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https://napavalleyartfestival.com/how-tall-is-1-66-meters-in-feet-update-new/
|
[
"Home » How Tall Is 1.66 Meters In Feet? Update New\n\n# How Tall Is 1.66 Meters In Feet? Update New\n\nLet’s discuss the question: how tall is 1.66 meters in feet. We summarize all relevant answers in section Q&A of website Napavalleyartfestival.com in category: MMO. See more related questions in the comments below.\n\n## How many feet is 5’7 meters?\n\nQuick Lookup Metres to Feet & Inches Common Conversions\nm ft & in\n1.65 5′ 5″\n1.70 5′ 7″\n1.75 5′ 9″\n1.80 5′ 11″\n\n## What is the feet of 1.65 meters?\n\nThus, 1.65 m in feet is the same as 1.65 m to ft, 1.65 meters to ft, and 1.65 meters to feet. There are 3.280839895 feet per meter. Therefore, to convert 1.65 meters to feet, we multiply 1.65 by 3.280839895. Below is the math and the answer.\n\n### 166 cm in feet and inches?\n\n166 cm in feet and inches?\n166 cm in feet and inches?\n\n6’2 = 187.96 cm.\n\n## How many cm is 5/10 feet?\n\n5’10 = 177.8 cm\n\nConvert 5 ft 10 to centimeters.\n\n## How do I find my height in meters?\n\nSince height is normally given in both feet and inches when these units are in play rather than using a decimal, the easiest way to convert height from feet to meters is to convert height entirely to inches and then divide by 39.37 to get meters. For example, 5 ft 10 in is 70″, and 70/39.37 = 1.778 m.\n\n## How many meters is 5 foot 11?\n\nSo, 5′ 11” is 1.8034 meters.\n\n## What is 1m 60cm in feet?\n\nThe following is the cm to feet and inches conversion table from 1 cm to 200 cm.\n\nConversion Chart.\nCentimeters Feet and Inches\n60 cm 1 feet and 11.622 inches\n61 cm 2 feet and 0.0157 inches\n62 cm 2 feet and 0.4094 inches\n63 cm 2 feet and 0.8031 inches\n\n## How do you measure height in feet?\n\nDivide by 12 to determine how tall someone is in feet.\n1. 12 inches = 1 foot.\n2. 24 inches = 2 feet.\n3. 36 inches = 3 feet.\n4. 48 inches = 4 feet.\n5. 60 inches = 5 feet.\n6. 72 inches = 6 feet.\n7. 84 inches = 7 feet.\n\n## What is 1m 52 cm in feet?\n\nThus, 1.52 m in feet is the same as 1.52 m to ft, 1.52 meters to ft, and 1.52 meters to feet. There are 3.280839895 feet per meter. Therefore, to convert 1.52 meters to feet, we multiply 1.52 by 3.280839895.\n\n## Is there such thing as 5 12?\n\nNot in the real world, if you want to make sense. You wouldn’t describe yourself as being 4 feet 24 inches tall, or 3 feet 36 inches tall. A foot is 12 inches. You can’t be 5 feet 12 inches, because you’re 6 feet.\n\n## What height is 170 cm?\n\n170 cm = 5’6.93\n\n170 cm is taller than about 20% of men and 81.6% of women in the USA. What is 170cm in feet and inches? Convert 170 centimeters to feet and inches.\n\n## How many cm is 5 5 feet?\n\n5’5 = 165.1 cm\n\nConvert 5 ft 5 to centimeters.\n\n### Ex: Convert Height in Feet and Inches to Inches, Centimeters, and Meters\n\nEx: Convert Height in Feet and Inches to Inches, Centimeters, and Meters\nEx: Convert Height in Feet and Inches to Inches, Centimeters, and Meters\n\n### Images related to the topicEx: Convert Height in Feet and Inches to Inches, Centimeters, and Meters",
null,
"Ex: Convert Height In Feet And Inches To Inches, Centimeters, And Meters\n\n## What size is 175 cm?\n\n175 cm is equal to 5 feet and 8.9 inches, rounded to one decimal place. There are 30.48 cm in a foot. The average height for men in the United States is 175.4 centimeters, which is about 5 feet 9 inches.\n\n## Is 177 cm a good height?\n\n177 cm is more than about 5’9” and a half, therefore inching more towards 5’10” and furthermore puts you at a very decent average height for a male. Plus, you’re 20 so chances are that your growth plates have already closed so gaining anymore height can be very difficult.\n\n## How tall are you if your 180cm?\n\n180 cm = 5’10.87\n\nConvert 180 centimeters to feet and inches.\n\n## What is m2 in height?\n\nApril 14, 2022 Body Mass Index (BMI) Calculator\n\nBody Mass Index is a simple calculation using a person’s height and weight. The formula is BMI = kg/m2 where kg is a person’s weight in kilograms and m2 is their height in metres squared. A BMI of 25.0 or more is overweight, while the healthy range is 18.5 to 24.9.\n\n## How tall is 2 Metres feet?\n\nMeters to feet conversion table\nMeters (m) Feet (ft)\n2 m 6.56168 ft\n3 m 9.84252 ft\n4 m 13.12336 ft\n5 m 16.40420 ft\n\n## How many inches is a 5’2 person?