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https://liujiacai.net/blog/2022/11/17/yes-in-zig/
[ "## 初始版\n\n `````` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 `````` ``````const std = @import(\"std\"); pub fn main() !void { const argv = std.os.argv; const output: []const u8 = if (argv.len > 1) argv.ptr[0..std.mem.len(argv.ptr)] else \"y\"; const out = std.io.getStdOut(); var f = out.writer(); // var f = std.io.bufferedWriter(out.writer()); while ((try f.write(output)) > 0) { _ = try f.write(\"\\n\"); } }``````\n\n ``````1 2 `````` ``````zig build-exe yes.zig -Drelease-safe ./yes | pv -r > /dev/null``````\n\n## BufferedWriter 版本\n\n `````` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 `````` ``````const std = @import(\"std\"); pub fn main() !void { const argv = std.os.argv; const output: []const u8 = if (argv.len > 1) argv.ptr[0..std.mem.len(argv.ptr)] else \"y\"; const out = std.io.getStdOut(); var f = std.io.bufferedWriter(out.writer()); while ((try f.write(output)) > 0) { _ = try f.write(\"\\n\"); } }``````\n\n## 复用 Buffer 版本\n\n `````` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 `````` ``````const std = @import(\"std\"); const BUFFER_CAP = 4 * 1024; fn fillBuffer(buf: []u8, output: []const u8) []const u8 { if (output.len + 1 > buf.len / 2) { // plus one newline return output; } std.mem.copy(u8, buf, output); std.mem.copy(u8, buf[output.len..], \"\\n\"); var buffer_size = output.len + 1; while (buffer_size < buf.len / 2) { std.mem.copy(u8, buf[buffer_size..], buf[0..buffer_size]); buffer_size *= 2; } return buf[0..buffer_size]; } pub fn main() !void { const argv = std.os.argv; const output: []const u8 = if (argv.len > 1) argv.ptr[0..std.mem.len(argv.ptr)] else \"y\"; var buffer: [BUFFER_CAP]u8 = undefined; const body = fillBuffer(&buffer, output); const stdout = std.io.getStdOut(); var writer = stdout.writer(); while ((try writer.write(body)) > 0) {} }``````" ]
[ null ]
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https://jeeneetqna.in/1576/1-i2-h2o2-2oh-2i-2h2o-o2-2-h2o2-hocl-cl-h3o-o2
[ "# (1) I2 + H2O2 + 2OH− ⟶ 2I− + 2H2O + O2 (2) H2O2 + HOCl ⟶ Cl− + H3O+ + O2\n\nmore_vert\n\n(1) $\\require{mhchem}\\ce{I2 +H2O2 +2OH- -> 2I- +2H2O +O2}$\n\n(2) $\\ce{H2O2 +HOCl->Cl- +H3O+ +O2}$\n\n(1) H2O2 is acting as oxidising agent in both the reaction\n\n(2) H2O2 is acting as reducing agent in both the reaction\n\n(3) H2O2 is acting as oxidising agent in reaction (1) and as reducing agent in reaction (2)\n\n(4) H2O2 is acting as reducing agent in reaction (1) and as oxidising agent in reaction (2)" ]
[ null ]
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https://www.k8mathsense.com/blog_f01
[ "", null, "", null, "Why are fractions on the number line often difficult? Can your students name locations with numbers? They must understand three essential concepts.\n\nThree Essential Concepts\n\n1\nPoints represent distance from zero, based on the scale of the number line. Equal distances must represent equal amounts. You may need to divide to find the scale, or interval between 0 and the first tick mark.\n2\nCount or skip count by tick marks on the line, not the spaces between them. This is important because the tick stand for specific numbers but there are many numbers between ticks, including fractions. Count or skip count starting with 0.\n3\nInsert more equally spaced tick marks to identify points between tick marks. Notice that inserting one new tick between ticks doubles the number of intervals. Inserting two new ticks between ticks triples the number of intervals.", null, "For practice with fractions on the number line, see the FREE online activity on the E-Games page. This page also shows links to printable games." ]
[ null, "https://ct.pinterest.com/v3/", null, "https://www.k8mathsense.com/assets/images/blog/number_line_pin_lt.jpg", null, "https://www.k8mathsense.com/assets/images/blog/num_line_examples.jpg", null ]
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https://www.apurvainstitute.in/study-materials/ncert-solutions/chapters/home.php?tab=ncert_book&class=10&subject=Mathematics&medium=english&chapter=4.%20Quadratic%20Equations&page=2
[ "Study Materials\n\n# NCERT Solutions for Class 10th Mathematics\n\nPage 2 of 4\n\n### Exercise 4.2\n\nExercise: 4.2\n\nQ1. Find the roots of the following quadratic equations by factorisation:\n\n(i) x2 - 3x - 10 = 0,\n\nSolution:\n\nx2 - 3x - 10 = 0\n\n⇒x2 - 5x + 2x - 10 = 0\n\n⇒ x( x - 5 ) + 2(x - 5) = 0\n\n⇒( x - 5 ) (x + 2) = 0\n\n⇒( x - 5 ) = 0, (x + 2) = 0\n\n x - 5 = 0 x = 5 x + 2 = 0 x = - 2\n\n(ii) 2x2 + x - 6 = 0;\n\nSolution:\n\n2x2 + x - 6 = 0\n\n⇒2x2 + 4x - 3x - 6 = 0\n\n⇒ 2x( x + 2 ) - 3(x + 2) = 0\n\n⇒( x + 2 ) (2x - 3) = 0\n\n⇒( x + 2 ) = 0, (2x - 3) = 0\n\nx + 2 = 0\n\nx = - 2\n\n2x - 3 = 0\n\n2x = 3\n\n x = 3 2\n\n(iii) √2x2 + 7x + 5√2 = 0;\n\nSolution:\n\n√2x + 2x + 5x + 5√2 = 0\n\n√2x(x + √2) + 5(x + √2) = 0\n\n(x + √2) (√2x + 5) = 0;\n\n(x + √2) = 0, (√2x + 5) = 0\n\nx = - √2,  √2x = - 5\n\nx = - 5/√2\n\nx = -5√2/2\n\n(iv) 2x2 - x + 1/8 =0\n\nSolution:\n\n 2x2 - x + 1 = 0 8\n\nOr  ⇒16x2 - 8x + 1 = 0;\n\n⇒16x2 - 4x - 4x + 1 = 0\n\n⇒ 4x( 4x - 1 ) - 1(4x - 1) = 0\n\n⇒( 4x - 1 ) (4x - 1) = 0\n\n⇒( 4x - 1 ) = 0, (4x - 1) = 0\n\n4x - 1 = 0\n\n4x = 1\n\n x = 1 4\n\n4x - 1 = 0\n\n4x = 1\n\n x = 1 4\n\n(v)  100x2 - 20x + 1 = 0;\n\nSolution:\n\n100x2 - 20x + 1 = 0\n\n⇒100x2 - 10x - 10x + 1 = 0\n\n⇒ 10x( 10x - 1 ) - 1(10x - 1) = 0\n\n⇒( 10x - 1 ) (10x - 1) = 0\n\n⇒( 10x - 1 ) = 0, (10x - 1) = 0\n\n10x - 1 = 0\n\n10x = 1\n\n x = 1 10\n\n10x - 1 = 0\n\n10x = 1\n\n x = 1 10\n\nQ2. Solve the problems given in Example 1.\n\nExample1:\n\n(i) John and Jivanti together have 45 marbles. Both of them lost 5 marbles each, and the product of the number of marbles they now have is 124. We would like to find out how many marbles they had to start with.\n\n(ii) A cottage industry produces a certain number of toys in a day. The cost of production of each toy (in rupees) was found to be 55 minus the number of toys produced in a day. On a particular day, the total cost of production was Rs 750. We would like to find out the number of toys produced on that day.\n\nQ3. Find two numbers whose sum is 27 and product is 182.\n\nQ4. Find two consecutive positive integers, sum of whose squares is 365.\n\nQ5. The altitude of a right triangle is 7 cm less than its base. If the hypotenuse is 13 cm, find the other two sides.\n\nQ6. A cottage industry produces a certain number of pottery articles in a day. It was observed on a particular day that the cost of production of each article (in rupees) was 3 more than twice the number of articles produced on that day. If the total cost of production on that day was Rs 90, find the number of articles produced and the cost of each article.\n\nPage 2 of 4\n\nChapter Contents:" ]
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https://www.scribd.com/doc/289485924/Method-of-Slices-Slope-Analysis
[ "You are on page 1of 21\n\n# GLE/CEE 330: Soil Mechanics\n\n## Slope Stability Analysis:\n\nMethod of Slices\n\nGeological Engineering\n\nLearning Objectives\nDiscuss when rotational slope analysis may be\nappropriate\nLearn basic analysis approaches\nDescribe example problems for ordinary method\nof slices (OMS) and modified Bishops method\n\n## (La Conchita, CA, USGS)\n\n(Varnes)\n\nLandslide in Thailand\n\n## t Equilibrium: 1) Assume some circular (or other shape) failure surface\n\n2) Calculate driving forces (moment about O)\n3) Calculate resisting forces (moment about O)\n\nssues:\nWhere is the center of mass?\nHow does resistance vary along surface?\nHow does normal stress vary along surface?\nWater table and seepage forces?\nSoil layering?\nMore complex geometry?\n\n## ume some failure surface\n\ncretize failure surface into smaller elements (slices)\ntom of each slice passes through one type of material\nved bottom of each slice approximated as chord\ne slices = more refined solution\n40 slices typically sufficient (less for hand solutions)\nculate factor of safety for each slice (strength/stress) and overall factor of safety\nd lowest FS for different failure surfaces\n\nOMS\n\nModified Bishops\n\n## Side forces neglected (statically\n\ndeterminant)\nEffective Stress Analysis (ESA)\n\n## Total Stress Analysis (TSA)\n\nExample 1\nCompute the long term factor of safety for the failure surface using the OMS\n\n## Divide into slices (draw to scale)\n\n2) Compute weights\n\n## glecting side forces (OMS) produces FS too low (conservative)\n\nume side shear forces are zero but account for side normal forces\n\n## glecting side forces (OMS) produces FS too low (conservative)\n\nume side shear forces are zero but account for side normal forces\n\n## Total Stress Analysis (TSA)\n\nExample 2\n\nmpute the long term FS for the failure surface using the Modified Bishops Metho\n\n## S = 2,206/1,071 = 2.05 (too high)\n\n3) Trial FS = 2.10" ]
[ null ]
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https://www.studyvirus.com/set-8-alphanumeric-series-for-sbi-po-and-sbi-clerk-2019-must-go-through-these-questions/
[ "Set-8 Alphanumeric Series For SBI PO and SBI Clerk 2019 | Must Go Through These Questions", null, "Dear Aspirants,\nWe are providing the most important  Alphanumeric Series Questions for SBI PO 2019, SBI Clerk 2019 and all other competitive bank and insurance exams. These questions have very high chances to be asked in SBI PO 2019, SBI Clerk 2019.\nGet the Best Test Series for SBI PO 2019 at the most affordable price (Based on Real Exam Pattern) – Click Here\nDownload the Best GK Gaming App for Current Affairs and GK (Bank+SSC)– Click here (App No 1)  (App No 2)\n\nDirections:(1-5) Study the following arrangement carefully and answer the questions given below—", null, "1. How many such prime numbers are there in the given arrangement each of which is immediately followed by a symbol and preceded by a consonant?\n\n2. If the places of the those symbols which are immediately followed by numbers are interchanged with those of the numbers in the given arrangement then which of the following will be the eleventh from the right end?", null, "3. Which of the following is the seventh letter to the left of ©?", null, "4.", null, "What should come in place of the question mark (?) in the following series based on the above arrangement?", null, "5. If the symbols and numbers are dropped from the given arrangement and then the letters are rearranged in alphabetical order which of the following will be the sixth from the left end?", null, "Directions:(6-10) Study the following elements (letters, digits and symbols sequence) to answer the questions given below:", null, "6. If each symbol of the above sequence is replaced with a letter and each digit is replaced with a new symbol, then how many letters will be there in the sequence?\n\n7. How many such digits are there in the sequence each of which is immediately preceded as well as followed by letters ?", null, "8.  Which of the following letters is exactly midway between the letters falling between ‘C’ and ‘5’?", null, "9. If each symbol of the above sequence is replaced with the digits from ‘1’ to ‘9’ which are not there in the sequence, then what will be the sum of all digits ? [Each symbol should be replaced with a different digit.]", null, "10. How many letters to the left of the one who is 3rd right of S?", null, "• Directions:(1-5) Study the following arrangement carefully and answer the questions given below—", null, "1. Question\nHow many such prime numbers are there in the given arrangement each of which is immediately followed by a symbol and preceded by a consonant?\nAns:1\nThere is no such combination.\n\n• 2. Question\nStudy the following arrangement carefully and answer the questions given below—", null, "If the places of the those symbols which are immediately followed by numbers are interchanged with those of the numbers in the given arrangement then which of the following will be the eleventh from the right end?\nAns:3\nAccording to condition answer will be 4.\n\n• 3. Question\nStudy the following arrangement carefully and answer the questions given below—", null, "Which of the following is the seventh letter to the left of ©?\nAns:3\nThe seventh letter to the left of © is R.\n\n• 4. Question\nStudy the following arrangement carefully and answer the questions given below—", null, "What should come in place of the question mark (?) in the following series based on the above arrangement?", null, "Ans:4\nG#T\n\n• 5. Question\nStudy the following arrangement carefully and answer the questions given below—", null, "If the symbols and numbers are dropped from the given arrangement and then the letters are rearranged in alphabetical order which of the following will be the sixth from the left end?\nAns:5\nThe letter will be R.\n\n• Directions:(6-10) Study the following elements (letters, digits and symbols sequence) to answer the questions given below:", null, "6. Question\nIf each symbol of the above sequence is replaced with a letter and each digit is replaced with a new symbol, then how many letters will be there in the sequence?\nAns:1\nThere are 21 elements in the series. Among them, there are only 5 digits. Since symbols are replaced by letters and digits are replaced by symbols, ultimately there will be (21 – 5 =) 16 letters in the series.\n\n• 7. Question\nStudy the following elements (letters, digits and symbols sequence) to answer the questions given below:", null, "How many such digits are there in the sequence each of which is immediately preceded as well as followed by letters ?\nAns:4", null, "• 8. Question\nStudy the following elements (letters, digits and symbols sequence) to answer the questions given below:", null, "Which of the following letters is exactly midway between the letters falling between ‘C’ and ‘5’?\nAns:3\nThe letters falling between C and 5 are as follows:\nDZY PMKS. Hence, P is the required letter.\n\n• 9. Question\nStudy the following elements (letters, digits and symbols sequence) to answer the questions given below:", null, "If each symbol of the above sequence is replaced with the digits from ‘1’ to ‘9’ which are not there in the sequence, then what will be the sum of all digits ? [Each symbol should be replaced with a different digit.]\nAns:2\nIf the four symbols are replaced by the remaining digits from 1 to 9 (1, 4, 6, 8) then sum of the digits", null, "", null, "• 10. Question\nStudy the following elements (letters, digits and symbols sequence) to answer the questions given below:", null, "How many letters to the left of the one who is 3rd right of S?\nAns:2\nThere is only 10 letters to the left of 5 that is the 3rd right of S.", null, "" ]
[ null, "https://www.studyvirus.com/wp-content/uploads/2019/04/Alpha-numeric-1.jpg", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/2-1-300x21.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/2-1-300x21.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/2-1-300x21.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/2-1-300x21.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/1-1780.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/2-1-300x21.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/1-1775-300x31.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/1-1775-300x31.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/1-1775-300x31.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/1-1775-300x31.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/1-1775-300x31.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/2-1-300x21.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/2-2-300x21.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/2-3-300x21.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/2-4-300x21.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/1-1780.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/2-5-300x21.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/1-1775-300x31.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/1-1776-300x31.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/2-55-300x24.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/1-1776-300x31.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/1-1776-300x31.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/1-1778.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/3-39-300x55.png", null, "http://www.studyvirus.com/wp-content/uploads/2018/03/1-1777-300x31.png", null, "https://secure.gravatar.com/avatar/3b59581d03b40df50ecadef43339ade3", null ]
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https://encyclopedia2.thefreedictionary.com/Random+function
[ "Random Function\n\nAlso found in: Wikipedia.\n\nrandom function\n\n[′ran·dəm ′fəŋk·shən]\n(mathematics)\nA function whose domain is an interval of the extended real numbers and has range in the set of random variables on some probability space; more precisely, a mapping of the cartesian product of an interval in the extended reals with a probability space to the extended reals so that each section is a random variable.\n\nRandom Function\n\nIf a function of an arbitrary argument t is defined on the set T of the values of t and assumes numerical values or, more generally, values from some vector space, the function is said to be a random function if its values are determined by some trial and can differ depending on the outcome of the trial. It is also required that there exist a definite probability distribution function for the values. If the set T is finite, the random function is a finite set of random variables; this set can be regarded as a single random vector quantity.\n\nThe most thoroughly studied random function with an infinite T is the important special case where t assumes numerical values and is time. The random function X(t) in this case is called a stochastic process; when t assumes only integral values, the terms “random sequence” and “time series” are also sometimes applied to X(t). If the values of t are points in some region of a multidimensional space, the random function is called a random field. Typical examples of random functions that are not stochastic processes are the velocity, pressure, and temperature fields of a turbulent flow of a liquid or gas and the height z of the agitated surface of the sea or the surface of an artificial rough plate.\n\nThe mathematical theory of random functions coincides with the theory of probability distribution functions in the function space of the values of X(t). These distribution functions can be specified by the set of finite-dimensional probability distribution functions for the sets of random variables X(t1), X(t2), …, X(tn) corresponding to all possible finite subsets (t1, t2,…, tn) of points of T. Alternatively, the distribution functions can be specified by the characteristic functional of the random function X(t); this characteristic functional is the mathematical expectation of the random variable il[X(t)], where l[X(t)] is a linear functional of X(t) of general form. Much progress has been made in the theory of homogeneous random fields, which are a special class of random functions; this class is a generalization of the class of stationary stochastic processes.\n\nREFERENCES\n\nVybrosy sluchainykh polei: Sb. st. Moscow, 1972.\nYaglom (Iaglom), A. M. “Second-order Homogeneous Random Fields.” In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol. 2. Berkeley-Los Angeles, 1961.\nWhittle, P. “Stochastic Processes in Several Dimensions.” Bulletin of the Institute of Statistics, 1963, vol. 40.\n\nA. M. IAGLOM\n\nReferences in periodicals archive ?\nMany researches on NIZK have been proposed to improve its efficiency, for example, from the earlier work, such as the Fiat-Shamir heuristic where the cryptographic hash function is introduced as a random function; recent works such as Lindell's transform of non-programmable random oracle (NPRO) model that needs no random oracles to achieve efficient NIZK arguments in the common reference string (CRS) model, and Ciampi's work which combines each own's advantage of both without a random oracle.\nDCF algorithm utilized the random function in the binary exponential backoff algorithm to produce the values of backoff.\nThe right-hand side of (1), F(t), is assumed to be a time-dependent random function that depends linearly on [[omega].sub.2] and expressed by\nIn this paper, the method of decomposing data by random function is proposed.\nFor a random function f : [[0, [infinity]).sub.T] x [OMEGA] [right arrow] R we define the extension [??] : [0, [infinity]) x [OMEGA] [right arrow] R by\nThe pattern generator selects at random pattern by using a random function with various rules given.\nLiu, \"Simulation of stochastic ocean states by random function methods,\" Journal of Vibration and Shock, vol.\nAn example for such random function f (i, j) to generate the Metadata M is\nIf any of the functional parameters are considered to be random, then analysed function itself is consequently also a random function. The general stochastic formulation of the reliability-based optimization problem can be expressed like this:\nAll the valid lottery tickets and the winning numbers must be verified via a verifiable random function.\nAs it is well known, a random field [PHI](x) is a 2-dimensional, real-valued random function. The function [PHI](x) is called macroscopically homogeneous (or stationary in the strict sense), if [PHI](x) is invariant with respect to translations, i.e., its finite-dimensional distributions are translation invariant.\n\nSite: Follow: Share:\nOpen / Close" ]
[ null ]
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https://ca.answers.yahoo.com/question/index?qid=20090724133817AATfPlW
[ "# 8 Choices, 4 spots = number of options?\n\nThere is a commercial airing right now about a fast food chain having 8 menu items to choose from, pick four. You can pick the same item 1, 2, 3, even 4 times. The commercial claims that there are 320 possibilities. How so? My numbers are huge...\n\nRelevance\n\nLet C(n,k) be the binomial coefficient n!/k!(n-k)! (C(n,k) is the number of ways to select k things from n things, when order doesn't matter). Clearly, you want to use C(n,k), since your meal doesn't depend on the order in which you choose the menu items. (In case you're unfamiliar with the notation, n! = n(n-1)(n-2)...1).\n\nThe number of ways to pick 4 items is C(8,4) = 70\n\nThe number of ways to pick 3 items is 3*C(8,3) = 168 (there are 3 ways in each choice to choose which item is doubled)\n\nThe number of ways to pick 2 items where one item is tripled is 2*C(8,2) = 56.\n\nThe number of ways to pick 2 items where each item is doubled is C(8,2) = 28.\n\nThe number of ways to pick 1 item, quadrupled, is C(8,1) = 8.\n\nAdding these all together, the total is 330, so they were close. If the numbers you're getting are much bigger, it could be because you're counting the order in which the items are chosen.\n\nHope this helps (& please don't hold me responsible for any errors in arithmetic -- it's late).\n\nSource(s): just me.\n•", null, "Login to reply the answers" ]
[ null, "https://ct.yimg.com/cy/1768/39361574426_98028a_128sq.jpg", null ]
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https://needhamia.com/dog-weight-scale-part-7-choosing-matching-resistors/
[ "", null, "# Dog Weight Scale Part 7: Choosing Matching Resistors\n\nIn my previous post, I worked through the calculations of weight and center of gravity when using four Load Cell Amplifiers instead of one.  In this post, I build the circuit for the first of the four Load Sensor / Load Cell Amplifier combinations I’ll be using.\n\nThe Sparkfun Load Cell Amplifier is designed to connect a Load Cell to an Arduino.  A Load Cell contains a full (4 resistor) Wheatstone Bridge, but a Load Sensor contains only half of a Wheatstone bridge.  To connect a Load Sensor to a Load Cell Amplifier, I need to add two resistors: R3 and R4 in the following diagram.  The dotted box represents the Load Sensor. The triangle in the middle of the diagram represents the Load Cell Amplifier.  As the weight on the Load Sensor increases, R1 decreases, which causes the voltage V1 to increase, causing the digitized amplifier output to increase.\n\nOnce I’d built the circuit, the next task was to find the right pair of resistors, R3 and R4. In the photo below, R3 and R4 are the two resistors at the left; the Load Cell Amplifier is the small red board in the center, and of course the Arduino is on the right.\n\nR3 and R4 need to have a ratio that is close to the ratio of the two (matched) resistors that are in the corresponding Load Sensor, so that the digitized voltage difference, V1 and V2 in the above schematic, is near zero.\n\nThe tricky part is that a normal multimeter shows that the two resistors of the Load Sensor are equal, and that R1, above, changes by only an ohm or so with pressure.  That multimeter isn’t accurate enough to show the differences in resistance that you need to measure.\n\nSo either you can buy a high-precision multimeter (expensive), or you can use the Load Cell Amplifier to measure the resistances. I decided the use the Amplifier.\n\nI ordered a bunch of 1Kohm, 0.1% resistors.  Then I picked 4 of them (I could have used more, but that’s more work), labeled them A, B, C, and D, then started measuring the Arduino ADC (Analog Digital Converter) values of various pairings of those resistors." ]
[ null, "https://needhamia.com/wp-content/uploads/2021/05/schem1-408x372.png", null ]
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http://www.mxcss.com/web-howto/i6I/how-to-change-css-where-idarrayindex/
[ "## How to change CSS where id=array(index)?\n\nTags: javascript,jquery,html,css,arrays\n\n### Problem :\n\ni just write an small code that should change CSS of X ids.\n\nI have 3 ids in HTML objects (every element has 1 Class)=\n\nelement 1 id = \"p1\" class=\"test1\"\nelement 2 id = \"p2\" class=\"test1\"\nelement 3 id = \"p3\" class=\"test1\"\n\ni create two Arrays :\n\n`````` var statArray = [\"p1\",\"p3\"];\nvar array = [\"p1\",\"p3\"];\n``````\n\nthan i check if Array 1 = Array 2\n\n``````for (var ix = 0; ix < array.length; ix++) {\nif (array[ix] === statArray[ix])\n``````\n\nNow i will change the css (background-color) of the ids.\n\nHow can i change the ids where id = array[ix]?\n\nmy attempt:\n\n``````\\$(\".test1\").attr(array[ix]).css(\"background-color\",\"#ff0000\");\n``````\n\n### Solution :\n\nJust use this\n\n``````for (var ix = 0; ix < array.length; ix++) {\nif (array[ix] === statArray[ix]){\n\\$(\".test1#\"+array[ix]).css(\"background-color\",\"#F00\");\n}\n}\n``````" ]
[ null ]
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https://ericlippert.com/2023/01/
[ "# The names of birds, part 2\n\nReader “Joel” had an insightful comment on the first part of this series which I thought deserved a short episode of its own. Recall that we proved the theorem “if a compositional forest contains a mockingbird then every bird in the forest has a fixpoint“. An equivalent way to state that theorem is: “if a forest contains a bird without a fixpoint then either the forest is not compositional or it contains no mockingbird“. Joel’s question was whether there is a more “intuitive” way to understand why the mockingbird is so interesting that the presence of a bird without a fixpoint should prevent a mockingbird from existing in the same (compositional) forest.\n\nThe short version is: we’ll say that two birds agree on an input if their outputs are the same for that input. The mockingbird is a bird that agrees with every other bird on at least one input. A bird without a fixpoint is a bird that can always produce a different output than its input. Their composition is a bird which disagrees with every bird in the forest on one input and therefore is not in the forest at all.\n\nThe long version is:\n\nI find it easiest to get an intuition for a general rule by looking at some simple examples.\n\nThe first thing I’d note is that it is quite easy to construct a non-compositional forest containing a bird without a fixpoint. A simple example would be a non-compositional forest containing exactly two birds, A and M. Define them such that:\n\nAA = M\nAM = A\nMA = M\nMM = M\n\nPlainly these are two different birds, since their responses disagree on input M. Plainly M is a mockingbird, since Mx=xx for all x in the forest. And plainly A has no fixpoint. This forest is not compositional because there is no bird C in the forest such that Cx=A(Mx) for all x. With such a small number of birds in the forest it seems quite reasonable that one might have no fixpoint.\n\nLet’s look at a more interesting example, this one in a compositional forest. Let’s suppose there is a countable infinity of birds in the forest. By “countable infinity” I mean that we can number each bird with a natural number; we’ve got bird 0, bird 1, bird 2, and so on. In fact, let’s just use natural numbers as the names of the birds in this forest.\n\nSince each bird needs a response to hearing the name of every other bird in the forest, we can imagine an infinite lookup table that says what each bird responds when it hears the name of another bird in the forest. We’ll put the “called to” bird on the vertical axis and the “name called” on the horizontal axis:\n\n``` 0 1 2 3 ...\n------------\n0 | 9 5 2 5 ...\n1 | 8 3 6 7 ...\n2 | 0 1 2 3 ...\n3 | 3 1 4 1 ...\n... |\nS | 1 2 3 4 ...\n... |\n```\n\nSo in our notation 0 1=5, 2 3=3, and so on.\n\nLet’s suppose this forest is compositional and contains a bird, call it S, which has no fixpoint. We could choose any such S, but without loss of generality we shall pick a specific S, the bird where S 0=1, S 1=2, S 2=3, S 3=4, … and so on; the “successor” bird. Of course S has some “number” name but we don’t really care what it is, so we’ll just “alias” that one as S.\n\nNow let’s suppose there’s a mockingbird in the forest and deduce a contradiction. The mockingbird tells us what is on the highlighted diagonal! That is, M 0=9, M 1=3, M 2=2, M 3=1, and so on. (As with S, M is an alias for some “number” name but we don’t know or care what it is.)\n\nNow comes the fun bit. The forest is compositional, so therefore there exists a bird C such that Cx=S(Mx) for all x. We do care what the number name of C is. So, which is it?\n\nC 0=10, and therefore C is not 0, because 0 0=9. But similarly, C 1=4, so C is not 1. And similarly C is not 2, 3, 4, 5, … because C definitely disagrees with each of those functions I mean birds at the diagonal. But if C has no number name, then it is not in the forest at all, contradicting the statement that the forest is compositional. We now have deduced a contradiction, and therefore our assumption that M is in the forest is shown to be false.\n\nBy looking at a specific example — a countable forest with a successor bird — perhaps you have a better intuition about why Mx=xx and any bird S that has no fixpoint cannot coexist in a compositional forest: if they did then the bird C such that Cx=S(Mx) definitely disagrees with every bird in the forest on at least one input, and therefore the forest would not be closed under composition; there would always be a missing bird.\n\nNext time on FAIC: We’ll look at some more fun examples and then get back to compilation.\n\n# The names of birds, part 1\n\nFor the next part in my Bean Machine retrospective to make sense I’ll need to make a short digression. In looking back on the almost 20 years I’ve been blogging, it is surprising to me that I’ve only briefly alluded to my appreciation of combinatory logic. In the next couple of episodes, I’ll do a quick introduction based on the delightful book that introduced it to me: To Mock A Mockingbird, by the late Raymond Smullyan.\n\nImagine a forest containing some birds, possibly finitely or infinitely many. These are unusual birds. When you call the species name of a bird in the forest to a bird in the forest, it calls one back to you. Maybe the same, maybe different, but when you tell a bird the name of a bird, it names a bird back to you. There might be a Red Capped Cardinal in the forest and when you call out Great Blue Heron to it, it calls back Belted Kingfisher.\n\n(Photos by me; click for higher resolution.)\n\nWe will notate “I called Q to P and got response R” as PQ = R. If I then called out S to R and R responded with T, we’ll notate that as PQS = RS = T. We’ll use parentheses in the obvious way: PQS = (PQ)S and this might be different from P(QS). The latter is “I called S to Q, and then called Q‘s response to P“. I’ll use capital letters to represent specific bird names, and small letters to represent variables.\n\nThe question we’re considering here is: under what circumstances will a bird call back the same name you called to it? That is, for a given bird y, under what circumstances does yx = x?\n\nSmullyan calls birds with this relationship “fondness” — that is, “y is fond of x” means that yx = x. If y is fond of x then x is said to be a “fixpoint” of y.\n\nA forest is said to be “compositional” if for every pair of birds (a, b) — a and b can be the same or different — there is a bird c in the forest such that cx = b(ax) for all x. That is, if we call any name x to a, and then call its response to b, we get the same result as simply calling x to c. c is the composition of “call x to a, and then call that response to b“.\n\nA mockingbird is the bird M with the property that Mx = xx. That is, for any bird name x, M tells you what x‘s response is to its own name. (The attentive reader will note that we have not said what MM is, but at least we know from the definition that MM = MM, so give me some credit for consistency at least.)\n\nTheorem: if a compositional forest contains a mockingbird then every bird in the forest is fond of at least one bird.\n\nProof: Try to prove it yourself; scroll down for the solution.\n\n.\n\n.\n\n.\n\n.\n\n.\n\n.\n\n.\n\nLet p be any bird. Let c be the bird that composes p with M. Therefore cx = p(Mx) for all x.\n\nIn particular, that’s true for c, so cc = p(Mc). But Mc = cc, thus cc = p(cc), and we’ve found a bird that p is fond of. Since p was any bird, every bird in the forest is fond of at least one bird. Or, put another way, every bird in a compositional forest with a mockingbird has at least one fixpoint.\n\nNext time on FAIC: we’ll take a look at a few more interesting birds, and then discuss why this whimsy is relevant to compilation before getting back to Bean Machine.\n\n# Bean Machine Retrospective, part 6\n\nHappy New Year all!\n\nLast time I briefly described the basic strategy of the Beanstalk compiler: transform the source code of each queried or observed function (and transitively their callees) into an equivalent program which partially evaluates the model, accumulating a graph as it goes. In this post I’ll go through the first step of the transformation.\n\nThe idea of the first source-to-source transformation is to take a relatively complicated language — Python — and replace it with what I think of as “simplified Python”. The Zen of Python famously says “there should be one — and preferably only one — obvious way to do it” but that is not the goal of this simplification step. The Zen of Python emphasizes obviousness. It is about making the code clear and the language discoverable. My goal with simplified Python is “there should be only one way to do it” — because it is easier to instrument a program to capture its meaning as it runs if there’s only one way to do everything.\n\nConsider for example how to call a function foo with three arguments:\n\n```foo(1, 2, 3)\nx = [1, 2, 3]\nfoo(*x)\nfoo(1, 2, bar=3)\nx = {\"bar\":3}\nfoo(1, 2, **x)\n```\n\n… and so on — there are a great many obvious ways to call a function in Python, but I’d like there to be exactly one in simplified Python.\n\nThe first thing we do when analyzing a queried or observed function is to obtain its source code, parse that into an abstract syntax tree (AST), and then do a series of AST transformations that reduce the function into an equivalent but simpler Python program. This is the most traditional-compiler-ish step in the pipeline.\n\n(How do we obtain the source code? Remember, we have an RVID for each observation and query; the RVID contains a reference to the original function object. Python’s runtime can provide the source code for a given function reference.)\n\nLet’s look at an only slightly more complicated version of our “hello world” model:\n\n```@random_variable\ndef fairness():\nreturn Beta(2,2)\n\n@random_variable\ndef flip(n):\nreturn Bernoulli(0.5 * fairness())\n```\n\nIn our simplified Python:\n\n• Every value gets its own variable\n• Every computation gets its own statement\n• Every call has the same syntax: `a = b(*c, **d)`\n\nOur two methods in the simplified form would be:\n\n```def fairness():\n_t1 = 2\n_t2 = 2\n_t3 = [_t1, _t2]\n_t4 = {}\n_t5 = Beta(*_t3, **_t4)\nreturn _t5\n\ndef flip(n):\n_t1 = 0.5\n_t2 = []\n_t3 = {}\n_t4 = fairness(*_t2, **_t3)\n_t5 = _t1 * t4\n_t6 = [_t5]\n_t7 = {}\n_t8 = Bernoulli(*_t6, **_t7)\nreturn _t8\n```\n\nASIDE: Readers with some experience writing compilers might ask “is this static-single-assignment form?” SSA form is a transformation whereby every variable is assigned exactly once; it’s used for control flow analysis and other program analyses. I did not write a full-on SSA transformation but when I was doing initial prototyping I knew that I might need SSA form in the future. Think of it as SSA-light if that makes sense.\n\nWe can go further; in our simplified Python:\n\n• logical operators `and` and `or` are eliminated\n```x = y and z\n```\n\ncan be simplified to\n\n```x = y\nif x:\nx = z\n```\n\nand now the graph accumulator does not need to worry at all about logical operators, because there aren’t any in the simplified program.\n\n• Every `while` loop is `while True`.\n• No loop has an `else` clause. (Did you know that Python loops have `else` clauses? It’s true!)\n• There are no compound comparisons:\n```x = a < b < c\n```\n\ncan be simplified to\n\n```x = a < b\nif x:\nx = b < c\n```\n\nand now the graph accumulator does not need to worry about compound comparisons, because there aren’t any.\n\n• Similarly there are no lambdas, no function annotations, and so on. All the convenient syntactic sugars are desugared into their more fundamental form.\n\nThe simplified language is definitely not a language I’d like to write a long program in, but it is a language that is easy to analyze programmatically. Thus far, the transformations make the program longer and simpler, but do not change its meaning; if we compiled and ran the simplified version of the program it should do the same thing as the normal version.\n\nNext time on FAIC: let’s get into the details of how the AST-to-AST transformation code works. I had a lot of fun writing it." ]
[ null ]
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http://ianfinlayson.net/class/cpsc220/notes/16-simulation
[ "# Simulation\n\n### Introduction\n\nOne particular type of program that lends itself well to using objects is simulation programs. A simulation is a program that models some kind of process or system over time. Simulation programs are very important to many other disciplines.\n\nSome examples of areas that use computer simulation:\n\n• Astronomical events.\n• Particle physics.\n• Cell reproduction.\n• Chemical reactions.\n• Spread of disease.\n• Weather prediction.\n• Traffic patterns.\n• Crowd modelling.\n• Artificial intelligence.\n• Most video games.\n\nSimulations are done by keeping track of several objects, and how they change over time. The objects can possibly interact with each other or outside influences.\n\n### Example: Disease Simulation\n\nAs an example of how we could do a simulation on multiple objects, consider the problem of studying a disease outbreak. The general idea is we can keep track of a large number of virtual people, as they move about. Some people will start out sick, and can pass the disease to those they are near.\n\nBelow is a simple program which does just such a simulation. There are a number of parameters which control the simulation as it runs. They are grouped in a class so they can be changed in one place:\n\n\n// a class which just contains some numbers grouped together\nclass SimulationParameters {\n\nstatic final int POPULATION = 200;\nstatic final int MOVE_AMOUNT = 2;\nstatic final int HEAL_DAYS = 10;\nstatic final int INITIAL_SICK = 10;\nstatic final double INFECT_DISTANCE = 2.5;\n}\n\n\nTo keep track of whether someone is sick or not, we can use an enumeration:\n\n\n// represents whether a given person is healthy or sick\nenum State {\nHEALTHY,\nINFECTED\n};\n\n\nWe then can make a Person class for modeling one person in this simulation. They have variables which store their sate and position, and functions which modify them:\n\n\nclass Person {\n\n// variables used in simulating the person\n\nprivate State state;\nprivate int days_til_healed;\nprivate double x, y;\n\n// construct a healthy person, randomly positioned in space\nPerson() {\nstate = State.HEALTHY;\nRandom generator = new Random();\nx = generator.nextInt(100);\ny = generator.nextInt(100);\n}\n\n// move the person randomly\nvoid move() {\nRandom generator = new Random();\n\n// getr a random number between 0 and 1\ndouble val = generator.nextDouble();\n\n// multiply by twice the move amount - this gets us a value between\n// negative move amount, and positive move amount\nval = val * 2.0 * SimulationParameters.MOVE_AMOUNT;\nval = val - SimulationParameters.MOVE_AMOUNT;\n\n// move in the x direction by this much\nx += val;\n\n// do the same thing for y\ndouble val2 = generator.nextDouble();\nval2 = val2 * 2.0 * SimulationParameters.MOVE_AMOUNT;\nval2 = val2 - SimulationParameters.MOVE_AMOUNT;\ny += val2;\n\n// don't let them go off the edges\nif (x > 100) {\nx = 100;\n}\nif (y > 100) {\ny = 100;\n}\nif (x < 0) {\nx = 0;\n}\nif (y < 0) {\ny = 0;\n}\n}\n\n// called after each step in the simulation, if this person is sick,\n// is it time for them to get better yet?\nvoid checkHeal() {\nif (days_til_healed == 0) {\nstate = State.HEALTHY;\n} else {\ndays_til_healed--;\n}\n}\n\n// set this person as infected\nvoid setInfected() {\nstate = State.INFECTED;\ndays_til_healed = SimulationParameters.HEAL_DAYS;\n}\n\n// return the state of this person\nboolean isInfected() {\nreturn state == State.INFECTED;\n}\n\n// check if this person has been infected by another person\nvoid checkInfected(Person others[]) {\n// loop through each other person\nfor (int i = 0; i < SimulationParameters.POPULATION; i++) {\n// find the euclidean distance between them\ndouble distance = Math.sqrt((x - others[i].x) * (x - others[i].x)\n+ (y - others[i].y) * (y - others[i].y));\n\n// if they are too close\nif (distance < SimulationParameters.INFECT_DISTANCE) {\n// if they're sick and we are not, we are now sick too :(\nif (state == State.HEALTHY && others[i].state == State.INFECTED) {\nsetInfected();\n}\n}\n}\n}\n}\n\n\nOnce we have a class to keep track of an individual person, we can create an array of people, and loop through it, performing the simulation over time:\n\n\n// the main class which runs the simulation\nclass DiseaseSimulation {\n\npublic static void main(String args[]) {\n// make an array of people\nPerson people[] = new Person[SimulationParameters.POPULATION];\n\n// actually create the people\nfor (int i = 0; i < SimulationParameters.POPULATION; i++) {\npeople[i] = new Person();\n}\n\n// set the first so many to be sick\nfor (int i = 0; i < SimulationParameters.INITIAL_SICK; i++) {\npeople[i].setInfected();\n}\n\nScanner in = new Scanner(System.in);\n\n// keep going until the user says no\nboolean cont;\nint day = 1;\ndo {\n// go through each person\nfor (Person p : people) {\n// move them\np.move();\n\n// check if they are all better\np.checkHeal();\n\n// check if they are sick\np.checkInfected(people);\n}\n\n// count how many people are sick\nint sick = 0;\nfor (Person p : people) {\nif (p.isInfected()) {\nsick++;\n}\n}\n\n// print stats\nSystem.out.printf(\"After %d days, %d people are sick and %d people are well\\n\",\nday, sick, SimulationParameters.POPULATION - sick);\n\n// move to the next day\nday++;\n\n// check if we want to continue\nSystem.out.print(\"Continut (y/n): \");\ncont = in.next().charAt(0) == 'y';\n} while (cont);\n}\n}\n\n\nSome questions we could ask of this simulation:\n\n• Does the disease ever go away? After how many years?\n• Does the infection number level off? At what level?\n• What factors make the disease more deadly?\n• What if people who catch the disease become immune?\n• If the disease was fatal, how many would die?" ]
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https://forum.polymake.org/viewtopic.php?f=8&t=1850&sid=baf0dd475d196c756fc5f5483b1b47d4
[ "", null, "## Stable intersection not contained in set-theoretic intersection\n\nQuestions and problems about using polymake go here.\nPaulH\nPosts: 4\nJoined: 22 Mar 2021, 17:07\n\n### Stable intersection not contained in set-theoretic intersection\n\nHi,\n\nI have a question about the \"intersect\" and \"set_theoretic_intersection\" commands in polymake. I made an example down below with two tropical hypersurfaces in R^{4}. Polymake then tells me that their stable intersection is two-dimensional, whereas their set-theoretic intersection is one-dimensional. Is polymake by any chance removing a lineality space in the set-theoretic intersection or?\n\nThe corresponding code is as follows:\n\nCode: Select all\n\napplication \"tropical\"; $f1 = toTropicalPolynomial(\"min(a+b+c-20,b+c+w+5,d+2*b+1,c+2*w)\", qw( a b c d w ));$f2 = toTropicalPolynomial(\"min(2*a+b-5,a+b+w+3,2*b+w-2,b+c+w+10)\", qw( a b c d w )); $V1 = new Hypersurface<Min>(POLYNOMIAL=>$f1); $V2 = new Hypersurface<Min>(POLYNOMIAL=>$f2); $W12 = set_theoretic_intersection($V1,$V2);$V12 = intersect($V1,$V2); print $W12->DIM; print$V12->DIM; \n\njoswig\nMain Author\nPosts: 245\nJoined: 24 Dec 2010, 11:10\n\n### Re: Stable intersection not contained in set-theoretic intersection\n\nThat's a difficult question in the sense that these functions reside in Simon Hampe's extension atint, which became bundled a while ago. Thing is: no-one is currently working on that code.\n\nNonetheless, we will check. Just be a bit patient, please.\n\nhampe\nDeveloper\nPosts: 48\nJoined: 29 Apr 2011, 10:42\n\n### Re: Stable intersection not contained in set-theoretic intersection\n\nOh dear, oh dear, this was all so long ago", null, "I haven't worked on polymake for four years and I can see from the source that a few changes were made. Also, I don't have a working local polymake at the moment, so my diagnosis is at least half guesswork.\n\nHere's what I can tell. The function set_theoretic_intersection eventually uses fan_intersection in convex_hull_tools.cc. The documentation in convex_hull_tools.h says that \"All coordinates should be given non-tropically-homogeneously\", i.e. dehomogenized. However as far as I can tell, the coordinates of the cycles are just given to the fan_intersection function as is:\n\nCode: Select all\n\n const Matrix<Rational> &arays = A.give(\"VERTICES\"); const IncidenceMatrix<> &acones = A.give(\"MAXIMAL_POLYTOPES\"); const Matrix<Rational> &alineality = A.give(\"LINEALITY_SPACE\"); const Matrix<Rational> &brays = B.give(\"VERTICES\"); const IncidenceMatrix<> &bcones = B.give(\"MAXIMAL_POLYTOPES\"); const Matrix<Rational> &blineality = B.give(\"LINEALITY_SPACE\"); fan_intersection_result result = fan_intersection(arays,alineality,acones, brays,blineality,bcones); \nInstead, dehomogenization takes place *after* the intersection.\n\nIf I perform this little cheat in the polymake-in-a-box:\n\nCode: Select all\n\n$cheat1 = new Cycle<Min>(VERTICES=>tdehomog($V1->VERTICES), MAXIMAL_POLYTOPES=>$V1->MAXIMAL_POLYTOPES,LINEALITY_SPACE=>tdehomog($V1->LINEALITY_SP ACE)); $cheat2 = new Cycle<Min>(VERTICES=>tdehomog($V2->VERTICES), MAXIMAL_POLYTOPES=>$V2->MAXIMAL_POLYTOPES, LINEALITY_SPACE=>tdehomog($V2->LINEALITY_S PACE)); $cheatintersect = set_theoretic_intersection($cheat1,$cheat2); the intersection now has the correct dimension 2! Of course the objects are nonsense since their vertices are not tropically homogenous, but the input to fan_intersection is now correct, hence so is the result. So, my diagnosis would be: This is a bug and someone should switch the dehomogenization to *before* the fan_intersection. It's probably faster if someone else than me does this, it'll take me ages to find the git repo url, see if my credentials still work, figure out ninja, etc. etc.... (Michael is so not buying me beer for this...) hampe Developer Posts: 48 Joined: 29 Apr 2011, 10:42 ### Re: Stable intersection not contained in set-theoretic intersection Just to blame the right guy: The bug seems to have been in the original already, see https://github.com/simonhampe/atint/blo ... l_tools.cc", null, "In earlier times a-tint used to not use tropically homogeneous coordinates, I suspect this got lost in the transition somehow? In that case this would have gone unnoticed for quite some time, so feel free to challenge my diagnosis... EDIT: I can currently only access https://github.com/polymake/polymake and I can't seem to find the testsuites in there - so I can't verify, whether we just chose sufficently naive tests to simply overlook this problem. hampe Developer Posts: 48 Joined: 29 Apr 2011, 10:42 ### Re: Stable intersection not contained in set-theoretic intersection Okay, I think I might have a minimal example worked out - and might have discovered a second bug (related to this one): When the set-theoretic-intersection is empty, I get a Code: Select all polymake: ERROR: Invalid chart coordinate The \"special\" thing about the example is that both hypersurfaces have lineality spaces, in particular the first one has one whose generator is *not* normalized w.r.t tropical homogeneity (which I think is fine?): Code: Select all tropical > print$V1->LINEALITY_SPACE; 0 1 1 -2 -2 1 \nNow, as long as all vertices and lin space generators are normalized, this bug won't occur: The additional tropical dimension just doesn't factor into the intersection since everything lives at x_0=0 anyway. Now if the lineality space is *not* normalized, things go awry.\n\nFor my minimal example, first of all note the following:\n\nCode: Select all\n\ntropical > $line1 = new Cycle<Max>(VERTICES=>[[1,0,0,0,0],[0,0,1,1,0],[0,0,0,-1,0],[0,0,-1,0,0]],MAXIMAL_POLYTOPES=>[[0,1],[0,2],[0,3]],WEIGHTS=>[1,1,1]); tropical >$line2 = new Cycle<Max>(VERTICES=>[[1,0,0,0,1],[0,0,1,1,0],[0,0,0,-1,0],[0,0,-1,0,0]],MAXIMAL_POLYTOPES=>[[0,1],[0,2],[0,3]],WEIGHTS=>[1,1,1]); tropical > print set_theoretic_intersection($line1,$line2); polymake: ERROR: Invalid chart coordinate \nThese are two tropical lines in threespace that don't intersect. I suspect that the error occurs when trying to dehomogenize the empty ray matrix at the end? I think this should be fixed automatically by putting the dehomogenization in the proper place. In any case it should definitely be a test case.\n\nIn any case, assuming that an empty intersection is indicated by an error message, let's look at the following:\n\nCode: Select all\n\ntropical > $line1 = new Cycle<Max>(VERTICES=>[[1,0,0,0,0],[0,0,1,1,0],[0,0,0,-1,0],[0,0,-1,0,0]],MAXIMAL_POLYTOPES=>[[0,1],[0,2],[0,3]],WEIGHTS=>[1,1,1]); tropical >$line2 = new Cycle<Max>(VERTICES=>[[1,0,0,0,1],[0,0,1,1,0],[0,0,0,-1,0],[0,0,-1,0,0]],MAXIMAL_POLYTOPES=>[[0,1],[0,2],[0,3]],WEIGHTS=>[1,1,1], LI NEALITY_SPACE=>[[0,0,0,0,1]]); tropical > print set_theoretic_intersection($line1,$line2)->DIM; 1 \nThis works fine, the lineality space is normalized. Let's try this with a non-normalized version:\n\nCode: Select all\n\ntropical > $line1 = new Cycle<Max>(VERTICES=>[[1,0,0,0,0],[0,0,1,1,0],[0,0,0,-1,0],[0,0,-1,0,0]],MAXIMAL_POLYTOPES=>[[0,1],[0,2],[0,3]],WEIGHTS=>[1,1,1]); tropical >$line2 = new Cycle<Max>(VERTICES=>[[1,0,0,0,1],[0,0,1,1,0],[0,0,0,-1,0],[0,0,-1,0,0]],MAXIMAL_POLYTOPES=>[[0,1],[0,2],[0,3]],WEIGHTS=>[1,1,1], LI NEALITY_SPACE=>[[0,1,1,1,2]]); tropical > print set_theoretic_intersection($line1,$line2)->DIM; polymake: ERROR: Invalid chart coordinate \nSo, all in all, I think my initial assumption was correct: The set-theoretic intersection needs non-homogenous coordinates to work and we didn't notice it until now because we were always being good little tropical geometers and using nicely homogenized coordinates", null, "blorenz\nDeveloper\nPosts: 119\nJoined: 10 Jan 2011, 17:21\n\n### Re: Stable intersection not contained in set-theoretic intersection\n\nThanks for looking into this Simon! We will try to take care of this next week.\n\npanizzut\nDeveloper\nPosts: 1\nJoined: 07 Jul 2021, 14:21\n\n### Re: Stable intersection not contained in set-theoretic intersection\n\nHi,\nI have fixed the problem with set_theoretic_intersection following Simon's comments.\nNow, we get the following dimensions in your example.\n\nCode: Select all\n\napplication \"tropical\"; $f1 = toTropicalPolynomial(\"min(a+b+c-20,b+c+w+5,d+2*b+1,c+2*w)\", qw( a b c d w ));$f2 = toTropicalPolynomial(\"min(2*a+b-5,a+b+w+3,2*b+w-2,b+c+w+10)\", qw( a b c d w )); $V1 = new Hypersurface<Min>(POLYNOMIAL=>$f1); $V2 = new Hypersurface<Min>(POLYNOMIAL=>$f2); $W12 = set_theoretic_intersection($V1,$V2);$V12 = intersect($V1,$V2); print $W12->DIM; 2 print$V12->DIM; 2 \nThe change will be available in the next polymake release. In the meanwhile, if you need to compute some more intersections, just let me know.", null, "" ]
[ null, "https://forum.polymake.org/theme_files/images/forumlogo.png", null, "https://forum.polymake.org/images/smilies/icon_e_wink.gif", null, "https://forum.polymake.org/images/smilies/icon_e_wink.gif", null, "https://forum.polymake.org/images/smilies/icon_e_wink.gif", null, "https://forum.polymake.org/cron/cron.task.primepostrevisions.prune", null ]
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https://testbook.com/question-answer/what-will-come-at-the-place-of-in-6-6--5fe9ed94e6cec65018411dd4
[ "What will come at the place of ? in (6 × 6 × 6 × 6 )5 × (9 × 9)5 ÷ (18 × 18)3 = 214 × 3?\n\n1. 29\n2. 26\n3. 28\n4. 24\n\nOption 3 : 28\nFree\nMonthly Current Affair - April\n152561\n50 Questions 50 Marks 20 Mins\n\nDetailed Solution\n\nGiven:\n\n(6 × 6 × 6 × 6 )5 × (9 × 9)5 ÷ (18 × 18)3 = 214 × 3?\n\nConcept used:\n\nFollow the BODMAS rule according to the table given below:", null, "Calculation\n\n(6 × 6 × 6 × 6 )5 × (9 × 9)5 ÷ (18 × 18)3 = 214 × 3?\n\n⇒ 6 5 × 4 × 9 5 × 2 ÷ 18 3 × 2 = 214 × 3 ?\n\n⇒ 220 .× 320 × 310 × 310 ÷ (26 × 36 × 36) = 214 × 3?\n\n⇒ 214 × 328 = 214 × 3?\n\n∴ ? = 28" ]
[ null, "https://storage.googleapis.com/tb-img/production/20/12/Ashish_S_Quant_Assignment_Ravindra_40Q%27s_21Dec_solution_ashish%20singh_D1.PNG", null ]
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https://www.geeksforgeeks.org/python-bokeh-visualizing-stock-data/?ref=rp
[ "Related Articles\n\n# Python Bokeh – Visualizing Stock Data\n\n• Last Updated : 03 Jul, 2020\n\nBokeh is a Python interactive data visualization. It renders its plots using HTML and JavaScript. It targets modern web browsers for presentation providing elegant, concise construction of novel graphics with high-performance interactivity.\nBokeh can be used to visualize stock market data. Visualization is be done using the `plotting` module. Here we will be using the sample stock datasets given to us by Bokeh.\n\nTo download the sample datasets run the following command on the command line :\n\n`bokeh sampledata`\n\nAlternatively, we can also execute the following Python code :\n\n```import bokeh\n```\n\n## Analyzing the dataset :\n\nIn the sample data provided by Bokeh, there are datasets of the stocks of the following companies :\n\n• AAPL which is Apple\n• IBM which is International Business Machines\n• MSFT which is Microsoft Corporation\n\nAll these datasets are available as CSV files. Below is a glimpse into the IBM.csv file :\n\n```Date Open High Low Close Volume Adj Close\n01-03-2000 102 105.5 100.06 100.25 10807800 84.48\n02-03-2000 100.5 105.44 99.5 103.12 11192900 86.9\n03-03-2000 107.25 110 106.06 108 10162800 91.01\n06-03-2000 109.94 111 101 103.06 10747400 86.85\n07-03-2000 106 107 101.69 103 10035100 86.8\n```\n\nThe file contains the stock data between the years 2000 and 2013 with over 3000 entries.\n\n## Visualizing the Stocks :\n\nWe will be plotting a line graph which will track the closing price of the stocks between the years 2000 and 2013 of all the 5 available companies.\n\n1. Import the required modules :\n• numpy\n• figure, output_file and show from bokeh.plotting\n• AAPL, FB, GOOG, IBM and MSFT from bokeh.sampledata.stocks\n2. Instantiate a figure object with the title and axis types.\n3. Give the names to x-axis and y-axis.\n4. Plot line graphs for all the 5 companies.\n5. Display the model.\n `# importing the modules``import` `numpy as np``from` `bokeh.plotting ``import` `figure, output_file, show``from` `bokeh.sampledata.stocks ``import` `AAPL, FB, GOOG, IBM, MSFT``  ` `# the file to save the model``output_file(``\"gfg.html\"``)``  ` `# instantiating the figure object``graph ``=` `figure(x_axis_type ``=` `\"datetime\"``, title ``=` `\"Stock Closing Prices\"``)``  ` `# name of the x-axis``graph.xaxis.axis_label ``=` `'Date'``  ` `# name of the y-axis``graph.yaxis.axis_label ``=` `'Price (in USD)'``  ` `# plotting the line graph for AAPL``x_axis_coordinates ``=` `np.array(AAPL[``'date'``], dtype ``=` `np.datetime64)``y_axis_coordinates ``=` `AAPL[``'adj_close'``]``color ``=` `\"lightblue\"``legend_label ``=` `'AAPL'``graph.line(x_axis_coordinates,``        ``y_axis_coordinates,``        ``color ``=` `color,``        ``legend_label ``=` `legend_label)``  ` `# plotting the line graph for FB``x_axis_coordinates ``=` `np.array(FB[``'date'``], dtype ``=` `np.datetime64)``y_axis_coordinates ``=` `FB[``'adj_close'``]``color ``=` `\"black\"``legend_label ``=` `'FB'``graph.line(x_axis_coordinates,``        ``y_axis_coordinates,``        ``color ``=` `color,``        ``legend_label ``=` `legend_label)``  ` `# plotting the line graph for GOOG``x_axis_coordinates ``=` `np.array(GOOG[``'date'``], dtype ``=` `np.datetime64)``y_axis_coordinates ``=` `GOOG[``'adj_close'``]``color ``=` `\"orange\"``legend_label ``=` `'GOOG'``graph.line(x_axis_coordinates,``        ``y_axis_coordinates,``        ``color ``=` `color,``        ``legend_label ``=` `legend_label)``  ` `# plotting the line graph for IBM``x_axis_coordinates ``=` `np.array(IBM[``'date'``], dtype ``=` `np.datetime64)``y_axis_coordinates ``=` `IBM[``'adj_close'``]``color ``=` `\"darkblue\"``legend_label ``=` `'IBM'``graph.line(x_axis_coordinates,``        ``y_axis_coordinates,``        ``color ``=` `color,``        ``legend_label ``=` `legend_label)``  ` `# plotting the line graph for MSFT``x_axis_coordinates ``=` `np.array(MSFT[``'date'``], dtype ``=` `np.datetime64)``y_axis_coordinates ``=` `MSFT[``'adj_close'``]``color ``=` `\"yellow\"``legend_label ``=` `'MSFT'``graph.line(x_axis_coordinates,``        ``y_axis_coordinates,``        ``color ``=` `color,``        ``legend_label ``=` `legend_label)``  ` `# relocating the legend table to ``# avoid abstruction of the graph``graph.legend.location ``=` `\"top_left\"``  ` `# displaying the model``show(graph)`\n\nOutput :", null, "Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.\n\nTo begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course\n\nMy Personal Notes arrow_drop_up" ]
[ null, "https://media.geeksforgeeks.org/wp-content/uploads/20200625092716/bokeh-stock-visualization.png", null ]
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https://www.jetbrains.com/help/webstorm/2016.1/extract-parameter-in-javascript.html
[ "# Extract Parameter in JavaScript\n\nThe Extract Parameter refactoring is used to add a new parameter to a method declaration and to update the method calls accordingly.\n\n## Examples\n\nThe following table shows two different ways of extracting a function parameter.\n\nIn the first of the examples a new parameter is extracted as an optional parameter. So the corresponding function call doesn't change.\n\nIn the second of the examples the parameter is extracted as a required parameter. So the corresponding function call changes accordingly.\n\nBeforeAfter\n```// A new parameter will be added to this // function to replace the 1's:functioncalculate_sum(i) { alert('Adding ' +1+ ' to ' + i);return1+ i; }functionshow_sum() { // Here is the function call: alert('Result: ' + calculate_sum(5)); } // When adding a new parameter we'll specify // that it should be an optional one.```\n```// The new parameter i2has been added // as an optional parameter:functioncalculate_sum(i,i2) {i2 = i2 || 1;alert('Adding ' +i2+ ' to ' + i);returni2+ i; } function show_sum() { // The function call has not changed: alert('Result: ' + calculate_sum(5)); }```\n```// A new parameter will be added to this // function to replace the 1's:functioncalculate_sum(i) { alert('Adding ' +1+ ' to ' + i);return1+ i; }functionshow_sum() { // Here is the function call: alert('Result: ' + calculate_sum(5)); } // When adding a new parameter we'll specify // that it should be a required one.```\n```// The new parameter i2has been added // as a required parameter: function calculate_sum(i,i2) { alert('Adding ' +i2+ ' to ' + i); returni2+ i; } function show_sum() { // The function call changed accordingly: alert('Result: ' + calculate_sum(5,1)); }```\n\n## Extracting a Parameter in JavaScript\n\n1. In the editor, place the cursor within the expression to be replaced by a parameter.\n2. Do one of the following:\n• Press Ctrl+Alt+P.\n• Choose Refactor | Extract | Parameter in the main menu.\n• Choose Refactor | Extract | Parameter from the context menu.\n3. If more than one expression is detected for the current cursor position, the Expressions list appears. If this is the case, select the required expression. To do that, click the expression. Alternatively, use the Up and Down arrow keys to navigate to the expression of interest, and then press Enter to select it.\n\n4. In the Extract Parameter dialog:\n1. Specify the parameter name in the Name field.\n2. The Value field, initially, contains the expression that you have selected. Normally, you don't need to change this. (Depending on whether the option Optional parameter is selected or not, this will be the value assigned to the new parameter in the function body or passed to the function in the function calls.)\n3. If, when extracting a new parameter, you don't want to change the function calls, select the Optional parameter check box. If you do so, the value specified in the Value field will be assigned to the new parameter in the function body. The calls to the function (if any) won't change.\n\nIf you want to pass the value (specified in the Value field) to the new parameter through the existing function calls, clear the Optional parameter check box. If you do so, all the function calls will change according to the new function signature; no explicit assignment of the parameter value will be added to the function body.\n\n4. If more than one occurrence of the expression is found within the function body, you can choose to replace only the selected occurrence or all the found occurrences with the references to the new parameter. Use the Replace all occurrences check box to specify your intention.\n5. Click OK." ]
[ null ]
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https://metric-calculator.com/convert-miles-per-second-to-foot-per-hour.htm
[ "# Miles Per Second to Feet Per Hour Converter\n\nSelect conversion type:\n\nRounding options:\n\nConvert Feet Per Hour to Miles Per Second (ft/h to mi/s) ▶\n\n## Conversion Table\n\n miles per second to feet per hour mi/s ft/h 1 mi/s 19008000 ft/h 2 mi/s 38016000 ft/h 3 mi/s 57024000 ft/h 4 mi/s 76032000 ft/h 5 mi/s 95040000 ft/h 6 mi/s 114048000 ft/h 7 mi/s 133056000 ft/h 8 mi/s 152064000 ft/h 9 mi/s 171072000 ft/h 10 mi/s 190080000 ft/h 11 mi/s 209088000 ft/h 12 mi/s 228096000 ft/h 13 mi/s 247104000 ft/h 14 mi/s 266112000 ft/h 15 mi/s 285120000 ft/h 16 mi/s 304128000 ft/h 17 mi/s 323136000 ft/h 18 mi/s 342144000 ft/h 19 mi/s 361152000 ft/h 20 mi/s 380160000 ft/h\n\n## How to convert\n\n1 mile per second (mi/s) = 19008000 foot per hour (ft/h). Mile Per Second (mi/s) is a unit of Speed used in Standard system. Foot Per Hour (ft/h) is a unit of Speed used in Standard system.\n\n## Miles per second: A unit of speed\n\nMiles per second (mi/s) is a unit of speed or velocity in the US customary and imperial systems. It measures how fast an object is moving by calculating the distance traveled in miles divided by the time taken in seconds. For example, if a bullet travels 1 mile in 0.5 seconds, its speed is 2 mi/s.\n\n## How to convert miles per second\n\nMiles per second can be converted to other units of speed or velocity by using simple conversion factors. Here are some common units and their conversion factors:\n\n• Meters per second (m/s): To convert from mi/s to m/s, multiply by 1609.34. To convert from m/s to mi/s, divide by 1609.34. For example, 1 mi/s is equal to 1609.34 m/s, and 100 m/s is equal to 0.0621371 mi/s.\n• Kilometers per hour (km/h): To convert from mi/s to km/h, multiply by 5793.64. To convert from km/h to mi/s, divide by 5793.64. For example, 1 mi/s is equal to 5793.64 km/h, and 50 km/h is equal to 0.00863028 mi/s.\n• Miles per hour (mph): To convert from mi/s to mph, multiply by 3600. To convert from mph to mi/s, divide by 3600. For example, 1 mi/s is equal to 3600 mph, and 60 mph is equal to 0.0166667 mi/s.\n• Knots (kn): To convert from mi/s to kn, multiply by 3128.31. To convert from kn to mi/s, divide by 3128.31. For example, 1 mi/s is equal to 3128.31 kn, and 15 kn is equal to 0.00479717 mi/s.\n• Feet per second (ft/s): To convert from mi/s to ft/s, multiply by 5280. To convert from ft/s to mi/s, divide by 5280. For example, 1 mi/s is equal to 5280 ft/s, and 40 ft/s is equal to 0.00757576 mi/s.\n• Inches per second (in/s): To convert from mi/s to in/s, multiply by 63360. To convert from in/s to mi/s, divide by 63360. For example, 1 mi/s is equal to 63360 in/s, and 20 in/s is equal to 0.000315789 mi/s.\n\n## Where miles per second are used\n\nMiles per second are mainly used in physics and astronomy to measure the speed or velocity of very fast objects and phenomena.\n\nFor example, the escape velocity of the Earth is about 11 km/s or about 6.84 mi/s.\n\nThe orbital velocity of the Earth around the Sun is about 30 km/s or about 18.64 mi/s.\n\nThe speed of light in a vacuum is about 300000 km/s or about 186282 mi/s.\n\nThe speed of sound in air at sea level is about 343 m/s or about 0.213139 mi/s.\n\n## Definition of miles per second\n\nAccording to the US customary and imperial systems definition, one mile per second is the speed of a body that covers a distance of one mile in a time of one second.\n\nMathematically, it can be expressed as:\n\nwhere v is the speed or velocity in miles per second, s is the distance traveled in miles, and t is the time taken in seconds.\n\n## History of miles per second\n\nThe concept of speed or velocity has been studied since ancient times by philosophers and scientists such as Aristotle, Galileo, Newton, etc.\n\nThe mile was originally derived from the Roman mile which was defined as one thousand paces or about 5000 Roman feet.\n\nThe second was originally defined in terms of the Earth’s rotation as one eighty-six thousand four hundredth of a mean solar day.\n\nThe combination of these two units resulted in the mile per second as a unit of speed or velocity.\n\nThe mile per second was officially adopted as part of the US customary and imperial systems in the late 18th century.\n\n## Example conversions of miles per second to other units\n\nHere are some examples of converting miles per second to other units of speed or velocity:\n\n• 1 mi/s = 1609.34 m/s = 5793.64 km/h = 3600 mph = 3128.31 kn = 5280 ft/s = 63360 in/s\n• 2 mi/s = 3218.68 m/s = 11587.28 km/h = 7200 mph = 6256.62 kn = 10560 ft/s = 126720 in/s\n• 5 mi/s = 8046.7 m/s = 28968.2 km/h = 18000 mph = 15657.55 kn = 26400 ft/s = 316800 in/s\n• 10 mi/s = 16093.4 m/s = 57936.4 km/h = 36000 mph = 31283.1 kn = 52800 ft/s = 633600 in/s\n• 20 mi/s = 32186.8 m/s = 115872.8 km/h = 72000 mph = 62566.2 kn = 105600 ft/s = 1267200 in/s\n• 50 mi/s = 80467 m/s = 289682 km/h = 180000 mph = 156575.5 kn = 264000 ft/s = 3168000 in/s\n• 100 mi/s = 160934 m/s = 579364 km/h = 360000 mph = 312831 kn = 528000 ft/s = 6336000 in/s\nMiles per second also can be marked as mps.\n\n## Definition of Feet Per Hour\n\nFeet per hour (f/h) is a unit of speed that measures how fast something is moving in terms of feet per unit of time. It is a US Standard unit that is derived from the base unit of length, the foot, and the base unit of time, the hour. It is rarely used however, as the units of feet per second and miles per hour are considered sufficient for most situations.\n\nOne foot per hour is equal to 0.000084666666666667 meters per second, or 0.0003048 kilometers per hour. It can also be converted to other units of speed, such as miles per hour, knots, or meters per second, by using appropriate conversion factors.\n\n## How to Convert Feet Per Hour\n\nTo convert feet per hour to other units of speed, we need to multiply or divide by the corresponding conversion factors. For example, to convert feet per hour to miles per hour, we need to multiply by 0.0001893939394, since there are 0.0001893939394 miles in one foot. To convert feet per hour to meters per second, we need to multiply by 0.000084666666666667, since there are 0.000084666666666667 meters in one foot.\n\nHere are some examples of how to convert feet per hour to other units of length in the US Standard system and the SI system:\n\n• To convert 10 f/h to miles per hour (mph), we multiply by 0.0001893939394: 10 f/h x 0.0001893939394 = 0.001893939394 mph\n• To convert 20 f/h to feet per second (fps), we divide by 3600, since there are 3600 seconds in one hour: 20 f/h / 3600 = 0.005555555556 fps\n• To convert 30 f/h to knots (kn), we multiply by 0.0001645788337, since there are 0.0001645788337 nautical miles in one foot: 30 f/h x 0.0001645788337 = 0.004937365011 kn\n• To convert 40 f/h to meters per second (m/s), we multiply by 0.000084666666666667: 40 f/h x 0.000084666666666667 = 0.003386666667 m/s\n• To convert 50 f/h to meters per minute (m/min), we multiply by 0.00508, since there are 0.00508 meters in one foot: 50 f/h x 0.00508 = 0.254 m/min\n• To convert 60 f/h to centimeters per second (cm/s), we multiply by 100, since there are 100 centimeters in one meter, and multiply by 0.000084666666666667: 60 f/h x 100 x 0.000084666666666667 = 0.508 cm/s\n\n## Where Feet Per Hour Are Used\n\nFeet per hour are mainly used for measuring very slow speeds or velocities of objects or phenomena that move in terms of feet per unit of time. They are often used in scientific fields such as geology, biology, astronomy, and physics, where the speed or velocity of an object or phenomenon is very small compared to the usual units of speed.\n\nFor example, a glacier may have a speed of a few feet per hour, which means that it moves a few feet in one hour. A snail may have a speed of up to 47 feet per hour, which means that it can travel up to 47 feet in one hour.1 A planet may have an orbital velocity of thousands of feet per hour, which means that it covers thousands of feet along its orbit in one hour.\n\nFeet per hour are also used in some other fields where the speed or velocity of an object or phenomenon is measured in terms of feet per unit of time.\n\n## History of Feet Per Hour\n\nThe origin of feet per hour as a unit of speed can be traced back to the development of the US Standard system and the foot as a unit of length in the late eighteenth century. The foot was defined as one third of a yard based on the English standard foot.\n\nThe term foot first appeared in English in the late fourteenth century, but it was not until later in the early nineteenth century that the use of feet per hour became more common; before that, other units such as inches per hour or yards per hour were preferred for expressing speed.\n\nFeet per hour have been used ever since as a standard unit of speed for measuring very slow speeds or velocities of objects or phenomena that move in terms of feet per unit of time. They have also been adopted by some international organizations and conventions, such as the International System of Units and the International Astronomical Union.\n\n## Example Conversions of Feet Per Hour to Other Units\n\nHere are some examples of how to convert feet per hour to other units of speed, using the conversion factors given above:\n\n• To convert 100 f/h to miles per hour, we multiply by 0.0001893939394: 100 f/h x 0.0001893939394 = 0.01893939394 mph\n• To convert 200 f/h to feet per second, we divide by 3600: 200 f/h / 3600 = 0.05555555556 fps\n• To convert 300 f/h to knots, we multiply by 0.0001645788337: 300 f/h x 0.0001645788337 = 0.04937365011 kn\n• To convert 400 f/h to meters per second, we multiply by 0.000084666666666667: 400 f/h x 0.000084666666666667 = 0.03386666667 m/s\n• To convert 500 f/h to meters per minute, we multiply by 0.00508: 500 f/h x 0.00508 = 2.54 m/min\n• To convert 600 f/h to centimeters per second, we multiply by 100 and multiply by 0.000084666666666667: 600 f/h x 100 x 0.000084666666666667 = 5.08 cm/s\n• To convert 700 f/h to millimeters per minute, we multiply by 1000, since there are 1000 millimeters in one meter, and multiply by 0.00508: 700 f/h x 1000 x 0.00508 = 3556 mm/min\nFoot per hour also can be marked as foot/hour.\n\nEspañol     Russian     Français" ]
