdV+c3o7gTcDMe+vuaCxculEsuuUQuu+wy2bJli7vOsLW4GQ/l0zHZvn27jBo1Ck0EAhHI6wSmU3PoE56WDRs22B/U0Ik1R44cad+nE00630m74YYbesxTlml+M3tj/hmygJvx0Dnm5s6da4/X+vXr5TOf+cyQ22eDngJuxkOvUKxevdp+IahXKSgIBCGQ1wnMAX300UftS4b6Y8M6v5G+8nRK+mzJzn16qwe3s54mubKysvSHWXYhkMt4aHP6nqTOdqCfUJwzZ46LHrBpukAu46EvCJ159PQy/L59+9KrZBkBXwTyPoHp+1f6HthNN90kmqxGjBghR44csXGbm5tFJ5TrrzC/WX8q7u/LdTz0xYRe8j333HNlwYIF7jtCDbZAruMxduxY+0WdfsG5qamJHwJgfwpEwNPpVAKJIEOjtbW18qtf/UpOPfVUue6662TYsGGyatUq+epXv2o/GerlxKuvvrrfGpjfrF8WV3e6GY/Nmzfb3w/TJ8uNGzfKaaedlhc//+UK1OXGbsZDL+fqn5aXXnpJ5s+f77I3bI7A0AUK9ntgejlKLxPqX6bC/GaZdLx7LNvx8K5FasokwHhk0uGxsAgUbAILywDQDwQQQACB3AQyn37kVidbIYAAAgggYFyABGacmAYQQAABBEwIkMBMqFInAggggIBxARKYcWIaQAABBBAwIUACM6FKnQgggAACxgVIYMaJaQABBBBAwIQACcyEKnUigAACCBgXIIEZJ6YBBBBAAAETAiQwE6rUiQACCCBgXIAEZpyYBhBAAAEETAiQwEyoUicCCCCAgHEBEphxYhpAAAEEEDAhQAIzoUqdCCCAAALGBUhgxolpAAEEEEDAhAAJzIQqdSKAAAIIGBcggRknpgEEEEAAARMCJDATqtSJAAIIIGBcgARmnJgGEEAAAQRMCPw/V5aSvbc+NLUAAAAASUVORK5CYII=", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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null, 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", null, 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", null, 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", null, 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", null, 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", null, 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hfPLxaWgTLBBhp/8c6Dv1YTraN744tjgcZfq8W4RwHM7EZgh9JXX31VAFSDBg2ExCVvMoANNx7MNJbJtgB6kuRGkfK3Nz9PnDhBjz76KH3wwQf0yCOPiNUVVKTOCNIHVjfGulidHT9+PFe5s/byexw33DgGV9uCzfKpp56iF154QexjBpXopk2bRHwbtkx31C6cVf7++29RJ+T3PZTNe6HlXFXKadfw3HTUrtMGCriCO/wt4KGK7iB9IZBevme+GENe+gw0/kJDBNtxoOzIHIj8NXt+PGoDM3aAlwWJXps2bWpbySNGSjp5IOgXWSvMyoxtFfRv2OWgPoPkgfRJrhIkMEhuRiooL0SpvjX27+pvSEEHDx6kFi1aCGlrxYoV4lRn9i/ZPiSwswxgRV54ieJbtKaiz78oD+lPzQHNAc0Bj3LAqwD2ww8/CMP/ggULaODAgTRr1ixq2bKlAISXX35ZOG/UqlXLtMyjV5nHxuC8ABsQHBIefPBBmjNnjsstWDlxAMAKwjuvbdu2tHXrVpfHa6wIhxs4nEDl2aZNG5EiCnWceSDKdhofPkqjF3xPwSdP0Zlliyjsp7UUsv+APKw/NQc0BzQHPMYBj6sQAUj4A2EVjz8jQSrDSj88PFwcgorKWGY8pyB/Q/qCSgDjQ6JabBkCScwZwb4ANY2ZBBYfH09VMrMpe9ZsCu76IFFUpLPm8nUcrv/ueAH+/vvvdPPNN4u+AWBwosE1Qa3Ys2dPh2OKWPgtNfx+KQ0uU4penTFF1E3t2pmi35lOSZPGOTxXH9Qc0BzwLQdgooFJB4vXQCGvSmCOmCDBS61jVqYeL6jvkJQghYCg4oSDiStSmLThmAEYdM7DwqMofsJkKnFrfYoZ+ToFH/7Ho5cEmyLA1+j+npdOdu/ebcu0gWtH4DJSREHtW61aNcumgs4kUpERr9Cfzw+lBUmXY/5S+vehiEUskR05anmuPqA5oDngew5MmDBB2Px9PxLXR+AzAHN9iN6riWBdaY9TewEIqKuQ7t27E27uG2+8YfuDpGIkAEdYWJj4Mx7D7yZZObT++SF0dt4nFMS7GBdv2pJC9u03q5qvMnhOgmCHyy+pEhjagBMNpDCER0AqtaIi/xtF6Y3voIh2bYSqVNrisq+5mlI7tKWo6TOtTtXlmgOaA37AAcwfBWHm8OSlXrEABmnpiSeeoO+//z4XP1UJDAdbtWpF3bp1E2pPqD4RHzZ//vxc5wHArCb5kD17KTyI6GBsUcqsXpXOjx9DKd06U9TM93O1k98CeE6C3AEwVQJDW1AjQi3pKIA57Kc1FP7DSjr/yksEbze4xKtqzJQB/Sjq088p6NRpNKlJc0BzwA85cIYX1Vi8BxJdsQA2e/ZscbPUiVbeOKMEBqnqlVdeEXY62Opg11NTY8nzrBw4cDycJ/lf2Z38pPKApDzSiyLnf+2xid1dAMMKDAl7b7jhBnlJQvKqWrWqNYAlp1DRZ19g8BpBOSUu6s6vueYaOwDLur4ipTdtTFFs/9OkOaA54J8cAIDhL5DoigQwSF/Tpk0Trv12AMYSFMgogRlvKKQMxHsZCQBmZv9CvfDVP9HBCuVFXJU8L7v8dZTe5E6KmjNXFrn16a4KEdIXbF4AbJWmT58unFnUMvk9hm16WRUrUFrH+2SRCDz/77//bL/xJfnJxwSARXz9rV25/qE5oDngHxzQEph/3Aeno0B2DWSUaNeunU1SiGTpoETtBsR6QiGZqTYwY4MIShYAxnFu4UuWUZFnhxNHiArnCVMA4zbDNmykYzWqimBmtb2UPo9Q5AcfEYfxq8X5+p4XCQxehe3bt7cLbDXav+Qgrr/+ehHILX/Lz+BjxymKx35+5MuySHxCAjMGnWfWqE5JUyZSzOgxFNe5O4X8cdDuHP1Dc0BzwLccgPZFqxB9ew+c9g6XcORsHDx4MCHoFpJCxLwvCZJETpEYClu3wakEVjwklFrv+4OKN2hMMa+9SZAqQvbuswSwsE1bKIsn9VhWxRk3fMyofxtll75KtOF08E4qAMAAvK7YwBDvhewa3333na1Vo/3LdsDiS/TkKZTWphVBRagS+Gon2V46mN68GZ1evZwybqlFxe5pR+HfL1FP0981BzQHfMQBaI/geKVViD66Aa52C3f4ihUr0u233y5UXQ3+PUZF/jeSzs75gNLYgy585SqHElg4u4S3H/4/qnU6kZLefI3O/LSCMhrcRmFbtloCWPhPP1NGoztEfkFkHzFSSt9HPeLMAQCDt6ArAAYJCcHV8K6U+fGsJDDjePEbbvGRDPwXBj+V6zDyXxolMFsljn9LHjaELvAf7H+aNAc0B3zPAQAXYsCQDkuGA/l+VM5HcEXZwLDC2DFxEr1d8xaKZLtTwhfzaUpGDh19ewJl1qpJ6Xc1pfAVq0wlsBz2xIvt1YeKvPIa7X3mKepRLIYymnDGfM4NmHFrbQawbZYAFvbjGkpnAEPKLHj0IV5LJQAn1HFh6zeoxXn+DhtYAu/J5SqAITwAGUfgiYkcbpDKzHZbNhtI9MS3KfX+DpR9Xblch6FCNNrAjJUy69Sm0B07jcWF9jdUts54UmgvXl+Y33MAqsNSpUqJraACSY14RQEYnBNGd+tBVY4eY6/AtRSxazf1jo2mv64rKx6wjNvqUvDxExR34iSL01fTtm1hdOBACJ3YeYJSqjekrHJlWQW2jDJYFaaCROatt1DoJQkMWUX++COEs9hHU79+xajxHcVp82+xlNGwgQiORlZwbB9jRzyu1F49KOq92XbFef0BCQzSpTo2qzYgIWHrEyTthRT2xx9/iBAASE9GYmzjTPQhvL1KOO8qEEWfTOSksPOXU/KgJ4xVxW+0YaZCVCtn3nyT4HXQpdg19Vhh/D5u3DjhNJTr3hfGi9XXFHAcgAQG8wMyBgWSGtHjqaT8/c69nvQardwRwSuNHI5XyqFDwYsoYWEYg00QxzCF0fc3P0FnNtfhuK/arGLMZnf5IDqfWJyycv6kOrsyqfnsNLr11hw7L8SMWjUo5NDflPIvdjIeRl9/XZLq10+nhg3T6Pbo36jdXwto9u/pVK9ehpCQoEZElguVUro9SCXqNxKSGGxiU6ZMoV69ell6Narnyu8AMEhg0Gc7o6NHjxIkpTvvvFMAGEBMppDCuWfPBtHKlRG0bFmk+AziGLayZbOoTJksSt6WRuNoNz2zMoe6dEnhfcfsezNz4rCvwb84TRdALGzHr0LyzXW8kBVgUYEJAvvjIYYQPNKkOeAvHABoAbxAWgLzl7tiMo4nnzxPM2cm8g7RZ2nIkPN8087RwoUVOM6pNOdwvIoGHhhEzegXdnA4Tr/8coJ2bTxESUXL0NEVu6l792TavTuMdyuuyN576xisLqkCWeral9CM5n7xDEsxabRjxzH68MMz1LdvMj0eOoPeumcR5xEszvukhQk1IrYbMVIOi+/pdzWjyE/nCZvUmDFjxAaYxnpWv5H5Hw8ebGBmLv7G86DOuvbaa8XWL0i0jHRRUn04fXoM86I0X0MM8yWDVYwnac+eY7RixUn6tPOntDLlDpr41ilxvGnTkpzo197tHkmQoUd3JglmMvCHbt9hHFqh/I1FBXYiv/feewWIaXViobzNAXtR8EDEhrJYZAUSgAWUBAb3dXeJvefZhf5yK8uXr2a1298s7QxmNVowxV5YTze2nUBU6mliMY1C5s4jqlmdSjWtRr3qXeB6WaxeTOFM9Z/xRpdjWcrKYXf0THru8FxqUewjqtEjkXXJA20dhP+8ljrP6klhndMZ+EpwEHRL3mH6oKlbelC/Ryn6sYF0svfDwiMIG0i6es2QvrCvGjYABXAgr6TVubAFoj4CllEH+51NnjyZwaoee2eW5N0CQujnn5NZIsPustAyRxOjKoW+PoZCZsyijHkfU5vGV9G93dLYozOMzy/O6sVkdgqxXbZwEEGwt5Q08L0XS5TYJBN7ioFC2AMzGB6cl+6r1Xgvt+o/3xzx12yUMI7DxoCFCe7Tww8/LDK6mNX1VhmyxEiHHW/14al288pfT/Wb33bwTMN8EKj8xZyBhSf4Dk/tQHkXAwrAXJEs8voAYsUBiSgsLIknf+Ido1MoPCKcrl28lOD2XfztqSLOCcK12n+xYh+zBNKRpZOaNGxYFL3VdRVV/XYUrQodZKsXyo4d4fwwnLuxEjWveobGjo1km1Nvqlt3oK2O3Xhr30LxbA87w1IYCDsjq33a1TX8QE5HeBWCII0he77V5npwJIEtDgmGZfszZnzLEkJliovL5i1UTvFqDGrSS52w9BA7cDDnwTpEpxctIARgy4MPPUScqT6OwSmMPvnkNE/OF8+BHQx2NUh5oMWLF9Py5cuFI4NMtxXCfCnGPMIYUCbHcrEF//6PFzwv40VdTHL47N+/PyE4PC/nu8sNxCdikgqUDS3zyl93+ePu+UifBq2D1TvnbvuePt/IX2y2izI8HzAvFOSz6cq1yTnDWPeKcuIwXjx+G2OWkIVjc6kSnNtvFYUvXUE5PMlntLgr16nIxpGTk0jDhyfxBH6c7n0kliqfOUvR/CBLivroE0rp8gCxnk4UtWmTSvfdt59ViS9yyqaLUoisKz55gkvt3oVKzF8oVul79uyxO+zoByQqrPAlMDlS38GBQwIL2ty1K5QeeqgK2+gyBQgBvFSK7f8ko2IOnVk0/yJ4qQf5+6hRZznDSDDb0i4n+zXyddWqVeIsNQVXVuVKFHThvHDJNzRZ6H6qWVqgqoEnqsqLQnfB+oICigNQG8IGhgV9IDlxXPEABrFZ9ZjDjdxdvhzHg62mqBnvETJlwFXeSFgRqBMQgnkzudq1Zy+KLUFnz4ltRFK7dbE79amnUnmVs5b69Inn1ZrdIfEjtfP/0dW791DLqtWEWzsK0Zaz7UgAYFICw0pKHZuxFwCYVO3BUaNTpxI0aNB5zvd4LpdDRsjveyhs42ZKens8sUeJsSnxm4U5tiueoffei6Eff7y4xxsADP1IMgMwdJZZvdoV4U4PAJOrSEi+WAAF0kQh76P+LJwckE4cALFAsoFd8QCGiVY1qEMCO3V9BQpidUAoZ9dI/b+Opk8sJiCjlLMzMoLKXHKRj/ziK4Jbfja73qsEyScnpzd7CmbSCy/EqofE95zi8bSDVXQDIqIomBEubex4Kn57Y4pv2ZaCD/2Vq74sQAwYJDCQVBfJY8ZPXC8A7KOPotmOV4zzQiayk0mysZr4HT39PUplD8kcBmxHlJCQxduunOUM/8X42oJsWU5wzt69e4V6BSmpjMCaUbMGeyIW7ngwxNpB4oKNRFKguSvLcevPwskBOHFICUwDWADdY9hqoP+VJERptiWlN7+LUnr1ZP+FyypBWQefZlLOltAQKs12IlDknE8ppUc38V39h9X3ddddTUOH/sKODxE0cGAcR7+rNYgWli5Jd/6+j7Znh1LkN9/R2Q9mUsqjD1Fcj0coKPGsfeVLv6QKET+N+m3jCdBxHz/+fzRxYhF2qjhFTZumGauI38H/HRNSZAo7lbhC996bStWrZ9L770fbARikr0aNGgkvJyOAZTKAhW4v3AAmwxqg3pWkAUxyQn/6AwdUCSyQNANXvAQGqQV7fGEFApKZ6JPGv0nJQ9kT0YIAEkYJbD1lU/z+AxS6YROr/c5SeotmpmcjI8eZM/s4D+FJ9gYM4QDXEmw4vXwrfkhLoaOcqWLBrTVpSpeOlFmvDiU/PZAyb6lFsY/0EwmHjQ2rAAZVFcaG/IxRb08zVmW399K0YUNbYe+qUsVEj3npjKj3Z1Naq7sp+9prcrVhVTB4cBK9+24RBquyNsl25cqV1KxZM6E2ywVgcKVnCSxQvLesrttROYz7uCfS+xJ1A01V4+j69LHA54AEsEBzo788awb+PcjXFUAigu1I2sEggeEmCmOQie1LdmImga1JT6Xov/+h6GkzKLVLZ5vzhjxHfiYkJHBmi7+Ep9+cOac5L2M63XNPSVqwAFk6goUX0D/Dn6Oz7DyiOnIkjXudDWIsYT0zTLi1y/bwqdrAMFnCiyhm7ASKmfSOHeDt3x/CMW7PsCfcWnaTtwYv1gNS5MdzKaVfH7Ubp9/r1MlgKSyDHVVuFTzF5I2kwU2aNDEFsKwKCRfbZI/FwkqqA4e8Ri2BSU7oT19zQHotaxWir+9EPvtXPeakBOasKTM13XF2mU+vkEDhy3+g1K4MYBYEAEM2DhDjJ2+UmUQjR55j1VsMZ8YoxQC2nX/XZ3vRXWw/2ifqiX8co3Fu1rsUyt6J8Xe1IiQWZtFFHDLawEIO/EFhP/5M2cXiCNnwQadPB4mA6piYSbzL9KUgbHEk9z/soJxZrarYPTr3UcclkMK++qoCS2BJHE/2s4g3g7MM7IZGCQwOMthqJefSGB23HJhHrQBMSv2BeVV61IWFA5C+EJuI9xMghphFxIIFAl3xEhhukuqJCAATEpiTu2cEMEgaUIMhSS12H84uW8ayBaSRggSmUvv2qfTtt6fYi49jx8LvYYDJ5pRD9djFfR7nH4yweSzmMCCdWfItJQ/oSzFvjBNAFrb6JyGBSScOSGDVlyxnEH2A0lu2IGymCRVl9+7FOcVVGgtXr9q50avjEN/Z4SBq5vuU8ljfXIdcKahbF1IYpLvH6fPPPxfqQ5xnCmBcjowcOZs2o4pPaMGCBezM8rjX+oY617hPnJbAvMbugGr40Ucf5TCcX306ZgAYQjug4oadFjFtgeLIoQGMHx3VExE3rkSJEk4fKKMKEavsUI73Sh06mM6PGe3wfKlCNKt07NhRzjmYSL16JXMqK2xxMJqzXYRTtWqleVfkeBFr9dPaKDrYoBOdWsUJdR/rQ7H9nqDyx4uwtFOZM2kwwP3RnMru/JNS+vflHZ8b0Ypvs+nuu0vRbbels/PIAZb6QqxBmkG4yAsvUTavxADE+aVnnjnPpw7hXIpr2UmkqWjGCsAyeCeAnM1b89uVW+fBgWf48OG0ZMkSkf3ErcYsTsazoXogolqgABgC4q8kKujr3bJli0hY4EseSw9EOYZAigULqEwcksGe/pSeiJho4NCBycUZAcBUJw6pJsq+5mpnp4pEvpg4IaZjtaMSPARlkDFL9Qxcv9OAAV+wGq4150YMp82bwzlTRiyrIDnAOiOI6/anrNCedCYnmIp8n063Nsqi3d/WpgrZf1DnKcEUnFWBFvzdiiZO/ouad4rkl+VfsQ+a6lBg6x/gNexFEfeV+OVc0/g3W10nX5C4OCbmMG/XMpDq1KkjalsBGDwRaeu2i7tScyaSgqRhw4ZR27ZtOWHxSjGRyLF6cgyQzgNVAmvZsiWrtt+35cn0JF/8rS3YgmrXrs2p1Da6tIh1d/zQ2ED1f+DAAXebcut86cAhGwkkRw4tgfFdkxIY1IcAJuQDc0bGyVgCmLPzcByrcaj7zJL6qgCGushtiDiqG27Ios6dUzgd1Vn64YeTnKbpGG/3cpxTEnE2kNcO0LNR19F3JxrSkLY76Jf0xvRA0xEce0T05z+RtK5Of2pNbC9jMrYvCvEP4PXcZfDKcUEKtZ1r8aV69S+42SEMYhdBycgzeVp2GU43VbGCyH4iywri8+uvvxbqmxEjRlCDBg3Y8WS9V7o1ezYCRQKDc5N0cPIKc/yoUUzkWFQi1VpBELQ9AM2DBw8WRHeWfWgAs2RNYByQNjCbB6ILwzaTwIxqIkfNqI4caj0jwFSuXFkAmFpHfi9RIpuzxmcwGB6kzytcQ5m31qZirdrTnzdVprNxhznj/jnel+wMlWpZhWAHA6lZOETBpX8xI1+nsM1bCJKXJ8ALzfbseQOv3FM4Q8fFAF4rAEPdYI65i/z8S3wtEMJiBapDJNeFzbB+/fqcB3OdV/q2AjB/tzNg3AjCvlKcTSANgfAOFgTJ+FN/A7BAWVzhHmkJjJkAFSKyU7jqgQjGmdnAMBG6SogFMzpy4FxseChViPgNCUx1pUfZtGnT7NIQyRiwpLGjKf2eu2lHm3vs1JvpjRtR+OqfhZRlBmDB/xyhqA/n0NmP3/cYeGGc7dq1o9GjQ0VcWFJSUC6eoY6kIM72AZANYmApCHr55ZfZLni3zT4HCQzu/sbdsj0xFisAw8rXnwnvA+hKATB5vWoKNG/eHwBYmTJlxDzgrSTAUF/PmDHD4WVoCUxhD1ZsyIwuCTcGWdXlagPleKFhvMSnJLMyeczbn9IGhgfYFQ9EjAcApmZsNrNzOBq3lSOHUQK7kfd+QVZ3+YDPmjWLXexH8qaZX9uat8WAseoTOQszOEmuOrasqjezv34whf66y1QCE3Fr97UjocqzteqZL7fcksE2sHTOlRjDme7jcrvRX+omiBcR6XfeQZFfXb4uz4zAvJUNGzaIbWTk0Qq8jxrsVL/99pss8tinFYChHFvb+CvJCV0DmHfuEN5bbCKLucRsMeuJXmF+ePXVV4VrvFV7ADB4IUq6Ym1gmMQxuS5dulTwAkbKF198kV3Bd3Gi2FfERAy1CTb2A2OHDh0qVBRmZZKZBfEJexQmEojyrnggYkx46KAvl8Bi5irtaOxwpZexYGo9I4BhbOgLi4Jt27bRm2++Sb1792ZHjkW209QYMBTKQGZbBXaPFVLYqp9yAVgQq00i531JyY/3t1X39JchQ5J4FYhEwMXsgNXYT2rnTgWmRoS3GQBVJagRvWEHA1AZ1cu4R2HssOLP4CBVnL4aIyb4J5/knRAKiCRgF6QEhve7YsWKXrODwX4JO9u+fUo8qYGfADB14X7FqhCnTp0qdgSW/AFIIWlst27d2JNugJh0AW6dO3dml/CunIHidk5ptEEAnrFMtgFggWcg/rxFcCvHg7R79267G+moP6kulJJOXiUwrPiNqy650aSqQsQYIIVhYu3bty9B9TV48GAhwUqdvVQhyvEC8FQPSZTDnR7xYkYVYjRvUJnGKa+yKySgmleoRo1MkW3kyy+vswN9Y2fYfy2YM9iH7NptPOTR33ihwR/Y5FTyFoBZPRtY9fqzGlFO6L4aI1Tn6kJNvVfe+I7rhUNXQdrAJIBBy+INwtwAwlxsRbi/qud1IElgHnWjHzJkCG3fvt22ilUnS6jpsBqAjUG6Kssy1DOWSWbff//9YvUABntjdSz7KVeunLjJsIvIrUbkMflpLMcEiJU1yhHJjofRWEeea/ysV6+e8HYCD3AuCBIZwAeApRJcewFcDzzwAIHHoCZNmnCc2C/Ur18/IdVUqlTJ1jdAFat+dSw5D3SkrGeepQtBGez4UUscy+H8j1mcLirkpx+oCF+DN4kFR07oG8nXGidCB+TWL2qf17BdMKt7Vyrx3fcUwmm0vEWQKBBGAAcZyXv0BZvduHHjxCRmGmZgGJBcxBiKc/3EMw9bh3o/UAnOQ+jfWJ6rAQ8V4FnLC2FBBY9cZGYoqDGq40O/MEmAz3gvvE3QqGAe+v333/N1vXj/80JYRFWvXl3Y4LGY9QaPsXgCHT58OFf78vnFOLA7u+wf31Emf+flmrxVV16HsX2PApixcbycWO2C8BJHRESIF9aVMtkWtqBHfUwo3hTtAZCwyyEY2dgPrgOTjbrtCsaHBwAxHLgueY78lON39Inzkam9SpUqohpUhFgBGtuAtAanj5deesl2rHnz5pyM9xMx6eLhVMeNmw0VmbGdGO7nk21bKXTjZvq3fjBFT3qHQuvVpXOlSsI90dFQ3T6Gd7t69eLM40fFggSTo0p4WTDe0NatKK5rTzo++CliHZtaxWPf4SaNhYfRPRwSEdTeq1evFs4zjjrEhCqlb0f1cAwre6iajfcD93///v1i8nDWhrvH8VzBDi3fPVfag9oaqm4898axu3K+O3XAXwAJCHZJPP/eJoS1YCG4bNmyPF8v5geoXKVJwZWxArSa8EIUwAdJ0xs8hmQHQNq8md955R1Xn1/5XMjjeAfwbsjfrlyLt+tIsDX241UvRDx00qED0gVeBlfL5ECxAkR6E4CENwkvOMhVGxjqgqlyErNSE6GeFd1zzz2ckf4722GoLrBSN1KXLl1EpgjVjtKqVSsRcInJ0cwGBpWrESS2vDKc1hWNofg+AyiuUxeRLip5oPdSKBmvA3uOZWb2tvHMeBy/M2tUo2yeDMJXrDI77JEyM/sXGsYi6bbbbvO4O72ZEwf6g6rGV+o59O+MMCFj8vO0DQztuuLtKReMUpXpbLzuHkc/8PrF2AqiTwCHIxUiQE11fsvP9eF8bGXkTIWoOnFgMS/tn/npsyDP8SqAJXDSWqiK4MAxZ84c3vm3EyGyH5HuUImBSVBnmZUVJBPQl1SvqMZMZ2OAClECmNUk5aiN1q1b2+n4jQ4c8lxMrEYAxzihhly8eLFdHkScA6DDOVADqHSEpbKFN1SgUxt+5q1e7qLU+ztw7NgtahWvfm/ZMpUlnFLsru5Y8E95qDvFvDXxYmYOL4wI98xKJeUNO5iZEwcuCxOFPwMYJnFsQurJMQIc2rRpwzsvLHB6Z30BYACUgrKDYeEJyQ0aFtiq8JxIgqT83HPP0U8//SSL8vUJSaphw4ZCojJLkwU1Lf7wLErC3BLJ6tRijZrzXn07ZLFffjqeSfIxZAAS/iT16dNHOGCo2S2gCoOEIMsw4RrL5PkF9SkBLC8SmCqG4+GzEnOtruGOO+4QKyx4CMEeYwVgVucjBdLcuXOF2kIdN8ALY8GY1AcTKgGh1+ZNOlP69bZq1mvl0AiWLr2Ivv++IXEOU0tK7dGVIucvpOiJ7zjck82yAScHrCQwnIZ4sHfeecdJC3k7jPtgTCWFFgIFwMwmvrxx4HLt+fPnE7QxcN7CgtYR4XnFwq0gpCGMA/3gPZLqbNinvEXQjmBhAABDOjk4bkHdV6MGp1VjgjkDxyWIOxoH7s/ChQvpoYceylUNEhg0XwBJOMVAw6AS+pAJfGU5xtM6jDdfZVtxXJeedO6DGZRR3/48WdfXn16VwOTFSaCSv/Hpapl6jje/SwDLiwRmBDCzScrRmOFGDelTelrlFcCgRty5c6eYCOFJqZKq3pTleW1fnufJzxtuWMUvZwK/G0HWzbLNMWnCWKHiDP31N+t6+TziSAKDPRKOA570CrMCMH/3QoSGBC7esOsYpfn8sB7S16RJk6hHjx4iaNxZG5AeoNIrCACDxIPJXAUwZ+Nz5zgkLgCFnDPAZ/WZ++GHH4S/gCsANm/ePHrttddyDQe2LPSDuQ2OYWZqRFyzusiVjbQLDae/27ampLGvU+xDfShs1Y/ykF99FgiA+dUVWwwGNxmOEEbXaovqohh15f5WmKRUG5Wj89RjUKd8++23oiivAIOXDSovM+8ngKtx0rFJYOoACvh76dKpbAf9k7dZsU9ibBxGFudGTB7yNBV96hm7DTmN9fLzG/fMGAMm24HDTs2aNT0a0GxlH8XE4Wn7krwOT3wCOKBOwyTriXFCSsCiDbGhiLl0JNkBUCA9VKtWrUAADBM5CPdESmCe4KFVGwAWSF+SoKpVU0rJXcxdcaT47LPPxLtuvEdYgICPWJQjYFo6xcg+8WkKYHxO4/RMOsQp6dLb3EtJUydRbP8nxf6C6rn+8F0D2KW7AO8j6aLu6o2BlCNBwmqV7awtGFgBXPBmzCuAoe327duburuqY5Nj8AcAA+hXqfIT20QR2OyYUnr3ouw4DlUYP9lxxTweBYBZ2cDQVALbbqXzUR6bzlUdqiKoy+VKW62AydJfjeUYMyRVLJI8ISliIp04cSI9/TQvSnhxBYkAajIrgvQFsMPELuMdrep6ohz3ARM91O9Q5+Fd9CYBnNWFJ1R8EsAgdcE7FSpW8MERySxHAENjEmLZBxZl4LcxJR3aBYCpDhwog90rizP3/Bl7MTVe+l1N6QLvEB/99lQc9ivSAHbpdkCcHzhwYJ5ujlECM5uknDUIVWqLFi3oyy+/FJOZMYjZ2fkIEp8+fXquapgkAKoq4QE383JU63j7O3hWsuRGDmgO4jg2J1n/L6kSo3lzzeD/HL/IeRm3IwkM7XgyM4K8B3i+jAQAkyt/4zFf/8aEDvsTnmlPSIrffPONUIlB4wCqW7euQzUiAARaETiBFYQKUdq/MDa8g65IPqibX5LgIs+Ht6dUIUL6gi0W4O1MhQjpq2PHjsLGhXAaldCHNI3IXS3U4/gOqQ33V6XwH1bT9mtK2z2bae3aUNiWbRR0/GJgtFrfl981gLnBfYCEO16Isms4Y8BLEyuhvKohsboCKBhJHRuOQR+ORMEI2PYlXQT9RHbiuEBvvFGUx+V4NNkJ5SnjtroUvnK144p5OOpMAsNqGI4GniAAGMAL98lIWPH7M4BJezCeS6N6yngtjn5D+powYQINGjTIxgcEDCM2yYoAYFDlQQIsaABDv96WwKQHorx+VYUI+1ezZs2E+hYgBP6ZEbwHoZZ98MEHxcLUDMCklIdnGoHaRkDEQiU3gK2i/ddXsNMOYCf49Dtup4hF35sNxWdlud8qnw0l8DpWQSK/KkRcdePGjYXjQF6lL0ccM6oQ8SIAxOSKzNG53jwmeQYAQ4b6OXMubrXiqM/0Jo0p/JIRGS++u+ACADMDfTkGVZ0jy/L7aWX/Qnv+LIGpEgnshe4A2Nq1a4UaFepuSZDAtm7dahkPBgkI9reCAjA8V+gLBAAzTvRy3J76hGpQtYGVLVtW8AhaErjOI1GBVGlaqVCxizjiamHfwsIUC1SV0Id83+HkBTOJUY2IBZRcqODcILbNhe7ZS/9xAnCjejutfRuK/GaR2oXPv2sAc+MWyMkYTWCiyqv0JLuGGyvUiJ4EMIxNqq/Qj1QfQsfvS8JkCOM9XOqxOefo0UXZWO/4MUxv1pjCflrDUc6ZQlIdNWqUW5fgDMDgdoyXV0rX7nTmaGEDycZTHn7ujNHsXBXA3AVaABXynqpSKCZcPKPIP2pGkIAkgBknUrP67pap14tJH56o3pSO4cQhpSOMHbwBGCEsBv0nsB0W7yq+W4EpvA8hfYHAT6MEZnQUMbOD4RpVCQwLxYx6dSiG+zVef3rLFhS681cKPvqv6NMf/jmeOfxhhH48BglgEM0h5kPqyS/1799fJDjO7/nG8zA26WCCY3i4fa0+xDgkz/C9Tp0MToWVQiNG5FaB4rikrBuuJ6gwwjZvFQZ9d7c8cQZgsP3AVghHjlDuM4ptcPklRwAGJwXYmIwTRX778uR56oSOCc4dCQyhHvDsNBLUiNiDzYxUFSLeLywQvUm4XpmfE9IKpCNv2sGM4IJrgxrxo48+orvuupwHFCBuBmCQtpAQokOHDoItkOCMAGaU8iCpmUlgKoBFcAYcOG1AKjMuHHJ4TkFScH9SI2oAc+OtuGjPOSdeLjz0xmwZeWkaAYxILeUpApiqEgQebjzkviYpgclxDB+exEGt4RzcLEvMP9ObshS2+kfxUiGHnCpdmp9hXQq+OFIh4szreQVc7O1pFNetF0VPnkoRXznPHGHWoyMAQ31MFP4IYJi8pGrJXRsYAEwG6Ko8QiYZKzuYVCFCOwGQN06majue+K4CNtrztis9VPqqChF9AsBwna4A2BdffCE2ZMW9AWFxavRCNAIYHDmieEEW8cVXFPTX3+I8LExkG9BwhP3IWXqamQMYTkhr15YivvYfNaIGMHEb8/dP2pkg6eTHAzF/vbp2FiQddZL3RwkMVxIbm8Mb7p3j7XbgEWWt3gSAwZEDEw3ISvUkDl76Z5VvDypMRwAW/Pdhmr73IJVmr6vExV/TuZlTqcgLL1PIgbxveeEMwLD6TYLtglVW/kSqTcgdGxjuFyZSrP6N5EwCk/YbAKm878Y2PPVbvV60WRAAJiU+eQ3wfoUZArGdkuzGoThzwFMRzl+SYH7Acy0WrQxEIIBkXNy1tHZtOLvlh1JCiXL0xsHDFPnJZxRe7w4qXrchDfv9ANXYtJVCDv7JGo4tQtORVel6oVY0WzSk3X0X28j20Ie8SaaVc4kcU0F8agBzg8uYBPHAOJuk3Ogi36dKcJUNYHXmDxKYlFrluPDZrl0qO7IQ73cWj0WgKcEDKnTfAeLZULxcrqgRkapLuiarjTpUIfIkUeyBbnSi0g303O11CAHVGfXrUcqAvhTb7wmi1LwBDZ4NR7ZRAFgtlvDienJuLUOGfnXMBf0dk5d0asAKPb9SIqQv2F7MtBMIUgZwmKnIIIFh8gZhHKgXunMX7xf3n1dYYZTAAAje8kSEOhR/RgnszjvvpOHDh9tlKYIKEQuAyLnzqHgDfkkuqVIRJwa+gk6dCqJffy3CfLqd9m04QMXuupd2Pv45qxRf4fRSd9Pzz8fS/fcXpzta3Uk30d9U8/RSurvuMepy/Rp6PeUNmjH/dhrXbBNN6LSXNt/SU7RppkIUBxhgk+5oSKemzzS9b6JOAf7TAOYGswESMMLj4fdHCUy1gQHA/MEGBgDDyk2VDnELZsxgbEgN4iwNFvYwViPBuFzz2AmRnNSZBIY+IHUa7QIwziNIFxKqGYXzpp9sPaeDfR6mg7y9hiRk7c++qhQVeekVWeTSJyYqR89Gqdg4KrN7DwUzYBQd8rxLbRZEJTzTUoWIz/zawKzUh7gGxEDCNmZUI+LZwMJQSmCQVM4yoMU+9CgVGf6SVy7fDMC8ZQO7KBnF2QEVLgoLzIcffth2fewlzyBXmorsP0Ax/xtF2bzYiRk7gaWpM8yf+2ny5Fp0552l6JZbStNTTxVjCWwRtet5D8Xs30GPLHyAymX+w2nq/mWvxpP023dbKDGqLNWp1Jc9HL+nhx/JolvvjqYt2Ucouy0HKj/6CB1s3oM6rnue008FC3sgbI/G9xSDO1TvVupGwRT+wUdUZNiLVKxdR4piQPMFaQBzg+vQz8MQjxWko1W2G13k+1SjE4e/ABj4hbgoYxoh9pug998/Qz/8EEEffGDuWg81YsOkC2J7CCmBWXlEYUICiGHlrhImRqQMs7pfkR99QimcTLgCq3NkZgRxPoPaubfHU/iyFXnKC4cJAAsdK6rPEt1pBvXEz+dS6NZtFD2Os/D7AUECkwDmjg3MEYDhMs3UiAAOSF1SasP3G79bQlkcExi2bgOFbtvuUQ4hvAQSppQ40TikP29JYAAw1QMR/fEQOK9pKL37bgw9/ngxfsZLsU3sanru2T7029axNLT2dzTqjvl078xu1LxZZeZNHz6eTWPGnOUch//R2tVHaV3x6vTrjbVo547/aMmoT2ht0CtUJf6iyr3IyNcpqHt7atKzDn36aR8aOrQ8JxLoxv2+yk5U6fTiiPM0fnYQtWyVzpvkFuN3JEKAmNE1H2PdWeYaQuKtmPUbxKIurU1rip7yrk80CB7PRo8L9BZZrZq91Z9sV7qem/UP+wBedkgWZsdlGwX9CdUUVv8YE14Y2IMQ7a+6Mhf0mGR/Ugoz8qtChRjOSJLOziyxVLVqOBuzs+Qp4jO5WRNqNnI0/ccxMq/yjgbFxoyn0IlvU1afRylz3OvwRbbVh6MHCICl9oPFBu6ZWmY76chRCv95LeW8O4WqxRYVkxpAEPUFMS+zeaPNorzyDOp0v3kbtsYufkEqKdwL0/64Sv1TZ2hnuTJ053XlKGvhFxTd7B4KZ6/LLAZRTxOAFJO1K4RnGuEEGDckA0hgVtfgqL1du3bRs88+a3luixo1KWXE/ygeqmPmNSioRlURUiL7q8xSav0F31HO6uWUxQuI2LETKOPb+aKuJ/7hWnGfcb3y/YBDBVR3cgzO+sEcgUWRK/zFMwmARNvLloVwXtBQXriFiMe3SZMszhifzer0dKpeLYv+7TCAZm8PoewaM+hPVhX2vn8rzVhfj56vVYbVjZ/ysHjllxlCYb0HU1JYOE1vdge9WjGWfqlTg7bFFaXOTw2hzOeHUvi69ZS2fRMNii8mMg7BGxEB07C72Zw4uLUJE3I4wXgo70xeQvADC0EjD06wzf9hyqCJPbpwQgJWfYPmfUFxa9ZRdrvWF38X0P+AAjDceF8QHmo8nGb9Qz2EnVwhjZkd98V40SfGA1sPxoQkntDpm6kDfDE+vBBY3ao2OUyuGCs23p08OZV69ixG8+adZhXT5Z2b/4qMoGt4oii7fhMt4+LU5Ssoc9F8ocYIYm9BSEi8NBWXJHMZQvJU7wsATPZlvPbod9+jtJbNKSmCU1yxqhGu9JiAVQ+6oPZtqcSroyl9z15Kwk7WTggre9gx1DGop9z0xyF6q2YVqoVnu0RxCv1wJsX9XzdK4o09YX/zFOE5hUoZE7UzwiSMSV0+05BYoXrFQsgsJZZVe2gD0hTim6yuvzF7tP1y8hSdZTVlBN/fYHZEuOXFV6lp9Rtt57TZvI028Kq/Cktg1L0rlXhnOqUuWUYZDRtYdZ2ncix2sMhQ3w9M6sZnx1Gjy5cvF1uVyMWuo7pQa0Pae++9THZgiqbHHjvPQJDGi7ZMaK8vEqu5Y0aNpfik7fR+1i7a9fRLF3mfWZvSax6lbkns/MPvd8TCb1mtOJ6yS5ag7/v0or0bNwi+HTp0iD69uRI9wBqIMN689vywIZQaGoIVnWgfuy5Ik4Lx3kybdoFBrCQlJNwvcrQirZVK0q6MeU+eG9W5E4W9P5uSmjZSq3rsu5UW4/KS1WNdXVkNQZqQk6I/XTlAQtrA/EV9KPkDiUY++LJM/WzRIo03QT1H3brF8wvEL90lwoS4JiaKig4YSPtKX0VfDOhDmbVvobPz51EQ9i5iEAu69IJK1aH8lG0A1G0SlSzEJ9syI+d+Rqk9u9lKzXIi5nBy4dQObSnkvQ9s9Rx9waRopa4MYRCMyEinDUGXpaLMWjUp9SHe0JNVPr4i6bAhV+YAMvzl1Q4mHThwrhlhi46YjVvodZ5oF1e7iVIe708XXniOvm3RlF7d/huFshdoyN59VG33XppR9uqLTfBedhcGPUExr48xazJfZUb7FxqB/Q02IDwvzggOFffffz9t27bNWVVxHAuBc+da0f/+F8uBy6fpiScucNb9S+DFEnvknLlUvGFTCtu0mZJmz6Rwfl8gDQrixcSYCuWoNfMtvgWr7sZPogssYSV+/SVdVekGmys9+ijOUt656W9TBsdu4ZlylcqUyaZp0xJ58fY078qQkus0LD6xILaNiWukdryPwn9Z5zUnm1yDuFSgAcyKMy6WY2WAVaYjQ72LTXm0GsYFFSJW3P7iQi8vEOBqtIHJY/Kzc+cUdq2/QF26lGD+XnxMMdF8Ub4snf3qU1rbuiX9tnevqJ7D13p2zgeUA1XdoKGiTEoLCBhVCROSUSUCL67kBT9TDi9G1I37EhLMs9KnPtSDQniSoRS2sjshAJjVsxG+YiUdq1WDTjH4qpT81OMiaDtszS9qcYF9x0IB4CXVaegYv/MDYKr0ancBbPsr+vwIOv+/4VSraRP6+eefbYeXl4ynxSwJx3XvxdvpDKH9rVrQfmV38dTuXSiYk8rCHukJMgMw2GrhPIJ32xlNmTJFVJFqa2f1t24tx67tPejDD+01DMF/HqLidzRjAPuUzr8xihIXLSDkAjVm4/jm+H90iFXMyb0fpjOrl1Ea79sF0U2NBcPzDy9HJAFAGAgbtZwNy+74nXemszv/FnYCeZDND3aHhPbklltusQOwHNYepN/FY//8K/vKXv6lAcxNBksJzGqV7Wbz+T5dTtKYQP0NwMAzRxLY+vXrac2aNQLA2rZNoQceKCGM2xs3RtC5q9itvUF93pKlilDv2RjEHm1J/NKHr/6RgtmWBeDCLteYnFQCcEZHl2Yjdhzb2EpyfrjSvIN4abr1mXb0YY3RalVhH5CqSPVAZvWqlMOr3ciF36jFpt/NAIyFPZ4EgimCAewchwcYgQEZDy48N5iKvDzSZhcybdxLhWYTen4ADFt9AMBYG8aecOH08cfRNHJkUerTpxhN7/YbHb26BqXxyh3hDiqAATROtGhG50e+LFSKx1kqtruPfK8vPDOIYsawytgDhLaNMVloVnXkwHOD7WCMEhmcHLCfX7t27QhqO2e0eHEEO0/0Y5Xh91Sv3mX1OM6L+uxzTlxdjxKXfCuyYci2MA4JpJAK0Wc475WX9uD/ESkb2ULljcUHFq6Qjoxu+rI9Vz+7dz/AquMstovZOyFBAgOAASRVSunyAEXyNbAhUC326ncNYG6yF5IObqTVKtvN5vN9OjKDQHUDNSIATLU35btRD50IALOSwPCCPvnkk4RMA6ARI5LYK+o8x7mEsbqlGa1evYABjW1FofVzBTNjFZjW6h6xggWAAeSMEtg//wRzCp7x/KIHCTXlypUn6e8fN9L84M706tpWrM4pxuB60RBhJYFhXJl9H6XIDz/GV4ckAezcuSD66qso6t+/GKuLSvN2GVfR9C23UyavWqXKTm0oteuD4ifUmgVNVgBmNk5HY9uxYy/99Vdbca0vvhgnQAx+NndWOkI7N2ZT1R2fCzDLzm4k7MhSJQWVPFRUaewoc3rdj1SMnUiMquC0TvdREGLDOLOEu2R2vWgTYwBwrFu3Tj5Ah3MAADNaSURBVCTXxY4R2M9MpXfffVfsyYd9/SSAQY1ttu3IjBkx9Mwzxdg5YhCHgqSpzYjv4d8vpdT72+cqV9NJwTMWwGRmE4J9EkAMk4GUwHI1loeC8uXLcD99aebMGE75xclLmRA2hPtklMBwLKPxnUIVD0/RgiINYG5yGpMx1HT+BmC4LIzJXwHMSgKbOnWqkEjkipM1I9S9ewq9804iJy59mR555FmOfUmjF16oyy/SBHY9trdRpPBqPfLTeXSKFxXI/qBOfNu2hfHL2JsSEnazAT2RV/7prHbJopivvqJ67eJoxcpTIiFG8+YlOQA6ROyxZCaBgbfZ97WnkH+OCtd3/LYiAFhERBG6774SrDKKZokkg1fsp2jlkM9pYsjTNPa9uryqT8mdlZ1n+vOvjKCYN8fb7HpWfaAckyf2heratSsNHjyYt6p5w15CdXSy4RhW8apLOQ5DAks//A+FrV1nqH3x5+rVq8UWIJg4z5wJovHjg1jKXMMxXtfTW28lMnidYN4n0vDnz9LAX3rR7EFraN2GkwzmGTze0rzY+ok+++x30ZgEMNkR3PkhVWD7EBvxAg3SmydUVlYABskHe+0hNmvYsGH0448/0r59+2gmP6MIeUjlzOyrPpnLmoIB4lnJZHtdzIhXqHjtBhTf9n6bihkqOAQTv/deNG9/coqv4/tc0lHIvv0UzIsuM8cUVYWIjW/hTWxFWKhiwYr7IOPorOo6K4dEd+LEOl5EJoqF3fnzQcLej/AGaDewOLRzCuJnNrXz/4mga2dte+q4BjA3OSlVdf4IYBgbAAwqB+lx5ObleuR0KwkMK0dMGC+//LLQsxs7w8R67bUxLKFdYDXMCZ5UM3j1W1G8XFu2XFohcrBzWrFSFLs7ges8yCvGD9mjMYZ6PFiUHnygGN1665e8mv7hsrcXdxI5fyEboTtwezlikoX9rVev4hwHlSCkI1NpkdVYqd06U9TsOcZh2v0GgC1aVJ0XEzli8oJdr1KlTKr1+5f0w4CPWPLARpcrOOYst4dtBqsXM+rWprguPTn+ab1du+oPOBHcd999IqYKu/hi2wyo7yAd5IcwocsYMHk+AKzyytVsl3qY4HxiJEzu//1XgRcXB3kcV9GKFdnsffgCffPNGWrShHWIlyhqxiwG5POU/OQAnsSzOQD3AtuDjlP16okMeu35dywvTIrZsnDgNKjnIV1gXCqlPtCRIr75Ns/ZUdQ28B2LHAA2O1rS22/H8PNXlBdIsbR3xwCKPvYG3VV+Na2ZwVkyWp+hxomj6cKoKHq/8wb6cujvNCK1Ix27dxaFPr2URu1kySstnRK/W0BZ5a/j0I5ZtGBBJO+sXJzvRxh9990pnvgzxcRvVO9FfL+E0ls0J7FNg2GAAFKAOgj3GvfXivCeA8A8oULEMwCwatLkD74/Gbx4jOf+jwvvXByDlifXPXmwE0UsXkpBpxEp5n3Km2XP++MJuB78GcCgZsDKHLFIeAn8heAFCNd+I40cOZI6d+5MTZs2FSBmPI6X5bbbbhPFJUrkcL2FPEliFdiHV8nxfI3ZbLfKpFV/raP4tIN0R4lInvy2U+1qNenGBV/QGzEbacxV0ezEUd3WNFbSQby6B1hIGjz4PAeHhrK66CoGzDKCh2bZ1FNZCivGLsqO6Ny5shzblkBLlpwRcT6iLi/JsW1F7NdP0udPn+ZJ7SAD0G0sZZ63m+xR99zUtymKg6tj+z5BmbxH04XnnqHMW2rZukRAN7bUgNq1b9++tnK4rs+cOdP2Oy9fzCQwuJnfsOInsblo7GMD6cxitv+x2zsrH3hijmT71pOs1q3I0u0KXjTdxTsuV2JVrCIx8QAgZUSPn0yJC9lOwgsASYxPLKFk8j28jXm0kttcz847MfR//5ck0owB/AEwuP+QCiRl3ViZg5sTKGIpOzK0byuL8/yJ642JKc2SfnF+V4J465c0xpEcqhD0F0WE/0GUdZaConi8ceyJeVNF2nT8Olq74zBlpLFEfUs9isgKo5RzObSdIqjE4nCq9S97FGZ+SWumRQnvwjad0qhHD2xsenEHZMRkGiXccAaw5KefMh27qkKECzv2UrMiABjeeVyTESStznFUDokOC8spU8ry+1CMVaANeDFcmxeAQaJ9AKsalJ3N9dOaN6Poqe/ShReHOWraI8e0BOYmG/0ZwCAVImARunzVo8zNS3b7dPDMKNVgBY+ND4cMGSJUH1AXGZ0bMIGpL37VqlV5tbmOnnvuPKuqjgtbGVaK38z/h7ZRLXql225KKPs1DfzpUepceRNVK3aYauz/w86NPnL+18ItXjWG4wInTEhkKTCEi1+zz8ihXH0WO3IEXUi2dB3G5H7u3HiW5o4LqUueGrrrN8rhWTurciWx4C5X7jXq3XsHZ2CIZ+COFZKArAuQSOn7CJ3a8BOl396A4h7sSeHfLRaHf/31V7YHPsAOKUPtwAsH5cQTfOw4RXz2BWU/9iQxsojzMCHhXCuSfN66NYzatCnBDgfF6OiuRnT2NIPIjHfpQNEatK7f1zR1agzbckpxNogYVotNpNWrDzJQ38plZQV42nkgsu2k6MDBlPzEY5RV5eZcXaNuSspfrNadxyBVh3mWwQG+UQwmpVhKDRESIcZlJEhhkfO+tCuGnWb8eI6Nwg1wgf77L4Mlr/v4ucjm2MNTHHh9nifr8/TcsWfp0ZmVqeeKVtTj27uox+cN6cnpZeij+eV5G5MFfJ1jWbrOpK8Wp9Dm/bE8kV9Pb765lVq3TqXmHUJoe6/XaXWpTnxvLoIXhgLVHp5h9X0MZtVs6B9/iq1KzIYLAJMqdUhgzlSIcOdHmi7TcBGzDhyUYcEADU5EBPH9TuTneDur7Wfw+xYmAAzXY6RkdjCJ/HCO2BzTeMzTvzWAuclRfwYwSGCQdPxJfQh2G70QETj71FNPiYkYqioEzmL1KF9aeYuMkgGcNGRKKSzo778/VXguxl5zjj7nJ7vUgoX0euJ5ESN2bvo7lNy/D7XnwGF5z+AfjK0h0u7vILuwfUIq+OCDM6yKuY+WLo2xldt9YRVKJksBobtzS5OoN2tWFDtkRfOY7NWDoTt+pUx2n5cE6ebmm3fT8uUn2DEllDc3Lcmq1Bhe+YbIKtCjUcrAAXTugxlUdPBzwhYFe2GvXr1YRdrzcj3+FpR4lqov+4EWHjlBxevfKbwlczZupqhLsWufffaZsJHZnaT8OHHiLHuBtmIpqDiraFMYRNLp2P7rqUHOWip/c0VquOdDem1lE/p9xSnhCPP669+w5LuaA7aLi5X5m2++yYuK54Tjg2w2etI7wpU75fF+ssjuE+oobHoJ550yZeI5BjCT1W+nWSpKZuklntW75YVUYXcS/0jr0I7C1m8gALUkBNiOGzeOPvnkE1lk+XnkSDAvUD7ixLiprHJNlHHwQlqEM0ZGnVtNzx07dizNnj3b7liFCsjkAYk4he9JMsW+2IsTUO8nOGdIwoRvlIygcsM+WxRlHi8HAMN5AGQ4cTgCMLzriL9TpSLZd34+5UJInnvDDXPYe3cR35/iDJKNhapSHpOfyGaf3qql2IZIlnnrUwOYm5zFZAzyVxsYAAwPoT+R0QaGFw46++7du9uGqboOy0IAmGqbQTZzrEiRnFclGJe/KhHPE/ZsqpGcSp91YXdjnhzS2MOrVGo6VTzyr6iOXZ6z42Ips+ZlMFHbgYNH587zea+yTgyU5tp2qPVCd+XeVXjXrlDecbooN/coT772AIhdbTNrVLd1BQCDh9+110ICOM1ADokyjDP0l6J77y1BkyYVIdj4GG8p4/b6lDRhLMX27k/nfvzJbg+5EAbSIkOfpxJ1bqe4X3+jN8KDaeeyRXT2s48peNpkipw+k4JYCkO8EnhuRvA227hxCtcpx2mOTrKXYDJLYMn0WfE+9HyF21hyO0Z79h2nZbN20tx9DanV9b+zh9pGdgmvZ2sO6qVB3XtQBZZ2o6Yx4HIsV9S7syhp0lt2bt+2Ey59QTZ2uNNjwpYEXiBDxd69I3kSPy2LbZ85xeMpvWkTivhyvq0Mkz3sZgBSo6Rvq8RfkHcTGSeys79kQD9lJ4QjRk+ACi+mzAgSlMzVKI8nsDrTLhaMg67Pv/ISh0O8SpzmQ1SDfQoaEZVg/0q79x61yO47wAhqR9wz9Gs8X60MAEPWFHcdOGSbUgKTv+FCf9ddx3gn9bNs13uZnZ3s1cSy3oXBAynq08+9vnuzBjDJ8Xx+SndWfwQwjM3fHDjAZqMEtmzZMla7tOYJ5LLEAQDDyyIJdjzE4KgqRHzH5C9T28i6MMqf4NRDyU8PpKltWtLhCxdVZ7C7fBATQTddWhFHfrVAgJo8z+yzefNgnlAnE5w6Tp7M/bpksjoMwKHSzz+Hs/2mBEuVUL38anddqBe2YydlKACGyQZqPRDPT7zPU6rwkgRY9O9/gW0aIawejeewgNJCKuo0pwvdXmQDnUxcQc8+VI06Vk2kHuX3U59WqTRyd2ea+/J62vvG+7TjurL09yW1W1C9uiJrSdSs2WKShTpOlXDXrw9jsC4ubIkREXMYNHeyfZERkwmSyNUMRstDs3kBcTHGJ/3u5pTMW8wUu7cDxX+zyGabRNBX9KQpYuuPmDfGUujveynzpsocfP4ZZV1fUbRn9Q8ABmncOPmOH49A76IskTUzPVWoEZUAWthlkCQYEh0kMSPxo8SSY1HOCViMRo36l/t8QTxHar3w5QxgbMvJC8HuCPuTSun3tqQMzhYjtuJhEIJ6r1atyzZMuNtDIk/nuDcrAmhBagO4O5K+cL5crBqlPKu2nZUbJTCZhaNjx1T2qN3Bat6urD7O3QoCsJGxJnoiS95epGAvtn1FNO3PEpgEVX9UIaoBocgjp27OhwdHxuDIhwjSF/T6csEgy5F01QhgWIFj1Zo8ZBCF8IukutJPy8mk4r/uEntLRSxZznE3udWHsm18whX/1KmR7CKeIryw4KmmUlbVKjYV4ocffsi2lNNsz4oXNrR77vk7t2TODYTs3W8ngWHiXrVqldqs+B4dncNquNMsHRwXNr7Fi0+yR1sKg2My3dRkMzUpN51GJcyggffuogefi6fGrzeg07Ub0TtfJLAt6SpevKxkNSjsJxdf8xS2TaRO+4IO701gYH2W3aPD2Pkjjlq1KsGSVjw1aZLG0tcJXsGPZf6VsI0HW8yc57yMf8iFwKUjKQP60ZmP3qMH9h2kB79ZTOFLllH8Xa1449FVwlEjcekiSpr8FqU81pcQ/O2M4F0H8DI6HHE4IwP3PPrzzxsZkOyDatFm+l1NeTuaMwwEF6VKSPNo5yVO+Ax1KVzfQYjF++KLKH7WSjKQhLOEeYK96w4J8FIXT0Fnz1EYZ7yHZJcXyiWBXToZkidyPBZ5fgTfx83sCXtZLRmxbDkH5t8mssA46gtSKYL7nQEYJE9oKTwFYEYJDAtiKQF26rSZVcanRGyb2diTBz0pPHyD/z5sdtgjZRrA3GSjtKdIsHCzOY+eLid7fwQwBCxDqoIUgNiW5pxhXiWjCtGoPpR1zQAMKkT5AgPIJIBhdf8Pq9DOsd0EKjjYr7BSdER4gaEq6tWLt2KPyBEZPNT6mVVuopA/D/FGgymcP64IZ2tIYNvLaVbtpYnksMYMLbCXZfPkiqBrSY15N0/Y8sycFOCgMWrUKFG1YsUsjvVKYY/FVLYHfUCl+5WnW+f3o8Zj76DWA0pSl64sgY08x67rp3jS/o8DiD9lFWuMADOOs6U6A+6iskm7KSdpOE/w97I790mqfnMqDar3I2348TAnlb3A0sh5oYJSVbXhS1fQhWaNTQOut7NqtmWJWIpmh6GiQ18QThqJnFXfzFFDXq+jz8cee4zH3SBXlfLlozh49hmaPz9KuLjb+Wewmg9hEEjBBAKAYcKH9NCz59PsRLGGP+M5MXRpDoaPYvtNMgeVw6MxW/BclepxPrK5ZHIyZagn80JWAAZHnLOz36NQVll32HtABAGz3pLCeOeDqA8+ZvVhS6fd4Ho2bdrk0IVeNoLrNkqx8lheP1UJDO8s3kO8E6BrrsFiY7AIEUCGFai4VcouW4aS2eYZ8s8Rtdij370OYDA8IqO3TCyL0SM2ZsuWLXbZn83KPHqlXmrMnyUwCa54CP2JAKxQi0AKW7FihZiwjAsAowoRk7s6qcrrAYDZ7dvFBwBgMjUQPvEbhGcQIJbBkgP2EcOk5wrB23Hv3t94080z/NyG84ozzqY2Qf7E7DLX0mvPEoNxR97H6QVWX7GOignPtFxEyH6wqzAmR5Vwn7AqN0phCN5dsmQJT9rz7ex8eKeQHQIpmKwI2th69c7wWMbyqh+qSGT5P0erxr9PvwY1oCEPr6au5wfSsPduoa4LHqZr+j0sQBh8xpgh7QriWQkSVXbrViKYGIsOlTZu3Eg317+Nkjhp7KkdGymtcyeRl0+tk5fvCAUwC1kAyKSk7Kavvz7F1xPOjjEcWK2YPlN69eDV/tcUdCaR1bHnWCpvLXYhnjXrVVbrNeEJfQetX39cOIb06JFss3fhenMB2IpVIq9fXsaNulAhwsZl5v2IBcvKp5+gp4PD6NoXXqbidRvyBqbDePeDFk61AGgbAAbPXGcSGOrCoxOBxp4g9AubGmy0WGzCyUouyrBIPH36oEhIvHZthIh3Q4o0laDGh93WW2Tfmxd6wV5AEOFffPFFkS4GCI6yvZyIFatLMMeszAtD8UqTALDvvvtOpG3ySgduNApQgGrEqJJxo0mPnYpJG9k4oD68++67c7ULNYVqA8MzYpxocBIyxhtViFYSGAATAbFBrA6DWgfpiFwhABgkpPj4HJZuTrJqLoQN2VFsSwoRq84+2dNo+Zo4niTv55d8ta1JAJARmKHmyjBxGoEEiv2ZVIJt8KabbhKTF4BeEsaCZLPOJim5esa4OUGHyL23Py6M9sYWoT5vTqA7DxykpPFj6NRm3gONVU8IUk5ku6PK57CNmwi5GSPYjgPvUGNoAwBMxuYJA54cpIc/MSaATalS2UJ6wq7ByJE5b14U8zxYSNJH67akt/oep8WLp/NcU4UlrRRWFR7jfbN+4DnmFb4/ud3qcwEYLw7CV62mNAc2KatLw4QO4FefW7Xuj0eP0JQWTVhdWJTj+ybT6fU/UfKzg4WHqVrP7DuABOQKgI0ZM4adf+41aybPZbjnmD9go5X2L9kIpDyo68uVyxTvBbY+atGiFDs9RcgqXv80d7HxULdYgeIlxlYD8EzaunWr2KIAwarIHYaVyoYNG4SjgbEMx0EAB7w0yOtnVDN5aJhOm8HYQXLlYTyhYcOGxiKf/8aDh5ce4r6UxHw+KGUAWMnhviL2C0lSQSp/AUwwxssygB0mCPlbNgVPREhgajkmJVw3yqA+hQoR3yE9IDZG1O3RlZADwxWqXbs2T5TzxHncDGfUSGVbTAzbjkqyDSWbkrNvph9ajKZblh5gME25CJL8zCAeCbxXxxa+6zfKeKCTXRnGACeWt99+W6grpT0GSWIR5wUPtK843RWybIAAGng/1HbFAcM/THawWch6AG+sorfcWZ+qdetB9Xt0oz316lBJvheZcz+kiF59qCZLB+XZhiLPCV/5o5C+8BsOM5AC5DF0B7UWFqJqmWEY+fqJ51dtE2CMRQzK5D2YMSOcJbIYDoKO4/ucTf8enkMtg5ZRmWu6chzYwEuekVEcy9ZGOHMAXEJZbAs69BflXIpFw6IGE7HsK5gBGxHHERbu81YXgzkCc1SFChUEj80WF3Dg6NChA2Wzty3k20syrlWTduWQ7qC1wGLKJh3b1cjbDyN/HZ0t3yF4dOK75BU2AMWzCSEE2pExY7IYwFJEZhw2P/L3TP4DqGXyc+2oB+fH5BxsrOlVAMPq88Ybb2Q33EeFCmTatGmshpkhPIQwEDw40FfjpYLXkFomfvA/uIED+aHW8NSqQrad109PPDh57TO/9TEJ4mFv3769Rx74/I7D6jwACRYnMNwDrEAqf/HCQuWHCRNSLlQYcoWrtonzAW74k5IDJDBMeGgPkhwWUnjRsJgCcKr9qG1ZfQeAjRgxwu68//0vhyWPDFZJhdC4RzdR0WmbxBjwMl9clZYTizWM3dYfX0vwnn0UUrcOhUgV3aVOofZBXUxy8J7D9a5cuZKzc7wjJi7YwTCBwKYHYz6cXmztWgwcfMXKWdaD1IYYqZI1a1Jk+7bi+YA016JFC5EZI4dBLKlJC1rCUmLodZXYXZKnh3NJlMW7H6MNABjuiWwPCwfwFvyRoGsxlDwXoz3ZD06GFACwAaEc7HvmGfxl8jOSyerBYCrDYQg3dx5GPf/6gyfasbbzb87OoTeDwoh4q5LoPw6KdE05/3c/ZXE4AhZQWBjIvoLZ+zDnnrttv0WHLvzDBAv+gudQI8r25KlQXcOBA/FjxmOyjqNPtItn3aiSdnSOo2NG/jqqC6DCHAz+471Ux485HAtEKSHyOowXdmm8sAkSu01PnBjFGXN46xxElLhBZmpZNOdVAINxHitAvIRQFSGwEKsIORhMKjCQm5XJa0VmBkkAOl8QxofVlVF94ouxuNonVv5YLT3//PN+OW4sbiDVdOvWTYwP0oGRv7BfIZMIVrN4gTB5G+uAH3jBIN3LFDuQ3NT7hZcezyLaQL9mbTjiK15OgAfU3nhhQeBv3bpJ/McJuP9mozZLVgAvTDSYqHAcLzYmNdkf0lbFXl2aEkNYc2/YAwxtIoXWwoULRRb9Tz/9VNjFwBcQ7F0ffPABe0I+IgAM+SJlu6KCyT+cC6kTKlYsZjABQZ2P7zgXgeC//PKLjW9o4smr4qn5wMeoR5cuFHTJ3iV2hub6WHRgcoZnJghqTdjusHjwNIF/xnZxTwGacrJU+5Se6ad5S4/e/3vVdv/D1m+k2If7UEVeyMyveB21/WKuOC32iUGUfXsTOnpwLzVmyRf8CGawj1v4jUiBlG5yf9T+jN+xuMJ4sWDCM2u8N4hXBIihnvGYsS2z37hnX375Zb7ONWvPjL9m9VCGZx7vDxYveNfU8eOdxDGjnZ3lFhZe+Hl68mKreWRnrqFYAbdXbWC4odDh40WqXr26UAEAwWWG70OHDgmGmJXlugJdUKg4gMkQE7yZ/UteqGoHg/rIzIkDdVVHDkzYeMEwUUgCEKIvTOCQcvJKULfgOYa0YkbZ5coSoqOupyAR44PJCgTpRLWBXXTguBzALCop/1Q72Ndffy3UTfIwVOyff/45e3ztEFJkAgfNOiOssqX9QtaFBIZJCIR3Uk0pBR4hpdfdD/ciXBOAS4DXpZOl2vfST6HKVAOYZbm3PqUdzFH7BxvWp1pBwULSCmP3/9iHetP5MaPpGO9aPPnvQ8L7Ew4VZz+ZTdvLXkO/ZIdQwrARVPzWBlS88d3CISf9jvybBHBfMK8ZCQssSKpWqjBjfeNvnCc1DMZj3v4tbamqC73sE+8ZNA6+Iq8CGPaMwUuBvGRYPeIlbNmypXjwsYLEpISgPrMyXzFE91swHMAKEMCiBnUae8bkK6XuXMZ2pTKkHumJCPUhVurqig2rRJTnF8DQFVbA8KY1JZ5cUipdT/Uio4SaxwrARABzTfOsH2gXqkMs7gBSsHPBLiYJaj5IkFDB58XmClugDJKG5gMAhkkWZAQwrPBhWwO/zEgFMLQJD0lHnpBmbbhThgkc84kj+pcXKQuLxbKH3/MieDhpykSxYzF4BunTNtmyVuX51Au04pGenPH/Vjr3/rt0cu9OOstqVKuUTo76lcewGLfLxnHpAKRyaSaRdQPlE88QwAvPH76rBGkYZiBfkVdViLgoeBwi1Y+qN0WAoVoGo6CxzFcM0f0WDAcwGWJSdrQihQSmApgjCWz16tVi4JjgjBOwdKV3F8DgcGRFZ8tfR7ce2EfxrO6E7QoECUxdNUMCS+UYNCuCpgIxUFCbI7gZNidJeH/gADB79mzOkjFJFjv9xOoZkw8IvIQ0KaVTqBAxKUFixf1A0C/2vbIi1IFtDn/Ydwypv6Ta1uocT5bj/mMh44igPv6pUkXqceBvOjdrum2XAcwxkBbxnMAxBs8JHFCq8ZYzKQqfHbXtyjEsDqwA7JVXXnGlCb+rIyUwxIEZAaxQS2DyTqjgldcyWV9/Fi4OYPuP4cOHO7woSGCYYEFWbvQ4pkpgWGGbARgmLHcBzEqFiDGc5Fiw6jlBNgksYuG3NOKrb6k8BzgLSkkVCWLVHIgXD9j/hxoR/QCsjISJF5QXCUxOPjhPquzxHQTAhPoVkiWcRwAOjjx9AWAACCQPhu3LEdhd7MGz/zF5wgbniCAN5HBw+qldW2zgJevDxihj7b7//nuxWFAXCbKeO58YI2xFAHlJMKXATgSNVCASrgnvD5ygjPZH6Yjnq+vyqgrRVxel+/V/DkAqspKo5OiNAGZVH5MwJmeoyNQYMNmOagOD6jI/BBUi+oBUZUb/lb6KqqRnCvVcyyS2fT33Am0vXowemv2J2PZeZOC49hrKiS9mdrqtDFIpJlWo1Y0EdSucPCCZukoqgEEygIpLJalGhPQFbzxIaFYEAIPjDeyX8KYraEKA8/bt2x12CwCTjjbGigAw2PjwnHzzzTe815i1NGw819XfcNrBhK9KYbB/wYMwv8+eq317qx7suLjnWBji+lSCBOZLFaIGMPVu6O9+xQGpQoTkhJgqKwDDqhAOC1CVQQIDYKmEF09KYHgR80OwqcH5Yffu3aanH43nF5wdSIp89jlNzQmhX54dTJMTytIv7K4d160XRXNWdqus92qDABxkHVedP9TjeXWacAZgcN+HKg1OI9gY0xFB0oWdDnY4R0DnqA13jrkLYNKhDAHiyAR0zz3WGeDdGadRjYi+1PyH7rTtq3PxHJktnLQE5qs7ovv1ew5IJw6otrD6B0hZEaQwuIsDqKSNR9ZVbWDurIIhhVmpERM5xutoTDRvnTGK3rjtVlofEiRULv82uZPOcZol7MCcUdPaA1GOFZ+OrlOt58p3ZwAGCQzOGAjARcymI0LORjh6yEBWR3W9cQzu+7DXQY1pRZAGjGoutS6kMMT0AYjxTHmDIOVCWgch/AJZ5AsDgEGyNNIVYQMzXrT+rTngCgcwEUH6gt3DSvqS7Ug7mDMbWH4lMPSDrB9WAAa7x9KbK1Pi3A8ps349sU+ZdKPPaMqJcJd/R6ndOZ9TARNWzZj0MT5MqpAOVMI1gbpw3Je/E6Q+jNeRGhEAZlzAqNcFAIOk7g31oewHAIbMKVADY4GA+ECAfyATYkqxGDISeI1kA3hPfUHWCm9fjEb3qTmgcADu8AAuOBmo3nxKFdtXVwAMtg93JTBIK2YEQ/3RWtWp/W11qdK/R0TQvgQw1FfjqczO91YZeAgJFDYZ/MkYMNkfVKNwxkC6t0AgqUa0Uv9BOnMkgcG7EwBjdb4neIDYRjgd1a9fX/y588x5YjyeaAM7ppt5DGNRgXcUCwezGMv169eL5MLektq1DcwTd1e34TUOQIKA1ONMApMqRKgbjV6I+I0JBZ5h7kpgyMaBDDJGgoQj7VbIHIJYMBXAjPUL8jdWzghYhiSGlbSRBg4caBc3ZzzuT7/hyWclgYHf8JRzJIFhkkUmf3eeA2f8gMMG1JRwyCkM4IXrxQLS6h3EgsHMkQPvwEMPPSTSATrjWX6PawDLL+f0eQXCAdjBMPk6k8AkgEGFaJzAMFnBewrg5s6EghcVK0lj9nswAgAm28ZYYIsDaHpr5ZkX5gPAEJsGXiJ1WyATPDER6G1GkL4wyZqF7ZjV12We4QDeNyOAYSHRu3dv3pX8Ka/a/zSAeeYe6la8xAFMugAMq9Wf7BYqRNg2oIs3SmCoIz0TzdQcsg1XPuHJhpgeI0GFKAEMIAFVHSQ1NSOI8ZyC+g0AQ/yT0YW+oPr3ZD+4z1AFy3R0atvO7F9qXf3dcxyAJyIWjiohgQXsrdig1JukbWDe5K5u220OAMCQBNWZBAbwQB2s/MykHoAaJjh3AQVACMnKSJDA1LahRoTThFQrGusX5G8AGBYBeXXBL8gxutoX7DCwgyHwGp6TKjmzf6l19XfPcQDv6Ny5c4WKHmEZSFeGVGgIVzCzm3muZyItgXmSm7otj3NAxp44AzB0DNWdmfSFYzhfSkj4nV9CkLErAAY7CNSWxsDP/PbrznkAMFBhkMBwHVZqREgBVkHMOE+TdzjQr18/GjRokPC2nDVrlogTnDlzptfCFNSr0BKYyg393e84gNUdyJkKEXUAYFAvmRGAzV31IdoFgKlpgmRfqgoRZZDA/EH6wlgKI4C9yzkMjQQJTAOYkSve/w0bc8eOHcWf93uz7yGgAAwuwb4gKQb7qv/8XDPcWwNpvLhGs/HKuCUAmdlxlTewT0GVZ1YPDhh40cyOqW04+w4ghFekkb/wgIOUJ9vH1hlQN8rfztr15nEAOwhSoT+Mx5VrNfJXPQeqUOTShHSrBn3DcQau6764RswRcB7BuAOBHPHXH8cv52Dj2AKD25dGjXRCviDJPF/1n59rhiQSSOPFNZqNV3oUAnzMjqu8QXZ0BKia1QO4QAIzO6a24ew72oA3o5G/cB5BclzZPrJawHFC/nbWrjePQxJEnBeAzB/G48q1GvmrnoPFCOyN8E6Vgdg4jsTPWGD44hphp4XTjlmIhTp2f/nuiL/+MkZ1HFYLAw1gKpcsvmNHZpAvXgyLITktDrQH1Iq/WNWOGTNGBKc64z9W3vgzq4c0RHDiMDvmlJlKBQApbGBG/kLyUwEMp0A6cLc/pWu3viIFFGxE/jIeZxdj5K+xPuxgyDEIqVsS7i+kXl9dI8DLV31LHrj66Yy/rrbj63raicPXd0D375QDkKzkIsJpZYsK2HjxxRdftDjqerGZDQyej5gQVC9E11vUNfPDATNHDgCYtoHlh5uBe44GsMC9d3rkPuCAGYBB+oI9JtCDhH3Aznx3CQBTM3LgHjjLwpHvzvSJfssBDWB+e2v0wPyRAwAwpGRSCZOnJ1z01Tb1d8ccQHZ32LxWrFghKuosHI75VViPagArrHdWX5dXOAAbGGwd2CZDEgDMX1zm5ZgK+yecacaPHy/ijwBeWn1Y2O+4+fVpADPniy7VHDDlAGxx2EdKDWY2xoCZnqgLPc4BZH2Hd+Xjjz9O//77r7Z/eZzD/t+gBjD/v0d6hH7GAaMdTKsQfXeD4JiDBcS4ceM0gPnuNvisZw1gPmO97jhQOWCUwABg2gPRN3cTYRbTpk0T2f+1B6Jv7oEvew2oODBfMkr3rTkgOQAJzKhC1AAmuVPwnwjQRg4+GfRe8CPQPfqKAxrAfMV53W/AcsAMwLQXom9vZ+PGjX07AN27TzigVYg+YbvuNJA5gMTCqgSGPIhaAgvkO6rHHqgc0AAWqHdOj9tnHDDawLQXos9uhe74CueABrAr/AHQl593DpipEHUcWN75qM/QHHCXAwUCYNiZ9sSJE7axQuWCRJz4lGRWJo/pT80Bf+KAEcC0G70/3R09liuJA14HsFGjRtHmzZvprbfeEqAF28Gzzz5Le/fupaFDh4pdPM3KrqSboK81sDgAFaK6qaUGsMC6f3q0hYcDXvVC3L17t8gR16FDB2rZsqV46ZcuXUqdO3emRo0aiQzeGzZsoCNHjuQqw3FNmgP+yAGjBAYbmHbi8Mc7pcdU2DngVQA7fPgwHTx4kEaMGCEkraeeekqkfKlTp47gKwIPkcMMaWCMZZLxTz/9NEEFiRx0M2fOlMU++cReQ4FCSHkUaNnRA4W/iDuC1kCONyUlhcqWLWv77a/PCLwnA4UC7fmVKcY0f73DgfT0dNOGvQpg2KUULzkADNuwL1iwQOzrhL2TQEiKikkWN99YJkfbq1cvYStDxD12vfUFYUdmrLp91X9+rjk6OlpsL5Gfc31xDnZMDhT+YndYjBUghu9I7Itn2J/HD/CCpIh3MhAo0J5fzA+w4wfKjsyBxl/M/2bkVQBLSEigbdu2iX4BAlipYgfVP//8k7DlOiSr+vXri5tuLJODrVmzpvwqJDXbjwL8gskJZLUKKMChuNwVFgaBNN5A4i94C+CCYxKAF8AQCPzOyMiwLRRdfpB8VDEQ+KmyBgsD8DdQdmQONP76BMAAVlATTpo0ifbv3y+cNkqVKiUSb65bt0689NiYrnLlyrnK1IdDf9cc8DcOyGBmrLyxkaLOxOFvd0iP50rggFclMDDwkUceEZIAdqyFFAZ66aWXRJlEVYizxjJRUf/THPBTDgDA4IkID0SQjgPz0xulh1WoOeB1AAP3JFCpnHS1TD1Hf9cc8BcOSACD+jAqKopCQkL8ZWh6HJoDVwwHvB4HdsVwUl/oFcUBAFhiYqIw3Gv14RV16/XF+hEHNID50c3QQwkcDqgSmAawwLlveqSFiwMawArX/dRXU0AcgPMGbGBQIWr7VwExXXejOWDggAYwA0P0T80BVzggJTCdRsoVbuk6mgPe4YAGMO/wVbdayDmgAayQ32B9eQHBAQ1gAXGb9CD9jQOqClHnQfS3u6PHc6VwQAPYlXKn9XV6lAOqBKYBzKOs1Y1pDrjMAQ1gLrNKV9QcuMwBpJCSThzaC/EyX/Q3zYGC5IAGsILktu6r0HAAKkTEgcGJQ0tghea26gv5//bOLNTGLozjz9eh5MJxYR6PWRlCcoXOnSQ3ppILKVMpSkmIPhlLoQwlZZ4vuHGhSEpSKBQy0zFFhigcU993/s/Xu2Pv97xn793+9n7f9/xWnXP2Xutd71rrt56znrWeNSWMAAosYRVGduNBQCbE+vp6P9CXEVg86oRcND8CKLDmV+eUuAQEdJq3jpDSnXeMwEoAlFdAoAgCKLAioBEFAiIgM2JdXR0KDHGAQIUIlOUw31KVrVKmmuAU/UqlXww/HZacpPyqjEnKr/hqIcfLly/9yqAk5F0jxaRcaJk0+VUboVs14FtMa1V8nEQpMB3bUwmnCy0lnJVKv5gyq0FNUn7VuCYpv+JbXV3tVaOT6OOedx13pQUnwc3nxchUOeMkTX5lTta9cEm50DJpfBsz02NCLOd/JWmlikDbtm29PJyFmKpqpTAJIoACS1BlkdV4EdAcmJx6szgIQKD8BFBg5WdOiikhECiwxswbKSkmxYBAbAmgwGJbNWQs7gS0FyyYH417XskfBNJIAAWWxlqlTGUhoDkwRl9lQU0iEAglgAILxYInBJomIBMi819Nc+IJCPxfBFBg/xdZ3pt6Ar169bIRI0akvpwUEAJxJYACi2vNkK/YE+jbt6/t2rUr9vkkgxBIKwEUWFprlnJBAAIQSDkBFFjKK5jiQQACEEgrARRYWmuWckEAAhBIOQEUWMormOJBAAIQSCsBFFhaa5ZyQQACEEg5ARRYyiuY4kEAAhBIK4G/Gu6v+ScphavUlRVfv361Y8eO2axZs5KCynQ/UVKqVld87N2712bOnGktWiTjhp8k8ZXQ7t+/36ZOnerXAiVBiJPG9+jRozZ+/HgLbiiIO+Ok8dX9cLoFPdslSoFlZ75c39++fWu1tbV269atciXZrNL5/v27DRkyxG7cuGG6VwlXegJDhw61s2fP+uWbpX87bxw9erTt27fPtDcQVz4CmBDLx5qUIAABCECghAQYgeUB88ePH3bz5k0bOXJkHk/zSKEEZOq8evWq89Xp7rjSE7h27ZppFCZTDK70BGQ9GDBgABaE0qONfCMKLBIPgRCAAAQgEFcCyZgxLxO9u3fv2sCBAzOpPX782Ce9O3XqZJ8+fbJnz55lwnSNfE1Njb148cI+fvzo/u3btzf94MIJhPFt1aqVdenSJRPh4cOH/jmYS/j586fdvn3bOnfubB06dMg8x4dcAvfu3bN+/fr5HWUKlfxm85WfJsO7du3qL0B+czk25tMU3w8fPtirV688utqHnj17+udsmW7s/fgXTqDq7wZXeLR0xZAJ6/Dhw3b8+HGbOHGiF06HtD59+tQuX75sWv3YunVru3DhgtXV1dm5c+fsyZMnponblStXmlYpyl9C+3tjnC5KxZcmjO/mzZu9Q3DlyhXnq4Z39+7d9uDBA7t06ZJ9+fLFG+MVK1b4ispDhw5550KXSOJyCcgEu3jxYps+fbpVVVVZGN9NmzbZu3fvfLHMnTt3bPjw4chvLspQn3z4Hjx40Nm+efPG5VcmxWyZ7t+/f+j78SyOACOwBm6nTp3yXqmWlgbu+vXrtmXLFnv9+rUv8dYS2Xnz5rmyWrJkiS1YsMC0/FtOjYaUlxoOXC6BbL5S+OoAbNu2zTTCWrhwoS9BFvOdO3dm/Pr06eMjrxkzZtiwYcPs9OnTtmjRotwEmrmPVsdevHjRevfu7STC+I4bN87DJ0+e7HznzJnj2xYUAfmNFqB8+Kp9ePTokS1btsznGdXhlcuW6QkTJkQnRmhBBFBgDbgmTZrk0M6fP5+Bp39q7fvSAo5169Zl/E+cOOGNrS4y1KhLJhjtYZKZYP78+TZo0KDMs3z4j0A2X5m1xFWjrPfv3/uoS+aX6upqj6C9YL9+/XJzjEyHch07dvTOhH/h1x8EBg8ebPpRR0AujK8Wx0h5yZ08edJGjRplz58/R36dSPSvfPjqDTLPar+dphrGjBljY8eOzZHp6JQILZQACiyEmBpPKaXt27f7/NbWrVt9tKARl3q6O3bs8FjdunWzAwcOuHlRCuzIkSMosBCe2V4a6c6ePdvWrFnjc4ZS+hq9BiNaPS8lpkY38FOdhG1kzH43381Nrtl8Ay7akK+RwtKlS50v8huQyf9vmPwqtvaByRIjq8LcuXOttmHvaCC/Ck/KJn3lNSmONcshNaWNtVow0K5dO1+oIYGUu3//vs/LBA2perBnzpzxsPr6eq6XdxL5/ZJpdsOGDSZTljYvt2nTJrMY5vPnz+6nSXCZGuU0H9mjR4/8Xs5TPlr9na+QaB5Rc2DLly/3xhT5LV5QsuVXbcSePXv8hUFnSxaFYIFXINPFp0jMMAKMwEKoqEHVni9NesvMNWXKFH9KjanmZQLXvXt3H4FJsWm0oLkaXH4EZDLcuHGjr+5Ub1Vu2rRptmrVKlOY5htrGlZ5qhOxevVq02ko69evz+/lPOUMf+cbzOVqtKt5RM3RSMFpBIb8Fi4w2fKr0ZUWGK1du9bbguDYuWyZLjwlYkQRYB9YBB31qqSY9BPlNGJjg2gUofCwb9++5ZgFw5jDN5xfU75hfMPiwDeMStN+YXw1t9uyZcs/IofJ9B8P8KVoAiiwotEREQIQgAAEKkkgemhRyZyRNgQgAAEIQCCCAAosAg5BEIAABCAQXwIosPjWDTmDAAQgAIEIAiiwCDgEQQACEIBAfAmgwOJbN+QMAhCAAAQiCKDAIuAQBAEIQAAC8SWAAotv3ZAzCEAAAhCIIIACi4BDEAQgAAEIxJcACiy+dUPOIAABCEAgggAKLAIOQRCAAAQgEF8CKLD41g05gwAEIACBCAIosAg4BEEAAhCAQHwJoMDiWzfkDAIQgAAEIgigwCLgEAQBCEAAAvElgAKLb92QMwhAAAIQiCCAAouAQxAEIAABCMSXAAosvnVDziAAAQhAIIIACiwCDkEQgAAEIBBfAv8CnqoeMT7DKacAAAAASUVORK5CYII=", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null, 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", null ]