\n\nHuman Height Conversion Table\nft in inches centimeters\n5’2” 62in 157.48cm\n5’3” 63in 160.02cm\n5’4” 64in 162.56cm\n5’5” 65in 165.10cm\n5 thg 2, 2010\n\n## What height is 1.8 m in feet?\n\nHeight Comparison Charts\nCentimeters Meters Feet, inches\n180 cm 1.8 m 5 feet, 10.9 in\n181 cm 1.81 m 5 feet, 11.3 in\n182 cm 1.82 m 5 feet, 11.7 in\n183 cm 1.83 m 6 feet, 0 in\n\n## What height is 5/10 cm?\n\n5 feet 10 inches in cm = [(5×12)+10] x 2.54 = 70 x 2.54 = 177.8 cm.\n\n## Is 160 cm short for a girl?\n\nMost of the world, women hover around 160 cm so you are within perfectly average height range.\n\n### ✅ How Many Feet In A Meter\n\n✅ How Many Feet In A Meter\n✅ How Many Feet In A Meter\n\n## How tall are you in USA?\n\nThe height of the average North American male is 175.5 centimeters, a little over 5 foot 9 inches. The average US female is 162.5 cm, or 5 feet 4 inches.\n4 feet 0 inches = 121.92 centimeters\n5 feet 9 inches = 175.26 centimeters\n5 feet 10 inches = 177.80 centimeters\n5 feet 11 inches = 180.34 centimeters\n\n## What height is 160?\n\n160 cm = 5’2.99\n\nWhat is 160cm in feet and inches? Convert 160 centimeters to feet and inches. Use the calculator above to calculate between feet and centimeters.\n\nRelated searches\n\n• 166 cm in feet\n• 1 66 m in cm\n• how tall is 1.44 meters in feet\n• how tall is 5 feet in meters\n• how tall is 1.79 metres\n• how tall is 6 feet in m\n• 1.66 height in feet\n• how tall is 1.66 in height\n• how tall is 1.89 meters\n• 1 66m in inches\n• 1.66 in feet\n• how tall is 3 feet in meters\n• how tall is 183 meters\n• how tall is 1.66 meters in feet and inches\n• how tall is 1.67m in feet\n• 1 66 in feet\n• 1.66m in inches\n• how tall is 1 66 in height\n• how tall is 12 meters in feet\n• 1 67 m in feet\n• how tall is 183 m in feet\n• 1 66 height in feet\n• how many feet is m\n• 1.66 m in cm\n• how tall is 1.66 meters in feet\n• 1 65 m in feet\n\n## Information related to the topic how tall is 1.66 meters in feet\n\nHere are the search results of the thread how tall is 1.66 meters in feet from Bing. You can read more if you want.\n\nYou have just come across an article on the topic how tall is 1.66 meters in feet. If you found this article useful, please share it. Thank you very much."
] |
[
null,
"https://i.ytimg.com/vi/P_rSFpvFVnQ/maxresdefault.jpg",
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|
https://kshow123tv.com/qa/what-is-a-3-5-grade.html
|
[
"",
null,
"# What Is A 3 5 Grade?\n\n## What is 11 out of 14 as a grade?\n\n78.571428571429%Convert fraction (ratio) 11 / 14 Answer: 78.571428571429%\n\n## What is 10 out of 14 as a grade?\n\n71.428571428571%Convert fraction (ratio) 10 / 14 Answer: 71.428571428571%\n\n## Is a 87 a good grade?\n\nA B+ letter grade is equivalent to a 3.3 GPA, or Grade Point Average, on a 4.0 GPA scale, and a percentage grade of 87–89….List of Common GPA Conversions.Letter GradePercent Grade4.0 GPA ScaleA-90–923.7B+87–893.3B83–863.0B-80–822.78 more rows\n\n## What is a 5th of 7?\n\n71.428571428571%Latest decimal numbers, fractions, rations or proportions converted to percentages5 / 7 = 71.428571428571%Jan 13 18:08 UTC (GMT)27 / 95 = 28.421052631579%Jan 13 18:07 UTC (GMT)2 / 3 = 66.666666666667%Jan 13 18:07 UTC (GMT)All decimal number, fractions, ratios or proportions converted to percentages10 more rows\n\n## What is 13 out of 14 as a grade?\n\n92.857142857143%Convert fraction (ratio) 13 / 14 Answer: 92.857142857143%\n\n## Is a 95 a good grade?\n\nA 95 is excellent. A 97 is also excellent. Yes, but only if you are a below average student. … Well, now if you are an above average student, there isn’t really any grade that is good.\n\n## Is 75 a good mark?\n\nThis is an above-average score, between 80% and 89% C – this is a grade that rests right in the middle. C is anywhere between 70% and 79% D – this is still a passing grade, and it’s between 59% and 69%\n\n## What is 12 out of 14 as a grade?\n\n85.714285714286%Convert fraction (ratio) 12 / 14 Answer: 85.714285714286%\n\n## Is a D+ passing?\n\nA D+ is technically a passing grade but you need an A (4.0 averaged with 1.3 is 2.65) or a B (3.0 averaged with 1.3 is 2.15) to offset. You typically need a 2.0 to graduate so if you’re a borderline student, it becomes a stretch goal.\n\n## Is 60 a passing grade in college?\n\nHowever, there are some schools that consider a C the lowest passing grade, so the general standard is that anything below a 60 or 70 is failing, depending on the grading scale. In college and universities, a D is considered to be an unsatisfactory passing grade."