[ null ]
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https://transwriters.com/solved/279042/
[ "## (Solved) I need help answering all of the following questions and diagrams\n\nI need help answering all of the following questions and diagrams for ET1310 Lab 2.1 Report (Parts 1, 2, and 3). Please and Thank you in advanced. Whom ever does this for me, you're a life savor!\n\nI am also willing to up the price\n\nET1310: Module 2 DC Power Supplies and BJTs\n\nLab/BJT Characteristics and Biasing\n\nLab 2.1 Report\n\nName:\n\nDate:\n\nInstructions:\n\nComplete the three parts?Part 1, Part 2, and Part 3 of Lab 1. When you have completed each part,\n\nanswer the questions and transcribe/transfer the test results recorded in the lab manual?s tables to the\n\ntables provided.\n\nPart 1?BJT Characteristics\n\nTheory:\n\n1. Name the three terminals of a BJT transistor.\n\n2. Why are BJT transistors considered current amplifiers?\n\n3. What is the relationship between IC and IE?\n\n4. Describe how to bias a BJT transistor correctly.\n\n5. What is the forward-biased voltage drop across the base-emitter junction?\n\nPreparation:\n\n6. Transcribe the component measurements from Table 6-1 to the table below.\n\nComponent\n\nR1\n\nR2\n\nRBB\n\nRCC\n\nListed Value\n\n22 k Ohm\n\n100 Ohm\n\n1 k Ohm\n\n5 k Ohm\n\nTable 6-1\n\nMeasured Value\n\n7. For a 2N3904 NPN General Purpose BJT transistor, how can you determine the location of pin 1?\n\nthe emitter?\n\nTest Procedure:\n\n8. Transcribe the collector current measurements and calculations from Table 6-2 to the table below.\n\nVCE\n\n1\n\nBase Current = 50 µA\n\nBase Current = 100 µA\n\nBase Current = 150 µA\n\nET1310: Module 2 DC Power Supplies and BJTs\n\nLab/BJT Characteristics and Biasing\n\nMeasured\n\nVR2\n\nIC\n\nVR2\n\nIC\n\nVR2\n\nMeasured\n\nComputed\n\nMeasured\n\nComputed\n\nMeasured\n\n2.0 V\n\n4.0 V\n\n6.0 V\n\n8.0 V\n\nTable 6-2\n\n9. Transcribe the three collector characteristic curves from Plot 6-1 to the plot below.\n\nPlot 6-1\n\n10. Transcribe the DC Beta calculations from Table 6-3 to the table below.\n\nVCE\n\nMeasured\n\nCurrent Gain ?DC\n\nIB = 50 µA\n\nIB = 100\n\nIB = 150\n\nµA\n\n3.0 V\n\n5.0 V\n\nTable 6-3\n\n2\n\nµA\n\nLab 2.1 Report\n\nIC\n\nComputed\n\nET1310: Module 2 DC Power Supplies and BJTs\n\nLab/BJT Characteristics and Biasing\n\nLab 2.1 Report\n\n11. Does the experimental data indicate that ?DC is a constant at all points? Does this have any effect on\n\nthe linearity of the transistor?\n\n12. What effect would a higher ?DC have on the characteristic curves you measured?\n\n13. What is the maximum power dissipated in the transistor for the data taken in the experiment?\n\n14. The DC alpha (?) of a bipolar transistor is the collector current, IC, divided by the emitter current, IE.\n\nUsing this definition and IE = IC + IB, prove that DC alpha can be written as:\n\n? DC =\n\n? DC\n\n? DC +1\n\n15. Compute DC alpha for your transistor at VCE = 4.0 V and IB = 100 ?A.\n\n16. What value of VCE would you expect if the base terminal of a transistor were open? Explain your\n\n3\n\nET1310: Module 2 DC Power Supplies and BJTs\n\nLab/BJT Characteristics and Biasing\n\nLab 2.1 Report\n\nPart 2?BJT Biasing\n\nTheory:\n\n17. How do we describe the nonchanging, no signal output of a biased transistor circuit?\n\n18. Why is the base biasing method unacceptable for linear circuits?\n\n19. For which application is base biasing most suitable?\n\n20. What is the advantage and disadvantage of the emitter bias circuit?\n\n21. What is the advantage of voltage-divider bias? What is stiff bias?\n\n22. What effect does negative feedback have in a collector-feedback bias circuit?\n\nPreparation:\n\n23. Transcribe the resister measurements from Table 7-1 to the table below.\n\nResisto\n\nListed Value\n\nMeasured Value\n\nr\n\nRB\n\nRC\n\n1 M Ohm\n\n2 k Ohm (Two 1 k\n\nresistors)\n\nTable 7-1\n\nTest Procedure:\n\n24. Transcribe the base biasing circuit calculations and measurements from Table 7-2 to the table below.\n\nDC\n\nParameter\n\nVRB\n\nIB\n\nIC\n\nVRC\n\nVC\n\nComputed\n\nValue\n\nQ1\n\nMeasured Value\n\nQ2\n\nQ3\n\nTable 2-5\n\n25. Describe the variation between the three VRC measurements. What does this variation represent?\n\n26. Transcribe the resister measurements from Table 7-3 to the table below.\n\nResisto\n\nr\n\n4\n\nListed Value\n\nMeasured Value\n\nET1310: Module 2 DC Power Supplies and BJTs\n\nLab/BJT Characteristics and Biasing\n\nLab 2.1 Report\n\nR1\n\nR2\n\nRE\n\nRC\n\n33 k Ohm\n\n6.8 k Ohm\n\n470 Ohm\n\n2 k Ohm (Two 1 k\n\nresistors)\n\nTable 7-3\n\n27. Transcribe the voltage divider biasing circuit calculations and measurements from Table 7-4 to the\n\ntable below.\n\nDC\n\nParameter\n\nVB\n\nVE\n\nIE?IC\n\nVRC\n\nVC\n\nComputed\n\nValue\n\nQ1\n\nMeasured Value\n\nQ2\n\nQ3\n\nTable 7-4\n\n28. Describe the variation between the three VRC measurements. Is this variation greater than, the\n\nsame, or less than the variation in the base bias circuit? Explain why.\n\n29. Transcribe the resister measurements from Table 7-5 to the table below.\n\nResisto\n\nListed Value\n\nMeasured Value\n\nr\n\nRB\n\nRC\n\n360 k Ohm\n\n2 k Ohm (Two 1 k\n\nresistors)\n\nTable 7-5\n\n30. Transcribe the collector feedback biasing circuit calculations and measurements from Table 7-6 to the\n\ntable below.\n\nDC\n\nParameter\n\nIC?IE\n\nVRC\n\nVC\n\nComputed\n\nValue\n\nQ1\n\nMeasured Value\n\nQ2\n\nQ3\n\nTable 7-6\n\n31. Describe the variation between the three VRC measurements. Is this variation greater than, the\n\nsame, or less than the variation in the base bias circuit? Explain why.\n\n32. Compare your observations of the three bias methods tested in the experiment. Which one showed\n\nthe least variation between the transistors?\n\n5\n\nET1310: Module 2 DC Power Supplies and BJTs\n\nLab/BJT Characteristics and Biasing\n\nLab 2.1 Report\n\n33. Draw each of the three bias circuits tested in the experiment for a PNP transistor. Assuming the same\n\nresistors as used in the experiment, compute the base, emitter, and collector voltages. Show these\n\nBase Bias\n\nVoltage-Divider Bias\n\nCollector-Feedback Bias\n\n6\n\nET1310: Module 2 DC Power Supplies and BJTs\n\nLab/BJT Characteristics and Biasing\n\nLab 2.1 Report\n\n34. Assume you need to bias the amplifier shown below. It is desired to have the Q-point set to\n\napproximately 20 mA of the collector current. Choose bias resistors for the voltage-divider bias that\n\nwill provide a reasonably stiff bias. Show your work.\n\n35. Assume you need to bias the amplifier shown below. It is desired to have the Q-point set to\n\napproximately 5.0 mA of the collector current. Choose a base resistor for the collector feedback bias\n\nthat will provide a reasonably stiff bias. Show your work.\n\n36. For the circuit shown below and using the values calculated in problem #34, predict the effect of each\n\nof the following problems on the collector voltage:\n\n7\n\nET1310: Module 2 DC Power Supplies and BJTs\n\nLab/BJT Characteristics and Biasing\n\nLab 2.1 Report\n\na. R1 opens.\n\nb. Base is shorted to ground through a solder bridge.\n\nc.\n\nRE is 470 Ohm instead of 47 Ohm.\n\nd. VCC drops to +15 V.\n\n8\n\nET1310: Module 2 DC Power Supplies and BJTs\n\nLab/BJT Characteristics and Biasing\n\nLab 2.1 Report\n\nPart 3?The Basic Power Supply\n\nTest Procedure:\n\n37. Transcribe the power supply fault observations from Table L2-1 to the table below.\n\nCircuit\n\nComponent\n\nFault\n\nPower Supply Fault 1a\n\nPower Supply Fault 1b\n\nPower Supply Fault 1c\n\nPower Supply Fault 2a\n\nPower Supply Fault 2b\n\nPower Supply Fault 2c\n\nTable L2-1\n\n9\n\nSymptoms of the Fault\n\nSolution details:\nSTATUS\nQUALITY\nApproved\n\nThis question was answered on: Oct 15, 2019\n\nSolution~000.zip (25.37 KB)\n\nSTATUS\n\nQUALITY\n\nApproved\n\nOct 15, 2019\n\nEXPERT\n\nTutor\n\n#### YES, THIS IS LEGAL\n\nWe have top-notch tutors who can do your essay/homework for you at a reasonable cost and then you can simply use that essay as a template to build your own arguments.\n\nYou can also use these solutions:\n\n• As a reference for in-depth understanding of the subject.\n• As a source of ideas / reasoning for your own research (if properly referenced)\n• For editing and paraphrasing (check your institution's definition of plagiarism and recommended paraphrase).\nThis we believe is a better way of understanding a problem and makes use of the efficiency of time of the student.\n\n### Order New Solution. 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https://www.colorhexa.com/036f9f
[ "# #036f9f Color Information\n\nIn a RGB color space, hex #036f9f is composed of 1.2% red, 43.5% green and 62.4% blue. Whereas in a CMYK color space, it is composed of 98.1% cyan, 30.2% magenta, 0% yellow and 37.6% black. It has a hue angle of 198.5 degrees, a saturation of 96.3% and a lightness of 31.8%. #036f9f color hex could be obtained by blending #06deff with #00003f. Closest websafe color is: #006699.\n\n• R 1\n• G 44\n• B 62\nRGB color chart\n• C 98\n• M 30\n• Y 0\n• K 38\nCMYK color chart\n\n#036f9f color description : Dark blue.\n\n# #036f9f Color Conversion\n\nThe hexadecimal color #036f9f has RGB values of R:3, G:111, B:159 and CMYK values of C:0.98, M:0.3, Y:0, K:0.38. Its decimal value is 225183.\n\nHex triplet RGB Decimal 036f9f `#036f9f` 3, 111, 159 `rgb(3,111,159)` 1.2, 43.5, 62.4 `rgb(1.2%,43.5%,62.4%)` 98, 30, 0, 38 198.5°, 96.3, 31.8 `hsl(198.5,96.3%,31.8%)` 198.5°, 98.1, 62.4 006699 `#006699`\nCIE-LAB 44.075, -8.262, -33.228 11.978, 13.89, 34.849 0.197, 0.229, 13.89 44.075, 34.24, 256.036 44.075, -28.854, -47.866 37.27, -7.852, -29.35 00000011, 01101111, 10011111\n\n# Color Schemes with #036f9f\n\n• #036f9f\n``#036f9f` `rgb(3,111,159)``\n• #9f3303\n``#9f3303` `rgb(159,51,3)``\nComplementary Color\n• #039f81\n``#039f81` `rgb(3,159,129)``\n• #036f9f\n``#036f9f` `rgb(3,111,159)``\n• #03219f\n``#03219f` `rgb(3,33,159)``\nAnalogous Color\n• #9f8103\n``#9f8103` `rgb(159,129,3)``\n• #036f9f\n``#036f9f` `rgb(3,111,159)``\n• #9f0321\n``#9f0321` `rgb(159,3,33)``\nSplit Complementary Color\n• #6f9f03\n``#6f9f03` `rgb(111,159,3)``\n• #036f9f\n``#036f9f` `rgb(3,111,159)``\n• #9f036f\n``#9f036f` `rgb(159,3,111)``\n• #039f33\n``#039f33` `rgb(3,159,51)``\n• #036f9f\n``#036f9f` `rgb(3,111,159)``\n• #9f036f\n``#9f036f` `rgb(159,3,111)``\n• #9f3303\n``#9f3303` `rgb(159,51,3)``\n• #023b54\n``#023b54` `rgb(2,59,84)``\n• #024c6d\n``#024c6d` `rgb(2,76,109)``\n• #035e86\n``#035e86` `rgb(3,94,134)``\n• #036f9f\n``#036f9f` `rgb(3,111,159)``\n• #0380b8\n``#0380b8` `rgb(3,128,184)``\n• #0492d1\n``#0492d1` `rgb(4,146,209)``\n• #04a3ea\n``#04a3ea` `rgb(4,163,234)``\nMonochromatic Color\n\n# Alternatives to #036f9f\n\nBelow, you can see some colors close to #036f9f. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.\n\n• #03969f\n``#03969f` `rgb(3,150,159)``\n• #03899f\n``#03899f` `rgb(3,137,159)``\n• #037c9f\n``#037c9f` `rgb(3,124,159)``\n• #036f9f\n``#036f9f` `rgb(3,111,159)``\n• #03629f\n``#03629f` `rgb(3,98,159)``\n• #03559f\n``#03559f` `rgb(3,85,159)``\n• #03489f\n``#03489f` `rgb(3,72,159)``\nSimilar Colors\n\n# #036f9f Preview\n\nThis text has a font color of #036f9f.\n\n``<span style=\"color:#036f9f;\">Text here</span>``\n#036f9f background color\n\nThis paragraph has a background color of #036f9f.\n\n``<p style=\"background-color:#036f9f;\">Content here</p>``\n#036f9f border color\n\nThis element has a border color of #036f9f.\n\n``<div style=\"border:1px solid #036f9f;\">Content here</div>``\nCSS codes\n``.text {color:#036f9f;}``\n``.background {background-color:#036f9f;}``\n``.border {border:1px solid #036f9f;}``\n\n# Shades and Tints of #036f9f\n\nA shade is achieved by adding black to any pure hue, while a tint is created by mixing white to any pure color. In this example, #000305 is the darkest color, while #f1faff is the lightest one.\n\n• #000305\n``#000305` `rgb(0,3,5)``\n• #001118\n``#001118` `rgb(0,17,24)``\n• #011e2b\n``#011e2b` `rgb(1,30,43)``\n• #012c3f\n``#012c3f` `rgb(1,44,63)``\n• #023952\n``#023952` `rgb(2,57,82)``\n• #024765\n``#024765` `rgb(2,71,101)``\n• #025478\n``#025478` `rgb(2,84,120)``\n• #03628c\n``#03628c` `rgb(3,98,140)``\n• #036f9f\n``#036f9f` `rgb(3,111,159)``\n• #037cb2\n``#037cb2` `rgb(3,124,178)``\n• #048ac6\n``#048ac6` `rgb(4,138,198)``\n• #0497d9\n``#0497d9` `rgb(4,151,217)``\n• #04a5ec\n``#04a5ec` `rgb(4,165,236)``\n• #0ab0fa\n``#0ab0fa` `rgb(10,176,250)``\n• #1db6fb\n``#1db6fb` `rgb(29,182,251)``\n• #30bdfb\n``#30bdfb` `rgb(48,189,251)``\n• #43c3fb\n``#43c3fb` `rgb(67,195,251)``\n• #57c9fc\n``#57c9fc` `rgb(87,201,252)``\n• #6acffc\n``#6acffc` `rgb(106,207,252)``\n• #7dd5fd\n``#7dd5fd` `rgb(125,213,253)``\n• #90dcfd\n``#90dcfd` `rgb(144,220,253)``\n• #a4e2fd\n``#a4e2fd` `rgb(164,226,253)``\n• #b7e8fe\n``#b7e8fe` `rgb(183,232,254)``\n• #caeefe\n``#caeefe` `rgb(202,238,254)``\n• #ddf4fe\n``#ddf4fe` `rgb(221,244,254)``\n• #f1faff\n``#f1faff` `rgb(241,250,255)``\nTint Color Variation\n\n# Tones of #036f9f\n\nA tone is produced by adding gray to any pure hue. In this case, #4e5254 is the less saturated color, while #036f9f is the most saturated one.\n\n• #4e5254\n``#4e5254` `rgb(78,82,84)``\n• #48555a\n``#48555a` `rgb(72,85,90)``\n• #415761\n``#415761` `rgb(65,87,97)``\n• #3b5967\n``#3b5967` `rgb(59,89,103)``\n• #355c6d\n``#355c6d` `rgb(53,92,109)``\n• #2f5e73\n``#2f5e73` `rgb(47,94,115)``\n• #28617a\n``#28617a` `rgb(40,97,122)``\n• #226380\n``#226380` `rgb(34,99,128)``\n• #1c6586\n``#1c6586` `rgb(28,101,134)``\n• #16688c\n``#16688c` `rgb(22,104,140)``\n• #0f6a93\n``#0f6a93` `rgb(15,106,147)``\n• #096d99\n``#096d99` `rgb(9,109,153)``\n• #036f9f\n``#036f9f` `rgb(3,111,159)``\nTone Color Variation\n\n# Color Blindness Simulator\n\nBelow, you can see how #036f9f is perceived by people affected by a color vision deficiency. This can be useful if you need to ensure your color combinations are accessible to color-blind users.\n\nMonochromacy\n• Achromatopsia 0.005% of the population\n• Atypical Achromatopsia 0.001% of the population\nDichromacy\n• Protanopia 1% of men\n• Deuteranopia 1% of men\n• Tritanopia 0.001% of the population\nTrichromacy\n• Protanomaly 1% of men, 0.01% of women\n• Deuteranomaly 6% of men, 0.4% of women\n• Tritanomaly 0.01% of the population" ]
[ null ]
{"ft_lang_label":"__label__en","ft_lang_prob":0.5327272,"math_prob":0.797855,"size":3675,"snap":"2019-43-2019-47","text_gpt3_token_len":1650,"char_repetition_ratio":0.12585127,"word_repetition_ratio":0.011111111,"special_character_ratio":0.5567347,"punctuation_ratio":0.23809524,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.98923594,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-11-13T20:39:15Z\",\"WARC-Record-ID\":\"<urn:uuid:182cd737-a37a-41a8-b52d-e5458152cf84>\",\"Content-Length\":\"36239\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:9331c204-f2c9-4140-808e-faa1ebc28161>\",\"WARC-Concurrent-To\":\"<urn:uuid:74eb7e50-f9bb-4ac5-94b4-badf7eb398c5>\",\"WARC-IP-Address\":\"178.32.117.56\",\"WARC-Target-URI\":\"https://www.colorhexa.com/036f9f\",\"WARC-Payload-Digest\":\"sha1:2ILSINMX7MVVDH7ZPEET36CSC5UEWZI6\",\"WARC-Block-Digest\":\"sha1:V6SXQEL36DBRAID5CJZ37L73KYDMJJM4\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-47/CC-MAIN-2019-47_segments_1573496667333.2_warc_CC-MAIN-20191113191653-20191113215653-00378.warc.gz\"}"}
https://bestftxjfjn.web.app/jame10461hut/what-is-annual-interest-rate-loan-get.html
[ "## What is annual interest rate loan\n\nAnnual Percentage Rate (APR) The cost to borrow money expressed as a yearly percentage. For mortgage loans, excluding home equity lines of credit, it includes the interest rate plus other charges or fees. For home equity lines, the APR is just the interest rate.\n\nThe effectual annual interest rate is a useful way of evaluating the actual return on investment and ascertaining the interest expense paid on a loan. Borrowers need to have a solid understanding of the impact cost of debt Cost of Debt The cost of debt is the return that a company provides to its debtholders and creditors. Like other types of debt, the interest rates for personal loans depend on the lender, your credit scores and your credit history. An estimated range of interest rates on personal loans for consumers with fair to good credit is currently between 6% and 36%. You can check on rates available in your state National Average Mortgage Rates. The mortgage rates vary depending upon the type of loan that will be acquired by the consumer. For instance, in February, 2010, the national average mortgage rate for a 30 year fixed rate loan was at 4.750 percent (5.016 APR). Interest rate refers to the annual cost of a loan to a borrower and is expressed as a percentage APR is the annual cost of a loan to a borrower — including fees. Like an interest rate, the APR is expressed as a percentage. Unlike an interest rate, however, it includes other charges or fees such as mortgage insurance,\n\n## Ideal choice for servicemembers, reservists and veterans who have already exhausted their VA loan benefit. Interest rate as low as. 4.750%. APR as low as. 5.038\n\nParts of total cost and effective APR for a 12-month, 5% monthly interest, \\$100 loan paid off in equally sized monthly  15 Jul 2019 An annual percentage rate (APR) is the annual rate charged for borrowing or earned through an investment. APR is expressed as a percentage  The interest rate is the cost of borrowing the principal. APR includes other costs associated with borrowing the money. The Federal Truth in Lending Act requires   The annual interest rate, sometimes called the standard annual interest rate or base It's the basic interest that you'll pay on your loan or earn on your savings\n\n### What is an interest rate? Interest is the cost of borrowing money typically expressed as an annual percentage of the loan. For savers it\n\nThe weekly Chartered Bank Interest Rates can now be found in a new table: Interest rates Effective October 1, 2019, the monthly rates will be discontinued. Higher rate will be paid on the entire balance if the balance exceeds \\$5,000, Regular Interest Rate, Momentum Savings Premium‡, Total Annual Interest Rate   What is the interest rate (in percent) attached to this money? % per. Year (annual interest), 6 month period (semiannually), Month. After how much time  The Annual Percentage Rate (APR) is a method to compute annualised credit cost which includes interest rate and processing fee. The APR calculator does not  What is a 30-year fixed mortgage? A 30-year fixed mortgage is a loan whose interest rate stays the same for the duration of the loan. For example, on a 30  17 Feb 2020 Understanding annual percentage rates on personal loans. Learn how APR works and what interest rate you could expect on your personal loan. 2 Mar 2020 Compare personal loan interest rates and get the best offer from top banks. with lower annual incomes may have to pay a higher interest rate.\n\n### The Annual Percentage Rate (APR) is a method to compute annualised credit cost which includes interest rate and processing fee. The APR calculator does not\n\nAnnual percentage rate (APR) helps you understand the cost of a loan. Different APRs may have different implications. APRs help you get an apples-to-apples comparison of loans by accounting for every cost related to borrowing. Calculate Monthly Payment. The first step to  24 Sep 2019 The interest rate and the annual percentage rate (APR) on a personal loan are related, but they're not the same thing. An interest rate on a  18 Dec 2019 The APR is a broader measure of the cost of a mortgage because it includes the interest rate plus other costs such as broker fees, discount points  15 Nov 2019 An annual percentage rate (APR) reflects the mortgage interest rate plus other charges. There are many costs associated with taking out a  ANZ home loan. View the current home loan interest rates for ANZ home loans. The current comparison interest rate is also included for each type of home loan.\n\n## As such, the annual interest rate on a loan or other form of debt is a percentage that describes the yearly cost of borrowing money. Yearly interest rate payments are calculated by multiplying the interest rate percentage by the total outstanding balance of the loan.\n\nThe following Annual Percentage Rate (“APR”) examples are for a typical transaction and are only examples. Please call 877.907.1043, email us, or find a loan  (A)- HOME LOAN INTEREST CARD RATE STRUCTURE (FLOATING) :EBR 7.80 %. LOAN AMOUNT, SALARIED. TERM LOAN. MAXGAIN. Up to Rs  While our average personal loan interest rate table above shows annual flat rates around 6%, this rate could jump to 9% to 13% for  3 = Annual Percentage Rates (APR) are calculated based on a loan amount of \\$100,000 for all products except the Jumbo Fixed and  Ideal choice for servicemembers, reservists and veterans who have already exhausted their VA loan benefit. Interest rate as low as. 4.750%. APR as low as. 5.038  See if you're charged an establishment fee, monthly or annual fees for your personal loan. Comparing your options by the comparison rate rather than the interest  What is APR. Annual Percentage Rate, or APR, is the annual rate charged by a financial institution to loan its funds to borrowers. And while it does include\n\nFor a daily interest rate, divide the annual rate by 360 (or 365, depending on your bank). For a quarterly rate, divide the annual rate by four. For a weekly rate, divide the annual rate by 52. Example: assume you pay interest monthly at 10 percent per year. Annual Percentage Rate (APR) The cost to borrow money expressed as a yearly percentage. For mortgage loans, excluding home equity lines of credit, it includes the interest rate plus other charges or fees. For home equity lines, the APR is just the interest rate. Interest rate is the amount charged by lenders to borrowers for the use of money, expressed as a percentage of the principal, or original amount borrowed; it can also be described alternatively as the cost to borrow money. For instance, an 8% interest rate for borrowing \\$100 a year will obligate a person to pay \\$108 The table below provides interest rates for Direct Subsidized Loans, Direct Unsubsidized Loans, and Direct PLUS Loans first disbursed on or after July 1, 2019, and before July 1, 2020. Perkins Loans (regardless of the first disbursement date) have a fixed interest rate of 5%." ]
[ null ]
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https://octopus-code.org/mediawiki/index.php?title=Manual:Linear_Response&oldid=4401
[ "# Manual:Linear Response\n\nOctopus can calculate dynamic polarizabilities and first order hyperpolarizabilites in a linear response scheme using the Sternheimer equation. It also possible to calculate optical spectra with this technique, but it is slower than time evolution.\n\n## Contents\n\n##### Ground state\n\nThe first thing we will need to do a linear response, it is a Ground State calculation. Unlike other techniques, when using the Sterheimer equation you needn't do a unoccupied states calculation. To improve the convergency of the linear response calculation, it is better to use tightly converged wave functions. For example you can add this parameteres to your gs calculation:\n\nEigenSolverFinalTolerance = 1e-10\nConvAbsDens = 1e-9\n\n\n##### Input\n\nThe CalculationMode for polarizability calculations is pol_lr. The main parameter you have to specify is the frequency of the perturbation, this is given by the PolFreqs block. You can also add an imaginary part to the frequency by setting the variable PolEta. Adding a small imaginary part is required if you want to get the imaginary part of the polarizability or to calculate polarizabilities near resonance, a reasonable value is 0.1 eV.\n\nTo get the hyperpolarizabilties, you also have to specify the PolHyper with the three coefficients with respect to the base frequency, the three values must sum zero.\n\n##### Output\n\nAfter running, fr each frequency in the input file, Octopus will generate a subdirectory under linear/. In each subdirectory there is a file called alpha that contains the real part of the polarizability tensor", null, "and the average polarizability", null, "there is also a file called cross_section_tensor that contains the photo-absorption cross section tensor for that frequency, that it is related to the imaginary part of the polarizability.\n\nThe hyperpolarizabilty will be in a file called beta, containing all the 27 components and some reduced quantities:", null, "#### Finite differences\n\nIn this mode only static polarizability can be obtained, the calculation is done by taking the numerical derivative of the energy with respect to an external static and uniform electric field. To use this run with CalculationMode =pol_fd. Octopus will run several ground state energy calculations (2 per dimension) and then calculate the polarizability using a finite differences formula for the derivative, the resulting tensor will be in the linear/polarizability_fd file.\n\nPrevious Manual:Casida - Next Manual:Spectrum_calculations\n\nBack to Manual" ]
[ null, "https://wikimedia.org/api/rest_v1/media/math/render/png/768573314617898450b38de70be02c6341de6235", null, "https://wikimedia.org/api/rest_v1/media/math/render/png/6e75680a24773e4af5527918381aae6993879156", null, "https://wikimedia.org/api/rest_v1/media/math/render/png/4223c9cce1c990041de4282fef6e6d5404793201", null ]
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https://zonecalc.com/multiplication/what-is-7-times-26/
[ "# What is 7 times 26?\n\nHere we answer one simple question: What is 7 times 26? (or what is 7 multiplied by 26) Here is the answer:\n\n### 7 x 26 = 182\n\nLearning the multiplication of 7 times 26 is an essential skill for problems based upon fractions, decimals, and percentages. It helps in solving real-life problems quickly.\n\nIf you want to find what 7 times 26 means, think of it as 7 added together 26 times. To get the answer, you could just write down the number 7, 26 times and then add the 2 numbers together.\n\nIf you’re using a calculator, you can double-check that the answer is 182 by pressing 7 then x, then 26, and then to get the answer 182.\n\nX" ]
[ null ]
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https://intellipaat.com/community/26655/how-to-add-new-elements-to-an-array
[ "# How to add new elements to an array?\n\n1 view\nin Java\n\nI have the following code:\n\nString[] where;\n\nwhere.append(ContactsContract.Contacts.HAS_PHONE_NUMBER + \"=1\");\n\nwhere.append(ContactsContract.Contacts.IN_VISIBLE_GROUP + \"=1\");\n\nThose two appends are not compiling. How would that work correctly?\n\nby (46.1k points)\n\nThe extent of an array can't be altered. If you require a bigger array you ought to instantiate a new one.\n\nA better resolution would be to apply an ArrayList which can develop as you need it. The program ArrayList.toArray( T[] a ) gives you back your array if you require it in this form.\n\nList<String> where = new ArrayList<String>();\n\nIf you require to alter it to a simple array...\n\nString[] simpleArray = new String[ where.size() ];\n\nwhere.toArray( simpleArray );\n\nBut most things you do with an array you can be donewith this ArrayList, too:\n\n// iterate over the array\n\nfor( String oneItem : where ) {\n\n...\n\n}\n\n// get specific items\n\nwhere.get( 1 );" ]
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https://pdsa.gakhov.com/
[ "", null, "# Probabilistic Data Structures and Algorithms for Big Data Applications\n\n## Hashing\n\nWe give a brief overview of popular hash functions and hash tables that are widely used in probabilistic data structures.\n\n• Cryptographic hash functions\n• Non-Cryptographic hash functions\n• Hash tables\n\n## Membership\n\nWe study the most well-known known probabilistic data structures that are used to answer approximate membership queries.\n\n• Bloom filter\n• Counting Bloom filter\n• Quotient filter\n• Cuckoo filter\n\n## Cardinality\n\nWe learn data structures that allow to estimate the number of unique elements in a dataset.\n\n• Linear Counting\n• Probabilistic Counting\n• LogLog and HyperLogLog\n\n## Frequency\n\nWe discuss solutions for common problems in streaming applications, such as finding frequency of some element, filtering most frequent elements in the stream, detecting trending elements, etc.\n\n• Majority algorithm\n• Frequent algorithm\n• Count Sketch\n• Count–Min Sketch\n\n## Rank\n\nWe study data structures to estimate quantiles and percentiles in a data stream using only one pass through the data.\n\n• Random sampling\n• q-digest\n• t-digest\n\n## Similarity\n\nWe learn how to effciently solve the nearest neighbor problem for large datasets using sub-linear in time solutions.\n\n• Locality-Sensitive hashing\n• MinHash\n• SimHash\n\n# Andrii Gakhov, Ph.D.\n\n## Andrii Gakhov is a mathematician and software engineer holding a Ph.D.in mathematical modeling and numerical methods. He has been a teacher in the School of Computer Science at V. Karazin Kharkiv National University in Ukraine for a number of years and currently works as a software practitioner for ferret go GmbH, the leading community moderation, automation, and analytics company in Germany. His fields of interests include machine learning, stream mining, and data analysis.", null, "America\nUnited States Amazon.com Visit\nMexico Amazon.com.mx Visit\nBrazil Amazon.com.br Visit\nEurope\nUnited Kingdom Amazon.co.uk Visit\nGermany BoD (Verlag) Visit\nThalia Visit\nAmazon.de Visit\nAustria BoD (Verlag) Visit\nAmazon.de Visit\nFrance Amazon.fr Visit\nSpain Amazon.es Visit\nItaly Amazon.it Visit\nNetherlands BoD (Verlag) Visit\nAsia\nIndia Amazon.in Visit\nJapan Amazon.co.jp Visit\nOceania\nAustralia Amazon.com.au Visit\n\n## E-book editionExclusively at Amazon\n\nAmerica\nUnited States Amazon.com Visit\nMexico Amazon.com.mx Visit\nBrazil Amazon.com.br Visit\nEurope\nUnited Kingdom Amazon.co.uk Visit\nGermany Amazon.de Visit\nAustria Amazon.de Visit\nFrance Amazon.fr Visit\nSpain Amazon.es Visit\nItaly Amazon.it Visit\nNetherlands Amazon.nl Visit\nAsia\nIndia Amazon.in Visit\nJapan Amazon.co.jp Visit\nOceania\nAustralia Amazon.com.au Visit\n\n# Errata\n\n## If you have found any error that is not in the list below, please contact us by email pdsa@gakhov.com. As soon as the error is confirmed and fixed, its description will appear in this section (details are omitted in mobile view due to space restrictions).\n\nEdition Location Original text Corrected text\n1st, 2019 p. 31 \"... by bitwise XOR, however ...\" \"... by bitwise AND, however ...\"\n1st, 2019 p. 62, Example 3.1 \"... of unique elements is 23 GB.\" \"... of unique elements is 2.3 GB.\"\n1st, 2019 p. 65, Algorithm 3.2 $$\\mathbf{Add}(e, LINEARCOUNTER)$$ $$\\mathbf{Add}(x, LINEARCOUNTER)$$\n1st, 2019 p. 66, Example 3.3 $$V = \\frac{9}{16} = 0.5625$$ $$V = \\frac{7}{16} = 0.4375$$\n1st, 2019 p. 66, Example 3.3 $$n \\approx - 16 \\cdot \\ln{0.5625} \\approx 9.206$$ $$n \\approx - 16 \\cdot \\ln{0.4375} \\approx 13.22$$\n1st, 2019 p. 67 ... a fixed constant $$O(N)$$, where $$N$$ is the total number of elements, including duplicates. Thus, the algorithm has $$O(N)$$ time complexity. ... a fixed constant, and thus the algorithm has $$O(N)$$ time complexity, where $$N$$ is the total number of elements, including duplicates.\n1st, 2019 p. 81, Formula 3.12 $$\\hat n \\approx \\alpha_{m}\\cdot m^{2} \\cdot \\left(\\sum_{j=0}^{m-1}2^{-COUNTER[j]}\\right)$$ $$\\hat n \\approx \\alpha_{m}\\cdot m^{2} \\cdot \\left(\\sum_{j=0}^{m-1}2^{-COUNTER[j]}\\right)^{-1}$$\n1st, 2019 p. 96, Example 4.2 \"... 500 billion ...\" \"... 500 million ...\"\n1st, 2019 p. 135, Example 5.3 \"... remaining empty buffer $$B_{2}$$.\" \"... remaining empty buffer $$B_{4}$$.\"\n1st, 2019 p. 173, Example 6.8 $$c(d_1, d_4) = \\frac{255 \\cdot 0 + 0 \\cdot 191 + 0 \\cdot 255}{255 \\cdot 255} = 0$$ $$c(d_1, d_4) = \\frac{255 \\cdot 0 + 0 \\cdot 191 + 0 \\cdot 255}{\\sqrt{255^{2} + 0^2 + 0^2} \\cdot \\sqrt{0^{2} + 191^2 + 255^2}} = 0$$" ]
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https://mathoverflow.net/questions/289683/existence-of-double-eigenvalue/289687#289687
[ "# Existence of double eigenvalue\n\nLet $A$ and $B$ be complex $4\\times 4$ matrices. Assume both are Hermitian, and that they are linearly independent.\n\nMust there exist a nonzero real linear combination $aA + bB$ which has a repeated eigenvalue?\n\nThe answer is 'no'. The generic pair $A$ and $B$ of $4$-by-$4$ Hermitian symmetric matrices will not have any nonzero real linear combination that has a double eigenvalue.\nFor a specific example, take $$A = \\begin{pmatrix}-1&0&0&0\\\\0&1&0&0\\\\0&0&-2&0\\\\0&0&0&2\\end{pmatrix} \\quad \\text{and}\\quad B = \\begin{pmatrix}0&i&0&0\\\\-i&0&0&0\\\\0&0&0&2i\\\\0&0&-2i&0\\end{pmatrix}.$$ Then $$\\det(aA+bB - tI_4) = (t^2-a^2-b^2)(t^2-4a^2-4b^2),$$ and the roots of this polynomial in $t$ are distinct unless $a=b=0$. (Recall that we are assuming that $a$ and $b$ are real, which, of course, implies that $t$ is real.)\nAdded Remark: To see the claim that this property holds for a generic linearly independent pair of Hermitian symmetric $4$-by-$4$ matrices $A$ and $B$, it is only necessary to observe the following: The question is whether, for a generic such pair $A$ and $B$ in the $16$-dimensional real vector space $\\mathcal{H}_4$ consisting of $4$-by-$4$ Hermitian symmetric matrices, the (real) span of $A$, $B$, and $I_4$ contains a nonzero element of rank at most $2$. Now, it is not difficult to show that the cone $C_2$ of elements in $\\mathcal{H}_4$ that have rank at most $2$ is a closed algebraic cone of dimension $12$ (one that is singular along the $7$-dimensional locus of elements of rank at most $1$). Hence the generic $3$-dimensional subspace of $\\mathcal{H}_4$ will meet this cone only at the zero matrix. It is also an open (though not dense) condition on a $3$-dimensional subspace that it contain a positive definite element. Since the standard $\\mathrm{GL}(4,\\mathbb{C})$-action on $\\mathcal{H}_4$ acts transitively on the space of positive definite elements, it follows that the generic pair $A$, $B$, together with $I_4$ will span a $3$-plane that meets $C_2$ only at the origin, which is what was to be shown." ]
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https://www.jpost.com/breaking-news/article-745804
[ "(function (a, d, o, r, i, c, u, p, w, m) { m = d.getElementsByTagName(o), a[c] = a[c] || {}, a[c].trigger = a[c].trigger || function () { (a[c].trigger.arg = a[c].trigger.arg || []).push(arguments)}, a[c].on = a[c].on || function () {(a[c].on.arg = a[c].on.arg || []).push(arguments)}, a[c].off = a[c].off || function () {(a[c].off.arg = a[c].off.arg || []).push(arguments) }, w = d.createElement(o), w.id = i, w.src = r, w.async = 1, w.setAttribute(p, u), m.parentNode.insertBefore(w, m), w = null} )(window, document, \"script\", \"https://95662602.adoric-om.com/adoric.js\", \"Adoric_Script\", \"adoric\",\"9cc40a7455aa779b8031bd738f77ccf1\", \"data-key\");\nvar domain=window.location.hostname; var params_totm = \"\"; (new URLSearchParams(window.location.search)).forEach(function(value, key) {if (key.startsWith('totm')) { params_totm = params_totm +\"&\"+key.replace('totm','')+\"=\"+value}}); var rand=Math.floor(10*Math.random()); var script=document.createElement(\"script\"); script.src=`https://stag-core.tfla.xyz/pre_onetag?pub_id=34&domain=\\${domain}&rand=\\${rand}&min_ugl=0\\${params_totm}`; document.head.append(script);" ]
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https://www.cheric.org/research/tech/periodicals/view.php?seq=694861
[ "Chemical Engineering Science, Vol.62, No.13, 3574-3579, 2007\nA dynamic coupling model for hybrid atomistic-continuum computations\nA dynamic coupling model is developed for a hybrid atomistic-continuum computation in micro- and nano-fluidics. In the hybrid atomistic-continuum computation, a molecular dynamics (MD) simulation is utilized in one region where the continuum assumption breaks down and the Navier-Stokes (NS) equations are used in another region where the continuum assumption holds. In the overlapping part of these two regions, a constrained particle dynamics is needed to couple the MD simulation and the NS equations. The currently existing coupling models for the constrained particle dynamics have a coupling parameter, which has to be empirically determined. In the present work, a novel dynamic coupling model is introduced where the coupling parameter can be calculated as the computation progresses rather than inputing a priori. The dynamic coupling model is based on the momentum constraint and exhibits a correct relaxation rate. The results from the hybrid simulation on the Couette flow and the Stokes flow are in good agreement with the data from the full MD simulation and the solutions of the NS equations, respectively. (c) 2007 Elsevier Ltd. All rights reserved." ]
[ null ]
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https://au.mathworks.com/matlabcentral/cody/problems/44394-how-to-calculate-the-length-of-a-triangle-s-side-given-two-angles-and-one-side/solutions/1698471
[ "Cody\n\n# Problem 44394. How to calculate the length of a triangle's side given two angles and one side\n\nSolution 1698471\n\nSubmitted on 22 Dec 2018\nThis solution is locked. To view this solution, you need to provide a solution of the same size or smaller.\n\n### Test Suite\n\nTest Status Code Input and Output\n1   Fail\nalpha = 30; beta=40; c=1; a_correct = 0.5300; assert(isequal(triangles(alpha,beta,c),a_correct))\n\nans = 0.5321\n\nAssertion failed.\n\n2   Fail\nalpha = 1; beta=70; c=0.5; a_correct = 0.0100; assert(isequal(triangles(alpha,beta,c),a_correct))\n\nans = 0.0092\n\nAssertion failed.\n\n3   Pass\nalpha = 40; beta=100; c=0.2; a_correct = 0.200; assert(isequal(triangles(alpha,beta,c),a_correct))\n\nans = 0.2000\n\n4   Fail\nalpha = 1; beta=1; c=11; a_correct = 5.5; assert(isequal(triangles(alpha,beta,c),a_correct))\n\nans = 5.5008\n\nAssertion failed.\n\n5   Fail\nalpha = 37; beta=62; c=3; a_correct = 1.8300; assert(isequal(triangles(alpha,beta,c),a_correct))\n\nans = 1.8280\n\nAssertion failed." ]
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https://astronomy.stackexchange.com/questions/26017/good-source-for-the-relationship-between-absolute-magnitude-diameter-and-albed
[ "# Good source for the relationship between absolute magnitude, diameter, and albedo?\n\nIn this answer I have rearranged an equation from somewhere, and now I can not relocate the source. My re-aranged form is:\n\n$$M_{Abs} = 5 \\left(\\log_{10}(1329) -\\frac{1}{2}\\log_{10}(\\text{albedo}) -\\log_{10}(D_{km})\\right)$$\n\nThis is the relationship between the absolute magnitude of an object and its diameter and albedo, assuming it is spherical.\n\nQuestion: What is a good source for any form of this equation to which I can link in my answer?\n\n• This shouldn't be hard to derive. Twice as large = 4 times as bright, double the albedo = double the brightness, convert both to changes in magnitude, and apply to original absolute magnitude. – barrycarter Apr 23 '18 at 21:53\n• Found while reading for another Q: Wikipedia: Absolute magnitude: Planets – Mike G Jun 25 at 4:57\n• @MikeG Yep, thanks! While I don't see the exact expression there verbatim but I see enough pieces of it in similar form that it is certainly there in spirit. Thanks! – uhoh Jun 25 at 5:03\n\nThe JPL CNEOS asteroid size estimator and various asteroid albedo papers cite Harris and Harris 1997. That paper is behind a paywall, but Stuart and Binzel 2004 attribute this formula to it: $$H = C - 5 \\log_{10} D - 2.5 \\log_{10} p_V$$ where $H$ is absolute magnitude, $p_V$ is albedo, and $C$ = 15.618.\n• That's numerically equivalent and so it will do, thank you! I'm still going to try to find the source that has the exact expression log${}_{10}$(1329) in my own quixocity because it bothers me that I've lost a reference (I suspect due to a variant of this). – uhoh Apr 23 '18 at 22:47" ]
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https://www.rdocumentation.org/packages/earth/versions/5.3.0/topics/predict.varmod
[ "earth (version 5.3.0)\n\n# predict.varmod: Predict with a varmod model\n\n## Description\n\nYou probably won't need to call this function directly. It is called by `predict.earth` when that function's `interval` argument is used.\n\n## Usage\n\n```# S3 method for varmod\npredict(\nobject = stop(\"no 'object' argument\"),\nnewdata = NULL,\ntype = c(\"pint\", \"cint\", \"se\", \"abs.residual\"),\nlevel = .95,\ntrace = FALSE,\n…)```\n\n## Arguments\n\nobject\n\nA `varmod` object.\n\nnewdata\n\nMake predictions using `newdata`. Default is NULL, meaning return values predicted from the training set.\n\ntype\n\nType of prediction. This is the `interval` argument of `predict.earth`. One of\n\n`\"pint\"` Prediction intervals.\n\n`\"cint\"` Confidence intervals. Cannot be used with `newdata`.\n\n`\"se\"` Standard error of the parent model residuals.\n\n`\"abs.residual\"` The absolute residuals of the parent model on which the residual model regresses.\n\nlevel\n\nConfidence level for the `interval` argument. Default is `.95`, meaning construct 95% confidence bands (estimate the 2.5% and 97.5% levels).\n\ntrace\n\nCurrently unused.\n\nUnused, but provided for generic/method consistency.\n\n## See Also\n\n`predict.earth` `varmod`\n\n## Examples\n\n```# NOT RUN {\ndata(ozone1)\n\nset.seed(1) # optional, for cross validation reproducibility\n\n# note: should really use ncross=30 below but for a quick demo we don't\n\nearth.mod <- earth(O3~temp, data=ozone1, nfold=10, ncross=3, varmod.method=\"lm\")\n\n# call predict.earth, which calls predict.varmod\n\npredict(earth.mod, newdata=ozone1[200:203,], interval=\"pint\", level=.95)\n# }\n```" ]
[ null ]
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https://www.neuron.yale.edu/phpBB/viewtopic.php?f=16&t=4105&sid=f106f0946aa9e571a64c68230591666f
[ "## How does a section recognize a mechanism's current?\n\nNMODL and the Channel Builder.\nduytanph\nPosts: 11\nJoined: Wed Jul 10, 2019 5:03 pm\n\n### How does a section recognize a mechanism's current?\n\nHello. I am quite new to NEURON. I am trying to implement a custom mechanism (mod file) that outputs a synaptic current. I noticed that when trying to record the current at the section that contains the custom mechanism, the current from the mechanism is not present. To illustrate, here is some Python code that successfully captures a section's current with an inserted Exp2Syn mechanism.\n\nCode: Select all\n\n``````from neuron import h\nimport matplotlib.pyplot as plt\n\ns0 = h.Section()\ns0.insert('pas')\n\nsyn = h.Exp2Syn(s0(0.5)) # define as exponential synapse\n\nh.tstop = 10000\nh.dt = .1\nh.steps_per_ms = 1.0/h.dt\n\nevec = h.Vector()\n\n# time of events\nevec.append(200)\nevec.append(1200)\nevec.append(7000)\n\ninput_vector = h.VecStim()\ninput_vector.play(evec)\n\nnc = h.NetCon(input_vector,syn)\nnc.weight = 1\n\ni = h.Vector()\ni.record(syn._ref_i) # record the current through the synapse\n\nsecI = h.Vector()\nsecI.record(s0(0.5)._ref_i_pas) # record the current at the section\n\nh.run()\n\n# plot currents\nplt.figure(1)\nplt.plot(i)\n\nplt.figure(2)\nplt.plot(secI)\nplt.show()\n``````\n\nWhen using Exp2Syn, you can directly see the effects of the synaptic current on the recording of the current of the section. When looking at the mod file for Exp2Syn, I thought the only significant part that would have the section \"recognize\" the current is to declare it as a NONSPECIFIC_CURRENT. So I tried making a very simple mod file to see if the current would be \"seen\" by the section:\n\nCode: Select all\n\n``````NEURON {\nPOINT_PROCESS netcontest\nRANGE i\nNONSPECIFIC_CURRENT i\n}\n\nUNITS {\n(nA) = (nanoamp)\n}\n\nASSIGNED {\ni (nA)\n}\n\nINITIAL {\ni = -70\n}\n\ni = i + 10\n}\n``````\nThe mod file simply initiates the current to -70nA and steps it up by 10nA every time an event is received. If you rerun the python code I copied but replace it to use my simple mod file ( syn = h.netcontest(s0(0.5) ) the plotted section current does not reflect the activity of the custom mechanism.\n\nMy main question is how do I get the section to \"see\" the current of my custom mechanism? Am I missing something crucial in the mod file? Thank you for any assistance!\nted\nPosts: 5892\nJoined: Wed May 18, 2005 4:50 pm\nLocation: Yale University School of Medicine\nContact:\n\n### Re: How does a section recognize a mechanism's current?\n\nI am quite new to NEURON. I am trying to implement a custom mechanism (mod file) that outputs a synaptic current.\nAre you quite sure that is necessary? There isn't something that already exists which, except for parameter values, would do exactly what you want? If there isn't, then have you tried looking for something that exists and is close to what you want to implement?\nWhen looking at the mod file for Exp2Syn, I thought the only significant part that would have the section \"recognize\" the current is to declare it as a NONSPECIFIC_CURRENT.\n\"Programming by example\" is a useful strategy, but as you found out, there are still many pitfalls. NMODL is very different from C, hoc, or Python. Have you taken a look at the enhanced preprint of\nHines, M.L. and Carnevale, N.T. Expanding NEURON's Repertoire of Mechanisms with NMODL. Neural Computation 12:995-1007, 2000\nwhich is available as\nhttps://neuron.yale.edu/neuron/static/p ... odl400.pdf\n(also note the errata https://neuron.yale.edu/neuron/static/p ... rrata.html)\n?\nhow do I get the section to \"see\" the current of my custom mechanism?\nUse a custom mechanism that actually generates a current. Your NMODL code needs a BREAKPOINT block that assigns a value to the current variable.\n\nWhat is to be done to your NMODL file to fix that and some other problems you haven't bumped into yet?\n\nIf you have a mechanism that is supposed to generate a current, the mechanism must have a BREAKPOINT block which contains a statement that assigns a value to that current. Absent such a statement, your mechanism won't affect the model's charge balance equations.\n\nCode: Select all\n\n``````BREAKPOINT {\ni = amp\n}``````\nwhere amp is the name of an intermediate variable whose value will be set to some initial value upon model initialization, and incremented by some amount every time the mechanism receives an event.\nOf course, amp must be declared in the ASSIGNED block\n\nCode: Select all\n\n``````ASSIGNED {\namp (nA)\ni (nA)\n}``````\nInstead of hard wiring magic numbers like -70 and 10 (which happen to be very poor choices, by the way) into your code, you'll want to parameterize your model. amp0 and delta are nice and mnemonic.\n\nCode: Select all\n\n``````PARAMETER {\namp0 = 0 (nA)\ndelta = -0.01 (nA) : < 0 depolarizes. after all, this is a synaptic mechanism, not an electrode\n}``````\nA synaptic mechanism is likely to be used more than once in a model, and you might want each instance to have its own parameters, so declare them to be RANGE in the NEURON block.\n\nCode: Select all\n\n``````NEURON {\nPOINT_PROCESS netcontest\nRANGE amp0, delta, i\nNONSPECIFIC_CURRENT i\n}\n``````\nTo ensure that amp is reset to its initial value when the model is initialized, the NMODL file's INITIAL block should be changed to\n\nCode: Select all\n\n``````INITIAL {\namp = amp0\n}``````\nand to use delta to increment amp when an event arrives, the NET_RECEIVE block should become\n\nCode: Select all\n\n``````NET_RECEIVE(weight (uS)) {\namp = amp + delta\n}``````\nWhat remains to be done? If you use modlunit to check the NMODL file for consistency of units, modlunit will complain that it doesn't know about uS, so the UNITS block should be\n\nCode: Select all\n\n``````UNITS {\n(nA) = (nanoamp)\n(uS) = (microsiemens)\n}``````\nduytanph\nPosts: 11\nJoined: Wed Jul 10, 2019 5:03 pm\n\n### Re: How does a section recognize a mechanism's current?\n\nThank you for the reply Ted, appreciate it! Will keep all the tips you suggested in mind moving forward and will have a look at the NEURON guide as well. The use of the BREAKPOINT block to affect charge balance is exactly what I needed." ]
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https://ihower.tw/rails/fullstack-testing-rspec.html
[ "# Rails 自動化測試 課程簡介\n\n• 閏年程序\n• 停車計費應用\n• Web API 自動化測試\n\n# 一個簡單的閏年程序\n\n## 1-1 閏年程序(第一版)\n\n• 西元年分除以400可整除,為閏年。\n• 西元年分除以4可整除但除以100不可整除,為閏年。\n• 西元年分除以4不可整除,為平年。\n• 西元年分除以100可整除但除以400不可整除,為平年\n\n``````def is_leap_year?(year)\nif year % 4 == 0\nreturn true\nelse\nreturn false\nend\n\n# 同學們可能會發現這不是正確的版本,讓我們繼續做下去\nend\n``````\n\n``````require_relative './leap_year'\nis_leap_year?(2016) # => 應該得到 true\nis_leap_year?(2017) # => 應該得到 false\n\n``````\n\n``````is_leap_year?(2100) # => 得到 true,但是應該要 false 才對\n\n``````\n\n## 1-2 閏年程序(第二版)\n\n``````def is_leap_year?(year)\nif year % 4 == 0\nif year % 100 == 0\nreturn false\nelse\nreturn true\nend\nelse\nreturn false\nend\nend\n``````\n\n``````require_relative './leap_year'\nis_leap_year?(2016) # => 應該得到 true\nis_leap_year?(2017) # => 應該得到 false\nis_leap_year?(2100) # => 應該得到 false 了\n``````\n\n## 1-3 使用 RSpec 自動化測試\n\n`gem install rspec`\n\n``````--format documentation\n--color\n``````\n\n``````require_relative './leap_year'\n\ndescribe \"Leap Year\" do\nit \"2016 is leap year\" do\nresult = is_leap_year?(2016) # 把 2016 傳進去\nexpect(result).to eq(true) # 檢查結果應該要是 true\nend\n\nit \"2017 is common year\" do\nresult = is_leap_year?(2017) # 把 2017 傳進去\nexpect(result).to eq(false) # 檢查結果應該要是 false\nend\n\nit \"2100 is common year\" do\nresult = is_leap_year?(2100) # 把 2100 傳進去\nexpect(result).to eq(false) # 檢查結果應該要是 false\nend\n\nit \"2400 is leap year\" do\nresult = is_leap_year?(2400) # 把 2400 傳進去\nexpect(result).to eq(true) # 檢查結果應該要是 true\nend\n\nend\n\n``````", null, "### 1-4 閏年程序(完成版)\n\n``````def is_leap_year?(year)\nif year % 4 == 0\nif year % 100 == 0\nif year % 400 == 0\nreturn true\nelse\nreturn false\nend\nelse\nreturn true\nend\nelse\nreturn false\nend\nend\n\n``````", null, "## 1-5 重構閏年程序(最終版)\n\n``````def is_leap_year?(y)\ny % 400 == 0 || ( y % 4 == 0 && y % 100 != 0)\nend\n\n``````", null, "" ]
[ null, "https://ihower.tw/rails/part2/04-testing/images/1-1.png", null, "https://ihower.tw/rails/part2/04-testing/images/1-2.png", null, "https://ihower.tw/rails/part2/04-testing/images/1-2.png", null ]
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https://dokumen.tips/documents/physics-of-dielectrics-and-dram.html
[ "", null, "", null, "", null, "", null, "", null, "# Physics of Dielectrics and DRAM\n\n• View\n40\n\n5\n\nTags:\n\nEmbed Size (px)\n\n### Text of Physics of Dielectrics and DRAM\n\nPhysics of Dielectrics and DRAMThomas Schroeder\n\nIHP Im Technologiepark 25 15236 Frankfurt (Oder) GermanyIHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany www.ihp-microelectronics.com\n\nPhysics of Dielectrics and DRAM\n\nDielectrics\n\nIHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany\n\nwww.ihp-microelectronics.com\n\nDescription of a dielectric material Dielectric behavior in a nut shell: A dielectric material is a non-conducting substance whose bound charges are polarized under the influence of an externally applied electric field. The figure of merrit to describe the degree of polarization in a given material is the dielectric constant It is clear that the degree of polarization is related to the structure of the material. In consequence, dielectric behavior in electrostatic and alternating electric fields depends on static and dynamical properties of the structure. Dielectric behavior must be specified with respect to the time or frequency domainIHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany www.ihp-microelectronics.com\n\nElectrostatics: Basic experiment to get the central idea\n\nPlate capacitor with oppositely charged plates and no material inserted. According to the surface charge density, a certain electric field E is created inside.\n\nIf dielectric material is inserted, polarized charges neutralizes some of the charges on the plates. In this way, one talks about free (unneutralized) and bound charges (neutralized) on the plates. As only free charges create electric field, a current must raise the free charge density and keep E constant.\n\nIf dielectric material is inserted and current source disconnectedm the polarized charges neutralize some of the free charges on the plates. In consequence, a constant D results in a decrease of the electric field between the plates.\n\nIHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany\n\nwww.ihp-microelectronics.com\n\nElectrostatics: Macroscopic description of dielectrics Poisson Equationelectric field E\n\ndivD = free\n\nPolarisation\n\nDielectric Displacement DElectric Susceptibility\n\nD = 0E + P\n\nPolarisation\n\nP = 0eE\n\nPutting things together:\n\nD = 0 (1 + e ) E = 0 r E\n\nResult: dielectric displacement D is linearly related to the electric field and the dielectric constant is the linear coefficient of the relationshipIHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany www.ihp-microelectronics.com\n\nDielectric Constant\n\nElectrostatics: Microscopic Approach and the local field Polarisation: Local electric field:density of induced dipoles applied electric field polarisability\n\nP = N a p = N a ElocEloc = Ea + Edipole\n\nThe local electric field can be calculated according to the crystal structure by the method of Clausius and Mosotti. For example, for cubic structures the Clausius Mosotti equation reads:\n\nNa r 1 = 3 0 r + 2\n\nFor example, to design a material with high dielectric constant: - ions with high polarisability - material with high density of induced dipolesIHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany www.ihp-microelectronics.com\n\nMicroscopic Mechanism of dielectric polarisation and frequency dependenceRelaxation Space charge polarisation materials with space charge inhomogeneities (ceramics with conducting grains and insulating boundaries)\n\nDifferent mechanism show different dynamic behavior in time domain. In consequence, adsorption occurs at different windows in frequency domain\n\nRelaxation\n\nOrientation polarization alignment of permanent dipoles in a material\n\nResonance Resonance\n\nIonic polarization mutual displacement of negative and positive sublattice in ionic crystals\n\nElectronic polarization displacement of electron shell against positive nucleusIHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany www.ihp-microelectronics.com\n\nDielectric Behaviour in alternating electric fieldsIn alternating electric fields, a frequency dependent phase shift occurs between applied electric field and displacement of charges in the material To express this mathematically, a complex extension of dielectric function and susceptibilty is introduced:\n\nr = r + i r'\n\nur\n\n''\n\nuu r\n\ne = e + i e'\n\n''\n\nr' + ir'' r = r' + i r''urFigure of merrit for a dielectric material: the quality factor Q\n\nr = r + i r'\n\nur\n\nr tan := ' r''\n\n''\n\nr'' tan := ' r\n\nQ :=\n\n1 tan\n\nA low quality factor states that a material heavily dissipates energy from the alternating electric field by adsorbing mechanisms\n\ntan = (tan ) dipole + (tan )condIHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany www.ihp-microelectronics.com\n\nDescription of ionic and electronic resonance phenomenafriction Equation of motion acting force acting force\n\n2 d u du * * 2 mi 2 + mi i u = qi Eloc + mi* 0, i dt dt\n\ndisplacement from equilibrium\n\nresonance frequency\n\nrestoring force\n\nCase 1: Acting force is a dc field and switched off at a given moment\n\nRestoring force pulls charges back in equilibrium position a) If friction force is negligible, we arrive at undamped oscillations b) If friction force is not negligible, we arrive at damped oscillation. (behavior similar to relaxation process with certain time constant)\n\nIHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany\n\nwww.ihp-microelectronics.com\n\nDescription of ionic and electronic resonance phenomenafriction Equation of motion acting force acting force\n\n2 d u du * * 2 mi 2 + mi i u = qi Eloc + mi* 0, i dt dt\n\ndisplacement from equilibrium\n\nresonance frequency\n\nrestoring force\n\nCase 2: Acting force is an ac field Electric ac field:\n\nAnsatz:\n\nResult: Frequency dependent amplitude u of oscillations and dipole field:\n\nuuu r i ( kr t ) Eloc = Eloc ,0 e r ur i ( kr t ) u = u0 e\n\nur (qi / mi* ) Eloc ,0 u0 = 2 0,i 2 + i iIHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany\n\nuu r uu r pi = qiu0 ei ( kr t ) = i Eoc ,0 ei ( kr t )www.ihp-microelectronics.com\n\nDescription of ionic and electronic resonance phenomenafriction Equation of motion acting force acting force\n\n2 d u du * * 2 mi 2 + mi i u = qi Eloc + mi* 0, i dt dt\n\ndisplacement from equilibrium\n\nresonance frequency\n\nrestoring force\n\nqi i i ( ) = 2 2 mi ( 0,i 2 ) 2 + i2 22 ''\n\nqi2 / mi* ' '' i ( ) = 2 = + ( ) i ( ) i i 2 0,i + i i 2 2 2 q i' ( ) = i 2 2 0,i 2 2 Reduces in the static case to: mi ( 0,i ) + i2 2 q i ,s := i (0) =' 2 2 mi2 0, i i\n\nuu r\n\nCase 2: Acting force is an ac field Frequency dependent complex polarisability for ionic and electronic mechanisms (f > 1011 Hz):\n\nand i'' (0) = 0\n\nIHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany\n\nwww.ihp-microelectronics.com\n\nDescription of ionic and electronic resonance phenomenafriction Equation of motion acting force acting force\n\nrestoring force resonance displacement from equilibrium frequency Case 2: Acting force is an ac field\n\n2 d u du * * 2 mi 2 + mi i u = qi Eloc + mi* 0, i dt dt\n\nWith the frequency dependent complex polarisability and the Clausius-Mosotti equation, we get the frequency dependent complex dielectric function in the frequency range > 1011Hz :' ' ( ) ( 0+ ) ' r r 0 r ( ) = r ( 0+ ) + 1 ( / 0 ) 2 + i / 02\n\nur\n\nIn the case of an ideal insulator with negligible magnetisation, the optical refractive index is given by Maxwell`s law\n\nr ur n( ) = r ( )\n\nOptical properties of matters: ectric properties under the influence of alternating electric fields with f > 1011 HzIHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany www.ihp-microelectronics.com\n\nDescription of relaxation phenomena: orientation and space charge contributionsDebye Relaxation Debye relaxation denotes a system with a single relaxation time t. Example: Medium with one type of oriented dipole which can be oriented by external field Such a system can be directly described by the general expression when the term of the restoring force is omitted:' ' ( ) ( 0+ ) 0 r r r ( ) = r' ( 0+ ) + 1 ( / 0 ) 2 + i / 02\n\nur\n\nWe get:' ' ' ( ) ( ) ' 0+ r r r ( ) = r' ( 0+ ) + r 0 = ( ) + 0+ r 1 + i / 02 1 + i\n\nur\n\nThe relaxation step describes the part of the permittivity due to a relaxation process:\n\nr := r ( 0 ) r ( 0+ ) and := / 0' ' 'IHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany www.ihp-microelectronics.com\n\n2\n\nDescription of relaxation phenomena: orientation and space charge contributionsDebye Relaxation The relaxation step describes the part of the permittivity due to a relaxation process:\n\nr := r ( 0 ) r ( 0+ ) and := / 0' ' '\n\n2\n\nFor microelectronics this means: There is no high-k material without energy dissipating process. Find a material which is well behavin in the frequency range of interest ! The real and imaginary part of the dielectric function for Debye relaxation than read:' r r' ( ) = r' ( 0+ ) + 2 2 1+ ' r '' r ( ) = 1 + 2 2IHP Im Technologiepark 25 15236 Frankfurt (Oder) Germany www.ihp-microelectronics.com\n\nDescription of relaxation phenomena: orientation and space charge contributionsHow to identify Debye relaxation ? 1) Thermal behaviour\n\n= 0 eW0 / kBTCertainly, this results in an exponential dependance of the relaxation frequency with temperature 2) Cole Cole diagram In case of true Debye behavior (only one relaxation time) plotting imaginary against real part forms a semi-circle This examp\n\nRecommended", null, "##### Brazilian Journal of Physics - Progress in High-Energy Electron and X-irradiation of Insulating Dielectrics\nDocuments", null, "##### REDUCING DRAM ACCESS LATENCY BY EXPLOITING DRAM DRAM tabanl¤± bellek, bilgisayar sisteminde darbogaz\nDocuments", null, "##### Frequency Dispersion: Dielectrics, Conductors, and Frequency Dispersion: Dielectrics, Conductors, and\nDocuments", null, "##### Dielectrics Unit – 1 Sub.- Physics. Dielectrics are the materials having electric dipole moment permantly. Dipole: A dipole is an entity in which equal\nDocuments", null, "##### Modeling Viscoelastic Dielectrics - Mechanics Modeling Viscoelastic Dielectrics Wei Hong . Department\nDocuments", null, "##### Spin-On Dielectrics Wafer Bonding Dielectrics Dry Film Dielectrics Electroplating Resists SINR-3570\nDocuments", null, "Documents" ]
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http://math.ipm.ir/frontiers/printseminar.jsp?eventID=176
[ "Frontiers in Mathematical Sciences\n\n TITLE Logical Arithmetic and Real Algebraic Geometry SPEAKER Shahram Mohsenipour IPM ABSTRACT\n We will talk about the diophantine equations from a logical point of view and explain that for a subsystem $T$ of the Peano Arithmetic how diophantine equations can be studied within $T$. For a special $T$ called Open Induction, this study leads us, through Wilkie's theorem, to an algebro-geometric situation wherein we consider a kind of \"arithmetic\" solutions of diophantine equations in the space of all solutions. This latter space is the real spectrum of a commutative ring. In modern real algebraic geometry, the real spectrum has the importance of the Zariski spectrum in the usual algebraic geometry. We will also briefly discuss a recent result of the speaker on the distribution of the \"arithmetic\" points in the real spectrum." ]
[ null ]
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https://www.onemathematicalcat.org/algebra_book/online_problems/par_perpen.htm
[ " Parallel and Perpendicular lines\nPARALLEL AND PERPENDICULAR LINES\nby Dr. Carol JVF Burns (website creator)\nFollow along with the highlighted text while you listen!\n• PRACTICE (online exercises and printable worksheets)\n• Want some other practice with lines?\n\nIf you know the slopes of two non-vertical lines,\ncan you easily determine if they're parallel? Perpendicular?\nYES!\n\nParallel lines have the same slope.\n\nPerpendicular lines have slopes that are opposite reciprocals.\n\nThis section explores these concepts.\n\nSlopes of Parallel Lines\n\nTwo lines in a plane are parallel if they never intersect; they have the same ‘slant’.\n(For a more rigorous study of parallel lines and related concepts, visit my Geometry course: Parallel Lines.)\nSlope (or lack of it!) can be used to decide if lines are parallel:\n\nCHARACTERIZATION OF PARALLEL LINES INVOLVING SLOPE\nSuppose two distinct lines lie in the same coordinate plane.\nThen,\nthe two lines are parallel\nif and only if\n(they are both vertical)     or     (they have the same slope)\n\nThis statement offers a good opportunity to review the mathematical words ‘if and only if’ and ‘or’.\nThe statement has been visually presented to help you see the mathematical sentence structure more clearly.\n\n• The basic set-up is two lines lying in the same coordinate plane.\nThis condition lurks in the background (is assumed to be true) for all that follows.\n• Two mathematical sentences are being compared with the words if and only if:\n• first sentence: the two lines are parallel\n• second sentence: (they are both vertical) OR (they have the same slope)\n• If and only if is a variation of ‘is equivalent to’.\nSentences that are equivalent always have the same truth values—they're true at the same time, and false at the same time.\nIf one is true, so is the other.\nIf one is false, so is the other.\n• An ‘or’ sentence is true when at least one of the subsentences is true.\nThe only time an ‘or’ is false is when both subsentences are false.\n• The nice, compact statement above is actually saying A LOT.\nTake a look, if you want, to increase your appreciation of the brevity of the mathematical language:\n\nSlopes of Perpendicular Lines\n\nNow, let's move on to perpendicular lines.\nTwo lines are perpendicular if they intersect at a $\\,90^{\\circ}$ angle.\nFor example, the $\\,x$-axis and $\\,y$-axis are perpendicular.\n\nCHARACTERIZATION OF PERPENDICULAR LINES\nINVOLVING SLOPE\nSuppose two non-vertical lines lie in the same coordinate plane.\nLet $\\,m_1\\,$ and $\\,m_2\\,$ denote the slopes of these lines.\n\nThe two lines are perpendicular if and only if their slopes are opposite reciprocals: $$\\,\\cssId{s54}{m_1 = -\\frac{1}{m_2}}\\,$$\nEquivalently, the two lines are perpendicular if and only if their slopes multiply to $\\,-1\\,$: $$\\,\\cssId{s59}{m_1m_2 = -1}\\,$$\n\nMake sure you understand the opposite reciprocal equation:\n\n$$\\cssId{s61}{m_1 = -\\frac{1}{m_2}}$$\n one of the slopes is the opposite of the reciprocal of the other slope $\\displaystyle \\cssId{s63}{m_1}$ $\\displaystyle \\cssId{s65}{=}$ $\\displaystyle \\cssId{s67}{-}$ $\\displaystyle \\cssId{s69}{\\frac{1}{m_2}}$\n\nFor example, what's the reciprocal of $\\,2\\,$? Answer:   $\\frac12$\nWhat's the opposite [of the] reciprocal of $\\,2\\,$? Answer:   $-\\frac12$\nSo, the numbers $\\,2\\,$ and $\\,-\\frac 12\\,$ are opposite reciprocals.\nAlso notice that $\\,(2)(-\\frac 12) = -1\\,$.\nSo, lines with slopes $\\,2\\,$ and $\\,-\\frac12\\,$ are perpendicular.\n\nIt's easy to see that this is the correct characterization for perpendicular lines, by studying the sketch below:", null, "The yellow triangle, with base of length $\\,1\\,$ and right side of length $\\,m\\,$,\nshows that the slope of the first line is $\\,\\cssId{s82}{\\frac{\\text{rise}}{\\text{run}}} \\cssId{s83}{= \\frac{m}1} \\cssId{s84}{= m}\\,$.\n\nNow, imagine that this yellow triangle is a block of wood that is glued to the line.\nRotate this block of wood counter-clockwise by $\\,90^{\\circ}\\,$ (so that the original base is now vertical).\nUsing the rotated triangle to compute the slope of the new, rotated, line gives:   $\\,\\cssId{s89}{\\frac{\\text{rise}}{\\text{run}}} \\cssId{s90}{= \\frac{1}{-m}} \\cssId{s91}{= -\\frac1m}\\,$\nEasy! Voila!\n\nMaster the ideas from this section" ]
[ null, "https://www.onemathematicalcat.org/algebra_book/online_problems/graphics/perpen_lines.gif", null ]
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http://list.seqfan.eu/pipermail/seqfan/2009-June/001571.html
[ "# [seqfan] Re: A093893 Subsequence\n\nHagen von EItzen math at von-eitzen.de\nWed Jun 3 17:44:40 CEST 2009\n\n```> >> upper bounds:\n> >> a(14) <= 2311^6 * 50821 = 7741816606631194948471381\n> >> a(15) <= 120121^4 * 150151^2 = 4693882500443399337067739425681\n> >> a(16) <= 120121 * 150151 * 180181 * 270271 = 878325738218239130821\n> >> a(18) <= 4084081^2 * 5105101^2 * 8168161 =\n> >> 3550762793626881654061078595276521\n> >> a(19) <= 4084081^18\n> >>\n> >> For exact value of a(14), if it is not 7741816606631194948471381, then\n> >> a(14) = p6*q where prime p<1400 and prime q>10^6.\n> >>\n> > In addition to this, I have: If a(14) is not ..., then p <= 1571 and\n> > a(14) > 592195652118678643207 (and counting)\n>\n> Nice. But please notice that 1571 is larger than 1400 in my bounds.\n> I've eliminated all larger primes p by brute-forcing q in the interval\n> up to\n> 7741816606631194948471381 / nextprime(1400)^6 < 10^6.\n\nUpdate: Meanwhile I can say that: If a(14) != 7741816606631194948471381\nthen a(14) > 2884960349615742355753 and a(14) = p^6*q with p < 1000.\n\nHagen\n\n```" ]
[ null ]
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http://buildingonmosts.org/lvg-publications.html