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https://evileg.com/en/post/541/
[ "# C ++ 17 - Lazy template functor with caching heavy function computation result\n\nDeveloping the idea of caching the result of calculations of heavy functions , I propose to write a small template class, which will take the function as an argument, namely the lambda function, as the most universal an instrument within which a heavy function will be performed.\n\nIn this case, the result of the function will not be calculated at the time of creation of the Functor, but at the time of calling the operator parentheses () . And at the same time, the result will be cached. That will allow not to call a heavy function more than once as part of the execution of a method.\n\n## Introduction\n\nOf course, you can first perform a heavy function and save the result in a variable, but if the logic of your program does not need to immediately calculate the desired value, but only if it is really needed. Then there is no need to call this function at all.\n\nI will try to show an example where this can be useful.\n\nFor example, in this artificial example_, which clearly requires refactoring, there are branches when a heavy function needs to be called several times, when only once, and when it does not need to be called at all.\n\n```int calc(int cases, int cases_2, int base)\n{\nint result = 0;\n\nswitch (cases)\n{\ncase 0:\n{\nresult = 3 * heavy_calc(base);\nbreak;\n}\ncase 1:\n{\nresult = 4;\nbreak;\n}\ncase 2:\n{\nresult = 2 * heavy_calc(base);\nbreak;\n}\ncase 3:\n{\nresult = 3 * heavy_calc(base);\nif (cases_2 < 2)\n{\nresult += heavy_calc(base) / 2;\n}\nbreak;\n}\ndefault:\nreturn heavy_calc(base);\n}\n\nswitch (cases_2)\n{\ncase 0:\n{\nresult = result * heavy_calc(base);\nbreak;\n}\ncase 1:\n{\nresult += result;\nbreak;\n}\ncase 2:\n{\nreturn result - 1;\n}\ndefault:\nreturn 2 * heavy_calc(base);\n}\nreturn result;\n}\n```\n\nOne of the possible solutions would be to call this function once at the very beginning and save the result in a variable, but then we will slow down the code execution in those branches where the heavy function call is not required. Therefore, it is worth considering how to rewrite the code so that the heavy function is called only once.\n\n## Lazy template functor with result caching\n\nTo do this, I propose to write a functor template class.\n\nATTENTION!!! This code only works when using the standard C ++ 17 or later\n\n```#ifndef LAZYCACHEDFUNCTION_H\n#define LAZYCACHEDFUNCTION_H\n\n#include <type_traits>\n\n// The template, as a template parameter, will take the type of function to be called\n// and will print the return type via std :: result_of_t from the C++17 standard\ntemplate <typename T, typename CachedType = typename std::result_of_t<T()>>\nclass LazyCachedFunction\n{\npublic:\n// The constructor of the functor takes as argument the function to be called\n// and move it to the m_function member\ntemplate<typename T>\nexplicit inline LazyCachedFunction(T&& function) :\nm_function(std::forward<T>(function))\n{\n}\n\n// Do the operator overload of parentheses,\n// so that calling a function inside a functor is like calling a regular function\nauto operator()() const\n{\n// Check that the cache exists\nif (!m_initialized)\n{\n// if not, then call the heavy function\nm_value = m_function();\nm_initialized = true;\n}\n// We return the result of calculations from the cache\nreturn m_value;\n}\n\nprivate:\nT m_function; ///< function called\nmutable CachedType m_value; ///< cached result\nmutable bool m_initialized {false}; ///< initialization flag, which indicates that the cache is full\n};\n\n#endif // LAZYCACHEDFUNCTION_H\n\n```\n\n## Example\n\nAnd now we use this functor\n\n```#include <iostream>\n#include <string>\n\n#include \"lazycachedfunction.h\"\n\nusing namespace std;\n\nint heavy_calc(int base)\n{\ncout << \"heavy_calc\" << std::endl;\n// sleep(+100500 years)\nreturn base * 25;\n}\n\nvoid calclulateValue(int base)\n{\n// Wrap heavy function i lambda function\n// this is the most convenient universal option\nauto foo = [&]()\n{\nreturn heavy_calc(base);\n};\n// We need to pass the function type to the template parameter, so we need to use decltype\nLazyCachedFunction<decltype(foo)> lazyCall(foo);\n// Next, call lazyCall() twice and see that the heavy function is called only once\nint fooFoo = lazyCall() + lazyCall();\ncout << fooFoo << std::endl;\n}\n\nint main()\n{\n// Let's double check by double calling calclulateValue\ncalclulateValue(1);\ncalclulateValue(10);\nreturn 0;\n}\n\n```\n\n#### Console Output\n\n```heavy_calc\n50\nheavy_calc\n500\n```\n\n#### Check that the function is not called if we do not call the parenthesis operator on the functor\n\nFor such a check, we can change the calclulateValue function as follows\n\n```void calclulateValue(int base)\n{\nauto foo = [&]()\n{\nreturn heavy_calc(base);\n};\nLazyCachedFunction<decltype(foo)> lazyCall(foo);\nint fooFoo = 10;\ncout << fooFoo << std::endl;\n}\n```\n\nThe output to the console will be as follows\n\n```10\n10\n```\n\nWhich proves that the function is not called when it is not required.\n\nAccordingly, in the case when it is not always necessary to perform some heavy calculations, such a functor can somewhat optimize the program.\nWithout greatly affecting the structure of the method that was written earlier, it will also help to keep the code readable.", null, "##### We recommend hosting TIMEWEB\nStable hosting, on which the social network EVILEG is located. For projects on Django we recommend VDS hosting.\n\nDo you like it? Share on social networks!\n\nOnly authorized users can post comments.\nL\n• Leo\n• Sept. 26, 2023, 11:43 a.m.\n\nC++ - Test 002. Constants\n\n• Result:41points,\n• Rating points-8\nL\n• Leo\n• Sept. 26, 2023, 11:32 a.m.\n\nC++ - Test 001. The first program and data types\n\n• Result:93points,\n• Rating points8\nQScintilla C++ example По горячим следам (с другого форума вопрос задали, пришлось в памяти освежить всё) решил дополнить. Качаем исходники с https://riverbankcomputing.com/software/qscintilla/downlo…\nQt/C++ - Lesson 048. QThread — How to work with threads using moveToThread Разве могут взаимодействовать объекты из разных нитей как-то, кроме как через сигнал-слоты?\" Могут. Выполняя оператор new , Вы выделяете под объект память в куче (heap), …\nAC\nAndrei CherniaevSept. 5, 2023, 3:37 a.m.\nQt/C++ - Lesson 048. QThread — How to work with threads using moveToThread Я поясню свой вопрос. Выше я писал \"Почему же в методе MainWindow::on_write_1_clicked() Можно обращаться к методам exampleObject_1? Разве могут взаимодействовать объекты из разных…\nn\nnvnAug. 31, 2023, 9:47 a.m.\nQML - Lesson 004. Signals and Slots in Qt QML Здравствуйте! Прекрасный сайт, отличные статьи. Не хватает только готовых проектов для скачивания. Многих комментариев типа appCore != AppCore просто бы не было )))\nDjango - Tutorial 023. Like Dislike system using GenericForeignKey Ваша ошибка связана с gettext from django.utils.translation import gettext_lazy as _ Поле должно выглядеть так vote = models.SmallIntegerField(verbose_name=_(\"Голос\"), choices=VOTES) …\nNow discuss on the forum\nИнтернационализация строк в QMessageBox Странная картина... Сделал минимально работающий пример - всё работает. Попробую на другой операционке. Может, дело в этом.\nПомогите добавить Ajax в проект В принципе ничего сложного с отправкой на сервер нет. Всё что ты хочешь отобразить на странице передаётся в шаблон и рендерится. Ты просто создаёшь файл forms.py в нём описываешь свою форму и в …\nРазмеры полей в TreeView Всем привет. Пытаюсь сделать дерево вот такого вида Пытаюсь организовать делегат для каждой строки в дереве. ТО есть отступ какого то размера и если при открытии есть под…\nКастомная QAbstractListModel и цвет фона, цвет текста и шрифт Похоже надо не абстрактный , а \"реальный\" типа QSqlTableModel Да, но не совсем. Решилось с помощью стайлшитов и setFont. Спасибо за отлик!\nВопрос: Нужно ли в деструкторе удалять динамически созданные QT-объекты. Напр: Зависит от того, как эти объекты были созданы. Если вы передаёте указатель на parent объект, то не нужно, Ядро Qt само разрулит удаление, если нет, то нужно удалять вручную, иначе будет ут…" ]
[ null, "https://evileg.com/media/technical_storage/timeweb-120-90.jpg", null ]
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https://mathematica.stackexchange.com/questions/29845/can-we-compile-using-only-integers-of-unusual-size