] |
[
null,
"https://mc.yandex.ru/watch/70851247",
null
] |
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|
https://www.virendrachandak.com/techtalk/how-to-sort-a-multi-dimension-array-by-value-in-php/
|
[
"# How to sort a multi-dimension array by value in PHP\n\nIn this article we will see how to sort a multi-dimension array by value of one of the keys of the array. We can use a few different methods to do this. One way to to use usort() function. Another way is to just identify the values and create another array with the values and then use it in the array_multisort() function. Using the multisort method we can easily sort a multi-dimension array based on its one or more values. Lets see how we can use both these methods.\n\nYou can view the demo of all these methods here.\n\n### Sort using usort\n\nThe first method to sort the array is by using the usort() function. Here is the code that we can use to perform this sort:\n\n```function cmp(\\$a, \\$b)\n{\nreturn strcmp(\\$a[\"name\"], \\$b[\"name\"]);\n}\nusort(\\$vc_array, \"cmp\");\n```\n\nView Output\n\nThe usort() function sorts the `\\$vc_array` by using the comparison function (`cmp`) that we created. The above code can be used to sort the multi-dimension array based on the value of the name column of the array. This is an easy way to sort the multi-dimension array based on the value of one keys.\n\nIf you want to sort the array based on values of multiple keys then, you might have to write some complex logic in the callback function to do that. However, there is an alternative way by using the array_multisort() function. The array_multisort() can be used to sort several arrays at once, or a multi-dimensional array by one or more dimensions.\n\n### Sort using array_multisort by value of 1 key\n\nLets now see how to use the array_multisort() function to do the same sorting as the one we did using usort above.\n\n```foreach (\\$vc_array as \\$key => \\$row)\n{\n\\$vc_array_name[\\$key] = \\$row['name'];\n}\narray_multisort(\\$vc_array_name, SORT_ASC, \\$vc_array);\n```\n\nView Output\n\nIn the above code we first create a new array `\\$vc_array_name` to store all the values that we want to sort the `\\$vc_array` by. Then we use the array_multisort() to sort the arrays. In this function we first sort the `\\$vc_array_name` ascending and then sort the `\\$vc_array`.\n\n### Sort using array_multisort by value of 2 keys\n\nNow, lets see how we can sort the same array by values of 2 keys of the array. In this example, we will sort by value ascending, name descending.\n\n```foreach (\\$vc_array as \\$key => \\$row)\n{\n\\$vc_array_value[\\$key] = \\$row['value'];\n\\$vc_array_name[\\$key] = \\$row['name'];\n}\narray_multisort(\\$vc_array_value, SORT_ASC, \\$vc_array_name, SORT_DESC, \\$vc_array);\n```\n\nView Output\n\nThis code first sorts the `\\$vc_array_values` ascending, `\\$vc_array_name` descending and then the `\\$vc_array` using both those sorted arrays. Using this method we can sort the multi-dimension array depending on values of multiple different keys.\n\n### Sort using array_multisort by value of 3 keys\n\nNow, lets see how we can sort the same array by values of 3 keys of the array. In this example, we will sort by value descending, order descending and name ascending.\n\n```foreach (\\$vc_array as \\$key => \\$row)\n{\n\\$vc_array_value[\\$key] = \\$row['value'];\n\\$vc_array_name[\\$key] = \\$row['name'];\n\\$vc_array_order[\\$key] = \\$row['order'];\n}\narray_multisort(\\$vc_array_value, SORT_DESC, \\$vc_array_order, SORT_DESC, \\$vc_array_name, SORT_ASC, \\$vc_array);\n```\n\nView Output\n\nThis code first sorts the `\\$vc_array_values` descending, `\\$vc_array_order` descending, `\\$vc_array_name` ascending and then the `\\$vc_array` using these sorted arrays. Using this method we can sort the multi-dimension array depending on values of multiple different keys. This method can be extended to sort an array by any number of values.\n\nView All Demo"