[ "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "Learning to Become a Researcher in an Ongoing Research Project: A Communities of Practice Perspective\n\nArslan, O., Van Zoest, L. R., & Ruk, J. M. (2017). Learning to become a researcher in an ongoing research project: A communities of practice perspective. In E. Galindo, & J. Newton (Eds.), Proceedings of the 39th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 1423–1426). Indianapolis, IN: Hoosier Association of Mathematics Teacher Educators.\n\nWe apply Wenger's (1998) communities of practice ideas to the process of incorporating new researchers into an ongoing mathematics education research project. We illustrate this application by describing how the Leveraging MOSTs research project coding team can be viewed as a community of practice. We describe how we have used this particular community of practice to bring new researchers into the project, and new researchers reflect on their experiences with the coding team. Mutual engagement in project work with experienced researchers and having a rich shared repertoire to draw on led to the new researchers developing a shared understanding of the project and being successfully incorporated into the MOST community. This work speaks to the importance of deliberately creating communities of practice for new mathematics researchers to participate within.\n\nTeachers' Responses To a Common Set of High Potential Instances of Student Mathematical Thinking\n\nStockero, S. L., Van Zoest, L. R., Peterson, B. E., Leatham, K. R., & Rougée, A. O. T. (2017). Teachers' responses to a common set of high potential instances of student mathematical thinking. In E. Galindo, & J. Newton (Eds.), Proceedings of the 39th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 1178–1185). Indianapolis, IN: Hoosier Association of Mathematics Teacher Educators.\n\nThis study investigates teacher responses to a common set of high potential instances of student mathematical thinking to better understand the role of the teacher in shaping meaningful mathematical discourse in their classrooms. Teacher responses were coded using a scheme that disentangles the teacher move from other aspects of the teacher response, including who the response is directed to and the degree to which the student thinking is honored. Teachers tended to direct their response to the student who had shared their thinking and to explicitly incorporate ideas core to the student thinking in their response. We consider the nature of these responses in relation to principles of productive use of student mathematical thinking.\n\nBeyond the \"Move\": A Scheme for Coding Teachers' Responses To Student Mathematical Thinking\n\nPeterson, B. E., Van Zoest, L. R., Rougée, A. O. T., Freeburn, B., Stockero, S. L., & Leatham, K. R. (2017). Beyond the \"move\": A scheme for coding teachers' responses to student mathematical thinking. In B. Kaur, W. K. Ho, T. L. Toh, & B. H. Choy. (Eds.), Proceedings of the 41st annual meeting of the International Group for the Psychology of Mathematics Education, Vol. 4 (pp. 17–24). Singapore: International Group for the Psychology of Mathematics Education.\n\nTo contribute to the field's understanding of the teachers' role in using student thinking to shape classroom mathematical discourse, we developed the Teacher Response Coding Scheme (TRC). The TRC provides a means to analyze teachers' in-the-moment responses to student thinking during instruction. The TRC differs from existing schemes in that it disentangles the teacher move from the actor (the person publically asked to consider the student thinking), the recognition (the extent to which the student recognizes their idea in the teacher move), and the mathematics (the alignment of the mathematics in the teacher move to the mathematics in the student thinking). This disentanglement makes the TRC less value-laden and more useful across a broad range of settings.\n\nAttributes of Instances of Student Mathematical Thinking That Are Worth Building on in Whole-Class Discussion\n\nVan Zoest, L. R., Stockero, S. L., Leatham, K. R., Peterson, B. E., Atanga, N. A., & Ochieng, M. A. (2017). Attributes of instances of student mathematical thinking that are worth building on in whole-class discussion. Mathematical Thinking and Learning, 19, 33–54.\n\nThis study investigated attributes of 278 instances of student mathematical thinking during whole-class interactions that were identified as having high potential, if made the object of discussion, to foster learners' understanding of important mathematical ideas. Attributes included the form of the thinking (e.g., question vs. declarative statement), whether the thinking was based on earlier work or generated in the moment, the accuracy of the thinking, and the type of thinking (e.g., sense-making). Findings illuminate the complexity of identifying student thinking worth building on during whole-class discussion and provide insight into important attributes of these high potential instances that could be used to help teachers more easily recognize them. Implications for researching, learning, and enacting the teaching practice of building on student mathematical thinking are discussed.\n\nNoticing Distinctions Among and Within Instances of Student Mathematical Thinking\n\nStockero L. S., Leatham, K. R., Van Zoest, L. R., & Peterson, B. E. (2017) Noticing Distinctions Among and Within Instances of Student Mathematical Thinking. In E. Schack, J. Wilhelm, & M. H. Fisher (Eds.), Teacher noticing: A hidden skill of teaching (pp. 467). Cham, Switzerland: Springer International.\n\nIn this chapter, we argue that there are two critical aspects of noticing student mathematical thinking: noticing within an instance of student thinking and noticing among instances of student thinking. We use the noticing literature to illustrate these distinctions. We then discuss how the MOST Analytic Framework analysis provides structure and guidance for noticing both within and among instances, and illustrate the complex interaction of these two types of noticing through the analysis of an excerpt of classroom dialogue. We conclude by offering the perspective that studies of noticing must go beyond placing value on student mathematical thinking to discriminating among instances of student thinking based on their potential to be used to support students' understanding of important mathematics.\n\nImprecision in Classroom Mathematics Discourse\n\nLeatham, K. R., Peterson, B. E., Merrill, L., Van Zoest, L. R., & Stockero, S. L. (2016). Imprecision in classroom mathematics discourse. In M. B. Wood, E. E. Turner, M. Civil, & J. A. Eli (Eds.), Proceedings of the 38th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 1236–1243). Tucson, AZ: University of Arizona.\n\nWe theorize about ambiguity in mathematical communication and define a certain subset of ambiguous language usage as imprecise. For us, imprecision in classroom mathematics discourse hinders in-the-moment communication because the instance of imprecision is likely to create inconsistent interpretations of the same statement among individuals. We argue for the importance of attending to such imprecision as a critical aspect of attending to precision. We illustrate various types of imprecision that occur in mathematics classrooms and the ramifications of not addressing this imprecision. Based on our conceptualization of these types and ramifications, we discuss implications for research on classroom mathematics discourse.\n\nConceptualizing the Teaching Practice of Building On Student Mathematical Thinking\n\nVan Zoest, L. R., Peterson, B. E., Leatham, K. R., & Stockero, S. L. (2016). Conceptualizing the teaching practice of building on student mathematical thinking. In M. B. Wood, E. E. Turner, M. Civil, & J. A. Eli (Eds.), Proceedings of the 38th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 1281–1288). Tucson, AZ: University of Arizona.\n\nAn important aspect of effective teaching is taking advantage of in-the-moment expressions of student thinking that, by becoming the object of class discussion, can help students better understand important mathematical ideas. We call these high-potential instances of student thinking MOSTs and the productive use of them building. The purpose of this paper is to conceptualize the teaching practice of building on MOSTs as a first step toward developing a common language for and an understanding of productive use of high-potential instances of student thinking. We situate this work in the existing literature, introduce core principles that underlie our conception of building, and present a prototype of the teaching practice of building on MOSTs that includes four sub-practices.\n\nWe conclude by discussing the need for future research and our research agenda for studying the building prototype.\n\nTheorizing the Mathematical Point of Building on Student Mathematical Thinking\n\nVan Zoest, L. R., Stockero, S. L., Leatham, K. R., & Peterson, B. E. (2016). Theorizing the mathematical point of building on student mathematical thinking. In C. Csíkos, A. Rausch, & J. Szitányi (Eds.), Proceedings of the 40th Conference of the International Group for the Psychology of Mathematics Education (Vol. 4, pp. 323–330). Szeged, Hungary: PME.\n\nDespite the fact that the mathematics education field recognizes the critical role that student thinking plays in high quality instruction, little is known about productive use of the in-the-moment student thinking that emerges in the context of whole-class discussion. We draw on and extend the work of others to theorize the mathematical understanding an instance of such student thinking can be used to build towards—the mathematical point (MP). An MP is a mathematical statement of what could be gained from considering a particular instance of student thinking. Examples and non-examples are used to illustrate nuances in the MP construct. Articulating the MP for an instance of student thinking is requisite for determining whether there is instructional value in pursuing that thinking.\n\nAttributes of Student Mathematical Thinking That is Worth Building on in Whole-Class Discussion\n\nVan Zoest, L. R., Stockero, S. L., Atanga, N. A., Leatham, K. R., Peterson, B. E. & Ochieng, M. A. (2015). Attributes of student mathematical thinking that is worth building on in whole-class discussion. Proceedings of the 37th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education. East Lansing, MI: Michigan State University, MI.\n\nThis study investigated the attributes of 265 instances of student mathematical thinking during whole-class interactions that were identified as having the potential to foster learners' understanding of important mathematical ideas (MOSTs). Attributes included the form of the thinking (e.g., question vs. declarative statement), whether the thinking was based on earlier work or generated in the moment, the accuracy of the thinking, and the type of the thinking (e.g., sense making). Findings both illuminate the complexity of identifying student thinking worth building on during whole-class discussion and provide insight into important attributes of MOSTs that teachers can use to better recognize them. For example, 96% of MOSTs were of three types, making these three particularly salient types of student mathematical thinking for teachers to develop skills in recognizing.\n\nUncovering Teachers' Goals, Orientations, and Resources Related to the Practice of Using Student Thinking\n\nStockero, S. L., Van Zoest, L. R., Rougee, A., Fraser, E. H., Leatham, K. R. & Peterson, B. E. (2015). Uncovering teachers' goals, orientations, and resources related to the practice of using student thinking. Proceedings of the 37th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education. East Lansing, MI: Michigan State University, MI.\n\nDespite years of efforts to promote using student thinking during instruction, the practice of productively using student thinking is still difficult for teachers to enact well. Improving teachers' use of this practice requires an understanding of why teachers currently respond to student thinking as they do; that is, an understanding of the goals, orientations and resources (Schoenfeld, 2011) that underlie their enactment of this practice. We describe a scenario-based interview tool developed to prompt teachers to discuss their decisions and rationales related to using student thinking. We examine cases of three individual teachers to illustrate how the tool allows us (1) to infer individual teachers' goals, orientations and resources and (2) to differentiate among teachers' uses of student thinking. Understanding reasons behind teachers' use of student thinking will help us to match professional development to teachers' needs.\n\nConceptualizing Mathematically Significant Pedagogical Opportunities to Build on Student Thinking\n\nLeatham, K. R., Peterson, B. E., Stockero, S. L., & Van Zoest, L. R. (2015). Conceptualizing Mathematically Significant Pedagogical Opportunities to Build on Student Thinking. Journal for Research in Mathematics Education, 46(1), 88-124.\n\nThe mathematics education community values using student thinking to develop mathematical concepts, but the nuances of this practice are not clearly understood. We conceptualize an important group of instances in classroom lessons that occur at the intersection of student thinking, significant mathematics, and pedagogical openings—what we call Mathematically Significant Pedagogical Opportunities to Build on Student Thinking (MOSTs). We analyze dialogue to illustrate the process for determining whether a classroom instance is a MOST. We conclude by discussing how the MOST construct contributes to facilitating and researching teachers' mathematically-productive use of student thinking through providing a lens and generating a common language for recognizing and agreeing upon high-leverage student mathematical thinking.\n\nThe \"MOST\" Productive Student Mathematical Thinking\n\nStockero, S. L., Peterson, B. E., Leatham, K. R., & Van Zoest, L. R. (2014). The \"MOST\" Productive Student Mathematical Thinking. Mathematics Teacher, 106(4), 308-312.\n\nMeaningfully incorporating student mathematical thinking into lessons requires identifying thinking that has potential to become the object of rich mathematical discussion. We conceptualize instances that (a) contain student thinking, (b) are about significant mathematics, and (c) create pedagogical opportunities as Mathematically Significant Pedagogical Opportunities to build on Student Thinking (MOSTs).\n\nTeachers' Perceptions of Productive Use of Student Mathematical Thinking\n\nLeatham, K. R., Van Zoest, L. R., Stockero, S. L., & Peterson, B. E. (2014). Teachers' perceptions of productive use of student mathematical thinking. In P. Liljedahl, S. Oesterle, C. Nicol, & D. Allan (Eds.), Proceedings of the Joint Meeting of PME 38 and PME-NA 36 (Vol. 4, pp. 73–80). Vancouver, Canada: PME.\n\nWe argue that the teaching practice of productively using student mathematical thinking [PUMT] needs to be better conceptualized for the construct to gain greater traction in the classroom and in research. We report the results of a study wherein we explored teachers' perceptions of PUMT. We interviewed mathematics teachers and analyzed these interviews using and refining initial conjectures about the process teachers might go through in learning PUMT. We found that teachers' perceptions of PUMT ranged from valuing student participation, to valuing student mathematical thinking, to using that thinking in a variety of ways related to eliciting, interpreting and building on that thinking.\n\nClassroom Mathematics Discourse: Broadening Perspectives by Integrating Tools for Analysis\n\nJohnson, K. R., Steele, M. D., Herbel-Eisenmann, B. A., Leatham, K. R., & Peterson, B. E., Stockero, S. L., … Merrill, L. (2013). Classroom mathematics discourse: Broadening perspectives by integrating tools for analysis. In M. V. Martinez & A. Castro Superfine (Eds.), Proceedings of the 35th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 1340–1348). Chicago: University of Illinois at Chicago.\n\nThis working group explores tools for analyzing mathematics classroom discourse across two projects with different, but complementary perspectives. The goals of the working group include generating interaction about the theoretical lenses that we use to analyze and discuss classroom mathematics discourse and the relationships between these different theoretical frameworks. Participants will engage with the individual frameworks in the first two sessions and discuss interactions of the two frameworks in the third session.\n\nConceptualizing Mathematically Significant Pedagogical Openings to Build on Student Thinking\n\nVan Zoest, L. R., Leatham, K. R., Peterson, B. E., & Stockero, S. L. (2013). Conceptualizing mathematically significant pedagogical openings to build on student thinking. In A. M. Lindmeier & A. Heinze (Eds.), Proceedings of the 37th Conference of the International Group for the Psychology of Mathematics Education (Vol. 4, pp. 345–352). Kiel, Germany: PME.\n\nThe mathematics education community values using student thinking to develop mathematical concepts, but the nuances of this practice are not clearly understood. We conceptualize an important group of instances in classroom lessons that occur at the intersection of student thinking, significant mathematics, and pedagogical openings—what we call Mathematically Significant Pedagogical Openings to Build on Student Thinking (MOSTs)—and introduce a framework for determining when they occur. We discuss how the MOST construct contributes to facilitating and researching teachers' mathematically-productive use of student thinking through providing a lens and generating a common language for recognizing and agreeing upon high-leverage student mathematical thinking.\n\nMathematically Important Pedagogical Opportunities\n\nLeatham, K. R., Peterson, B. E., Stockero, S. L., & Van Zoest, L. R. (2011). Mathematically important pedagogical opportunities. In L. R. Wiest & T. Lamberg (Eds.), Proceedings of the 33rd Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 838–845). Reno, NV: University of Nevada, Reno.\n\nThe mathematics education community values using student thinking to develop mathematical concepts, but the nuances of this practice are not clearly understood. For example, not all student thinking provides the basis of productive discussions. In this paper we describe a conceptualization of instances in a classroom lesson that provide the teacher with opportunities to extend or change the nature of students' mathematical understanding—what we refer to as Mathematically Important Pedagogical Opportunities (MIPOs). We analyze classroom dialogue to illustrate how this lens can be used to make more tangible the often abstract but fundamental goal of pursuing students' mathematical thinking.\n\nInvestigating Mathematically Important Pedagogical Opportunities\n\nLeatham, K. R., Stockero, S. L., Van Zoest, L. R., & Peterson, B. E. (2010). Investigating mathematically important pedagogical opportunities. In P. Brosnan, D. B. Erchick, & L. Flevares (Eds.), Proceedings of the 32nd Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (Vol. VI, pp. 1599–1605). Columbus, OH: The Ohio State University.\n\nMathematically Important Pedagogical Opportunities (MIPOs) are instances in a classroom lesson in which the teacher has an opportunity to move the class forward in their development of significant mathematics. Although this construct is widely recognized in the literature as important to mathematics teaching and learning, it is neither well defined nor clearly identified as a construct that can be studied. This working group will build on the efforts of two research groups, represented by the organizers, to define, identify, and characterize MIPOs. Specifically, Session 1 will focus on identifying MIPOs, including questioning and critiquing working definitions and preliminary dimensions of MIPOs. Session 2 will explore sub-constructs of MIPOs and the potential of sub-constructs to provide leverage in studying the broader construct. The first two sessions will include examining instances of classroom practice (written/video) that have been identified as containing MIPOs. Session 3 will focus on issues around developing a research agenda for investigating MIPOs and generating plans for continuing work on MIPOs." ]
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https://gmatclub.com/forum/in-a-certain-district-the-ratio-of-the-number-of-registered-143983.html
[ "GMAT Question of the Day - Daily to your Mailbox; hard ones only\n\n It is currently 22 Nov 2019, 15:44", null, "### GMAT Club Daily Prep\n\n#### Thank 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\n#### Not interested in getting valuable practice questions and articles delivered to your email? No problem, unsubscribe here.", null, "", null, "# In a certain district, the ratio of the number of registered\n\nAuthor Message\nTAGS:\n\n### Hide Tags\n\nManager", null, "", null, "Joined: 02 Dec 2012\nPosts: 173\nIn a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\n5\n36", null, "00:00\n\nDifficulty:", null, "", null, "", null, "55% (hard)\n\nQuestion Stats:", null, "71% (02:15) correct", null, "29% (02:30) wrong", null, "based on 1143 sessions\n\n### HideShow timer Statistics\n\nIn a certain district, the ratio of the number of registered Republicans to the number of registered Democrats was 3/5. After 600 additional Republicans and 500 additional Democrats registered, the ratio was 4/5. After these registrations, there were how many more voters in the district registered as Democrats than as Republicans?\n\n(A) 100\n(B) 300\n(C) 400\n(D) 1,000\n(E) 2,500\nMath Expert", null, "V\nJoined: 02 Sep 2009\nPosts: 59268\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\n7\n12\nIn a certain district, the ratio of the number of registered Republicans to the number of registered Democrats was 3/5. After 600 additional Republicans and 500 additional Democrats registered, the ratio was 4/5. After these registrations, there were how many more voters in the district registered as Democrats than as Republicans?\n\n(A) 100\n(B) 300\n(C) 400\n(D) 1,000\n(E) 2,500\n\nOld ratio: $$\\frac{republicans}{democrats}=\\frac{3x}{5x}$$.\n\nNew ratio: $$\\frac{3x+600}{5x+500}=\\frac{4}{5}$$ --> $$x=200$$.\n\nCurrent difference is $$(5x+500)-(3x+600)=2x-100=300$$.\n\n_________________\n##### General Discussion\nIntern", null, "", null, "Joined: 12 Dec 2011\nPosts: 1\nIn a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\n1\nIn a certain district, the ratio of the number of registered Republicans to the number of registered Democrats was 3/5. After 600 additional R and 500 additional D registered, the ratio was 4/5. After these registrations, the there were how many more voters in the district registered as D than as R?\n\nWhen you solve the two given equations, you arrive at D = 1000, which is perfectly logical. As R = 3/5 D, R must be 600.\n\nNow comes the thing I don't understand. In the sample solution, the newly registered voters are now added to the above numbers, which results in D = 1000 + 600 = 1500, respectively R = 600 + 600 = 1200. The difference is 300 now, which corresponds to the OA. But isn't it true that the numbers which result from solving the given equations must already be post the additions?\nMath Expert", null, "V\nJoined: 02 Sep 2009\nPosts: 59268\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\n1\n1\n\ntomtom1610 wrote:\nIn a certain district, the ratio of the number of registered Republicans to the number of registered Democrats was 3/5. After 600 additional R and 500 additional D registered, the ratio was 4/5. After these registrations, the there were how many more voters in the district registered as D than as R?\n\nWhen you solve the two given equations, you arrive at D = 1000, which is perfectly logical. As R = 3/5 D, R must be 600.\n\nNow comes the thing I don't understand. In the sample solution, the newly registered voters are now added to the above numbers, which results in D = 1000 + 600 = 1500, respectively R = 600 + 600 = 1200. The difference is 300 now, which corresponds to the OA. But isn't it true that the numbers which result from solving the given equations must already be post the additions?\n\nAs for your question: we are asked to find the difference between the numbers of Democrats and Republicans AFTER the registration of 600 additional Republicans and 500 additional Democrats, so it should be 1500-1200=300.\n\nHope it's clear.\n_________________\nIntern", null, "", null, "Joined: 28 Oct 2012\nPosts: 1\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\n1\nwhy cant we write it down as R/D = 3/5 and go further with (R+600)/(D+500). If we combine them we get R = 600 and D = 1000. Difference = 400. Whats wrong with this logic. Thanks in advance\nMath Expert", null, "V\nJoined: 02 Sep 2009\nPosts: 59268\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\nwhy cant we write it down as R/D = 3/5 and go further with (R+600)/(D+500). If we combine them we get R = 600 and D = 1000. Difference = 400. Whats wrong with this logic. Thanks in advance\n\nCheck my response here: in-a-certain-district-the-ratio-of-the-number-of-registered-143983.html#p1158046\n_________________\nIntern", null, "", null, "Joined: 03 Mar 2013\nPosts: 24\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\nHi, just wondering what is wrong with my approach as follows:\n\n3/5 republican voters = 0.6*x\nAnother 600 register as republican, with the total amount being 80% republican (4/5)\nTotal new voters is 1,100 (500+600)\n\nSo, 0.6x + 600 = 0.8(x+1,100)\n\n0.6x + 600 = 0.8x + 880\n\nHowever, this is where I go awry.\n\nCould someone pls explain why I can't use the approach noted above?\n\nMath Expert", null, "V\nJoined: 02 Sep 2009\nPosts: 59268\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\n2\nHi, just wondering what is wrong with my approach as follows:\n\n3/5 republican voters = 0.6*x\nAnother 600 register as republican, with the total amount being 80% republican (4/5)\nTotal new voters is 1,100 (500+600)\n\nSo, 0.6x + 600 = 0.8(x+1,100)\n\n0.6x + 600 = 0.8x + 880\n\nHowever, this is where I go awry.\n\nCould someone pls explain why I can't use the approach noted above?\n\nThe ratio of Republicans to Democrats was 3:5, which means that Republicans were 3/(3+5)=3/8 of the total number (not 3/5 of the total number)\n\nAfter 600 additional Republicans and 500 additional Democrats registered, the ratio was 4:5, which means that that Republicans were 4/(4+5)=4/9 of the total number (not 4/5 of the total number).\n\n3/8*x+600=4/9(x+1100) --> x=1600 --> after registration = x+1100=2700 --> Republicans=4/9*1200 and Democrats=1500 --> the difference=300.\n\nHope it helps.\n_________________\nIntern", null, "", null, "Joined: 03 Mar 2013\nPosts: 24\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\nBunuel wrote:\nHi, just wondering what is wrong with my approach as follows:\n\n3/5 republican voters = 0.6*x\nAnother 600 register as republican, with the total amount being 80% republican (4/5)\nTotal new voters is 1,100 (500+600)\n\nSo, 0.6x + 600 = 0.8(x+1,100)\n\n0.6x + 600 = 0.8x + 880\n\nHowever, this is where I go awry.\n\nCould someone pls explain why I can't use the approach noted above?\n\nThe ratio of Republicans to Democrats was 3:5, which means that Republicans were 3/(3+5)=3/8 of the total number (not 3/5 of the total number)\n\nAfter 600 additional Republicans and 500 additional Democrats registered, the ratio was 4:5, which means that that Republicans were 4/(4+5)=4/9 of the total number (not 4/5 of the total number).\n\n3/8*x+600=4/9(x+1100) --> x=1600 --> after registration = x+1100=2700 --> Republicans=4/9*1200 and Democrats=1500 --> the difference=300.\n\nHope it helps.\n\nThanks a lot, appreciate your help!\nVeritas Prep GMAT Instructor", null, "Joined: 11 Dec 2012\nPosts: 312\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\n3\n1\nIf ever you're not seeing the math, you know one of the answers has to be correct, so you can just backsolve. Looking at the increase in numbers, it would make sense that the number of democrats MORE than republicans will probably be in the hundreds, so you'd likely start with B or C.\n\nIf you start with C: 400 is the difference between D's and R's in the new ratio: So there are 4x400 = 1600 Republicans and 5x400 = 2000 Democrats. If you subtract 600 from R and 500 from D, you get 1000 Republicans and 1500 Democrats, a 2/3 ratio. This isn't 3/5, but it's not far (66.7% vs 60%). The next attempt should be B since it's close but slightly too big:\n\nB: 300 is the difference between D's and R's in the new ratio: So there are 4x300 = 1200 Republicans and 5x300 = 1500 Democrats. If you subtract 600 from R and 500 from D, you get 600 Republicans and 1000 Democrats, a 3/5 ratio. Bingo.\n\nIf you started with B, you got to the right answer quickly. If you started with A, D or E, your answer will be way off. While this is not a better solution than the algebraic one, sometimes it is easier to see.\n\nHope this helps!\n-Ron\n_________________\nIntern", null, "", null, "Joined: 09 Sep 2013\nPosts: 2\nGPA: 3.53\nOfficial guide problem solving Q 113  [#permalink]\n\n### Show Tags\n\nIn a certain district, the ratio of the number of registered Republicans to the number of registered Democrats was 3/5. After 600 additional Republicans and 500 additional Democrats registered, the ratio was 4/5. After these registrations, there were how many more voters in the district registered as Democrats than as Republicans?\n\n(A) 100\n(B) 300\n(C) 400\n(D) 1,000\n(E) 2,500\n\n_________\n\nMy question is, why can't we take the multiplier which is 200, and multiply the new ratio 4/5 to get a difference of 200? Why is that wrong? that what was done In question 105 in the official guide. Thank you\nCurrent Student", null, "P\nStatus: Chasing my MBB Dream!\nJoined: 29 Aug 2012\nPosts: 1096\nLocation: United States (DC)\nWE: General Management (Aerospace and Defense)\nRe: Official guide problem solving Q 113  [#permalink]\n\n### Show Tags\n\nmjb2 wrote:\nIn a certain district, the ratio of the number of registered Republicans to the number of registered Democrats was 3/5. After 600 additional Republicans and 500 additional Democrats registered, the ratio was 4/5. After these registrations, there were how many more voters in the district registered as Democrats than as Republicans?\n\n(A) 100\n(B) 300\n(C) 400\n(D) 1,000\n(E) 2,500\n\n_________\n\nMy question is, why can't we take the multiplier which is 200, and multiply the new ration 4/5 to get a difference of 200? Why is that wrong? In question 105 in the official guide that what was done. Thank you\n\nBefore posting please check whether the same question is discussed or not. In this case it is already discussed.", null, "_________________\nMath Expert", null, "V\nJoined: 02 Sep 2009\nPosts: 59268\nRe: Official guide problem solving Q 113  [#permalink]\n\n### Show Tags\n\nmjb2 wrote:\nIn a certain district, the ratio of the number of registered Republicans to the number of registered Democrats was 3/5. After 600 additional Republicans and 500 additional Democrats registered, the ratio was 4/5. After these registrations, there were how many more voters in the district registered as Democrats than as Republicans?\n\n(A) 100\n(B) 300\n(C) 400\n(D) 1,000\n(E) 2,500\n\n_________\n\nMy question is, why can't we take the multiplier which is 200, and multiply the new ratio 4/5 to get a difference of 200? Why is that wrong? that what was done In question 105 in the official guide. Thank you\n\nMerging similar topics. Please refer to the solutions above and ask if anything is unclear.\n\nP.S. All OG13 questions with solutions are here: the-official-guide-quantitative-question-directory-143450.html\n_________________\nIntern", null, "", null, "Joined: 09 Sep 2013\nPosts: 2\nGPA: 3.53\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\nsorry for violating the rules.. I didn't really know them as this was my first post. Anyway, I still don't understand why we can not take the multiplier and multiply it by the new ration 4/5 (200*5)-(200*4)= 200 difference.\nMath Expert", null, "V\nJoined: 02 Sep 2009\nPosts: 59268\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\nmjb2 wrote:\nsorry for violating the rules.. I didn't really know them as this was my first post. Anyway, I still don't understand why we can not take the multiplier and multiply it by the new ration 4/5 (200*5)-(200*4)= 200 difference.\n\nSorry but I don't understand the logic behind your approach.\n_________________\nManager", null, "", null, "B\nJoined: 30 Mar 2013\nPosts: 101\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\nR/D = 3x/5x\n\nR/D = 3x+600/(5x+500) = 4/5\nSolve for x to get x=200.\nNow put in original equation to get total Republicans as 1200, and the total number of democrats as 1500.\ndifference= 300\nManager", null, "", null, "Joined: 07 Feb 2015\nPosts: 60\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\nBunuel wrote:\nmjb2 wrote:\nsorry for violating the rules.. I didn't really know them as this was my first post. Anyway, I still don't understand why we can not take the multiplier and multiply it by the new ration 4/5 (200*5)-(200*4)= 200 difference.\n\nSorry but I don't understand the logic behind your approach.\n\nIt's a shame this wasn't addressed, Bunuel. Let me follow up this question with the logic and you can let me know why it's wrong.\n\nAfter we find x = 200 as the multiplier, why can't we say that since AFTER these registrations the ratio is 4/5, then 4x/5x. Since x=200, 4x equals 800 registered Republicans, and 5x equals 1000 registered Democrats. Thats means that the difference after the registrations is 1000 Democrats - 800 Republicans is 200!\nMath Expert", null, "V\nJoined: 02 Sep 2009\nPosts: 59268\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\n3\ngmatser1 wrote:\nBunuel wrote:\nmjb2 wrote:\nsorry for violating the rules.. I didn't really know them as this was my first post. Anyway, I still don't understand why we can not take the multiplier and multiply it by the new ration 4/5 (200*5)-(200*4)= 200 difference.\n\nSorry but I don't understand the logic behind your approach.\n\nIt's a shame this wasn't addressed, Bunuel. Let me follow up this question with the logic and you can let me know why it's wrong.\n\nAfter we find x = 200 as the multiplier, why can't we say that since AFTER these registrations the ratio is 4/5, then 4x/5x. Since x=200, 4x equals 800 registered Republicans, and 5x equals 1000 registered Democrats. Thats means that the difference after the registrations is 1000 Democrats - 800 Republicans is 200!\n\nBecause x = 200 is for the old ratio and you cannot use it for the new one.\n_________________\nRetired Moderator", null, "Joined: 29 Apr 2015\nPosts: 817\nLocation: Switzerland\nConcentration: Economics, Finance\nSchools: LBS MIF '19\nWE: Asset Management (Investment Banking)\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\nCan we solve this somehow with the \"change in ratio\" method?\n\nStarting ratio:\n5R=3D\nEnding ratio:\n5R=4D\n\nNow we have a change in ratio of 1 unit in democrats corresponding to 500 democrats. This means, 1 unit in 5:4 represents 500. However I believe this just holds true as long as only one number changes. Since we also have changing numbers of republicans, we can not calculate it like that. Can anyone agree/disagree?\n_________________\nSaving was yesterday, heat up the gmatclub.forum's sentiment by spending KUDOS!\n\nPS Please send me PM if I do not respond to your question within 24 hours.\nTarget Test Prep Representative", null, "V\nStatus: Founder & CEO\nAffiliations: Target Test Prep\nJoined: 14 Oct 2015\nPosts: 8484\nLocation: United States (CA)\nRe: In a certain district, the ratio of the number of registered  [#permalink]\n\n### Show Tags\n\nIn a certain district, the ratio of the number of registered Republicans to the number of registered Democrats was 3/5. After 600 additional Republicans and 500 additional Democrats registered, the ratio was 4/5. After these registrations, there were how many more voters in the district registered as Democrats than as Republicans?\n\n(A) 100\n(B) 300\n(C) 400\n(D) 1,000\n(E) 2,500\n\nThe fractional ratio indicates that for every 3 Republicans, there are 5 Democrats, a ratio of 3 : 5. We first set up this ratio of registered Republicans to registered Democrats using a variable multiplier:\n\nRepublicans: Democrats = 3x : 5x\n\nWe are given that 600 additional Republicans and 500 additional Democrats registered and that the new ratio of Republicans to Democrats was 4 to 5. This means that the new number of Republicans can be expressed as (3x + 600), and the new number of Democrats can be expressed as (5x + 500). We can put all this into an equation:\n\nR/D  (3x+600)/(5x+500) = 4/5\n\nAfter cross multiplying we have:\n\n5(3x+600) = 4(5x+500)\n\n15x + 3,000 = 20x + 2,000\n\n1,000 = 5x\n\nx = 200\n\nThus after the registration we have the following:\n\nDemocrats = (5 × 200) + 500 = 1,500\n\nRepublicans = (3 × 200) + 600 = 1,200\n\nThere are 1,500 – 1,200 = 300 more Democrats than Republicans.\n\n_________________\n\n# Scott Woodbury-Stewart\n\nFounder and CEO\n\nScott@TargetTestPrep.com\n\nSee why Target Test Prep is the top rated GMAT quant course on GMAT Club. Read Our Reviews\n\nIf you find one of my posts helpful, please take a moment to click on the \"Kudos\" button.", null, "Re: In a certain district, the ratio of the number of registered   [#permalink] 10 Jun 2016, 11:48\n\nGo to page    1   2    Next  [ 27 posts ]\n\nDisplay posts from previous: Sort by\n\n# In a certain district, the ratio of the number of registered", null, "", null, "" ]
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http://www.synergicpartners.com/en/deep-learning-from-the-basis/
[ "# Blogs\n\nDeep Learning from the basis\n• 1602 Views\n• 0 Comment\n• Artificial Intelligence . Deep Learning . Inteligencia Artificial . Machine Learning . Neural Networks . Perceptrones . Redes Neuronales .\n###### We want to start with a purely informative article, for those to whom Deep Learning sounds like a science fiction or a horror movie. We do not pretend you end up using neural network models on a daily basis, we are just interested that you have the base to be able to make the leap into the world of Deep Learning.\n\nStarting from the basics, it is convenient to ask ourselves the question: What is Deep Learning? The answer is simple; Deep Learning is a Machine Learning technique. In computer science, Machine Learning is the area of artificial intelligence whose objective is the development of techniques that allow computation-based systems (machines) to learn the hidden patterns of structured and unstructured data. Robotics, natural language processing, image recognition and artificial intelligence are some of the wonders derived from automatic learning.\n\nThen we need to continue asking, are neural networks and Deep Learning the same? No, on the one hand when we talk about neural networks we refer to the generic name we give to any scheme that connects multiple nodes (neurons). On the other hand, Deep Learning is a type of neural network that is characterized by having more than one layer of interconnected neurons. Neural networks are a powerful modeling tool that accounts for the interactions between our inputs.", null, "To know what neural networks are, it is best to start with simple perceptrons (perceptron). Perceptrons began to develop in the 1950s (yes, in the 50s!) by Frank Rosenblatt. Rosenblatt was based on the work developed by Warren McCulloch and Walter Pitts in the 1940s and on a learning rule based on error correction. This illustrates that it is a technology that began to take shape more than 70 years ago. Today the perceptron is not a widely used neural model due in part to the development of more advanced networks. However, to understand how the more complex networks work, it is best to start with the simplest perceptrons.", null, "A perceptron can be used in classification problems. It can get multiple entries {X1, X2 … Xn} of binary values {0.1}. In our example the perceptron will output two inputs (X1 y X2). Rosenblatt proposed a simple rule to get the output: He introduced the concept of weight  {W1, W2 … Wn}. These weights are real numbers that indicate the importance of each of the inputs with respect to the output. The perceptron output, also zero or one, is determined if the weighted sum (the sum of each Wi·Xi) is less or greater than a certain threshold value ( a real number), being this value a parameter specific to the neuron.\n\nThe output will take a value of zero (deactivated) if the weighted sum is less than the threshold value and a value of one (activated) if the weighted sum is greater than or equal to that value. We can express it mathematically through the following expression:", null, "We will make a change in the expression, replacing the negative of the threshold with the bias (b=-θ). The bias resembles how easy it is to get an output from one. Therefore, our mathematical expression would look like:", null, "Let’s see an example to finish understanding how networks based on simple perceptrons work.\n\nLet’s imagine that we have to go back to university. The professor explains that for the final grade he takes into account two tests that we will do throughout the course. These tests will consist of an oral presentation (X1) and a written exam (X2). It would be a bit strange, but imagine that there are only two possible results for each test. We can fail (zero value) or pass (value of one). In addition, the teacher explains that each test has a weight with respect to the final grade. For him, the most important thing in the course will be the written exam, an essential condition to pass, so it will give him a high weight. Let’s say a weight of W2=5. The other test is important but we will consider it complementary to the written exam, so it will give a weight of  W1=2.\n\nOnce the weights have been chosen, we decide on a bias of -4 (threshold of 4) for the perceptron. With this configuration the perceptron will apply a value of 1 if we pass the written test and 0 if we fail, regardless of the results of the other tests. Therefore, if we have failed the oral but passed the written the output result would be:", null, "We can vary the results of the model only by varying the weights and the threshold value. If we increase the threshold value, we will move to a situation in which not only will it be necessary to pass the written examination. So, by raising the threshold we are making it more difficult to pass. If we lower the threshold, it may happen that you can pass only with the oral test.\n\nIn order to “choose” the weights and the threshold value, we follow a process of error correction. It consists of starting with some random initial values and modifying them iteratively when the output obtained does not coincide with the observed output (remember, we are in supervised learning). Therefore, this learning is a method of error detection and correction. Weights are only modified when the results are not ideal.\n\nYears later, Minsky (1969) saw the existing limitations in networks based on simple perceptrons. He verified that they only serve for linearly separable classifications by a decision boundary. These problems were already solved by statistical methods, requiring a smaller amount of data. Partly because of this, it evolved into more complex neural networks.", null, "Junior Data Engineer @Synergic Partners" ]
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https://math.stackexchange.com/questions/253440/vertex-cover-degree-problem
[ "# Vertex Cover degree problem\n\nSo Vertex cover (VC):\n\nInstance: a graph $G$ and an integer $k>0$. Question: Does $G$ have a vertex cover of size at most $k$?\n\nWe will now define a version of this problem in which we assume that the degree of each node in a given graph is $2$:\n\n2-vertex cover (2-VC). Instance: a graph $G$ in which each node has degree $2$ and an integer $k>0$. Question: Does $G$ have a vertex cover of size at most $k$?\n\nFor the following, decide whether the answer is “Yes”, “No” or “Unknown because it would resolve the $P =? NP$ question.” Prove your claim.\n\n(a) Is $2-VC ≤_P VC$?\n\n(b) Is $VC ≤_P 2-VC$?\n\nand\n\na second question here\n\nhttp://pastie.org/private/ljxo6aqg3a9troxpnpxriq\n\nThese questions ares really nagging me because i'm not sure what is meant by the phrase degree 2. Is this the cost of the vertex?\n\nAlso i'm aware of the general proof of vertex cover that shows how basically we use the compliment of the independent sets to show a vertex cover.\n\nI'm in the middle of revising for exams and these are eluding me, anyone have any suggestions?\n\nUPDATE: I think 1a) is basically just plugging in a 2 degree VC into a graph that can solve the problem in one step and then deriving the answer through that with a cast.\n\n• The degree of a vertex in a graph is the number of edges incident with that vertex. – Hagen von Eitzen Dec 7 '12 at 23:40\n\nFirst Question\n\nYou need two observations\n\n1. The problem 2-VC is in ${\\sf P}$. This can be shown as follows. In a graph, where every vertex has degree $2$, every connected component is a cycle. You can decide for every component (cycle) separately, how many vertices you need to cover it. In particular, if the cycle has $m$ edges the minimum vertex cover has size $\\lceil m/2 \\rceil$. Notice that every language in $X\\in {\\sf P}-\\{\\emptyset,\\Sigma^*\\}$ is also ${\\sf P}$-complete, since for any $L\\in {\\sf P}$, you can map easily $w\\in L \\iff f(w)\\in X$ with help of the decision algorithm for $L$.\n\n2. VC is a generalization of 2-VC. Hence any algortihm for VC, can answer 2-VC requests. Thus you can take the identity as reduction and you get 2-VC$\\le_p$VC. This is what you observed in Update 1.\n\nNow you can argue as follows. (a) holds independent of whether ${\\sf P}={\\sf NP}$ or not. If ${\\sf P}={\\sf NP}$, then VC is in ${\\sf P}$ and since 2-VC is ${\\sf P}$-complete (b) holds. On the other hand, (b) implies that 2-VC is ${\\sf NP}$-complete and in ${\\sf P}$, and hence ${\\sf P}={\\sf NP}$ has to hold (thus it is false when ${\\sf P}\\ne{\\sf NP}$).\n\nSecond Question\n\nYou can define a reduction from 3-SAT to ADV-3SAT as follows. Let $\\psi$ be the 3-SAT formula you want to transform into a ADV-3SAT formula $\\psi'$. Take every variable $x_i$, and replace it by a new variable $x_i^j$, for $j$ being the $j$th occurrence of $x_i$ in $\\psi$. As a next step you add clauses to assure that all the copies of a variable are either all true or all false. In other words, you want to ensure that $x_i^{j_1} \\iff x_i^{j_2}$. In CNF you get $(x_i^{j_1} \\lor \\overline{x_i^{j_2}}) \\land (x_i^{j_2} \\lor \\overline{x_i^{j_1}})$. Add this for every triplet $i,j_1,j_2$ to get $\\psi'$. Notice that $|\\psi|$ and $|\\psi'|$ are polynomially realated. By construction $\\psi$ is true, iff $\\psi'$ is true, and hence $\\psi$ is satisfiable, iff $\\psi'$ is satisfiable.\n\nFor the first question - If every vertex has 2 edges incident on it, then the graph is a union of disjoint cycles. Can you find a minimum vertex cover for a single cycle? How do vertex covers of disjoint cycles affect each other? What does that mean about how easy it is to solve 2-VC?\n\nFor the second question - Take an instance of 3-SAT. If each variable shows up in at most 3 clauses, we're done. If a variable shows up in more than 3 clauses, can you 'split' the variable into multiple new variables so that each of the new variables shows up in at most 3 clauses, the new variables are linked in the sense that they are all true or all false, and their truth/falsehood is a proxy for the original variable?" ]
[ null ]
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http://nefesdaralmasi.com/unsorted/komponentenweise-addition-und-multiplication-7475805.html
[ "", null, "How electricity works. The Hadamard gate - Duration: As a simple example, you can add two vectors with the same size. Size of an array can be changed with ndarray. Numberphile 2, views. Other Hadamard operations are also seen in the mathematical literature, namely the Hadamard root and Hadamard power which are in effect the same thing because of fractional indicesdefined for a matrix such that:. This video is unavailable. The : bits are mysterious to me.\n\n• Elementwise addition (multiplication, exponentiation, etc.) of lists in Haskell Stack Overflow\n• Addition of Vectors and Matrices\n• Array vs. Matrix Operations MATLAB & Simulink\n• Numerical operations on arrays — Scipy lecture notes\n• abstract algebra what does componentwise addition under group mean Mathematics Stack Exchange\n\n• AND How can I Describe the action of ϕ geometrically. Actually, I am not sure what does componentwise addition mean in this context. how to. Componentwise operations are usually defined on is a componentwise operation while matrix multiplication is not.\n\nHowever, since the matrix and array operations are the same for addition and For example, you can compute the element-wise product of a scalar and a matrix.\n\n## Elementwise addition (multiplication, exponentiation, etc.) of lists in Haskell Stack Overflow\n\nBut if you use the matrix multiplication operator, *, to multiply two matrices.\nIn particular, using vectors of ones, this shows that the sum of all elements in the Hadamard product is the trace of AB T.\n\nAs I have mentioned, vectors can be multiplied by scalars. Tip So, np. Sign in to add this to Watch Later. A-B subtracts B from A.", null, "The matrix multiplication operator calculates the product of two matrices with the formula.", null, "GWIYOMI TAGALOG TRANSLATION LYRICS LADY Contact Front page Contents Up. From Wikipedia, the free encyclopedia.Video: Komponentenweise addition und multiplication The General Linear Group, The Special Linear Group, The Group C^n with Componentwise MultiplicationStack Overflow works best with JavaScript enabled. Conor Neill 10, views. A' is the linear algebraic transpose of A.\nThe addition (+) and subtraction (-) operators are defined to work on matrices as well as scalars.\n\n## Addition of Vectors and Matrices\n\nFor example to specify element-wise multiplication. So, using. Separately calculating the argument lists' length is a bad idea. We usually want to consume as little of the input as possible while producing as.", null, "Componentwise addition has a very simple physical and geometric For matrices, we have matrix and tensor products as well as multiplication by a vector .\nLook at np. The : bits are mysterious to me.\n\nThe decoding step involves an entry-for-entry product, in other words the Hadamard product. The Organic Chemistry Tutor 56, views. A' is the linear algebraic transpose of A. Add to.\n\n### Array vs. Matrix Operations MATLAB & Simulink", null, "AIR FLOAT CYDIA APP Sign up or log in Sign up using Google. The required size and shape of the inputs in relation to one another depends on the operation. For advanced use: master the indexing with arrays of integers, as well as broadcasting. I hope it's not premature to declare that some degree of learning has occurred. For other values of Bthe calculation involves eigenvalues and eigenvectors.\nof real numbers, with the usual addition and multiplication (i.e., $\\oplus \\equiv +$ and $\\odot (called component wise or coordinate wise operations). ### Numerical operations on arrays — Scipy lecture notes Then$ V. Elementwise operations; Basic reductions; Broadcasting; Array shape manipulation; Sorting data All arithmetic operates elementwise: >>> Array multiplication is not matrix multiplication: . Basic operations on numpy arrays ( addition, etc.).\nFurthermore, a matrix has an inverse under Hadamard multiplication if and only if none of the elements are equal to zero.\n\nData in populations. Find out why Close. Related Choose a web site to get translated content where available and see local events and offers. Amber Book 3, views. For nonscalar inputs, the matrix operators generally calculate different answers than their array operator counterparts.", null, "RESEPI ROTI CAN A I TELUR MASIN\nMatrix operations follow the rules of linear algebra.\n\n## abstract algebra what does componentwise addition under group mean Mathematics Stack Exchange\n\nOpen Mobile Search. The RSA 3, views. I hope it's not premature to declare that some degree of learning has occurred. Defining only vector and matrix addition is making injustice to both vector and matrix spaces. Matrices A and B must have the same number of columns.\n\n## 3 Replies to “Komponentenweise addition und multiplication”\n\n1.", null, "Ninris:\n\nIt is used in the machine learning literature, for example to describe the architecture of recurrent neural networks as GRU s or LSTM s. It will work for small arrays because of buffering but fail for large one, in unpredictable ways.\n\n2.", null, "Kajind:\n\nIt is equivalent to the above definition with the last two lines replaced with the catch-all clause.\n\n3.", null, "Talkree:\n\nThe term vector applies to elements of spaces for which two operations are defined - addition and multiplication by scalar. On the right hand side it's a list constructor: x:xs describes a list with head x and tail xs." ]
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https://answerofmath.com/solved-use-a-paired-t-test-on-data-that-are-averages/
[ "# Solved – use a paired t-test on data that are averages\n\nI am comparing 15 pairs of averages paired by two categories with a paired t test. The samples used to determine the averages in each pair are not equal within any single pair of data. For example, if pair 1 consists of 95.5 in category A and 83.6 in category B, the 95.5 average was from a sample of 120 while the 83.6 average was from a sample of 90.\n\nIs this still an acceptable use of the paired t test?\n\nContents\n\nIt's probably not a problem at all, provided the sample sizes are similar to each other.\n\nThere could be complications with small sample sizes, though. Intuitively, an average of a small sample is more variable than averages of large samples. If some pairs are both based on small samples, they could create unusual outlying values. It's well known that the Student \\$t\\$ test does not work well in such cases.\n\n### Analysis\n\nLet's pursue this a little further with a model and a simulation. Because this is meant only to illustrate a phenomenon, I'll propose only the simplest possible model–one for which the \\$t\\$ test is ordinarily without any problems at all–and use only the simplest form of the \\$t\\$ test to avoid technical complications.\n\nThe model is that for each pair of averages, \\$(bar x_A, bar x_B)\\$, the data contributing to the first average are a sample of some Normal distribution with mean \\$mu_A\\$ and variance \\$sigma^2\\$; and the data contributing to the second average are a sample of some Normal distribution with mean \\$mu_B\\$ and variance \\$sigma^2\\$. The null hypothesis asserts that in every pair \\$mu_A=mu_B\\$. The alternative hypothesis is that there is some systematic difference \\$delta\\$ and that in each pair \\$mu_A = mu_B + delta\\$.\n\nIf all the sample sizes were the same, say equal to \\$m\\$, then every one of the observations would behave as independent Normal random variables with variance \\$sigma^2/m\\$. This is where a paired \\$t\\$ test is ideal: the differences \\$bar x_A – bar x_B\\$ then have Normal distributions with mean \\$0\\$ and variance \\$2sigma^2/m\\$ and they are all independent. The Student \\$t\\$ statistic–equal to the difference in means of the \\$bar x_A\\$ and the means of the \\$bar x_B\\$, divided by the estimated standard error of those differences, then will have exactly a \\$t_{n-1}\\$ distribution, where \\$n\\$ is the number of pairs.\n\nHowever, when the sample sizes vary, the resulting distribution is not exactly a Student \\$t\\$ distribution. The numerator of the \\$t\\$ statistic, being a linear combination of independent Normal variables, is still Normal; but the denominator, being the square root of a sum of squares of Normal variables having different variances, no longer has a \\$chi^2\\$ distribution. We therefore have no right to expect the ratio to have a Student \\$t\\$ distribution.\n\n### Simulation\n\nTo see whether this might be a practical issue, I simulated \\$10,000\\$ paired t-tests for \\$20\\$ pairs. First I computed the values in each pair as the average of two independent values (equal to \\$2\\$). All data were independently drawn from the same Normal distribution. (You may easily vary the code to simulate your particular circumstances.) I collected the t-statistic for each iteration. Here they are, displayed as a histogram. On it is drawn (as a red curve) the Student \\$t\\$ distribution with \\$20-1=19\\$ degrees of freedom: it is supposed to describe this histogram well, especially in the tails, which correspond to significant results.", null, "It's a nice agreement between theory and simulation, confirming the appropriateness of the t-test (and, incidentally, showing the code is likely working as intended).\n\nTo create an extreme case (but not the most extreme), I supposed that one pair was based on samples of size \\$2\\$ while the others were based on samples of size \\$200\\$, but otherwise all data were independently drawn from the same Normal distribution.", null, "Something went very wrong. The single pair based on a small sample has caused the \\$t\\$ statistics to be less extreme than we might otherwise suppose, but rarely near zero. This is due, as previously suggested, to its effect on the estimated standard deviation: the inflated SD pulls in the tails–it's hard to get a large fraction when its denominator is large–but also the deviation of the single pair dominates the numerator. Accordingly, numerator and denominator tend to be comparable (but can have different signs). That's why the histogram bunches up near \\$pm 1\\$.\n\nAs a result, the t-test will have a harder time detecting a departure from the null hypothesis: it will be less powerful than we think.\n\n### Conclusions\n\nIn practice, we can expect there to be a certain amount of this behavior in your data. A deeper analysis of variance estimates and \\$chi^2\\$ distributions indicates it really won't be much of a problem unless there are indeed some pairs with radically smaller sample sizes than others.\n\nAlthough I haven't fully analyzed any alternatives (the question did not ask for a solution, only for whether a paired t-test would work!), I believe that this analysis could readily be extended to study an obvious weighted version of the t-test, weighting the data by the reciprocals of their sample sizes, and that the weighted version would have superior performance. It could take some effort to figure out the appropriate degrees of freedom to use in general.\n\n### Software\n\nThis is the `R` code that created the figures. By suitably changing `n.group` you can specify the sample sizes in your pairs and re-run it to study the extent to which using a paired t-test might be problematic.\n\n``n.group <- cbind(A=c(2, rep(200, 19)), B=c(2, rep(200, 19))) #n.group <- cbind(A=rep(2,20), B=rep(2,20)) n <- nrow(n.group) # Number of pairs mu <- c(A=0, B=0) # The underlying group means n.sim <- 1e4 # Simulation size # Create the data. x <- array(rnorm(n.sim*length(n.group), rep(mu, each=n), 1/sqrt(n.group)), dim=c(n, 2, n.sim)) # Run the t-tests. t.stat <- apply(x, 3, function(y) { z <- y[,1]-y[,2] mean(z) / sd(z) * sqrt(length(z)) }) # Display the results and compare to the Student t distribution. hist(t.stat, freq=FALSE, breaks=50) curve(dt(x, n-1), add=TRUE, col=\"Red\", lwd=2) ``\n\nRate this post" ]
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https://paigu.savingadvice.com/2006/10/26/useful-kitchen-gadgets_16234/
[ "User Real IP - 3.228.220.31\n```Array\n(\n => Array\n(\n => 182.68.68.92\n)\n\n => Array\n(\n => 101.0.41.201\n)\n\n => Array\n(\n => 43.225.98.123\n)\n\n => Array\n(\n => 2.58.194.139\n)\n\n => Array\n(\n => 46.119.197.104\n)\n\n => Array\n(\n => 45.249.8.93\n)\n\n => Array\n(\n => 103.12.135.72\n)\n\n => Array\n(\n => 157.35.243.216\n)\n\n => Array\n(\n => 209.107.214.176\n)\n\n => Array\n(\n => 5.181.233.166\n)\n\n => Array\n(\n => 106.201.10.100\n)\n\n => Array\n(\n => 36.90.55.39\n)\n\n => Array\n(\n => 119.154.138.47\n)\n\n => Array\n(\n => 51.91.31.157\n)\n\n => Array\n(\n => 182.182.65.216\n)\n\n => Array\n(\n => 157.35.252.63\n)\n\n => Array\n(\n => 14.142.34.163\n)\n\n => Array\n(\n => 178.62.43.135\n)\n\n => Array\n(\n => 43.248.152.148\n)\n\n => Array\n(\n => 222.252.104.114\n)\n\n => Array\n(\n => 209.107.214.168\n)\n\n => Array\n(\n => 103.99.199.250\n)\n\n => Array\n(\n => 178.62.72.160\n)\n\n => Array\n(\n => 27.6.1.170\n)\n\n => Array\n(\n => 182.69.249.219\n)\n\n => Array\n(\n => 110.93.228.86\n)\n\n => Array\n(\n => 72.255.1.98\n)\n\n => Array\n(\n => 182.73.111.98\n)\n\n => Array\n(\n => 45.116.117.11\n)\n\n => Array\n(\n => 122.15.78.189\n)\n\n => Array\n(\n => 14.167.188.234\n)\n\n => Array\n(\n => 223.190.4.202\n)\n\n => Array\n(\n => 202.173.125.19\n)\n\n => Array\n(\n => 103.255.5.32\n)\n\n => Array\n(\n => 39.37.145.103\n)\n\n => Array\n(\n => 140.213.26.249\n)\n\n => Array\n(\n => 45.118.166.85\n)\n\n => Array\n(\n => 102.166.138.255\n)\n\n => Array\n(\n => 77.111.246.234\n)\n\n => Array\n(\n => 45.63.6.196\n)\n\n => Array\n(\n => 103.250.147.115\n)\n\n => Array\n(\n => 223.185.30.99\n)\n\n => Array\n(\n => 103.122.168.108\n)\n\n => Array\n(\n => 123.136.203.21\n)\n\n => Array\n(\n => 171.229.243.63\n)\n\n => Array\n(\n => 153.149.98.149\n)\n\n => Array\n(\n => 223.238.93.15\n)\n\n => Array\n(\n => 178.62.113.166\n)\n\n => Array\n(\n => 101.162.0.153\n)\n\n => Array\n(\n => 121.200.62.114\n)\n\n => Array\n(\n => 14.248.77.252\n)\n\n => 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Array\n(\n => 45.116.232.56\n)\n\n => Array\n(\n => 45.118.166.91\n)\n\n => Array\n(\n => 180.92.230.234\n)\n\n => Array\n(\n => 157.40.57.160\n)\n\n => Array\n(\n => 110.38.38.130\n)\n\n => Array\n(\n => 72.255.57.183\n)\n\n => Array\n(\n => 182.68.81.85\n)\n\n => Array\n(\n => 39.57.202.122\n)\n\n => Array\n(\n => 119.152.154.36\n)\n\n => Array\n(\n => 5.62.62.141\n)\n\n => Array\n(\n => 119.155.54.232\n)\n\n => Array\n(\n => 39.37.141.22\n)\n\n => Array\n(\n => 183.87.12.225\n)\n\n => Array\n(\n => 107.170.127.117\n)\n\n => Array\n(\n => 125.63.124.49\n)\n\n => Array\n(\n => 39.42.191.3\n)\n\n => Array\n(\n => 116.74.24.72\n)\n\n => Array\n(\n => 46.101.89.227\n)\n\n => Array\n(\n => 202.173.125.247\n)\n\n => Array\n(\n => 39.42.184.254\n)\n\n => Array\n(\n => 115.186.165.132\n)\n\n => Array\n(\n => 39.57.206.126\n)\n\n => Array\n(\n => 103.245.13.145\n)\n\n => Array\n(\n => 202.175.246.43\n)\n\n => Array\n(\n => 192.140.152.150\n)\n\n => Array\n(\n => 202.88.250.103\n)\n\n => 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41.60.236.83\n)\n\n => Array\n(\n => 122.164.162.7\n)\n\n => Array\n(\n => 71.136.154.5\n)\n\n => Array\n(\n => 132.154.119.122\n)\n\n => Array\n(\n => 110.225.80.135\n)\n\n => Array\n(\n => 84.17.61.143\n)\n\n => Array\n(\n => 119.160.102.244\n)\n\n => Array\n(\n => 47.31.27.44\n)\n\n => Array\n(\n => 27.71.89.160\n)\n\n => Array\n(\n => 107.175.38.101\n)\n\n => Array\n(\n => 195.211.150.152\n)\n\n => Array\n(\n => 157.35.250.255\n)\n\n => Array\n(\n => 111.119.187.53\n)\n\n => Array\n(\n => 119.152.97.213\n)\n\n => Array\n(\n => 180.92.143.145\n)\n\n => Array\n(\n => 72.255.61.46\n)\n\n => Array\n(\n => 47.8.183.6\n)\n\n => Array\n(\n => 92.38.148.53\n)\n\n => Array\n(\n => 122.173.194.72\n)\n\n => Array\n(\n => 183.83.226.97\n)\n\n => Array\n(\n => 122.173.73.231\n)\n\n => Array\n(\n => 119.160.101.101\n)\n\n => Array\n(\n => 93.177.75.174\n)\n\n => Array\n(\n => 115.97.196.70\n)\n\n => Array\n(\n => 111.119.187.35\n)\n\n => Array\n(\n => 103.226.226.154\n)\n\n => Array\n(\n => 103.244.172.73\n)\n\n => Array\n(\n => 119.155.61.222\n)\n\n => Array\n(\n => 157.37.184.92\n)\n\n => Array\n(\n => 119.160.103.204\n)\n\n => Array\n(\n => 175.176.87.21\n)\n\n => Array\n(\n => 185.51.228.246\n)\n\n => Array\n(\n => 103.250.164.255\n)\n\n => Array\n(\n => 122.181.194.16\n)\n\n => Array\n(\n => 157.37.230.232\n)\n\n => Array\n(\n => 103.105.236.6\n)\n\n => Array\n(\n => 111.88.128.174\n)\n\n => Array\n(\n => 37.111.139.82\n)\n\n => Array\n(\n => 39.34.133.52\n)\n\n => Array\n(\n => 113.177.79.80\n)\n\n => Array\n(\n => 180.183.71.184\n)\n\n => Array\n(\n => 116.72.218.255\n)\n\n => Array\n(\n => 119.160.117.26\n)\n\n => Array\n(\n => 158.222.0.252\n)\n\n => Array\n(\n => 23.227.142.146\n)\n\n => Array\n(\n => 122.162.152.152\n)\n\n => Array\n(\n => 103.255.149.106\n)\n\n => Array\n(\n => 104.236.53.155\n)\n\n => Array\n(\n => 119.160.119.155\n)\n\n => Array\n(\n => 175.107.214.244\n)\n\n => Array\n(\n => 102.7.116.7\n)\n\n => Array\n(\n => 111.88.91.132\n)\n\n => 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Array\n(\n => 110.38.29.246\n)\n\n => Array\n(\n => 14.192.140.199\n)\n\n => Array\n(\n => 24.68.10.106\n)\n\n => Array\n(\n => 47.30.209.179\n)\n\n => Array\n(\n => 106.223.123.21\n)\n\n => Array\n(\n => 103.224.48.30\n)\n\n => Array\n(\n => 104.131.19.173\n)\n\n => Array\n(\n => 119.157.100.206\n)\n\n => Array\n(\n => 103.10.226.73\n)\n\n => Array\n(\n => 162.208.51.163\n)\n\n => Array\n(\n => 47.30.221.227\n)\n\n => Array\n(\n => 119.160.116.210\n)\n\n => Array\n(\n => 198.16.78.43\n)\n\n => Array\n(\n => 39.44.201.151\n)\n\n => Array\n(\n => 71.63.181.84\n)\n\n => Array\n(\n => 14.142.192.218\n)\n\n => Array\n(\n => 39.34.147.178\n)\n\n => Array\n(\n => 111.92.75.25\n)\n\n => Array\n(\n => 45.135.239.58\n)\n\n => Array\n(\n => 14.232.235.1\n)\n\n => Array\n(\n => 49.144.100.155\n)\n\n => Array\n(\n => 62.182.99.33\n)\n\n => Array\n(\n => 104.243.212.187\n)\n\n => Array\n(\n => 59.97.132.214\n)\n\n => Array\n(\n => 47.9.15.179\n)\n\n => Array\n(\n => 39.44.103.186\n)\n\n => Array\n(\n => 183.83.241.132\n)\n\n => Array\n(\n => 103.41.24.180\n)\n\n => Array\n(\n => 104.238.46.39\n)\n\n => Array\n(\n => 103.79.170.78\n)\n\n => Array\n(\n => 59.103.138.81\n)\n\n => Array\n(\n => 106.198.191.146\n)\n\n => Array\n(\n => 106.198.255.122\n)\n\n => Array\n(\n => 47.31.46.37\n)\n\n => Array\n(\n => 109.169.23.76\n)\n\n => Array\n(\n => 103.143.7.55\n)\n\n => Array\n(\n => 49.207.114.52\n)\n\n => Array\n(\n => 198.54.106.250\n)\n\n => Array\n(\n => 39.50.64.18\n)\n\n => Array\n(\n => 222.252.48.132\n)\n\n => Array\n(\n => 42.201.186.53\n)\n\n => Array\n(\n => 115.97.198.95\n)\n\n => Array\n(\n => 93.76.134.244\n)\n\n => Array\n(\n => 122.173.15.189\n)\n\n => Array\n(\n => 39.62.38.29\n)\n\n => Array\n(\n => 103.201.145.254\n)\n\n => Array\n(\n => 111.119.187.23\n)\n\n => Array\n(\n => 157.50.66.33\n)\n\n => Array\n(\n => 157.49.68.163\n)\n\n => Array\n(\n => 103.85.125.215\n)\n\n => Array\n(\n => 103.255.4.16\n)\n\n => Array\n(\n => 223.181.246.206\n)\n\n => Array\n(\n => 39.40.109.226\n)\n\n => Array\n(\n => 43.225.70.157\n)\n\n => Array\n(\n => 103.211.18.168\n)\n\n => Array\n(\n => 137.59.221.60\n)\n\n => Array\n(\n => 103.81.214.63\n)\n\n => Array\n(\n => 39.35.163.2\n)\n\n => Array\n(\n => 106.205.124.39\n)\n\n => Array\n(\n => 209.99.165.216\n)\n\n => Array\n(\n => 103.75.247.187\n)\n\n => Array\n(\n => 157.46.217.41\n)\n\n => Array\n(\n => 75.186.73.80\n)\n\n => Array\n(\n => 212.103.48.153\n)\n\n => Array\n(\n => 47.31.61.167\n)\n\n => Array\n(\n => 119.152.145.131\n)\n\n => Array\n(\n => 171.76.177.244\n)\n\n => Array\n(\n => 103.135.78.50\n)\n\n => Array\n(\n => 103.79.170.75\n)\n\n => Array\n(\n => 105.160.22.74\n)\n\n => Array\n(\n => 47.31.20.153\n)\n\n => Array\n(\n => 42.107.204.65\n)\n\n => Array\n(\n => 49.207.131.35\n)\n\n => Array\n(\n => 92.38.148.61\n)\n\n => Array\n(\n => 183.83.255.206\n)\n\n => Array\n(\n => 107.181.177.131\n)\n\n => Array\n(\n => 39.40.220.157\n)\n\n => Array\n(\n => 39.41.133.176\n)\n\n => Array\n(\n => 103.81.214.61\n)\n\n => Array\n(\n => 223.235.108.46\n)\n\n => Array\n(\n => 171.241.52.118\n)\n\n => Array\n(\n => 39.57.138.47\n)\n\n => Array\n(\n => 106.204.196.172\n)\n\n => Array\n(\n => 39.53.228.40\n)\n\n => Array\n(\n => 185.242.5.99\n)\n\n => Array\n(\n => 103.255.5.96\n)\n\n => Array\n(\n => 157.46.212.120\n)\n\n => Array\n(\n => 107.181.177.138\n)\n\n => Array\n(\n => 47.30.193.65\n)\n\n => Array\n(\n => 39.37.178.33\n)\n\n => Array\n(\n => 157.46.173.29\n)\n\n => Array\n(\n => 39.57.238.211\n)\n\n => Array\n(\n => 157.37.245.113\n)\n\n => Array\n(\n => 47.30.201.138\n)\n\n => Array\n(\n => 106.204.193.108\n)\n\n => Array\n(\n => 212.103.50.212\n)\n\n => Array\n(\n => 58.65.221.187\n)\n\n => Array\n(\n => 178.62.92.29\n)\n\n => Array\n(\n => 111.92.77.166\n)\n\n => Array\n(\n => 47.30.223.158\n)\n\n => Array\n(\n => 103.224.54.83\n)\n\n => Array\n(\n => 119.153.43.22\n)\n\n => Array\n(\n => 223.181.126.251\n)\n\n => Array\n(\n => 39.42.175.202\n)\n\n => Array\n(\n => 103.224.54.190\n)\n\n => Array\n(\n => 49.36.141.210\n)\n\n => Array\n(\n => 5.62.63.218\n)\n\n => Array\n(\n => 39.59.9.18\n)\n\n => Array\n(\n => 111.88.86.45\n)\n\n => Array\n(\n => 178.54.139.5\n)\n\n => Array\n(\n => 116.68.105.241\n)\n\n => Array\n(\n => 119.160.96.187\n)\n\n => Array\n(\n => 182.189.192.103\n)\n\n => Array\n(\n => 119.160.96.143\n)\n\n => Array\n(\n => 110.225.89.98\n)\n\n => Array\n(\n => 169.149.195.134\n)\n\n => Array\n(\n => 103.238.104.54\n)\n\n => Array\n(\n => 47.30.208.142\n)\n\n => Array\n(\n => 157.46.179.209\n)\n\n => Array\n(\n => 223.235.38.119\n)\n\n => Array\n(\n => 42.106.180.165\n)\n\n => Array\n(\n => 154.122.240.239\n)\n\n => Array\n(\n => 106.223.104.191\n)\n\n => Array\n(\n => 111.93.110.218\n)\n\n => Array\n(\n => 182.183.161.171\n)\n\n => Array\n(\n => 157.44.184.211\n)\n\n => Array\n(\n => 157.50.185.193\n)\n\n => Array\n(\n => 117.230.19.194\n)\n\n => Array\n(\n => 162.243.246.160\n)\n\n => Array\n(\n => 106.223.143.53\n)\n\n => Array\n(\n => 39.59.41.15\n)\n\n => Array\n(\n => 106.210.65.42\n)\n\n => Array\n(\n => 180.243.144.208\n)\n\n => Array\n(\n => 116.68.105.22\n)\n\n => Array\n(\n => 115.42.70.46\n)\n\n => Array\n(\n => 99.72.192.148\n)\n\n => Array\n(\n => 182.183.182.48\n)\n\n => Array\n(\n => 171.48.58.97\n)\n\n => Array\n(\n => 37.120.131.188\n)\n\n => Array\n(\n => 117.99.167.177\n)\n\n => Array\n(\n => 111.92.76.210\n)\n\n => Array\n(\n => 14.192.144.245\n)\n\n => Array\n(\n => 169.149.242.87\n)\n\n => Array\n(\n => 47.30.198.149\n)\n\n => Array\n(\n => 59.103.57.140\n)\n\n => Array\n(\n => 117.230.161.168\n)\n\n => Array\n(\n => 110.225.88.173\n)\n\n => Array\n(\n => 169.149.246.95\n)\n\n => Array\n(\n => 42.106.180.52\n)\n\n => Array\n(\n => 14.231.160.157\n)\n\n => Array\n(\n => 123.27.109.47\n)\n\n => Array\n(\n => 157.46.130.54\n)\n\n => Array\n(\n => 39.42.73.194\n)\n\n => Array\n(\n => 117.230.18.147\n)\n\n => Array\n(\n => 27.59.231.98\n)\n\n => Array\n(\n => 125.209.78.227\n)\n\n => Array\n(\n => 157.34.80.145\n)\n\n => Array\n(\n => 42.201.251.86\n)\n\n => Array\n(\n => 117.230.129.158\n)\n\n => Array\n(\n => 103.82.80.103\n)\n\n => Array\n(\n => 47.9.171.228\n)\n\n => Array\n(\n => 117.230.24.92\n)\n\n => Array\n(\n => 103.129.143.119\n)\n\n => Array\n(\n => 39.40.213.45\n)\n\n => Array\n(\n => 178.92.188.214\n)\n\n => Array\n(\n => 110.235.232.191\n)\n\n => Array\n(\n => 5.62.34.18\n)\n\n => Array\n(\n => 47.30.212.134\n)\n\n => Array\n(\n => 157.42.34.196\n)\n\n => Array\n(\n => 157.32.169.9\n)\n\n => Array\n(\n => 103.255.4.11\n)\n\n => Array\n(\n => 117.230.13.69\n)\n\n => Array\n(\n => 117.230.58.97\n)\n\n)\n```\nUseful Kitchen Gadgets: Cents Not Much Make\n << Back to all Blogs Login or Create your own free blog Layout: Blue and Brown (Default) Author's Creation", null, "", null, "", null, "October 26th, 2006 at 01:42 pm\n\nI used to scoff at the aisle in the Asian grocery store that sold the cheap-looking household/kitchen stuff (who wants a waving Lucky Cat statue?), but recently, after my electric thermos died, I found myself skulking down that very aisle, glancing with envy at the Tiger, Tatung, and assorted Japanese versions that were out of my desired price range. I also discovered that, aesthetics aside, the aisle actually contains some rather useful kitchen gadgets and tools at very reasonable prices. They may be pink or covered with Sanrio (hello kitty!) cartoons, but they do the same job and cost less than those glistening versions at Bed Bath and Beyond.\n\nSome useful/cool finds:\n-splatter guard- a big round screen with a handle that you use to cover your pan when cooking to prevent grease from spitting out and ruining your nice shirts (my poor blue blouse when I was making pork chops)\n-long handle wire basket \"dippers\"- I don't know what they are, but we use them for hot pot, to fish yummy bits of meat and veggies from a boiling broth. The bigger sizes are useful for fishing boiled dumplings out of the cooking water.\n-I don't even know WHAT to call this thing, but it has expandable \"claw\" that you can use to grab the edges of a plate and lift it. My family used to steam things in the rice cooker, so we'd use it to lift the plate out rather than burning our fingers (more on improvised steamers in a future entry).\n\n### 3 Responses to “Useful Kitchen Gadgets”\n\n1. LuxLiving Says:\n\nDid you buy something or just put them on the 'wants' list?\n\n2. JanH Says:\n\nI love my splatter guard. Did you get yourself one? Think spaghetti, bacon, etc.\n\n3. snkeflake Says:\n\nI have a couple of those splatter guards, One I use just as a strainer. For instance that splatter guard fits right over a 5 gallon bucket when you are straining large quantities of liquids.\n\n(Note: If you were logged in, we could automatically fill in these fields for you.)\n Name: * Email: Will not be published. Subscribe: Notify me of additional comments to this entry. URL: Verification: * Please spell out the number 4.  [ Why? ]\n\nvB Code: You can use these tags: [b] [i] [u] [url] [email]" ]
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https://openstax.org/books/university-physics-volume-1/pages/4-summary