[ "# Can we compile using only Integers Of Unusual Size?\n\nI coined Integers Of Unusual Size to mean \"bignum\" using GMP, the underpinning of Mathematica's ability to craft very big numbers.\n\nI have these functions from this question:\n\nascendingQ[x_] := 3 == Mod[x, 4]\nuniqueQ[x_] := 0 != Mod[2 x - 1, 3]\nuniqueRank[n_, m_] := Block[{a = If[1 != n && OddQ[n], n - 1, n]},\n(n + IntegerExponent[a, 2]) 2^m - 1]\n\n\nThe first two need \"bignum\" inputs and the third needs \"bignum\" output\n\nAnd I have this variation of countOrbit which we will use for our research:\n\ntotalOrbit[x_] :=\nBlock[{h = x, t, c = 0, m, n, mt = 0, nt = 0},\nWhile[1 != h,\nh = (t = -1 + (3/2)^(m = IntegerExponent[h + 1, 2]) (h + 1))/\n2^(n = IntegerExponent[t, 2]);\nmt += m;\nnt += n;\n];\nc = 2 mt + nt\n]\n\n\nThis needs \"bignum\" everywhere. When we get a sufficiently frisky totalOrbit I will recast countOrbit.\n\nAfter I execute the compiled totalOrbit, I get error messages about exceeding machine precision,\n\ncto = Compile[{x}, totalOrbit[x]]\n\n\nso, I need a way to indicate that all numbers are \"bignums.\"\n\n• Unfortunately Compile supports only machine-size integers, ruling out bignums. – kirma Aug 5 '13 at 10:54\n\nUnfortunately Compile supports only machine-size integers, ruling out bignums.\n\n• Where is \"machine-size integer\" defined and documented? Does it vary between machines, and how can I determine what it is on my machine? – rogerl Dec 25 '15 at 2:26\n• @rogerl Please see $MaxMachineInteger. You can get the maximum machine-size integer with Needs[\"Developer\"]; Developer$MaxMachineInteger. $MinMachineInteger interestingly enough doesn't exist, but on all even remotely modern architectures (that is, two's complement arithmetic) it would be equal to -Developer$MaxMachineInteger-1. – kirma Dec 25 '15 at 7:21\n• Actually my statement on minimum machine-size integer seems to be false. DeveloperMachineIntegerQ[-Developer$MaxMachineInteger - 1] returns false, but DeveloperMachineIntegerQ[-Developer$MaxMachineInteger] returns true. This also corresponds to the documentation... but it's not consistent with how \"native\" signed types work on real hardware. – kirma Dec 25 '15 at 7:30\n• Thanks. I didn't know about that package. And so given this (on my machine, this is $2^{63}-1$, as I expected), can I expect compiled programs with integer arguments to function properly up to this bound? (I realize that this is exactly what the documentation for Compile says.) – rogerl Dec 25 '15 at 13:14\n• Oh, and with respect to the minimum machine-size integer, I think you would expect it to be equal to -$MaxMachineInteger + 1, not -$MaxMachineInteger-1$. But in any case, -$MaxMachineInteger returning True is indeed unexpected. – rogerl Dec 25 '15 at 13:19\n\nAs pointed out in the comments, Compile uses machine numbers only. Also note that IntegerExponent is not a compilable function as can be seen from this example:\n\nf = Compile[{{in, _Integer, 0}}, IntegerExponent[in]];\nNeeds[\"CompiledFunctionTools\"]\nCompilePrint[f]\n\n\nNote the call to MainEvaluate in the output. This indicates that the function can not be compiled.\n\nConcerning your top level functions, the switch between bignum and machine integer is opaque in Mathematica. If a calculation needs big integers it will internally switch to such a representation. No action on your part is required for that." ]
[ null ]
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https://notes.dzackgarza.com/Quick_Notes/2021-12-06-quick_note.html
[ "# 2021-12-06\n\nTags: #web/quick-notes\n\nRefs: ?\n\n## Ali Daemi: Signature functions and basic knots (15:04)\n\n• Assumptions: $$X\\in {\\mathsf{sm}}{\\mathsf{Mfd}}^4$$, $$b_1({{\\partial}}X) = 0$$, so get a nondegenerate quadratic form $$q: H_2(X){ {}^{ \\scriptstyle\\otimes_{{\\mathbf{Z}}}^{2} } } \\to {\\mathbf{R}}$$.\n\n• Get positive/negative definite eigenspaces of dimensions $$b^+, b^-$$, define $$\\operatorname{sig}(X) = b^+ - b^-$$.\n\n• Take $$K\\hookrightarrow S^3$$, find a surface $$F \\subseteq {\\mathbb{B}}^4$$ with $${{\\partial}}F = K$$.\n\n• Write $$\\Sigma_n(F)$$ for the $$n$$th cyclic branched cover of $${\\mathbb{B}}^4$$ branched along $$F$$. Boundary is a $$\\mathbf{Q}\\kern-0.5pt\\operatorname{HS}^3$$, and $$\\operatorname{sig}(\\Sigma_n(F))$$ is determined by $$K$$. For $$n=2$$, recovers ordinary signature of $$K$$.\n\n• Covering map induces an action $$C_n \\curvearrowright H_2(\\Sigma_n(F))$$, so take eigenspaces. Write $$C_n = \\left\\langle{t}\\right\\rangle$$, take $$\\ker(t-\\zeta_n)$$ for $$\\zeta_n$$ an $$n$$th root of unity (working over $${\\mathbf{C}}$$ now).\n\n• Define $$\\sigma_K(w)$$ for the signature restructure to $$\\ker(t-w)$$ for $$w\\coloneqq\\zeta_n$$ – the Levine-Tristam, signature.\n\n• Find a rep-theoretic description of $$\\sigma_K(w)$$: consider $$\\mathop{\\mathrm{Hom}}(\\pi, G)$$ for $$G\\in \\mathsf{Lie}{\\mathsf{Grp}}$$. We’ll take $$G \\coloneqq{\\operatorname{SU}}_2$$ and $$\\pi \\coloneqq\\pi_1(K)$$.\n\n• Set $$P\\coloneqq\\mathop{\\mathrm{Hom}}(\\pi_1(T), {\\operatorname{SU}}_2) \\cong \\mathop{\\mathrm{Hom}}({\\mathbf{Z}}{ {}^{ \\scriptscriptstyle\\times^{2} } }, {\\operatorname{SU}}_2)/\\sim$$ where $${\\operatorname{SU}}_2$$ acts by conjugation. Equivalently $$P = \\left\\{{(\\theta_1, \\theta_2) \\in S^1\\times S^1 {~\\mathrel{\\Big\\vert}~}(\\theta_1, \\theta_2) = (- \\theta_1, - \\theta_2)}\\right\\}$$. This yields a pillowcase:\n\n• Define $$\\chi^*(K) \\coloneqq{\\operatorname{Homeo}}(\\pi, {\\operatorname{SU}}_2)$$ where $$\\pi \\coloneqq\\pi_1(S^3\\setminus\\nu(K))$$ and we take homeomorphisms that do not have abelian image, modulo conjugation as before.\n• For $$\\alpha\\in [0, 1/2]$$, define $$\\chi_\\alpha^*(K) \\coloneqq\\left\\{{\\phi\\in \\chi^*(K) {~\\mathrel{\\Big\\vert}~}\\phi(\\mu) \\sim { \\begin{bmatrix} {e^{2\\pi i \\alpha}} & {0} \\\\ {0} & {e^{-2\\pi i \\alpha}} \\end{bmatrix} } }\\right\\}$$ for $$\\mu$$ a meridian of $$K$$.\n• Morally: $$\\sigma_K(e^{2\\pi i \\alpha})$$ is a signed count of $$\\chi_\\alpha^*(K)$$.\n• There is a map $$r: \\chi^*(K) \\to P$$ given by restriction to $${{\\partial}}\\nu(K)$$.\n• Generically $$\\chi^*(K)$$ is a 1-dimensional variety with boundary, and its image under $$r$$ is the bottom line of the pillowcase. We can also write $$\\chi_\\alpha^*(K) = r^{-1}(C_\\alpha)$$ where $$C_\\alpha \\coloneqq\\left\\{{(\\alpha, t) {~\\mathrel{\\Big\\vert}~}t\\in [0, 1/2]}\\right\\} \\subseteq P$$ is a vertical line.\n• Need to remove a finite set $$S_k$$, the $$\\alpha$$ for which $$e^{4\\pi i \\alpha}$$ is a root of the Alexander polynomial.\n• Get a lower bound for the number of elements in the character variety: $$# \\chi_\\alpha^*(K) \\leq {1\\over 2}{\\left\\lvert {\\sigma_K(e^{4\\pi i \\alpha})} \\right\\rvert}$$.\n• Say $$K$$ is $${\\operatorname{SU}}_2{\\hbox{-}}$$basic if $$\\chi_\\alpha^*(K)$$ is as small as possible, so equality in this inequality, plus some transversality conditions.\n• Examples: $$T_{p, q}$$.\n• Also the pretzel knot $$P(-2,3,7)$$\n• Question: can we classify all $${\\operatorname{SU}}_2{\\hbox{-}}$$basic knots?\n• Theorem: if $$K$$ is $${\\operatorname{SU}}_2{\\hbox{-}}$$basic, $$S:K\\to K'$$ a concordance, then any $$\\phi\\in \\chi^*(K)$$ extends to $$\\tilde\\phi\\in \\mathop{\\mathrm{Hom}}_{\\mathsf{Grp}}(\\pi_1(S^4\\times I \\setminus S), {\\operatorname{SU}}_2)$$.\n• Proof uses instanton Floer homology, Yang-Mills gauge theory.\n• Definition of ribbon concordance: $$S:K\\to K'$$ is ribbon if, noting $$S \\subseteq I \\times S^3$$, the projection $$S\\to I$$ is Morse without any critical points of index 2.\n• Theorem: let $$S:K\\to K'$$ be a ribbon concordance, then the same kind of lift exists.\n• Question: if $$K$$ is $${\\operatorname{SU}}_2$$ basic and $$S:K\\to K'$$ is a concordance, can $$S$$ be exchanged for $$S'$$ a ribbon concordance?\n• Slice-ribbon conjecture for $$K=U$$ implies that the answer is yes. The theorem says that a negative answer wouldn’t be useful in disproving this conjecture.\n• Other examples of $${\\operatorname{SU}}_2$$ basic knots:\n• Cables $$C_{pqk + 1, k}(T_{p, q})$$..\n• Twisted torus knots $$T(3, 3n-1, 2, 1)$$. Interestingly, these are all $$L{\\hbox{-}}$$space knots.\n• Question: can $${\\operatorname{SU}}_2$$ basic knots be classified using Dehn surgery.\n\n#web/quick-notes" ]
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https://answers.everydaycalculation.com/add-fractions/6-28-plus-4-9
[ "Solutions by everydaycalculation.com\n\n6/28 + 4/9 is 83/126.\n\n1. Find the least common denominator or LCM of the two denominators:\nLCM of 28 and 9 is 252\n2. For the 1st fraction, since 28 × 9 = 252,\n6/28 = 6 × 9/28 × 9 = 54/252\n3. Likewise, for the 2nd fraction, since 9 × 28 = 252,\n4/9 = 4 × 28/9 × 28 = 112/252\n54/252 + 112/252 = 54 + 112/252 = 166/252\n5. 166/252 simplified gives 83/126\n6. So, 6/28 + 4/9 = 83/126\n\nMathStep (Works offline)", null, "Download our mobile app and learn to work with fractions in your own time:" ]
[ null, "https://answers.everydaycalculation.com/mathstep-app-icon.png", null ]
{"ft_lang_label":"__label__en","ft_lang_prob":0.6506099,"math_prob":0.9992273,"size":352,"snap":"2020-45-2020-50","text_gpt3_token_len":164,"char_repetition_ratio":0.22988506,"word_repetition_ratio":0.0,"special_character_ratio":0.53409094,"punctuation_ratio":0.05376344,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99842036,"pos_list":[0,1,2],"im_url_duplicate_count":[null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-12-05T11:56:42Z\",\"WARC-Record-ID\":\"<urn:uuid:d1d42a1e-bf90-4c23-93d7-56c328d1e77c>\",\"Content-Length\":\"8291\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:b2afb766-7925-4a3c-8774-9b5391d731ee>\",\"WARC-Concurrent-To\":\"<urn:uuid:49ead437-752d-4306-8389-69f2a384eac8>\",\"WARC-IP-Address\":\"96.126.107.130\",\"WARC-Target-URI\":\"https://answers.everydaycalculation.com/add-fractions/6-28-plus-4-9\",\"WARC-Payload-Digest\":\"sha1:XOZOUCOBAZ7XOQP4DGWOHTKFMLZDI3EH\",\"WARC-Block-Digest\":\"sha1:T2MWQO4SEYIMN4EHD6RIAOVZIVTQYGLD\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-50/CC-MAIN-2020-50_segments_1606141747774.97_warc_CC-MAIN-20201205104937-20201205134937-00406.warc.gz\"}"}