] |
[
null
] |
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|
https://ftp.aimsciences.org/article/doi/10.3934/cpaa.2021149
|
[
"",
null,
"",
null,
"",
null,
"",
null,
"doi: 10.3934/cpaa.2021149\nOnline First\n\nOnline First articles are published articles within a journal that have not yet been assigned to a formal issue. This means they do not yet have a volume number, issue number, or page numbers assigned to them, however, they can still be found and cited using their DOI (Digital Object Identifier). Online First publication benefits the research community by making new scientific discoveries known as quickly as possible.\n\nReaders can access Online First articles via the “Online First” tab for the selected journal.\n\n## Classification of positive radial solutions to a weighted biharmonic equation\n\n School of Mathematical Sciences, East China Normal University, Shanghai 200241, China\n\nReceived May 2021 Revised July 2021 Early access September 2021\n\nFund Project: The author is partially supported by NSFC (No. 11431005)\n\nIn this paper, we consider the weighted fourth order equation\n $\\Delta(|x|^{-\\alpha}\\Delta u)+\\lambda \\text{div}(|x|^{-\\alpha-2}\\nabla u)+\\mu|x|^{-\\alpha-4}u = |x|^\\beta u^p\\quad \\text{in} \\quad \\mathbb{R}^n \\backslash \\{0\\},$\nwhere\n $n\\geq 5$\n,\n $-n<\\alpha , $ p>1 $and $ (p,\\alpha,\\beta,n) $belongs to the critical hyperbola $ \\frac{n+\\alpha}{2}+\\frac{n+\\beta}{p+1} = n-2. $We prove the existence of radial solutions to the equation for some $ \\lambda $and $ \\mu $. On the other hand, let $ v(t): = |x|^{\\frac{n-4-\\alpha}{2}}u(|x|) $, $ t = -\\ln |x| $, then for the radial solution $ u $with non-removable singularity at origin, $ v(t) $is a periodic function if $ \\alpha \\in (-2,n-4) $and $ \\lambda $, $ \\mu $satisfy some conditions; while for $ \\alpha \\in (-n,-2] $, there exists a radial solution with non-removable singularity and the corresponding function $ v(t) $is not periodic. We also get some results about the best constant and symmetry breaking, which is closely related to the Caffarelli-Kohn-Nirenberg type inequality. Citation: Yuhao Yan. Classification of positive radial solutions to a weighted biharmonic equation. Communications on Pure & Applied Analysis, doi: 10.3934/cpaa.2021149 ##### References: M. Bhakta and R. Musina, Entire solutions for a class of variational problems involving the biharmonic operator and Rellich potentials, Nonlinear Anal., 75 (2012), 3836-3848. doi: 10.1016/j.na.2012.02.005.",
null,
"",
null,
"Google Scholar P. Caldiroli and G. Cora, Entire solutions for a class of fourth-order semilinear elliptic equations with weights, Mediterr. J. Math., 13 (2016), 657-675. doi: 10.1007/s00009-015-0519-1.",
null,
"",
null,
"Google Scholar P. Caldiroli and R. Musina, On Caffarelli-Kohn-Nirenberg type inequalities for the weighted biharmonic operator in cones, Milan J. Math., 79 (2011), 657-687. doi: 10.1007/s00032-011-0167-2.",
null,
"",
null,