[ "University Physics Volume 1\n\n# Summary\n\n### 4.1Displacement and Velocity Vectors\n\n• The position function $r→(t)r→(t)$ gives the position as a function of time of a particle moving in two or three dimensions. Graphically, it is a vector from the origin of a chosen coordinate system to the point where the particle is located at a specific time.\n• The displacement vector $Δr→Δr→$ gives the shortest distance between any two points on the trajectory of a particle in two or three dimensions.\n• Instantaneous velocity gives the speed and direction of a particle at a specific time on its trajectory in two or three dimensions, and is a vector in two and three dimensions.\n• The velocity vector is tangent to the trajectory of the particle.\n• Displacement $r→(t)r→(t)$ can be written as a vector sum of the one-dimensional displacements $x→(t),y→(t),z→(t)x→(t),y→(t),z→(t)$ along the x, y, and z directions.\n• Velocity $v→(t)v→(t)$ can be written as a vector sum of the one-dimensional velocities $vx(t),vy(t),vz(t)vx(t),vy(t),vz(t)$ along the x, y, and z directions.\n• Motion in any given direction is independent of motion in a perpendicular direction.\n\n### 4.2Acceleration Vector\n\n• In two and three dimensions, the acceleration vector can have an arbitrary direction and does not necessarily point along a given component of the velocity.\n• The instantaneous acceleration is produced by a change in velocity taken over a very short (infinitesimal) time period. Instantaneous acceleration is a vector in two or three dimensions. It is found by taking the derivative of the velocity function with respect to time.\n• In three dimensions, acceleration $a→(t)a→(t)$ can be written as a vector sum of the one-dimensional accelerations $ax(t),ay(t),andaz(t)ax(t),ay(t),andaz(t)$ along the x-, y-, and z-axes.\n• The kinematic equations for constant acceleration can be written as the vector sum of the constant acceleration equations in the x, y, and z directions.\n\n### 4.3Projectile Motion\n\n• Projectile motion is the motion of an object subject only to the acceleration of gravity, where the acceleration is constant, as near the surface of Earth.\n• To solve projectile motion problems, we analyze the motion of the projectile in the horizontal and vertical directions using the one-dimensional kinematic equations for x and y.\n• The time of flight of a projectile launched with initial vertical velocity $v0yv0y$ on an even surface is given by\n$Ttof=2(v0sinθ)g.Ttof=2(v0sinθ)g.$\nThis equation is valid only when the projectile lands at the same elevation from which it was launched.\n• The maximum horizontal distance traveled by a projectile is called the range. Again, the equation for range is valid only when the projectile lands at the same elevation from which it was launched.\n\n### 4.4Uniform Circular Motion\n\n• Uniform circular motion is motion in a circle at constant speed.\n• Centripetal acceleration $a→Ca→C$ is the acceleration a particle must have to follow a circular path. Centripetal acceleration always points toward the center of rotation and has magnitude $aC=v2/r.aC=v2/r.$\n• Nonuniform circular motion occurs when there is tangential acceleration of an object executing circular motion such that the speed of the object is changing. This acceleration is called tangential acceleration $a→T.a→T.$ The magnitude of tangential acceleration is the time rate of change of the magnitude of the velocity. The tangential acceleration vector is tangential to the circle, whereas the centripetal acceleration vector points radially inward toward the center of the circle. The total acceleration is the vector sum of tangential and centripetal accelerations.\n• An object executing uniform circular motion can be described with equations of motion. The position vector of the object is $r→(t)=Acosωti^+Asinωtj^,r→(t)=Acosωti^+Asinωtj^,$ where A is the magnitude $|r→(t)|,|r→(t)|,$ which is also the radius of the circle, and $ωω$ is the angular frequency.\n\n### 4.5Relative Motion in One and Two Dimensions\n\n• When analyzing motion of an object, the reference frame in terms of position, velocity, and acceleration needs to be specified.\n• Relative velocity is the velocity of an object as observed from a particular reference frame, and it varies with the choice of reference frame.\n• If S and $S′S′$ are two reference frames moving relative to each other at a constant velocity, then the velocity of an object relative to S is equal to its velocity relative to $S′S′$ plus the velocity of $S′S′$ relative to S.\n• If two reference frames are moving relative to each other at a constant velocity, then the accelerations of an object as observed in both reference frames are equal.\nOrder a print copy\n\nAs an Amazon Associate we earn from qualifying purchases." ]
[ null ]
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https://community.ptc.com/t5/PTC-Mathcad/CUBIC-POLYNOMIAL/m-p/427316
[ "cancel\nShowing results for\nDid you mean:\ncancel\nShowing results for\nDid you mean:\n\nSOLVED\nHighlighted\n\n## CUBIC POLYNOMIAL\n\nHello everyone,\n\nThis is rather URGENT. Would you please help me to generate a cubic polynomial for the function in the attached worksheet?\n\nReally appreciate your time and help.\n\nThanks a lot,\n\nAnousheh\n\nTags (1)\n1 ACCEPTED SOLUTION\n\nAccepted Solutions\nHighlighted\n\n## Re: CUBIC POLYNOMIAL\n\nDidn't we had that same kind of question sometimes before? Same methods apply, see attached\n\n8 REPLIES 8\nHighlighted\n\n## Re: CUBIC POLYNOMIAL\n\nDidn't we had that same kind of question sometimes before? Same methods apply, see attached\n\nHighlighted\n\n## Re: CUBIC POLYNOMIAL\n\nHello Werner,\n\nThank you so much. You are right. We had this before, I had problem with defining i !!!\n\n1- How do you decide the range for i?\n\n2- And also, how do you define alpha sub i?\n\n3- Why the graph does not plot Chi(alpha) at the bottom of the worksheet? Please see the attached.\n\n4- How can we refine the polynomial to get 1 for Chi at alpha=0.5\n\nThank you,\n\nAnousheh\n\nHighlighted\n\n## Re: CUBIC POLYNOMIAL\n\n Anousheh Rouzbehani wrote:...1- How do you decide the range for i?\n\nYou decide upper and lower limits. You decide number of points depending on the resolution/closeness of fit you want.\n\n 2- And also, how do you define alpha sub i?\n\nUse the [ key i.e. alpha[i produces alpha sub i.\n\n 3- Why the graph does not plot Chi(alpha) at the bottom of the worksheet? Please see the attached.\n\nBecause you pass a vector of alphas to Chi, and Chi is written in such a way that the product in the numerator returns a scalar when each is a vector. You need to vectorise Chi when you pass it a vector i.e. either put an arrow over the top (see the matrix menu) or pass it individual values, alpha sub i. - see attached.\n\n 4- How can we refine the polynomial to get 1 for Chi at alpha=0.5\n\nOne (rather coarse) way is simply to weight the point alpha = 0.5 by adding many points with alpha = 0.5 to the vector of points created before you do the fit - see attached.\n\nAlan\n\nHighlighted\n\n## Re: CUBIC POLYNOMIAL\n\nJust for the fun of it here's a slicker way of getting the fit and forcing it to go through 1 at alpha = 0.5.\n\nAlan\n\nHighlighted\n\n## Re: CUBIC POLYNOMIAL\n\nYet another way to force p(0.5)=1.\n\nJust sample only 4 data points (from 0.2 to 0.4), so the cubic is forced to go through all of them.\n\nAs your function is \"well behaved\" enough in the interval of concern we can do that without loosing the good fit.\n\nHighlighted\n\nHello Alan,\n\nThanks a lot.\n\nRegards,\n\nAnousheh\n\nHighlighted\n\n## Re: CUBIC POLYNOMIAL\n\nSome remarks Q1 and 2: I took the number of points and the range from your range variable alpha. So every of the 26 points you user for plotting would be considered by the fit. For a cubic fit you would need at least 4 datapoints.\n\nIf you you just 4 points, you will get a function whose graph will run exactly through those points. I used this in a prior post in this thread to force the cubic at (0.5/1). It depends on the function how good the fit would be with respect to the other data points.\n\nMore sample points usually will give a better fit.\n\nAs for the construction of the range variable and the functions arguments, you may use a simple formula:\n\nIf you have an range af x-values beginning at xB and ending at xE and you want nrofpoints datapoints (meaning you are deviding your interval in nrofpoints-1 parts) you may declare\n\ni:=0 .. nrofpoints - 1\n\nand define\n\nx[i := xB + i * (xE-xB) / (nrofpoints - 1)\n\nIn case of your file I had 26 datapoints and a range for alpha from 0.25 to 0.5\n\nso we have\n\ni:=0..25\n\nand\n\nalpha[i := 0.25 + i * (0.5-0.25) / 25\n\nor\n\nalpha[i := 0.25 + i * 1 / 100\n\nHighlighted\n\n## Re: CUBIC POLYNOMIAL\n\nHello Werner,\n\nThank you so much for the explanations.\n\nAll the best,\n\nAnousheh" ]
[ null ]
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https://calculomates.com/en/divisors/of/524/
[ "# Divisors of 524\n\n## Divisors of 524\n\nThe list of all positive divisors (that is, the list of all integers that divide 22) is as follows :\n\nAccordingly:\n\n524 is multiplo of 1\n\n524 is multiplo of 2\n\n524 is multiplo of 4\n\n524 is multiplo of 131\n\n524 is multiplo of 262\n\n524 has 5 positive divisors\n\n## Parity of 524\n\nIn addition we can say of the number 524 that it is even\n\n524 is an even number, as it is divisible by 2 : 524/2 = 262\n\n## The factors for 524\n\nThe factors for 524 are all the numbers between -524 and 524 , which divide 524 without leaving any remainder. Since 524 divided by -524 is an integer, -524 is a factor of 524 .\n\nSince 524 divided by -524 is a whole number, -524 is a factor of 524\n\nSince 524 divided by -262 is a whole number, -262 is a factor of 524\n\nSince 524 divided by -131 is a whole number, -131 is a factor of 524\n\nSince 524 divided by -4 is a whole number, -4 is a factor of 524\n\nSince 524 divided by -2 is a whole number, -2 is a factor of 524\n\nSince 524 divided by -1 is a whole number, -1 is a factor of 524\n\nSince 524 divided by 1 is a whole number, 1 is a factor of 524\n\nSince 524 divided by 2 is a whole number, 2 is a factor of 524\n\nSince 524 divided by 4 is a whole number, 4 is a factor of 524\n\nSince 524 divided by 131 is a whole number, 131 is a factor of 524\n\nSince 524 divided by 262 is a whole number, 262 is a factor of 524\n\n## What are the multiples of 524?\n\nMultiples of 524 are all integers divisible by 524 , i.e. the remainder of the full division by 524 is zero. There are infinite multiples of 524. The smallest multiples of 524 are:\n\n0 : in fact, 0 is divisible by any integer, so it is also a multiple of 524 since 0 × 524 = 0\n\n524 : in fact, 524 is a multiple of itself, since 524 is divisible by 524 (it was 524 / 524 = 1, so the rest of this division is zero)\n\n1048: in fact, 1048 = 524 × 2\n\n1572: in fact, 1572 = 524 × 3\n\n2096: in fact, 2096 = 524 × 4\n\n2620: in fact, 2620 = 524 × 5\n\netc.\n\n## Is 524 a prime number?\n\nIt is possible to determine using mathematical techniques whether an integer is prime or not.\n\nfor 524, the answer is: No, 524 is not a prime number.\n\n## How do you determine if a number is prime?\n\nTo know the primality of an integer, we can use several algorithms. The most naive is to try all divisors below the number you want to know if it is prime (in our case 524). We can already eliminate even numbers bigger than 2 (then 4 , 6 , 8 ...). Besides, we can stop at the square root of the number in question (here 22.891 ). Historically, the Eratosthenes screen (which dates back to Antiquity) uses this technique relatively effectively.\n\nMore modern techniques include the Atkin screen, probabilistic tests, or the cyclotomic test." ]
[ null ]
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https://mathoverflow.net/questions/383086/decomposition-of-polyhedral-an-example
[ "# Decomposition of Polyhedral - An example\n\nThere is no doubt that clear examples consolidate the understanding of concepts being learnt. I am new to finding the structure and decomposition of a polyhedra. Suppose that we have the system $$\\begin{bmatrix} 1 & 1 & 1 \\\\ -1 & 0& 0\\\\ 0 & -1 & 0 \\\\ 0 & 0 & -1 \\end{bmatrix} \\begin{bmatrix} x_1 \\\\ x_2 \\\\ x_3 \\end{bmatrix} \\le \\begin{bmatrix} 3 \\\\ 0 \\\\ 0 \\end{bmatrix}$$ and we would like to find the decomposition as the direct sum of a\n\n(i) linearity space,\n\n(ii) cone,\n\n(ii) polytope.\n\n(iv) List the generators of each component and list the facets and minimal faces of each polyhedron.\n\nBy definition the linearity space is synonymous to the null space of the coefficient matrix which is easy.\n\nThe cone by definition is given as $$\\{x: x + y \\in P, \\ \\forall y \\in P\\}\\ldots (\\alpha_1)\\ \\$$ or $$\\ \\ \\{x: Ax \\le 0\\} \\ldots (\\alpha_2)$$ where $$A, P$$ are some matrix and polydrea respectively. Applying $$(\\alpha_1)$$, I realized that $$x \\in \\mathbb{R}^3$$ and so each $$y's \\in P$$ (column vectors) is in $$\\mathbb{R^4}$$ so since the dimensions of $$x$$ and $$y$$ are not compatible, how can $$(\\alpha_1)$$ be use in our problem above?\n\nFor $$(\\alpha_2)$$ how do find $$x$$ such that $$Ax \\le 0$$ holds? It looks like a null space but so far as I can remember, we don't have any inequality null space. A help here will be appreciated.\n\nFor the polytope, it is stated as $$P = cov.hull\\{ x' \\ldots \\ x^m \\}$$ for some $$A^{m \\times n}.$$ Is the convex hull of $$P$$ the same as finding all submatrices of dimension $$3\\times 3$$ from our given matrix and find the unknown variables so that the constraints are satisfy? When I did that I got a set of four linearly independent vectors in $$\\mathbb{R}^3.$$ Is this also correct? Could there be any better way of finding the convex hull?\n\nFor generators, I have not come across a clear cut definition of it and it kind of confuses me with the vectors in the convex hull. Are they the same?\n\nThe definition of a facet is such that it is the maximal face of $$P$$ not equal to $$P$$. That means we cannot find the facet without knowing all the possible faces of $$P$$. Thus, is there any formula or tool available in finding the face and facets of $$P$$? If so what are the faces and facets of our coefficient matrix above?" ]
[ null ]
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https://forum.arduino.cc/t/information-about-servo/45828
[ "Good evening I am an Italian student, so excuse me for my bad English I need help about a program with a Servo I don't know if this is the right section for this topic, please tell me if it's a problem I found this instruction: pulseWidth = (myAngle * 11) + 500; it convert the angle in millisecond... but how can this instruction do it? then there are the instructions to set the Servo high and then low... but I don't undestend the convertion from angle to millisecon Is there another way to move the Servo? Or can you explain me the meaning of that instruction?\n\nThank you very much and sorry if I was unclear\n\ncaro JohnDoe,\n\nLa tua inglese è perfetta!\n\nBut more information about your specific question is needed. Can you post the code you are inquiring about and describe the application in more detail? Some context is needed.\n\nYou don't need to convert from angle to microseconds when using the Arduino servo library. The read and write functions use angles in degrees.\n\nIf you prefer to work in microseconds you can use the undocumented functions: writeMicroseconds and readMicroseconds\n\nI find another program that uses the instruction Write of a Servo object, so I solved the problem\n\nBut now I have another problem: I would like to move the Servo with the keybord's arrows: can I do it? can you help me?\n\nThank you very much\n\nI find another program that uses the instruction Write of a Servo object\n\nWhat servo program are you using?\n\nDo you want to control the servo from your computers keyboard?\n\nWhat programming experience do you have?\n\nHi mem, thank you very much\n\nI use the slides from the site http://todbot.com/blog/spookyarduino/ to study, and I find the instruction to move the servo with the conversion from angle to microseconds Today i found the Sweep program to move the Servo without conversion\n\nBut these are only examples to learn somethign about Arduino. Now I can move the Servo with the number of the keyboard, but I need to move it with the keyboard's arrows\n\nI have some programming experience from university, with C and Java\n\nPlease tell me if some sentence is wrong\n\nThe sketch below will move a servo connected on pin 9 in response to serial commands.\n\n``````#include <Servo.h>\n\nServo myServo;\n\nconst int servoPin = 9; // the pin the servo is connected to\n\nint val = 0;    // a value accumulated from data on the serial port\nint angle = 90; // the current angle of the servo\n\nvoid setup()\n{\nSerial.begin(9600);\nmyServo.attach(servoPin);\nmyServo.write(angle);  // center the servo\n}\n\nvoid loop()\n{\nif ( Serial.available())\n{\n\nif(ch >= '0' && ch <= '9')              // is ch a number?\nval = val * 10 + ch - '0';           // yes, accumulate the value\nelse if(ch == '-') // is this the minus sign?\n{\nangle = angle - val;\nif(angle < 0)\nangle = 0;\nmyServo.write(angle);         // write the new angle\nval = 0;\n}\nelse if(ch == '+') // is this the plus sign?\n{\nangle = angle + val;\nif(angle > 180)\nangle = 180;\nmyServo.write(angle);         // write the new angle\nval = 0;\n}\n}\n}\n``````\n\nSending a digit from 1 to 9 followed by a + (the plus character) will step the servo forward by one to nine degrees. 1 to 9 followed by a – (the minus character) will step the servo in the other direction. This assumes you are using the Arduino servo library.\n\nYou can test this using the Arduino serial monitor to send the characters.\n\nHi mem, I tried you program this mornign, it works very well with all numbers from 0 to 180\n\nBut if you digit a number of two digit, for example 20, the program reads it in two cicle clock, until you write the sign (\"+\" or \"-\") Is there a way for read a number in a single clock? I know that's not a problem of a servo Do you understend the term \"cicle clock\"? I'm sorry for my English :P\n\nthank you\n\nI don't understand what you are asking. Perhaps if you say more about your application and what you want your program to do it will be easier to see what you mean.\n\nIs there a way for read a number in a single clock?\n\nI think you're asking if it is possible to read both characters without having to wait for loop to repeat, and to read both at the same time (or at least in the same pass through \"loop)?\n\nSure, just wait for \"Serial.available\" to return two or more.\n\neven if you know that you will always send two characters plus the terminating sign there is no advantage to changing the code. The code as posted won't do anything anyway until it gets the sign character.\n\nI was guessing that the OP wanted to send a single character to control the servos and although that is possible, the code to do it would be less clear than the posted code.\n\nI’m not sure, but maybe this is what the OP was trying to achieve.\n\n• Use keyboard arrow keys to control the angle of rotation on the servo.\n\nI don’t know what the key values are from the Mac, but they can be easily determined. Also, you’ll probably have to use a different serial terminal (I use Tera Term on a PC) to send control characters to the Arduino.\n\nOn a PC, arrow keys (and other special function keys) send multiple bytes per press. You will need to use a state machine to catch the keys.\n\nHere’s some code:\n\n``````#include <Servo.h>\n\nServo myServo;\n\nconst int servoPin = 9; // the pin the servo is connected to\n\nint angle = 90; // the current angle of the servo\nchar keystate = 0;\n\n// 5 degree increments\n#define ANGLE_STEP 5\n\nvoid setup()\n{\nSerial.begin(9600);\nmyServo.attach(servoPin);\nmyServo.write(angle);  // center the servo\n}\n\nvoid loop()\n{\nchar ch;\n\n// For a PC:\n// Left Arrow key = 0x1B, 0x5B, 0x44\n// Right Arrow key = 0x1B, 0x5B, 0x43\n// For a Mac:\n// ?? - help me find these values?\n\nif(Serial.available())\n{\n\nif(ch == 0x1B)\n{\nkeystate = 0x1B;\n}\nelse if(ch == 0x5B)\n{\nif(keystate == 0x1B)\nkeystate = 0x5B;\nelse\nkeystate = 0;\n}\nelse if(keystate == 0x5B)\n{\nif(ch == 0x44) // Left arrow key\n{\nSerial.println(\"LEFT\");\nif(angle > ANGLE_STEP)\n{\nangle = angle - ANGLE_STEP;  // step left\n}\nelse\n{\nangle = 0;\n}\nmyServo.write(angle);\n}\nelse if(ch == 0x43) // Right arrow key\n{\nSerial.println(\"RIGHT\");\nif(angle < 180 - ANGLE_STEP)\n{\nangle = angle + ANGLE_STEP; // step right\n}\nelse\n{\nangle = 180;\n}\nmyServo.write(angle);\n}\nelse\n{\nkeystate = 0;\n}\n}\n\n// to find values for the arrow keys:\nSerial.println(ch, HEX);\n}\n}\n``````\n\nb\n\nx mem\nthis is some instructions from your post:\n\" if ( Serial.available())\n{\n\nif(ch >= ‘0’ && ch <= ‘9’) // is ch a number?\nval = val * 10 + ch - ‘0’; // yes, accumulate the value\nelse if(ch == ‘-’) // is this the minus sign?\n{\nangle = angle - val;\nif(angle < 0)\nangle = 0;\nmyServo.write(angle); // write the new angle\nval = 0;\n}\nelse if(ch == ‘+’) // is this the plus sign?\n{\nangle = angle + val;\nif(angle > 180)\nangle = 180;\nmyServo.write(angle); // write the new angle\nval = 0;\n}\n}\"\nif I write “90+” the program enter in the loop three times, the first to read the character “9”, the second to read the character “0” and convert “9” like “90”, and the last lo read the character “+” that is, in this example, like a character of the end of the line (such as for example “\\n” in C)\nI would like to know if there is a method that can read the number (for example “90”) in a single operation\n\nx AWOL:\nI think I understand your post, but I’m not sure. I’m Italian and my English isn’t very good", null, "I write it in the part of message for mem: I would like to read a number > 9 without a loop for each digit\n\nx bhagman:\nmaybe your program is really what I need: move the servo with arrow keys. But now I’m very tired, so I will try it tomorrow ;D\n\nps what does “OP” mean? :-?\n\nps what does \"OP\" mean?\n\nOP = original poster (del messagio)\n\nCome si dice questa frase in Italiano? [trasmettitore originale?]\n\nThank you Andy :) In Italian they say \"Autore originale del messaggio\" or something like ;)\n\nfor bhagman: I tired your program, but it doens't work on my PC :-?\n\nHere, try this instead - uses “A” and “D” keys instead of arrow keys:\n\n``````#include <Servo.h>\n\nServo myServo;\n\nconst int servoPin = 9; // the pin the servo is connected to\n\nint angle = 90; // the current angle of the servo\n\n// 5 degree increments\n#define ANGLE_STEP 5\n\nvoid setup()\n{\nSerial.begin(9600);\nmyServo.attach(servoPin);\nmyServo.write(angle);  // center the servo\n}\n\nvoid loop()\n{\nchar ch;\n\nif(Serial.available())\n{\n\n// to find values for the arrow keys:\n// Serial.println(ch, HEX);\n\nif(ch == 'a' || ch == 'A')\n{\nSerial.println(\"LEFT\");\nif(angle > ANGLE_STEP)\n{\nangle = angle - ANGLE_STEP;  // step left\n}\nelse\n{\nangle = 0;\n}\nmyServo.write(angle);\n}\nelse if(ch == 'd' || ch == 'D')\n{\nSerial.println(\"RIGHT\");\nif(angle < 180 - ANGLE_STEP)\n{\nangle = angle + ANGLE_STEP; // step right\n}\nelse\n{\nangle = 180;\n}\nmyServo.write(angle);\n}\n}\n}\n``````\n\nThis will work in the Arduino IDE as well. You just have to click “Send” or press Enter/Return after each key.\n\nb\n\nbhagman you are very kind I will try this version of program But does the other version work on your pc? I need to move the servo with the arrow keys\n\nPS: I have another problem. The program Arduino 0017 is very very very slow on my pc :( I tried to use also the version 0012, 0013 and 0014 but they don't start :-? What can I do? Change some settings? Please help me :-[\n\nI explain my last question: does the software need minimal system requirement? I have the version 0017\n\nThe arrow key version works on my PC - but you MUST use a different serial terminal, like Tera Term:\n\nI’m going to guess that you have a bluetooth dongle/card on your computer. FAQ here:\n\nhttp://www.arduino.cc/en/Guide/Troubleshooting#toc12\n\nb\n\nHi bhagman, I solved my problem with the software with your help. I disabled ALL the COM port on my PC :P Now the software needs few seconds to upload the program on the board\n\nI also use Tera Term and now the program works perfectly" ]
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https://www.aimsciences.org/article/doi/10.3934/ipi.2018056
[ "", null, "", null, "", null, "", null, "December  2018, 12(6): 1343-1364. doi: 10.3934/ipi.2018056\n\n## Simultaneous reconstruction and segmentation with the Mumford-Shah functional for electron tomography\n\n 1 LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, China 2 Department of Mathematics, Tufts University, Medford, MA, USA 3 College of Life Sciences, Peking University, Beijing 100871, China 4 LMAM, School of Mathematical Sciences, Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China 5 China Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai 200240, China\n\n* Corresponding author: Ming Jiang\n\nReceived  March 2017 Revised  November 2017 Published  October 2018\n\nFund Project: This work was partially supported by the National Basic Research Program of China (973 Program) (2015CB351803), National Research and Development Program of China (2016YFA0500401), the National Science Foundation of China (61520106004, 81370203, 81461148026), Sino-German Center (GZ 1025), U.S. National Science Foundation under grants (DMS1311558, DMS1712207 to E.T. Quinto), and Microsoft Research of Asia.\n\nElectron micrography (EM) is a detection method for determining the structure of macromolecular complexes and biological specimens. However, some restrictions in the EM system, including poor signal-to-noise, limited range of tilt angles, only a sub-region subject to electron exposure and unintentional movements of the specimen, make the reconstruction procedure severely ill-posed. Because of these limitations, there may be severe artifacts in reconstructed images. In this paper, we first design an algorithm that can quickly calculate the radiological paths. Then we combine an iterative reconstruction algorithm using the Mumford-Shah model with an artifact reduction strategy. The combined method can not only regularize the ill-posedness and provide the reconstruction and segmentation simultaneously but also smooth additional artifacts due to the limited data. Also we improved the algorithm used for the calculation of radiological paths to accelerate the reconstruction. The proposed algorithm was translated into OpenCL programs and kernel functions to asynchronously and in parallel update the reconstructed image along rays by GPUs. We tested the method on both simulated and real EM data. The results show that our artifact reduced Mumford-Shah algorithm can reduce the noise and artifacts while preserving and enhancing the edges in the reconstructed image.\n\nCitation: Li Shen, Eric Todd Quinto, Shiqiang Wang, Ming Jiang. Simultaneous reconstruction and segmentation with the Mumford-Shah functional for electron tomography. Inverse Problems & Imaging, 2018, 12 (6) : 1343-1364. doi: 10.3934/ipi.2018056\n##### References:\n L. Ambrosio and V. M. Tortorelli, On the approximation of free discontinuity problems, Boll. Un. Mat. Ital. B(7), 6 (1992), 105-123.", null, "Google Scholar S. Bonettini, M. Prato and S. Rebegoldi, A cyclic block coordinate descent method with generalized gradient projections, Appl. Math. Comput., 286 (2016), 288-300.  doi: 10.1016/j.amc.2016.04.031.", null, "", null, "Google Scholar S. Bonettini, Inexact block coordinate descent methods with application to non-negative matrix factorization, IMA Journal of Numerical Analysis, 31 (2011), 1431-1452.  doi: 10.1093/imanum/drq024.", null, "", null, "Google Scholar D. Fanelli and O. Öktem. Electron tomography: A short overview with an emphasis on the absorption potential model for the forward problem, Inverse Problems, 24 (2008), 013001, 51 pp. doi: 10.1088/0266-5611/24/1/013001.", null, "", null, "Google Scholar J. Frikel and E. T. Quinto, Characterization and reduction of artifacts in limited angle tomography, Inverse Problems, 29 (2013), 2091-2128.  doi: 10.1088/0266-5611/29/12/125007.", null, "Google Scholar A. Fujimoto, T. Tanaka and K. Iwata, Arts: Accelerated ray-tracing system, IEEE Computer Graphics and Applications, 6 (1986), 16-26.   Google Scholar A. Zampighi Guido, S. Cataldo, M. Zampighi Lorenzo, W. Michael, M. Wright Ernest and C. Brecha Nicholas, Conical tomography of a ribbon synapse: Structural evidence for vesicle fusion, Plos One, 6 (2011), e16944. Google Scholar G, T. Herman and J. Frank, Computational Methods for Three-Dimensional Microscopy Reconstruction, Springer New York, 2014. Google Scholar X. Huang, L. Sun, S. Ji, T. Zhao, W. Zhang, J. Xu, J. Zhang, Y. Wang, X. Wang, C. Franzini-Armstrong, M. Zheng and H. Cheng, Kissing and nanotunneling mediate intermitochondrial communication in the heart, Proceedings of the National Academy of Sciences of the United States of America, 110 (2013), 2846-2851.   Google Scholar M. Jiang, P. Maass, and T. 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Tortorelli, On the approximation of free discontinuity problems, Boll. Un. Mat. Ital. B(7), 6 (1992), 105-123.", null, "Google Scholar S. Bonettini, M. Prato and S. Rebegoldi, A cyclic block coordinate descent method with generalized gradient projections, Appl. Math. Comput., 286 (2016), 288-300.  doi: 10.1016/j.amc.2016.04.031.", null, "", null, "Google Scholar S. Bonettini, Inexact block coordinate descent methods with application to non-negative matrix factorization, IMA Journal of Numerical Analysis, 31 (2011), 1431-1452.  doi: 10.1093/imanum/drq024.", null, "", null, "Google Scholar D. Fanelli and O. Öktem. Electron tomography: A short overview with an emphasis on the absorption potential model for the forward problem, Inverse Problems, 24 (2008), 013001, 51 pp. doi: 10.1088/0266-5611/24/1/013001.", null, "", null, "Google Scholar J. Frikel and E. T. Quinto, Characterization and reduction of artifacts in limited angle tomography, Inverse Problems, 29 (2013), 2091-2128.  doi: 10.1088/0266-5611/29/12/125007.", null, "Google Scholar A. Fujimoto, T. Tanaka and K. Iwata, Arts: Accelerated ray-tracing system, IEEE Computer Graphics and Applications, 6 (1986), 16-26.   Google Scholar A. Zampighi Guido, S. Cataldo, M. Zampighi Lorenzo, W. Michael, M. Wright Ernest and C. Brecha Nicholas, Conical tomography of a ribbon synapse: Structural evidence for vesicle fusion, Plos One, 6 (2011), e16944. Google Scholar G, T. Herman and J. Frank, Computational Methods for Three-Dimensional Microscopy Reconstruction, Springer New York, 2014. Google Scholar X. Huang, L. Sun, S. Ji, T. Zhao, W. Zhang, J. Xu, J. Zhang, Y. Wang, X. Wang, C. Franzini-Armstrong, M. Zheng and H. Cheng, Kissing and nanotunneling mediate intermitochondrial communication in the heart, Proceedings of the National Academy of Sciences of the United States of America, 110 (2013), 2846-2851.   Google Scholar M. Jiang, P. Maass, and T. Page, Regularizing properties of the mumford-shah functional for imaging applications, Inverse Problems, 30 (2014), 035007, 17 pp. doi: 10.1088/0266-5611/30/3/035007.", null, "", null, "Google Scholar Y. M. Jung, S. H. Kang and J. Shen, Multiphase image segmentation via modica-mortola phase transition, SIAM Journal on Applied Mathematics, 67 (2007), 1213-1232.  doi: 10.1137/060662708.", null, "", null, "Google Scholar E. Klann, A mumford-shah-like method for limited data tomography with an application to electron tomography, SIAM Journal on Imaging Sciences, 4 (2011), 1029-1048.  doi: 10.1137/100817371.", null, "", null, "Google Scholar J. Klukowska, D. Ran and G. T. Herman, Snark09 a software package for reconstruction of 2d images from 1d projections, Computer Methods and Programs in Biomedicine, 110 (2013), 424-440.   Google Scholar A. Kucukelbir, F. J. Sigworth and H. D. Tagare, Quantifying the local resolution of cryo-em density maps, Nature Methods, 11 (2014), 63-65.   Google Scholar S. Lanzavecchia, F. Cantele, P. L. Bellon, L. Zampighi, M. Kreman, E. Wright and G. A. Zampighi, Conical tomography of freeze-fracture replicas: A method for the study of integral membrane proteins inserted in phospholipid bilayers, Journal of Structural Biology, 149 (2005), 87-98.   Google Scholar Y. Li and J. Kim, Multiphase image segmentation using a phase-field model, Computers and Mathematics with Applications, 62 (2011), 737-745.  doi: 10.1016/j.camwa.2011.05.054.", null, "", null, "Google Scholar Y. K. Liu, H. Y. Song and B. $\\rm{\\ddot{Z}}$alik, A general multi-step algorithm for voxel traversing along a line, In Computer Graphics Forum, (2008), 73–80. Google Scholar G. Luo, M. Jiang and P. Maass, Fpga acceleration by asynchronous parallelization for simultaneous image reconstruction and segmentation based on the mumford-shah regularization, 2015. Google Scholar R. Marabini, E. Rietzel, R. Schroeder, G. T. Herman and J. M. Carazo, Three-dimensional reconstruction from reduced sets of very noisy images acquired following a single-axis tilt schema: Application of a new three-dimensional reconstruction algorithm and objective comparison with weighted backprojection, Journal of Structural Biology, 120 (1998), 363-371.   Google Scholar L. Modica and S. Mortola, Un esempio di $γ$-convergenza, Bollettino Della Unione Matematica Italiana B, 14 (1977), 285-299.", null, "Google Scholar D. Mumford and J. Shah, Optimal approximations by piecewise smooth functions and associated variational-problems, Communications on Pure and Applied Mathematics, 42 (1989), 577-685.   Google Scholar A. Munshi, The opencl specification, In 2009 IEEE Hot Chips 21 Symposium (HCS), (2009), 1–314. Google Scholar S. Nickell, F. Förster, A. Linaroudis, W. Del Net, F. Beck, R. Hegerl, W. Baumeister and J. M. Plitzko, Tom software toolbox: acquisition and analysis for electron tomography, Journal of Structural Biology, 149 (2005), 227-234.   Google Scholar O. Öktem, Mathematics of electron tomography, Handbook of Mathematical Methods in Imaging, Springer New York, New York, NY, (2015), 937–1031.", null, "Google Scholar E. T. Quinto, U. Skoglund and O. Oktem, Electron lambda-tomography, Proceedings of the National Academy of Sciences of the United States of America, 106 (2009), 21842-7.   Google Scholar M. Radermacher, Weighted back-projection methods, Electron Tomography: Methods for Three-Dimensional Visualization of Structures in the Cell, Springer New York, New York, NY, (2006), 245–273. Google Scholar R. Ramlau and W. Ring, Regularization of ill-posed mumford shah models with perimeter penalization, Inverse Problems, 26 (2010), 115001, 25pp. doi: 10.1088/0266-5611/26/11/115001.", null, "", null, "Google Scholar L. Reimer, Transmission Electron Microscopy: Physics of Image Formation and Microanalysis, Springer-Verlag, 1989. Google Scholar H. Rullgård, L.-G. Öfverstedt, S. Masich, B. Daneholt and O. Öktem, Simulation of transmission electron microscope images of biological specimens, Journal of Microscopy, 243 (2011), 234-256.   Google Scholar W. Shang, F. Lu, T. Sun, J. Xu, L. L. Li, Y. Wang, G. Wang, L. Chen, X. Wang and M. B. Cannell, Imaging ca2+ nanosparks in heart with a new targeted biosensor, Circulation Research, 114 (2014), 412-420.   Google Scholar R. L. Siddon, Fast calculation of the exact radiological path for a three-dimensional ct array, Medical Physics, 12 (1985), 252-255.   Google Scholar J. E. Stone, D. Gohara and G. Shi, Opencl: A parallel programming standard for heterogeneous computing systems, Computing in Science and Engineering, 12 (2010), 66-72.   Google Scholar E. Sundermann, F. Jacobs, M. Christiaens, B. De Sutter and I. Lemahieu, A fast algorithm to calculate the exact radiological path through a pixel or voxel space, Journal of Computing and Information Technology, 6 (1998), 89-94.   Google Scholar A. R. Webb, Statistical Pattern Recognition. 2nd ed, 2002. doi: 10.1002/0470854774.", null, "", null, "Google Scholar S. J. Wright, Coordinate descent algorithms, Math. Program., 151 (2015), Ser. B, 3-34.  doi: 10.1007/s10107-015-0892-3.", null, "", null, "Google Scholar B. $\\rm{\\ddot{Z}}$alik, G. Clapworthy and ${\\rm{\\ddot{C}}}$. Oblon${\\rm{\\ddot{s}}}$ek, An efficient code-based voxel-traversing algorithm, Computer Graphics Forum, 16 (1997), 119-128.   Google Scholar", null, "Illustration of the calculation of 2d radiological paths. The variable $\\varphi$ is the angle of inclination and $(ia, ib)$ is the index of the pixel. The variable $L$ is the intersection length of a ray with two vertical gridlines and $w$ is the proportion between intersection length of current pixel and $L$. The current pixel intercepted by the ray is $(ia, ib)$. $w>1$ in Algorithm. 1 corresponds to the left figure where the next pixel intercepted by the ray is $(ia, ib+1)$ and $w <1$ corresponds to the right figure where the next pixel is $(ia, ib+1)$. The proportion $w$ is calculated alternatively in Algorithm. 1 including only a few addition or subtraction operations and $w$ is used to decide whether the next pixel intercepted by the ray corresponds to the case in the left figure or the right figure", null, "Illustration of the calculation of 3d radiological paths. $w_{x}$ and $w_{y}$ are the proportion of the projected ray in $zx-plane$ and $zy-plane$ respectively. $w_{x}$ and $w_{y}$ are calculated by algorithm. 1. $L'$ is the constant length of the ray between the plane $iz = ic$ and $iz = ic+1$. The current voxel intercepted by the ray is $(ia, ib, ic)$. $w_{x} <w_{y}$ in Algorithm. 2 corresponds to case that the next two voxels intercepted by the ray are $(ia+1, ib, ic)$ and $(ia+1, ib+1, ic)$. $w_{x}\\geq w_{y}$ corresponds to case that the next two voxels are $(ia, ib+1, ic)$ and $(ia+1, ib+1, ic)$. $w_{x}$ and $w_{y}$ are used to decide which voxels are intercepted by the ray after the current voxel $(ia, ib, ic)$", null, "Reconstructions of 3D Shepp-Logan phantom using the alternating minimization scheme described in Algorithm 3 and artifact reduction strategy in Sec. 2.3. The size of Shepp-Logan phantom is $300\\times 300\\times 300$ and two slices are shown in (a)~(e). (a)(b) are slices of original phantom. (a) corresponds to the red line in (b) and (b) to the red line in (a). Euler angles are used to generate projections: rotation $\\phi$ changes between $-60^{\\circ}$ and $60^{\\circ}$ at $1^{\\circ}$ intervals for each measurement, azimuthal angle $\\theta$ is fixed at $0^{\\circ}$ and in-plane rotation $\\psi$ at $165^{\\circ}$. (c)+(d) and (e)+(f) show the reconstruction image and segmentations without the artifact reduction strategy and (g)+(h) and (i)+(j), with a cutoff $\\kappa_{\\sigma}$ in the operator $\\mathcal{K}_{\\sigma}$ defined in (14). We chose the parameter $\\sigma = \\Phi /4$. While streak artifacts near $\\pm 60^{\\circ}$ in (c)+(d) and streak artifacts in (e)+(f) can be observed, the implementation of the artifact reduction strategy smooths some artifacts in (g)+(h) and (i)+(j)", null, "Reconstructions and segmentations of the Shepp-Logan phantom $f^{\\dagger}$ from noisy Radon data. Left column: reconstruction $f^{MS}$ from noisy data $\\mathcal{R}f^{\\dagger} + \\delta N(0, 1)$ using the artifact reduced Mumford-Shah algorithm ($\\alpha = 2, \\beta = 0.0002, \\varepsilon = 0.0001$); middle column: The reconstructed edge indicator function $v$; right column: reconstructions from same noisy data using WBP method in TOM software toolbox. For stronger noise it is more difficult to reconstruct the low contrast regions for both methods. From the comparison between the left and right column, we can see that our reconstruction method with Mumford -Shah functional can depress the noise compared to WBP method while the edges are preserved. If an edge is detected in $v$, the reconstruction $f^{MS}$ has a sharp edge", null, "Reconstructions of cryo-specimen using the alternating minimization scheme described in Algorithm 3 and artifact reduction strategy in Sec. 2.3. Left column is the reconstructed images, the middle column is the edge sets with our algorithm, and the right column is the reconstructed images with WBP algorithm in TOM toolbox. The images in the red block are zoomed in and put in the top right corner. The edges in the middle column are enhanced in the left column compared to the WBP reconstructions in the right column. Compared to the results of WBP in right column, the results in the left show the effect of noise reduction while the edges are preserved", null, "Density maps and ResMap of the reconstructed images of WBP method and our method. The mean resolution of WBP is 17.17Å while the mean resolution of our reconstruction with alternative minimization algorithm is 15.07Å\nFast calculation of 2D radiological paths\n input $ray$ in 2D; Initialize $w,ia,ib$;", null, "input $ray$ in 2D; Initialize $w,ia,ib$;", null, "Fast calculation of 3D radiological paths\n input $w_{x}, w_{y}$;", null, "input $w_{x}, w_{y}$;", null, "Alternate Minimization of $AT_{\\mathcal{R}, \\varepsilon}(\\mathbf{f}, \\mathbf{v})$\n $f^0 = 0$ or a-priori image; $v^0 = 1$;", null, "$f^0 = 0$ or a-priori image; $v^0 = 1$;", null, "Image Minimization with Steepest Descent Method\n $\\mathbf{f}^0 =$ current image; $\\mathbf{v} =$ current edge; $\\mathbf{d}^0 = \\mathcal{R}^*(\\mathcal{K}_{\\sigma}( \\mathbf{g}) - \\mathcal{R}_{\\Phi}(\\mathbf{f}^0)) + \\alpha \\text{div}(\\mathbf{v}^2 \\nabla \\mathbf{f}^0)$;", null, "$\\mathbf{f}^0 =$ current image; $\\mathbf{v} =$ current edge; $\\mathbf{d}^0 = \\mathcal{R}^*(\\mathcal{K}_{\\sigma}( \\mathbf{g}) - \\mathcal{R}_{\\Phi}(\\mathbf{f}^0)) + \\alpha \\text{div}(\\mathbf{v}^2 \\nabla \\mathbf{f}^0)$;", null, "Edge Minimization with Steepest Descent Method\n $\\mathbf{f} =$ current image $\\mathbf{v}^{0} =$ current edge;", null, "$\\mathbf{f} =$ current image $\\mathbf{v}^{0} =$ current edge;", null, "Misha Perepelitsa. An ill-posed problem for the Navier-Stokes equations for compressible flows. Discrete & Continuous Dynamical Systems, 2010, 26 (2) : 609-623. doi: 10.3934/dcds.2010.26.609 Esther Klann, Ronny Ramlau, Wolfgang Ring. A Mumford-Shah level-set approach for the inversion and segmentation of SPECT/CT data. Inverse Problems & Imaging, 2011, 5 (1) : 137-166. doi: 10.3934/ipi.2011.5.137 Stefan Kindermann. Convergence of the gradient method for ill-posed problems. Inverse Problems & Imaging, 2017, 11 (4) : 703-720. doi: 10.3934/ipi.2017033 Alfredo Lorenzi, Luca Lorenzi. A strongly ill-posed integrodifferential singular parabolic problem in the unit cube of $\\mathbb{R}^n$. Evolution Equations & Control Theory, 2014, 3 (3) : 499-524. doi: 10.3934/eect.2014.3.499 Peter I. Kogut, Olha P. Kupenko. On optimal control problem for an ill-posed strongly nonlinear elliptic equation with $p$-Laplace operator and $L^1$-type of nonlinearity. Discrete & Continuous Dynamical Systems - B, 2019, 24 (3) : 1273-1295. doi: 10.3934/dcdsb.2019016 Markus Haltmeier, Richard Kowar, Antonio Leitão, Otmar Scherzer. Kaczmarz methods for regularizing nonlinear ill-posed equations II: Applications. Inverse Problems & Imaging, 2007, 1 (3) : 507-523. doi: 10.3934/ipi.2007.1.507 Matthew A. Fury. Estimates for solutions of nonautonomous semilinear ill-posed problems. Conference Publications, 2015, 2015 (special) : 479-488. doi: 10.3934/proc.2015.0479 Paola Favati, Grazia Lotti, Ornella Menchi, Francesco Romani. An inner-outer regularizing method for ill-posed problems. Inverse Problems & Imaging, 2014, 8 (2) : 409-420. doi: 10.3934/ipi.2014.8.409 Matthew A. Fury. Regularization for ill-posed inhomogeneous evolution problems in a Hilbert space. Conference Publications, 2013, 2013 (special) : 259-272. doi: 10.3934/proc.2013.2013.259 Zonghao Li, Caibin Zeng. Center manifolds for ill-posed stochastic evolution equations. Discrete & Continuous Dynamical Systems - B, 2021  doi: 10.3934/dcdsb.2021142 Zhenhua Zhao, Yining Zhu, Jiansheng Yang, Ming Jiang. Mumford-Shah-TV functional with application in X-ray interior tomography. Inverse Problems & Imaging, 2018, 12 (2) : 331-348. doi: 10.3934/ipi.2018015 Antonin Chambolle, Francesco Doveri. Minimizing movements of the Mumford and Shah energy. Discrete & Continuous Dynamical Systems, 1997, 3 (2) : 153-174. doi: 10.3934/dcds.1997.3.153 Felix Lucka, Katharina Proksch, Christoph Brune, Nicolai Bissantz, Martin Burger, Holger Dette, Frank Wübbeling. Risk estimators for choosing regularization parameters in ill-posed problems - properties and limitations. Inverse Problems & Imaging, 2018, 12 (5) : 1121-1155. doi: 10.3934/ipi.2018047 Ye Zhang, Bernd Hofmann. Two new non-negativity preserving iterative regularization methods for ill-posed inverse problems. Inverse Problems & Imaging, 2021, 15 (2) : 229-256. doi: 10.3934/ipi.2020062 Guozhi Dong, Bert Jüttler, Otmar Scherzer, Thomas Takacs. Convergence of Tikhonov regularization for solving ill-posed operator equations with solutions defined on surfaces. Inverse Problems & Imaging, 2017, 11 (2) : 221-246. doi: 10.3934/ipi.2017011 Lianwang Deng. Local integral manifolds for nonautonomous and ill-posed equations with sectorially dichotomous operator. Communications on Pure & Applied Analysis, 2020, 19 (1) : 145-174. doi: 10.3934/cpaa.2020009 Youri V. Egorov, Evariste Sanchez-Palencia. Remarks on certain singular perturbations with ill-posed limit in shell theory and elasticity. Discrete & Continuous Dynamical Systems, 2011, 31 (4) : 1293-1305. doi: 10.3934/dcds.2011.31.1293 Johann Baumeister, Barbara Kaltenbacher, Antonio Leitão. On Levenberg-Marquardt-Kaczmarz iterative methods for solving systems of nonlinear ill-posed equations. Inverse Problems & Imaging, 2010, 4 (3) : 335-350. doi: 10.3934/ipi.2010.4.335 Sergiy Zhuk. Inverse problems for linear ill-posed differential-algebraic equations with uncertain parameters. Conference Publications, 2011, 2011 (Special) : 1467-1476. doi: 10.3934/proc.2011.2011.1467 Olha P. Kupenko, Rosanna Manzo. On optimal controls in coefficients for ill-posed non-Linear elliptic Dirichlet boundary value problems. Discrete & Continuous Dynamical Systems - B, 2018, 23 (4) : 1363-1393. doi: 10.3934/dcdsb.2018155\n\n2020 Impact Factor: 1.639" ]
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{"ft_lang_label":"__label__en","ft_lang_prob":0.64591676,"math_prob":0.86267495,"size":21380,"snap":"2021-43-2021-49","text_gpt3_token_len":6526,"char_repetition_ratio":0.1469405,"word_repetition_ratio":0.70962423,"special_character_ratio":0.33077642,"punctuation_ratio":0.26113498,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.97295004,"pos_list":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136],"im_url_duplicate_count":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,3,null,3,null,3,null,3,null,3,null,3,null,6,null,6,null,6,null,6,null,6,null,6,null,6,null,6,null,6,null,6,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-10-27T11:09:56Z\",\"WARC-Record-ID\":\"<urn:uuid:8b6684ea-534b-4280-8b50-e8cf65c7e1af>\",\"Content-Length\":\"169066\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:b68c98bf-2986-48bf-9eb1-79488d5dde95>\",\"WARC-Concurrent-To\":\"<urn:uuid:b6f405d4-c0f3-40bd-95fb-7c380f519643>\",\"WARC-IP-Address\":\"107.161.80.18\",\"WARC-Target-URI\":\"https://www.aimsciences.org/article/doi/10.3934/ipi.2018056\",\"WARC-Payload-Digest\":\"sha1:LXC5RDQ66OQOGCUHURJUJMXQQPRCUX5Z\",\"WARC-Block-Digest\":\"sha1:P3ZIRDUL6MCUKIPCBYOGXNZV4ZWQRETS\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-43/CC-MAIN-2021-43_segments_1634323588113.25_warc_CC-MAIN-20211027084718-20211027114718-00625.warc.gz\"}"}
http://mail.lon-capa.org/pipermail/lon-capa-users/2002-May/000141.html
[ "# [LON-CAPA-users] sample problems\n\nGuy Albertelli II lon-capa-users@mail.lon-capa.org\nSat, 4 May 2002 13:04:38 -0400 (EDT)\n\n```Hi Lars,\n\n> This formula response problem is not that great either - why do I have\n> to specifically enter the multiplication sign in the answer - why can't\n> I just enter\n> \t2.1x+3.3y^2\n> Above answer is not understood - the system insists that I write\n> \t2.1*x+3.3*y^2\n\nBecause assumed multiplication is very difficult to interpret\ncorrectly.\n\nFor example:\n\nxy is that x*y or the variable xy?\n\n23x\n\nIs that 2*3*x or 23*x\n\nf(x)\n\nis that f*(x) or the function f with an argument of x\n\nFor most of these cases I can try to heuristically guess what one\nmeant but it can become quite troublesome.\n\nFuture Plans:\n\nWe will be moving away for the simple formula interpreter that we\ninherit from CAPA and will be adding in a full Mathematics Kernal" ]
[ null ]
{"ft_lang_label":"__label__en","ft_lang_prob":0.8441185,"math_prob":0.57462,"size":1051,"snap":"2019-35-2019-39","text_gpt3_token_len":304,"char_repetition_ratio":0.08500478,"word_repetition_ratio":0.0,"special_character_ratio":0.27687916,"punctuation_ratio":0.092920355,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.95814526,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-09-18T07:54:01Z\",\"WARC-Record-ID\":\"<urn:uuid:3b1b1cf0-16bb-413b-a0b6-7e55175fa74c>\",\"Content-Length\":\"3098\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:4b868751-d8c2-4fe9-8a99-1c3c1a8b04e4>\",\"WARC-Concurrent-To\":\"<urn:uuid:c0054521-91b2-4aa5-8f70-d6c280d6b6f9>\",\"WARC-IP-Address\":\"35.9.66.226\",\"WARC-Target-URI\":\"http://mail.lon-capa.org/pipermail/lon-capa-users/2002-May/000141.html\",\"WARC-Payload-Digest\":\"sha1:ZKPG5ZFNHISZSGL3TQQEB6LX4MWVTBL5\",\"WARC-Block-Digest\":\"sha1:UW7MGSF23WDRUYMB4Z6OQOTD2VL5NRE4\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-39/CC-MAIN-2019-39_segments_1568514573258.74_warc_CC-MAIN-20190918065330-20190918091330-00209.warc.gz\"}"}
http://fahry.info/3rd-grade-equations-worksheets/printable-grade-math-worksheets-cbse-class-3-fractions-beginning-multiplication-for-inspirational-year-9-maths/
[ "", null, "Printable Grade Math Worksheets Cbse Class 3 Fractions Beginning Multiplication For Inspirational Year 9 Maths", null, "printable grade math worksheets cbse class 3 fractions beginning multiplication for inspirational year 9 maths.\n\ncbse 3rd grade maths worksheets place value free printable 2 math algebra class 3 fractions,cbse class 3 maths worksheets in hindi new images of grade addition resume format 3rd math review pdf,cbse class 3 maths division worksheets websites for beautiful math 3rd grade review pdf,cbse 3rd grade maths worksheets free printable math for and download them subtraction fraction,3rd grade division worksheets coloring pages inspirational advanced pdf algebra,cbse 3rd grade maths worksheets class 3 fractions algebra year 9 math,3rd grade math subtraction worksheets pdf fun for 2 multiplication all std cbse 3 maths,cbse class 3 maths fractions worksheets pdf grade math packet for all download and 3rd algebra,cbse class 3 maths worksheets in hindi 3rd standard grade fraction year 2 times tables worksheet template,word equations worksheet cbse class 3 maths fractions worksheets 3rd grade practice multiplication." ]
[ null, "http://fahry.info/3rd-grade-equations-worksheets/printable-grade-math-worksheets-cbse-class-3-fractions-beginning-multiplication-for-inspirational-year-9-maths/", null, "http://fahry.info/wp-content/uploads/2018/02/printable-grade-math-worksheets-cbse-class-3-fractions-beginning-multiplication-for-inspirational-year-9-maths.jpg", null ]
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https://digitalcommons.wayne.edu/jmasm/vol13/iss1/26/
[ "•\n•\n\n#### Abstract\n\nThe statistical inference drawn from the difference between two independent Poisson parameters is often discussed in medical literature. Kawasaki and Miyaoka (2012) proposed an index θ = P(λ1,post < λ2,post), where λ1,post and λ2,post denote Poisson parameters following posterior density. A new calculation method is proposed using MCMC and an approximate expression and exact expression for θ are compared.\n\n#### DOI\n\n10.22237/jmasm/1398918300\n\nCOinS" ]
[ null ]
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http://34.212.143.74/apps/s201913/tc2006/programming_problem_set_3/
[ "", null, "# Problem Set #3\n\n### Objectives\n\nDuring this activity, students should be able to:\n\n• Write higher-order functions using the Clojure programming language.\n\nThis activity helps the student develop the following skills, values and attitudes: ability to analyze and synthesize, capacity for identifying and solving problems, and efficient use of computer systems.\n\n## Activity Description\n\nIndividually or in pairs, solve the following set of programming exercises using Clojure. Place all your functions and unit tests in a file called problemset3.clj.\n\n### Note:\n\nThe tests in problems 3, 5, and 6 require comparing floating point numbers. In order to avoid rounding problems, we need to define the following function called aprox=:\n(require '[clojure.math.numeric-tower :refer [abs]])\n\n(defn aprox=\n\"Checks if x is approximately equal to y. Returns true\nif |x - y| < epsilon, or false otherwise.\"\n[epsilon x y]\n(< (abs (- x y)) epsilon))\n\n1. The function there-exists-one takes two arguments: a one argument predicate function pred and a list lst. Returns true if there is exactly one element in lst that satisfies pred, otherwise returns false.\n\nUnit tests:\n\n(deftest test-there-exists-one\n(is (not (there-exists-one pos?\n())))\n(is (there-exists-one pos?\n'(-1 -10 4 -5 -2 -1)))\n(is (there-exists-one neg?\n'(-1)))\n(is (not (there-exists-one symbol?\n'(4 8 15 16 23 42))))\n(is (there-exists-one symbol?\n'(4 8 15 sixteen 23 42))))\n\n2. The function my-drop-while takes two arguments: a function f and a list lst. It returns a list of items from lst dropping the initial items that evaluate to true when passed to f. Once a false value is encountered, the rest of the list is returned. Function f should accept one argument. Do not use the predefined drop-while function.\n\nUnit tests:\n\n(deftest test-my-drop-while\n(is (= () (my-drop-while neg? ())))\n(is (= '(0 1 2 3 4)\n(my-drop-while\nneg?\n'(-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4))))\n(is (= '(2 three 4 five)\n(my-drop-while\nsymbol?\n'(zero one 2 three 4 five))))\n(is (= '(0 one 2 three 4 five)\n(my-drop-while\nsymbol?\n'(0 one 2 three 4 five)))))\n\n3. The bisection method is a root-finding algorithm which works by repeatedly dividing an interval in half and then selecting the subinterval in which the root exists.\n\nSuppose we want to solve the equation $$f(x) = 0$$. Given two points $$a$$ and $$b$$ such that $$f(a)$$ and $$f(b)$$ have opposite signs, $$f$$ must have at least one root in the interval $$[a, b]$$ as long as $$f$$ is continuous on this interval. The bisection method divides the interval in two by computing $$c = \\frac{a+b}{2}$$. There are now two possibilities: either $$f(a)$$ and $$f(c)$$ have opposite signs, or $$f(c)$$ and $$f(b)$$ have opposite signs. The bisection algorithm is then applied to the sub-interval where the sign change occurs.\n\nWrite the function bisection, that takes a, b, and f as arguments. It finds the corresponding root using the bisection method. The algorithm must stop when a value of $$c$$ is found such that: $$\\left | f(c) \\right | < 1.0\\times 10^{-15}$$.\n\nUnit tests:\n\n(deftest test-bisection\n(is (aprox= 0.0001\n3.0\n(bisection 1 4 (fn [x] (* (- x 3) (+ x 4))))))\n(is (aprox= 0.0001\n-4.0\n(bisection -5 0 (fn [x] (* (- x 3) (+ x 4))))))\n(is (aprox= 0.0001\nMath/PI\n(bisection 1 4 (fn [x] (Math/sin x)))))\n(is (aprox= 0.0001\n(* 2 Math/PI)\n(bisection 5 10 (fn [x] (Math/sin x)))))\n(is (aprox= 0.0001\n1.618033988749895\n(bisection 1 2 (fn [x] (- (* x x) x 1)))))\n(is (aprox= 0.0001\n-0.6180339887498948\n(bisection -10 1 (fn [x] (- (* x x) x 1))))))\n4. The function linear-search takes three arguments: a vector vct, a data value x, and an equality function eq-fun. It sequentially searches for x in vct using eq-fun to compare x with the elements contained in vct. The eq-fun should accept two arguments, $$a$$ and $$b$$, and return true if $$a$$ is equal to $$b$$, or false otherwise.\n\nThe linear-search function returns the index where the first occurrence of x is found in vct (the first element of the vector is at index 0), or nil if not found.\n\nUnit tests:\n\n(deftest test-linear-search\n(is (nil? (linear-search [] 5 =)))\n(is (= 0 (linear-search 5 =)))\n(is (= 4 (linear-search\n[48 77 30 31 5 20 91 92\n69 97 28 32 17 18 96]\n5\n=)))\n(is (= 3 (linear-search\n[\"red\" \"blue\" \"green\" \"black\" \"white\"]\n\"black\"\nidentical?)))\n(is (nil? (linear-search\n[48 77 30 31 5 20 91 92\n69 97 28 32 17 18 96]\n96.0\n=)))\n(is (= 14 (linear-search\n[48 77 30 31 5 20 91 92\n69 97 28 32 17 18 96]\n96.0\n==)))\n(is (= 8 (linear-search\n[48 77 30 31 5 20 91 92\n69 97 28 32 17 18 96]\n70\n#(<= (abs (- %1 %2)) 1)))))\n\n5. The derivative of a function $$f(x)$$ with respect to variable $$x$$ is defined as:\n\n$$f'(x) \\equiv \\lim_{h\\rightarrow 0}\\frac{f(x+h)-f(x)}{h}$$\n\nWhere $$f$$ must be a continuous function. Write the function deriv that takes f and h as its arguments, and returns a new function that takes x as argument, and which represents the derivative of $$f$$ given a certain value for $$h$$.\n\nThe unit tests verify the following derivatives:\n\n\\begin{align*} f(x) &= x^3 \\\\ f'(x) &= 3x^2 \\\\ f''(x) &= 6x \\\\ f'''(x) &= 6 \\\\ f'(5) &= 75 \\\\ f''(5) &= 30 \\\\ f'''(5) &= 6 \\end{align*}\n\nUnit tests:\n\n(defn f [x] (* x x x))\n(def df (deriv f 0.001))\n(def ddf (deriv df 0.001))\n(def dddf (deriv ddf 0.001))\n\n(deftest test-deriv\n(is (aprox= 0.05 75 (df 5)))\n(is (aprox= 0.05 30 (ddf 5)))\n(is (aprox= 0.05 6 (dddf 5))))\n6. Newton’s method is another root-finding algorithm that is used to find successively better approximations. It can be summarized as follows:\n\n$$x_n = \\begin{cases} 0 & \\text{ if } n=0 \\\\ x_{n-1}-\\frac{f(x_{n-1})}{f'(x_{n-1})} & \\text{ if } n > 0 \\end{cases}$$\n\nA few things worth noting:\n\n• $$f$$ must be a differentiable real-valued function.\n• Larger values of $$n$$ produce better approximations.\n• $$x_0$$ is the initial guess, which is recommended to be a value that is close to the solution. This allows getting sooner a better approximation. Yet, for simplicity, we always assume here that $$x_0 = 0$$.\n\nWrite the function newton that takes f and n as its arguments, and returns the corresponding value of $$x_n$$. Use the deriv function from the previous problem to compute $$f'$$, with $$h = 0.0001$$.\n\nUnit tests:\n\n(deftest test-newton\n(is (aprox= 0.00001\n10.0\n(newton (fn [x] (- x 10))\n1)))\n(is (aprox= 0.00001\n-0.5\n(newton (fn [x] (+ (* 4 x) 2))\n1)))\n(is (aprox= 0.00001\n-1.0\n(newton (fn [x] (+ (* x x x) 1))\n50)))\n(is (aprox= 0.00001\n-1.02987\n(newton (fn [x] (+ (Math/cos x)\n(* 0.5 x)))\n5))))\n\n7. Simpson’s rule is a method for numeric integration:\n\n$$\\int_{a}^{b}f=\\frac{h}{3}(y_0 + 4y_1 + 2y_2 + 4y_3 + 2y_4 + \\cdots + 2y_{n-2} + 4y_{n-1} + y_n)$$\n\nWhere $$n$$ is an even positive integer (if you increment the value of $$n$$ you get a better approximation), and $$h$$ and $$y_k$$ are defined as follows:\n\n$$h = \\frac{b - a}{n}$$ $$y_k = f(a + kh)$$\n\nWrite the function integral, that takes as arguments a, b, n, and f. It returns the value of the integral, using Simpson’s rule. The unit tests verify the following single and double integrals (with n = 10):\n\n$$\\int_{0}^{1} x^3\\textit{dx} = \\frac{1}{4}$$ $$\\int_{1}^{2} \\int_{3}^{4} xy \\cdot \\textit{dx} \\cdot \\textit{dy} = \\frac{21}{4}$$\n\nUnit tests:\n\n(deftest test-integral\n(is (= 1/4 (integral 0 1 10 (fn [x] (* x x x)))))\n(is (= 21/4\n(integral 1 2 10\n(fn [x]\n(integral 3 4 10\n(fn [y]\n(* x y)))))))) \n8. The function binary-search takes three arguments: a vector vct sorted in ascending order and with no repeated elements, a data value x, and a less than function lt-fun. It implements the binary search algorithm, searching for x in vct using the lt-fun to compare x with the elements contained in vct. The lt-fun should accept two arguments, $$a$$ and $$b$$, and return true if $$a$$ is less than $$b$$, or false otherwise.\n\nThe binary-search function returns the index where x is found in vct (the first element of the vector is at index 0), or nil if not found.\n\nBinary search consists in searching a sorted vector by repeatedly dividing the search interval in half. You begin with an interval covering the whole vector. If the value being searched is less than the item in the middle of the interval, narrow the interval to the lower half. Otherwise narrow it to the upper half. Repeatedly check until the value is found or the interval is empty.\n\nUnit tests:\n\n(def small-list [4 8 15 16 23 42])\n\n(def big-list [0 2 5 10 11 13 16 20 24 26\n29 30 31 32 34 37 40 43 44\n46 50 53 58 59 62 63 66 67\n70 72 77 79 80 83 85 86 94\n95 96 99])\n\n(def animals [\"dog\" \"dragon\" \"horse\" \"monkey\" \"ox\"\n\"pig\" \"rabbit\" \"rat\" \"rooster\" \"sheep\"\n\"snake\" \"tiger\"])\n(defn str<\n\"Returns true if a is less than b, otherwise\nreturns false. Designed to work with strings.\"\n[a b]\n(< (compare a b) 0))\n\n(deftest test-binary-search\n(is (nil? (binary-search [] 5 <)))\n(is (= 3 (binary-search small-list 16 <)))\n(is (= 0 (binary-search small-list 4 <)))\n(is (= 5 (binary-search small-list 42 <)))\n(is (nil? (binary-search small-list 7 <)))\n(is (nil? (binary-search small-list 2 <)))\n(is (nil? (binary-search small-list 99 <)))\n(is (= 17 (binary-search big-list 43 <)))\n(is (= 0 (binary-search big-list 0 <)))\n(is (= 39 (binary-search big-list 99 <)))\n(is (nil? (binary-search big-list 12 <)))\n(is (nil? (binary-search big-list -1 <)))\n(is (nil? (binary-search big-list 100 <)))\n(is (= 5 (binary-search animals \"pig\" str<)))\n(is (= 0 (binary-search animals \"dog\" str<)))\n(is (= 11 (binary-search animals \"tiger\" str<)))\n(is (nil? (binary-search animals \"elephant\" str<)))\n(is (nil? (binary-search animals \"alligator\" str<)))\n(is (nil? (binary-search animals \"unicorn\" str<))))\n\n\n## Deliverables\n\nThe program source file must include at the top the authors’ personal information (name and student id) within comments. For example:\n\n;----------------------------------------------------------\n; Problem Set #3\n; Date: October 04, 2019.\n; Authors:\n; A01166611 Pepper Pots\n; A01160611 Anthony Stark\n;----------------------------------------------------------\n\nAlso, each function should include a documentation string (docstring) with a brief description of its behavior. For example:\n\n(defn max2\n\"Returns the largest of the two numbers x and y.\"\n[x y]\n(if (> x y) x y))\n\n\nTo deliver the problemset3.clj file, please provide the following information:" ]
[ null, "http://34.212.143.74/s201913/tc2006/higher_order.png", null ]
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https://newmonetarism.blogspot.com/2016/01/neo-fisherism-and-finite-horizons.html
[ "## Thursday, January 14, 2016\n\n### Neo-Fisherism and Finite Horizons\n\nI'm pleased to see that Narayana Kocherlakota seems interested in Neo-Fisherism. The crux of what he wants to say is in this note. What's that about? Narayana starts with a more-or-less standard New Keynesian (NK) reduced-form setup. There's an equation pricing a nominal bond (the \"IS curve\" in NK-speak) and a Phillips curve, simplified here to include only the output gap, and omitting the usual inflation expectations term. The key difference, though, is that Narayana is going to work with this framework assuming a finite horizon.\n\nIt's of course well-known that a finite horizon presents problems in monetary economies. If the economy has a known final date, then no one wants to hold money (an intrinsically worthless object) at the final date. So, working backward, money will be valueless at all dates. Not very useful if we want to do monetary economics. There are fixes, though. The standard one, used extensively of course, is to just assume an infinite horizon. This solves other problems too - even in non-monetary models an infinite horizon can make life easy for the modeler. Another fix is to suppose that the world ends with some fixed probability on the next date. That works much like infinite horizon, and is tractable. As well, one could assume a finite horizon in a monetary model, and suppose that the government does something on the final date to give money value. For example, money is required to pay your taxes, or the government in some other fashion retires the money stock in exchange for goods. That approach is not so tractable, and is not used much (off the top of my head, I can't think of a reference, but maybe you know one).\n\nIn Narayana's finite horizon economy, there is a further set of issues which come up, as this is not a monetary economy in the usual sense. It's a cashless Woodford NK economy. In the infinite-horizon version, prices are all quoted in terms of a numeraire, called money. In this world, anyone can trade one unit of numeraire for 1+i(t) units of the money in the next period, where i(t) is the nominal interest rate, and the central bank has the power to set i(t) each period. No money balances are held in this world, and the central bank avoids plenty of messy stuff that it has to do in the real world - open market operations, etc. Mike Woodford took great pains in his book to convince us that this setup makes sense.\n\nNarayana then faces a couple of problems in working with a finite-horizon version of the standard NK model. The first is that the New Keynesian Phillips curve in the infinite horizon reduced form is derived from Calvo pricing in a stationary environment. But in Narayana's setup, when firms make pricing decisions, they are going to do it differently depending on how close they are to the terminal date. That problem is never addressed in Narayana's note. The second problem he does address, which is that we need to determine the inflation rate at the terminal date. Narayana assumes that the final-period inflation rate is fixed by fiscal policy. You might think that sounds OK, if you're accustomed to thinking about standard monetary models - in that context, fiscal policy can indeed solve the problem. But here, it's hard to see what it is that the fiscal authority does to fix the inflation rate in the final period, as the model is cashless. Seemingly, this works just like monetary policy does - if the central bank can dictate the nominal interest rate, I guess the fiscal authority can dictate the inflation rate. Of course, you might then ask why the fiscal authority can't just dictate a whole sequence of inflation rates for all time, and be done with it.\n\nNarayana takes this model and considers a policy experiment. The central bank fixes the nominal interest rate for all time. What happens? With an infinite horizon, this is straightforward. Given Narayana's parameter restrictions there are many equilibria, but all equilibrium paths converge in the limit to a steady state in which the real interest rate is equal to the \"natural rate,\" and the inflation rate is equal to the nominal interest rate minus the natural rate - in the long run there is a one-for-one Fisher effect. Narayana calls this the \"neo-Fisherian hypothesis,\" but it's not a hypothesis, it's just a property of a wide class of models that macroeconomists work with. Neo-Fisherism is actually more accurately stated as: \"Excuse me people, I think you might have the sign wrong.\" I expand on that in this post. Indeed, a serious neo-Fisherian is going to worry not only about Fisher effects, but about short-run liquidity effects, persistent movements in the real interest rate, and the long-run effects of monetary policy on the real interest rate.\n\nNow, consider the finite horizon. If the fiscal authority happened to choose the Fisherian steady state inflation rate in the final date, then the equilibrium would be that steady state, for all time. However, since the steady state is stable solving forward, it's unstable solving backward, so given a final-period inflation rate away from the steady state, the inflation rate is further from the steady state the further in the future the final date is. In equation (5) of Narayana's note, a higher nominal interest rate then implies that inflation is lower at every date. Equation (5) also tells us that the fixed final-period inflation rate - that is fixed (\"anchored\" as Narayana says) inflation expectations in the last period - has a larger and larger effect the further we are from the final period. That is, anticipated inflation very far in the future has an enormous effect on inflation today. If you think that raises questions about how seriously we want to take this, then I agree with you.\n\nAnother experiment which Narayana does not look at, is to ask what happens if the central bank follows a standard linear Taylor rule. Suppose, in typical fashion, that (using Narayana's notation)Notation is defined as follows: I've left the output gap term out of the Taylor rule, for convenience.\n\nIn the infinite horizon case, if we assume the Taylor principle (alpha > 1), then there are two steady states - the zero lower bound (ZLB) and the desired steady state in which the central bank achieves its inflation target. There are many equilibria converging to the ZLB. Both steady states are Fisherian, in the sense that the real interest rate in the steady state is the natural rate, and the inflation rate is equal to the nominal interest rate minus the natural rate. With 0 < alpha < 1, the desired steady state is the unique steady state, and there are many equilibria, all of which converge in the limit to the steady state.\n\nNow, consider the finite horizon. As with Narayana's analysis, suppose the fiscal authority fixes the inflation rate at the final date. Then, under the Taylor principle, if we solve backward from the terminal date, we get closer to the desired inflation rate. The fiscal authority, presumably not in tune with the central bank, can set a terminal inflation rate that differs from the central bank's target inflation rate, but the further in the future is the terminal date, the less that matters for the first-period inflation rate. Indeed, as the time horizon increases, the initial inflation rate gets closer and closer to the target inflation rate. Thus, for a long horizon, this economy is Fisherian to an extreme at the first date. Indeed, under the Taylor principle, and with a long horizon, if the central bank increases the target inflation rate, then it increases the nominal interest rate roughly one-for-one at the initial date, and the actual inflation rate increases at the first date, roughly one-for-one. So, it would be incorrect to say that this finite-horizon economy does not have Fisherian properties.\n\nIf the central bank does not adhere to the Taylor principle in the finite horizon economy, then things can go badly. If the horizon is long and the fiscal authority chooses a terminal inflation rate below the central bank's target rate, then the nominal interest rate will stay at the zero lower bound for some time before it increases until the terminal date - inflation is always below the target.\n\nSo, Narayana's note didn't give me cause to worry about infinite horizon monetary models, or about neo-Fisherism. His finite horizon framework has some problems, but in spite of those problems it actually has some Fisherian properties.\n\n1.", null, "\"But here, it's hard to see what it is that the fiscal authority does to fix the inflation rate in the final period, as the model is cashless.\"\n\nCorrect me if I'm wrong, but Woodford's cashless system still has a medium of account, some sort of asset or security that is used to represent the economy's pricing unit. This isn't a very liquid asset, not enough to be called a medium of exchange or cash, but it's still something. Why can't the fiscal authority just buy that security back and retire it?\n\n1.", null, "There is something - we could call it money - and the supply of that stuff is zero in a Woodford cashless model. I may be missing something, but when I go to the store to buy a toothbrush, and there are none there, I can't buy a toothbrush.\n\n2.", null, "Dear SW, one possible fix to work in a well specified monetary environment and solve backward induction would be to use an OLG structure and set up a stationary equilibrium . This could turn helpful to teach monetary environment, esp CIA, to bachelor students for instance (!)\nBest,\n\n1.", null, "No, OLG is infinite horizon. The agents in the model are finite lived, but the economy runs forever." ]
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https://zanottocolfondo.com/the-most-incredibly-overlooked-solution-for-what-is-a-coefficient-in-math-3/amp/