https://chemistry.stackexchange.com/questions/600/why-is-nitric-acid-such-a-strong-oxidizing-agent/103313
[ "# Why is nitric acid such a strong oxidizing agent?\n\nIn my teaching-lab experiments I've seen that nitric acid solutions are able to facilitate the dissolution of metals such as silver, even though they are more active than hydrogen. Does anyone know why nitric acid is so special? Why is it such a strong oxidizing agent?\n\n• Its not that special- concentrated sulfuric acid and all the oxyacids of chlorine are oxidising particularly perchloric HCL04. – user2617804 May 28 '14 at 23:14\n\nBecause, unlike in other metal dissolution reactions, the $$\\ce{H+}$$ of $$\\ce{HNO3}$$ isn't reduced—the $$\\ce{NO3-}$$ is. The following data and balanced reactions are taken from Wikipedia:\n\n\\begin{align} \\ce{NO3- + 4 H+ + 3 e- &-> NO + 2 H2O} & E^\\circ_\\mathrm{red} &= \\pu{0.96 V} \\\\ \\ce{NO3- + 2 H+ + e- &-> NO2 + H2O} & E^\\circ_\\mathrm{red} &= \\pu{0.79 V} \\\\ \\ce{Ag+(aq) + e- &-> Ag(s)} & E^\\circ_\\mathrm{red} &= \\pu{0.799 V} \\end{align}\n\nSince the standard reduction potential (SRP) of the $$\\ce{NO2}$$ reaction looks smaller than that of $$\\ce{Ag+}$$ (I may be wrong, but by significant digits it can't be greater than $$0.799$$), one can conclude that it's the $$\\ce{NO}$$ reaction that's occurring here. And the $$\\ce{NO}$$ reaction has a large enough SRP to oxidise $$\\ce{Ag+}$$.\n\nUsually nitrogen compounds are pretty versatile when it comes to redox reactions, since nitrogen shows many oxidation states. So the simple reason for why $$\\ce{HNO3}$$ is so strong an oxidising agent (with respect to other acids) is that it has a different, better path available to it to get reduced.\n\nNote that the exact reduction path (i.e. final reduction products/oxidation state) depends upon the concentration of nitric acid—so much that copper can be oxidised in three different ways.\n\n• what about platinum? the E reduction of platinum is greater than NO3, yet NO3 still oxidized Pt. also the same case for Ag – Lifeforbetter Jan 28 at 13:35\n\nManishEarth did a good job explaining already. Perhaps I could point out one more you could think of to understand why nitric acid has such a high reduction potential. The nitrogen centre in nitric acid is in +5 oxidation state, which is also the highest known oxidation state of nitrogen. As a result, the acid exists in high energy state to begin with as it is really unstable." ]
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https://www.synonym.com/synonyms/first-rate
[ "Synonyms\nAntonyms\nEtymology\n\n### 1. first\n\nadjective. ['ˈfɝːst'] preceding all others in time or space or degree.\n\n• prototypical\n• firstborn\n• archetypical\n• premiere\n• eldest\n• prototypic\n• primary\n• basic\n• prototypal\n• initial\n• prime\n• first-year\n• early\n• archetypal\n• original\n• front\n• premier\n• introductory\n\n• late\n• middle\n• last\n• secondary\n• back\n\n#### Etymology\n\n• first (Middle English (1100-1500))\n\n### Words that Rhyme with First Rate\n\n• a42128\n• circumnavigate\n• recriminate\n• solid-state\n• silverplate\n• remunerate\n• multistate\n• intrastate\n• interrelate\n• disinflate\n• demodulate\n• translate\n• stagflate\n• reinstate\n• procreate\n• desecrate\n• conjugate\n• commutate\n• underrate\n• tri-state\n• recreate\n• postdate\n• interstate\n• conflate\n• upstate\n• telerate\n• sumgait\n• restate\n• reflate\n• prorate\n\n### 2. first\n\nadjective. ['ˈfɝːst'] indicating the beginning unit in a series.\n\n• ordinal\n\n• empty\n• immature\n\n#### Etymology\n\n• first (Middle English (1100-1500))\n\n### 3. first\n\n• foremost\n• first of all\n• firstly\n\n• present\n• old\n• past\n• uncreative\n\n#### Etymology\n\n• first (Middle English (1100-1500))\n\n### 4. first\n\n• conventional\n\n#### Etymology\n\n• first (Middle English (1100-1500))\n\n### 5. first\n\nnoun. ['ˈfɝːst'] the first or highest in an ordering or series.\n\n• rank\n• former\n\n• refrain\n• leave office\n\n#### Etymology\n\n• first (Middle English (1100-1500))\n\n### 6. first\n\nnoun. ['ˈfɝːst'] the first element in a countable series.\n\n#### Synonyms\n\n• ordinal\n• number 1\n• no.\n• ordinal number\n\n• stop\n• close\n• stand still\n• go off\n\n#### Etymology\n\n• first (Middle English (1100-1500))\n\n### 7. first\n\n• aft\n\n#### Etymology\n\n• first (Middle English (1100-1500))\n\n### 8. rate\n\nnoun. ['ˈreɪt'] a magnitude or frequency relative to a time unit.\n\n#### Synonyms\n\n• deathrate\n• pace\n• oftenness\n• THz\n• rate of flow\n• kilohertz\n• words per minute\n• gigacycle per second\n• dose rate\n• fatality rate\n• MHz\n• acceleration\n• gigacycle\n• growth rate\n• metabolic rate\n• deceleration\n• kph\n• kilometres per hour\n• attrition rate\n• hertz\n• flow rate\n• pulse rate\n• sed rate\n• mortality rate\n• megahertz\n• miles per hour\n• erythrocyte sedimentation rate\n• km/h\n• megacycle\n• quantitative relation\n• Mc\n• rev\n• respiratory rate\n• bps\n• death rate\n• heart rate\n• mph\n• kilocycle per second\n• cps\n• gigahertz\n• sampling rate\n• GHz\n• wpm\n• magnitude relation\n• ESR\n• cycles/second\n• kHz\n• rpm\n• gait\n• rate of respiration\n• speed\n• rate of return\n• sedimentation rate\n• crime rate\n• rate of inflation\n• pulse\n• Hz\n• birth rate\n• frequence\n• bits per second\n• birthrate\n• terahertz\n• mortality\n• tempo\n• spacing\n• kilometers per hour\n• inflation rate\n• kc\n• flux\n• frequency\n• megacycle per second\n• Gc\n• data rate\n• natality\n• cycle\n• velocity\n• jerk\n• rate of attrition\n• rate of growth\n• solar constant\n• kilocycle\n• flow\n• fertility\n• revolutions per minute\n• fertility rate\n\n• acceleration\n• walk\n• push\n• pull\n\n#### Etymology\n\n• raten (Middle English (1100-1500))\n• hrata (Old Norse)\n\n### 9. rate\n\nnoun. ['ˈreɪt'] amount of a charge or payment relative to some basis.\n\n#### Synonyms\n\n• linage\n• payment rate\n• installment rate\n• freight rate\n• pay rate\n• rate of pay\n• depreciation rate\n• lineage\n• excursion rate\n• freightage\n• rate of depreciation\n• room rate\n• footage\n• rate of exchange\n• interest rate\n• rate of payment\n• freight\n• exchange rate\n• charge\n• charge per unit\n• tax rate\n• repayment rate\n\n• decelerate\n• immortality\n• permanence\n• distribution\n\n#### Etymology\n\n• raten (Middle English (1100-1500))\n• hrata (Old Norse)\n\n### 10. rate\n\nverb. ['ˈreɪt'] assign a rank or rating to.\n\n#### Synonyms\n\n• superordinate\n• rank\n• shortlist\n• judge\n• range\n• prioritise\n• place\n• pass judgment\n• order\n• prioritize\n• reorder\n• sequence\n• seed\n• evaluate" ]
[ null ]
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https://optimization-online.org/2017/09/6216/
[ "# The Vertex k-cut Problem\n\nGiven an undirected graph G = (V, E), a vertex k-cut of G is a vertex subset of V the removing of which disconnects the graph in at least k connected components. Given a graph G and an integer k greater than or equal to two, the vertex k-cut problem consists in finding a vertex k-cut of G of minimum cardinality. We first prove that the problem is NP-hard for any fixed k greater than or equal to three. We then present a compact formulation, and an extended formulation from which we derive a column generation and a branching scheme. Extensive computational results prove the effectiveness of the proposed methods." ]
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http://benediktahrens.net/supervision/student-projects/
[ "### Suggestions for BSc and MSc projects\n\nBelow you can find some suggestions for student projects.\n\n#### Computer-checked proofs of results in mathematics and computer science\n\nThis project is suitable for students with an interest in logic and formal proofs.\n\nComputer proof assistants are computer programmes that check the correctness of mathematical proofs. For this to work, the proofs, and the results that they prove, have to be written in a formal language that can be understood by a computer.\n\nComputer proof assistants can, in principle, check results in any area of endeavour, as long as these results are the product of rigorous logical reasoning. However, so far they have predominantly been used to check results in mathematics and computer science.\n\nAn important example of a computer-checked proof in mathematics is the Kepler Conjecture from the 17th century.\nA computer-checked proof of this conjecture was presented in the year 2017.\n\nIn computer science, examples of computer-checked software are the CompCert verified compiler for the C programming language, and the seL4 certified microkernel.\n\nMany different computer proof assistants exist, based on various different logics.\n\nIn this project, we propose formulating and proving some result – to be chosen in accordance with the interests of the student – of mathematics or computer science in a suitable computer proof assistant. Such a result could be, e.g.:\n\n• The correctness of a sorting algorithm, i.e., programming the algorithm and showing that the output of the algorithm is always sorted.