"Google Scholar P. Caldiroli and R. Musina, Rellich inequalities with weights, Calc. Var. Partial Differ. Equ., 45 (2012), 147-164. doi: 10.1007/s00526-011-0454-3.",
null,
"",
null,
"Google Scholar R. Frank and T. König, Classification of positive solutions to a nonlinear biharmonic equation with critical exponent, Anal. Partial Differ. Equ., 12 (2019), 1101-1113. doi: 10.2140/apde.2019.12.1101.",
null,
"",
null,
"Google Scholar R. Frank and T. König, Singular solution to a semilinear biharmonic equation with a general critical nonlinearity, Rend. Lincei Mat. Appl., 30 (2019), 817-846. doi: 10.4171/RLM/871.",
null,
"",
null,
"Google Scholar Z. M. Guo, X. Huang, L. P. Wang and J. C. Wei, On Delaunay solutions of a biharmonic elliptic equation with critical exponent, J. Anal. Math., 140 (2020), 371-394. doi: 10.1007/s11854-020-0096-5.",
null,
"",
null,
"Google Scholar Z. M. Guo, F. S. Wan and L. P. Wang, Embeddings of weighted Sobolev spaces and a weighted fourth-order elliptic equation, Commun. Contemp. Math., 22 (2020), 1950057, 40 pp. doi: 10.1142/S0219199719500573.",
null,
"",
null,
"Google Scholar X. Huang and L. P. Wang, Classification to the positive radial solutions with weighted biharmonic equation, Discrete Contin. Dyn. Syst., 40 (2020), 4821-4837. doi: 10.3934/dcds.2020203.",
null,
"",
null,
"Google Scholar X. Huang and D. Ye, Hardy-Rellich type equalities, preprint. Google Scholar C. S. Lin, A classification of solutions of a conformally invariant fourth order equation in$\\mathbb{R}^N$, Comment. Math. Helv., 73 (1998), 206-231. doi: 10.1007/s000140050052.",
null,
"",
null,
"Google Scholar P. L. Lions, The concentration-compactness principle in the calculus of variations. The locally compact case, I, Ann. Inst. H. Poincaré Anal. Non Linéaire, 1 (1984), 109–145.",
null,
"Google Scholar P. L. Lions, The concentration-compactness principle in the calculus of variations. The locally compact case, II, Ann. Inst. H. Poincaré Anal. Non Linéaire, 1 (1984), 223–283.",
null,
"Google Scholar R. Musina, Weighted Sobolev spaces of radially symmetric functions, Ann. Mat. Pura Appl., 193 (2014), 1626-1659. doi: 10.1007/s10231-013-0348-4.",
null,
"",
null,
"Google Scholar show all references ##### References: M. Bhakta and R. Musina, Entire solutions for a class of variational problems involving the biharmonic operator and Rellich potentials, Nonlinear Anal., 75 (2012), 3836-3848. doi: 10.1016/j.na.2012.02.005.",
null,
"",
null,
"Google Scholar P. Caldiroli and G. Cora, Entire solutions for a class of fourth-order semilinear elliptic equations with weights, Mediterr. J. Math., 13 (2016), 657-675. doi: 10.1007/s00009-015-0519-1.",
null,
"",
null,
"Google Scholar P. Caldiroli and R. Musina, On Caffarelli-Kohn-Nirenberg type inequalities for the weighted biharmonic operator in cones, Milan J. Math., 79 (2011), 657-687. doi: 10.1007/s00032-011-0167-2.",
null,
"",
null,
"Google Scholar P. Caldiroli and R. Musina, Rellich inequalities with weights, Calc. Var. Partial Differ. Equ., 45 (2012), 147-164. doi: 10.1007/s00526-011-0454-3.",
null,
"",
null,
"Google Scholar R. Frank and T. König, Classification of positive solutions to a nonlinear biharmonic equation with critical exponent, Anal. Partial Differ. Equ., 12 (2019), 1101-1113. doi: 10.2140/apde.2019.12.1101.",
null,
"",
null,
"Google Scholar R. Frank and T. König, Singular solution to a semilinear biharmonic equation with a general critical nonlinearity, Rend. Lincei Mat. Appl., 30 (2019), 817-846. doi: 10.4171/RLM/871.",
null,
"",
null,