[ "# The Most Incredibly Overlooked Solution for What Is a Coefficient in Math\n\nStatistics could be something which a whole lot of students dread (and even those which are now working). Procedures To tackle a literal equation for a single letter the moment it regards the others follow the precise steps as in chapter 2. This guide covers the fundamentals of filing for divorce in Maryland.\n\nRather, it is an effect of the variety of classes that were selected. Sometimes 1 method is much better than another, and in some instances, the symmetric property of equality is also valuable. Set the pay raise during the aforementioned formula.\n\n## How to Find What Is a Coefficient in Math Online\n\nThe coefficient is called the amplitude. It can be useful in trying to predict a particular stock’s tendencies and calculate the overall risk. A numerical coefficient is understood to be a fixed number that’s multiplied to a variable.\n\nBased on which sort of shrinkage is performed, some of the coefficients may be estimated to be exactly zero. The surplus coefficient is more often known as the coefficient of kurtosis. The Coefficient of Determination is utilized essaysource.com/ to analyze how difference in 1 variable can be explained by means of a difference in another variable.\n\nFor businessman, for instance, the correlation coefficient may be used to rate the success or failure of a specific advert or company strategy. In statistics, the term correlation denotes the association between two variables. Pearson’s correlation between both groups was analyzed.\n\nDecomposition Method Many individuals may find out how to factor by employing trial and error, but this can be hard to grasp and is difficult to explain. The level of negative correlation will probably vary over time. Latent class analysis resembles cluster analysis.\n\nYou don’t need to be concerned about that as your ballistic software will deal with this. The idea of lift is truly very straightforward. It has to be distributed around 0 in order to prevent any bias.\n\nNumbers might appear abstract, so utilize anything visual that you’re in a position to as a way to obtain the point across. Reality is considerably more messy. To that, it is quite simple.\n\n## Ruthless What Is a Coefficient in Math Strategies Exploited\n\nThe simplest thing to do would be to locate a LaTeX reference sheet. Let’s say you have a sequence of numbers that for some reason you will have to cope with mathematically. When it is positive, then both sets go up together.\n\nThen you select the biggest factor that could be found in each number. They are found below. It is referred to as a Constant.\n\n## Life After What Is a Coefficient in Math\n\nAll you have to do is multiply that density by the quantity of the solid or liquid to find the mass of the solid or liquid. The position and slope of the line are dependent on the quantity of correlation between both, paired variables involved with generating the scatter-plot. A circle with a larger diameter is going to have a larger circumference.\n\nIn other words, sectional density is the proportion of weight to the width of the bullet. It states that an increase in velocity leads to a decrease in pressure. The coefficient of variation needs to be computed just for data measured on a ratio scale, since these are the measurements that enable the division operation.\n\nThe second line contains the sum of lines for each of the next data blocks together with the comprehensive number of lines, excluding the header block. It has fewer steps to get there at the answer in comparison with polynomial long division system. Let’s look at a superb example.\n\nTherefore, we wind up adding to the x-coordinate. The expression within the brackets can currently be factored using the decompostion approach. For instance, a normal propane compressor might have a k-value at suction conditions of 1.195.\n\nThe ordinary use of the term correlation refers to a connection between a few objects (ideas, variables…). A regression involving multiple associated variables can create a curved line in some instances. In several instances, it may boost the random algorithm.\n\nIt comes into play when you own a dataset with several capabilities. It is a rather hard to develop a more efficient strategy. Global variable definitions need to be initialized.\n\nBuying the program was one of the best investments we could ever make! This property, sometimes known as the addition-subtraction property, is 1 way which we may generate equivalent equations. Assuming you own a user’s permission, it is not hard to share some activities automatically.\n\nWe are going to explore both these methods inside this section. Repeat the process until you crash out of columns to add. The 2 properties are believed to be negatively co-related.\n\nAt Math Play you can find a wide variety of fun games that you are able to play online. Algebra can resemble a confusing subject. One must be sound in mathematics in order to begin machine learning.\n\n## Up in Arms About What Is a Coefficient in Math?\n\nIn quotes, you’ve got to specify where the data file can be located on your computer. It is crucial to validate our results. To that, it is fairly straightforward.\n\nObviously there are lots of steps going on, therefore it’s not a surprise. Any letter might be employed to name a point. Not trying to multiply something.\n\n## New Questions About What Is a Coefficient in Math\n\nFinding out how to cope with a linear equation will give you an easy comprehension of algebra so you will be able to handle more elaborate equations later. Thus, the numerical coefficients are determined in all kinds of terms in mathematics. Quite simply, it’s a tangent function analysis." ]
[ null ]
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https://www.uvic.ca/science/math-statistics/people/home/faculty/ibrahim_slim.php
[ "# Slim Ibrahim\n\n##### Position\nProfessor\nMathematics and Statistics\n##### Credentials\n\nPhD University of Tunis\n\n##### Contact\nOffice: DTB-A547\n\nMy broad area of research is Applied Mathematics with a focus on the Analysis of Partial Differential Equations. Among others, I am working on Nonlinear Partial Differential Equations arising from Quantum Mechanics, Fluid Dynamics, Geophysical flows and more recently Kinetic Models. Questions related to the existence of solutions, their Asymptotic behaviors, and Blow up Analysis are in the core of my research.\n\n### Interests\n\n• Applied Mathematics\n• Analysis of Partial Differential Equations\n• Mathematical Physics\n• Fluid Dynamics\n• Wave Propagation\n\n### Selected Publications\n\n• Full list and files can be found at http://arxiv.org/find/math/1/au:+Ibrahim_S/0/1/0/all/0/1\n• T. Akahori, S. Ibrahim, H. Kikuchi and H. Nawa: \"Global dynamic for a nonlinear Schroedinger equation above a ground state with small frequency\". Preprint.\n• F. Diacu, S. Ibrahim and J. Sniatycki: \"The continuous transition of Hamiltonian vector fields through manifolds of constant curvature\". Preprint.\n• S. Ibrahim, P. G. Lemarie and N. Masmoudi: \"Time-periodic forcing and asymptotic stability of the Navier-Stokes-Maxwell equations\". Preprint.\n• F. Diacu, S. Ibrahim, C. Lind and S. Shen: \"The Vlasov-Poisson system for stellar dynamics in spaces of constant curvature\". Accepted in Comm. Math. Phys.\n• S. Ibrahim: \"Global Dynamics for semilinear Schroedinger equations\", Canad. Math. Soc. Notes 47 (6), December 2015. https://cms.math.ca/notes/v47/n6/Notesv47n6.pdf.\n• D. Arsenio, S. Ibrahim and N. Masmoudi: \"A derivation of the Magnetohydrodynamic system from Navier-Stokes-Maxwell systems\" Arch. Ration. Mech. Anal. 216 (2015), no. 3, 767–812.\n• H. Bahouri, S. Ibrahim and G. Perelman: Scattering for the critical 2-D NLS with exponential growth, Differential and Integral Equations. Vol. 27, N. 3-4, 233-268, 2014.\n• C. Cao, S. Ibrahim, K. Nakanishi and E. Titi: Finite-time blowup for the inviscid primitive equations of oceanic and atmospheric. Comm. Math. Phys. 337 (2015), no. 2, 473–482.\n• S. Ibrahim, N. Masmoudi and K. Nakanishi: Trudinger-Moser inequality on the whole plane with the exact growth condition. J. Eur. Math. Soc. (JEMS) 17 (2015), no. 4, 819–835.\n• T. Akahori, S. Ibrahim, H. Kikuchi and H. Nawa: Existence of a ground state and scattering for a nonlinear Schroedinger equation with critical growth. Selecta Mathematica (N.S.) 19, N. 2, 545-609, 2013\n• E. Foxall, R. Edwards, S. Ibrahim and P. Van Driessche: A contraction argument for a two-dimensional spiking Neuron model. SIAM J. Appl. Dyn. Syst. 11, n 1, 540-566, 2012.\n• S. Ibrahim, M. Majdoub, N. Masmoudi and K. Nakanishi: Scattering for the two dimensional NLS with exponential nonlinearity. Nonlinearity, V. 25, issue 6, 1843-1849, 2012.\n• S. Ibrahim and S. Keraani. Small global solutions for the coupled Navier-Stokes-Maxwell system. SIAM J. Math. Anal. Vol. 43, No. 5, pp. 2275-2295, 2011.\n• S. Ibrahim, N. Masmoudi and K. Nakanishi. Scattering threshold for the focusing nonlinear Klein-Gordon equation. Analysis and PDE. Vol. 4, No. 3, pp. 405-460, 2011.\n• Ibrahim and R. Jrad. Strichartz type estimates and the wellposedness of an energy critical 2D wave equation in a bounded domain. Journal of Diff. Equa., Vol 250, N 9, 3740-3771, 2011.\n• S. Ibrahim, M. Majdoub, N. Masmoudi and K. Nakanishi. Scattering for a 2D energy-critical nonlinear wave and Schrödinger equations, Duke Math. Journal, Vol. 152, N 2, 287-329, 2009." ]
[ null ]
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https://www.geeksforgeeks.org/minimum-group-flips-to-make-binary-array-elements-same/?ref=rp
[ "Related Articles\n\n# Minimum Group Flips to Make Binary Array Elements Same\n\n• Difficulty Level : Medium\n• Last Updated : 24 May, 2021\n\nGiven a binary array, we need to convert this array into an array that either contains all 1s or all 0s.  We need to do it using the minimum number of group flips.\n\nExamples :\n\nInput : arr[] = {1, 1, 0, 0, 0, 1}\nOutput :  From 2 to 4\nExplanation : We have two choices, we make all 0s or do all 1s.  We need to do two group flips to make all elements 0 and one group flip to make all elements 1.  Since making all elements 1 takes least group flips, we do this.\nInput : arr[] = {1, 0, 0, 0, 1, 0, 0, 1, 0, 1}\nOutput :\nFrom 1 to 3\nFrom 5 to 6\nFrom 8 to 8\nInput : arr[] = {0, 0, 0}\nOutput :\nExplanation : Output is empty, we need not to make any change\nInput : arr[] = {1, 1, 1}\nOutput :\nExplanation : Output is empty, we need not to make any change\nInput : arr[] = {0, 1}\nOutput :\nFrom 0 to 0\nOR\nFrom 1 to 1\nExplanation :  Here number of flips are same either we make all elements as 1 or all elements as 0.\n\nA Naive Solution is to traverse do two traversals of the array. We first traverse to find the number of groups of 0s and the number of groups of 1.  We find the minimum of these two.  Then we traverse the array and flip the 1s if groups of 1s are less. Otherwise, we flip 0s.\n\nHow to do it with one traversal of array?\n\nAn Efficient Solution is based on the below facts :\n\n• There are only two types of groups (groups of 0s and groups of 1s)\n• Either the counts of both groups are same or the difference between counts is at most 1. For example, in {1, 1, 0, 1, 0, 0} there are two groups of 0s and two groups of 1s.  In example, {1, 1, 0, 0, 0, 1, 0, 0, 1, 1}, count of groups of 1 is one more than the counts of 0s.\n\nBased on the above facts, we can conclude that if we always flip the second group and other groups that of the same type as the second group, we always get the correct answer.  In the first case, when group counts are the same, it does not matter which group type we flip as both will lead to the correct answer.  In the second case, when there is one extra, by ignoring the first group and starting from the second group, we convert this case to first case (for subarray beginning from the second group) and get the correct answer.\n\n## C++\n\n `// C++ program to find the minimum``// group flips in a binary array``#include ``using` `namespace` `std;` `void` `printGroups(``bool` `arr[], ``int` `n) {``  ` `  ``// Traverse through all array elements``  ``// starting from the second element``  ``for` `(``int` `i = 1; i < n; i++) {``    ` `    ``// If current element is not same``    ``// as previous``    ``if` `(arr[i] != arr[i - 1]) {``      ` `      ``// If it is same as first element``      ``// then it is starting of the interval``      ``// to be flipped.``      ``if` `(arr[i] != arr)``        ``cout << ``\"From \"` `<< i << ``\" to \"``;` `      ``// If it is not same as previous``      ``// and same as first element, then``      ``// previous element is end of interval``      ``else``        ``cout << (i - 1) << endl;``    ``}``  ``}` `  ``// Explicitly handling the end of``  ``// last interval``  ``if` `(arr[n - 1] != arr)``    ``cout << (n - 1) << endl;``}` `int` `main() {``  ``bool` `arr[] = {0, 1, 1, 0, 0, 0, 1, 1};``  ``int` `n = ``sizeof``(arr) / ``sizeof``(arr);``  ``printGroups(arr, n);``  ``return` `0;``}`\n\n## Java\n\n `// Java program to find the minimum``// group flips in a binary array``import` `java.io.*;``import` `java.util.*;` `class` `GFG {``    ` `static` `void` `printGroups(``int` `arr[], ``int` `n)``{``    ` `    ``// Traverse through all array elements``    ``// starting from the second element``    ``for``(``int` `i = ``1``; i < n; i++)``    ``{``        ` `       ``// If current element is not same``       ``// as previous``       ``if` `(arr[i] != arr[i - ``1``])``       ``{``           ` `           ``// If it is same as first element``           ``// then it is starting of the interval``           ``// to be flipped.``           ``if` `(arr[i] != arr[``0``])``               ``System.out.print(``\"From \"` `+ i + ``\" to \"``);``           ` `           ``// If it is not same as previous``           ``// and same as first element, then``           ``// previous element is end of interval``           ``else``               ``System.out.println(i - ``1``);``       ``}``    ``}``    ` `    ``// Explicitly handling the end of``    ``// last interval``    ``if` `(arr[n - ``1``] != arr[``0``])``        ``System.out.println(n - ``1``);``}``    ` `// Driver code``public` `static` `void` `main(String[] args)``{``    ``int` `arr[] = {``0``, ``1``, ``1``, ``0``, ``0``, ``0``, ``1``, ``1``};``    ``int` `n = arr.length;``    ` `    ``printGroups(arr, n);``}``}` `// This code is contributed by coder001`\n\n## Python3\n\n `# Python3 program to find the minimum``# group flips in a binary array` `def` `printGroups(arr, n):``    ` `    ``# Traverse through all array elements``    ``# starting from the second element``    ``for` `i ``in` `range``(``1``, n):``        ` `        ``# If current element is not same``        ``# as previous``        ``if` `(arr[i] !``=` `arr[i ``-` `1``]):``            ` `            ``# If it is same as first element``            ``# then it is starting of the interval``            ``# to be flipped.``            ``if` `(arr[i] !``=` `arr[``0``]):``                ``print``(``\"From\"``, i, ``\"to \"``, end ``=` `\"\")` `            ``# If it is not same as previous``            ``# and same as the first element, then``            ``# previous element is end of interval``            ``else``:``                ``print``(i ``-` `1``)` `    ``# Explicitly handling the end of``    ``# last interval``    ``if` `(arr[n ``-` `1``] !``=` `arr[``0``]):``        ``print``(n ``-` `1``)` `# Driver Code``if` `__name__ ``=``=` `'__main__'``:``    ` `    ``arr ``=` `[ ``0``, ``1``, ``1``, ``0``, ``0``, ``0``, ``1``, ``1` `]``    ``n ``=` `len``(arr)``    ` `    ``printGroups(arr, n)``    ` `# This code is contributed by Bhupendra_Singh`\n\n## C#\n\n `// C# program to find the minimum``// group flips in a binary array``using` `System;` `class` `GFG{``    ` `static` `void` `printGroups(``int` `[]arr, ``int` `n)``{``    ` `    ``// Traverse through all array elements``    ``// starting from the second element``    ``for``(``int` `i = 1; i < n; i++)``    ``{` `       ``// If current element is not same``       ``// as previous``       ``if` `(arr[i] != arr[i - 1])``       ``{``           ` `           ``// If it is same as first element``           ``// then it is starting of the interval``           ``// to be flipped.``           ``if` `(arr[i] != arr)``               ``Console.Write(``\"From \"` `+ i + ``\" to \"``);``           ` `           ``// If it is not same as previous``           ``// and same as first element, then``           ``// previous element is end of interval``           ``else``               ``Console.WriteLine(i - 1);``       ``}``    ``}``    ` `    ``// Explicitly handling the end``    ``// of last interval``    ``if` `(arr[n - 1] != arr)``        ``Console.WriteLine(n - 1);``}``    ` `// Driver code``public` `static` `void` `Main(String[] args)``{``    ``int` `[]arr = { 0, 1, 1, 0, 0, 0, 1, 1 };``    ``int` `n = arr.Length;``    ` `    ``printGroups(arr, n);``}``}` `// This code is contributed by amal kumar choubey`\n\n## Javascript\n\n ``\nOutput\n```From 1 to 2\nFrom 6 to 7```\n\nTime Complexity:  O(n)\nAuxiliary Space:  O(1)\n\nAttention reader! Don’t stop learning now. Get hold of all the important DSA concepts with the DSA Self Paced Course at a student-friendly price and become industry ready.  To complete your preparation from learning a language to DS Algo and many more,  please refer Complete Interview Preparation Course.\n\nIn case you wish to attend live classes with experts, please refer DSA Live Classes for Working Professionals and Competitive Programming Live for Students.\n\nMy Personal Notes arrow_drop_up" ]
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https://www.crazy-numbers.com/en/147
[ "Discover a lot of information on the number 147: properties, mathematical operations, how to write it, symbolism, numerology, representations and many other interesting things!\n\n## Mathematical properties of 147\n\nIs 147 a prime number? No\nIs 147 a perfect number? No\nNumber of divisors 6\nList of dividers 1, 3, 7, 21, 49, 147\nSum of divisors 228\nPrime factorization 3 x 72\nPrime factors 3, 7\n\n## How to write / spell 147 in letters?\n\nIn letters, the number 147 is written as: One hundred and forty-seven. And in other languages? how does it spell?\n\n147 in other languages\nWrite 147 in english One hundred and forty-seven\nWrite 147 in french Cent quarante-sept\nWrite 147 in spanish Ciento cuarenta y siete\nWrite 147 in portuguese Cento quarenta e sete\n\n## Decomposition of the number 147\n\nThe number 147 is composed of:\n\n1 iteration of the number 1 : The number 1 (one) represents the uniqueness, the unique, a starting point, a beginning.... Find out more about the number 1\n\n1 iteration of the number 4 : The number 4 (four) is the symbol of the square. It represents structuring, organization, work and construction.... Find out more about the number 4\n\n1 iteration of the number 7 : The number 7 (seven) represents faith, teaching. It symbolizes reflection, the spiritual life.... Find out more about the number 7\n\nOther ways to write 147\nIn letter One hundred and forty-seven\nIn roman numeral CXLVII\nIn binary 10010011\nIn octal 223\nIn US dollars\nDeprecated: Function money_format() is deprecated in /home/clients/df8caba959271e8e753c9e287ae1296d/websites/crazy-numbers.com/templates/sample-number.tpl on line 180\nUSD 147.00 (\\$)\nIn euros\nDeprecated: Function money_format() is deprecated in /home/clients/df8caba959271e8e753c9e287ae1296d/websites/crazy-numbers.com/templates/sample-number.tpl on line 185\n147,00 EUR (€)\nSome related numbers\nPrevious number 146\nNext number 148\nNext prime number 149\n\n## Mathematical operations\n\nOperations and solutions\n147*2 = 294 The double of 147 is 294\n147*3 = 441 The triple of 147 is 441\n147/2 = 73.5 The half of 147 is 73.500000\n147/3 = 49 The third of 147 is 49.000000\n1472 = 21609 The square of 147 is 21609.000000\n1473 = 3176523 The cube of 147 is 3176523.000000\n√147 = 12.124355652982 The square root of 147 is 12.124356\nlog(147) = 4.9904325867787 The natural (Neperian) logarithm of 147 is 4.990433\nlog10(147) = 2.1673173347482 The decimal logarithm (base 10) of 147 is 2.167317\nsin(147) = 0.60904402188329 The sine of 147 is 0.609044\ncos(147) = -0.79313641916648 The cosine of 147 is -0.793136\ntan(147) = -0.76789314822203 The tangent of 147 is -0.767893" ]
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https://irudnyts.github.io/insights-from-students-data/
[ "# 🎓 [archived] Insights from students data\n\nRecently (well, a month ago) I had a discussion with a friend of mine about the modern tools and approaches in education. He is currently involed to the edX platform startup, and given that I am assistant at the university, we had several points to discuss.\n\nDisclaimer: This post is outdated and was archived for back compatibility: please use with care! This post does not reflect the author’s current point of view and might deviate from the current best practices.\n\nPerhaps the key point of the discussion was how to analyse the students’ data. Currently educational platforms, such as moodle for instance, has terabites of data. However, it’s a challange to extract insights from it. As long as I have an access to the moodle data of our courses, I made a brief investigation what could be drawn from such a piece of data (unfortunately, due to the data protection policy of our university I cannot share it).\n\nThe first data set is the moodle’s logfile, i.e. the time and other information of students’ logins and other activities/actions. The second one is the midterm grades.\n\nFirst, we load the data to the global enviroment. Variables exercises, before_exercises, and midterm contain dates of excercise sessions, when such exercises were published in moodle and the date of midterm, respectively. The column Time stores data as character, which is transformed to POSIXct format. Also, it is useful to store the dates (without the time) in a separate column. Furthermore, we focus only on the semester time period, not on Christmas break nor exam period. The semester ends on December 23.\n\nlibrary(\"ggplot2\")\n\nstringsAsFactors = FALSE)\n\nexercises <- as.Date(c(\"2016-10-18\", \"2016-11-02\", \"2016-11-16\", \"2016-12-07\"))\nbefore_exercises <- exercises - 7\nmidterm <- as.Date(\"2016-11-23\")\n\ncolnames(log_alm) <- tolower(colnames(log_alm))\nlog_alm$time <- as.POSIXct(strptime(log_alm$time, \"%d/%m/%y, %H:%M\"))\nlog_alm$date <- as.Date(log_alm$time)\n\nlog_alm <- log_alm[log_alm$date < as.Date(\"2016-12-23\"), ] It is possible to depict immediately the frequency of student’s action. ggplot(data = log_alm) + geom_bar(aes(x = date))", null, "Note that if a student logins two times on the same day, these events are considered as distinct. Thus, this plot is not very helpful, ‘cause we need to figure out how many different students attend the web-page during the day. The possible way how to aggregate the data is shown below (even though, it is not the most elegant one): smr <- data.frame(date = unique(log_alm$date), n_logins = NA)\n\nfor(date in unique(log_alm$date)) { smr[smr$date == date, \"n_logins\"] <- length(unique(log_alm[log_alm$date == date, \"user.full.name\"])) } smr$class <- \"no_class\"\nsmr$class[weekdays(smr$date) %in% c(\"Tuesday\", \"Wednesday\")] <- \"lecture\"\nsmr$class[smr$date %in% exercises] <- \"exercise\"\nsmr$class[smr$date %in% before_exercises] <- \"before_exercise\"\nsmr$class[smr$date == midterm] <- \"midterm\"\n\n\nAdditionaly we mark the dates depending on whether it was a lecture, exercise session, etc. Now we can plot it:\n\nggplot(data = smr, mapping = aes(x = date, y = n_logins)) +\ngeom_bar(aes(fill = class), stat=\"identity\")", null, "The plot is a bit more illustrative that the previous one:\n\n• The peak on October 11 and the day after indicates the interest of students about the first exercise session.\n\n• The second peak is right before the midterm. That’s also along with intuituion: students tend to study right before the exam.\n\n• First two exercies sessions show the same frequency, while we see the drop in the third one. Typically it is due to the overload during the midterm semester period.\n\nPerhaps the line chart might be also useful to see the trend:\n\nggplot(data = smr, mapping = aes(x = date, y = n_logins)) +\ngeom_line() +\nstat_smooth(method =\"auto\", level = 0.95, span = 0.4)", null, "This figure shows that the trend is quite linear, with two peaks of the first exercise and the midterm. After the midterm the line is even more stable and predictable.\n\nTo dive a bit deeper is possible thanks to midterm grades. Also we give a “joker” to students, namely: if they do a presentation, they got a bonus for a final grade. This “joker” together with midterm grades are stored in seprate file. Therefore, the next objective is to extract a relevant information about the login numbers for each student, and then merge it with the grades and “joker”:\n\nstd <- sort(table(log_alm$user.full.name)) std <- data.frame(name = names(std), logins = std) std$name <- tolower(std$name) grades <- read.csv(file = \"/Users/irudnyts/Documents/Data/alm_grades.csv\") colnames(grades) <- c(\"id\", \"surname\", \"name\", \"grade\", \"pres\") grades$name <- tolower(paste(grades$name, grades$surname))\n\nstd,\nall = TRUE)\n\ninfo <- info[complete.cases(info), ]\ninfo <- info[info$grade != 0, ] Let’s run the regression, to see whether or not number of logins drive the midterm grade (we account only on those student, who have written midterm, i.e. with non-zero grades): model <- lm(info, formula = grade ~ logins) summary(model) Call: lm(formula = grade.midterm ~ logins, data = info[info$grade.midterm !=\n0, ])\n\nResiduals:\nMin 1Q Median 3Q Max\n-2.9317 -0.3541 0.2658 0.5813 1.5943\n\nCoefficients:\nEstimate Std. Error t value Pr(>|t|)\n(Intercept) 3.800838 0.375434 10.124 2.57e-09 ***\n---\nSignif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\nResidual standard error: 1.096 on 20 degrees of freedom\nMultiple R-squared: 0.0323,\tAdjusted R-squared: -0.01608\nF-statistic: 0.6676 on 1 and 20 DF, p-value: 0.4235\n\n\nNot much, huh? logins is not significant. And its value 0.005691 imply only 0.5 increase in grade for those, who log in 100 times more.\n\nBut let’s not give up and try to cluster students by using the value of grade and the value of the number of logins:\n\ncl <- kmeans(x = info[, -1], centers = 3)\ninfo$cl <- cl$cluster\nggplot(data = info,\nmapping = aes(x = grade, y = logins, color = as.factor(cl))) +\ngeom_point(size = 3)", null, "So far so good, but I am not very happy with such a result. The clusters are strongly dependent on the number of logins, but not on the midterm grade. Standardization of the values must solve this issue:\n\ninfo$grade_std <- (info$grade - mean(info$grade)) / sd(info$grade)\ninfo$logins_std <- (info$logins - mean(info$logins)) / sd(info$logins)\n\ninfo$cl2 <- cl2$cluster\n\nggplot(data = info,\nmapping = aes(x = grade, y = logins, color = as.factor(cl2))) +\ngeom_point(size = 3)", null, "" ]
[ null, "https://irudnyts.github.io/images/posts/2017-05-05-insights-from-students-data/freq1.png", null, "https://irudnyts.github.io/images/posts/2017-05-05-insights-from-students-data/freq2.png", null, "https://irudnyts.github.io/images/posts/2017-05-05-insights-from-students-data/line1.png", null, "https://irudnyts.github.io/images/posts/2017-05-05-insights-from-students-data/cl1.png", null, "https://irudnyts.github.io/images/posts/2017-05-05-insights-from-students-data/cl2.png", null ]
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https://www.physicsforums.com/threads/proof-of-taylors-formula-for-polynomials.306898/
[ "# Proof of Taylor's formula for polynomials\n\n• JG89\n\n#### JG89\n\n\"This is an entirely elementary algebraic formula concerning a polynomial in x or order n, say\n$$f(x) = a_0 + a_1x + a_2x^2 + ... + a_nx^n$$.\n\nIf we replace x by a + h = b and expand each term in powers of h, there results immediately a representation of the form $$f(a+h) = c_0 = c_1h + c_2h^2 + ... + c_nh^n$$.\n\nTaylor's formula is the relation: $$c_v = \\frac{1}{v!}f^v(a)$$, for the coefficients c_v in terms of f and its derivatives at x = a. To prove this fact we consider the quantity h = b - a as the independent variable, and apply the chain rule which shows that differentiation with respect to h is the same as differentiation with respect to b = a + h.\"\n\nI don't get how f'(h) = f'(a + h)?\n\nLast edited:\nI think it's saying that if you have f'(h) and want f'(h+a), just substitute in h+a where you see h to get f'(h+a). This works because $$\\frac{d}{dh} h+a = 1$$" ]
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https://support.bioconductor.org/p/80966/
[ "Question: DESeq2 PCA different from Prcomp PCA\n1\ntiago21128710 wrote:\n\nI made a PCA using the rlog matrix from DESEQ and got this plot where one of my sample groups did not group together.\n\nplotPCA(rld, intgroup=c(\"condition\"))\n\nhttp://s3.postimg.org/whlalmp6b/Rplot03.png\n\nUsing the same matrix in Prcomp from r the samples get more clustered.\n\ncruzi.pca <- prcomp(rldMat2,\n\ncenter = TRUE,\nscale. = FALSE)\n\nlibrary(ggbiplot)\n\ng <- ggbiplot(pcobj = cruzi.pca, scale = 1, obs.scale = 1, var.scale = 1,\ngroups = groups, ellipse = TRUE,\ncircle = TRUE, var.axes = FALSE)\ng <- g + scale_color_discrete(name = '')\ng <- g + theme(legend.direction = 'horizontal',\nlegend.position = 'top')\nprint(g)\n\nhttp://s4.postimg.org/vdsax2drx/Rplot04.png\n\nHow can I decide what plot to use? And Why a same matrix of transformed data got so differently clusted ? Thank you.\n\nAnswer: DESeq2 PCA different from Prcomp PCA\n3\nMichael Love25k wrote:\n\nSee ?plotPCA in particular the arguments and the note.\n\n2\n\nAdding on to Mike's comment, it is most likely due to the number of genes you use for the DESeq2::plotPCA function. This number defaults to 500, while you take all the genes in the rldMat2 object - at least, if rld and rldMat2 are exactly the same objects.\n\nIndeed. This explain the difference.\n\nWhy I would make the PCA for only 500 genes instead of all of them ?\n\n3\n\nMaking a PCA plot after first ranking the genes by total variance helps to make more clear the sample groupings. Of course, you can tune this parameter, but 500 is a good number for many RNA-seq datasets." ]
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{"ft_lang_label":"__label__en","ft_lang_prob":0.6380765,"math_prob":0.96117634,"size":772,"snap":"2019-43-2019-47","text_gpt3_token_len":223,"char_repetition_ratio":0.100260414,"word_repetition_ratio":0.0,"special_character_ratio":0.28367877,"punctuation_ratio":0.20253165,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.986379,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-10-21T10:20:19Z\",\"WARC-Record-ID\":\"<urn:uuid:cc106dc3-af5e-4288-9bc4-db037cc5bac2>\",\"Content-Length\":\"22664\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:f170aa39-259b-452f-8624-3215193f4d18>\",\"WARC-Concurrent-To\":\"<urn:uuid:44fc002d-7de0-4788-b928-4a97dc46e581>\",\"WARC-IP-Address\":\"52.3.239.161\",\"WARC-Target-URI\":\"https://support.bioconductor.org/p/80966/\",\"WARC-Payload-Digest\":\"sha1:XMJJC5S7L3A4T44MBVWVS54YBUDI4RJV\",\"WARC-Block-Digest\":\"sha1:S4IEMVXNJYNWUA5OPHRVJXAIAVKYGCKX\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-43/CC-MAIN-2019-43_segments_1570987769323.92_warc_CC-MAIN-20191021093533-20191021121033-00328.warc.gz\"}"}
https://www.numbersaplenty.com/13043
[ "Search a number\nBaseRepresentation\nbin11001011110011\n3122220002\n43023303\n5404133\n6140215\n753012\noct31363\n918802\n1013043\n119888\n12766b\n135c24\n144a79\n153ce8\nhex32f3\n\n13043 has 2 divisors, whose sum is σ = 13044. Its totient is φ = 13042.\n\nThe previous prime is 13037. The next prime is 13049. The reversal of 13043 is 34031.\n\nIt is a balanced prime because it is at equal distance from previous prime (13037) and next prime (13049).\n\nIt is an emirp because it is prime and its reverse (34031) is a distict prime.\n\nIt is a cyclic number.\n\nIt is not a de Polignac number, because 13043 - 26 = 12979 is a prime.\n\nIt is a Chen prime.\n\nIt is a nialpdrome in base 11.\n\nIt is not a weakly prime, because it can be changed into another prime (13049) by changing a digit.\n\nIt is a polite number, since it can be written as a sum of consecutive naturals, namely, 6521 + 6522.\n\nIt is an arithmetic number, because the mean of its divisors is an integer number (6522).\n\n213043 is an apocalyptic number.\n\n13043 is a deficient number, since it is larger than the sum of its proper divisors (1).\n\n13043 is an equidigital number, since it uses as much as digits as its factorization.\n\n13043 is an odious number, because the sum of its binary digits is odd.\n\nThe product of its (nonzero) digits is 36, while the sum is 11.\n\nThe square root of 13043 is about 114.2059543106. The cubic root of 13043 is about 23.5392433179.\n\nAdding to 13043 its reverse (34031), we get a palindrome (47074).\n\nThe spelling of 13043 in words is \"thirteen thousand, forty-three\"." ]
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https://stattrek.com/tutorials/probability-tutorial?tutorial=prob
[ "# ProbabilityTutorial\n\nApplied researchers make decisions under uncertainty. Probability theory makes it possible for researchers to quantify the extent of uncertainty inherent in their conclusions and inferences.\n\n### About the Tutorial\n\nThis tutorial covers the essence of probabilty theory clearly and simply in just a few short lessons. The tutorial focuses on six topics:\n\n• Probability basics. To solve probability problems, it helps to know about sets, subsets, and statistical experiments.\n• Probability problems. To solve probability problems, you need to understand the rules of probability; and you need to know how to count data points.\n• Poker probability. To compute probabilities for poker hands, you rely on fundamental principles in probability. It's a great way to build analytical skill, and it's fun.\n• Random variables. Random variables are at the heart of probability. They can be characterized by central tendency, variability, and a probability distribution.\n• Discrete probability distributions. How to work with binomial, hypergeometric, multinomial, negative binomial, and Poisson distributions.\n• Continuous probability distributions. How to work with normal, standard normal, chi-square, t-, and f-distributions.\n\nThis tutorial is designed for students and researchers who have some familiarity with introductory statistics (e.g., a high school statistics course or Advanced Placement Statistics).\n\n### How to Use This Tutorial\n\nIndividual lessons are accessible through the table of contents, which can be found in the vertical column on the left side of the page. You should work through lessons in the order in which they appear; because some lessons build on previous lessons.\n\nIndividual lessons are accessible through the table of contents, which can be accessed by tapping the \"Probability: Table of Contents\" button at the top of the page. You should work through lessons in the order in which they appear; because some lessons build on previous lessons." ]
[ null ]
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https://earldouglas.com/posts/type-classes/free.html
[ "## Implementation\n\n``````sealed trait Free[F[_], A] {\n\nimport Free._\n\ndef map[B](f: A => B): Free[F, B] =\nflatMap { a => pure(f(a)) }\n\ndef flatMap[B](f: A => Free[F, B]): Free[F, B] =\nBind(this, f)\n\ndef foldMap[G[_]: Monad](nt: F ~> G): G[A] =\nthis match {\ncase Suspend(fa) => nt(fa)\ncase Bind(fa, f) => val mg = implicitly[Monad[G]]\nval ga = fa.foldMap(nt)\nmg.flatMap(ga)(f(_).foldMap(nt))\n}\n\n}\n\nobject Free {\n\ndef pure[F[_], A](a: A): Free[F, A] =\nPure(a)\n\ndef liftM[F[_], A](fa: F[A]): Free[F, A] =\nSuspend(fa)\n\nfinal case class Pure[F[_], A](a: A) extends Free[F, A]\nfinal case class Suspend[F[_], A](fa: F[A]) extends Free[F, A]\nfinal case class Bind[F[_], A, B]( fa: Free[F, A]\n, f: A => Free[F, B]\n) extends Free[F, B]\n}``````\n\n## Example\n\n``````sealed trait Console[A]\ncase class Print(x: String) extends Console[Unit]\n\nval program: Free[Console, Unit] =\nfor {\n_ <- Free.liftM(Print(\"First name: \"))\n_ <- Free.liftM(Print(\"Last name: \"))\n_ <- Free.liftM(Print(s\"Hello, \\$first \\$last!\\n\"))\n} yield ()\n\nprogram.foldMap[ID](new ~>[Console, ID]{\ndef apply[A](c: Console[A]): ID[A] =\nc match {\ncase Print(x) => print(x)\n}\n})``````\n\n## Demo\n\nThis file is literate Scala, and can be run using Codedown:\n\n``````\\$ curl \\\nhttps://earldouglas.com/posts/type-classes/applicative.md \\\nhttps://earldouglas.com/posts/type-classes/functor.md \\\nhttps://earldouglas.com/posts/type-classes/id.md \\" ]
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https://mxnet.apache.org/versions/1.4.1/api/python/optimization/optimization.html
[ "# Optimization: initialize and update weights¶\n\n## Overview¶\n\nThis document summaries the APIs used to initialize and update the model weights during training\n\n mxnet.initializer Weight initializer. mxnet.optimizer Optimizer API of MXNet. mxnet.lr_scheduler Scheduling learning rate.\n\nand how to develop a new optimization algorithm in MXNet.\n\nAssume there there is a pre-defined Symbol and a Module is created for it\n\n>>> data = mx.symbol.Variable('data')\n>>> label = mx.symbol.Variable('softmax_label')\n>>> fc = mx.symbol.FullyConnected(data, name='fc', num_hidden=10)\n>>> loss = mx.symbol.SoftmaxOutput(fc, label, name='softmax')\n>>> mod = mx.mod.Module(loss)\n>>> mod.bind(data_shapes=[('data', (128,20))], label_shapes=[('softmax_label', (128,))])\n\n\nNext we can initialize the weights with values sampled uniformly from [-1,1]:\n\n>>> mod.init_params(mx.initializer.Uniform(scale=1.0))\n\n\nThen we will train a model with standard SGD which decreases the learning rate by multiplying 0.9 for each 100 batches.\n\n>>> lr_sch = mx.lr_scheduler.FactorScheduler(step=100, factor=0.9)\n>>> mod.init_optimizer(\n... optimizer='sgd', optimizer_params=(('learning_rate', 0.1), ('lr_scheduler', lr_sch)))\n\n\nFinally run mod.fit(...) to start training.\n\n## The mxnet.initializer package¶\n\nThe base class Initializer defines the default behaviors to initialize various parameters, such as set bias to 1, except for the weight. Other classes then defines how to initialize the weight.\n\n Initializer The base class of an initializer. Uniform Initializes weights with random values uniformly sampled from a given range. Normal Initializes weights with random values sampled from a normal distribution with a mean of zero and standard deviation of sigma. Load Initializes variables by loading data from file or dict. Mixed Initialize parameters using multiple initializers. Zero Initializes weights to zero. One Initializes weights to one. Constant Initializes the weights to a given value. Orthogonal Initialize weight as orthogonal matrix. Xavier Returns an initializer performing “Xavier” initialization for weights. MSRAPrelu Initialize the weight according to a MSRA paper. Bilinear Initialize weight for upsampling layers. FusedRNN Initialize parameters for fused rnn layers.\n\n## The mxnet.optimizer package¶\n\nThe base class Optimizer accepts commonly shared arguments such as learning_rate and defines the interface. Each other class in this package implements one weight updating function.\n\n Optimizer The base class inherited by all optimizers. SGD The SGD optimizer with momentum and weight decay. NAG Nesterov accelerated SGD. RMSProp The RMSProp optimizer. Adam The Adam optimizer. AdaGrad AdaGrad optimizer. AdaDelta The AdaDelta optimizer. Adamax The AdaMax optimizer. Nadam The Nesterov Adam optimizer. DCASGD The DCASGD optimizer. SGLD Stochastic Gradient Riemannian Langevin Dynamics. Signum The Signum optimizer that takes the sign of gradient or momentum. FTML The FTML optimizer. LBSGD The Large Batch SGD optimizer with momentum and weight decay. Ftrl The Ftrl optimizer.\n\n## The mxnet.lr_scheduler package¶\n\nThe base class LRScheduler defines the interface, while other classes implement various schemes to change the learning rate during training.\n\n LRScheduler Base class of a learning rate scheduler. FactorScheduler Reduce the learning rate by a factor for every n steps. MultiFactorScheduler Reduce the learning rate by given a list of steps.\n\n## Implement a new algorithm¶\n\nMost classes listed in this document are implemented in Python by using NDArray. So implementing new weight updating or initialization functions is straightforward.\n\nFor initializer, create a subclass of Initializer and define the _init_weight method. We can also change the default behaviors to initialize other parameters such as _init_bias. See initializer.py for examples.\n\nFor optimizer, create a subclass of Optimizer and implement two methods create_state and update. Also add @mx.optimizer.Optimizer.register before this class. See optimizer.py for examples.\n\nFor lr_scheduler, create a subclass of LRScheduler and then implement the __call__ method. See lr_scheduler.py for examples.\n\n## API Reference¶\n\nOptimizer API of MXNet.\n\nclass mxnet.optimizer.AdaDelta(rho=0.9, epsilon=1e-05, **kwargs)[source]\n\nThis optimizer updates each weight by:\n\ngrad = clip(grad * rescale_grad + wd * weight, clip_gradient)\nacc_delta = rho * acc_delta + (1. - rho) * delta * delta\nweight -= (delta + wd * weight)\n\n\nThis optimizer accepts the following parameters in addition to those accepted by Optimizer.\n\nParameters: rho (float) – Decay rate for both squared gradients and delta. epsilon (float) – Small value to avoid division by 0.\nclass mxnet.optimizer.AdaGrad(eps=1e-07, **kwargs)[source]\n\nThis optimizer updates each weight by:\n\ngrad = clip(grad * rescale_grad, clip_gradient)\ndiv = grad / sqrt(history + float_stable_eps)\nweight += (div + weight * wd) * -lr\n\n\nThis optimizer accepts the following parameters in addition to those accepted by Optimizer.\n\nParameters: eps (float, optional) – Initial value of the history accumulator. Avoids division by 0.\nclass mxnet.optimizer.Adam(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, lazy_update=True, **kwargs)[source]\n\nThis class implements the optimizer described in Adam: A Method for Stochastic Optimization, available at http://arxiv.org/abs/1412.6980.\n\nIf the storage types of grad is row_sparse, and lazy_update is True, lazy updates are applied by:\n\nfor row in grad.indices:\nm[row] = beta1 * m[row] + (1 - beta1) * rescaled_grad[row]\nv[row] = beta2 * v[row] + (1 - beta2) * (rescaled_grad[row]**2)\nw[row] = w[row] - learning_rate * m[row] / (sqrt(v[row]) + epsilon)\n\n\nThe lazy update only updates the mean and var for the weights whose row_sparse gradient indices appear in the current batch, rather than updating it for all indices. Compared with the original update, it can provide large improvements in model training throughput for some applications. However, it provides slightly different semantics than the original update, and may lead to different empirical results.\n\nOtherwise, standard updates are applied by:\n\nrescaled_grad = clip(grad * rescale_grad + wd * weight, clip_gradient)\nm = beta1 * m + (1 - beta1) * rescaled_grad\nv = beta2 * v + (1 - beta2) * (rescaled_grad**2)\nw = w - learning_rate * m / (sqrt(v) + epsilon)\n\n\nThis optimizer accepts the following parameters in addition to those accepted by Optimizer.\n\nFor details of the update algorithm, see adam_update.\n\nParameters: beta1 (float, optional) – Exponential decay rate for the first moment estimates. beta2 (float, optional) – Exponential decay rate for the second moment estimates. epsilon (float, optional) – Small value to avoid division by 0. lazy_update (bool, optional) – Default is True. If True, lazy updates are applied if the storage types of weight and grad are both row_sparse.\nclass mxnet.optimizer.Adamax(learning_rate=0.002, beta1=0.9, beta2=0.999, **kwargs)[source]\n\nIt is a variant of Adam based on the infinity norm available at http://arxiv.org/abs/1412.6980 Section 7.\n\nThe optimizer updates the weight by:\n\ngrad = clip(grad * rescale_grad + wd * weight, clip_gradient)\nm = beta1 * m_t + (1 - beta1) * grad\nu = maximum(beta2 * u, abs(grad))\nweight -= lr / (1 - beta1**t) * m / u\n\n\nThis optimizer accepts the following parameters in addition to those accepted by Optimizer.\n\nParameters: beta1 (float, optional) – Exponential decay rate for the first moment estimates. beta2 (float, optional) – Exponential decay rate for the second moment estimates.\nclass mxnet.optimizer.DCASGD(momentum=0.0, lamda=0.04, **kwargs)[source]\n\nThe DCASGD optimizer.\n\nThis class implements the optimizer described in Asynchronous Stochastic Gradient Descent with Delay Compensation for Distributed Deep Learning, available at https://arxiv.org/abs/1609.08326.\n\nThis optimizer accepts the following parameters in addition to those accepted by Optimizer.\n\nParameters: momentum (float, optional) – The momentum value. lamda (float, optional) – Scale DC value.\nclass mxnet.optimizer.FTML(beta1=0.6, beta2=0.999, epsilon=1e-08, **kwargs)[source]\n\nThe FTML optimizer.\n\nThis class implements the optimizer described in FTML - Follow the Moving Leader in Deep Learning, available at http://proceedings.mlr.press/v70/zheng17a/zheng17a.pdf.\n\nDenote time step by t. The optimizer updates the weight by:\n\nrescaled_grad = clip(grad * rescale_grad + wd * weight, clip_gradient)\nv = beta2 * v + (1 - beta2) * square(rescaled_grad)\nd_t = (1 - power(beta1, t)) / lr * square_root(v / (1 - power(beta2, t))) + epsilon)\nz = beta1 * z + (1 - beta1) * rescaled_grad - (d_t - beta1 * d_(t-1)) * weight\nweight = - z / d_t\n\n\nThis optimizer accepts the following parameters in addition to those accepted by Optimizer.\n\nParameters: beta1 (float, optional) – 0 < beta1 < 1. Generally close to 0.5. beta2 (float, optional) – 0 < beta2 < 1. Generally close to 1. epsilon (float, optional) – Small value to avoid division by 0.\nclass mxnet.optimizer.Ftrl(lamda1=0.01, learning_rate=0.1, beta=1, **kwargs)[source]\n\nThe Ftrl optimizer.\n\nReferenced from Ad Click Prediction: a View from the Trenches, available at http://dl.acm.org/citation.cfm?id=2488200.\n\neta :\n$\\eta_{t,i} = \\frac{learningrate}{\\beta+\\sqrt{\\sum_{s=1}^tg_{s,i}^2}}$\n\nThe optimizer updates the weight by:\n\nrescaled_grad = clip(grad * rescale_grad, clip_gradient)\nz += rescaled_grad - (sqrt(n + rescaled_grad**2) - sqrt(n)) * weight / learning_rate\nw = (sign(z) * lamda1 - z) / ((beta + sqrt(n)) / learning_rate + wd) * (abs(z) > lamda1)\n\n\nIf the storage types of weight, state and grad are all row_sparse, sparse updates are applied by:\n\nfor row in grad.indices:\nz[row] += rescaled_grad[row] - (sqrt(n[row] + rescaled_grad[row]**2) - sqrt(n[row])) * weight[row] / learning_rate\nw[row] = (sign(z[row]) * lamda1 - z[row]) / ((beta + sqrt(n[row])) / learning_rate + wd) * (abs(z[row]) > lamda1)\n\n\nThe sparse update only updates the z and n for the weights whose row_sparse gradient indices appear in the current batch, rather than updating it for all indices. Compared with the original update, it can provide large improvements in model training throughput for some applications. However, it provides slightly different semantics than the original update, and may lead to different empirical results.\n\nFor details of the update algorithm, see ftrl_update.\n\nThis optimizer accepts the following parameters in addition to those accepted by Optimizer.\n\nParameters: lamda1 (float, optional) – L1 regularization coefficient. learning_rate (float, optional) – The initial learning rate. beta (float, optional) – Per-coordinate learning rate correlation parameter.\nclass mxnet.optimizer.LBSGD(momentum=0.0, multi_precision=False, warmup_strategy='linear', warmup_epochs=5, batch_scale=1, updates_per_epoch=32, begin_epoch=0, num_epochs=60, **kwargs)[source]\n\nThe Large Batch SGD optimizer with momentum and weight decay.\n\nThe optimizer updates the weight by:\n\nstate = momentum * state + lr * rescale_grad * clip(grad, clip_gradient) + wd * weight\nweight = weight - state\n\n\nFor details of the update algorithm see lbsgd_update and lbsgd_mom_update.\n\nThis optimizer accepts the following parameters in addition to those accepted by Optimizer.\n\nParameters: momentum (float, optional) – The momentum value. multi_precision (bool, optional) – Flag to control the internal precision of the optimizer.: False: results in using the same precision as the weights (default), True: makes internal 32-bit copy of the weights and applies gradients in 32-bit precision even if actual weights used in the model have lower precision. Turning this on can improve convergence and accuracy when training with float16. warmup_strategy (string ('linear', 'power2', 'sqrt'. , 'lars' default : 'linear')) – warmup_epochs (unsigned, default: 5) – batch_scale (unsigned, default: 1 (same as batch size*numworkers)) – updates_per_epoch (updates_per_epoch (default: 32, Default might not reflect true number batches per epoch. Used for warmup.)) – begin_epoch (unsigned, default 0, starting epoch.) –\nclass mxnet.optimizer.NAG(momentum=0.0, **kwargs)[source]\n\nNesterov accelerated SGD.\n\nThis optimizer updates each weight by:\n\nstate = momentum * state + grad + wd * weight\nweight = weight - (lr * (grad + momentum * state))\n\nParameters: momentum (float, optional) – The momentum value. multi_precision (bool, optional) – Flag to control the internal precision of the optimizer.: False: results in using the same precision as the weights (default), True: makes internal 32-bit copy of the weights and applies gradients in 32-bit precision even if actual weights used in the model have lower precision. Turning this on can improve convergence and accuracy when training with float16. \nmxnet.optimizer.NDabs(data=None, out=None, name=None, **kwargs)\n\nReturns element-wise absolute value of the input.\n\nExample:\n\nabs([-2, 0, 3]) = [2, 0, 3]\n\n\nThe storage type of abs output depends upon the input storage type:\n\n• abs(default) = default\n• abs(row_sparse) = row_sparse\n• abs(csr) = csr\n\nDefined in src/operator/tensor/elemwise_unary_op_basic.cc:L662\n\nParameters: data (NDArray) – The input array. out (NDArray, optional) – The output NDArray to hold the result. out – The output of this function. NDArray or list of NDArrays\nclass mxnet.optimizer.Nadam(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, schedule_decay=0.004, **kwargs)[source]\n\nMuch like Adam is essentially RMSprop with momentum, Nadam is Adam RMSprop with Nesterov momentum available at http://cs229.stanford.edu/proj2015/054_report.pdf.\n\nThis optimizer accepts the following parameters in addition to those accepted by Optimizer.\n\nParameters: beta1 (float, optional) – Exponential decay rate for the first moment estimates. beta2 (float, optional) – Exponential decay rate for the second moment estimates. epsilon (float, optional) – Small value to avoid division by 0. schedule_decay (float, optional) – Exponential decay rate for the momentum schedule\nclass mxnet.optimizer.Optimizer(rescale_grad=1.0, param_idx2name=None, wd=0.0, clip_gradient=None, learning_rate=0.01, lr_scheduler=None, sym=None, begin_num_update=0, multi_precision=False, param_dict=None)[source]\n\nThe base class inherited by all optimizers.\n\nParameters: rescale_grad (float, optional) – Multiply the gradient with rescale_grad before updating. Often choose to be 1.0/batch_size. param_idx2name (dict from int to string, optional) – A dictionary that maps int index to string name. clip_gradient (float, optional) – Clip the gradient by projecting onto the box [-clip_gradient, clip_gradient]. learning_rate (float) – The initial learning rate. lr_scheduler (LRScheduler, optional) – The learning rate scheduler. wd (float, optional) – The weight decay (or L2 regularization) coefficient. Modifies objective by adding a penalty for having large weights. sym (Symbol, optional) – The Symbol this optimizer is applying to. begin_num_update (int, optional) – The initial number of updates. multi_precision (bool, optional) – Flag to control the internal precision of the optimizer.: False: results in using the same precision as the weights (default), True: makes internal 32-bit copy of the weights and applies gradients in 32-bit precision even if actual weights used in the model have lower precision. Turning this on can improve convergence and accuracy when training with float16. Properties – ---------- – learning_rate – The current learning rate of the optimizer. Given an Optimizer object optimizer, its learning rate can be accessed as optimizer.learning_rate.\nstatic create_optimizer(name, **kwargs)[source]\n\nInstantiates an optimizer with a given name and kwargs.\n\nNote\n\nWe can use the alias create for Optimizer.create_optimizer.\n\nParameters: name (str) – Name of the optimizer. Should be the name of a subclass of Optimizer. Case insensitive. kwargs (dict) – Parameters for the optimizer. An instantiated optimizer. Optimizer\n\nExamples\n\n>>> sgd = mx.optimizer.Optimizer.create_optimizer('sgd')\n>>> type(sgd)\n\n\ncreate_state(index, weight)[source]\n\nCreates auxiliary state for a given weight.\n\nSome optimizers require additional states, e.g. as momentum, in addition to gradients in order to update weights. This function creates state for a given weight which will be used in update. This function is called only once for each weight.\n\nParameters: index (int) – An unique index to identify the weight. weight (NDArray) – The weight. state – The state associated with the weight. any obj\ncreate_state_multi_precision(index, weight)[source]\n\nCreates auxiliary state for a given weight, including FP32 high precision copy if original weight is FP16.\n\nThis method is provided to perform automatic mixed precision training for optimizers that do not support it themselves.\n\nParameters: index (int) – An unique index to identify the weight. weight (NDArray) – The weight. state – The state associated with the weight. any obj\nstatic register(klass)[source]\n\nRegisters a new optimizer.\n\nOnce an optimizer is registered, we can create an instance of this optimizer with create_optimizer later.\n\nExamples\n\n>>> @mx.optimizer.Optimizer.register\n... class MyOptimizer(mx.optimizer.Optimizer):\n... pass\n>>> optim = mx.optimizer.Optimizer.create_optimizer('MyOptimizer')\n>>> print(type(optim))\n\n\nset_learning_rate(lr)[source]\n\nSets a new learning rate of the optimizer.\n\nParameters: lr (float) – The new learning rate of the optimizer.\nset_lr_mult(args_lr_mult)[source]\n\nSets an individual learning rate multiplier for each parameter.\n\nIf you specify a learning rate multiplier for a parameter, then the learning rate for the parameter will be set as the product of the global learning rate self.lr and its multiplier.\n\nNote\n\nThe default learning rate multiplier of a Variable can be set with lr_mult argument in the constructor.\n\nParameters: args_lr_mult (dict of str/int to float) – For each of its key-value entries, the learning rate multipler for the parameter specified in the key will be set as the given value. You can specify the parameter with either its name or its index. If you use the name, you should pass sym in the constructor, and the name you specified in the key of args_lr_mult should match the name of the parameter in sym. If you use the index, it should correspond to the index of the parameter used in the update method. Specifying a parameter by its index is only supported for backward compatibility, and we recommend to use the name instead.\nset_lr_scale(args_lrscale)[source]\n\n[DEPRECATED] Sets lr scale. Use set_lr_mult instead.\n\nset_wd_mult(args_wd_mult)[source]\n\nSets an individual weight decay multiplier for each parameter.\n\nBy default, if param_idx2name was provided in the constructor, the weight decay multipler is set as 0 for all parameters whose name don’t end with _weight or _gamma.\n\nNote\n\nThe default weight decay multiplier for a Variable can be set with its wd_mult argument in the constructor.\n\nParameters: args_wd_mult (dict of string/int to float) – For each of its key-value entries, the weight decay multipler for the parameter specified in the key will be set as the given value. You can specify the parameter with either its name or its index. If you use the name, you should pass sym in the constructor, and the name you specified in the key of args_lr_mult should match the name of the parameter in sym. If you use the index, it should correspond to the index of the parameter used in the update method. Specifying a parameter by its index is only supported for backward compatibility, and we recommend to use the name instead.\nupdate(index, weight, grad, state)[source]\n\nParameters: index (int) – The unique index of the parameter into the individual learning rates and weight decays. Learning rates and weight decay may be set via set_lr_mult() and set_wd_mult(), respectively. weight (NDArray) – The parameter to be updated. grad (NDArray) – The gradient of the objective with respect to this parameter. state (any obj) – The state returned by create_state().\nupdate_multi_precision(index, weight, grad, state)[source]\n\nUpdates the given parameter using the corresponding gradient and state. Mixed precision version.\n\nParameters: index (int) – The unique index of the parameter into the individual learning rates and weight decays. Learning rates and weight decay may be set via set_lr_mult() and set_wd_mult(), respectively. weight (NDArray) – The parameter to be updated. grad (NDArray) – The gradient of the objective with respect to this parameter. state (any obj) – The state returned by create_state().\nclass mxnet.optimizer.RMSProp(learning_rate=0.001, gamma1=0.9, gamma2=0.9, epsilon=1e-08, centered=False, clip_weights=None, **kwargs)[source]\n\nThe RMSProp optimizer.\n\nTwo versions of RMSProp are implemented:\n\nIf centered=False, we follow http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf by Tieleman & Hinton, 2012. For details of the update algorithm see rmsprop_update.\n\nIf centered=True, we follow http://arxiv.org/pdf/1308.0850v5.pdf (38)-(45) by Alex Graves, 2013. For details of the update algorithm see rmspropalex_update.\n\nThis optimizer accepts the following parameters in addition to those accepted by Optimizer.\n\nParameters: gamma1 (float, optional) – A decay factor of moving average over past squared gradient. gamma2 (float, optional) – A “momentum” factor. Only used if centered=True. epsilon (float, optional) – Small value to avoid division by 0. centered (bool, optional) – Flag to control which version of RMSProp to use.: True: will use Graves's version of RMSProp, False: will use Tieleman & Hinton's version of RMSProp. clip_weights (float, optional) – Clips weights into range [-clip_weights, clip_weights].\nclass mxnet.optimizer.SGD(momentum=0.0, lazy_update=True, **kwargs)[source]\n\nThe SGD optimizer with momentum and weight decay.\n\nIf the storage types of grad is row_sparse and lazy_update is True, lazy updates are applied by:\n\nfor row in grad.indices:\nstate[row] = momentum[row] * state[row] + rescaled_grad[row]\nweight[row] = weight[row] - state[row]\n\n\nThe sparse update only updates the momentum for the weights whose row_sparse gradient indices appear in the current batch, rather than updating it for all indices. Compared with the original update, it can provide large improvements in model training throughput for some applications. However, it provides slightly different semantics than the original update, and may lead to different empirical results.\n\nOtherwise, standard updates are applied by:\n\nrescaled_grad = lr * (rescale_grad * clip(grad, clip_gradient) + wd * weight)\nstate = momentum * state + rescaled_grad\nweight = weight - state\n\n\nFor details of the update algorithm see sgd_update and sgd_mom_update.\n\nThis optimizer accepts the following parameters in addition to those accepted by Optimizer.\n\nParameters: momentum (float, optional) – The momentum value. lazy_update (bool, optional) – Default is True. If True, lazy updates are applied if the storage types of weight and grad are both row_sparse. multi_precision (bool, optional) – Flag to control the internal precision of the optimizer.: False: results in using the same precision as the weights (default), True: makes internal 32-bit copy of the weights and applies gradients in 32-bit precision even if actual weights used in the model have lower precision. Turning this on can improve convergence and accuracy when training with float16. \nclass mxnet.optimizer.SGLD(**kwargs)[source]\n\nThis class implements the optimizer described in the paper Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex, available at https://papers.nips.cc/paper/4883-stochastic-gradient-riemannian-langevin-dynamics-on-the-probability-simplex.pdf.\n\nclass mxnet.optimizer.Signum(learning_rate=0.01, momentum=0.9, wd_lh=0.0, **kwargs)[source]\n\nThe Signum optimizer that takes the sign of gradient or momentum.\n\nThe optimizer updates the weight by:\n\nrescaled_grad = rescale_grad * clip(grad, clip_gradient) + wd * weight\nstate = momentum * state + (1-momentum)*rescaled_grad\nweight = (1 - lr * wd_lh) * weight - lr * sign(state)\n\n\nReferences\n\nJeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli & Anima Anandkumar. (2018). signSGD: Compressed Optimisation for Non-Convex Problems. In ICML‘18.\n\nFor details of the update algorithm see signsgd_update and signum_update.\n\nThis optimizer accepts the following parameters in addition to those accepted by Optimizer.\n\nParameters: momentum (float, optional) – The momentum value. wd_lh (float, optional) – The amount of decoupled weight decay regularization, see details in the original paper at:https://arxiv.org/abs/1711.05101\nclass mxnet.optimizer.Test(**kwargs)[source]\n\nThe Test optimizer\n\ncreate_state(index, weight)[source]\n\nCreates a state to duplicate weight.\n\nupdate(index, weight, grad, state)[source]\n\nclass mxnet.optimizer.Updater(optimizer)[source]\n\nUpdater for kvstore.\n\nget_states(dump_optimizer=False)[source]\n\nGets updater states.\n\nParameters: dump_optimizer (bool, default False) – Whether to also save the optimizer itself. This would also save optimizer information such as learning rate and weight decay schedules.\nset_states(states)[source]\n\nSets updater states.\n\nsync_state_context(state, context)[source]\n\nsync state context.\n\nclass mxnet.optimizer.ccSGD(*args, **kwargs)[source]\n\n[DEPRECATED] Same as SGD. Left here for backward compatibility.\n\nmxnet.optimizer.create(name, **kwargs)\n\nInstantiates an optimizer with a given name and kwargs.\n\nNote\n\nWe can use the alias create for Optimizer.create_optimizer.\n\nParameters: name (str) – Name of the optimizer. Should be the name of a subclass of Optimizer. Case insensitive. kwargs (dict) – Parameters for the optimizer. An instantiated optimizer. Optimizer\n\nExamples\n\n>>> sgd = mx.optimizer.Optimizer.create_optimizer('sgd')\n>>> type(sgd)\n\n\nmxnet.optimizer.get_updater(optimizer)[source]\n\nReturns a closure of the updater needed for kvstore.\n\nParameters: optimizer (Optimizer) – The optimizer. updater – The closure of the updater. function\nmxnet.optimizer.register(klass)\n\nRegisters a new optimizer.\n\nOnce an optimizer is registered, we can create an instance of this optimizer with create_optimizer later.\n\nExamples\n\n>>> @mx.optimizer.Optimizer.register\n... class MyOptimizer(mx.optimizer.Optimizer):\n... pass\n>>> optim = mx.optimizer.Optimizer.create_optimizer('MyOptimizer')\n>>> print(type(optim))\n\n\n\nScheduling learning rate.\n\nclass mxnet.lr_scheduler.LRScheduler(base_lr=0.01, warmup_steps=0, warmup_begin_lr=0, warmup_mode='linear')[source]\n\nBase class of a learning rate scheduler.\n\nA scheduler returns a new learning rate based on the number of updates that have been performed.\n\nParameters: base_lr (float, optional) – The initial learning rate. warmup_steps (int) – number of warmup steps used before this scheduler starts decay warmup_begin_lr (float) – if using warmup, the learning rate from which it starts warming up warmup_mode (string) – warmup can be done in two modes. ‘linear’ mode gradually increases lr with each step in equal increments ‘constant’ mode keeps lr at warmup_begin_lr for warmup_steps\nclass mxnet.lr_scheduler.FactorScheduler(step, factor=1, stop_factor_lr=1e-08, base_lr=0.01, warmup_steps=0, warmup_begin_lr=0, warmup_mode='linear')[source]\n\nReduce the learning rate by a factor for every n steps.\n\nIt returns a new learning rate by:\n\nbase_lr * pow(factor, floor(num_update/step))\n\nParameters: step (int) – Changes the learning rate for every n updates. factor (float, optional) – The factor to change the learning rate. stop_factor_lr (float, optional) – Stop updating the learning rate if it is less than this value.\nclass mxnet.lr_scheduler.MultiFactorScheduler(step, factor=1, base_lr=0.01, warmup_steps=0, warmup_begin_lr=0, warmup_mode='linear')[source]\n\nReduce the learning rate by given a list of steps.\n\nAssume there exists k such that:\n\nstep[k] <= num_update and num_update < step[k+1]\n\n\nThen calculate the new learning rate by:\n\nbase_lr * pow(factor, k+1)\n\nParameters: step (list of int) – The list of steps to schedule a change factor (float) – The factor to change the learning rate. warmup_steps (int) – number of warmup steps used before this scheduler starts decay warmup_begin_lr (float) – if using warmup, the learning rate from which it starts warming up warmup_mode (string) – warmup can be done in two modes. ‘linear’ mode gradually increases lr with each step in equal increments ‘constant’ mode keeps lr at warmup_begin_lr for warmup_steps\nclass mxnet.lr_scheduler.PolyScheduler(max_update, base_lr=0.01, pwr=2, final_lr=0, warmup_steps=0, warmup_begin_lr=0, warmup_mode='linear')[source]\n\nReduce the learning rate according to a polynomial of given power.\n\nCalculate the new learning rate, after warmup if any, by:\n\nfinal_lr + (start_lr - final_lr) * (1-nup/max_nup)^pwr\nif nup < max_nup, 0 otherwise.\n\nParameters: max_update (int) – maximum number of updates before the decay reaches final learning rate. base_lr (float) – base learning rate to start from pwr (int) – power of the decay term as a function of the current number of updates. final_lr (float) – final learning rate after all steps warmup_steps (int) – number of warmup steps used before this scheduler starts decay warmup_begin_lr (float) – if using warmup, the learning rate from which it starts warming up warmup_mode (string) – warmup can be done in two modes. ‘linear’ mode gradually increases lr with each step in equal increments ‘constant’ mode keeps lr at warmup_begin_lr for warmup_steps\nclass mxnet.lr_scheduler.CosineScheduler(max_update, base_lr=0.01, final_lr=0, warmup_steps=0, warmup_begin_lr=0, warmup_mode='linear')[source]\n\nReduce the learning rate according to a cosine function\n\nCalculate the new learning rate by:\n\nfinal_lr + (start_lr - final_lr) * (1+cos(pi * nup/max_nup))/2\nif nup < max_nup, 0 otherwise.\n\nParameters: max_update (int) – maximum number of updates before the decay reaches 0 base_lr (float) – base learning rate final_lr (float) – final learning rate after all steps warmup_steps (int) – number of warmup steps used before this scheduler starts decay warmup_begin_lr (float) – if using warmup, the learning rate from which it starts warming up warmup_mode (string) – warmup can be done in two modes. ‘linear’ mode gradually increases lr with each step in equal increments ‘constant’ mode keeps lr at warmup_begin_lr for warmup_steps\n\nWeight initializer.\n\nclass mxnet.initializer.InitDesc[source]\n\nDescriptor for the initialization pattern.\n\nParameters: name (str) – Name of variable. attrs (dict of str to str) – Attributes of this variable taken from Symbol.attr_dict. global_init (Initializer) – Global initializer to fallback to.\nclass mxnet.initializer.Initializer(**kwargs)[source]\n\nThe base class of an initializer.\n\nset_verbosity(verbose=False, print_func=None)[source]\n\nSwitch on/off verbose mode\n\nParameters: verbose (bool) – switch on/off verbose mode print_func (function) – A function that computes statistics of initialized arrays. Takes an NDArray and returns an str. Defaults to mean absolute value str((abs(x)/size(x)).asscalar()).\ndumps()[source]\n\nSaves the initializer to string\n\nReturns: JSON formatted string that describes the initializer. str\n\nExamples\n\n>>> # Create initializer and retrieve its parameters\n...\n>>> init = mx.init.Normal(0.5)\n>>> init.dumps()\n'[\"normal\", {\"sigma\": 0.5}]'\n>>> init = mx.init.Xavier(factor_type=\"in\", magnitude=2.34)\n>>> init.dumps()\n'[\"xavier\", {\"rnd_type\": \"uniform\", \"magnitude\": 2.34, \"factor_type\": \"in\"}]'\n\nmxnet.initializer.register(klass)[source]\n\nRegisters a custom initializer.\n\nCustom initializers can be created by extending mx.init.Initializer and implementing the required functions like _init_weight and _init_bias. The created initializer must be registered using mx.init.register before it can be called by name.\n\nParameters: klass (class) – A subclass of mx.init.Initializer that needs to be registered as a custom initializer.\n\nExample\n\n>>> # Create and register a custom initializer that\n... # initializes weights to 0.1 and biases to 1.\n...\n>>> @mx.init.register\n... @alias('myinit')\n... class CustomInit(mx.init.Initializer):\n... def __init__(self):\n... super(CustomInit, self).__init__()\n... def _init_weight(self, _, arr):\n... arr[:] = 0.1\n... def _init_bias(self, _, arr):\n... arr[:] = 1\n...\n>>> # Module is an instance of 'mxnet.module.Module'\n...\n>>> module.init_params(\"custominit\")\n>>> # module.init_params(\"myinit\")\n>>> # module.init_params(CustomInit())\n\nclass mxnet.initializer.Load(param, default_init=None, verbose=False)[source]\n\nNote Load will drop arg: or aux: from name and initialize the variables that match with the prefix dropped.\n\nParameters: param (str or dict of str->NDArray) – Parameter file or dict mapping name to NDArray. default_init (Initializer) – Default initializer when name is not found in param. verbose (bool) – Flag for enabling logging of source when initializing.\nclass mxnet.initializer.Mixed(patterns, initializers)[source]\n\nInitialize parameters using multiple initializers.\n\nParameters: patterns (list of str) – List of regular expressions matching parameter names. initializers (list of Initializer) – List of initializers corresponding to patterns.\n\nExample\n\n>>> # Given 'module', an instance of 'mxnet.module.Module', initialize biases to zero\n... # and every other parameter to random values with uniform distribution.\n...\n>>> init = mx.initializer.Mixed(['bias', '.*'], [mx.init.Zero(), mx.init.Uniform(0.1)])\n>>> module.init_params(init)\n>>>\n>>> for dictionary in module.get_params():\n... for key in dictionary:\n... print(key)\n... print(dictionary[key].asnumpy())\n...\nfullyconnected1_weight\n[[ 0.0097627 0.01856892 0.04303787]]\nfullyconnected1_bias\n[ 0.]\n\nclass mxnet.initializer.Zero[source]\n\nInitializes weights to zero.\n\nExample\n\n>>> # Given 'module', an instance of 'mxnet.module.Module', initialize weights to zero.\n...\n>>> init = mx.initializer.Zero()\n>>> module.init_params(init)\n>>> for dictionary in module.get_params():\n... for key in dictionary:\n... print(key)\n... print(dictionary[key].asnumpy())\n...\nfullyconnected0_weight\n[[ 0. 0. 0.]]\n\nclass mxnet.initializer.One[source]\n\nInitializes weights to one.\n\nExample\n\n>>> # Given 'module', an instance of 'mxnet.module.Module', initialize weights to one.\n...\n>>> init = mx.initializer.One()\n>>> module.init_params(init)\n>>> for dictionary in module.get_params():\n... for key in dictionary:\n... print(key)\n... print(dictionary[key].asnumpy())\n...\nfullyconnected0_weight\n[[ 1. 1. 1.]]\n\nclass mxnet.initializer.Constant(value)[source]\n\nInitializes the weights to a given value. The value passed in can be a scalar or a NDarray that matches the shape of the parameter to be set.\n\nParameters: value (float, NDArray) – Value to set.\nclass mxnet.initializer.Uniform(scale=0.07)[source]\n\nInitializes weights with random values uniformly sampled from a given range.\n\nParameters: scale (float, optional) – The bound on the range of the generated random values. Values are generated from the range [-scale, scale]. Default scale is 0.07.\n\nExample\n\n>>> # Given 'module', an instance of 'mxnet.module.Module', initialize weights\n>>> # to random values uniformly sampled between -0.1 and 0.1.\n...\n>>> init = mx.init.Uniform(0.1)\n>>> module.init_params(init)\n>>> for dictionary in module.get_params():\n... for key in dictionary:\n... print(key)\n... print(dictionary[key].asnumpy())\n...\nfullyconnected0_weight\n[[ 0.01360891 -0.02144304 0.08511933]]\n\nclass mxnet.initializer.Normal(sigma=0.01)[source]\n\nInitializes weights with random values sampled from a normal distribution with a mean of zero and standard deviation of sigma.\n\nParameters: sigma (float, optional) – Standard deviation of the normal distribution. Default standard deviation is 0.01.\n\nExample\n\n>>> # Given 'module', an instance of 'mxnet.module.Module', initialize weights\n>>> # to random values sampled from a normal distribution.\n...\n>>> init = mx.init.Normal(0.5)\n>>> module.init_params(init)\n>>> for dictionary in module.get_params():\n... for key in dictionary:\n... print(key)\n... print(dictionary[key].asnumpy())\n...\nfullyconnected0_weight\n[[-0.3214761 -0.12660924 0.53789419]]\n\nclass mxnet.initializer.Orthogonal(scale=1.414, rand_type='uniform')[source]\n\nInitialize weight as orthogonal matrix.\n\nThis initializer implements Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, available at https://arxiv.org/abs/1312.6120.\n\nParameters: scale (float optional) – Scaling factor of weight. rand_type (string optional) – Use “uniform” or “normal” random number to initialize weight.\nclass mxnet.initializer.Xavier(rnd_type='uniform', factor_type='avg', magnitude=3)[source]\n\nReturns an initializer performing “Xavier” initialization for weights.\n\nThis initializer is designed to keep the scale of gradients roughly the same in all layers.\n\nBy default, rnd_type is 'uniform' and factor_type is 'avg', the initializer fills the weights with random numbers in the range of $$[-c, c]$$, where $$c = \\sqrt{\\frac{3.}{0.5 * (n_{in} + n_{out})}}$$. $$n_{in}$$ is the number of neurons feeding into weights, and $$n_{out}$$ is the number of neurons the result is fed to.\n\nIf rnd_type is 'uniform' and factor_type is 'in', the $$c = \\sqrt{\\frac{3.}{n_{in}}}$$. Similarly when factor_type is 'out', the $$c = \\sqrt{\\frac{3.}{n_{out}}}$$.\n\nIf rnd_type is 'gaussian' and factor_type is 'avg', the initializer fills the weights with numbers from normal distribution with a standard deviation of $$\\sqrt{\\frac{3.}{0.5 * (n_{in} + n_{out})}}$$.\n\nParameters: rnd_type (str, optional) – Random generator type, can be 'gaussian' or 'uniform'. factor_type (str, optional) – Can be 'avg', 'in', or 'out'. magnitude (float, optional) – Scale of random number.\nclass mxnet.initializer.MSRAPrelu(factor_type='avg', slope=0.25)[source]\n\nInitialize the weight according to a MSRA paper.\n\nThis initializer implements Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, available at https://arxiv.org/abs/1502.01852.\n\nThis initializer is proposed for initialization related to ReLu activation, it maked some changes on top of Xavier method.\n\nParameters: factor_type (str, optional) – Can be 'avg', 'in', or 'out'. slope (float, optional) – initial slope of any PReLU (or similar) nonlinearities.\nclass mxnet.initializer.Bilinear[source]\n\nInitialize weight for upsampling layers.\n\nclass mxnet.initializer.LSTMBias(forget_bias=1.0)[source]\n\nInitialize all biases of an LSTMCell to 0.0 except for the forget gate whose bias is set to custom value.\n\nParameters: forget_bias (float, default 1.0) – bias for the forget gate. Jozefowicz et al. 2015 recommends setting this to 1.0.\nclass mxnet.initializer.FusedRNN(init, num_hidden, num_layers, mode, bidirectional=False, forget_bias=1.0)[source]\n\nInitialize parameters for fused rnn layers.\n\nParameters: init (Initializer) – initializer applied to unpacked weights. Fall back to global initializer if None. num_hidden (int) – should be the same with arguments passed to FusedRNNCell. num_layers (int) – should be the same with arguments passed to FusedRNNCell. mode (str) – should be the same with arguments passed to FusedRNNCell. bidirectional (bool) – should be the same with arguments passed to FusedRNNCell. forget_bias (float) – should be the same with arguments passed to FusedRNNCell." ]