\n• Error-correction properties of linear codes. This project involves implementing matrices and operations on them, as well as proving correctness of those operations.\n\n### A tool to build certified natural deduction proofs via drag-and-drop\n\nThis project is suitable for students with an interest in logic and GUI programming.\n\nNatural deduction proofs for propositional logic are tree-shaped derivations built from basic building blocks called inference rules.\nSuch proofs are shown, e.g., in this course.\n\nThis project consists in building a tool that allows to build proof trees by drag-and-drop. The programme should check that only valid proofs can be built, and reject invalid (ill-formed) proof trees.\n\n### A type checker for (directed) type theory\n\nDifficult This project is suitable for students with an interest in dependent type theory and functional programming.\n\nComputer proof assistants mechanically check the correctness of mathematical proofs written in a formal language. Two such computer proof assistants are Agda and Coq. They are based on type theory; type theory is a strongly typed (functional) programming language similar to Haskell or Ocaml.\n\nThe core of these computer proof assistants is a type checker. A type checker takes, as an input, two terms t and T and check whether “t : T” (read: t is of type T) holds. A type checker is often accompanied by a type inferrer: a function that, given a term t, returns a term T such that “t : T” holds (type synthesis).\n\nA project could consist in writing a type checker for type theory.\n\nEven more difficult In a recent work (click here), Riehl and Shulman present a directed type theory, a powerful, but complicated generalization of type theory. One could attempt to write a type-checker for this directed type theory. This type checker could form the core of a proof checker for directed type theory.\n\nThis site is compiled with nanoc. Built from code written by Cyril Cohen." ]
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https://syntaxbug.com/1fedb39928/
[ "# [SVM Classification] Data classification based on convolutional neural network combined with support vector machine CNN-SVM with matlab code\n\n?About the author: A Matlab simulation developer who loves scientific research. He cultivates his mind and improves his technology simultaneously. For cooperation on MATLAB projects, please send a private message.\n\nPersonal homepage: Matlab Research Studio\n\nPersonal credo: Investigate things to gain knowledge.\n\nFor more Matlab simulation content click\n\nIntelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system\n\nSignal processing Image processing Path planning Cellular automaton Drone\n\n## Content introduction\n\nSVM classification is a commonly used machine learning algorithm that performs well in data classification problems. However, with the rise of convolutional neural networks (CNN), researchers have begun to explore methods of combining CNN with SVM to further improve the accuracy and efficiency of data classification. In this article, we will introduce a data classification method based on convolutional neural network combined with support vector machine-CNN-SVM.\n\nConvolutional neural network is a deep learning model widely used in the fields of image recognition and computer vision. It can effectively extract feature information in images through multi-layer convolution and pooling operations. CNN has achieved great success in image classification tasks, but its computational complexity is high and its training time is long when processing large-scale data sets. This provides us with an opportunity to introduce SVM.\n\nSupport vector machine is a binary classification model that separates data points of different categories by constructing an optimal hyperplane in the feature space. SVM has good generalization ability when processing high-dimensional data and can effectively handle large-scale data sets. However, SVM itself cannot directly process image data because image data is high-dimensional and has spatial structure. Therefore, we need to combine CNN with SVM to take full advantage of both models.\n\nThe basic idea of CNN-SVM is that during the training process, we first use CNN to extract features of the image, and then use these features as the input of SVM. Specifically, we can take the output of the last convolutional layer of CNN as a feature vector and then use SVM for classification. The advantage of this method is that we can use the feature extraction capability of CNN to convert image data into low-dimensional feature vectors, thereby reducing the dimensionality of the data. This not only reduces the computational complexity of SVM, but also improves the accuracy of classification.\n\nIn practical applications, we can use pre-trained CNN models, such as VGG16 or ResNet, to extract features of images. We then input these features into SVM for training and classification. In this way, we can make full use of CNN’s ability to train on large-scale data sets, while taking advantage of SVM’s advantages on high-dimensional data.\n\nHowever, CNN-SVM also has some challenges and limitations. First, since CNN and SVM are two different models, their training processes are separated, thus requiring additional computing resources and time. Secondly, CNN-SVM may face memory and computing resource limitations when processing large-scale data sets. In addition, the performance of CNN-SVM is also affected by factors such as the selection of the CNN model and the adjustment of SVM parameters.\n\nIn summary, the data classification method based on convolutional neural network combined with support vector machine – CNN-SVM, is a potential classification algorithm. It can make full use of the advantages of CNN in image feature extraction, and at the same time, it can take advantage of the advantages of SVM in high-dimensional data. However, we need to pay attention to its computing and resource requirements when using CNN-SVM, and we need to make reasonable selection and parameter adjustment of the model. It is hoped that through further research and practice, we can further improve the performance of CNN-SVM and make it useful in a wider range of data classification problems.\n\n## Core code\n\n`%% Clear environment variables</code><code>warning off % Close alarm information</code><code>close all % Close open figure window</code><code>clear % Clear variables</code><code>clc % clear command line</code><code>?</code><code>%% import data</code><code>res = xlsread('dataset.xlsx');</code><code>?</code><code>%% divide the training set and test set</code><code>temp = randperm(357);</code><code>?</code><code>P_train = res(temp(1: 240), 1: 12)';</code><code>T_train = res(temp(1: 240), 13)';</code><code>M = size(P_train, 2);</code><code>?</code><code>P_test = res(temp(241: end), 1: 12)';</code><code>T_test = res(temp(241: end), 13)';</code><code>N = size(P_test, 2);</code><code>?</code><code>%% data normalization化</code><code>[p_train, ps_input] = mapminmax(P_train, 0, 1);</code><code>p_test = mapminmax('apply', P_test, ps_input );</code><code>t_train = T_train;</code><code>t_test = T_test ;</code><code>?</code><code>%% transpose to fit the model</code><code>p_train = p_train\\ '; p_test = p_test';</code><code>t_train = t_train'; t_test = t_test';</code><code>?</code><code>%% Create model</code> <code>c = 10.0; % penalty factor</code><code>g = 0.01; % radial basis function parameters</code><code>cmd = ['-t 2', '-c\\ ', num2str(c), '-g', num2str(g)];</code><code>model = svmtrain(t_train, p_train, cmd);`\n\n## Operation results", null, "", null, "", null, "", null, "## ? References\n\n Zhang Dandan. Research on CNN classification model based on SVM and RF and its application in face detection [D]. Nanjing University of Posts and Telecommunications, 2016.\n\n Sun Jingyang. Design and implementation of emotion analysis and social sharing system based on ECG data [D]. Beijing University of Posts and Telecommunications, 2018.\n\n Wang Zheng, Li Haoyue, Xu Hongshan, et al. Emotional classification of Chinese paintings based on convolutional neural network and SVM [J]. Journal of Nanjing Normal University: Natural Science Edition, 2017, 40(3):7.DOI: 10.3969/j.issn.1001-4616.2017.03.011.\n\n Yang Hongyun, Huang Qiong, Sun Aizhen, et al. Rice seed image classification and recognition based on convolutional neural network and support vector machine [J]. Chinese Journal of Cereals and Oils, 2021(012):036." ]
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http://www.technicalbookspdf.com/mechanical-engineering/fluid-mechanics/
[ "## Introduction to Fluid Mechanics By William S. Janna\n\nDescription The ability to understand the area of fluid mechanics is enhanced by using equations to mathematically model those phenomena encountered in everyday life. Helping those new to fluid mechanics make sense of its concepts and calculations, Introduction to Fluid Mechanics, Fourth Edition makes learning a visual experience by introducing the types of problems that students are likely to encounter in practice – and then presenting methods to solve them. A time-tested book that has proven useful in various fluid mechanics and turbomachinery courses, this volume assumes knowledge of calculus and physics…" ]
[ null ]
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