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"• 防诈骗中心\n• 客服中心 |\n• 网站导航 |\n• 设为主页 |\n• 加入收藏\n• 您当前位置： 首页> 产品库>吉林省>松原市\n相关分类：\n• 湖南\n• 长沙市\n• 常德市\n• 郴州市\n• 衡阳市\n• 怀化市\n• 娄底市\n• 邵阳市\n• 湘潭市\n• 湘西土家族苗族自治州\n• 益阳市\n• 永州市\n• 岳阳市\n• 张家界市\n• 株洲市\n• 山西\n• 长治市\n• 大同市\n• 晋城市\n• 晋中市\n• 临汾市\n• 吕梁市\n• 朔州市\n• 太原市\n• 忻州市\n• 阳泉市\n• 运城市\n• 安徽\n• 安庆市\n• 蚌埠市\n• 亳州市\n• 巢湖市\n• 池州市\n• 滁州市\n• 阜阳市\n• 合肥市\n• 淮北市\n• 淮南市\n• 黄山市\n• 六安市\n• 马鞍山市\n• 宿州市\n• 铜陵市\n• 芜湖市\n• 宣城市\n• 广西\n• 百色市\n• 北海市\n• 崇左市\n• 防城港市\n• 贵港市\n• 桂林市\n• 河池市\n• 贺州市\n• 来宾市\n• 柳州市\n• 南宁市\n• 钦州市\n• 梧州市\n• 玉林市\n• 河南\n• 安阳市\n• 鹤壁市\n• 焦作市\n• 开封市\n• 洛阳市\n• 漯河市\n• 南阳市\n• 平顶山市\n• 濮阳市\n• 三门峡市\n• 商丘市\n• 新乡市\n• 信阳市\n• 许昌市\n• 郑州市\n• 周口市\n• 驻马店市\n• 吉林\n• 白城市\n• 白山市\n• 长春市\n• 吉林市\n• 辽源市\n• 四平市\n• 松原市\n• 通化市\n• 延边朝鲜族自治州\n• 广东\n• 潮州市\n• 东莞市\n• 佛山市\n• 广州市\n• 河源市\n• 惠州市\n• 江门市\n• 揭阳市\n• 茂名市\n• 梅州市\n• 清远市\n• 汕头市\n• 汕尾市\n• 韶关市\n• 深圳市\n• 阳江市\n• 云浮市\n• 湛江市\n• 肇庆市\n• 中山市\n• 珠海市\n• 辽宁\n• 鞍山市\n• 本溪市\n• 朝阳市\n• 大连市\n• 丹东市\n• 抚顺市\n• 阜新市\n• 葫芦岛市\n• 锦州市\n• 辽阳市\n• 盘锦市\n• 沈阳市\n• 铁岭市\n• 营口市\n• 湖北\n• 鄂州市\n• 恩施土家族苗族自治州\n• 黄冈市\n• 黄石市\n• 荆门市\n• 荆州市\n• 直辖行政单位\n• 十堰市\n• 随州市\n• 武汉市\n• 咸宁市\n• 襄阳市\n• 孝感市\n• 宜昌市\n• 江西\n• 抚州市\n• 赣州市\n• 吉安市\n• 景德镇市\n• 九江市\n• 南昌市\n• 萍乡市\n• 上饶市\n• 新余市\n• 宜春市\n• 鹰潭市\n• 浙江\n• 杭州市\n• 湖州市\n• 嘉兴市\n• 金华市\n• 丽水市\n• 宁波市\n• 衢州市\n• 绍兴市\n• 台州市\n• 温州市\n• 舟山市\n• 青海\n• 果洛藏族自治州\n• 海北藏族自治州\n• 海东地区\n• 海南藏族自治州\n• 海西蒙古族藏族自治州\n• 黄南藏族自治州\n• 西宁市\n• 玉树藏族自治州\n• 甘肃\n• 白银市\n• 定西市\n• 甘南藏族自治州\n• 嘉峪关市\n• 金昌市\n• 酒泉市\n• 兰州市\n• 临夏回族自治州\n• 陇南市\n• 平凉市\n• 庆阳市\n• 天水市\n• 武威市\n• 张掖市\n• 贵州\n• 安顺市\n• 毕节市\n• 贵阳市\n• 六盘水市\n• 黔东南苗族侗族自治州\n• 黔南布依族苗族自治州\n• 黔西南布依族苗族自治州\n• 铜仁地区\n• 遵义市\n• 陕西\n• 安康市\n• 宝鸡市\n• 汉中市\n• 商洛市\n• 铜川市\n• 渭南市\n• 西安市\n• 咸阳市\n• 延安市\n• 榆林市\n• 西藏\n• 阿里地区\n• 昌都地区\n• 拉萨市\n• 林芝地区\n• 那曲地区\n• 日喀则地区\n• 山南地区\n• 宁夏\n• 固原市\n• 石嘴山市\n• 吴忠市\n• 银川市\n• 中卫市\n• 福建\n• 福州市\n• 龙岩市\n• 南平市\n• 宁德市\n• 莆田市\n• 泉州市\n• 三明市\n• 厦门市\n• 漳州市\n• 内蒙古\n• 阿拉善盟\n• 巴彦淖尔市\n• 包头市\n• 赤峰市\n• 鄂尔多斯市\n• 呼和浩特市\n• 呼伦贝尔市\n• 通辽市\n• 乌海市\n• 乌兰察布市\n• 锡林郭勒盟\n• 兴安盟\n• 云南\n• 保山市\n• 楚雄彝族自治州\n• 大理白族自治州\n• 德宏傣族景颇族自治州\n• 迪庆藏族自治州\n• 红河哈尼族彝族自治州\n• 昆明市\n• 丽江市\n• 临沧市\n• 怒江傈僳族自治州\n• 曲靖市\n• 思茅市\n• 文山壮族苗族自治州\n• 西双版纳傣族自治州\n• 玉溪市\n• 昭通市\n• 新疆\n• 阿克苏地区\n• 阿勒泰地区\n• 巴音郭楞蒙古自治州\n• 博尔塔拉蒙古自治州\n• 昌吉回族自治州\n• 哈密地区\n• 和田地区\n• 喀什地区\n• 克拉玛依市\n• 克孜勒苏柯尔克孜自治州\n• 直辖行政单位\n• 塔城地区\n• 吐鲁番地区\n• 乌鲁木齐市\n• 伊犁哈萨克自治州\n• 黑龙江\n• 大庆市\n• 大兴安岭地区\n• 哈尔滨市\n• 鹤岗市\n• 黑河市\n• 鸡西市\n• 佳木斯市\n• 牡丹江市\n• 七台河市\n• 齐齐哈尔市\n• 双鸭山市\n• 绥化市\n• 伊春市\n• 香港\n• 香港\n• 九龙\n• 新界\n• 澳门\n• 澳门\n• 其它地区\n• 台湾\n• 台中市\n• 台南市\n• 高雄市\n• 台北市\n• 基隆市\n• 嘉义市\n•",
null,
"长春吊顶天花板-想要购买高质量的吊顶天花板找哪家\n\n品牌:誉寰球,,\n\n出厂地:吉林省 松原市\n\n报价：面议\n\n吉林誉寰球铝业有限公司\n\n黄金会员：",
null,
"主营：吉林铝单板,吉林铝方通,吉林双曲板,吉林蜂窝板,吉林铝塑板\n\n•",
null,
"内蒙古密拼铝单板-在哪里能买到实惠的密拼铝单板\n\n品牌:誉寰球,,\n\n出厂地:吉林省 松原市\n\n报价：面议\n\n吉林誉寰球铝业有限公司\n\n黄金会员：",
null,
"主营：吉林铝单板,吉林铝方通,吉林双曲板,吉林蜂窝板,吉林铝塑板\n\n•",
null,
"阿拉善盟铝方通|为您推荐吉林誉寰球铝业性价比高的铝方通\n\n品牌:誉寰球,,\n\n出厂地:吉林省 松原市\n\n报价：面议\n\n吉林誉寰球铝业有限公司\n\n黄金会员：",
null,
"主营：吉林铝单板,吉林铝方通,吉林双曲板,吉林蜂窝板,吉林铝塑板\n\n•",
null,
"佳木斯密拼铝单板_在哪能买到有品质的密拼铝单板\n\n品牌:誉寰球,,\n\n出厂地:吉林省 松原市\n\n报价：面议\n\n吉林誉寰球铝业有限公司\n\n黄金会员：",
null,
"主营：吉林铝单板,吉林铝方通,吉林双曲板,吉林蜂窝板,吉林铝塑板\n\n•",
null,
"密拼铝单板价格_买超值的密拼铝单板优选吉林誉寰球铝业\n\n品牌:誉寰球,,\n\n出厂地:吉林省 松原市\n\n报价：面议\n\n吉林誉寰球铝业有限公司\n\n黄金会员：",
null,
"主营：吉林铝单板,吉林铝方通,吉林双曲板,吉林蜂窝板,吉林铝塑板\n\n•",
null,
"巴彦淖尔铝方通-出售松原超值的铝方通\n\n品牌:誉寰球,,\n\n出厂地:吉林省 松原市\n\n报价：面议\n\n吉林誉寰球铝业有限公司\n\n黄金会员：",
null,
"主营：吉林铝单板,吉林铝方通,吉林双曲板,吉林蜂窝板,吉林铝塑板\n\n•",
null,
"盘锦密拼铝单板_有品质的密拼铝单板推荐\n\n品牌:誉寰球,,\n\n出厂地:吉林省 松原市\n\n报价：面议\n\n吉林誉寰球铝业有限公司\n\n黄金会员：",
null,
"主营：吉林铝单板,吉林铝方通,吉林双曲板,吉林蜂窝板,吉林铝塑板\n\n•",
null,
"兴安盟金属保温一体板|哪儿有卖专业金属保温一体板\n\n品牌:誉寰球,,\n\n出厂地:吉林省 松原市\n\n报价：面议\n\n吉林誉寰球铝业有限公司\n\n黄金会员：",
null,