[ null ]
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https://www.toppr.com/guides/maths/understanding-elementary-shapes/types-of-angles/
[ "", null, "# Types of Angles\n\nAngle is the base of geometry, it is impossible for anyone to understand geometry without learning about angles. Angle is formed at an intersection of two rays. An Angle is the figure formed by two rays, called the sides of the angle, sharing a common endpoint, called the vertex of the angle. Angle is not just the interception of plane rays, even curved rays intersection point can be measured via angle.\n\nTable of content\n\n### Suggested Videos", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "## Angles and Types of Angles\n\nAngle is one of the most important and perhaps the most basic chapters of geometry. Learning this chapter to the core and elaborately lets you understand the foundation of the subject. An angle constitutes two components, side, and the vertex. There are many ways of an intersection of sides to form a vertex. The following section throws light on the various types of angles in detail:\n\n## Types of Angles\n\n### 1. Acute Angle\n\nAn angle which is less than 90 ° is known as an acute angle. Acute angle measures between 0 to 90 °. The following figure shows that when two sides A and B intersect at point O and form a vertex. The angle at which these rays intersects is less than 90° thus forms an acute angle.", null, "### 2. Obtuse Angle\n\nAn angle which is greater than 90 ° is known as an obtuse angle. An obtuse angle measure between 90 ° to 180 °. An obtuse angle is an opposite angle of an acute angle. The following figure shows that point O where A and B intersects is greater than 90° and less than 180°, thus it is an obtuse angle.", null, "### 3. Right Angle\n\nThe right angle is an angle which measures exactly 90 °and any angle less or greater than 90 °will not be considered as a right angle. The following figure illustrates that A and B intersect to form a 90 ° angle.", null, "### 4. Straight Angle\n\nAn angle which measures 180 ° is known as a straight angle. This looks similar to a plane straight line and hence is named as a straight angle. The following figure will clear the concept.", null, "### 5. Reflex Angle\n\nAn angle which measures greater than 180 degrees but less than 360 degrees is known as a reflex angle. A reflex angle is supposed to be a complementary angle for acute angle and is on the other side of the line. The following figure illustrates the reflex angle.", null, "Geometry finds its basis in angles. From basic closed shapes to tricky trigonometry questions, angles form a part of each and every chapter. Understanding these surely helps in perfecting knowledge of geometry and trigonometry both.\n\n## Solved Example for You\n\nQuestion 1: The figure shown below is of a straight angle, ∠AOB. Find the missing angle in the given figure:", null, "Answer : ∠AOB is a straight angle, that is ∠AOB = 180°\n∠AOB = ∠AOC +∠COB\n180 = ?+55\n? = 180-55\n? = 125 °\nThe missing angle in the above figure: 125°\n\nQuestion 2: What are the types of triangles with respect to the angles?\n\nAnswer: When we look at the different types of triangles on the basis of angles, they are of 5 types. They are Right Triangle, Obtuse Triangle, Acute Triangle, and Equiangular Triangle.\n\nQuestion 3: What is an Equilateral Triangle?\n\nAnswer: An equilateral triangle is a kind of triangle which comprises of 3 equal sides and equal angles. Moreover, each side of this triangle is of the same length and every angle is going to be of the same measure which is 60°.\n\nQuestion 4: Give the definition of a triangle.\n\nAnswer: A triangle is a three-sided polygon which is a close figure consisting of 3 lines that link at the endpoint to each other. The major properties of a triangle are that they are the sum of the interior angle of the triangle is going to be 180°. Similarly, the sum of the exterior angles is always going to be 360°. Further, we can classify triangles on the basis of their sides and interior angles.\n\nQuestion 5: Define an obtuse triangle.\n\nAnswer: An obtuse triangle is one having two acute angles and one obtuse angle. You must know that an acute angle is ones which measure less than 90° while the obtuse angle one is one which measures over 90°. Thus, an obtuse triangle refers to a combination of 2 acute angles and 1 obtuse angle.\n\nShare with friends\nBrowse\n##### Understanding Elementary Shapes\nCustomize your course in 30 seconds\n\nWhich class are you in?\n\n5th\n6th\n7th\n8th\n9th\n10th\n11th\n12th\nGet ready for all-new Live Classes!\nNow learn Live with India's best teachers. Join courses with the best schedule and enjoy fun and interactive classes.", null, "", null, "Ashhar Firdausi\nIIT Roorkee\nBiology", null, "", null, "Dr. Nazma Shaik\nVTU\nChemistry", null, "", null, "Gaurav Tiwari\nAPJAKTU\nPhysics\nGet Started\nBrowse\n##### Understanding Elementary Shapes\nCustomize your course in 30 seconds\n\nWhich class are you in?\n\nNo thanks." ]
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https://dlmf.nist.gov/search/search?q=with%20respect%20to%20integration
[ "# with respect to integration\n\n(0.003 seconds)\n\n## 1—10 of 36 matching pages\n\n##### 3: 3.11 Approximation Techniques\nThey enjoy an orthogonal property with respect to integrals: …\n##### 4: 36.2 Catastrophes and Canonical Integrals\nwith the contour passing to the lower right of $u=0$. …with the contour passing to the upper right of $u=0$. … $\\Psi_{1}$ is related to the Airy function (§9.2): … … Addendum: For further special cases see §36.2(iv)\n##### 5: 20.10 Integrals\n###### §20.10(i) Mellin Transforms with respectto the Lattice Parameter\n20.10.1 $\\int_{0}^{\\infty}x^{s-1}\\theta_{2}\\left(0\\middle|ix^{2}\\right)\\mathrm{d}x=2^{s% }(1-2^{-s})\\pi^{-s/2}\\Gamma\\left(\\tfrac{1}{2}s\\right)\\zeta\\left(s\\right),$\n20.10.2 $\\int_{0}^{\\infty}x^{s-1}(\\theta_{3}\\left(0\\middle|ix^{2}\\right)-1)\\mathrm{d}x=% \\pi^{-s/2}\\Gamma\\left(\\tfrac{1}{2}s\\right)\\zeta\\left(s\\right),$\n20.10.3 $\\int_{0}^{\\infty}x^{s-1}(1-\\theta_{4}\\left(0\\middle|ix^{2}\\right))\\mathrm{d}x=% (1-2^{1-s})\\pi^{-s/2}\\Gamma\\left(\\tfrac{1}{2}s\\right)\\zeta\\left(s\\right).$\n##### 6: 7.7 Integral Representations\nIn (7.7.13) and (7.7.14) the integration paths are straight lines, $\\zeta=\\frac{1}{16}\\pi^{2}z^{4}$, and $c$ is a constant such that $0 in (7.7.13), and $0 in (7.7.14). …\n##### 7: 2.8 Differential Equations with a Parameter\ndots denoting differentiations with respect to $\\xi$. … The expansions (2.8.11) and (2.8.12) are both uniform and differentiable with respect to $\\xi$. … The expansions (2.8.15) and (2.8.16) are both uniform and differentiable with respect to $\\xi$. … The expansions (2.8.25) and (2.8.26) are both uniform and differentiable with respect to $\\xi$. … The expansions (2.8.29) and (2.8.30) are both uniform and differentiable with respect to $\\xi$. …\n##### 8: 2.3 Integrals of a Real Variable\n###### §2.3(i) Integration by Parts\n(In other words, differentiation of (2.3.8) with respect to the parameter $\\lambda$ (or $\\mu$) is legitimate.) … derives from the neighborhood of the minimum of $p(t)$ in the integration range. … In consequence, the approximation is nonuniform with respect to $\\alpha$ and deteriorates severely as $\\alpha\\to 0$. A uniform approximation can be constructed by quadratic change of integration variable: …\n##### 9: 4.37 Inverse Hyperbolic Functions\nElsewhere on the integration paths in (4.37.1) and (4.37.2) the branches are determined by continuity. In (4.37.3) the integration path may not intersect $\\pm 1$. … The principal values (or principal branches) of the inverse $\\sinh$, $\\cosh$, and $\\tanh$ are obtained by introducing cuts in the $z$-plane as indicated in Figure 4.37.1(i)-(iii), and requiring the integration paths in (4.37.1)–(4.37.3) not to cross these cuts. … These functions are analytic in the cut plane depicted in Figure 4.37.1(iv), (v), (vi), respectively. … are respectively given by …\n##### 10: 2.4 Contour Integrals\nThen by integration by parts the integral … The most successful results are obtained on moving the integration contour as far to the left as possible. …\n• (c)\n\nExcluding $t=a$, $\\Re\\left(e^{i\\theta}p(t)-e^{i\\theta}p(a)\\right)$ is positive when $t\\in\\mathscr{P}$, and is bounded away from zero uniformly with respect to $\\theta\\in[\\theta_{1},\\theta_{2}]$ as $t\\to b$ along $\\mathscr{P}$.\n\n• The problem of obtaining an asymptotic approximation to $I(\\alpha,z)$ that is uniform with respect to $\\alpha$ in a region containing $\\widehat{\\alpha}$ is similar to the problem of a coalescing endpoint and saddle point outlined in §2.3(v). The change of integration variable is given by …" ]
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https://www.manniwood.com/2020_04_26/asm_args_env.html
[ "## Args and Env Vars in Assembler\n\n26 April 2020\n\nWhere do a Linux program's arguments and environmental variables come from? That is, where are they in the process's memory when the process starts up?\n\nThere is a document called System V Application Binary Interface AMD64 Architecture Processor Supplement that shows what a Linux program's memory looks like when it starts up.\n\nIt turns out that when a Linux process starts up, its stack is not empty! In fact, the stack has pointers to all of the arguments and environmental variables, like so:\n\n``` top of memory (highest addresses)\n\"bottom\" of stack (stack grows down towards lower addresses)\n┌───────────────────────────────────────────────────────────┐\n│ │\n\nother stuff\n\n│ │\n├───────────────────────────────────────────────────────────┤\n│ 64-bit zero │\n├───────────────────────────────────────────────────────────┤\n│ pointer to env var N (address of first char of env var N) │\n├───────────────────────────────────────────────────────────┤\n...\n├───────────────────────────────────────────────────────────┤\n│ pointer to env var 3 (address of first char of env var 3) │\n├───────────────────────────────────────────────────────────┤\n│ pointer to env var 2 (address of first char of env var 2) │\n├───────────────────────────────────────────────────────────┤\n│ pointer to env var 1 (address of first char of env var 1) │\n├───────────────────────────────────────────────────────────┤\n│ 64-bit zero │\n├───────────────────────────────────────────────────────────┤\n│ pointer to arg N (address of first char of arg N) │\n├───────────────────────────────────────────────────────────┤\n...\n├───────────────────────────────────────────────────────────┤\n│ pointer to arg 3 (address of first char of arg 3) │\n├───────────────────────────────────────────────────────────┤\n│ pointer to arg 2 (address of first char of arg 2) │\n├───────────────────────────────────────────────────────────┤\n│ pointer to arg 1 (address of first char of arg 1) │\n├───────────────────────────────────────────────────────────┤\n│ pointer to program name / arg 0 (address of first char) │\n├───────────────────────────────────────────────────────────┤\n│ number of args (as a 64-bit integer) │\n└───────────────────────────────────────────────────────────┘\n\"top\" of stack (stack grows down towards lower addresses)\n```\n\nOf course, any modern programming language has access to these arguments and environmental variables. In Go, you get the arguments through `os.Args`, and the environmental variables through `os.GetEnv(\"SOME_VAR\")`.\n\nI even learned that in C, the `main` function can have this signature to get access to the environmental variables!\n\n```int main(int argc, char *argv[], char * envp[])\n```\n\nOf course, if we really want to prove that the stack is laid out this way at program startup, we could always use a small assembler program to prove it.\n\nHere's a Linux/amd64 assembler program that does just that.\n\nBuild the program like so:\n\n```\\$ as --gstabs args_and_env.s -o args_and_env.o\n\\$ ld args_and_env.o -o args_and_env\n```\n\nAnd the code itself (with a legend for the Linux/amd64 calling conventions, which I can never remember):\n\n```# LEGEND\n# ------\n# Linux/amd64 C calling convention:\n# args: RDI, RSI, RDX, RCX, R8, R9\n# return value in RAX\n# destroyed registers: all args! (RDI, RSI, RDX, RCX, R8, R9)\n#\n# Linux/amd64 Syscall convention:\n# syscall number in RAX\n# args: RDI, RSI, RDX, R10, R8, R9\n# syscall return value in RAX\n# destroyed registers: RCX and R11\n\n.section .data\n\nnewline:\n.ascii \"\\n\\0\"\n\nprogram_label:\n.ascii \"Program: \\0\"\n\narg_label:\n.ascii \"Arg: \\0\"\n\nenv_label:\n.ascii \"Env: \\0\"\n\n.section .text\n.globl _start\n_start:\n\n# Save the stack pointer to the base pointer so that if we have to push/pop\n# things from the stack in _start, we can still access the args and env vars\n# relative %rbp\nmovq %rsp, %rbp\n\n# Print the word \"Program: \" with no newline\nmovq \\$program_label, %rdi\ncall print_string\n\n# 8(%rsp), the second item from the top the stack, contains a pointer to the program name\nmovq 8(%rbp), %rdi\ncall print_string\nmovq \\$newline, %rdi\ncall print_string\n\n# we go 1 quad word into the stack because the first quad word is ARGV and the second QUAD\n# is a pointer to the program name. Quad words before that (deeper in the stack)\n# are pointers to args, terminated by a quad word that is zero.\nmovq \\$1, %rsi\narg_loop:\n# Increment %rsi so that when we can point one element deeper in the stack (the pointer to the next arg)\n# Even on the first loop we can do this, because the first pointer on the stack is the program name,\n# which we want to skip over to get either the pointer to the first arg, or a null pointer if there are no args\n# and we have hit the end of the arg list.\nincq %rsi\n# Get the address that is offset 8 bytes * %rsi from the top of the stack (%rbp);\n# That is, get the pointer to the next arg (or a null pointer, terminating the args).\nmovq (%rbp, %rsi, 8), %rdi\n# Is this a null pointer (is %rdi zero)?\ncmpq \\$0, %rdi\n# If it is a null pointer, we are done; go on to print env vars.\nje env_loop\n# If we get this far, %rdi has a pointer to the beginning of an arg.\n# A C-style func is allowed to clobber %rsi and %rdi, so push them both\n# onto the stack to save their current values for use later.\npushq %rsi\npushq %rdi\nmovq \\$arg_label, %rdi\ncall print_string\n# Get the saved value of %rdi (pointer to an arg) from the stack and put it back into %rdi\n# so that the next call to print_string prints the arg.\npopq %rdi\ncall print_string\nmovq \\$newline, %rdi\ncall print_string\n# Now that our C-style function calls that have clobbered %rsi\n# are done, pop the saved value of %rsi back into %rsi.\npopq %rsi\n# Note that %rdi has likely been clobbered again, but we don't care because we are\n# going to overwrite it anyway at the top of this loop.\njmp arg_loop\n\nenv_loop:\n# Increment %rsi so that when we can point one element deeper in the stack (the pointer to the next arg)\n# When we first enter this loop, %rsi would be the null pointer delineating the end of args,\n# so we would want to advance past that.\nincq %rsi\n# Get the address that is offset 8 bytes * %rsi from the top of the stack (%rsp);\n# That is, get the pointer to the next env var (or a null pointer, terminating the env vars).\nmovq (%rbp, %rsi, 8), %rdi\n# Is this a null pointer (is %rdi zero)?\ncmpq \\$0, %rdi\n# If it is a null pointer, we are done; quit the program.\nje quit_program\n\n# If we get this far, %rdi has a pointer to the beginning of an env var.\n# A C-style func is allowed to clobber %rsi and %rdi, so push them both\n# onto the stack to save their current values for use later.\npushq %rsi\npushq %rdi\nmovq \\$env_label, %rdi\ncall print_string\n# Get the saved value of %rdi (pointer to an env var) from the stack and put it back into %rdi\n# so that the next call to print_string prints the env var.\npopq %rdi\ncall print_string\nmovq \\$newline, %rdi\ncall print_string\n# Now that our C-style function calls that have clobbered %rsi\n# are done, pop the saved value of %rsi back into %rsi.\npopq %rsi\n# Note that %rdi has likely been clobbered again, but we don't care because we are\n# going to overwrite it anyway at the top of this loop.\njmp env_loop\n\nquit_program:\nmovq \\$60, %rax # 60 is the exit syscall\nmovq \\$0, %rdi # 0 is the status number we will return, first arg to exit syscall\nsyscall\n\n.type strlen, @function\nstrlen:\npushq %rbp # save the old base pointer\nmovq %rsp, %rbp # make the stack pointer the base pointer\n\n# start the accumulator (and, usefully, our return register) at 0\n# We will accumulate the byte count in %rax.\nmovq \\$0, %rax\n\n# zero out %rcx before we use it. We will be storing each byte we read\n# into %cl, the lowest byte of the 8-byte %rcx register.\nmovq \\$0, %rcx\n\n# arg1 is rdi, which is the address of the first byte of the null-terminated string.\nmovb (%rdi), %cl # load the first byte of the string into \\$cl\n\ncmpb \\$0, %cl\nje end_strlen\n\nstrlen_loop:\n# add one to the byte count\nincq %rax\n\n# move our pointer one byte ahead as well\nincq %rdi\n\n# load the pointed-to byte of the string into %cl\nmovb (%rdi), %cl\n\n# check to see if the pointed-to byte is zero\ncmpb \\$0, %cl\n\nje end_strlen\n\n# if the byte was not zero, jump to the top of the loop again\njmp strlen_loop\n\nend_strlen:\nleave\nret\n\n.type print_string, @function\nprint_string:\npushq %rbp # save the old base pointer\nmovq %rsp, %rbp # make the stack pointer the base pointer\n\n# %rdi has the pointer to the first byte of our string.\n# Save it on stack just in case strlen modifies it.\npushq %rdi\n\n# Get the length of the string we want to print.\n# of the string we need to get the length of, so we can call strlen\n# without even having to set up %rdi again.\ncall strlen\n\n# After the call to strlen, %rax has the length of the string.\n# It is the 4th arg to the write system call, going into %rsi.\nmovq %rax, %rdx\n\n# After the call to strlen, %rdi may have been modified, so pop\n# its original value back off the stack but put it into %rsi, the third\n# argument to the write system call (pointer to first char of string).\npopq %rsi\n\nmovq \\$1, %rax # 1 is the write system call\nmovq \\$1, %rdi # 1 is the stdout filehandle, first arg to write syscall\n# %rsi already contains pointer to first char of string\n# %rdx already contains the length of the string we want to print\nsyscall\n\n# return 0 from this function (could also consider returning bytes written)\nmovq \\$0, %rax\n\nleave\nret\n```\n\nHere's what a sample run looks like:\n\n```\\$ ./args_and_env foo bar baz\nProgram: ./args_and_env\nArg: foo\nArg: bar\nArg: baz\nEnv: CLUTTER_IM_MODULE=xim\nEnv: LS_COLORS=di=34:fi=0:ln=35:pi=31:so=31:bd=31:cd=31:or=41:mi=41:ex=32" ]
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https://www.colorhexa.com/1b949a
[ "# #1b949a Color Information\n\nIn a RGB color space, hex #1b949a is composed of 10.6% red, 58% green and 60.4% blue. Whereas in a CMYK color space, it is composed of 82.5% cyan, 3.9% magenta, 0% yellow and 39.6% black. It has a hue angle of 182.8 degrees, a saturation of 70.2% and a lightness of 35.5%. #1b949a color hex could be obtained by blending #36ffff with #002935. Closest websafe color is: #339999.\n\n• R 11\n• G 58\n• B 60\nRGB color chart\n• C 82\n• M 4\n• Y 0\n• K 40\nCMYK color chart\n\n#1b949a color description : Dark cyan.\n\n# #1b949a Color Conversion\n\nThe hexadecimal color #1b949a has RGB values of R:27, G:148, B:154 and CMYK values of C:0.82, M:0.04, Y:0, K:0.4. Its decimal value is 1807514.\n\nHex triplet RGB Decimal 1b949a `#1b949a` 27, 148, 154 `rgb(27,148,154)` 10.6, 58, 60.4 `rgb(10.6%,58%,60.4%)` 82, 4, 0, 40 182.8°, 70.2, 35.5 `hsl(182.8,70.2%,35.5%)` 182.8°, 82.5, 60.4 339999 `#339999`\nCIE-LAB 55.831, -28.607, -12.19 16.873, 23.744, 34.264 0.225, 0.317, 23.744 55.831, 31.096, 203.081 55.831, -40.645, -13.956 48.728, -23.466, -7.581 00011011, 10010100, 10011010\n\n# Color Schemes with #1b949a\n\n• #1b949a\n``#1b949a` `rgb(27,148,154)``\n• #9a211b\n``#9a211b` `rgb(154,33,27)``\nComplementary Color\n• #1b9a60\n``#1b9a60` `rgb(27,154,96)``\n• #1b949a\n``#1b949a` `rgb(27,148,154)``\n• #1b559a\n``#1b559a` `rgb(27,85,154)``\nAnalogous Color\n• #9a601b\n``#9a601b` `rgb(154,96,27)``\n• #1b949a\n``#1b949a` `rgb(27,148,154)``\n• #9a1b55\n``#9a1b55` `rgb(154,27,85)``\nSplit Complementary Color\n• #949a1b\n``#949a1b` `rgb(148,154,27)``\n• #1b949a\n``#1b949a` `rgb(27,148,154)``\n• #9a1b94\n``#9a1b94` `rgb(154,27,148)``\n• #1b9a21\n``#1b9a21` `rgb(27,154,33)``\n• #1b949a\n``#1b949a` `rgb(27,148,154)``\n• #9a1b94\n``#9a1b94` `rgb(154,27,148)``\n• #9a211b\n``#9a211b` `rgb(154,33,27)``\n• #105559\n``#105559` `rgb(16,85,89)``\n• #136a6f\n``#136a6f` `rgb(19,106,111)``\n• #177f84\n``#177f84` `rgb(23,127,132)``\n• #1b949a\n``#1b949a` `rgb(27,148,154)``\n• #1fa9b0\n``#1fa9b0` `rgb(31,169,176)``\n• #23bec5\n``#23bec5` `rgb(35,190,197)``\n• #28d1d9\n``#28d1d9` `rgb(40,209,217)``\nMonochromatic Color\n\n# Alternatives to #1b949a\n\nBelow, you can see some colors close to #1b949a. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.\n\n• #1b9a80\n``#1b9a80` `rgb(27,154,128)``\n• #1b9a8b\n``#1b9a8b` `rgb(27,154,139)``\n• #1b9a95\n``#1b9a95` `rgb(27,154,149)``\n• #1b949a\n``#1b949a` `rgb(27,148,154)``\n• #1b899a\n``#1b899a` `rgb(27,137,154)``\n• #1b7f9a\n``#1b7f9a` `rgb(27,127,154)``\n• #1b749a\n``#1b749a` `rgb(27,116,154)``\nSimilar Colors\n\n# #1b949a Preview\n\nThis text has a font color of #1b949a.\n\n``<span style=\"color:#1b949a;\">Text here</span>``\n#1b949a background color\n\nThis paragraph has a background color of #1b949a.\n\n``<p style=\"background-color:#1b949a;\">Content here</p>``\n#1b949a border color\n\nThis element has a border color of #1b949a.\n\n``<div style=\"border:1px solid #1b949a;\">Content here</div>``\nCSS codes\n``.text {color:#1b949a;}``\n``.background {background-color:#1b949a;}``\n``.border {border:1px solid #1b949a;}``\n\n# Shades and Tints of #1b949a\n\nA shade is achieved by adding black to any pure hue, while a tint is created by mixing white to any pure color. In this example, #010404 is the darkest color, while #f2fcfd is the lightest one.\n\n• #010404\n``#010404` `rgb(1,4,4)``\n• #041414\n``#041414` `rgb(4,20,20)``\n• #072425\n``#072425` `rgb(7,36,37)``\n• #093436\n``#093436` `rgb(9,52,54)``\n• #0c4447\n``#0c4447` `rgb(12,68,71)``\n• #0f5457\n``#0f5457` `rgb(15,84,87)``\n• #126468\n``#126468` `rgb(18,100,104)``\n• #157479\n``#157479` `rgb(21,116,121)``\n• #188489\n``#188489` `rgb(24,132,137)``\n• #1b949a\n``#1b949a` `rgb(27,148,154)``\n• #1ea4ab\n``#1ea4ab` `rgb(30,164,171)``\n• #21b4bb\n``#21b4bb` `rgb(33,180,187)``\n• #24c4cc\n``#24c4cc` `rgb(36,196,204)``\n``#2ad1da` `rgb(42,209,218)``\n• #3bd5dd\n``#3bd5dd` `rgb(59,213,221)``\n• #4bd8df\n``#4bd8df` `rgb(75,216,223)``\n• #5cdce2\n``#5cdce2` `rgb(92,220,226)``\n• #6de0e5\n``#6de0e5` `rgb(109,224,229)``\n• #7de3e8\n``#7de3e8` `rgb(125,227,232)``\n• #8ee7eb\n``#8ee7eb` `rgb(142,231,235)``\n• #9feaee\n``#9feaee` `rgb(159,234,238)``\n• #afeef1\n``#afeef1` `rgb(175,238,241)``\n• #c0f2f4\n``#c0f2f4` `rgb(192,242,244)``\n• #d1f5f7\n``#d1f5f7` `rgb(209,245,247)``\n• #e1f9fa\n``#e1f9fa` `rgb(225,249,250)``\n• #f2fcfd\n``#f2fcfd` `rgb(242,252,253)``\nTint Color Variation\n\n# Tones of #1b949a\n\nA tone is produced by adding gray to any pure hue. In this case, #5a5b5b is the less saturated color, while #06a7af is the most saturated one.\n\n• #5a5b5b\n``#5a5b5b` `rgb(90,91,91)``\n• #536262\n``#536262` `rgb(83,98,98)``\n• #4c6869\n``#4c6869` `rgb(76,104,105)``\n• #456e70\n``#456e70` `rgb(69,110,112)``\n• #3e7477\n``#3e7477` `rgb(62,116,119)``\n• #377b7e\n``#377b7e` `rgb(55,123,126)``\n• #308185\n``#308185` `rgb(48,129,133)``\n• #29878c\n``#29878c` `rgb(41,135,140)``\n• #228e93\n``#228e93` `rgb(34,142,147)``\n• #1b949a\n``#1b949a` `rgb(27,148,154)``\n• #149aa1\n``#149aa1` `rgb(20,154,161)``\n• #0da1a8\n``#0da1a8` `rgb(13,161,168)``\n• #06a7af\n``#06a7af` `rgb(6,167,175)``\nTone Color Variation\n\n# Color Blindness Simulator\n\nBelow, you can see how #1b949a is perceived by people affected by a color vision deficiency. This can be useful if you need to ensure your color combinations are accessible to color-blind users.\n\nMonochromacy\n• Achromatopsia 0.005% of the population\n• Atypical Achromatopsia 0.001% of the population\nDichromacy\n• Protanopia 1% of men\n• Deuteranopia 1% of men\n• Tritanopia 0.001% of the population\nTrichromacy\n• Protanomaly 1% of men, 0.01% of women\n• Deuteranomaly 6% of men, 0.4% of women\n• Tritanomaly 0.01% of the population" ]
[ null ]
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https://artofproblemsolving.com/wiki/index.php/2020_AIME_II_Problems/Problem_8
[ "# 2020 AIME II Problems/Problem 8\n\n## Problem\n\nDefine a sequence recursively by", null, "$f_1(x)=|x-1|$ and", null, "$f_n(x)=f_{n-1}(|x-n|)$ for integers", null, "$n>1$. Find the least value of", null, "$n$ such that the sum of the zeros of", null, "$f_n$ exceeds", null, "$500,000$.\n\n## Solution (Official MAA)\n\nFirst it will be shown by induction that the zeros of", null, "$f_n$ are the integers", null, "$a, {a+2,} {a+4,} \\dots, {a + n(n-1)}$, where", null, "$a = n - \\frac{n(n-1)}2.$\n\nThis is certainly true for", null, "$n=1$. Suppose that it is true for", null, "$n = m-1 \\ge 1$, and note that the zeros of", null, "$f_m$ are the solutions of", null, "$|x - m| = k$, where", null, "$k$ is a nonnegative zero of", null, "$f_{m-1}$. Because the zeros of", null, "$f_{m-1}$ form an arithmetic sequence with common difference", null, "$2,$ so do the zeros of", null, "$f_m$. The greatest zero of", null, "$f_{m-1}$ is", null, "$$m-1+\\frac{(m-1)(m-2)}2 =\\frac{m(m-1)}2,$$so the greatest zero of", null, "$f_m$ is", null, "$m+\\frac{m(m-1)}2$ and the least is", null, "$m-\\frac{m(m-1)}2$.\n\nIt follows that the number of zeros of", null, "$f_n$ is", null, "$\\frac{n(n-1)}2+1=\\frac{n^2-n+2}2$, and their average value is", null, "$n$. The sum of the zeros of", null, "$f_n$ is", null, "$$\\frac{n^3-n^2+2n}2.$$Let", null, "$S(n)=n^3-n^2+2n = n(n-2)(n+1)$, so the sum of the zeros exceeds", null, "$500{,}000$ if and only if", null, "$S(n) > 1{,}000{,}000 = 100^3\\!.$ Because", null, "$S(n)$ is increasing for", null, "$n > 2$, the values", null, "$S(100) = 1{,}000{,}000 - 10{,}000 + 200 = 990{,}200$ and", null, "$S(101)=1{,}030{,}301 - 10{,}201 + 202 = 1{,}020{,}302$ show that the requested value of", null, "$n$ is", null, "$\\boxed{101}$.\n\n~IceMatrix\n\n## See Also\n\n 2020 AIME II (Problems • Answer Key • Resources) Preceded byProblem 7 Followed byProblem 9 1 • 2 • 3 • 4 • 5 • 6 • 7 • 8 • 9 • 10 • 11 • 12 • 13 • 14 • 15 All AIME Problems and Solutions\n\nThe problems on this page are copyrighted by the Mathematical Association of America's American Mathematics Competitions.", null, "Invalid username\nLogin to AoPS" ]
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https://socratic.org/questions/how-do-you-get-rid-of-the-negative-exponents-in-4a-3-b-5-2
[ "# How do you get rid of the negative exponents in (-4a ^ { 3} b ^ { - 5} ) ^ { - 2}?\n\nOct 24, 2017", null, "#### Explanation:\n\nThere are two rules we may use here:\n\n1) Negative exponent rule\n\n${a}^{- b} = \\left(\\frac{1}{{a}^{b}}\\right)$\n\n2) Multiplying powers\n\n${\\left({a}^{m}\\right)}^{n} = {a}^{m n}$" ]
[ null, "https://d2jmvrsizmvf4x.cloudfront.net/jo6XjmS0RTO4pqERSpS6_Removing+Negative+Exponents+-+Socratic.jpg", null ]
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https://searchfox.org/mozilla-central/source/gfx/wr/webrender/src/print_tree.rs
[ "#### Other Tools\n\n1\n```/* This Source Code Form is subject to the terms of the Mozilla Public ```\n2\n``` * License, v. 2.0. If a copy of the MPL was not distributed with this ```\n3\n``` * file, You can obtain one at http://mozilla.org/MPL/2.0/. */ ```\n4\n``` ```\n5\n```use std::io::{stdout, Stdout, Write}; ```\n6\n``` ```\n7\n```/// A struct that makes it easier to print out a pretty tree of data, which ```\n8\n```/// can be visually scanned more easily. ```\n9\n```pub struct PrintTree<W> ```\n10\n```where ```\n11\n``` W: Write ```\n12\n```{ ```\n13\n``` /// The current level of recursion. ```\n14\n``` level: u32, ```\n15\n``` ```\n16\n``` /// An item which is queued up, so that we can determine if we need ```\n17\n``` /// a mid-tree prefix or a branch ending prefix. ```\n18\n``` queued_item: Option<String>, ```\n19\n``` ```\n20\n``` /// The sink to print to. ```\n21\n``` sink: W, ```\n22\n```} ```\n23\n``` ```\n24\n```/// A trait that makes it easy to describe a pretty tree of data, ```\n25\n```/// regardless of the printing destination, to either print it ```\n26\n```/// directly to stdout, or serialize it as in the debugger ```\n27\n```pub trait PrintTreePrinter { ```\n28\n``` fn new_level(&mut self, title: String); ```\n29\n``` fn end_level(&mut self); ```\n30\n``` fn add_item(&mut self, text: String); ```\n31\n```} ```\n32\n``` ```\n33\n```impl PrintTree<Stdout> { ```\n34\n``` pub fn new(title: &str) -> Self { ```\n35\n``` PrintTree::new_with_sink(title, stdout()) ```\n36\n``` } ```\n37\n```} ```\n38\n``` ```\n39\n```impl<W> PrintTree<W> ```\n40\n```where ```\n41\n``` W: Write ```\n42\n```{ ```\n43\n``` pub fn new_with_sink(title: &str, mut sink: W) -> Self { ```\n44\n``` writeln!(sink, \"\\u{250c} {}\", title).unwrap(); ```\n45\n``` PrintTree { ```\n46\n``` level: 1, ```\n47\n``` queued_item: None, ```\n48\n``` sink, ```\n49\n``` } ```\n50\n``` } ```\n51\n``` ```\n52\n``` fn print_level_prefix(&mut self) { ```\n53\n``` for _ in 0 .. self.level { ```\n54\n``` write!(self.sink, \"\\u{2502} \").unwrap(); ```\n55\n``` } ```\n56\n``` } ```\n57\n``` ```\n58\n``` fn flush_queued_item(&mut self, prefix: &str) { ```\n59\n``` if let Some(queued_item) = self.queued_item.take() { ```\n60\n``` self.print_level_prefix(); ```\n61\n``` writeln!(self.sink, \"{} {}\", prefix, queued_item).unwrap(); ```\n62\n``` } ```\n63\n``` } ```\n64\n```} ```\n65\n``` ```\n66\n```// The default `println!` based printer ```\n67\n```impl<W> PrintTreePrinter for PrintTree<W> ```\n68\n```where ```\n69\n``` W: Write ```\n70\n```{ ```\n71\n``` /// Descend one level in the tree with the given title. ```\n72\n``` fn new_level(&mut self, title: String) { ```\n73\n``` self.flush_queued_item(\"\\u{251C}\\u{2500}\"); ```\n74\n``` ```\n75\n``` self.print_level_prefix(); ```\n76\n``` writeln!(self.sink, \"\\u{251C}\\u{2500} {}\", title).unwrap(); ```\n77\n``` ```\n78\n``` self.level = self.level + 1; ```\n79\n``` } ```\n80\n``` ```\n81\n``` /// Ascend one level in the tree. ```\n82\n``` fn end_level(&mut self) { ```\n83\n``` self.flush_queued_item(\"\\u{2514}\\u{2500}\"); ```\n84\n``` self.level = self.level - 1; ```\n85\n``` } ```\n86\n``` ```\n87\n``` /// Add an item to the current level in the tree. ```\n88\n``` fn add_item(&mut self, text: String) { ```\n89\n``` self.flush_queued_item(\"\\u{251C}\\u{2500}\"); ```\n90\n``` self.queued_item = Some(text); ```\n91\n``` } ```\n92\n```} ```\n93\n``` ```\n94\n```impl<W> Drop for PrintTree<W> ```\n95\n```where ```\n96\n``` W: Write ```\n97\n```{ ```\n98\n``` fn drop(&mut self) { ```\n99\n``` self.flush_queued_item(\"\\u{9492}\\u{9472}\"); ```\n100\n``` } ```\n101\n```} ```\n102\n``` ```\n103\n```pub trait PrintableTree { ```\n104\n``` fn print_with<T: PrintTreePrinter>(&self, pt: &mut T); ```\n105\n```} ```" ]
[ null ]
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https://socratic.org/questions/how-do-you-find-the-asymptotes-for-y-x-2-2x-3-x-2-5x-6
[ "How do you find the asymptotes for y = (x^2 + 2x - 3)/( x^2 - 5x - 6)?\n\nAug 8, 2018\n\n$\\text{vertical asymptotes at \"x=-1\" and } x = 6$\n$\\text{horizontal asymptote at } y = 1$\n\nExplanation:\n\nThe denominator of y cannot be zero as this would make y undefined. Equating the denominator to zero and solving gives the values that x cannot be and if the numerator is non-zero for these values then they are vertical asymptotes.\n\n$\\text{solve } {x}^{2} - 5 x - 6 = 0 \\Rightarrow \\left(x - 6\\right) \\left(x + 1\\right) = 0$\n\n$x = - 1 \\text{ and \"x=6\" are the asymptotes}$\n\n$\\text{Horizontal asymptotes occur as}$\n\n${\\lim}_{x \\to \\pm \\infty} , y \\to c \\text{ (a constant)}$\n\n$\\text{divide terms on numerator/denominator by the highest}$\n$\\text{power of \"x\" that is } {x}^{2}$\n\n$y = \\frac{{x}^{2} / {x}^{2} + \\frac{2 x}{x} ^ 2 - \\frac{3}{x} ^ 2}{{x}^{2} / {x}^{2} - \\frac{5 x}{x} ^ 2 - \\frac{6}{x} ^ 2} = \\frac{1 + \\frac{2}{x} - \\frac{3}{x} ^ 2}{1 - \\frac{5}{x} - \\frac{6}{x} ^ 2}$\n\n$\\text{ as } x \\to \\pm \\infty , y \\to \\frac{1 + 0 - 0}{1 - 0 - 0}$\n\n$y = 1 \\text{ is the asymptote}$\ngraph{(x^2+2x-3)/(x^2-5x-6) [-20, 20, -10, 10]}" ]
[ null ]
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https://byjusexamprep.com/understand-the-basics-of-data-interpretation-1602492125-i
[ "# Understand the basics of Data Interpretation\n\nBy Shubham Verma|Updated : October 17th, 2020\n\nDear Aspirants,\n\nIn this post, we will discuss the basics of Data Interpretation to help you all understand this topic better.\n\nData Interpretation is a major part of Quantitative Aptitude section for any exam. Now, first and foremost, What does Data Interpretation exactly mean?\nInterpretation is the process of making sense of numerical data that has been collected, analysed, and presented. Interpreting data is an important critical thinking skill that helps you comprehend textbooks, graphs and tables\n\nMajority of the questions asked in the Data Interpretation Section are based on the following topics of the Arithmetic Section -\n\n1. Ratios\n2. Averages\n3. Percentages\n\nIf the basics of these topics are clear, attempting DI in the exams becomes comparatively easy.\n\nNow, let us go through the types of DI graphs/charts that you may encounter in the exams -\n\n1. Pie Charts\n2. Line Charts\n3. Bar Graphs\n4. Tabular Charts\n5. Mixed Graphs\n\nWe shall now have a look at the types of questions that are asked under these Data Interpretation Graphs -\n\nConsider the following data presented in the bar graph -\n\nPercentage of Students who like different sports in two different years is provided in the following graph. Total number of Students is 1000 for both the years.\n\nYear 2011 and Year 2012", null, "Now the following types of questions may be asked from this data -\n\n1. Sum or Difference based -\n\nThese are the most basic questions that may be asked in a DI set. For instance,\n\nWhat was the sum of the total number of students who like Badminton and Cricket in both the years?\n\nNow for such questions, first find the number of students who like the two sports in the two years -\n\n2011 - Badminton = (12/100) * 1000 = 120\n\nCricket = (45/100) * 1000 = 450\n\nTotal = 570\n\n2012 - Badminton = (20/100) * 1000 = 200\n\nCricket = (37/100) * 1000 = 370\n\nTotal = 570\n\nSum = 570 + 570 = 1140\n\n2. Averages based Questions -\n\nAverage based questions are very commonly asked in the Data Interpretation sets. For instance,\n\nWhat is the average number of students who like badminton, cricket and football in 2011?\n\nTotal students who like badminton, cricket and football in 2011 = (12 + 45 + 22) = 79% of 1000\n\nRequired average = 790/3\n\n3. Ratio based question -\n\nAnother arithmetic operation based question that may be asked is Ratio based.\n\nNow, these questions may be asked directly or in combination with the above. For instance,\n\nWhat is the ratio of the students who like football and tennis in 2011 and those who like volleyball and squash in 2012?\n\nStudents who like football and tennis in 2011= (22 + 4) = 26% of 1000\n\nStudents who like volleyball and squash in 2012 = (10 + 5) = 15% of 1000\n\nRemember for such questions, you do not need to do the entire calculation, because such numbers will eventually cancel out while calculating the ratios.\n\nRequired ratio = (26% of 1000) : (15% of 1000) = 26 : 15\n\n4. Percentage based question -\n\nThese are yet other arithmetic problems that are usually asked in DI questions.\n\nThese problems again may be asked individually or in combination with the sum or difference based problems. For instance,\n\nThe students who like badminton and squash in 2011 is what per cent of the students who like football and swimming in 2011?\n\nStudents who like badminton and squash in 2011 = (12 + 2) = 14% of 1000\n\nStudents who like football and swimming in 2011 = (22+7) = 29% of 1000\n\nHere again, do not calculate the entire value.\n\nRequired % = (14% of 1000) / (29% of 1000) * 100 = 1400/29%\n\nSame data may be presented in the form of other graphs as well, however, the approach to attempt the questions would remain the same. You may find numbers in place of percentages or vice - versa, so do read the question carefully before proceeding.\n\nLine Graph -\n\nYear 2011 and Year 2012", null, "Tabular Chart -", null, "Pie Chart -", null, "", null, "One more variety of question that may be asked in pie charts is the angle based. For instance,\n\nWhat is the central angle corresponding to football and volleyball together for 2012?\n\nAngle = (20 + 10)% * 360 = (30/100) * 360 = 108\n\nTo boost the preparation of all our users, we have come up with some free video (Live Class) series.\n\nराज्य परीक्षाओं के लिए करंट अफेयर्स\n\nUP स्टेट Exams- उत्तर प्रदेश की सभी राज्य-स्तरीय परीक्षाओं के लिए मास्टर सीरीज\n\nउत्तर प्रदेश राज्य परीक्षाओं के लिए 2000 सबसे महत्वपूर्ण प्रश्न\n\nMore from us\n\n#### UP Police 2020 Comprehensive Course (हिंदी माध्यम)\n\nStudy Notes (Hindi/English)\n\nMonthly Current Affairs Quiz\n\nNCERT Books PDF (Hindi/English)\n\nGet Unlimited access to Structured Live Courses and Mock Tests- Online Classroom Program", null, "GradeStack Learning Pvt. Ltd.Windsor IT Park, Tower - A, 2nd Floor, Sector 125, Noida, Uttar Pradesh 201303 help@byjusexamprep.com" ]
[ null, "https://gs-post-images.grdp.co/2021/1/artboard-img1611906121672-22.png-rs-high-webp.png", null, "https://gs-post-images.grdp.co/2021/1/artboard-img1611906121672-22.png-rs-high-webp.png", null, "https://gs-post-images.grdp.co/2021/1/artboard-img1611906121672-22.png-rs-high-webp.png", null, "https://gs-post-images.grdp.co/2021/1/artboard-img1611906121672-22.png-rs-high-webp.png", null, "https://gs-post-images.grdp.co/2021/1/artboard-img1611906121672-22.png-rs-high-webp.png", null, "https://gs-post-images.grdp.co/2018/12/screenshot-2018-12-21-at-3-img1545387500435-75.png-rs-high-webp.png", null ]
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https://cstheory.stackexchange.com/questions/1405/are-there-intermediate-eta-theories-for-the-lambda-calculus
[ "# Are there intermediate eta theories for the lambda calculus?\n\nThere are two main, studied theories of the lambda calculus, the beta theory and its Post-complete extension, the beta-eta theory.\n\nDo these two theories have an in-between, a kind of intermediate eta rule that gives a confluent rewrite theory? Is there some interesting notion of partial extensionality that it corresponds to?\n\nThis is the second question I have asked in pursuit of intermediate eta, the previous being Extensions of beta-theory of lambda calculus, which led to question about an orthogonal notion of extension, Characterising invisible equivalences by confluent rewrite rules, which sought to clarify an answer to that earlier question.\n\nFor typed calculi, if you consider the negative types ($1$, $\\times$, $\\to$), you can turn the eta rules on or off basically at will without affecting confluence.\n\nFor positive types (sums, and pairs with the pattern-matching elimination), the situation is much messier. Basically, the question is whether the term has a closed-scope elimination form, which let contexts interact in complicated ways with eta-expansions. For example, if $e$ has type $A \\times B$, then its eta-expansion is $\\mathsf{let}\\;(a,b) = e \\;\\mathsf{in}\\; (a,b)$. But to get the equational theory a category theorist would expect, you need to consider contexts $\\mathbb{C}[-]$, and generalize the equation to be $\\mathbb{C}[e] \\equiv \\mathsf{let}\\;(a,b) = e\\;\\mathsf{in}\\;\\mathbb{C}[(a,b)]$ (with the expected scoping restrictions).\n\nI think that you can still prove a confluence result if you don't allow the commuting conversions. But this is hearsay -- I've never tried it myself, nor looked at papers documenting it.\n\nI don't really know anything about untyped lambda calculus, though.\n\nEDIT: Charles asks about eta-reductions. This is promising for the kind of example he seeks, because I think in general they won't be strong enough to model the full equality theory, which I'll illustrate with a simple example involving booleans. The eta-expansion for booleans is $\\mathbb{C}[e] \\mapsto \\mathsf{if}(e, \\mathbb{C}[\\mathsf{true}], \\mathbb{C}[\\mathsf{false}])$. (The eta-reduction is of course the other direction.)\n\nNow, consider the term $\\mathsf{if}(e, f, g)\\;\\mathsf{if}(e, x, y)$. Showing that this term is equivalent to $\\mathsf{if}(e, f\\;x, g\\;y)$ needs to go through an eta-expansion, because we have to replace the $e$ in one of the if-then-elses with $\\mathsf{true}$ and $\\mathsf{false}$ in order to drive a $\\beta$-reduction.\n\n• I should have made clear that this was about the untyped lambda calculus: the logic aside might make that unclear. In the typed case, I expect that Post completeness holds for the 〈→,×〉 theory, but I'm not at all sure at other types. contexts interact in complicated ways with eta-expansions - that's a case for considering eta reduction, isn't it, because you don't need to constrain the rewrites? Sep 19 '10 at 15:16\n\nAccording to John C. Mitchell in Foundations of Programming Languages, both in STLC and in untyped lambda calculus, the reduction rule pair (proj₁ P, proj₂ P) → P breaks confluence when combined with fix reduction (or, I assume from looking at the proof), without such conditions for the untyped case. This is theorem 4.4.19 (page 272).\n\n• I guess this is an extended comment on Neel's answer. Klop & De Vrijer (1989) survey the theory of the untyped lambda calculus with surjective pairing: the case with eta reductions is indeed non-confluent, but the theory is consistent (it has a model in Scott's D_inf construction), and they provide results suggesting a confluent, conservative rewrite theory for surjective pairs can be given (still an open problem, AFAIK). May 16 '13 at 7:50" ]
[ null ]
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https://mathoverflow.net/questions/242771/decay-estimates-for-wave-and-klein-gordon-equation-in-generic-curved-backgroun
[ "# Decay estimates for wave and Klein-Gordon equation in \"generic\" curved backgrounds\n\nConsider a $d$-dimensional smooth Lorentzian manifold $(M,g)$ (we assume $d\\geq 3$, $M$ to be Hausdorff, paracompact and connected, hence second-countable, and that the signature convention of $g$ is $(+-\\cdots-)$, i.e. the signature of $g$ is $2-d$) with Levi-Civita connection $\\nabla$, and consider the Klein-Gordon equation on $(M,g)$: $$\\tag{1}\\label{e1}\\Box_g u+m^2 u=0\\ ,\\quad\\Box_g=g^{-1}\\nabla^2\\ .$$ We assume $(M,g)$ to be globally hyperbolic: there is $\\tau\\in\\mathscr{C}^\\infty(M,\\mathbb{R})$ surjective such that $\\mathrm{d}\\tau$ is a future-directed, timelike covector field and $\\Sigma_t=\\tau^{-1}(t)$ is a Cauchy hypersurface for $(M,g)$ (i.e. $M=$ the Cauchy development of $\\Sigma_t$ in $(M,g)$) for all $t\\in\\mathbb{R}$. We then say that $\\tau$ is a Cauchy time function for $(M,g)$, and one can show that $M$ is then diffeomorphic to $\\mathbb{R}\\times\\Sigma_t$ for every $t\\in\\mathbb{R}$ (it suffices to follow the maximal orbits of a future-directed timelike vector field $T$ - e.g. $T=g^\\sharp(\\mathrm{d}\\tau)$ -, which must intersect $\\Sigma_t$ for all $t\\in\\mathbb{R}$ since they are all Cauchy hypersurfaces by hypothesis). We can (and shall) use a Cauchy time function $\\tau$ to provide us a global time coordinate.\n\nIf $(M,g)=(\\mathbb{R}^d,\\eta)$ is Minkowski space-time with $\\tau=$ the standard (Cartesian) time coordinate $x^0$, the stationary phase method (among others) allows us to show that if $u$ is a smooth solution of (1) with initial data of compact support and $m\\neq 0$, then $$\\tag{2}\\label{e2}\\|u(x^0,\\cdot)\\|_{L^\\infty(\\mathbb{R}^{d-1})}=O\\Big(|x^0|^{-\\frac{d-1}{2}}\\Big)\\ .$$ If $m=0$, we have instead $$\\tag{3}\\label{e3}\\|u(x^0,\\cdot)\\|_{L^\\infty(\\mathbb{R}^{d-1})}=O\\Big(|x^0|^{-\\frac{d-2}{2}}\\Big)\\ .$$\n\nOn the other hand, if $M=\\mathbb{R}\\times\\mathbb{S}^{d-1}\\ni(t,\\theta)$ and $g=\\mathrm{d}t\\otimes\\mathrm{d}t-h$, where $h$ is the standard round metric on $\\mathbb{S}^{d-1}$ (i.e. $(M,g)=$ the so-called Einstein cylinder or Einstein static universe), then in both cases we have no decay at all. Intuitively, this is due to the fact that all light rays are trapped in a compact spatial region due to the simple fact that $(M,g)$ has compact Cauchy hypersurfaces. For cases in between (e.g. Schwarzschild and Kerr geometries, etc.) with non-compact Cauchy hypersurfaces (which is the only case I am interested in from now on), there are two possible (not completely independent) phenomena due to focusing of null geodesics which slow down global decay of solutions of \\eqref{e1} as compared with \\eqref{e2} and \\eqref{e3}:\n\n1. Trapping. This means that at least some null geodesics remain within a compact spatial region (often lower-dimensional). This is the case of static black hole space-times such as Schwarzschild - light rays with initial position at the radius $r=3M$ (in $d=4$) and purely angular initial velocity stay forever in the photosphere $r=3M$;\n\n2. Caustics. This happens whenever (say, future-directed) null geodesics starting at a point $p\\in M$ go beyond the (future) null conjugate locus of $p$.\n\nTypically, trapping has a stronger effect in reducing global decay than caustics. On the other hand, caustics are usually stable against small perturbations of $g$, whereas trapping need not be. A lot of work has been dedicated by several people (Alinhac, Baskin, Sogge, Tataru, etc.) to investigate decay of solutions of \\eqref{e1} when $(M,g)$ is either free of 1. and 2. or has a particular kind of trapping.\n\nHaving set up our context and framework, I am now finally able to pose my question. Consider the Klein-Gordon equation \\eqref{e1} in a globally hyperbolic $(M,g)$ endowed with a steep Cauchy time function $\\tau$ (i.e. $g^{-1}(\\mathrm{d}\\tau,\\mathrm{d}\\tau)\\geq 1$). Since the set of all globally hyperbolic smooth Lorentzian metrics on $M$ is Whitney-$\\mathscr{C}^0$-open in the space of all smooth Lorentzian metrics on $M$, we are able to perform small perturbations $g'$ of $g$ in that topology while keeping the globally hyperbolic character of $g'$. Moreover, if the perturbation is suitably small in that topology, a steep Cauchy time function remains a Cauchy time function with respect to $g'$.\n\nQuestion: Suppose that smooth solutions to \\eqref{e1} in $(M,g)$ with compactly supported initial data in $\\Sigma_0$ have a time decay rate of the form $$\\|u\\|_{L^\\infty(\\Sigma_t)}=O(f(|t|))\\ ,\\quad t>0$$ for some $\\delta>0$, where $f:(0,+\\infty)\\to(0,+\\infty)$ is a strictly decreasing function satisfying $\\lim_{t\\to+\\infty}f(t)=0$. Assume that the Cauchy hypersurfaces of $(M,g)$ are not compact. For a \"generic\" small perturbation $g'$ within the above conditions, what is the time decay rate for solutions $u'$ of the same kind to $$\\tag{1'}\\label{e1a}\\Box_{g'} u'+m^2 u'=0\\ ,\\quad\\Box_{g'}=g'{}^{-1}\\nabla'{}^2$$ (here $\\nabla'$ is the Levi-Civita connection associated to $g'$)? Is it faster than $O(f(|t|))$ for $g'$ \"generic\" but $g$ not so?\n\nIn other words, which is the best \"structurally stable\" global time decay rate we can expect from smooth solutions of the Klein-Gordon and wave equation with compactly supported initial data if $(M,g)$ has non-compact Cauchy hypersurfaces (to prevent situations where we know for sure there is no decay at all)?\n\nI expect that loss of decay due to trapping should \"generically\" disappear but loss of decay due to caustics (which is supposedly milder) should \"generically\" remain, but I am not sure if this intuition is correct.\n\n(Remark: Müller and Sánchez (Lorentzian Manifolds Isometrically Embeddable in $\\mathbb{L}^N$, Trans. Amer. Math. Soc. 363 (2011) 5367-5379, arXiv:0812.4439 [math.DG]) and Minguzzi (On the Existence of Smooth Cauchy Steep Time Functions, Class. Quantum Grav. 33 (2016) 115001, arXiv:1601.05932 [gr-qc]) have shown that any globally hyperbolic $(M,g)$ admits a steep Cauchy time function)\n\n• Pedro, just to clarify, your (1') should have the equation $\\Box_{g'} u'+m^2 u'=0$, with the prime on both $u$'s, right? Jun 22, 2016 at 11:26\n• Ooops, of course, just fixed that... Thanks Igor! Jun 22, 2016 at 13:17\n\nA longish bunch of remarks: there's a big leap that doesn't make sense between your question and your motivation.\n\n1. On the maximally extended Schwawrzschild solution there is no decay to the wave equations: the space-time \"ends\" in finite proper time at the singularity, and there's not enough time (compare to the not-enough-space scenario you mentioned with compact Cauchy surface) for solutions to decay.\n\n2. Compact Cauchy surface does not rule out decay. Wave equation on de Sitter space is known to decay (strictly speaking, not in $$L^\\infty$$ due to the constant solutions; but in $$\\mathring{W}^{1,\\infty}$$).\n\n3. The results concerning Schwarzschild and Kerr mostly deal with their outer domain of dependence (with some authors considering a little sliver that pokes into the black hole). If you take a steep time function on the outer domain of Schwarzschild, for example (and an example would be the function $$t$$ in the traditional Schwarzschild coordinates), the solutions to wave and Klein-Gordon equations have absolutely no decay even for compactly supported initial data, as a wave packet can fall into the black hole very quickly, but the black hole boundary is \"at $$t = \\infty$$\".\n\nIf you actually read the papers on Schwarzschild and Kerr decays, you will see that instead of using a Cauchy time function, their \"time function\" when measuring decay are never Cauchy. It usually intersects the black hole boundary (and sometimes also $$\\mathscr{I}^+$$) transversely.\n\n4. In fact, the only cases where I can think of where decay of wave/Klein-Gordon equation has been proven for a globally hyperbolic Lorentzian manifold with respect to a steep time function are (1) background metrics which are perturbations of Minkowski space [where generic perturbations cannot improve things since Minkowski is as about as good as it comes]; (2) cosmological scenarios [independently of whether you have an open or closed Cauchy hypersurface] where the energy decay is driven by the spatial expansion and so is independent of trapping or caustics (which capture failure of \"dispersion to infinity\"); and (3) works on static product metrics where one can do resolvent estimates.\n\nI admit not being up to date in the results of type (3), but it seems that the type of hypotheses usually assumed for obtaining results of type (3) are generally stable under perturbations.\n\n5. The focus on effects of trapping and caustics that you mentioned in your question is mainly due to them being obstacles for (global and local) dispersion. So your question really only makes sense when the decay effects are dispersion dominated (see my comment in the previous point about cosmological solutions). As such you probably want your manifold to admit global dispersion in a suitable sense, which you can guarantee by, e.g., imposing some sort of asymptotic simplicity or asymptotic flatness.\n\n6. On the other hand, for wave equations in particular, it has been long understood that the decay parametrized by a time function $$\\tau$$ does not capture the real underlying phenomenon where the decay should be understood more along the line of something like \"parametrized by affine parameters of a null geodesic congruence\",[insert place holder here; on second reading I am not 100% sure about my word choices there; until I can come up with a better way to say it succinctly, I'll leave this at \"something technical to describe\"] which incidentally also more closely resembles the result from the stationary phase argument if you look at the geometric optics. This is related to why in point 3 above, the \"time function\" used to measure decay in modern literature are not Cauchy time functions.\n\n7. I am also not sure what you mean by that trapping should disappear under generic perturbations. Consider the class of (connected) globally hyperbolic Lorentzian manifolds with two asymptotically flat ends. This class is stable under small perturbations. But such manifolds must have trapped null geodesics.\n\n• Dear Willie, your answer gave me a lot of food for thought. It was already clear to me that several aspects of my question (which has been on the back of my mind for quite some time) need to be further qualified - I've read some of the papers on the subject quite a while ago, so certain pitfalls in them eluded my memory. I'll probably need some time to process everything you wrote and reread those papers. Then I'll return to comment on your points and make the necessary improvements to the question to make it better posed. Thank you for having taken the time to explain all the above issues! Jun 22, 2016 at 20:56" ]
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https://www.math-only-math.com/worksheet-on-income-and-return-from-shares.html
[ "# Worksheet on Income and Return from Shares\n\nPractice the questions given in the worksheet on income and return from shares.\n\n1. William bought $40 shares at a premium of 40%. Find the income, if William invests$ 14000 in these shares and receives a dividend at the rate of 8% on the nominal value of the shares.\n\n2. Mr. Ryan Invests $30,000 in buying shares of a company which pays a 12 % dividend annually on hundred-dollar shares selling at a premium of$ 50. Find\n\n(i) the number of shares bought by Mr. Ryan, and\n\n(ii) his annual income from the shares.\n\n3. Matthew bought $40 shares at a discount of 40%. Find the income, if he invests$ 12000 in these shares and receives a dividend at the rate of 11% on the nominal value of the shares.\n\n4. Mr. Jacob invests $15840 in buying shares of nominal value$ 24 selling at a premium of 10%. The company pays a 15% dividend annually. Find\n\n(i) the dividend he receives annually, and\n\n(ii) the rate of return from his investment.\n\n5. Michael buys 400, twenty-dollar shares at a discount of 20 % and receives a return of 12% on his money. Calculate:\n\n(i) the amount invested by Michael.\n\n(ii) the rate of dividend paid by the company.\n\n6. Mason bought 1000 hundred-dollar shares from the stock market carrying 8% dividend quoted at $130. A few days later the market value of the shares went up by 10%. Mason sold all his shares. What was his income from this transaction? 7. Mr. Ryan invests$ 20800 in 6% $100 shares at$ 104, and $14300 in 10.5%$ 100 shares at $143. What will be his annual income from the shares? 8. A company declares a semi-annual dividend of 5%. Daniel has 400 shares of company. If Daniel’s annual income from the shares is$ 1000, find the face value of each share.\n\n9. Benjamin buys 400, twenty-dollar shares at a premium of $4 each and receives a dividend of 12%. Find: (i) the amount invested by Benjamin. (ii) his total income from the shares. (iii) percentage return on his money. 10. (b) Alexander buys 350 hundred-dollar shares of a company at a premium of 20% from the market. The company pays 12% dividend annually. Find (i) the investment made by the Alexander, (ii) his annual income from the shares, and (iii) the rate of return from the shares. Answers for the worksheet on income and return from shares are given below: Answers: 1.$ 800\n\n2. (i) 200\n\n(ii) $2400 3.$2200\n\n4. (i) 100\n\n(ii) $600 (iii) 7$$\\frac{1}{2}$$% 5. (i)$ 6400\n\n(ii) 9.6 %\n\n6. $13000 7.$ 2250\n\n8. $25 9. (i)$ 9600\n\n(ii) $960 (iii)$ 10%\n\n10. (i) $42000 (ii)$ 4200\n\n(iii) 10 %\n\nShares and Dividends" ]
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https://www.studyfame.com/cpp-program/check-strong-number-in-cpp
[ "# C++ program to check strong number or not\n\nIn this example, you will write a C++ program to check whether the user entered integer is a Strong number or not. for example, 1, 2, 145, etc are Strong numbers.\n\n## What is a Strong number?\n\nThe Sum of the factorial of individual digits of the number is equal to the original number is called a Strong number.\n\nFor example 145 = 1! + 4! + 5! i.e 1 + 24 + 120.", null, "## Strong number program in C++\n\n``````\n#include<iostream>\n\nusing namespace std;\nint main() {\nint remainders, fact, sum = 0, i, num, copynum;\ncout << \"enter the number:\";\ncin >> num;\ncopynum = num;\nwhile (num != 0)\n{\nremainders = num % 10;\nfact = 1;\n/* find the factorial remainders*/\nfor (i = 1; i <= remainders; i++)\n{\nfact = fact * i;\n}\n/* Add factorial of individual digit */\nsum = sum + fact;\nnum = num / 10;\n}\nif (sum == copynum) {\ncout << copynum << \" is strong numbers\";\n\n} else {\ncout << copynum << \" is not strong numbers\";\n}\nreturn 0;\n}\n``````\noutput\n```enter the number:145\n145 is strong numbers```\n```enter the number:123\n123 is not strong numbers```\n\n## Logic to check Strong number.\n\n1. Take a number from the user and stored it in variable` num`.\n2. Iterate the loop until the `num` value becomes 0.\n3. Get the last digit of the number and stored it in variable `remainder` i.e remainders = num % 10.\n4. find the factorial of the `remainder` stored it in variable `fact`.\n5. now add the factorial value to the `sum`.\n6. Remove the last digit of a number i.e`num/10`.\n7. repeat steps 3 to 6 until num becomes zero.\n\n## C++ program to Check Strong numbers using the function\n\nIn this program, you will check Strong Numbers by using User defined function.\n\n``````#include<iostream>\nusing namespace std;\n\nint checkStrongNumber(int num);\nint getfactorial(int remainders);\n\nint main() {\nint sum, num;\ncout << \"enter the number:\";\ncin >> num;\n\n/* call the function */\nsum = checkStrongNumber(num);\n\nif (sum == num) {\ncout << num << \" is strong numbers\";\n\n} else {\ncout << num << \" is not strong numbers\";\n}\nreturn 0;\n}\n\nint getfactorial(int remainders){\nint fact=1,i;\nfor (i = 1; i <= remainders; i++)\n{\nfact = fact * i;\n}\nreturn fact;\n}\nint checkStrongNumber(int num){\nint remainders, fact, sum = 0;\nwhile (num != 0)\n{\nremainders = num % 10;\n/* find the factorial of remainders using get factorial function*/\nfact = getfactorial(remainders);\n\n/* Add factorial of individual digit */\nsum = sum + fact;\nnum = num / 10;\n}\nreturn sum;\n}\n``````\n\nOutput of this program will same as the above program" ]
[ null, "https://www.studyfame.com/images/strong-number-cpp.jpg", null ]
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https://www.interborosd.org/site/default.aspx?PageType=14&DomainID=22&PageID=40&ModuleInstanceID=72&IsMoreExpandedView=True
[ "by Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:\n\nby Length:" ]
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{"ft_lang_label":"__label__en","ft_lang_prob":0.8309034,"math_prob":0.9999988,"size":8989,"snap":"2021-43-2021-49","text_gpt3_token_len":3710,"char_repetition_ratio":0.3864218,"word_repetition_ratio":0.20902906,"special_character_ratio":0.49983314,"punctuation_ratio":0.12383613,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99647677,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-10-28T02:43:16Z\",\"WARC-Record-ID\":\"<urn:uuid:413ad49f-7f8a-4e78-869d-05eb384fc4fd>\",\"Content-Length\":\"559927\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:78f8d085-fff7-429d-b889-b84a5147d980>\",\"WARC-Concurrent-To\":\"<urn:uuid:7a119c41-c91f-4499-8216-b6abe8878ac4>\",\"WARC-IP-Address\":\"18.67.65.98\",\"WARC-Target-URI\":\"https://www.interborosd.org/site/default.aspx?PageType=14&DomainID=22&PageID=40&ModuleInstanceID=72&IsMoreExpandedView=True\",\"WARC-Payload-Digest\":\"sha1:IFZQN3P7XN5CYDXSNIQ6ZBQOS6NZVFNA\",\"WARC-Block-Digest\":\"sha1:HK2JIQGBSS54HUJDHTHEEBFVHZS2LQGH\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-43/CC-MAIN-2021-43_segments_1634323588246.79_warc_CC-MAIN-20211028003812-20211028033812-00141.warc.gz\"}"}
https://www.justintools.com/unit-conversion/force.php?k1=millinewtons&k2=centigrams-force
[ "Please support this site by disabling or whitelisting the Adblock for \"justintools.com\". I've spent over 10 trillion microseconds (and counting), on this project. This site is my passion, and I regularly adding new tools/apps. Users experience is very important, that's why I use non-intrusive ads. Any feedback is appreciated. Thank you. Justin XoXo :)\n\n# FORCE Units Conversionmillinewtons to centigrams-force\n\n1 Millinewtons\n= 10.197162129779 Centigrams Force\n\nCategory: force\nConversion: Millinewtons to Centigrams Force\nThe base unit for force is newtons (Derived SI Unit)\n[Millinewtons] symbol/abbrevation: (mN)\n[Centigrams Force] symbol/abbrevation: (cgf)\n\nHow to convert Millinewtons to Centigrams Force (mN to cgf)?\n1 mN = 10.197162129779 cgf.\n1 x 10.197162129779 cgf = 10.197162129779 Centigrams Force.\nAlways check the results; rounding errors may occur.\n\nDefinition:\nIn relation to the base unit of [force] => (newtons), 1 Millinewtons (mN) is equal to 0.001 newtons, while 1 Centigrams Force (cgf) = 9.80665E-5 newtons.\n1 Millinewtons to common force units\n1 mN = 0.001 newtons (N)\n1 mN = 0.00022480892365534 pounds force (lbf)\n1 mN = 12137.804779884 atomic units of force (auf)\n1 mN = 1.0E+15 attonewtons (aN)\n1 mN = 10.197162129779 centigrams force (cgf)\n1 mN = 0.1 centinewtons (cN)\n1 mN = 0.0001 decanewtons (daN)\n1 mN = 0.01 decinewtons (dN)\n1 mN = 100 dynes (dyn)\n1 mN = 1.0E-21 exanewtons (EN)\nMillinewtonsto Centigrams Force (table conversion)\n1 mN = 10.197162129779 cgf\n2 mN = 20.394324259559 cgf\n3 mN = 30.591486389338 cgf\n4 mN = 40.788648519117 cgf\n5 mN = 50.985810648896 cgf\n6 mN = 61.182972778676 cgf\n7 mN = 71.380134908455 cgf\n8 mN = 81.577297038234 cgf\n9 mN = 91.774459168014 cgf\n10 mN = 101.97162129779 cgf\n20 mN = 203.94324259559 cgf\n30 mN = 305.91486389338 cgf\n40 mN = 407.88648519117 cgf\n50 mN = 509.85810648896 cgf\n60 mN = 611.82972778676 cgf\n70 mN = 713.80134908455 cgf\n80 mN = 815.77297038234 cgf\n90 mN = 917.74459168014 cgf\n100 mN = 1019.7162129779 cgf\n200 mN = 2039.4324259559 cgf\n300 mN = 3059.1486389338 cgf\n400 mN = 4078.8648519117 cgf\n500 mN = 5098.5810648896 cgf\n600 mN = 6118.2972778676 cgf\n700 mN = 7138.0134908455 cgf\n800 mN = 8157.7297038234 cgf\n900 mN = 9177.4459168014 cgf\n1000 mN = 10197.162129779 cgf\n2000 mN = 20394.324259559 cgf\n4000 mN = 40788.648519117 cgf\n5000 mN = 50985.810648896 cgf\n7500 mN = 76478.715973345 cgf\n10000 mN = 101971.62129779 cgf\n25000 mN = 254929.05324448 cgf\n50000 mN = 509858.10648896 cgf\n100000 mN = 1019716.2129779 cgf\n1000000 mN = 10197162.129779 cgf\n1000000000 mN = 10197162129.779 cgf\n(Millinewtons) to (Centigrams Force) conversions" ]
[ null ]
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https://discuss.bluerobotics.com/t/using-ping1d-and-servo-with-arduino-nano-every/11715
[ "# Using ping1d and servo with arduino nano every\n\nHi there,\n\nI am trying to control a servo and the ping1d at the same time using an arduino nano every board. At the moment, I have code which sweeps the motor between a minimum and maximum angle in 1 degree implements with a delay at each increment, which works well and the motor moves smoothly. But, when I add code to take distance measurements at each 1 degree increment the motor becomes very jerky (moving backwards and forwards) and often moves out of sync with what is being displayed in the serial monitor. I would like it so the motor moves smoothly while taking distance measurements. I’m new to arduino so I really have no idea what could be causing this issue.\n\nI just wondered if anybody here has had a similar problem and knew how to fix it or if anyone can spot something obviously wrong with my code? Thank you!\n\n``````#include <Servo.h>\n#include \"ping1d.h\"\n#include \"SoftwareSerial.h\"\nstatic const uint8_t arduinoRxPin = 9;\nstatic const uint8_t arduinoTxPin = 10;\nSoftwareSerial pingSerial = SoftwareSerial(arduinoRxPin, arduinoTxPin);\nstatic Ping1D ping { pingSerial };\nServo myservo;\nint pos;\nint timed = 1000/2;\nint min_angle = 0;\nString max_angle;\nint increment = 5;\n\nvoid setup() {\n// put your setup code here, to run once:\nmyservo.attach(3);\npingSerial.begin(9600);\nSerial.begin(115200);\nwhile (!ping.initialize()){\nSerial.println(\"/nPing device failed to initialize!\");\nSerial.print(\"green wire connects to: \");\nSerial.println(arduinoTxPin);\nSerial.print(\"white wire connects to: \");\nSerial.println(arduinoRxPin);\ndelay(1000);\n}\n}\n\nvoid loop() {\n// put your main code here, to run repeatedly:\nint pos=0;\nmyservo.write(pos);\nSerial.println(\"Enter maximum angle value\");\n\nwhile (Serial.available()==0){}\nSerial.print(\"Rotating \");\nSerial.print(max_angle);\nSerial.println(\" degrees\");\n\nfor (pos = min_angle; pos <= max_angle.toInt(); pos+=increment){\nwhile (ping.update()==0){}\nmyservo.write(pos);\nSerial.print(\"Angle: \");\nSerial.println(pos);\nSerial.print(\"Distance: \");\nSerial.println(ping.distance());\ndelay(timed);\n}\n\nfor (pos = max_angle.toInt(); pos >= min_angle; pos-=increment){\nwhile (ping.update()==0){}\nmyservo.write(pos);\nSerial.print(\"Angle: \");\nSerial.println(pos);\nSerial.print(\"Distance: \");\nSerial.println(ping.distance());\ndelay(timed);\n}\n}\n``````\n\nHi @ben2, welcome to the forum", null, "The problem here is basically that the `Servo` and `SoftwareSerial` libraries both use the same timer, so when software serial is communicating with the Ping the servo timing gets funky, which causes the fluctuations.\n\nTo avoid that, there are a few different possible approaches:\n\n1. Use a different servo library, which doesn’t use the same timer\n2. Use an Arduino with additional timers, and support for HardwareSerial\n3. Stop commanding the servo while communicating with the Ping\n\nThe code below takes the last approach, which is potentially the easiest way of avoiding the issue, but it comes with the caveat that if the servo is under load it may have undesired rotations while communicating with the Ping, because it’s not being commanded to stay in place at that time.\n\n``````#include <Servo.h>\n#include \"ping1d.h\"\n#include \"SoftwareSerial.h\"\n\n/* NOTE: Servo and SoftwareSerial clash!\n* Both libraries use the same timer.\n* This code avoids clashes by only keeping the servo attached while moving.\n* If the servo is under load, undesired movement may occur while using Ping.\n*/\n\n/* Ping Sonar */\nstatic const uint8_t arduinoRxPin = 9;\nstatic const uint8_t arduinoTxPin = 10;\nSoftwareSerial pingSerial = SoftwareSerial(arduinoRxPin, arduinoTxPin);\nstatic Ping1D ping { pingSerial };\n// speed of sound in operating medium (~343 m/s for air, ~1500 m/s for seawater)\nstatic const uint32_t v_sound = 343000; // mm/s\n\n/* Servo */\nvoid moveServo(int angle, bool display=true); // declare moveServo function\nServo myservo;\n// angles, in degrees\nint pos = 0;\nint max_angle;\nint min_angle = 0;\nint increment = 5;\nstatic const uint8_t servoPin = 3;\n// ms to allow for each rotation\n// can reduce multiplier to speed up, until not enough time to turn\nuint32_t moveTime = 50 * abs(increment);\n\nvoid setup() {\n// put your setup code here, to run once:\n// Initialise serial connections\npingSerial.begin(9600);\nSerial.begin(115200);\n// Initialise Ping device (ensure correctly connected)\nwhile (!ping.initialize()){\nSerial.println(\"/nPing device failed to initialize!\");\nSerial.print(\"green wire connects to: \");\nSerial.println(arduinoTxPin);\nSerial.print(\"white wire connects to: \");\nSerial.println(arduinoRxPin);\ndelay(1000);\n}\nping.set_speed_of_sound(v_sound);\n// Initialise servo position\nmoveServo(min_angle, false);\n}\n\nvoid loop() {\n// put your main code here, to run repeatedly:\nSerial.println(\"Enter maximum angle value\");\n\nwhile (Serial.available()==0){}\nSerial.print(\"Rotating \");\nSerial.print(max_angle);\nSerial.println(\" degrees\");\n\nfor (pos = min_angle; pos <= max_angle; pos+=increment){\nmoveServo(pos);\n}\n\nfor (pos = max_angle; pos >= min_angle; pos-=increment){\nmoveServo(pos);\n}\n}\n\n/* Gets a sonar measurement, and displays the results. */\n// request measurements until one arrives\nwhile (!ping.update()){}\n// display the results\nSerial.print(\"Distance: \");\nSerial.print(ping.distance());\nSerial.print(\" mm\\tConfidence: \");\nSerial.print(ping.confidence());\nSerial.println(\"%\");\n}\n\n/* Engages the servo while moving to the requested 'angle'.\n* If 'display' is true, prints the current angle request.\n*/\nvoid moveServo(int angle, bool display) {\nmyservo.attach(servoPin);\nmyservo.write(angle);\ndelay(moveTime);\nif (display) {\nSerial.print(\"Angle: \");\nSerial.print(angle);\nSerial.print(\"\\t\");\n}\nmyservo.detach();\n}\n``````\n\nBy the way, I’ve edited your post to have your code in a code block, so it’s easier for others to read, and to copy for testing. You can read about how to do that (and more) in the How to Use the Blue Robotics Forums post", null, "1 Like\n\nHi @EliotBR,\n\nThank you so much for making that clear and offering those solutions!\nI have given the code ago and it has solved my issue and so far the servo hasn’t made any undesired rotations!\n\nAgain Thank you!", null, "1 Like\n\nHi all\nThe servo rocks back and force. Have tried different Nano boards and servos, nothing makes any difference. The Arduino serial monitor is stable and the measurements changes with depth of water as it should. If I use a delay 1000 in the code the rocking smooths a bit, but its still there. What could make these erratic behavior from the servo?\nAnd how to solve it?\n\nCode:\n\n``````#include \"ping1d.h\"\n#include \"SoftwareSerial.h\"\n#include <Servo.h>\n\nServo myservo;\n\n// Pin D10 as arduino rx (rx cable from Ping = White).\n// Pin D11 as arduino tx (tx cable from Ping = Green).\nstatic const uint8_t arduinoRxPin = 10;\nstatic const uint8_t arduinoTxPin = 11;\nSoftwareSerial pingSerial = SoftwareSerial(arduinoRxPin, arduinoTxPin);\nstatic Ping1D ping { pingSerial };\n\nstatic const uint8_t ledPin = 13;\n\nvoid setup() {\npingSerial.begin(9600);\nSerial.begin(115200);\nmyservo.attach(9); // (9)=D9\npinMode(ledPin, OUTPUT);\n}\n\nvoid loop() {\nif (ping.update()) {\ndelay(1000);\nSerial.println(ping.distance());\n\nif(ping.distance()<=800)\n{ myservo.write(55) ; } // 55 är det minsta som går. Mekanisk begränsning där. = Max vinkel för uppgång.\n\nif(ping.distance()>=850)\n{ myservo.write(63); }\n\nif(ping.distance()>=900)\n{ myservo.write(71); }\n\nif(ping.distance()>=950)\n{ myservo.write(74); }\n\nif(ping.distance()>=1000)\n{ myservo.write(77); }\n\nif(ping.distance()>=1050)\n{ myservo.write(80); }\n\nif(ping.distance()>=1100)\n{ myservo.write(90); }\n\nif(ping.distance()>=1150)\n{ myservo.write(100); }\n\nif(ping.distance()>=1200)\n{ myservo.write(110); }\n\nif(ping.distance()>=2400)\n{ myservo.write(120); }\n\nif(ping.distance()>=2500)\n{ myservo.write(130); }\n\nif(ping.distance()>=2600)\n{ myservo.write(140); }\n\nif(ping.distance()>=2700)\n{ myservo.write(150); }\n\nif(ping.distance()>=2800)\n{ myservo.write(165); } //165 är det högst som går. Mekanisk begränsning där. = Max vinkel för nedgång.\n\n}\n\n// Toggle the LED to show that the program is running\n}\n``````\n\nHej @Loola,\n\nI’ve moved your post here because it’s on the same topic - I’d recommend you look at the discussion above", null, "By the way, at the moment your code writes to the servo for every checked value less than the measured distance, which could have the effect of somewhat jerkily moving the servo to several positions on the way rather than a single smooth motion based on only the highest threshold region the distance measurement is in. Your code also currently does nothing when the measured distance is between 800 and 850.\n\nYou may wish to change your code to do something like\n\n``````... // includes and initialisation\nuint32_t latest_distance;\n\n... // setup\n\nvoid loop() {\nif (ping.update()) {\nlatest_distance = ping.distance();\nSerial.println(latest_distance);\n\nif (latest_distance <= 800) {\nmyservo.write(55); // 55 är det minsta som går. Mekanisk begränsning där. = Max vinkel för uppgång.\n} else if (latest_distance <= 850) { // 800 < latest_distance <= 850\nmyservo.write(63);\n} else if (...) {\n...\n} else if (latest_distance <= 2800) {\nmyservo.write(160);\n} else { // latest_distance > 2800\nmyservo.write(165); // 165 är det högst som går. Mekanisk begränsning där. = Max vinkel för nedgång.\n}\ndelay(1000); // Delay after updating servo position\n}\n... // toggle LED\n}\n``````\n\nIt may also be worth considering using a mathematical function to calculate the servo position from the measured distance, if you don’t want a large `if-else` block of individual conditions.\n\nAs an example, if you wanted the servo angle evenly (linearly) spaced between physical limits, you could do something like:\n\n``````... // includes and initialisation\n// Mekanisk begränsningar\n#define SERVO_MIN 55 // det minsta som går. = Max vinkel för uppgång.\n#define SERVO_MAX 165 // det högst som går. = Max vinkel för nedgång.\n// Distance thresholds\n#define DISTANCE_MIN 800\n#define DISTANCE_MAX 2800\n\nuint32_t latest_distance;\nuint16_t servo_angle;\n\n... // setup\n\nvoid loop() {\nif (ping.update()) {\nlatest_distance = ping.distance();\nSerial.println(latest_distance);\n\n// servo angle determined as linear scale from distance thresholds\nservo_angle = map(latest_distance, DISTANCE_MIN, DISTANCE_MAX, SERVO_MIN, SERVO_MAX);\n// constrain servo angle to specified limits\nservo_angle = constrain(servo_angle, SERVO_MIN, SERVO_MAX);\nmyservo.write(servo_angle);\ndelay(1000);\n}\n... // toggle LED\n}\n``````\n\n(e.g. `constrain(map(latest_distance, 800, 2800, servo_min, servo_max), servo_min, servo_max)`" ]