"主营：吉林铝单板,吉林铝方通,吉林双曲板,吉林蜂窝板,吉林铝塑板\n\n•",
null,
"张家口铝塑板-松原地区质量好的铝塑板\n\n品牌:誉寰球,,\n\n出厂地:吉林省 松原市\n\n报价：面议\n\n吉林誉寰球铝业有限公司\n\n黄金会员：",
null,
"主营：吉林铝单板,吉林铝方通,吉林双曲板,吉林蜂窝板,吉林铝塑板\n\n•",
null,
"幕墙铝单板价格|吉林好的幕墙铝单板供应\n\n品牌:誉寰球,,\n\n出厂地:吉林省 松原市\n\n报价：面议\n\n吉林誉寰球铝业有限公司\n\n黄金会员：",
null,
"主营：吉林铝单板,吉林铝方通,吉林双曲板,吉林蜂窝板,吉林铝塑板\n\n• 没有找到合适的松原市供应商？您可以发布采购信息\n\n没有找到满足要求的松原市供应商？您可以搜索 批发 公司\n\n### 最新入驻厂家\n\n相关产品:\n长春吊顶天花板 内蒙古密拼铝单板 阿拉善盟铝方通 佳木斯密拼铝单板 密拼铝单板价格 巴彦淖尔铝方通 盘锦密拼铝单板 兴安盟金属保温一体板 张家口铝塑板 幕墙铝单板价格"
] |
[
null,
"http://image-ali.bianjiyi.com/1/2019/0112/16/15472808093113.png",
null,
"http://www.shengyibao.com/Public/Images/ForeApps/grade2.png",
null,
"http://image-ali.bianjiyi.com/1/2018/1229/10/15460495696636.jpg",
null,
"http://www.shengyibao.com/Public/Images/ForeApps/grade2.png",
null,
"http://image-ali.bianjiyi.com/1/2019/0416/09/15553795157454.jpg",
null,
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null,
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null,
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null,
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null,
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null
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|
https://www.brightstorm.com/tag/inequality-notation/page/1
|
[
"# inequality notation 45 videos\n\n• #### Linear Inequalities\n\n##### AlgebraSolving and Graphing Inequalities\n\nHow to notate solving and graphing inequalities.\n\ninequality notation\n• #### Solving a Three-part Linear Inequality\n\n##### Algebra 2Linear Inequalities\n\nHow to solve a three part inequality.\n\ninterval notation inequality notation set builder notation three part inequality\n• #### Solving a Three-part Linear Inequality\n\n##### PrecalculusLinear Equations and Inequalities\n\nHow to solve a three part inequality.\n\ninterval notation inequality notation set builder notation three part inequality\n• #### Function Notation\n\n##### Algebra 2Functions\n\nHow to define function notation.\n\nfunction notation f(x)\n• #### Solving and Graphing Inequalities using Addition or Subtraction\n\n##### AlgebraSolving and Graphing Inequalities\n\nHow to solve and graph one variable inequalities using addition or subtraction.\n\ninequality notation\n• #### Scientific Notation\n\n##### AlgebraExponents\n\nHow to move between scientific notation and standard notation of numbers.\n\npower scientific notation\n• #### Scientific Notation\n\nHow to move between scientific notation and standard notation of numbers.\n\npower scientific notation\n• #### Scientific Notation\n\n##### Algebra 2Exponents\n\nHow to move between scientific notation and standard notation of numbers.\n\nscientific notation standard notation big numbers small numbers\n• #### Scientific Notation\n\n##### ChemistryIntroduction to Chemistry\n\nHow to use and interpret scientific notation.\n\nscientific notation exponents\n• #### Scientific Notation\n\n##### PhysicsIntroduction to Physics\n\nHow to use and interpret scientific notation.\n\nscientific notation exponents\n• #### Solving and Graphing Inequalities using Multiplication or Division\n\n##### AlgebraSolving and Graphing Inequalities\n\nHow to solve and graph one variable inequalities using multiplication or division.\n\ninequality notation solve\n• #### Solving and Graphing Multistep Inequalities\n\n##### AlgebraSolving and Graphing Inequalities\n\nHow to solve and graph multistep inequalities.\n\ninequality solve\n• #### Function Notation with Logs and Exponentials\n\n##### Algebra 2Inverse, Exponential and Logarithmic Functions\n\nHow to use function notation to solve log equations.