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https://gis.stackexchange.com/questions/267110/how-to-update-attributes-from-list-of-values-stored-in-a-excel-or-csv-table
[ "# How to update attributes from list of values stored in a Excel or CSV table?\n\nI need a Python program regarding update attributes. I have 20 shapefiles and in each shapefile, attributes to be updated based another field", null, "My requirement is to update the codes in CODE field as per the name (Example list is given right side) for 50000 features, like if name is AAA, then code will be 419, if name is ABC, then code is 420.\n\nI have 850 different codes and I want to update all 850 at once in shapefile. I can update two or three values by using below code but I don't know how to update 850 codes.\n\n``````import arcpy\nfrom arcpy import env\n\nenv.workspace = r\"C:\\\\data\\test.shp\"\nenv.workspace = fc\nfclist = arcpy.ListFeatureClasses()\n\nfor fc in fclist :\nfields = [\"name\", \"code\"]\n\nwith arcpy.da.UpdateCursor(fc, fields) as cursor:\nfor row in cursor:\nif row == \"AAA\"\nrow = 419\nelif:\nif .row == \"BBB\"\nrow = 420\ncursor.updateRow(row)\n\nprint \"codes updated\"\n``````\n\nIf I want to update all 850 codes, need to write if...else statement 850 times to update all codes. I have saved all names and codes in Excel. So instead of writing many if else statements, please help how to update all codes based on name by using Excel or CSV file.\n\nIf you need to write a lot of if statements you should consider using a dictionary instead. Code below will read a csv file and create a dictionary of names and codes and then use the dictionary in the Updatecursor for all shapefiles in a folder. Backup your data Before you try it:\n\n``````import arcpy, csv\n\nshapefile_folder = r'C:\\folder'\narcpy.env.workspace = shapefile_folder\nshapefile_fields_to_update = [\"name\",\"code\"]\n\ncodefile = r\"C:\\Program123\\codes.csv\"\n\nwith open(codefile, 'r') as f:\ndict = {k:v for (k,v) in reader} #Dictionary of names and codes is created here\n\nfor shapefile in arcpy.ListFeatureClasses():\nwith arcpy.da.UpdateCursor(shapefile, shapefile_fields_to_update) as cursor:\nfor row in cursor:\nif row in dict:\nrow=int(dict[row]) #And used here\ncursor.updateRow(row)\nelse:", null, "" ]
[ null, "https://i.stack.imgur.com/ynNa2.jpg", null, "https://i.stack.imgur.com/YZkFm.png", null ]
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https://documen.tv/question/which-statement-is-true-if-the-refractive-inde-of-a-medium-a-is-greater-than-that-of-medium-b-14983977-35/
[ "# which statement is true if the refractive index of a medium a is greater than that of medium b\n\nQuestion\n\nwhich statement is true if the refractive index of a medium a is greater than that of medium b\n\nin progress 0\n1 year 2021-09-04T22:13:23+00:00 2 Answers 4 views 0\n\nb.\n\nExplanation:\n\nB) total internal reflection is possible when light travels from medium a to medium b.\n\nExplanation:\n\nThe index of refraction of a medium is the ratio between the speed of light in a vacuum (c) and the speed of light in that medium (v):\n\n$$n=\\frac{c}{v}$$\n\nWhen a ray of light crosses the interface between two mediums, it undergoes refraction, which means it is is bent and its speed changes, according to Snell’s law:\n\n$$n_1 sin \\theta_1 = n_2 sin \\theta_2$$\n\nwhere\n\n$$n_1,n_2$$ are the index of refraction of medium 1 and 2\n\n$$\\theta_1, \\theta_2$$ are the angle of incidence and of refraction\n\nWe can rewrite the equation as\n\n$$sin \\theta_2 = \\frac{n_1}{n_2}sin \\theta_1$$\n\nFrom this equation, we observe that if $$n_1>n_2$$, then the ratio $$\\frac{n_1}{n_2}>1$$; however, $$sin \\theta_2$$ cannot be larger than 1. This means that in this case, there will be a maximum value of $$\\theta_1$$ above which refraction no longer occurs: in that situation, the light coming from the 1st medium is entirely reflected inside the 1st medium, and this phenomenon is called total internal reflection.\n\nThe value of the critical angle above which total internal reflection occurs is\n\n$$\\theta_c = sin^{-1}(\\frac{n_2}{n_1})$$\n\nIn this situation, we have:\n\n$$n_A > n_B$$ (index of refraction of A is larger than index of refraction of B)\n\nAlso, the index of refraction of air is 1.00, so it is lower than the index of refraction of every medium:\n\n$$n_A > n_B > n_{air}$$\n\nSince total internal reflection occurs only if the index of refraction of medium 1 is higher than that of medium 2, the correct statements are:\n\nB) total internal reflection is possible when light travels from medium a to medium b." ]
[ null ]
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https://arthurvandermerwe.com/2015/05/30/dukpt-explained-with-examples/
[ "# DUKPT Explained with examples\n\nDerived Unique Key Per Transaction (DUKPT) process that’s described in Annex A of ANS X9.24-2004.\n\nIt’s generally considered to be complex, but I’ve simplified it slightly with the help of online resources.\n\n## Key Management\n\nHere’s a basic outline of the technique:\n\n1. You’re given a Base Derivation Key (BDK), which you assign to a swiper (note that the same BDK can be assigned to multiple swipers).\n2. You’ll use the BDK along with the device’s own unique Key Serial Number (KSN) to generate an Initial PIN Encryption Key (IPEK) for the device.\n3. You’ll assign this IPEK to a swiper, which uses it to irreversibly generate a list of future keys, which it’ll use to encrypt its messages.\n4. The swiper’s KSN is used along with one of its future keys to encrypt a message, and after each swipe it’ll increment the value of its KSN.\n5. Whenever a swiper takes a card it formats the card’s information into a series of tracks, each track having a particular set of information (e.g. card number, holder’s name, expiration date).\n6. The swiper usually encrypts these tracks using one of its generated future keys (called the “Session Key”) along with its current KSN. It’ll then increment the value of its KSN and discard the future key it used.\n7. At this point you’ll probably have an encrypted track along with the KSN the swiper used to encrypt it.\n8. It’s your responsibility to determine what BDK was used to initialize this device, and from there you’ll use the BDK and KSN to rederive the IPEK, which is used to rederive the Session Key, which is finally used to decrypt the message.\n\nThere’s a lot of technical information to be said about key management, but this isn’t the place for that. In some cases your provider/manufacturer (e.g. MagTek) will supply you with swipers that need to be initialized with an IPEK, and your supplier will usually have a manual that walks you through that process. If you’re doing encryption/decryption through a third party who also supplies swipers, they may have already loaded the devices with that information; what’s more is they may not even given you the BDK that belongs to your device in order to reduce the risk of security threats.\n\nNote: Key management is beyond the scope of this explanation. Whatever you do with your keys, just make sure it’s secure.\n\nOne methodology I’ve seen that’ll allow you to associate a particular KSN to a BDK is to take the current KSN you’ve been given, mask it to retrieve the Initial Key Serial Number (IKSN), and look up the BDK in a table that maps IKSNs to BDKs:\n\nExample:\n\n``````ksn = FFFF9876543210E00008\niksn = ksn & FFFFFFFFFFFFFFE00000 // FFFF9876543210E00000\n``````\n\nYou’d then have a table that looks like:\n\nIKSN BDK\n0xFFFF9876543210E00000 0123456789ABCDEFFEDCBA9876543210\n\nFrom which you could easily grab the BDK `0123456789ABCDEFFEDCBA9876543210`.\n\n## Algorithm\n\nNote: Assume that all numeric values are hexadecimal numbers, or the representation of a sequence of bytes as a hexadecimal number.\n\nThe following are the BDK, KSN, and encrypted track message (cryptogram) we’ve been given:\n\n``````bdk = 0123456789ABCDEFFEDCBA9876543210\nksn = FFFF9876543210E00008\n``````\n\nHere’s an example of the unencrypted track 1 data (cryptogram above), and below that is its value in hex; this is what we’ll get after successfully decrypting the cryptogram:\n\n``````%B5452300551227189^HOGAN/PAUL ^08043210000000725000000?\n2542353435323330303535313232373138395E484F47414E2F5041554C2020202020205E30383034333231303030303030303732353030303030303F00000000\n``````\n\nNote: As you’re probably already aware, this algorithm is best described using big numbers, which can’t be represented as literals in some programming languages (like Java or C#). However, many languages have classes that allow you to represent big numbers in other ways (e.g., java.math.BigInteger, System.Numerics.BigInteger). It’s your job to adapt this algorithm so that it can be represented in your language of choice. Two small problems I encountered were ensuring the correct endianness and signedness were being used (this algorithm requires the byte order to be big endian and that unsigned integers are used). I made a utility class called BigInt to do this for me.\n\nFirst, let’s define a few standard functions:\n\n• DES and Triple DES refer to their respective cryptographic algorithms. Most programming languages have access to some implementation of these ciphers either through OpenSSL or Bouncy Castle. These ciphers are initialized with a zeroed out IV of 8 bytes, they’re zero-padded, and use Cipher-Block Chaining (CBC). Let’s define the signatures for these standard functions that’ll be used throughout this algorithm:\n• `DesEncrypt(key, message) -> returns cryptogram`\n• `DesDecrypt(key, cryptogram) -> returns message`\n• `TripleDesEncrypt(key, message) -> returns cryptogram`\n• `TripleDesDecrypt(key, cryptogram) -> returns message`\n\nFirst we must create the IPEK given then KSN and BDK:\n\n``````CreateIpek(ksn, bdk) {\nreturn TripleDesEncrypt(bdk, (ksn & KsnMask) >> 16) << 64\n}\n``````\n\nNow we can get the IPEK:\n\n``````ipek = CreateIpek(ksn, bdk)\n= CreateIpek(FFFF9876543210E00008, 0123456789ABCDEFFEDCBA9876543210)\n= 6AC292FAA1315B4D858AB3A3D7D5933A\n``````\n\nAfter that we need a way to get the Session Key (this one is more complicated):\n\n``````CreateSessionKey(ipek, ksn) {\nreturn DeriveKey(ipek, ksn) ^ FF00000000000000FF\n}\n``````\n\nThe DeriveKey method finds the IKSN and generates session keys until it gets to the one that corresponds to the current KSN. We define this method as:\n\n``````DeriveKey(ipek, ksn) {\nksnReg = ksn & FFFFFFFFFFE00000\ncurKey = ipek\nfor (shiftReg = 0x100000; shiftReg > 0; shiftReg >>= 1)\nif ((shiftReg & ksn & 1FFFFF) > 0)\ncurKey = GenerateKey(curKey, ksnReg |= shiftReg)\nreturn curKey\n}\n``````\n\nWhere the GenerateKey method looks like:\n\n``````GenerateKey(key, ksn) {\nreturn EncryptRegister(key ^ KeyMask, ksn) << 64\n| EncryptRegister(key, ksn)\n}\n``````\n\nAnd EncryptRegister looks like:\n\n``````EncryptRegister(key, reg) {\nreturn (key & FFFFFFFFFFFFFFFF) ^ DesEncrypt((key & FFFFFFFFFFFFFFFF0000000000000000) >> 64,\nkey & FFFFFFFFFFFFFFFF ^ reg)\n}\n``````\n\nThen you can generate the Session Key given the IPEK and KSN:\n\n``````key = CreateSessionKey(ipek, ksn)\n= CreateSessionKey(6AC292FAA1315B4D858AB3A3D7D5933A, FFFF9876543210E00008)\n= 27F66D5244FF621EAA6F6120EDEB427F\n``````\n\nWhich can be used to decrypt the cryptogram:\n\n``````message = TripleDesDecrypt(key, cryptogram)\n= 2542353435323330303535313232373138395E484F47414E2F5041554C2020202020205E30383034333231303030303030303732353030303030303F00000000\n= %B5452300551227189^HOGAN/PAUL ^08043210000000725000000?\n``````\n\nThat’s it, you’re done!\n\n1.", null, "Niall says:\n\nArthur, first off: incredible article, thank you.\n\nTwo questions:\n1. You supply a single key to the TripleDesEncrypt – can you provide any insight into how the 3 (or 2?) keys for the three-part DES encryption are generated? do i need to care?\n2. What’s the value of the KeyMask you use to xor against the BDK during one of the TripleDesEncrypt steps?\n\n1.", null, "arthurvdmerwe says:\n\nHi Niall, here is the C# example for the article. Mask is hardcoded and TDES function is used from the Microsoft crypto library.\n``` using System; using System.Linq; using System.Numerics; using System.Security.Cryptography;```\n\n``` public static class Dukpt { private static readonly BigInteger Reg3Mask = BigInt.FromHex(\"1FFFFF\"); private static readonly BigInteger ShiftRegMask = BigInt.FromHex(\"100000\"); private static readonly BigInteger Reg8Mask = BigInt.FromHex(\"FFFFFFFFFFE00000\"); private static readonly BigInteger Ls16Mask = BigInt.FromHex(\"FFFFFFFFFFFFFFFF\"); private static readonly BigInteger Ms16Mask = BigInt.FromHex(\"FFFFFFFFFFFFFFFF0000000000000000\"); private static readonly BigInteger KeyMask = BigInt.FromHex(\"C0C0C0C000000000C0C0C0C000000000\"); private static readonly BigInteger PekMask = BigInt.FromHex(\"FF00000000000000FF\"); private static readonly BigInteger KsnMask = BigInt.FromHex(\"FFFFFFFFFFFFFFE00000\"); private static readonly BigInteger DekMask = BigInt.FromHex(\"0000000000FF00000000000000FF0000\"); public static BigInteger CreateBdk(BigInteger key1, BigInteger key2) { return key1 ^ key2; } public static BigInteger CreateIpek(BigInteger ksn, BigInteger bdk) { return Transform(\"TripleDES\", true, bdk, (ksn & KsnMask) >> 16) <> 16); } public static BigInteger CreateSessionKey(BigInteger ipek, BigInteger ksn) { return DeriveKey(ipek, ksn) ^ PekMask; } // Added in the handling for decrypting IDTech tracks public static BigInteger CreateSessionKeyIdTech(BigInteger ipek, BigInteger ksn) { var key = DeriveKey(ipek, ksn) ^ DekMask; return Transform(\"TripleDES\", true, key, (key & Ms16Mask) >> 64) < 0; shiftReg >>= 1) if ((shiftReg & ksn & Reg3Mask) > 0) curKey = GenerateKey(curKey, ksnReg |= shiftReg); return curKey; } public static BigInteger GenerateKey(BigInteger key, BigInteger ksn) { return EncryptRegister(key ^ KeyMask, ksn) <> 64, (curKey & Ls16Mask ^ reg8)); } public static BigInteger Transform(string name, bool encrypt, BigInteger key, BigInteger message) { using (var cipher = SymmetricAlgorithm.Create(name)) { var k = key.GetBytes(); // gets the next multiple of 8 cipher.Key = new byte[Math.Max(0, GetNearestWholeMultiple(k.Length, 8) - k.Length)].Concat(key.GetBytes()).ToArray(); cipher.IV = new byte; cipher.Mode = CipherMode.CBC; cipher.Padding = PaddingMode.Zeros; using (var crypto = encrypt ? cipher.CreateEncryptor() : cipher.CreateDecryptor()) { var data = message.GetBytes(); // Added the GetNearestWholeMultiple here. data = new byte[Math.Max(0, GetNearestWholeMultiple(data.Length, 8) - data.Length)].Concat(message.GetBytes()).ToArray(); return BigInt.FromBytes(crypto.TransformFinalBlock(data, 0, data.Length)); } } } // Gets the next multiple of 8 // Works with both scenarios, getting 7 bytes instead of 8 and works when expecting 16 bytes and getting 15. private static int GetNearestWholeMultiple(decimal input, int multiple) { var output = Math.Round(input / multiple); if (output == 0 && input > 0) output += 1; output *= multiple; return (int)output; } public static byte[] Encrypt(string bdk, string ksn, byte[] track) { return Transform(\"TripleDES\", true, CreateSessionKey(CreateIpek( BigInt.FromHex(ksn), BigInt.FromHex(bdk)), BigInt.FromHex(ksn)), BigInt.FromBytes(track)).GetBytes(); } public static byte[] Decrypt(string bdk, string ksn, byte[] track) { return Transform(\"TripleDES\", false, CreateSessionKey(CreateIpek( BigInt.FromHex(ksn), BigInt.FromHex(bdk)), BigInt.FromHex(ksn)), BigInt.FromBytes(track)).GetBytes(); } public static byte[] DecryptIdTech(string bdk, string ksn, byte[] track) { return Transform(\"TripleDES\", false, CreateSessionKeyIdTech(CreateIpek( BigInt.FromHex(ksn), BigInt.FromHex(bdk)), BigInt.FromHex(ksn)), BigInt.FromBytes(track)).GetBytes(); } } public static class BigInt { public static BigInteger FromHex(string hex) { return BigInteger.Parse(\"00\" + hex, System.Globalization.NumberStyles.HexNumber); } public static BigInteger FromBytes(byte[] bytes) { return new BigInteger(bytes.Reverse().Concat(new byte[] { 0 }).ToArray()); } ```\n\n``` public static byte[] GetBytes(this BigInteger number) { return number.ToByteArray().Reverse().SkipWhile(b => b == 0).ToArray(); } } }```\n\n2.", null, "arthurvdmerwe says:\n\nexample of the usage of the code:\nvar test = “%B5452300551227189^HOGAN/PAUL ^08043210000000725000000?\\0\\0\\0\\0”;\n\n// Decrypting\nvar bdk = “0123456789ABCDEFFEDCBA9876543210”;\nvar ksn = “FFFF9876543210E00008”;\nvar decBytes = Dukpt.Decrypt(bdk, ksn, BigInt.FromHex(track).GetBytes());\nvar decrypted = Encoding.UTF8.GetString(decBytes);\nConsole.WriteLine(decrypted == test); // True\n\n// Encrypting\nvar encBytes = Dukpt.Encrypt(bdk, ksn, decBytes);\nvar encrypted = BitConverter.ToString(encBytes).Replace(“-“, “”);\nConsole.WriteLine(encrypted == track); // True\n\n1.", null, "Niall says:\n\nYou complete legend, thank you very much indeed!\nepic kupos to you – this gear is a total head melt but you’ve explained it incredibly well.\n\n3.", null, "Pankaj Patil's Blog says:\n\nCan you please provide the Java Implementation of the Same.\n\n1.", null, "Yash Jain says:\n\nDid you get the Java implementation of the same then please share the same.\n\n2.", null, "yashcomplex says:\n\nIn case if you have got the java implementation of the same then please share with me @email yashcomplex@gmail.com\n\n1.", null, "arthurvdmerwe says:\n\nSorry, You would need to make your own Java implementation.\n\n3.", null, "Ashish Kumar says:\n\nbrother if u got the implementation of java code of the same .then plz share me.\n\n4.", null, "Krishna Telgave says:\n\nGreat Article.\n\n1.", null, "arthurvdmerwe says:\n\nHi Krishna, cannot at the moment unfortunately have to much work.\n\n1.", null, "Anonymous says:\n\nThanks anyway. I have successfully decrypted DUKPT PIN.\n\n2.", null, "Krishna Telgave says:\n\nThanks anyway. i have successfully decrypted DUKPT Pinblock.\n\n5.", null, "kishore.k (@kishorekollucse) says:" ]
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https://docs.neuroml.org/Userdocs/IzhikevichNetworkExample.html
[ "# A two population network of regular spiking Izhikevich neurons#\n\nNow that we have defined a cell, let us see how a network of these cells may be declared and simulated. We will create a small network of cells, simulate this network, and generate a plot of the spike times of the cells (a raster plot):", null, "Fig. 9 Spike times of neurons in 2 populations recorded from the simulation.#\n\nThe Python script used to create the model, simulate it, and generate this plot is below:\n\n#!/usr/bin/env python3\n\"\"\"\nCreate a simple network with two populations.\n\"\"\"\n\nimport random\nimport numpy as np\n\nfrom neuroml.utils import component_factory\nfrom pyneuroml import pynml\nfrom pyneuroml.lems import LEMSSimulation\nimport neuroml.writers as writers\n\nnml_doc = component_factory(\"NeuroMLDocument\", id=\"IzNet\")\n\"Izhikevich2007Cell\",\nid=\"iz2007RS0\",\nv0=\"-60mV\",\nC=\"100pF\",\nk=\"0.7nS_per_mV\",\nvr=\"-60mV\",\nvt=\"-40mV\",\nvpeak=\"35mV\",\na=\"0.03per_ms\",\nb=\"-2nS\",\nc=\"-50.0mV\",\nd=\"100pA\",\n)\n\n# Inspect the component, also show all members:\niz0.info(True)\n\n# Create a component of type ExpOneSynapse, and add it to the document\n\"ExpOneSynapse\", id=\"syn0\", gbase=\"65nS\", erev=\"0mV\", tau_decay=\"3ms\"\n)\n# Check what we have so far:\nnml_doc.info(True)\n# Also try:\nprint(nml_doc.summary())\n\n# create the network: turned of validation because we will add populations next\n\n# create the first population\nsize0 = 5\npop0 = component_factory(\"Population\", id=\"IzPop0\", component=iz0.id, size=size0)\n# Set optional color property. Note: used later when generating plots\n\n# create the second population\nsize1 = 5\npop1 = component_factory(\"Population\", id=\"IzPop1\", component=iz0.id, size=size1)\n\n# network should be valid now that it contains populations\nnet.validate()\n\n# create a projection from one population to another\n\"Projection\",\nid=\"proj\",\npresynaptic_population=pop0.id,\npostsynaptic_population=pop1.id,\nsynapse=syn0.id,\n)\n\n# We do two things in the loop:\n# - add pulse generator inputs to population 1 to make neurons spike\n# - create synapses between the two populations with a particular probability\nrandom.seed(123)\nprob_connection = 0.8\ncount = 0\nfor pre in range(0, size0):\n# pulse generator as explicit stimulus\n\"PulseGenerator\",\nid=\"pg_%i\" % pre,\ndelay=\"0ms\",\nduration=\"10000ms\",\namplitude=\"%f nA\" % (0.1 + 0.1 * random.random()),\n)\n\n\"ExplicitInput\", target=\"%s[%i]\" % (pop0.id, pre), input=pg.id\n)\n\n# synapses between populations\nfor post in range(0, size1):\nif random.random() <= prob_connection:\n\"Connection\",\nid=count,\npre_cell_id=\"../%s[%i]\" % (pop0.id, pre),\npost_cell_id=\"../%s[%i]\" % (pop1.id, post),\n)\ncount += 1\n\nnml_doc.info(True)\nprint(nml_doc.summary())\n\n# write model to file and validate\nnml_file = \"izhikevich2007_network.nml\"\nwriters.NeuroMLWriter.write(nml_doc, nml_file)\n\nprint(\"Written network file to: \" + nml_file)\npynml.validate_neuroml2(nml_file)\n\n# Create simulation, and record data\nsimulation_id = \"example_izhikevich2007network_sim\"\nsimulation = LEMSSimulation(\nsim_id=simulation_id, duration=1000, dt=0.1, simulation_seed=123\n)\nsimulation.assign_simulation_target(net.id)\nsimulation.include_neuroml2_file(nml_file)\n\nsimulation.create_event_output_file(\n\"pop0\", \"%s.0.spikes.dat\" % simulation_id, format=\"ID_TIME\"\n)\nfor pre in range(0, size0):\n\"pop0\", pre, \"IzPop0[{}]\".format(pre), \"spike\"\n)\n\nsimulation.create_event_output_file(\n\"pop1\", \"%s.1.spikes.dat\" % simulation_id, format=\"ID_TIME\"\n)\nfor pre in range(0, size1):\n\"pop1\", pre, \"IzPop1[{}]\".format(pre), \"spike\"\n)\n\nlems_simulation_file = simulation.save_to_file()\n\n# Run the simulation\npynml.run_lems_with_jneuroml_neuron(\nlems_simulation_file, max_memory=\"2G\", nogui=True, plot=False\n)\n\n# Load the data from the file and plot the spike times\n# using the pynml generate_plot utility function.\ntimes_0 = data_array_0[:, 1]\ntimes_1 = data_array_1[:, 1]\nids_0 = data_array_0[:, 0]\nids_1 = [id + size0 for id in data_array_1[:, 0]]\npynml.generate_plot(\n[times_0, times_1],\n[ids_0, ids_1],\n\"Spike times\",\nsave_figure_to=\"%s-spikes.png\" % simulation_id,\nxaxis=\"time (s)\",\nyaxis=\"cell ID\",\ncolors=[\"b\", \"r\"],\nlinewidths=[\"0\", \"0\"],\nmarkers=[\".\", \".\"],\n)\n\n\nAs with the previous example, we will step through this script to see how the various components of the network are declared in NeuroML before running the simulation and generating the plot. We will use the same helper functions to inspect the model as we build it: component_factory, add, info, summary.\n\n## Declaring the model in NeuroML#\n\nTo declare the complete network model, we must again first declare its core entities:\n\nnml_doc = component_factory(\"NeuroMLDocument\", id=\"IzNet\")\n\"Izhikevich2007Cell\",\nid=\"iz2007RS0\",\nv0=\"-60mV\",\nC=\"100pF\",\nk=\"0.7nS_per_mV\",\nvr=\"-60mV\",\nvt=\"-40mV\",\nvpeak=\"35mV\",\na=\"0.03per_ms\",\nb=\"-2nS\",\nc=\"-50.0mV\",\nd=\"100pA\",\n)\n\n# Inspect the component, also show all members:\niz0.info(True)\n\n# Create a component of type ExpOneSynapse, and add it to the document\n\"ExpOneSynapse\", id=\"syn0\", gbase=\"65nS\", erev=\"0mV\", tau_decay=\"3ms\"\n)\n\n\nHere, we create a new document, declare the Izhikevich neuron, and also declare the synapse that we are going to use to connect one population of neurons to the other. We use the ExpOne Synapse here, where the conductance of the synapse increases instantaneously by a constant value gbase on receiving a spike, and then decays exponentially with a decay constant tauDecay.\n\nLet’s inspect our model document so far:\n\nnml_doc.info(True)\n\nValid members for NeuroMLDocument are:\n...\n* izhikevich2007_cells (class: Izhikevich2007Cell, Optional)\n* Contents ('ids'/<objects>): ['iz2007RS0']\n\n* fitz_hugh_nagumo_cells (class: FitzHughNagumoCell, Optional)\n* fitz_hugh_nagumo1969_cells (class: FitzHughNagumo1969Cell, Optional)\n* pinsky_rinzel_ca3_cells (class: PinskyRinzelCA3Cell, Optional)\n* pulse_generators (class: PulseGenerator, Optional)\n* pulse_generator_dls (class: PulseGeneratorDL, Optional)\n* id (class: NmlId, Required)\n* Contents ('ids'/<objects>): IzNet\n\n* sine_generators (class: SineGenerator, Optional)\n...\n* IF_curr_exp (class: IF_curr_exp, Optional)\n* exp_one_synapses (class: ExpOneSynapse, Optional)\n* Contents ('ids'/<objects>): ['syn0']\n\n* IF_cond_alpha (class: IF_cond_alpha, Optional)\n* exp_two_synapses (class: ExpTwoSynapse, Optional)\n...\n\n\nLet’s also get a summary:\n\nprint(nml_doc.summary())\n*******************************************************\n* NeuroMLDocument: IzNet\n*\n* ExpOneSynapse: ['syn0']\n* Izhikevich2007Cell: ['iz2007RS0']\n*\n*******************************************************\n\n\nWe can now declare our network with 2 populations of these cells. Note: setting a color as a property is optional, but is used in when we generate our plots below.\n\n# create the network: turned of validation because we will add populations next\n\n# create the first population\nsize0 = 5\npop0 = component_factory(\"Population\", id=\"IzPop0\", component=iz0.id, size=size0)\n# Set optional color property. Note: used later when generating plots\n\n# create the second population\nsize1 = 5\npop1 = component_factory(\"Population\", id=\"IzPop1\", component=iz0.id, size=size1)\n\n\nWe can test to see if the network is now valid, since we have added the required populations to it:\n\nnet.validate()\n\n\nThis function does not return anything if the component is valid. If it is invalid, however, it will throw a ValueError.\n\nWe can now create projections between the two populations based on some probability of connection. To do this, we iterate over each post-synaptic neuron for each pre-synaptic neuron and draw a random number between 0 and 1. If the drawn number is less than the required probability of connection, the connection is created.\n\nWhile we are iterating over all our pre-synaptic cells here, we also add external inputs to them using ExplicitInputs (this could have been done in a different loop, but it is convenient to also do this here).\n\n# create a projection from one population to another\n\"Projection\",\nid=\"proj\",\npresynaptic_population=pop0.id,\npostsynaptic_population=pop1.id,\nsynapse=syn0.id,\n)\n\n# We do two things in the loop:\n# - add pulse generator inputs to population 1 to make neurons spike\n# - create synapses between the two populations with a particular probability\nrandom.seed(123)\nprob_connection = 0.8\ncount = 0\nfor pre in range(0, size0):\n# pulse generator as explicit stimulus\n\"PulseGenerator\",\nid=\"pg_%i\" % pre,\ndelay=\"0ms\",\nduration=\"10000ms\",\namplitude=\"%f nA\" % (0.1 + 0.1 * random.random()),\n)\n\n\"ExplicitInput\", target=\"%s[%i]\" % (pop0.id, pre), input=pg.id\n)\n\n# synapses between populations\nfor post in range(0, size1):\nif random.random() <= prob_connection:\n\"Connection\",\nid=count,\npre_cell_id=\"../%s[%i]\" % (pop0.id, pre),\npost_cell_id=\"../%s[%i]\" % (pop1.id, post),\n)\ncount += 1\n\n\n\nLet us inspect our model again to confirm that we have it set up correctly.\n\nnml_doc.info(True)\n\nValid members for NeuroMLDocument are:\n* biophysical_properties (class: BiophysicalProperties, Optional)\n* SpikeSourcePoisson (class: SpikeSourcePoisson, Optional)\n* cells (class: Cell, Optional)\n* networks (class: Network, Optional)\n* Contents ('ids'/<objects>): ['IzNet']\n\n* cell2_ca_poolses (class: Cell2CaPools, Optional)\n...\n* izhikevich2007_cells (class: Izhikevich2007Cell, Optional)\n* Contents ('ids'/<objects>): ['iz2007RS0']\n\n* fitz_hugh_nagumo_cells (class: FitzHughNagumoCell, Optional)\n* fitz_hugh_nagumo1969_cells (class: FitzHughNagumo1969Cell, Optional)\n* pinsky_rinzel_ca3_cells (class: PinskyRinzelCA3Cell, Optional)\n* pulse_generators (class: PulseGenerator, Optional)\n* Contents ('ids'/<objects>): ['pg_0', 'pg_1', 'pg_2', 'pg_3', 'pg_4']\n\n* pulse_generator_dls (class: PulseGeneratorDL, Optional)\n* id (class: NmlId, Required)\n* Contents ('ids'/<objects>): IzNet\n\n...\n* exp_one_synapses (class: ExpOneSynapse, Optional)\n* Contents ('ids'/<objects>): ['syn0']\n\n* IF_cond_alpha (class: IF_cond_alpha, Optional)\n..\n\nprint(nml_doc.summary())\n*******************************************************\n* NeuroMLDocument: IzNet\n*\n* ExpOneSynapse: ['syn0']\n* Izhikevich2007Cell: ['iz2007RS0']\n* PulseGenerator: ['pg_0', 'pg_1', 'pg_2', 'pg_3', 'pg_4']\n*\n* Network: IzNet\n*\n* 10 cells in 2 populations\n* Population: IzPop0 with 5 components of type iz2007RS0\n* Properties: color=0 0 .8;\n* Population: IzPop1 with 5 components of type iz2007RS0\n* Properties: color=.8 0 0;\n*\n* 20 connections in 1 projections\n* Projection: proj from IzPop0 to IzPop1, synapse: syn0\n* 20 connections: [(Connection 0: 0 -> 0), ...]\n*\n* 0 inputs in 0 input lists\n*\n* 5 explicit inputs (outside of input lists)\n* Explicit Input of type pg_0 to IzPop0(cell 0), destination: unspecified\n* Explicit Input of type pg_1 to IzPop0(cell 1), destination: unspecified\n* Explicit Input of type pg_2 to IzPop0(cell 2), destination: unspecified\n* Explicit Input of type pg_3 to IzPop0(cell 3), destination: unspecified\n* Explicit Input of type pg_4 to IzPop0(cell 4), destination: unspecified\n*\n*******************************************************\n\n\n\nWe can now save and validate our model.\n\n# write model to file and validate\nnml_file = \"izhikevich2007_network.nml\"\nwriters.NeuroMLWriter.write(nml_doc, nml_file)\n\nprint(\"Written network file to: \" + nml_file)\npynml.validate_neuroml2(nml_file)\n\n\n### The generated NeuroML model#\n\nLet us take a look at the generated NeuroML model\n\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xs=\"http://www.w3.org/2001/XMLSchema\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2.3.xsd\" id=\"IzNet\">\n<expOneSynapse id=\"syn0\" gbase=\"65nS\" erev=\"0mV\" tauDecay=\"3ms\"/>\n<izhikevich2007Cell id=\"iz2007RS0\" C=\"100pF\" v0=\"-60mV\" k=\"0.7nS_per_mV\" vr=\"-60mV\" vt=\"-40mV\" vpeak=\"35mV\" a=\"0.03per_ms\" b=\"-2nS\" c=\"-50.0mV\" d=\"100pA\"/>\n<pulseGenerator id=\"pg_0\" delay=\"0ms\" duration=\"10000ms\" amplitude=\"0.105236 nA\"/>\n<pulseGenerator id=\"pg_1\" delay=\"0ms\" duration=\"10000ms\" amplitude=\"0.153620 nA\"/>\n<pulseGenerator id=\"pg_2\" delay=\"0ms\" duration=\"10000ms\" amplitude=\"0.124516 nA\"/>\n<pulseGenerator id=\"pg_3\" delay=\"0ms\" duration=\"10000ms\" amplitude=\"0.131546 nA\"/>\n<pulseGenerator id=\"pg_4\" delay=\"0ms\" duration=\"10000ms\" amplitude=\"0.102124 nA\"/>\n<network id=\"IzNet\">\n<population id=\"IzPop0\" component=\"iz2007RS0\" size=\"5\" type=\"population\">\n<property tag=\"color\" value=\"0 0 .8\"/>\n</population>\n<population id=\"IzPop1\" component=\"iz2007RS0\" size=\"5\" type=\"population\">\n<property tag=\"color\" value=\".8 0 0\"/>\n</population>\n<projection id=\"proj\" presynapticPopulation=\"IzPop0\" postsynapticPopulation=\"IzPop1\" synapse=\"syn0\">\n<connection id=\"0\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"1\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"2\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"3\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"4\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"5\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"6\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"7\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"8\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"9\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"10\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"11\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"12\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"13\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"14\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"15\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"16\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"17\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"18\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n<connection id=\"19\" preCellId=\"../IzPop0\" postCellId=\"../IzPop1\"/>\n</projection>\n<explicitInput target=\"IzPop0\" input=\"pg_0\"/>\n<explicitInput target=\"IzPop0\" input=\"pg_1\"/>\n<explicitInput target=\"IzPop0\" input=\"pg_2\"/>\n<explicitInput target=\"IzPop0\" input=\"pg_3\"/>\n<explicitInput target=\"IzPop0\" input=\"pg_4\"/>\n</network>\n</neuroml>\n\n\nIt should now be easy to see how the model is clearly declared in the NeuroML file. Observe how entities are referenced in NeuroML depending on their location in the document architecture. Here, population and projection are at the same level. The synaptic connections using the connection tag are at the next level. So, in the connection tags, populations are to be referred to as ../ which indicates the previous level. The explicitinput tag is at the same level as the population and projection tags, so we do not need to use ../ here to reference them.\n\nAnother point worth noting here is that because we have defined a population of the same components by specifying a size rather than by individually adding components to it, we can refer to the entities of the population using the common [..] index operator.\n\nThe advantage of such a declarative format is that we can also easily get information on our model from the NeuroML file. Similar to the summary() function that we have used so far, pyNeuroML also includes the helper pynml-summary script that can be used to get summaries of NeuroML models from their NeuroML files:\n\n$pynml-summary izhikevich2007_network.nml ******************************************************* * NeuroMLDocument: IzNet * * ExpOneSynapse: ['syn0'] * Izhikevich2007Cell: ['iz2007RS0'] * PulseGenerator: ['pulseGen_0', 'pulseGen_1', 'pulseGen_2', 'pulseGen_3', 'pulseGen_4'] * * Network: IzNet * * 10 cells in 2 populations * Population: IzPop0 with 5 components of type iz2007RS0 * Population: IzPop1 with 5 components of type iz2007RS0 * * 20 connections in 1 projections * Projection: proj from IzPop0 to IzPop1, synapse: syn0 * 20 connections: [(Connection 0: 0 -> 0), ...] * * 0 inputs in 0 input lists * * 5 explicit inputs (outside of input lists) * Explicit Input of type pulseGen_0 to IzPop0(cell 0), destination: unspecified * Explicit Input of type pulseGen_1 to IzPop0(cell 1), destination: unspecified * Explicit Input of type pulseGen_2 to IzPop0(cell 2), destination: unspecified * Explicit Input of type pulseGen_3 to IzPop0(cell 3), destination: unspecified * Explicit Input of type pulseGen_4 to IzPop0(cell 4), destination: unspecified * ******************************************************* We can also generate a graphical summary of our model using pynml from pyNeuroML: $ pynml izhikevich2007_network.nml -graph 3\n\n\nThis generates the following model summary diagram:", null, "Fig. 10 A summary graph of the model generated using pynml and the dot tool.#\n\nOther options for pynml produce other views, e.g individual connections:\n\n$pynml izhikevich2007_network.nml -graph -1", null, "Fig. 11 Model summary graph showing individual connections between cells in the populations.# In our very simple network here, neurons do not have morphologies and are not distributed in space. In later examples, however, we will also see how summary figures of the network that show the morphologies, locations of different layers and neurons, and so on can also be generated using the NeuroML tools. ## Simulating the model# Now that we have our model set up, we can proceed to simulating it. We create our simulation, and setup the information we want to record from it. # Create simulation, and record data simulation_id = \"example_izhikevich2007network_sim\" simulation = LEMSSimulation( sim_id=simulation_id, duration=1000, dt=0.1, simulation_seed=123 ) simulation.assign_simulation_target(net.id) simulation.include_neuroml2_file(nml_file) simulation.create_event_output_file( \"pop0\", \"%s.0.spikes.dat\" % simulation_id, format=\"ID_TIME\" ) for pre in range(0, size0): simulation.add_selection_to_event_output_file( \"pop0\", pre, \"IzPop0[{}]\".format(pre), \"spike\" ) simulation.create_event_output_file( \"pop1\", \"%s.1.spikes.dat\" % simulation_id, format=\"ID_TIME\" ) for pre in range(0, size1): simulation.add_selection_to_event_output_file( \"pop1\", pre, \"IzPop1[{}]\".format(pre), \"spike\" ) lems_simulation_file = simulation.save_to_file() The generated LEMS file is here: <Lems> <!-- This LEMS file has been automatically generated using PyNeuroML v0.7.2 (libNeuroML v0.4.1) --> <!-- Specify which component to run --> <Target component=\"example_izhikevich2007network_sim\"/> <!-- Include core NeuroML2 ComponentType definitions --> <Include file=\"Cells.xml\"/> <Include file=\"Networks.xml\"/> <Include file=\"Simulation.xml\"/> <Include file=\"izhikevich2007_network.nml\"/> <Simulation id=\"example_izhikevich2007network_sim\" length=\"1000ms\" step=\"0.1ms\" target=\"IzNet\" seed=\"123\"> <!-- Note seed: ensures same random numbers used every run --> <EventOutputFile id=\"pop0\" fileName=\"example_izhikevich2007network_sim.0.spikes.dat\" format=\"ID_TIME\"> <EventSelection id=\"0\" select=\"IzPop0\" eventPort=\"spike\"/> <EventSelection id=\"1\" select=\"IzPop0\" eventPort=\"spike\"/> <EventSelection id=\"2\" select=\"IzPop0\" eventPort=\"spike\"/> <EventSelection id=\"3\" select=\"IzPop0\" eventPort=\"spike\"/> <EventSelection id=\"4\" select=\"IzPop0\" eventPort=\"spike\"/> </EventOutputFile> <EventOutputFile id=\"pop1\" fileName=\"example_izhikevich2007network_sim.1.spikes.dat\" format=\"ID_TIME\"> <EventSelection id=\"0\" select=\"IzPop1\" eventPort=\"spike\"/> <EventSelection id=\"1\" select=\"IzPop1\" eventPort=\"spike\"/> <EventSelection id=\"2\" select=\"IzPop1\" eventPort=\"spike\"/> <EventSelection id=\"3\" select=\"IzPop1\" eventPort=\"spike\"/> <EventSelection id=\"4\" select=\"IzPop1\" eventPort=\"spike\"/> </EventOutputFile> </Simulation> </Lems> Where we had generated a graphical summary of the model before, we can now also generate graphical summaries of the simulation using pynml and the -lems-graph option. This dives deeper into the LEMS definition of the cells, showing more of the underlying dynamics of the components: $ pynml LEMS_example_izhikevich2007network_sim.xml -lems-graph\n\n\nHere is the generated summary graph:", null, "Fig. 12 A summary graph of the model generated using pynml -lems-graph.#\n\nIt shows a top-down breakdown of the simulation: from the network, to the populations, to the cell types, leading up to the components that these cells are made of (more on Components later). Let us add the necessary code to run our simulation, this time using the well known NEURON simulator:\n\n# Run the simulation\npynml.run_lems_with_jneuroml_neuron(\nlems_simulation_file, max_memory=\"2G\", nogui=True, plot=False\n)\n\n\n## Plotting recorded spike times#\n\nTo analyse the outputs from the simulation, we can again plot the information we recorded. In the previous example, we had recorded and plotted the membrane potentials from our cell. Here, we have recorded the spike times. So let us plot them to generate our figure:\n\n# Load the data from the file and plot the spike times\n# using the pynml generate_plot utility function.\ntimes_0 = data_array_0[:, 1]\ntimes_1 = data_array_1[:, 1]\nids_0 = data_array_0[:, 0]\nids_1 = [id + size0 for id in data_array_1[:, 0]]\npynml.generate_plot(\n[times_0, times_1],\n[ids_0, ids_1],\n\"Spike times\",\n\nObserve how we are using the same generate_plot utility function as before: it is general enough to plot different recorded quantities. Under the hood, it passes this information to Python’s Matplotlib library. This produces the raster plot shown at the top of the page." ]
[ null, "https://docs.neuroml.org/_images/example_izhikevich2007network_sim-spikes.png", null, "https://docs.neuroml.org/_images/IzNet.gv.png", null, "https://docs.neuroml.org/_images/IzNet-1.gv.png", null, "https://docs.neuroml.org/_images/LEMS_example_izhikevich2007network_sim.png", null ]
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https://www.civilprojectsonline.com/building-construction/understanding-stresses-and-modular-ratio-rcc-structures/
[ "# Understanding Stresses and Modular ratio | RCC Structures\n\n#### Stresses in Steel and Concrete | Building Construction\n\nIn one of our previous articles, we discussed “Basic definitions and formulas”.\n\nNow we will move on with our discussion on “Permissible stresses in concrete and steel” and “Understanding Modular ratio”.\n\n#### Permissible Stresses in Concrete\n\nReinforced concrete designs make use of M15 grade concrete. The permissible stresses for different grades of concrete is different. They are given below:\n\n Sr. No. Concrete Grade M15 M20 M25 M30 1. Stress in compression Bending 5 7 8.5 10 Direct 4 5 6 8 2. Stress in bond (average) for plain bars 0.6 0.8 0.9 1.0 3. Characteristics compressive strength 15 20 25 30\n\nAlso refer for other values in IS:456-1978\n\n#### Permissible Stresses in Steel\n\nThe permissible stresses for different grades of steel are given in the table above.\n\nThe different grades steel available in the market with their market names are as follows:\n\nMild Steel\n\nGrade I steel is known as mild steel. The abbreviation used for Mild steel is (m.s.)\n\nHigh Tensile deformed steel has two types. They are as follows:\n\n1. Grade Fe415 (Tor-40 or Tistrong I)\n2. Grade Fe500 (Tor-50 or Tistrong II)\n\nThe names of the high tensile deformed steel have been derived from their manufacturers.\n\n#### For example:\n\n• Tor-Isteg Steel Corporation in Calcutta manufactures Tor-40 and Tor-50. Hence, the name.\n• Tata Iron and Steel Co. Ltd, Calcutta manufactures Tistrong I and Tistrong II.\n\n(Being aware of the names of the manufacturers is important for students especially those studying Civil and Structural Engineering.)\n\n#### Understanding Modular Ratios\n\nIt is defined as the ratio of moduli of steel to the moduli of concrete. It is denoted by the letter “m”.\n\nm=Es/Ec\n\nThe modular ratio is not constant for all grades of concrete. It varies with the grade of concrete. Es/Ec is generally not used to calculate modular ratio for reinforced concrete designs.\n\nAs per IS: 456-1978;\n\nm is calculated by the following formula:\n\nm = 280/3σcbc\n\nwhere,\n\nσcbc = permissible compressive stress in concrete in bending.\n\n#### Calculation of Modular ratio values for different grades of concrete\n\n Grade of concrete Modular ratio M15 m = 280/3×5 = 18.66 M20 m = 280/3×7 = 13.33 M25 m = 280/3×8.5 = 10.98 M30 m = 280/3×10 = 9.33\n\nIt should be remembered that rounding off the modular ratio values is not permitted by Indian Standard.\n\n### 5 thoughts on “Understanding Stresses and Modular ratio | RCC Structures”\n\n1.", null, "What is the modular ratio m ? how much FSI permissible for Construction of Residetial Building ?\n\n2.", null, "Any one can clarify my doubt. The modular ratio is m = 280/3σcbc.How to get the numerical value of 280.Why the Characteristics compressive strength of concrete is 3 times higher than bending Stress in compression of concrete.\n\n3.", null, "very good and understanding material available.\n\n4.", null, "5.", null, "" ]
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https://www.eggintongroup.co.uk/7vw53h/canberra-distance-python-0b2ac6
[ "It is a networking forum for meeting other locals interes I wasn't able to find any appropriate libraries that do this so I wrote my own. When u [i] and v [i] are 0 for given i, then the fraction 0/0 = 0 is used in the calculation. (from 0 to P-1) of P elements. . sum_i(abs(x_i - y_i) / (abs(x_i) + abs(y_i))). Hamming Distance. The two vectors may have an arbitrary cardinality (i.e. COLOR PICKER. Features: 30+ algorithms; Pure python implementation; Simple usage; More than two sequences comparing; Some algorithms have more than one implementation in one class. Python Exercises, Practice and Solution: Write a Python program to compute the distance between the points (x1, y1) and (x2, y2). KITSUNE is a toolkit for evaluation of the length of k-mer in a given genome dataset for alignment-free phylogenimic analysis. J. So some of this comes down to what purpose you're using it for. The following are 6 code examples for showing how to use scipy.spatial.distance.braycurtis().These examples are extracted from open source projects. Here I demonstrate the distance matrix computations using the R function dist(). You can vote up the ones you like or vote down the ones you don't like, Python Analysis of Algorithms Linear Algebra Optimization Functions Graphs ... Cosine distance is an example of a dissimilarity for points in a real vector space. Canberra distance using stats::dist is ambiguous. With respect to the agglomeration method, Ward’s is widely used. Python module for computing edit distances and alignments between sequences. This reference manual details functions, modules, and objects included in mlpy. Python Tutorial for Beginners. You may also want to check out all available functions/classes of the module Welcome to this project-based course on Analyzing Text Data with Yellowbrick. Euclidean distance is harder by hand bc you're squaring anf square rooting. If all the variables are categorical, then you can use Hamming distance, which counts the number of mismatches. k is PyNNDescent is a Python nearest neighbor descent for approximate nearest neighbors. Calculates the Canberra distance between two vectors. CanberraDistance[u, v] gives the Canberra distance between vectors u and v. Y = pdist(X, 'braycurtis') Computes the Bray-Curtis distance between the points. where p is the number of elements and k is the number of ... We are seeking a Data Scientist to join our Data and Analytics team in our Canberra office. The resulting Xptr external pointer object needs to be passed to parDist using the func parameter.. Parameters: These examples are extracted from open source projects. If you’ve got a large herd, you’d start by measuring all the criteria for each animal. Parameters X {array-like, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples). TextDistance – python library for comparing distance between two or more sequences by many algorithms.. Eerily dead Civic Square always strikes one as the built, paved equivalent of the dead parrot in the classic Monty Python sketch. So if the numbers are 7 and 15, they are 0111 and 1111 in binary, here the MSb is different, so the Hamming distance is 1. Perform DBSCAN clustering from features, or distance matrix. How do I change the distance metric of k mean clustering to canberra distance or any other distance metric? Please follow the given Python program to compute Euclidean Distance. don't need same length). Python scipy.spatial.distance.canberra() Examples The following are 8 code examples for showing how to use scipy.spatial.distance.canberra(). The shonky pet … NbClust Package for determining the best number of clusters. First, we’ll create a dataset that displays the exam score of 20 students along with the number of hours they spent studying, the number of prep exams they took, and their current grade in the course: Machine Learning PYthon (mlpy) is a high-performance Python library for predictive modeling. Calculate Euclidean distance between two points using Python. If metric is “precomputed”, X is assumed to be a distance … the location parameter, if k=None will be set to P. The lower the indicator value, the higher the stability of the lists. This is a common situation. Offered by Coursera Project Network. Computes the Canberra distance between the points. Sort by. We have to find the Hamming distance of them. x and y. The reduced distance, defined for some metrics, is a computationally more efficient measure which preserves the rank of the true distance. ... would calculate the pair-wise distances between the vectors in X using the Python function sokalsneath. A float value, representing the Euclidean distance between p and q: Python Version: 3.8 Math Methods. Manhattan Distance: There are various other distance metrics, please see dist in R or pdist in Python. Click on the distance between link to see distane map. If metric is a string, it must be one of the options allowed by scipy.spatial.distance.pdist for its metric parameter, or a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS. Comput. LIKE US. I recently submitted a scikit-learn pull request containing a brand new ball tree and kd-tree for fast nearest neighbor searches in python. , or try the search function On 06/02/2010 11:31 AM, Christophe Genolini wrote: > The definition I use is the on find in the book \"Cluster analysis\" by > Brian Everitt, Sabine Landau and Morven Leese. Python jobs in Canberra, ACT All Filter 47 jobs within 25 km. (N(N-1))/2 non trivial values of the distance matrix (computed In brief, the closer to 1 the more similar the vectors. The Canberra distance between two points u and v is. Back. Created using, Canberra Distances and Stability Indicator of Ranked Lists, Canberra Distance with Location Parameter. sklearn.linear_model.LogisticRegression(). A position list of length P contains the position Deakin, ACT We are now seeking Python Developers for our large Federal Government client in Canberra Duration: Till 30th June 2021 with 2 X3 months extension Australian citizens with minim… ; Clustered heatmaps are superior to ordinary heatmaps as the heatmap cells are arranged … Canberra är Australiens huvudstad. Step 1: Create the dataset. k is the location parameter, if k=None will be set to P. Enter search terms or a module, class or function name. Firstly let’s prepare a small dataset to work with: # set seed to make example reproducible set.seed(123) test <- data.frame(x=sample(1:10000,7), y=sample(1:10000,7), z=sample(1:10000,7)) test x y z 1 2876 8925 1030 2 7883 5514 8998 3 4089 4566 2461 4 8828 9566 421 5 9401 4532 3278 6 456 6773 9541 7 … Any salary. Distance matrices¶ What if you don’t have a nice set of points in a vector space, but only have a pairwise distance matrix providing the distance between each pair of points? a, b = input().split() Type Casting. We discuss Python and related open source technologies. Does anyone have the original definition given in the Lance & Williams paper from Aust. Python Jobs in Canberra, ACT Browse 1 Python jobs with Upwork - the top freelancing website. This group is a regular gathering of Python users and developers in the Canberra area. chebyshev: the Chebyshev distance. fit (X, y = None, sample_weight = None) [source] ¶. It provides a python implementation of Nearest Neighbor Descent for k-neighbor-graph construction and approximate nearest neighbor search, as per the paper: Dong, Wei, Charikar Moses, and Kai Li. ... would calculate the pair-wise distances between the vectors in X using the Python function sokalsneath. The haversine can be expressed in trignometric function as: The haversine of the central angle (which is d/r) is calculated by the following formula: @StefanS, OP wants to have Euclidean Distance - which is pretty well defined and is a default method in pdist, if you or OP wants another method (minkowski, cityblock, seuclidean, sqeuclidean, cosine, correlation, hamming, jaccard, chebyshev, canberra, etc. sum ... Because of the Python object overhead involved in calling the python function, this will be fairly slow, but it will have the same scaling as other distances. Distance. - there are altogether 22 different metrics) you can simply specify it as a metric argument – MaxU Aug 29 '16 at 15:08 This would result in sokalsneath being called \\({n \\choose 2}\\) times, which is inefficient. The Euclidean distance between two vectors, A and B, is calculated as:. positions to consider. The Canberra distance is a way to use all these criteria together to separate individuals according to how similar or dissimilar they are. Create alert All Python Developer Save. ... canberra: the Canberra distance. NbClust package provides 30 indices for determining the number of clusters and proposes to user the best clustering scheme from the different results obtained by varying all combinations of number of clusters, distance … 50km. The Canberra distance is a metric function often used for data scattered around an origin. Returns the Canberra distance between two position lists, Manhattan distance is easier to calculate by hand, bc you just subtract the values of a dimensiin then abs them and add all the results. Because the Canberra distance metric handles the relatively large number of empty occurrences well, it is an interesting option (Desagulier 2014, 163). Linear Algebra using Python | Euclidean Distance Example: Here, we are going to learn about the euclidean distance example and its implementation in Python. Python Programmer jobs in Canberra ACT Filter. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. Canberra Python meetup Renewables Hub 19 Moore St Canberra ACT 2601, Australia 07 Feb. from 7am UTC to 8:30am UTC, 2019 The dist function computes the Euclidean distance between two points of the same dimension. Compute canberra distance between two vectors. Calculates Dice-Sorensen's index between two vectors of features. Returns the Canberra stability indicator between N position It is a weighted version of L₁ distance. canberradouble. Den är belägen på federalt territorium , Australian Capital Territory (ACT). dist_to_rdist: Convert the true distance to the reduced distance. The following are 8 This would result in sokalsneath being called times, which is inefficient. is EUCLIDEAN, MAHALANOBIS, MINKOWSKY, BLOCK, CHEBYCHEV, CANBERRA, JACCARD, PEARSON, COSINE, ANGULAR COSINE, or HAMMING and defines the type of distance to compute; and where is a matrix where the resulting distance matrix is saved. Usage: cd = canb_dist(p,q) Where p and q are vectors of same dimensions and cd is the variable that stores value of the distance. Defining and compiling a user-defined C++ distance function, as well as creating an external pointer to the function can easily be achieved with the cppXPtr function of the RcppXPtrUtils package. Sort by Relevance Date Job type Any job type Contract Full time Casual/Temporary permanent Listed date Any time Last 24 hours Last 7 days Last 14 days Last 30 days. Perhaps you have a complex custom distance measure; perhaps you have strings and are using Levenstein distance, etc. Distance Between Canberra and Surrounding Cities. Distance. From my understanding, sklearn only supports euclidean distance and nltk doesn't seem to support canberra distance but I may be wrong. The Canberra distance between vectors u and v. Notes. In our case, we’ll separate the herd according to how sheepy or goaty they are. Tasks such as assessing document similarity, topic modelling and other text mining endeavors are predicated on the notion of \"closeness\" or \"similarity\" between documents. lists, where x is an (N, P) matrix. Staden har drygt 350 000 invånare (2009) och är Australiens åttonde största stad samt den största stad som inte ligger vid kusten. In this introductory workshop we will focus on continuous features, but be aware that distance measures for categorical variables exists, such as, the Jaccard index, Gower distance and polychoric correlation. - tuzz/canberra_distance >>> from scipy.spatial import distance >>> distance.canberra( [1, 0, 0], … It has also been used to analyze the gut microbiome in … if k=None will be set to P. Returns the expected value of the Canberra location distance, Shows the distance from Canberra to the North Pole, Equator, South Pole and any place in the world. It is calculated using Minkowski Distance formula by setting p’s value to 2. edit_distance. In Python split() function is used to take multiple inputs in the same line. > They cite, as definition paper for Canberra distance, an article of > Lance and Williams \"Computer programs for hierarchical polythetic > classification\" Computer Journal 1966. Filter. Distances and divergences between distributions implemented in python. The hcluster Python library has an interface that is very similar to MATLAB's suite of hierarchical clustering functions found in the Statistics Toolbox. Overview: The function clustermap() in seaborn draws a hierarchically clustered heatmap. Use the following steps to calculate the Mahalanobis distance for every observation in a dataset in Python. Examples. “canberra” CanberraDistance. Returns the Canberra distance between two P-vectors x and y: The Haversine formula calculates the shortest distance between two points on a sphere using their latitudes and longitudes measured along the surface. Google allows users to search the Web for images, news, products, video, and other content. Training instances to cluster, or distances between instances if metric='precomputed'.If a sparse matrix is provided, it will be converted into a sparse csr_matrix. Wasserstein distance python implementation. This will update the distance ‘d’ formula as below: Euclidean distance formula can be used to calculate the distance between two data points in a plane. This reference manual details functions, modules, and objects included in mlpy. In this course, we define various distance metrics (e.g. cityblock: the Manhattan distance. ... would calculate the pair-wise distances between the vectors in X using the Python function sokalsneath. Example Suppose there are four strings 010, 011, 101 and 111. Machine Learning PYthon (mlpy) is a high-performance Python library for predictive modeling. ; Dentograms are drawn for the columns and the rows of the heatmap. “canberra” CanberraDistance: sum ... Because of the Python object overhead involved in calling the python function, this will be fairly slow, but it will have the same scaling as other distances. See .. mlpy.canberra_location(x, y, k=None)¶ Returns the Canberra distance between two position lists, x and y.A position list of length P contains the position (from 0 to P-1) of P elements. value of the Canberra metric. The heatmap cells are all clustered using a similarity algorithm. Salary estimate. Includes air line, route, centre, route planner, flight route and interactive map. Euclidean distance = √ Σ(A i-B i) 2 To calculate the Euclidean distance between two vectors in Python, we can use the numpy.linalg.norm function: #import functions import numpy as np from numpy. We will also perform simple demonstration and comparison with Python and the SciPy library. The hamming distance is the number of bit different bit count between two numbers. The stability is computed by the mean distance of all the k is the location parameter, These examples are extracted from open source projects. Distance. This would result in sokalsneath being called times, which is inefficient. Get Started When there are zeros at certain position in both vectors, they are not omitted as documented in the function (see below). scipy.spatial.distance Salary estimate Any salary \\$30,000+ \\$50,000+ \\$70,000+ \\$90,000+ \\$110,000+ Refine search. HOW TO. For object arrays, provide an … Surrounding cities of Canberra shown on map, and the distance between Canberra to other cities and states listed below the map. Where. code examples for showing how to use scipy.spatial.distance.canberra(). The Canberra distance is a numerical measure of the distance between pairs of points in a vector space, introduced in 1966 and refined in 1967 by Godfrey N. Lance and William T. Williams. The metric to use when calculating distance between instances in a feature array. and go to the original project or source file by following the links above each example. I needed a way to compute edit distances between sequences in Python. choosing 1 cell to be called `Root` painting the `Root` at the top creating lower layer for each cell in the graph depending on their distance from the root -the. Very similar to the Jaccard Index jaccard but Dice-Sorensen is the harmonic mean of the ratio. What. ; A clustered heatmap is different from an ordinary heatmap on the following terms:. Rogers's genetic distance also is shown to be less sensitive to the overestimation of distance produced by heterozygous loci and finite sample size than the Manhattan metric, Cavalli-Sforza and Edwards's distances, or Hillis's (1984, Syst. - 1.5.2 - a Python package on PyPI - Libraries.io Tabs Dropdowns Accordions Side Navigation Top Navigation Modal Boxes Progress Bars Parallax Login Form HTML Includes Google Maps Range Sliders Tooltips Slideshow Filter List var x = [ 2, 4, 5, 3, 8, 2 ], y = [ 3, 1, 5, -3, 7, 2 ]; var d = canberra( x, y ); // returns ~1.87. Details User-defined distance functions custom. Euclidean Distance: Euclidean distance is one of the most used distance metrics. 50km. P contains the position (from 0 to P-1) of P elements. Methods. Submitted by Anuj Singh, on June 20, 2020 . It is important for use in navigation. The Canberra distance between two points u and v is. Back Refine Clear. K-mer based approach is simple and fast yet has been widely used in many applications including biological sequence comparison. The Canberra distance has been used as a metric for comparing ranked lists and for intrusion detection in computer security. 1, 15-20, 1967? by canberra_location()) scaled by the expected (average) Canberra Distance with Location Parameter¶. The Python example finds the Euclidean distance between two points in a two-dimensional plane. >>>. Python Analysis of Algorithms Linear Algebra Optimization Functions Graphs ... Cosine distance is an example of a dissimilarity for points in a real vector space. canberra ( x, y [, accessor] ) Computes the Canberra distance between two arrays. You may check out the related API usage on the sidebar. Y = cdist(XA, XB, 'braycurtis') Computes the Bray-Curtis distance between the points. This role is offered on a flexible, full-time basis. Canberra distance. © Copyright 2011, mlpy Developers. @StefanS, OP wants to have Euclidean Distance - which is pretty well defined and is a default method in pdist, if you or OP wants another method (minkowski, cityblock, seuclidean, sqeuclidean, cosine, correlation, hamming, jaccard, chebyshev, canberra, etc. dist : Optional output distances from the input vectors to the corresponding neighbors. Although sensitive to outliers (i.e. A position list of length Distances and divergences between distributions implemented in the best way I found in python. For categorical variables (male/female, or small/medium/large), we can define the distance as 0 if two points are in the same category, and 1 otherwise. - LucaCappelletti94/dictances View 459 Python jobs in Canberra ACT at Jora, create free email alerts and never miss another career opportunity again. It is a method of changing an entity from one data type to another. In a set of strings of equal lengths, the minimum Hamming distance is the smallest Hamming distance between all possible pairs of strings in that set. A computationally more efficient measure which preserves the rank of the heatmap cells are all using. Cities of Canberra shown on map, and objects included in mlpy you! At certain position in both vectors, they are not omitted as documented in the Statistics Toolbox more! Of Canberra shown on map, and other content u and v is (... If you’ve got a large herd, you’d start by measuring all the variables are categorical, you..., or distance matrix metrics ( e.g, centre, route planner, flight route and map! Most used distance metrics ( e.g I wrote my own or a module, class or function name video. Link to see distane map, news, products, video, and other content been used... On the distance between two points on a flexible, full-time basis below.... Original definition given in the best way I found in Python ) of P elements, XB 'braycurtis. This group is a regular gathering of Python users and developers in the function clustermap ( ) in seaborn a... Submitted by Anuj Singh, on June 20, 2020 using Levenstein distance, etc used in many including! This course, we define various distance metrics clustering functions found in Python is on... Data and Analytics team in our case, we’ll separate the herd according how! } of shape ( n_samples, n_features ), or try the search function analysis., accessor ] ) Computes the Bray-Curtis distance between link to see distane map times, which is...., provide an … Python jobs in Canberra, ACT Browse 1 Python jobs with -... Canberra office the Web for images, news, products, video, and included. How sheepy or goaty they are not omitted as documented in the Statistics Toolbox v is course on Text! Scipy.Spatial.Distance.Canberra ( ) the heatmap based approach is simple and fast yet has been widely used in many including. Array-Like, sparse matrix } of shape ( n_samples, n_samples ) in a feature....: Optional output distances from the input vectors to the agglomeration method Ward... A way to use scipy.spatial.distance.braycurtis ( ).split ( ).These examples are extracted open. The Web for images, news, products, video, and objects included in mlpy ball... The top freelancing website Hamming distance, etc length P contains the position ( from 0 to P-1 ) P. Type to another is widely used ACT Browse 1 Python jobs with Upwork - the top freelancing website are. Latitudes and longitudes measured along the surface images, news, products, video, and objects included in.! Use all these criteria together to separate individuals according to how similar or they... Compute edit distances between sequences in Python then you can use Hamming distance is one of the module scipy.spatial.distance or... Freelancing website given Python program to compute edit distances between the vectors in X using the Python sokalsneath! Some metrics, is a high-performance Python library has an interface that is similar!, sample_weight = None ) [ source ] ¶ very similar to reduced... Is used to take multiple inputs in the Canberra stability indicator of ranked lists Canberra! Function is used to take multiple inputs in the best way I found in Python calculate the pair-wise distances the... May also want to check out the related API usage on the are! \\ ( { N \\choose 2 } \\ ) times, which is inefficient ( mlpy ) is regular! The corresponding neighbors ) in seaborn draws a hierarchically canberra distance python heatmap is different from an heatmap... Try the search function the Lance & Williams paper from Aust I was n't able to find the distance. Bit different bit count between two numbers from one Data Type to another 1 the more the! The harmonic mean of the same line ) matrix sparse matrix } shape! Some of this comes down to what purpose you 're squaring anf square rooting zeros at position! Be wrong a Python nearest neighbor descent for approximate nearest neighbors N, P ) matrix nearest neighbor for... Python program to compute edit distances and divergences between distributions implemented in.! Between Canberra to other cities and states listed below the map.split ( ), is computationally. Count between two points on a sphere using their latitudes and longitudes measured along the surface to how or! And the SciPy library an origin herd according to how similar or dissimilar they are for computing edit distances the... Or a module, class or function name the most used distance metrics nearest.. Search the Web for images, news, products, video, and objects included in.... Are drawn for the columns and the distance between two points of the true distance 90,000+ \\$ 110,000+ Refine.. As documented in the same line full-time basis closer to 1 the more similar the vectors ] ¶ used many... And v is flexible, full-time basis use all these criteria together to individuals! Searches in Python split ( ).split ( ).These examples are extracted from source. Python program to compute edit distances and stability indicator between N position lists, X and y sklearn supports... Includes air line, route, centre, route planner, flight route interactive!, 2020 reference manual details functions, modules, and objects included in mlpy I be! Being called \\ ( { N \\choose 2 } \\ ) times, is. Examples are extracted from open source projects are not omitted as documented in the Lance Williams. We are seeking a Data Scientist to join our Data and Analytics team in our case, we’ll the... In seaborn draws a hierarchically clustered heatmap vectors, a and b, is a Python! \\$ 50,000+ \\$ 70,000+ \\$ 90,000+ \\$ 110,000+ Refine search an entity from one Data Type to another detection... Details functions, modules, and other content n't able to find any appropriate libraries that do this I. Are 8 code examples for showing how to use all these criteria together to separate individuals according to similar... Lance & Williams paper from Aust a way to compute edit distances between the vectors the agglomeration,! Position lists, X and y distance and nltk does n't seem to Canberra! 90,000+ \\$ 110,000+ Refine search Dentograms are drawn for the columns and the rows of the heatmap cells all! Goaty they are as a metric function often used for Data scattered around an origin 2... Squaring anf square rooting N, P ) matrix more efficient measure which preserves the rank of the true.... Singh, on June 20, 2020 around an origin setting p’s to! May be wrong one of the heatmap Data scattered around an origin an interface that very! Using their latitudes and longitudes measured along the surface by setting p’s value to 2 biological comparison... The dist function Computes the Canberra distance between instances in a given genome dataset for alignment-free phylogenimic analysis,. } of shape ( n_samples, n_samples ) and other content products, video, the! To 2 LucaCappelletti94/dictances distances and divergences between distributions implemented in Python jobs with Upwork - the top freelancing website position! Usage canberra distance python the sidebar Capital Territory ( ACT ) in our Canberra.! The shortest distance between the points where X is an ( N, )! To find the Hamming distance of them distance formula by setting p’s value to 2 \\$ 50,000+ 70,000+! ( i.e extracted from open source projects, n_samples ) anyone have the original definition in... As documented in the Canberra distance between link to see distane map points on flexible... ) is a high-performance Python library has an interface that is very similar to MATLAB 's of. Of shape ( n_samples, n_features ), or ( n_samples, n_features ), or try the function... \\$ 110,000+ Refine search for each animal strings and are using Levenstein distance, which is inefficient sequences in.... Interactive map ( n_samples, n_features ), or ( n_samples, n_samples ) alignments between sequences in.! Den är belägen på federalt territorium, Australian Capital Territory ( ACT ) from 0 P-1. På federalt territorium, Australian Capital Territory ( ACT ) scattered around an origin the sidebar ( see )..., centre, route, centre, route planner, flight route and interactive map similar the vectors in using! \\Choose 2 } \\ ) times, which is inefficient k-mer based approach is and! Module for computing edit distances between sequences in Python split ( canberra distance python.These examples are extracted from open source...., flight route and interactive map June 20, 2020 toolkit for evaluation of the module scipy.spatial.distance, distance! Omitted as documented in the same dimension created using, Canberra distance with Location Parameter function Computes Bray-Curtis... Gathering of Python users and developers in the best number of mismatches team in our Canberra office estimate any \\$... \\$ 110,000+ Refine search jobs in Canberra, ACT all Filter 47 within... Separate individuals according to how similar or dissimilar they are, 101 and 111 for alignment-free phylogenimic.! And objects included in mlpy on a sphere using their latitudes and measured! A two-dimensional plane Python nearest neighbor searches in Python split ( ).These examples are extracted open! Allows users to search the Web for images, news, products,,. Territory ( ACT ) from an ordinary heatmap on the sidebar and Analytics team in our Canberra.! For approximate nearest neighbors fast nearest neighbor descent for approximate nearest neighbors distributions in... To other cities and states listed below the map square rooting be wrong position list length. Är Australiens åttonde största stad samt den största stad samt den största stad som inte ligger vid kusten Capital (. And divergences between distributions implemented in the Canberra distance has been used a...\n\nLegion Furniture 30 Vanity, Ball Lock Gas Post, Volvo S90 T8, Military Ethical Dilemmas Case Studies, Eye Makeup Brushes Names, Supreme Shoulder Bag Replica, Catfish Jackson Rdr2, John Deere 6115d Specs," ]
[ null ]
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https://www.yuanzifan.com/344.html
[ "# QTP生成随机字符(纯字母)的VBS语句\n\nDim i, N, sUCharRand ,sTemp, sRDA\n\nFunction UCharRand(N)\nFor i=1 to N\nRandomize 'ASC(MID(sRDA,1,1))\nsTemp = cint(25*Rnd)\nsTemp = sTemp + 65\nUCharRand = UCharRand & chr(sTemp) 'chr方法可根据数字给出的ascii码值返回该ascii码对应的字母或数字\nNext\nEnd Function\n\n'''''''''''''''''''''''''''''''''分隔符'''''''''''''''''''''''''''''''''''''''''''''''\n\n'下列代码可生成纯数字的随机字符串,参数N为字符串长度\n\nDim i, N, sUCharRand ,sTemp, sRDA\n\nFunction UCharRand(N)\n\nFor i=1 to N\n\nRandomize 'ASC(MID(sRDA,1,1))\n\nsTemp = cint(10*Rnd)\n\nsTemp = sTemp + 48\n\nsUCharRand = sUCharRand & chr(sTemp) 'chr方法可根据数字给出的ascii码值返回该ascii码对应的字母或数字\n\nNext\n\nEnd Function\n\n11月\n16\n• QTP\n• 随机字符串" ]
[ null ]
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https://www.yousazoe.top/archives/1492ea3c.html
[ "### 问题\n\n19603,039,332,4011.3340,781,960\n19613,080,114,3611.8056,083,390\n19623,136,197,7512.1969,508,948\n19633,205,706,6992.1971,110,065\n19643,276,816,7642.0869,021,089\n…………\n\n### 群体的增长趋势是什么?\n\n#### 马尔萨斯人口论\n\n##### 基本原理\n• 食物为人类生存所必需\n• 两性间的情欲是必然,而且几乎会保持现状\n##### 基本论述\n\n• 人口有 几何增长 的趋势(即按 指数 函数增长的趋势),如级数(1,2,4,8,16,… )\n\n• 食物供应只有 算术增长 的趋势(即按 线性 函数增长的趋势),如级数(1,2,3,4,5,… )\n\n### 马尔萨斯人口模型\n\n$P(t)$ $t$ 时刻的人口数量\n\n1. 已知当前或过去某个时刻的人口数量,预测未来某个时刻的人口?\n2. 遥远未来的趋势($t$ 趋于无穷)?\n\n#### 高尔斯的推导\n\n$\\frac{dP(t)}{dt}=rP(t)$ –> $P(t_0)=P_0$ –> $P(t)=P_0e^{r(t-t_0)}$\n\n### Logistic模型\n\nLogistic模型中自然增长率 $r$ 依旧可以认为是一个常数,但是这个常数现在我们需要对它进行校正。下式可以发现相比高尔斯的模型多了一个参数 $K$,直观意义是地球最多能养活多少人,也就是 环境承载量。不同的社会学家和人口学家对这个问题没有统一的意见,但他们的共识是 $K$ 这个参数一定是存在的\n\n$r(t)=r(P(t))=r(1-\\frac{P(t)}{K})$\n\n#### 推导\n\n$\\frac{dN(t)}{dt}=r(1-\\frac{N(t)}{K})N(t)$\n\n$N(t_0)=N_0$\n\n$N(t)$表示时间 $t$ 处人口的数量,换个符号以区别于马尔萨斯模型\n\n$N(t)=\\frac{K}{1+Ce^{-r(t-t_0)}}$\n\n$C=\\frac{K-P_0}{P_0}$\n\n#### 模型离散化\n\n$\\frac{dN}{dt}=rN(1-\\frac{N}{K})$ –> $\\frac{\\Delta N}{\\Delta t}=rN(1-\\frac{N}{K})$\n\n$\\Delta N = N_{t+1}-N_t$\n\n$\\Delta t = 1$\n\n$N_{t+1}-N_t=rN_t(1-\\frac{N_t}{K})$\n\n$N_{t+1}=(1+r)N_t-\\frac{r}{K}N_t^{2}$\n\n### Leslie模型\n\n$n_{0,t+1}=F_0n_{0,t}+F_1n_{1,t}+…+F_sn_{s,t}$\n\n$n_{1,t+1}=p_0n_{0,t}$\n\n……\n\n$n_{s,t+1}=p_{s-1}n_{s-1,t}$\n\n$N_{n+1}=LN_t$\n\n$L = \\begin{pmatrix} F_0 & F_1 & F_2 & … & F_s \\\\ p_0 & 0 & 0 & … & 0 \\\\ 0 & p_1 & 0 & … & 0 \\\\ 0 & 0 & … & p_{s-1} & 0\\end{pmatrix}$\n\n$N_0=c_0v_0+\\sum^S_{i=1}c_i(\\lambda_i)^t\\nu_t$\n\n$N_t=c_0(\\lambda_0)^t\\nu_0+\\sum^S_{i=1}c_i(\\lambda_i)^t\\nu_i$\n\n• 地域差异\n• 移民的影响\n\nAcknowledgement\nvibin ks" ]
[ null ]
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http://www.netlib.org/linalg/old_html_templates/subsubsection2.7.4.3.5.html
[ "### Blocking over systems of partial differential equations\n\nIf the physical problem has several variables per grid point, that is, if there are several coupled partial differential equations, it is possible to introduce blocking in a natural way.\n\nBlocking of the equations (which gives a small number of very large blocks) was used by Axelsson and Gustafsson for the equations of linear elasticity, and blocking of the variables per node (which gives many very small blocks) was used by Aarden and Karlsson for the semiconductor equations. A systematic comparison of the two approaches was made by Bank, et al. .", null, "" ]
[ null, "http://www.netlib.org/xnl4/icons/invis_anchor.xbm", null ]
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https://www.colorhexa.com/00e359
[ "# #00e359 Color Information\n\nIn a RGB color space, hex #00e359 is composed of 0% red, 89% green and 34.9% blue. Whereas in a CMYK color space, it is composed of 100% cyan, 0% magenta, 60.8% yellow and 11% black. It has a hue angle of 143.5 degrees, a saturation of 100% and a lightness of 44.5%. #00e359 color hex could be obtained by blending #00ffb2 with #00c700. Closest websafe color is: #00cc66.\n\n• R 0\n• G 89\n• B 35\nRGB color chart\n• C 100\n• M 0\n• Y 61\n• K 11\nCMYK color chart\n\n#00e359 color description : Pure (or mostly pure) cyan - lime green.\n\n# #00e359 Color Conversion\n\nThe hexadecimal color #00e359 has RGB values of R:0, G:227, B:89 and CMYK values of C:1, M:0, Y:0.61, K:0.11. Its decimal value is 58201.\n\nHex triplet RGB Decimal 00e359 `#00e359` 0, 227, 89 `rgb(0,227,89)` 0, 89, 34.9 `rgb(0%,89%,34.9%)` 100, 0, 61, 11 143.5°, 100, 44.5 `hsl(143.5,100%,44.5%)` 143.5°, 100, 89 00cc66 `#00cc66`\nCIE-LAB 79.418, -73.634, 53.44 29.27, 55.656, 18.651 0.283, 0.537, 55.656 79.418, 90.982, 144.029 79.418, -72.876, 78.555 74.603, -60.521, 37.4 00000000, 11100011, 01011001\n\n# Color Schemes with #00e359\n\n• #00e359\n``#00e359` `rgb(0,227,89)``\n• #e3008a\n``#e3008a` `rgb(227,0,138)``\nComplementary Color\n• #19e300\n``#19e300` `rgb(25,227,0)``\n• #00e359\n``#00e359` `rgb(0,227,89)``\n• #00e3cb\n``#00e3cb` `rgb(0,227,203)``\nAnalogous Color\n• #e30019\n``#e30019` `rgb(227,0,25)``\n• #00e359\n``#00e359` `rgb(0,227,89)``\n• #cb00e3\n``#cb00e3` `rgb(203,0,227)``\nSplit Complementary Color\n• #e35900\n``#e35900` `rgb(227,89,0)``\n• #00e359\n``#00e359` `rgb(0,227,89)``\n• #5900e3\n``#5900e3` `rgb(89,0,227)``\n• #8ae300\n``#8ae300` `rgb(138,227,0)``\n• #00e359\n``#00e359` `rgb(0,227,89)``\n• #5900e3\n``#5900e3` `rgb(89,0,227)``\n• #e3008a\n``#e3008a` `rgb(227,0,138)``\n• #00973b\n``#00973b` `rgb(0,151,59)``\n• #00b045\n``#00b045` `rgb(0,176,69)``\n• #00ca4f\n``#00ca4f` `rgb(0,202,79)``\n• #00e359\n``#00e359` `rgb(0,227,89)``\n• #00fd63\n``#00fd63` `rgb(0,253,99)``\n• #17ff72\n``#17ff72` `rgb(23,255,114)``\n• #31ff81\n``#31ff81` `rgb(49,255,129)``\nMonochromatic Color\n\n# Alternatives to #00e359\n\nBelow, you can see some colors close to #00e359. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.\n\n• #00e320\n``#00e320` `rgb(0,227,32)``\n• #00e333\n``#00e333` `rgb(0,227,51)``\n• #00e346\n``#00e346` `rgb(0,227,70)``\n• #00e359\n``#00e359` `rgb(0,227,89)``\n• #00e36c\n``#00e36c` `rgb(0,227,108)``\n• #00e37f\n``#00e37f` `rgb(0,227,127)``\n• #00e392\n``#00e392` `rgb(0,227,146)``\nSimilar Colors\n\n# #00e359 Preview\n\nThis text has a font color of #00e359.\n\n``<span style=\"color:#00e359;\">Text here</span>``\n#00e359 background color\n\nThis paragraph has a background color of #00e359.\n\n``<p style=\"background-color:#00e359;\">Content here</p>``\n#00e359 border color\n\nThis element has a border color of #00e359.\n\n``<div style=\"border:1px solid #00e359;\">Content here</div>``\nCSS codes\n``.text {color:#00e359;}``\n``.background {background-color:#00e359;}``\n``.border {border:1px solid #00e359;}``\n\n# Shades and Tints of #00e359\n\nA shade is achieved by adding black to any pure hue, while a tint is created by mixing white to any pure color. In this example, #000b04 is the darkest color, while #f7fffa is the lightest one.\n\n• #000b04\n``#000b04` `rgb(0,11,4)``\n• #001f0c\n``#001f0c` `rgb(0,31,12)``\n• #003214\n``#003214` `rgb(0,50,20)``\n• #00461b\n``#00461b` `rgb(0,70,27)``\n• #005a23\n``#005a23` `rgb(0,90,35)``\n• #006d2b\n``#006d2b` `rgb(0,109,43)``\n• #008133\n``#008133` `rgb(0,129,51)``\n• #00953a\n``#00953a` `rgb(0,149,58)``\n• #00a842\n``#00a842` `rgb(0,168,66)``\n• #00bc4a\n``#00bc4a` `rgb(0,188,74)``\n• #00cf51\n``#00cf51` `rgb(0,207,81)``\n• #00e359\n``#00e359` `rgb(0,227,89)``\n• #00f761\n``#00f761` `rgb(0,247,97)``\n• #0bff6b\n``#0bff6b` `rgb(11,255,107)``\n• #1fff77\n``#1fff77` `rgb(31,255,119)``\n• #32ff83\n``#32ff83` `rgb(50,255,131)``\n• #46ff8f\n``#46ff8f` `rgb(70,255,143)``\n• #5aff9b\n``#5aff9b` `rgb(90,255,155)``\n• #6dffa6\n``#6dffa6` `rgb(109,255,166)``\n• #81ffb2\n``#81ffb2` `rgb(129,255,178)``\n• #95ffbe\n``#95ffbe` `rgb(149,255,190)``\n• #a8ffca\n``#a8ffca` `rgb(168,255,202)``\n• #bcffd6\n``#bcffd6` `rgb(188,255,214)``\n• #cfffe2\n``#cfffe2` `rgb(207,255,226)``\n• #e3ffee\n``#e3ffee` `rgb(227,255,238)``\n• #f7fffa\n``#f7fffa` `rgb(247,255,250)``\nTint Color Variation\n\n# Tones of #00e359\n\nA tone is produced by adding gray to any pure hue. In this case, #697a70 is the less saturated color, while #00e359 is the most saturated one.\n\n• #697a70\n``#697a70` `rgb(105,122,112)``\n• #60836e\n``#60836e` `rgb(96,131,110)``\n• #578c6c\n``#578c6c` `rgb(87,140,108)``\n• #4f946a\n``#4f946a` `rgb(79,148,106)``\n• #469d68\n``#469d68` `rgb(70,157,104)``\n• #3da666\n``#3da666` `rgb(61,166,102)``\n• #34af64\n``#34af64` `rgb(52,175,100)``\n• #2cb762\n``#2cb762` `rgb(44,183,98)``\n• #23c061\n``#23c061` `rgb(35,192,97)``\n• #1ac95f\n``#1ac95f` `rgb(26,201,95)``\n• #11d25d\n``#11d25d` `rgb(17,210,93)``\n• #09da5b\n``#09da5b` `rgb(9,218,91)``\n• #00e359\n``#00e359` `rgb(0,227,89)``\nTone Color Variation\n\n# Color Blindness Simulator\n\nBelow, you can see how #00e359 is perceived by people affected by a color vision deficiency. This can be useful if you need to ensure your color combinations are accessible to color-blind users.\n\nMonochromacy\n• Achromatopsia 0.005% of the population\n• Atypical Achromatopsia 0.001% of the population\nDichromacy\n• Protanopia 1% of men\n• Deuteranopia 1% of men\n• Tritanopia 0.001% of the population\nTrichromacy\n• Protanomaly 1% of men, 0.01% of women\n• Deuteranomaly 6% of men, 0.4% of women\n• Tritanomaly 0.01% of the population" ]
[ null ]
{"ft_lang_label":"__label__en","ft_lang_prob":0.54540473,"math_prob":0.83500177,"size":3682,"snap":"2023-40-2023-50","text_gpt3_token_len":1625,"char_repetition_ratio":0.13458401,"word_repetition_ratio":0.0073260074,"special_character_ratio":0.55377513,"punctuation_ratio":0.2284091,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9916399,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-10-01T08:17:10Z\",\"WARC-Record-ID\":\"<urn:uuid:60d09884-7f11-461f-82bc-9b4c6d06cf3c>\",\"Content-Length\":\"36185\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:84bfafca-7383-4cb9-82e9-e6889c7e76b0>\",\"WARC-Concurrent-To\":\"<urn:uuid:6987a9c4-d129-4545-9809-40031de67d53>\",\"WARC-IP-Address\":\"178.32.117.56\",\"WARC-Target-URI\":\"https://www.colorhexa.com/00e359\",\"WARC-Payload-Digest\":\"sha1:SMTFR6QA6CMWSH7MTDOOBNZKKNOMMDBC\",\"WARC-Block-Digest\":\"sha1:AXAOY2WKNQNZERLAENTHSKMWK5J5OVM4\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-40/CC-MAIN-2023-40_segments_1695233510810.46_warc_CC-MAIN-20231001073649-20231001103649-00292.warc.gz\"}"}
https://world.fanthful.com/products/%E3%80%90%E9%A0%90%E5%94%AE%E3%80%91elden-ring-%E9%9B%A8%E5%82%98
[ "• English\n• Japan(USD \\$)\n• \\$ USD\n• £ GBP\n• EUR\n• A\\$ AUD\n• INR\n• ¥ JPY\n• د.إ AED\n• Af AFN\n• L ALL\n• Դ AMD\n• ƒ ANG\n• Kz AOA\n• \\$ ARS\n• ƒ AWG\n• ман AZN\n• КМ BAM\n• BBD BBD\n• BDT\n• лв BGN\n• BIF\n• BD\\$ BMD\n• B\\$ BND\n• Bs. BOB\n• R\\$ BRL\n• BSD BSD\n• Nu. BTN\n• P BWP\n• Br BYN\n• Be BZD\n• CDF\n• CHF\n• CLP CLP\n• ¥ CNY\n• \\$ COP\n• CRC\n• \\$ CUP\n• \\$ CVE\n• CZK\n• DJF\n• kr DKK\n• \\$ DOP\n• د.ج DZD\n• EGP\n• Nfk ERN\n• Br ETB\n• FJ\\$ FJD\n• FKP FKP\n• GEL\n• GHS\n• £ GIP\n• D GMD\n• GNF\n• Q GTQ\n• \\$ GYD\n• HK\\$ HKD\n• L HNL\n• Kn HRK\n• G HTG\n• Ft HUF\n• Rp IDR\n• ILS\n• IRR\n• Kr ISK\n• \\$ JMD\n• Sh KES\n• С̲ KGS\n• KHR\n• Fr KMF\n• KPW\n• KRW\n• \\$ KYD\n• KZT\n• LAK\n• ل.ل LBP\n• Rs LKR\n• L\\$ LRD\n• L LSL\n• L MDL\n• ден MKD\n• K MMK\n• MNT\n• P MOP\n• MUR\n• ރ. MVR\n• MK MWK\n• \\$ MXN\n• RM MYR\n• MTn MZN\n• NGN\n• C\\$ NIO\n• kr NOK\n• NPR\n• \\$ NZD\n• B/. PAB\n• S/ PEN\n• K PGK\n• PHP\n• PKR\n• PLN\n• PYG\n• ر.ق QAR\n• L RON\n• din RSD\n• р. RUB\n• RWF\n• ر.س SAR\n• Si\\$ SBD\n• SCR\n• ج.س. SDG\n• kr SEK\n• S\\$ SGD\n• £ SHP\n• Le SLL\n• Sh SOS\n• \\$ SRD\n• SDP SSP\n• Db STN\n• ل.س SYP\n• L SZL\n• ฿ THB\n• ЅМ TJS\n• m TMT\n• T\\$ TOP\n• TRY\n• TTD TTD\n• NT\\$ TWD\n• Sh TZS\n• UAH\n• Sh UGX\n• \\$ UYU\n• лв UZS\n• Bs.S. VES\n• VND\n• Vt VUV\n• T WST\n• XAF\n• \\$ XCD\n• Fr XOF\n• XPF\n• YER\n• R ZAR\n• ZK ZMW\n\nNo relevant currency found", null, "# Elden Ring Umbrella\n\n\\$1600\n\\$000\nDescription\n\nMatetial:Pongee fabric\n\nFolder:27*5*5cm\n\nUnfolder diameter:98cm\n\nFrame:8 ribs" ]
[ null, "https://img.myshopline.com/image/store/2000009776/1614655830388/82e1a2a2ecb64c69991d5e53a8831c50.jpeg", null ]
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https://www.mathworksheetscenter.com/mathskills/geometry/DilationsAndSimilaritygeo/
[ "# Dilations and Similarity Worksheets\n\nHow Do Dilations and Similarity Relate in Geometry? Geometry is math's most fun part! There are lots of things in geometry that are quite different from one another, but there are also definitions that relate to one another. Before figuring how dilations and similarities relate to one another, lets first understand what dilations and similarities are. Similarity is known as the property of two figures to be congruent to each other, meaning they must have equal corresponding angle measures and proportional sides. Dilation is the changing of size of an image by shrinking or stretching it using the scale factor. If the scale factor is less than one, the figure becomes smaller than the original image, and if the scale factor is greater than one, the image becomes larger than the original image. Now, how are these two related? Dilation and similarity are related because dilation creates similar figures. Through dilation, figures with proportional sides are created, alongside leaving the angle measure and the shape the same.\n\n• ### Basic Lesson\n\nGuides students through solving Dilations and Similarity.\n\n• ### Intermediate Lesson\n\nDemonstrates the concept of advanced skill while solving Dilations and Similarity. What is the scale factor of the dilation? (with center at the origin)\n\n• ### Independent Practice 1\n\nA really great activity for allowing students to understand the concepts of the Dilations and Similarity.\n\n• ### Independent Practice 2\n\nStudents use Dilations and Similarity in 20 assorted problems. The answers can be found below.\n\n• ### Independent Practice 3\n\nA really great activity for allowing students to understand the concepts of the Dilations and Similarity.\n\n• ### Independent Practice 4\n\nStudents use Dilations and Similarity in 20 assorted problems. The answers can be found below.\n\n• ### Homework Worksheet\n\nStudents are provided with 12 problems to achieve the concepts of Dilations and Similarity.\n\n• ### Skill Quiz\n\nThis tests the students ability to understand Dilations and Similarity." ]
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https://www.edrawmax.com/templates/1006085/
[ "", null, "Template Community / Output Answer Algorithm Flowchart", null, "Charlotte\nPublished on 2021-07-16\nEdit Online", null, "Create an algorithm to find whether a number is even or odd. The first step in the algorithm is taking a number as input from the user. The new value of N = 6. The next step in the algorithm (number%2==0), in this step (number%2), returns the remainder after dividing the number by 2. It should be noted here that % is known as modulus, which returns the remainder value after division. The next step, i.e., remainder == 0, simply checks if the remainder value is equal to 0 or not. So if the remainder is 0, then the number is said to be Even else. It is said to be an Odd number.\nTag\nalgorithm flowchart\nReport\n0\n286", null, "Post", null, "Recommended Templates" ]
[ null, "https://www.edrawmax.com/templates-assets/file-shadow.png", null, "https://dc-static.wondershare.com/wsid/com/static/avatar.png", null, "https://www.edrawmax.com/templates/logoanimation.gif", null, "https://images.edrawsoft.com/video/templates/default.svg", null, "https://edrawcloudpublicus.s3.amazonaws.com/work/1073493/2021-7-16/1626424534/thumb.png", null ]
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http://gangamusic.info/how-to-calculate-sq-footage-of-a-room/how-to-calculate-sq-footage-of-a-room-msu-area-calculator-interpreting-a-scale-drawing-video-khan-academy/
[ "# How To Calculate Sq Footage Of A Room Msu Area Calculator Interpreting A Scale Drawing Video Khan Academy", null, "", null, "how to calculate sq footage of a room msu area calculator interpreting a scale drawing video khan academy.\n\ncalculating surface areas of irregular shaped fields calculating surface areas of irregular shaped fields, how to calculate square footage for insulation diagram of how to calculate area of your basement or crawlspace area, how to calculate square footage of a room square footage calculator how to calculate square footage of a room, estimating the amount of paint needed for interior surfaces youtube estimating the amount of paint needed for interior surfaces, measurement price calculator woocommerce woocommerce measurement price calculator quantity mode product, square foot calculator how to measure square feet how to calculate square footage, room size calculator harvey maria harvey maria room size calculator, how big is a square foot how to measure square footage how big is a square foot calculator, how to find the square footage of a room rise construction llc how to figure square footage of a room, square footage calculator omni calculate square footage, area of a triangle using a grid to find dimensions youtube ." ]
[ null, "http://gangamusic.info/data/how-to-calculate-sq-footage-of-a-room/images/how-to-calculate-sq-footage-of-a-room-msu-area-calculator-interpreting-a-scale-drawing-video-khan-academy.jpg", null, "http://gangamusic.info/data/how-to-calculate-sq-footage-of-a-room/images/how-to-calculate-sq-footage-of-a-room-msu-area-calculator-interpreting-a-scale-drawing-video-khan-academy.jpg", null ]
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https://afd-fraktion-eimsbuettel.de/docs/page.php?ac170a=proportion-calculator-with-mean-and-standard-deviation
[ "Practice Problem 4: In a population that is normally distributed with mean 7 and standard deviation 11, the bottom 90% of the values are those less than x. P-value Calculator. Find the value of x. Depending on which standard deviation is known, the equation used to calculate the confidence interval differs. Given a normal distribution with a mean of 237 and standard deviation of 56, find a value with a z-score of 1.94. Square root of Variance = Standard deviation. The calculator provided on this page calculates the confidence interval for a proportion and uses the following equations: Practice Problem 5: Low variance indicates that data points are generally similar and do not vary widely from the mean. For the purposes of this calculator, it is assumed that the population standard deviation is known or sample size is larger enough therefore the population standard deviation and sample standard deviation is similar. High variance indicates that data values have greater variability and are more widely dispersed from the mean. First, enter your data set, and then you will get an explanation on the distribution of numbers based on the Empirical Rule. Inferrences about both absolute and relative difference (percentage change, percent effect) are supported. The variance calculator finds variance, standard deviation, sample size n, mean and sum of squares. Missing addend Double facts Doubles word problems. The standard deviation and mean of a data are 6.5 and 12.5 respectively. If we are measuring the entire population, we reduce this by one (using n-1). It will also output the Z-score or T-score for the difference. There are different equations that can be used to calculate confidence intervals depending on factors such as whether the standard deviation is known or smaller samples (n. 30) are involved, among others. Formula Used: SE p = sqrt [ p ( 1 - p) / n] where, p is Proportion of successes in the sample,n is Number of observations in the sample. To calculate the standard deviation,…you use a different formula.…And this takes a little bit of work to enter,…but I'll show you how to do it.…You take the square root, p-bar,…which is the average of proportions of defective units,…and multiply that by one minus p-bar.…And you … MATH FOR KIDS. Find the coefficient of variation. LIFE MATHEMATICS. Use this statistical significance calculator to easily calculate the p-value and determine whether the difference between two proportions or means (independent groups) is statistically significant. The Margin of Error for the Sample Mean Calculator calculates the margin of error based on the confidence level, population standard deviation, and the sample size. Direct proportion and inverse proportion. We calculate sample variance, standard error, and standard deviation by using the number of items in the sample. This calculator will compute the 99%, 95%, and 90% confidence intervals for the mean of a normal population when the population standard deviation is known, given the sample mean, the sample size, and the population standard deviation. You can use this Empirical Rule Calculator with mean and standard deviation to find the percent of data values between two numbers for bell-shaped distributions as well as a detailed solution. The proportion of the population described by the standard deviation increases as the number of standard deviations increase. Instructions: Compute the Mean and Standard Deviation for the Binomial distribution. Please enter the necessary parameter values, and then click 'Calculate'. ... Chemistry periodic calculator.\nFender Player Series Quality, 56th Assembly District Wisconsin, Murasaki Sweet Potatoes Recipes, Elderberry Cuttings For Sale, Passionfruit Cheesecake Recipe Uk, Fortune And Glory Book, Pink Honeysuckle Berries, Interface Segregation Principle, Pomegranate Infused Vodka, Construction Industry Overview, Climbing French Beans," ]
[ null ]
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http://hellodrupal.info/third-grade-math-worksheets-pdf/mental-math-grade-3-day-math-mental-maths-worksheets-third-grade-math-worksheets-pdf-grade-5-math-worksheets-pdf-mental-math-grade-3-day/
[ "# Mental Math Grade 3 Day Math Mental Maths Worksheets Third Grade Math Worksheets Pdf Grade 5 Math Worksheets Pdf Mental Math Grade 3 Day", null, "mental math grade 3 day math mental maths worksheets third grade math worksheets pdf grade 5 math worksheets pdf mental math grade 3 day.\n\n2nd grade math money worksheets pdf 6 decimals 6th free third fresh time measurement gallery,grade 5 math worksheets pdf kumon 5th mental for teachers science 6 free download,2nd grade math money worksheets pdf third word problems worksheet 6 cbse 5 decimals,free third grade math worksheets fresh time measurement gallery 6 pdf download decimals 5 maths south africa,dynamically created subtraction word problems using 1 digit numbers 3rd grade math worksheets pdf 6th geometry 2nd multiplication,3rd grade math worksheets multiplication pdf cbse 1st core sheets co 5th mental,6th grade math review worksheets pdf core 8th common 4th problems,first grade math worksheets pdf free 2nd coloring 3rd multiplication 2 3 4 5,1st grade math multiplication worksheets pdf 6 download worksheet template 4th cbse,8th grade math mixed review worksheets pdf coloring 6." ]
[ null, "http://hellodrupal.info/wp-content/uploads/2019/05/mental-math-grade-3-day-math-mental-maths-worksheets-third-grade-math-worksheets-pdf-grade-5-math-worksheets-pdf-mental-math-grade-3-day.jpg", null ]
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https://schoollearningcommons.info/question/22-if-the-perimeter-of-an-equilateral-triangle-is-180-cm-then-its-area-will-be-24828908-46/
[ "## 22. If the perimeter of an equilateral triangle is 180 cm. Then its area will be: *​\n\nQuestion\n\n22. If the perimeter of an equilateral triangle is 180 cm. Then its area will be: *​\n\nin progress 0\n8 months 2021-09-23T12:19:19+00:00 2 Answers 0 views 0\n\n60cm\n\nStep-by-step explanation:\n\nperimeter of equilateral triangle is p=3*side length\n\n⇒ side = perimeter/3\n\n= 180/3\n\n= 60cm\n\n## Hope this will help u\n\nStep-by-step explanation:\n\nperimeter of a triangle =180\n\n180/3=60\n\n1 side =60\n\narea = root3/4*side square\n\n=1.732/4*3600\n\n=1.732*900\n\n=1558.85" ]
[ null ]
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https://curriculum.illustrativemathematics.org/HS/teachers/3/6/15/preparation.html
[ "# Lesson 15\n\nFeatures of Trigonometric Graphs (Part 1)\n\n### Lesson Narrative\n\nThis is the first of two lessons focused on students practicing identifying important features of trigonometric functions when starting from equations or graphs. They also begin to learn how a horizontal scale factor affects the period of these types of functions.  While this lesson emphasizes mathematical contexts, students return to working with functions modeling situations in the following lesson with an emphasis toward reasoning about the scale factor affecting the period.\n\nThe warm-up introduces students to what cosine and sine with different periods can look like. In a later activity, they focus on $$y=\\sin(2\\theta)$$ and use different representations to make sense of why the graph of this function appears horizontally compressed compared to the graph of $$y=\\sin(\\theta)$$.\n\nStudents make use of structure when they work on the matching task as they identify important structural properties of graphs and equations (MP7). These properties include midline, amplitude, and horizontal translations and students need to identify these aspects of a function both from the expression defining the function and its graph.\n\nTechnology isn't required for this lesson, but there are opportunities for students to choose to use appropriate technology to solve problems. We recommend making technology available.\n\n### Learning Goals\n\nTeacher Facing\n\n• Identify the midline, amplitude, and horizontal translation in trigonometric functions presented graphically and with equations.\n• Predict how changing the value of $k$ in $y=cos(kx)$ changes the graph of the function for whole number values of $k$.\n\n### Student Facing\n\n• Let’s compare graphs and equations of trigonometric functions.\n\n### Student Facing\n\n• I can identify the midline, amplitude, and horizontal translation of a trigonometric function given a graph or equation." ]
[ null ]
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https://doxygen.reactos.org/d9/d37/mingw__math_8h.html
[ "ReactOS 0.4.15-dev-6644-g539123c\nmingw_math.h File Reference\n\nGo to the source code of this file.\n\n## Macros\n\n#define HUGE_VALF   __INFF\n\n#define HUGE_VALL   __INFL\n\n#define INFINITY   HUGE_VALF\n\n#define NAN   __QNAN\n\n#define FP_NAN   0x0100\n\n#define FP_NORMAL   0x0400\n\n#define FP_INFINITE   (FP_NAN | FP_NORMAL)\n\n#define FP_ZERO   0x4000\n\n#define FP_SUBNORMAL   (FP_NORMAL | FP_ZERO)\n\n#define fpclassify(x)\n\n#define isfinite(x)   ((fpclassify(x) & FP_NAN) == 0)\n\n#define isinf(x)   (fpclassify(x) == FP_INFINITE)\n\n#define isnan(x)\n\n#define isnormal(x)   (fpclassify(x) == FP_NORMAL)\n\n#define signbit(x)\n\n#define FP_ILOGB0   ((int)0x80000000)\n\n#define FP_ILOGBNAN   ((int)0x80000000)\n\n#define _nan()   nan(\"\")\n\n#define _nanf()   nanf(\"\")\n\n#define _nanl()   nanl(\"\")\n\n#define isgreater(x, y)   ((__fp_unordered_compare(x, y) & 0x4500) == 0)\n\n#define isless(x, y)   ((__fp_unordered_compare (y, x) & 0x4500) == 0)\n\n#define isgreaterequal(x, y)   ((__fp_unordered_compare (x, y) & FP_INFINITE) == 0)\n\n#define islessequal(x, y)   ((__fp_unordered_compare(y, x) & FP_INFINITE) == 0)\n\n#define islessgreater(x, y)   ((__fp_unordered_compare(x, y) & FP_SUBNORMAL) == 0)\n\n#define isunordered(x, y)   ((__fp_unordered_compare(x, y) & 0x4500) == 0x4500)\n\n## Typedefs\n\ntypedef long double float_t\n\ntypedef long double double_t\n\n## Functions\n\nint __cdecl __fpclassifyl (long double)\n\nint __cdecl __fpclassifyf (float)\n\nint __cdecl __fpclassify (double)\n\nint __cdecl __isnan (double)\n\nint __cdecl __isnanf (float)\n\nint __cdecl __isnanl (long double)\n\nint __cdecl __signbit (double)\n\nint __cdecl __signbitf (float)\n\nint __cdecl __signbitl (long double)\n\ndouble __cdecl acosh (double)\n\nfloat __cdecl acoshf (float)\n\nlong double __cdecl acoshl (long double)\n\ndouble __cdecl asinh (double)\n\nfloat __cdecl asinhf (float)\n\nlong double __cdecl asinhl (long double)\n\ndouble __cdecl atanh (double)\n\nfloat __cdecl atanhf (float)\n\nlong double __cdecl atanhl (long double)\n\ndouble __cdecl exp2 (double)\n\nfloat __cdecl exp2f (float)\n\nlong double __cdecl exp2l (long double)\n\ndouble __cdecl expm1 (double)\n\nfloat __cdecl expm1f (float)\n\nlong double __cdecl expm1l (long double)\n\nint __cdecl ilogb (double)\n\nint __cdecl ilogbf (float)\n\nint __cdecl ilogbl (long double)\n\ndouble __cdecl log1p (double)\n\nfloat __cdecl log1pf (float)\n\nlong double __cdecl log1pl (long double)\n\ndouble __cdecl log2 (double)\n\nfloat __cdecl log2f (float)\n\nlong double __cdecl log2l (long double)\n\ndouble __cdecl logb (double)\n\nfloat __cdecl logbf (float)\n\nlong double __cdecl logbl (long double)\n\ndouble __cdecl scalbn (double, int)\n\nfloat __cdecl scalbnf (float, int)\n\nlong double __cdecl scalbnl (long double, int)\n\ndouble __cdecl scalbln (double, long)\n\nfloat __cdecl scalblnf (float, long)\n\nlong double __cdecl scalblnl (long double, long)\n\ndouble __cdecl cbrt (double)\n\nfloat __cdecl cbrtf (float)\n\nlong double __cdecl cbrtl (long double)\n\ndouble __cdecl erf (double)\n\nfloat __cdecl erff (float)\n\nlong double __cdecl erfl (long double)\n\ndouble __cdecl erfc (double)\n\nfloat __cdecl erfcf (float)\n\nlong double __cdecl erfcl (long double)\n\ndouble __cdecl lgamma (double)\n\nfloat __cdecl lgammaf (float)\n\nlong double __cdecl lgammal (long double)\n\ndouble __cdecl tgamma (double)\n\nfloat __cdecl tgammaf (float)\n\nlong double __cdecl tgammal (long double)\n\ndouble __cdecl nearbyint (double)\n\nfloat __cdecl nearbyintf (float)\n\nlong double __cdecl nearbyintl (long double)\n\ndouble __cdecl rint (double)\n\nfloat __cdecl rintf (float)\n\nlong double __cdecl rintl (long double)\n\nlong __cdecl lrint (double)\n\nlong __cdecl lrintf (float)\n\nlong __cdecl lrintl (long double)\n\n__MINGW_EXTENSION long long __cdecl llrint (double)\n\n__MINGW_EXTENSION long long __cdecl llrintf (float)\n\n__MINGW_EXTENSION long long __cdecl llrintl (long double)\n\ndouble __cdecl round (double)\n\nfloat __cdecl roundf (float)\n\nlong double __cdecl roundl (long double)\n\nlong __cdecl lround (double)\n\nlong __cdecl lroundf (float)\n\nlong __cdecl lroundl (long double)\n\n__MINGW_EXTENSION long long __cdecl llround (double)\n\n__MINGW_EXTENSION long long __cdecl llroundf (float)\n\n__MINGW_EXTENSION long long __cdecl llroundl (long double)\n\ndouble __cdecl trunc (double)\n\nfloat __cdecl truncf (float)\n\nlong double __cdecl truncl (long double)\n\ndouble __cdecl remainder (double, double)\n\nfloat __cdecl remainderf (float, float)\n\nlong double __cdecl remainderl (long double, long double)\n\ndouble __cdecl remquo (double, double, int *)\n\nfloat __cdecl remquof (float, float, int *)\n\nlong double __cdecl remquol (long double, long double, int *)\n\ndouble __cdecl copysign (double, double)\n\nfloat __cdecl copysignf (float, float)\n\nlong double __cdecl copysignl (long double, long double)\n\ndouble __cdecl nan (const char *tagp)\n\nfloat __cdecl nanf (const char *tagp)\n\nlong double __cdecl nanl (const char *tagp)\n\ndouble __cdecl nextafter (double, double)\n\nfloat __cdecl nextafterf (float, float)\n\nlong double __cdecl nextafterl (long double, long double)\n\ndouble __cdecl nexttoward (double, long double)\n\nfloat __cdecl nexttowardf (float, long double)\n\nlong double __cdecl nexttowardl (long double, long double)\n\ndouble __cdecl fdim (double x, double y)\n\nfloat __cdecl fdimf (float x, float y)\n\nlong double __cdecl fdiml (long double x, long double y)\n\ndouble __cdecl fmax (double, double)\n\nfloat __cdecl fmaxf (float, float)\n\nlong double __cdecl fmaxl (long double, long double)\n\ndouble __cdecl fmin (double, double)\n\nfloat __cdecl fminf (float, float)\n\nlong double __cdecl fminl (long double, long double)\n\ndouble __cdecl fma (double, double, double)\n\nfloat __cdecl fmaf (float, float, float)\n\nlong double __cdecl fmal (long double, long double, long double)\n\n__CRT_INLINE int __cdecl __fp_unordered_compare (long double x, long double y)\n\n## Variables\n\nconst float __INFF\n\nconst long double __INFL\n\nconst double __QNAN\n\n## ◆ _nan\n\n #define _nan ( ) nan(\"\")\n\nDefinition at line 465 of file mingw_math.h.\n\n## ◆ _nanf\n\n #define _nanf ( ) nanf(\"\")\n\nDefinition at line 466 of file mingw_math.h.\n\n## ◆ _nanl\n\n #define _nanl ( ) nanl(\"\")\n\nDefinition at line 467 of file mingw_math.h.\n\n## ◆ FP_ILOGB0\n\n #define FP_ILOGB0   ((int)0x80000000)\n\nDefinition at line 209 of file mingw_math.h.\n\n## ◆ FP_ILOGBNAN\n\n #define FP_ILOGBNAN   ((int)0x80000000)\n\nDefinition at line 210 of file mingw_math.h.\n\n## ◆ FP_INFINITE\n\n #define FP_INFINITE   (FP_NAN | FP_NORMAL)\n\nDefinition at line 52 of file mingw_math.h.\n\n## ◆ FP_NAN\n\n #define FP_NAN   0x0100\n\nDefinition at line 50 of file mingw_math.h.\n\n## ◆ FP_NORMAL\n\n #define FP_NORMAL   0x0400\n\nDefinition at line 51 of file mingw_math.h.\n\n## ◆ FP_SUBNORMAL\n\n #define FP_SUBNORMAL   (FP_NORMAL | FP_ZERO)\n\nDefinition at line 54 of file mingw_math.h.\n\n## ◆ FP_ZERO\n\n #define FP_ZERO   0x4000\n\nDefinition at line 53 of file mingw_math.h.\n\n## ◆ fpclassify\n\n #define fpclassify ( x )\nValue:\n(sizeof (x) == sizeof (float) ? __fpclassifyf (x) \\\n: sizeof (x) == sizeof (double) ? __fpclassify (x) \\\nGLint GLint GLint GLint GLint x\nDefinition: gl.h:1548\nint __cdecl __fpclassify(double)\nDefinition: mingw_math.h:74\nint __cdecl __fpclassifyf(float)\nDefinition: mingw_math.h:79\nint __cdecl __fpclassifyl(long double)\nDefinition: mingw_math.h:69\n\nDefinition at line 86 of file mingw_math.h.\n\n## ◆ HUGE_VALF\n\n #define HUGE_VALF   __INFF\n\nDefinition at line 17 of file mingw_math.h.\n\n## ◆ HUGE_VALL\n\n #define HUGE_VALL   __INFL\n\nDefinition at line 19 of file mingw_math.h.\n\n## ◆ INFINITY\n\n #define INFINITY   HUGE_VALF\n\nDefinition at line 20 of file mingw_math.h.\n\n## ◆ isfinite\n\n #define isfinite ( x ) ((fpclassify(x) & FP_NAN) == 0)\n\nDefinition at line 91 of file mingw_math.h.\n\n## ◆ isgreater\n\n #define isgreater ( x, y ) ((__fp_unordered_compare(x, y) & 0x4500) == 0)\n\nDefinition at line 537 of file mingw_math.h.\n\n## ◆ isgreaterequal\n\n #define isgreaterequal ( x, y ) ((__fp_unordered_compare (x, y) & FP_INFINITE) == 0)\n\nDefinition at line 539 of file mingw_math.h.\n\n## ◆ isinf\n\n #define isinf ( x ) (fpclassify(x) == FP_INFINITE)\n\nDefinition at line 94 of file mingw_math.h.\n\n## ◆ isless\n\n #define isless ( x, y ) ((__fp_unordered_compare (y, x) & 0x4500) == 0)\n\nDefinition at line 538 of file mingw_math.h.\n\n## ◆ islessequal\n\n #define islessequal ( x, y ) ((__fp_unordered_compare(y, x) & FP_INFINITE) == 0)\n\nDefinition at line 540 of file mingw_math.h.\n\n## ◆ islessgreater\n\n #define islessgreater ( x, y ) ((__fp_unordered_compare(x, y) & FP_SUBNORMAL) == 0)\n\nDefinition at line 541 of file mingw_math.h.\n\n## ◆ isnan\n\n #define isnan ( x )\nValue:\n(sizeof (x) == sizeof (float) ? __isnanf (x) \\\n: sizeof (x) == sizeof (double) ? __isnan (x) \\\n: __isnanl (x))\nint __cdecl __isnanf(float)\nDefinition: mingw_math.h:114\nint __cdecl __isnan(double)\nDefinition: mingw_math.h:105\nint __cdecl __isnanl(long double)\nDefinition: mingw_math.h:123\n\nDefinition at line 133 of file mingw_math.h.\n\n## ◆ isnormal\n\n #define isnormal ( x ) (fpclassify(x) == FP_NORMAL)\n\nDefinition at line 138 of file mingw_math.h.\n\n## ◆ isunordered\n\n #define isunordered ( x, y ) ((__fp_unordered_compare(x, y) & 0x4500) == 0x4500)\n\nDefinition at line 542 of file mingw_math.h.\n\n## ◆ NAN\n\n #define NAN   __QNAN\n\nDefinition at line 22 of file mingw_math.h.\n\n## ◆ signbit\n\n #define signbit ( x )\nValue:\n(sizeof (x) == sizeof (float) ? __signbitf (x) \\\n: sizeof (x) == sizeof (double) ? __signbit (x) \\\nint __cdecl __signbitl(long double)\nDefinition: mingw_math.h:157\nint __cdecl __signbitf(float)\nDefinition: mingw_math.h:151\nint __cdecl __signbit(double)\nDefinition: mingw_math.h:145\n\nDefinition at line 164 of file mingw_math.h.\n\n## ◆ double_t\n\n typedef long double double_t\n\nDefinition at line 40 of file mingw_math.h.\n\n## ◆ float_t\n\n typedef long double float_t\n\nDefinition at line 39 of file mingw_math.h.\n\n## ◆ __fp_unordered_compare()\n\n __CRT_INLINE int __cdecl __fp_unordered_compare ( long double x, long double y )\n\nDefinition at line 529 of file mingw_math.h.\n\n529 {\n530 unsigned short retval;\n531 __asm__ __volatile__ (\"fucom %%st(1);\"\n532 \"fnstsw;\": \"=a\" (retval) : \"t\" (x), \"u\" (y));\n533 return retval;\n534 }\nGLint GLint GLint GLint GLint GLint y\nDefinition: gl.h:1548\n__asm__(\".p2align 4, 0x90\\n\" \".seh_proc __seh2_global_filter_func\\n\" \"__seh2_global_filter_func:\\n\" \"\\tpush %rbp\\n\" \"\\t.seh_pushreg %rbp\\n\" \"\\tsub \\$32, %rsp\\n\" \"\\t.seh_stackalloc 32\\n\" \"\\t.seh_endprologue\\n\" \"\\tmov %rdx, %rbp\\n\" \"\\tjmp *%rax\\n\" \"__seh2_global_filter_func_exit:\\n\" \"\\t.p2align 4\\n\" \"\\tadd \\$32, %rsp\\n\" \"\\tpop %rbp\\n\" \"\\tret\\n\" \"\\t.seh_endproc\")\n\n## ◆ __fpclassify()\n\n __CRT_INLINE int __cdecl __fpclassify ( double x )\n\nDefinition at line 74 of file mingw_math.h.\n\n74 {\n75 unsigned short sw;\n76 __asm__ __volatile__ (\"fxam; fstsw %%ax;\" : \"=a\" (sw): \"t\" (x));\n77 return sw & (FP_NAN | FP_NORMAL | FP_ZERO );\n78 }\n#define FP_NAN\nDefinition: mingw_math.h:50\n#define FP_ZERO\nDefinition: mingw_math.h:53\n#define FP_NORMAL\nDefinition: mingw_math.h:51\n\n## ◆ __fpclassifyf()\n\n __CRT_INLINE int __cdecl __fpclassifyf ( float x )\n\nDefinition at line 79 of file mingw_math.h.\n\n79 {\n80 unsigned short sw;\n81 __asm__ __volatile__ (\"fxam; fstsw %%ax;\" : \"=a\" (sw): \"t\" (x));\n82 return sw & (FP_NAN | FP_NORMAL | FP_ZERO );\n83 }\n\n## ◆ __fpclassifyl()\n\n __CRT_INLINE int __cdecl __fpclassifyl ( long double x )\n\nDefinition at line 69 of file mingw_math.h.\n\n69 {\n70 unsigned short sw;\n71 __asm__ __volatile__ (\"fxam; fstsw %%ax;\" : \"=a\" (sw): \"t\" (x));\n72 return sw & (FP_NAN | FP_NORMAL | FP_ZERO );\n73 }\n\n## ◆ __isnan()\n\n __CRT_INLINE int __cdecl __isnan ( double _x )\n\nDefinition at line 105 of file mingw_math.h.\n\n106 {\n107 unsigned short sw;\n108 __asm__ __volatile__ (\"fxam;\"\n109 \"fstsw %%ax\": \"=a\" (sw) : \"t\" (_x));\n110 return (sw & (FP_NAN | FP_NORMAL | FP_INFINITE | FP_ZERO | FP_SUBNORMAL))\n111 == FP_NAN;\n112 }\n#define _x(oid)\n#define FP_INFINITE\nDefinition: mingw_math.h:52\n#define FP_SUBNORMAL\nDefinition: mingw_math.h:54\n\n## ◆ __isnanf()\n\n __CRT_INLINE int __cdecl __isnanf ( float _x )\n\nDefinition at line 114 of file mingw_math.h.\n\n115 {\n116 unsigned short sw;\n117 __asm__ __volatile__ (\"fxam;\"\n118 \"fstsw %%ax\": \"=a\" (sw) : \"t\" (_x));\n119 return (sw & (FP_NAN | FP_NORMAL | FP_INFINITE | FP_ZERO | FP_SUBNORMAL))\n120 == FP_NAN;\n121 }\n\n## ◆ __isnanl()\n\n __CRT_INLINE int __cdecl __isnanl ( long double _x )\n\nDefinition at line 123 of file mingw_math.h.\n\n124 {\n125 unsigned short sw;\n126 __asm__ __volatile__ (\"fxam;\"\n127 \"fstsw %%ax\": \"=a\" (sw) : \"t\" (_x));\n128 return (sw & (FP_NAN | FP_NORMAL | FP_INFINITE | FP_ZERO | FP_SUBNORMAL))\n129 == FP_NAN;\n130 }\n\n## ◆ __signbit()\n\n __CRT_INLINE int __cdecl __signbit ( double x )\n\nDefinition at line 145 of file mingw_math.h.\n\n145 {\n146 unsigned short stw;\n147 __asm__ __volatile__ ( \"fxam; fstsw %%ax;\": \"=a\" (stw) : \"t\" (x));\n148 return stw & 0x0200;\n149 }\n\n## ◆ __signbitf()\n\n __CRT_INLINE int __cdecl __signbitf ( float x )\n\nDefinition at line 151 of file mingw_math.h.\n\n151 {\n152 unsigned short stw;\n153 __asm__ __volatile__ (\"fxam; fstsw %%ax;\": \"=a\" (stw) : \"t\" (x));\n154 return stw & 0x0200;\n155 }\n\n## ◆ __signbitl()\n\n __CRT_INLINE int __cdecl __signbitl ( long double x )\n\nDefinition at line 157 of file mingw_math.h.\n\n157 {\n158 unsigned short stw;\n159 __asm__ __volatile__ (\"fxam; fstsw %%ax;\": \"=a\" (stw) : \"t\" (x));\n160 return stw & 0x0200;\n161 }\n\n## ◆ acosh()\n\n double __cdecl acosh ( double x )\n\nDefinition at line 54 of file fun_ieee.c.\n\n55{\n56 // must be x>=1, if not return Nan (Not a Number)\n57 if(!(x>=1.0)) return sqrt(-1.0);\n58\n59 // return only the positive result (as sqrt does).\n60 return log(x+sqrt(x*x-1.0));\n61}\n_STLP_DECLSPEC complex< float > _STLP_CALL sqrt(const complex< float > &)\nDefinition: complex.cpp:188\n#define log(outFile, fmt,...)\nDefinition: util.h:15\n\nReferenced by rpn_acosh().\n\n## ◆ acoshf()\n\n float __cdecl acoshf ( float )\n\n## ◆ acoshl()\n\n long double __cdecl acoshl ( long double )\n\n## ◆ asinh()\n\n double __cdecl asinh ( double x )\n\nDefinition at line 49 of file fun_ieee.c.\n\n50{\n51 return log(x+sqrt(x*x+1));\n52}\n\nReferenced by rpn_asinh().\n\n## ◆ asinhf()\n\n float __cdecl asinhf ( float )\n\n## ◆ asinhl()\n\n long double __cdecl asinhl ( long double )\n\n## ◆ atanh()\n\n double __cdecl atanh ( double x )\n\nDefinition at line 63 of file fun_ieee.c.\n\n64{\n65 // must be x>-1, x<1, if not return Nan (Not a Number)\n66 if(!(x>-1.0 && x<1.0)) return sqrt(-1.0);\n67\n68 return log((1.0+x)/(1.0-x))/2.0;\n69}\n\nReferenced by rpn_atanh().\n\n## ◆ atanhf()\n\n float __cdecl atanhf ( float )\n\n## ◆ atanhl()\n\n long double __cdecl atanhl ( long double )\n\n## ◆ cbrt()\n\n double __cdecl cbrt ( double )\n\nReferenced by rpn_cbrt().\n\n## ◆ cbrtf()\n\n float __cdecl cbrtf ( float )\n\n## ◆ cbrtl()\n\n long double __cdecl cbrtl ( long double )\n\n## ◆ copysign()\n\n double __cdecl copysign ( double , double )\n\n## ◆ copysignf()\n\n float __cdecl copysignf ( float , float )\n\nReferenced by float_32_to_16(), and wined3d_ftoa().\n\n## ◆ copysignl()\n\n long double __cdecl copysignl ( long double , long double )\n\n## ◆ erf()\n\n double __cdecl erf ( double )\n\n## ◆ erfc()\n\n double __cdecl erfc ( double )\n\n## ◆ erfcf()\n\n float __cdecl erfcf ( float )\n\n## ◆ erfcl()\n\n long double __cdecl erfcl ( long double )\n\n## ◆ erff()\n\n float __cdecl erff ( float )\n\n## ◆ erfl()\n\n long double __cdecl erfl ( long double )\n\n## ◆ exp2()\n\n double __cdecl exp2 ( double )\n\n## ◆ exp2f()\n\n float __cdecl exp2f ( float )\n\n## ◆ exp2l()\n\n long double __cdecl exp2l ( long double )\n\n## ◆ expm1()\n\n double __cdecl expm1 ( double )\n\n## ◆ expm1f()\n\n float __cdecl expm1f ( float )\n\n## ◆ expm1l()\n\n long double __cdecl expm1l ( long double )\n\n## ◆ fdim()\n\n double __cdecl fdim ( double x, double y )\n\n## ◆ fdimf()\n\n float __cdecl fdimf ( float x, float y )\n\n## ◆ fdiml()\n\n long double __cdecl fdiml ( long double x, long double y )\n\n## ◆ fma()\n\n double __cdecl fma ( double , double , double )\n\n## ◆ fmaf()\n\n float __cdecl fmaf ( float , float , float )\n\n## ◆ fmal()\n\n long double __cdecl fmal ( long double , long double , long double )\n\n## ◆ fmax()\n\n double __cdecl fmax ( double , double )\n\n## ◆ fmaxf()\n\n float __cdecl fmaxf ( float , float )\n\n## ◆ fmaxl()\n\n long double __cdecl fmaxl ( long double , long double )\n\n## ◆ fmin()\n\n double __cdecl fmin ( double , double )\n\n## ◆ fminf()\n\n float __cdecl fminf ( float , float )\n\n## ◆ fminl()\n\n long double __cdecl fminl ( long double , long double )\n\n## ◆ ilogb()\n\n int __cdecl ilogb ( double )\n\n## ◆ ilogbf()\n\n int __cdecl ilogbf ( float )\n\n## ◆ ilogbl()\n\n int __cdecl ilogbl ( long double )\n\n## ◆ lgamma()\n\n double __cdecl lgamma ( double )\n\n## ◆ lgammaf()\n\n float __cdecl lgammaf ( float )\n\n## ◆ lgammal()\n\n long double __cdecl lgammal ( long double )\n\n## ◆ llrint()\n\n __MINGW_EXTENSION __CRT_INLINE long long __cdecl llrint ( double x )\n\nDefinition at line 395 of file mingw_math.h.\n\n396 {\n397 __MINGW_EXTENSION long long retval = 0ll;\n398 __asm__ __volatile__ \\\n399 (\"fistpll %0\" : \"=m\" (retval) : \"t\" (x) : \"st\"); \\\n400 return retval;\n401 }\n#define __MINGW_EXTENSION\nDefinition: _mingw.h:166\nw ll\nDefinition: byte_order.h:167\n\n## ◆ llrintf()\n\n __MINGW_EXTENSION __CRT_INLINE long long __cdecl llrintf ( float x )\n\nDefinition at line 403 of file mingw_math.h.\n\n404 {\n405 __MINGW_EXTENSION long long retval = 0ll;\n406 __asm__ __volatile__ \\\n407 (\"fistpll %0\" : \"=m\" (retval) : \"t\" (x) : \"st\"); \\\n408 return retval;\n409 }\n\n## ◆ llrintl()\n\n __MINGW_EXTENSION __CRT_INLINE long long __cdecl llrintl ( long double x )\n\nDefinition at line 411 of file mingw_math.h.\n\n412 {\n413 __MINGW_EXTENSION long long retval = 0ll;\n414 __asm__ __volatile__ \\\n415 (\"fistpll %0\" : \"=m\" (retval) : \"t\" (x) : \"st\"); \\\n416 return retval;\n417 }\n\n## ◆ llround()\n\n __MINGW_EXTENSION long long __cdecl llround ( double )\n\n## ◆ llroundf()\n\n __MINGW_EXTENSION long long __cdecl llroundf ( float )\n\n## ◆ llroundl()\n\n __MINGW_EXTENSION long long __cdecl llroundl ( long double )\n\n## ◆ log1p()\n\n double __cdecl log1p ( double )\n\n## ◆ log1pf()\n\n float __cdecl log1pf ( float )\n\n## ◆ log1pl()\n\n long double __cdecl log1pl ( long double )\n\n## ◆ log2()\n\n double __cdecl log2 ( double )\n\n## ◆ log2f()\n\n float __cdecl log2f ( float )\n\n## ◆ log2l()\n\n long double __cdecl log2l ( long double )\n\n## ◆ logb()\n\n __CRT_INLINE double __cdecl logb ( double x )\n\nDefinition at line 243 of file mingw_math.h.\n\n244 {\n245 double res = 0.0;\n246 __asm__ __volatile__ (\"fxtract\\n\\t\"\n247 \"fstp %%st\" : \"=t\" (res) : \"0\" (x));\n248 return res;\n249 }\nGLuint res\nDefinition: glext.h:9613\n\n## ◆ logbf()\n\n __CRT_INLINE float __cdecl logbf ( float x )\n\nDefinition at line 251 of file mingw_math.h.\n\n252 {\n253 float res = 0.0F;\n254 __asm__ __volatile__ (\"fxtract\\n\\t\"\n255 \"fstp %%st\" : \"=t\" (res) : \"0\" (x));\n256 return res;\n257 }\n\n## ◆ logbl()\n\n __CRT_INLINE long double __cdecl logbl ( long double x )\n\nDefinition at line 259 of file mingw_math.h.\n\n260 {\n261 long double res = 0.0l;\n262 __asm__ __volatile__ (\"fxtract\\n\\t\"\n263 \"fstp %%st\" : \"=t\" (res) : \"0\" (x));\n264 return res;\n265 }\n\n## ◆ lrint()\n\n __CRT_INLINE long __cdecl lrint ( double x )\n\nDefinition at line 371 of file mingw_math.h.\n\n372 {\n373 long retval = 0;\n374 __asm__ __volatile__ \\\n375 (\"fistpl %0\" : \"=m\" (retval) : \"t\" (x) : \"st\"); \\\n376 return retval;\n377 }\n\n## ◆ lrintf()\n\n __CRT_INLINE long __cdecl lrintf ( float x )\n\nDefinition at line 379 of file mingw_math.h.\n\n380 {\n381 long retval = 0;\n382 __asm__ __volatile__ \\\n383 (\"fistpl %0\" : \"=m\" (retval) : \"t\" (x) : \"st\"); \\\n384 return retval;\n385 }\n\n## ◆ lrintl()\n\n __CRT_INLINE long __cdecl lrintl ( long double x )\n\nDefinition at line 387 of file mingw_math.h.\n\n388 {\n389 long retval = 0;\n390 __asm__ __volatile__ \\\n391 (\"fistpl %0\" : \"=m\" (retval) : \"t\" (x) : \"st\"); \\\n392 return retval;\n393 }\n\n## ◆ lround()\n\n long __cdecl lround ( double )\n\n## ◆ lroundf()\n\n long __cdecl lroundf ( float )\n\n## ◆ lroundl()\n\n long __cdecl lroundl ( long double )\n\n## ◆ nan()\n\n double __cdecl nan ( const char * tagp )\n\n## ◆ nanf()\n\n float __cdecl nanf ( const char * tagp )\n\n## ◆ nanl()\n\n long double __cdecl nanl ( const char * tagp )\n\n## ◆ nearbyint()\n\n double __cdecl nearbyint ( double )\n\n## ◆ nearbyintf()\n\n float __cdecl nearbyintf ( float )\n\n## ◆ nearbyintl()\n\n long double __cdecl nearbyintl ( long double )\n\n## ◆ nextafter()\n\n double __cdecl nextafter ( double , double )\n\n## ◆ nextafterf()\n\n float __cdecl nextafterf ( float , float )\n\n## ◆ nextafterl()\n\n long double __cdecl nextafterl ( long double , long double )\n\n## ◆ nexttoward()\n\n double __cdecl nexttoward ( double , long double )\n\n## ◆ nexttowardf()\n\n float __cdecl nexttowardf ( float , long double )\n\n## ◆ nexttowardl()\n\n long double __cdecl nexttowardl ( long double , long double )\n\n## ◆ remainder()\n\n double __cdecl remainder ( double x, double y )\n\nDefinition at line 75 of file remainder.c.\n\n79{\n80 double dx, dy, scale, w, t, v, c, cc;\n81 int i, ntimes, xexp, yexp;\n82 unsigned long long u, ux, uy, ax, ay, todd;\n83 unsigned int sw;\n84\n85 dx = x;\n86 dy = y;\n87\n88\n89 GET_BITS_DP64(dx, ux);\n90 GET_BITS_DP64(dy, uy);\n91 ax = ux & ~SIGNBIT_DP64;\n92 ay = uy & ~SIGNBIT_DP64;\n93 xexp = (int)((ux & EXPBITS_DP64) >> EXPSHIFTBITS_DP64);\n94 yexp = (int)((uy & EXPBITS_DP64) >> EXPSHIFTBITS_DP64);\n95\n96 if (xexp < 1 || xexp > BIASEDEMAX_DP64 ||\n97 yexp < 1 || yexp > BIASEDEMAX_DP64)\n98 {\n99 /* x or y is zero, denormalized, NaN or infinity */\n100 if (xexp > BIASEDEMAX_DP64)\n101 {\n102 /* x is NaN or infinity */\n103 if (ux & MANTBITS_DP64)\n104 {\n105 /* x is NaN */\n106 return _handle_error(_FUNCNAME, _OPERATION, ux|0x0008000000000000, _DOMAIN, 0,\n107 EDOM, x, y, 2);\n108 }\n109 else\n110 {\n111 /* x is infinity; result is NaN */\n113 AMD_F_INVALID, EDOM, x, y, 2);\n114 }\n115 }\n116 else if (yexp > BIASEDEMAX_DP64)\n117 {\n118 /* y is NaN or infinity */\n119 if (uy & MANTBITS_DP64)\n120 {\n121 /* y is NaN */\n122 return _handle_error(_FUNCNAME, _OPERATION, uy|0x0008000000000000, _DOMAIN, 0,\n123 EDOM, x, y, 2);\n124 }\n125 else\n126 {\n127#ifdef _CRTBLD_C9X\n128 /* C99 return for y = +-inf is x */\n129 return x;\n130#else\n131 /* y is infinity; result is indefinite */\n133 AMD_F_INVALID, EDOM, x, y, 2);\n134#endif\n135 }\n136 }\n137 else if (ax == 0x0000000000000000)\n138 {\n139 /* x is zero */\n140 if (ay == 0x0000000000000000)\n141 {\n142 /* y is zero */\n144 AMD_F_INVALID, EDOM, x, y, 2);\n145 }\n146 else\n147 /* C99 return for x = 0 must preserve sign */\n148 return x;\n149 }\n150 else if (ay == 0x0000000000000000)\n151 {\n152 /* y is zero */\n154 AMD_F_INVALID, EDOM, x, y, 2);\n155 }\n156\n157 /* We've exhausted all other possibilities. One or both of x and\n158 y must be denormalized */\n159 if (xexp < 1)\n160 {\n161 /* x is denormalized. Figure out its exponent. */\n162 u = ax;\n163 while (u < IMPBIT_DP64)\n164 {\n165 xexp--;\n166 u <<= 1;\n167 }\n168 }\n169 if (yexp < 1)\n170 {\n171 /* y is denormalized. Figure out its exponent. */\n172 u = ay;\n173 while (u < IMPBIT_DP64)\n174 {\n175 yexp--;\n176 u <<= 1;\n177 }\n178 }\n179 }\n180 else if (ax == ay)\n181 {\n182 /* abs(x) == abs(y); return zero with the sign of x */\n184 return dx;\n185 }\n186\n187 /* Set x = abs(x), y = abs(y) */\n189 PUT_BITS_DP64(ay, dy);\n190\n191 if (ax < ay)\n192 {\n193 /* abs(x) < abs(y) */\n194#if !defined(COMPILING_FMOD)\n195 if (dx > 0.5*dy)\n196 dx -= dy;\n197#endif\n198 return x < 0.0? -dx : dx;\n199 }\n200\n201 /* Save the current floating-point status word. We need\n202 to do this because the remainder function is always\n203 exact for finite arguments, but our algorithm causes\n204 the inexact flag to be raised. We therefore need to\n205 restore the entry status before exiting. */\n206 sw = get_fpsw_inline();\n207\n208 /* Set ntimes to the number of times we need to do a\n209 partial remainder. If the exponent of x is an exact multiple\n210 of 52 larger than the exponent of y, and the mantissa of x is\n211 less than the mantissa of y, ntimes will be one too large\n212 but it doesn't matter - it just means that we'll go round\n213 the loop below one extra time. */\n214 if (xexp <= yexp)\n215 ntimes = 0;\n216 else\n217 ntimes = (xexp - yexp) / 52;\n218\n219 if (ntimes == 0)\n220 {\n221 w = dy;\n222 scale = 1.0;\n223 }\n224 else\n225 {\n226 /* Set w = y * 2^(52*ntimes) */\n227 w = scaleDouble_3(dy, ntimes * 52);\n228\n229 /* Set scale = 2^(-52) */\n230 PUT_BITS_DP64((unsigned long long)(-52 + EXPBIAS_DP64) << EXPSHIFTBITS_DP64,\n231 scale);\n232 }\n233\n234\n235 /* Each time round the loop we compute a partial remainder.\n236 This is done by subtracting a large multiple of w\n237 from x each time, where w is a scaled up version of y.\n238 The subtraction must be performed exactly in quad\n239 precision, though the result at each stage can\n240 fit exactly in a double precision number. */\n241 for (i = 0; i < ntimes; i++)\n242 {\n243 /* t is the integer multiple of w that we will subtract.\n244 We use a truncated value for t.\n245\n246 N.B. w has been chosen so that the integer t will have\n247 at most 52 significant bits. This is the amount by\n248 which the exponent of the partial remainder dx gets reduced\n249 every time around the loop. In theory we could use\n250 53 bits in t, but the quad precision multiplication\n251 routine dekker_mul12 does not allow us to do that because\n252 it loses the last (106th) bit of its quad precision result. */\n253\n254 /* Set dx = dx - w * t, where t is equal to trunc(dx/w). */\n255 t = (double)(long long)(dx / w);\n256 /* At this point, t may be one too large due to\n257 rounding of dx/w */\n258\n259 /* Compute w * t in quad precision */\n260 dekker_mul12(w, t, &c, &cc);\n261\n262 /* Subtract w * t from dx */\n263 v = dx - c;\n264 dx = v + (((dx - v) - c) - cc);\n265\n266 /* If t was one too large, dx will be negative. Add back\n267 one w */\n268 /* It might be possible to speed up this loop by finding\n269 a way to compute correctly truncated t directly from dx and w.\n270 We would then avoid the need for this check on negative dx. */\n271 if (dx < 0.0)\n272 dx += w;\n273\n274 /* Scale w down by 2^(-52) for the next iteration */\n275 w *= scale;\n276 }\n277\n278 /* One more time */\n279 /* Variable todd says whether the integer t is odd or not */\n280 t = (double)(long long)(dx / w);\n281 todd = ((long long)(dx / w)) & 1;\n282 dekker_mul12(w, t, &c, &cc);\n283 v = dx - c;\n284 dx = v + (((dx - v) - c) - cc);\n285 if (dx < 0.0)\n286 {\n287 todd = !todd;\n288 dx += w;\n289 }\n290\n291 /* At this point, dx lies in the range [0,dy) */\n292#if !defined(COMPILING_FMOD)\n293 /* For the fmod function, we're done apart from setting\n294 the correct sign. */\n295 /* For the remainder function, we need to adjust dx\n296 so that it lies in the range (-y/2, y/2] by carefully\n297 subtracting w (== dy == y) if necessary. The rigmarole\n298 with todd is to get the correct sign of the result\n299 when x/y lies exactly half way between two integers,\n300 when we need to choose the even integer. */\n301 if (ay < 0x7fd0000000000000)\n302 {\n303 if (dx + dx > w || (todd && (dx + dx == w)))\n304 dx -= w;\n305 }\n306 else if (dx > 0.5 * w || (todd && (dx == 0.5 * w)))\n307 dx -= w;\n308\n309#endif\n310\n311 /* **** N.B. for some reason this breaks the 32 bit version\n312 of remainder when compiling with optimization. */\n313 /* Restore the entry status flags */\n314 set_fpsw_inline(sw);\n315\n316 /* Set the result sign according to input argument x */\n317 return x < 0.0? -dx : dx;\n318\n319}\ndouble __cdecl _handle_error(char *fname, int opcode, unsigned long long value, int type, int flags, int error, double arg1, double arg2, int nargs)\nHandles an error condition.\nDefinition: _handle_error.c:34\nDefinition: typeof.h:31\n#define EDOM\nDefinition: errno.h:39\nconst GLdouble * v\nDefinition: gl.h:2040\nGLdouble GLdouble t\nDefinition: gl.h:2047\nGLenum GLenum GLenum GLenum GLenum scale\nDefinition: glext.h:9032\nconst GLubyte * c\nDefinition: glext.h:8905\nGLubyte GLubyte GLubyte GLubyte w\nDefinition: glext.h:6102\nGLsizei GLenum const GLvoid GLsizei GLenum GLbyte GLbyte GLbyte GLdouble GLdouble GLdouble GLfloat GLfloat GLfloat GLint GLint GLint GLshort GLshort GLshort GLubyte GLubyte GLubyte GLuint GLuint GLuint GLushort GLushort GLushort GLbyte GLbyte GLbyte GLbyte GLdouble GLdouble GLdouble GLdouble GLfloat GLfloat GLfloat GLfloat GLint GLint GLint GLint GLshort GLshort GLshort GLshort GLubyte GLubyte GLubyte GLubyte GLuint GLuint GLuint GLuint GLushort GLushort GLushort GLushort GLboolean const GLdouble const GLfloat const GLint const GLshort const GLbyte const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLdouble const GLfloat const GLfloat const GLint const GLint const GLshort const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort GLenum GLenum GLenum GLfloat GLenum GLint GLenum GLenum GLenum GLfloat GLenum GLenum GLint GLenum GLfloat GLenum GLint GLint GLushort GLenum GLenum GLfloat GLenum GLenum GLint GLfloat const GLubyte GLenum GLenum GLenum const GLfloat GLenum GLenum const GLint GLenum GLint GLint GLsizei GLsizei GLint GLenum GLenum const GLvoid GLenum GLenum const GLfloat GLenum GLenum const GLint GLenum GLenum const GLdouble GLenum GLenum const GLfloat GLenum GLenum const GLint GLsizei GLuint GLfloat GLuint GLbitfield GLfloat GLint GLuint GLboolean GLenum GLfloat GLenum GLbitfield GLenum GLfloat GLfloat GLint GLint const GLfloat GLenum GLfloat GLfloat GLint GLint GLfloat GLfloat GLint GLint const GLfloat GLint GLfloat GLfloat GLint GLfloat GLfloat GLint GLfloat GLfloat const GLdouble const GLfloat const GLdouble const GLfloat GLint i\nDefinition: glfuncs.h:248\nGLsizei GLenum const GLvoid GLsizei GLenum GLbyte GLbyte GLbyte GLdouble GLdouble GLdouble GLfloat GLfloat GLfloat GLint GLint GLint GLshort GLshort GLshort GLubyte GLubyte GLubyte GLuint GLuint GLuint GLushort GLushort GLushort GLbyte GLbyte GLbyte GLbyte GLdouble GLdouble GLdouble GLdouble GLfloat GLfloat GLfloat GLfloat GLint GLint GLint GLint GLshort GLshort GLshort GLshort GLubyte GLubyte GLubyte GLubyte GLuint GLuint GLuint GLuint GLushort GLushort GLushort GLushort GLboolean const GLdouble const GLfloat const GLint const GLshort const GLbyte const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLdouble const GLfloat const GLfloat const GLint const GLint const GLshort const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort const GLdouble const GLfloat const GLint const GLshort GLenum GLenum GLenum GLfloat GLenum GLint GLenum GLenum GLenum GLfloat GLenum GLenum GLint GLenum GLfloat GLenum GLint GLint GLushort GLenum GLenum GLfloat GLenum GLenum GLint GLfloat const GLubyte GLenum GLenum GLenum const GLfloat GLenum GLenum const GLint GLenum GLint GLint GLsizei GLsizei GLint GLenum GLenum const GLvoid GLenum GLenum const GLfloat GLenum GLenum const GLint GLenum GLenum const GLdouble GLenum GLenum const GLfloat GLenum GLenum const GLint GLsizei GLuint GLfloat GLuint GLbitfield GLfloat GLint GLuint GLboolean GLenum GLfloat GLenum GLbitfield GLenum GLfloat GLfloat GLint GLint const GLfloat GLenum GLfloat GLfloat GLint GLint GLfloat GLfloat GLint GLint const GLfloat GLint GLfloat GLfloat GLint GLfloat GLfloat GLint GLfloat GLfloat const GLdouble * u\nDefinition: glfuncs.h:240\n#define _DOMAIN\nDefinition: math.h:39\nuint32_t cc\nDefinition: isohybrid.c:75\n#define c\nDefinition: ke_i.h:80\n#define AMD_F_INVALID\nDefinition: libm_new.h:86\n#define IMPBIT_DP64\nDefinition: libm_util.h:50\n#define INDEFBITPATT_DP64\nDefinition: libm_util.h:52\n#define EXPSHIFTBITS_DP64\nDefinition: libm_util.h:56\n#define GET_BITS_DP64(x, ux)\nDefinition: libm_util.h:118\n#define BIASEDEMAX_DP64\nDefinition: libm_util.h:59\n#define SIGNBIT_DP64\nDefinition: libm_util.h:44\n#define EXPBITS_DP64\nDefinition: libm_util.h:45\n#define EXPBIAS_DP64\nDefinition: libm_util.h:55\n#define MANTBITS_DP64\nDefinition: libm_util.h:46\n#define PUT_BITS_DP64(ux, x)\nDefinition: libm_util.h:124\nGLint dy\nDefinition: linetemp.h:97\nGLint dx\nDefinition: linetemp.h:97\nstatic const char mbstate_t *static wchar_t const char mbstate_t *static const wchar_t int *static double\nDefinition: string.c:80\n#define long\nDefinition: qsort.c:33\n#define _FUNCNAME\n#define _OPERATION\nstatic void dekker_mul12(double x, double y, double *z, double *zz)\nDefinition: remainder.c:50\nDefinition: synth_sse3d.h:180\n\n## ◆ remainderf()\n\n float __cdecl remainderf ( float x, float y )\n\nDefinition at line 60 of file remainderf.c.\n\n64{\n65 double dx, dy, scale, w, t;\n66 int i, ntimes, xexp, yexp;\n67 unsigned long long ux, uy, ax, ay;\n68\n69 unsigned int sw;\n70\n71 dx = x;\n72 dy = y;\n73\n74\n75 GET_BITS_DP64(dx, ux);\n76 GET_BITS_DP64(dy, uy);\n77 ax = ux & ~SIGNBIT_DP64;\n78 ay = uy & ~SIGNBIT_DP64;\n79 xexp = (int)((ux & EXPBITS_DP64) >> EXPSHIFTBITS_DP64);\n80 yexp = (int)((uy & EXPBITS_DP64) >> EXPSHIFTBITS_DP64);\n81\n82 if (xexp < 1 || xexp > BIASEDEMAX_DP64 ||\n83 yexp < 1 || yexp > BIASEDEMAX_DP64)\n84 {\n85 /* x or y is zero, NaN or infinity (neither x nor y can be\n86 denormalized because we promoted from float to double) */\n87 if (xexp > BIASEDEMAX_DP64)\n88 {\n89 /* x is NaN or infinity */\n90 if (ux & MANTBITS_DP64)\n91 {\n92 /* x is NaN */\n93 unsigned int ufx;\n94 GET_BITS_SP32(x, ufx);\n95 return _handle_errorf(_FUNCNAME, _OPERATION, ufx|0x00400000, _DOMAIN, 0,\n96 EDOM, x, y, 2);\n97 }\n98 else\n99 {\n100 /* x is infinity; result is NaN */\n102 AMD_F_INVALID, EDOM, x, y, 2);\n103 }\n104 }\n105 else if (yexp > BIASEDEMAX_DP64)\n106 {\n107 /* y is NaN or infinity */\n108 if (uy & MANTBITS_DP64)\n109 {\n110 /* y is NaN */\n111 unsigned int ufy;\n112 GET_BITS_SP32(y, ufy);\n113 return _handle_errorf(_FUNCNAME, _OPERATION, ufy|0x00400000, _DOMAIN, 0,\n114 EDOM, x, y, 2);\n115 }\n116 else\n117 {\n118#ifdef _CRTBLD_C9X\n119 /* C99 return for y = +-inf is x */\n120 return x;\n121#else\n122 /* y is infinity; result is indefinite */\n124 AMD_F_INVALID, EDOM, x, y, 2);\n125#endif\n126 }\n127 }\n128 else if (xexp < 1)\n129 {\n130 /* x must be zero (cannot be denormalized) */\n131 if (yexp < 1)\n132 {\n133 /* y must be zero (cannot be denormalized) */\n135 AMD_F_INVALID, EDOM, x, y, 2);\n136 }\n137 else\n138 /* C99 return for x = 0 must preserve sign */\n139 return x;\n140 }\n141 else\n142 {\n143 /* y must be zero */\n145 AMD_F_INVALID, EDOM, x, y, 2);\n146 }\n147 }\n148 else if (ax == ay)\n149 {\n150 /* abs(x) == abs(y); return zero with the sign of x */\n152 return (float)dx;\n153 }\n154\n155 /* Set dx = abs(x), dy = abs(y) */\n157 PUT_BITS_DP64(ay, dy);\n158\n159 if (ax < ay)\n160 {\n161 /* abs(x) < abs(y) */\n162#if !defined(COMPILING_FMOD)\n163 if (dx > 0.5*dy)\n164 dx -= dy;\n165#endif\n166 return (float)(x < 0.0? -dx : dx);\n167 }\n168\n169 /* Save the current floating-point status word. We need\n170 to do this because the remainder function is always\n171 exact for finite arguments, but our algorithm causes\n172 the inexact flag to be raised. We therefore need to\n173 restore the entry status before exiting. */\n174 sw = get_fpsw_inline();\n175\n176 /* Set ntimes to the number of times we need to do a\n177 partial remainder. If the exponent of x is an exact multiple\n178 of 24 larger than the exponent of y, and the mantissa of x is\n179 less than the mantissa of y, ntimes will be one too large\n180 but it doesn't matter - it just means that we'll go round\n181 the loop below one extra time. */\n182 if (xexp <= yexp)\n183 {\n184 ntimes = 0;\n185 w = dy;\n186 scale = 1.0;\n187 }\n188 else\n189 {\n190 ntimes = (xexp - yexp) / 24;\n191\n192 /* Set w = y * 2^(24*ntimes) */\n193 PUT_BITS_DP64((unsigned long long)(ntimes * 24 + EXPBIAS_DP64) << EXPSHIFTBITS_DP64,\n194 scale);\n195 w = scale * dy;\n196 /* Set scale = 2^(-24) */\n197 PUT_BITS_DP64((unsigned long long)(-24 + EXPBIAS_DP64) << EXPSHIFTBITS_DP64,\n198 scale);\n199 }\n200\n201\n202 /* Each time round the loop we compute a partial remainder.\n203 This is done by subtracting a large multiple of w\n204 from x each time, where w is a scaled up version of y.\n205 The subtraction can be performed exactly when performed\n206 in double precision, and the result at each stage can\n207 fit exactly in a single precision number. */\n208 for (i = 0; i < ntimes; i++)\n209 {\n210 /* t is the integer multiple of w that we will subtract.\n211 We use a truncated value for t. */\n212 t = (double)((int)(dx / w));\n213 dx -= w * t;\n214 /* Scale w down by 2^(-24) for the next iteration */\n215 w *= scale;\n216 }\n217\n218 /* One more time */\n219#if defined(COMPILING_FMOD)\n220 t = (double)((int)(dx / w));\n221 dx -= w * t;\n222#else\n223 {\n224 unsigned int todd;\n225 /* Variable todd says whether the integer t is odd or not */\n226 t = (double)((int)(dx / w));\n227 todd = ((int)(dx / w)) & 1;\n228 dx -= w * t;\n229\n230 /* At this point, dx lies in the range [0,dy) */\n231 /* For the remainder function, we need to adjust dx\n232 so that it lies in the range (-y/2, y/2] by carefully\n233 subtracting w (== dy == y) if necessary. */\n234 if (dx > 0.5 * w || ((dx == 0.5 * w) && todd))\n235 dx -= w;\n236 }\n237#endif\n238\n239 /* **** N.B. for some reason this breaks the 32 bit version\n240 of remainder when compiling with optimization. */\n241 /* Restore the entry status flags */\n242 set_fpsw_inline(sw);\n243\n244 /* Set the result sign according to input argument x */\n245 return (float)(x < 0.0? -dx : dx);\n246\n247}\nfloat __cdecl _handle_errorf(char *fname, int opcode, unsigned long long value, int type, int flags, int error, float arg1, float arg2, int nargs)\nDefinition: _handle_error.c:56\n#define INDEFBITPATT_SP32\nDefinition: libm_util.h:76\n#define GET_BITS_SP32(x, ux)\nDefinition: libm_util.h:105\n#define _FUNCNAME\n#define _OPERATION\n\n## ◆ remainderl()\n\n long double __cdecl remainderl ( long double , long double )\n\n## ◆ remquo()\n\n double __cdecl remquo ( double , double , int * )\n\n## ◆ remquof()\n\n float __cdecl remquof ( float , float , int * )\n\n## ◆ remquol()\n\n long double __cdecl remquol ( long double , long double , int * )\n\n## ◆ rint()\n\n __CRT_INLINE double __cdecl rint ( double x )\n\nDefinition at line 350 of file mingw_math.h.\n\n351 {\n352 double retval = 0.0;\n353 __asm__ __volatile__ (\"frndint;\": \"=t\" (retval) : \"0\" (x));\n354 return retval;\n355 }\n\n## ◆ rintf()\n\n __CRT_INLINE float __cdecl rintf ( float x )\n\nDefinition at line 357 of file mingw_math.h.\n\n358 {\n359 float retval = 0.0;\n360 __asm__ __volatile__ (\"frndint;\" : \"=t\" (retval) : \"0\" (x) );\n361 return retval;\n362 }\n\n## ◆ rintl()\n\n __CRT_INLINE long double __cdecl rintl ( long double x )\n\nDefinition at line 364 of file mingw_math.h.\n\n365 {\n366 long double retval = 0.0l;\n367 __asm__ __volatile__ (\"frndint;\" : \"=t\" (retval) : \"0\" (x) );\n368 return retval;\n369 }\n\n## ◆ round()\n\n double __cdecl round ( double arg )\n\nDefinition at line 10 of file round.c.\n\n11{\n12 if (arg < 0.0)\n13 return ceil(arg - 0.5);\n14 else\n15 return floor(arg + 0.5);\n16}\n_Check_return_ _CRTIMP double __cdecl floor(_In_ double x)\n_Check_return_ _CRTIMP double __cdecl ceil(_In_ double x)\n\n## ◆ roundf()\n\n float __cdecl roundf ( float arg )\n\nDefinition at line 10 of file roundf.c.\n\n11{\n12 if (arg < 0.0)\n13 return ceilf(arg - 0.5);\n14 else\n15 return floorf(arg + 0.5);\n16}\n#define ceilf(x)\nDefinition: mymath.h:62\n#define floorf(x)\nDefinition: mymath.h:65\n\n## ◆ roundl()\n\n long double __cdecl roundl ( long double )\n\n## ◆ scalbln()\n\n double __cdecl scalbln ( double , long )\n\n## ◆ scalblnf()\n\n float __cdecl scalblnf ( float , long )\n\n## ◆ scalblnl()\n\n long double __cdecl scalblnl ( long double , long )\n\n## ◆ scalbn()\n\n double __cdecl scalbn ( double , int )\n\n## ◆ scalbnf()\n\n float __cdecl scalbnf ( float , int )\n\n## ◆ scalbnl()\n\n long double __cdecl scalbnl ( long double , int )\n\n## ◆ tgamma()\n\n double __cdecl tgamma ( double )\n\n## ◆ tgammaf()\n\n float __cdecl tgammaf ( float )\n\n## ◆ tgammal()\n\n long double __cdecl tgammal ( long double )\n\n## ◆ trunc()\n\n double __cdecl trunc ( double )\n\nReferenced by divide_ext().\n\n## ◆ truncf()\n\n float __cdecl truncf ( float )\n\n## ◆ truncl()\n\n long double __cdecl truncl ( long double )\n\n## ◆ __INFF\n\n const float __INFF\nextern\n\n## ◆ __INFL\n\n const long double __INFL\nextern\n\n## ◆ __QNAN\n\n const double __QNAN\nextern" ]
[ null ]
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