\n\nfunction notation solving log equations\n• #### Function Notation with Logs and Exponentials\n\n##### PrecalculusExponential and Logarithmic Functions\n\nHow to use function notation to solve log equations.\n\nfunction notation solving log equations\n• #### Solving and Graphing Compound Inequalities\n\n##### AlgebraSolving and Graphing Inequalities\n\nHow to solve inequalities that use the word \"and\" or \"or.\"\n\ninequality solve compound\n• #### Solving and Graphing Compound Inequalities\n\n##### PrecalculusLinear Equations and Inequalities\n\nHow to solve inequalities that use the word \"and\" or \"or.\"\n\ninequality solve compound\n• #### Systems of Inequalities\n\n##### AlgebraSolving Systems of Equations\n\nHow to find the solution region for a system of inequalities.\n\nsystem of inequality inequality greater than less than greater than or equal to less than or equal to solution region\n• #### Triangle Side Inequalities\n\n##### GeometryTriangles\n\nHow to determine the triangle side inequalities.\n\nshortest distance side length inequalities\n• #### Solving Linear Inequalities\n\n##### Algebra 2Linear Inequalities\n\nHow to solve linear inequalities.\n\nlinear inequalities less than greater than\n• #### Solving Linear Inequalities\n\n##### PrecalculusLinear Equations and Inequalities\n\nHow to solve linear inequalities.\n\nlinear inequalities less than greater than"
] |
[
null
] |
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|
http://cds.cern.ch/collection/DIRAC%20Notes?ln=sv
|
[
"Books, e-books, journals, periodicals, proceedings, standards and loaning procedures will be migrated on 21st of April to a new website under development. See here for more information.\n\n# DIRAC Notes\n\nSenast inlagda poster:\n2017-02-14\n09:49\n The estimation of production rates of $K^+ K^−$ and $p\\bar{p}$ atoms in proton-nucleus interactions at 450 GeV/c / Gorchakov, O ; Nemenov, L DIRAC-Note-2016-07. - 2016. - 11 p. Fulltext\n\n2017-02-14\n09:33\n An improved $\\pi$K atom lifetime measurement / Yazkov, V (SINP, Moscow) ; Zhabitsky, M (Dubna, JINR) This note describes details of analysis of data samples collected by DIRAC experiment on a Pt target in 2007 and Ni targets in 2008–2010 in order to estimate the lifetime of πK atoms. [...] DIRAC-Note-2016-06. - 2016. - 9 p. Fulltext\n\n2016-08-11\n14:06\n Estimation of beam time needed for measurement of pion-kaon s-wave scattering length combination $a_{\\bar{0}}$ with a statistical accuracy 5% / Yazkov, V (SINP, Moscow) Measurement of s-wave pion-kaon scattering length combina tion $a_{\\bar{0}} = 1/3( a^{1/2}_0 − a^{3/2}_0)$ with an accuracy 5% provides check of prediction made by Chir al Perturbation theory and Lattice QCD. [...] DIRAC-Note-2016-05. - 2016. - 14 p. Fulltext\n\n2016-08-11\n14:03\n The estimation of production rates of $π^+ K^−, π^− K^+$ and $π^+π^−$ atoms in proton-nucleus interactions at 24 and 450 GeV/c / Gorchakov, O ; Nemenov, L Short-lived ( τ ∼ 3 × 10 − 15 s ) π + K − , K + π − and π + π − atoms as well as long-lived ( τ ≥ 1 × 10 − 11 s) π + π − atoms produced in proton-nucleus interactions at 24 GeV/c are observed and studied in the DIRAC experiment at the CERN P S. [...] DIRAC-Note-2016-04. - 2016. - 19 p.\n\n2016-08-11\n14:00\n Update in the SFD detector response simulation / Benelli , A (Dubna, JINR) ; Yazkov, V (SINP, Moscow) DIRAC-Note-2016-03. - 2016. - 12 p. Fulltext\n\n2016-03-09\n09:27\n Update in the Multiple Scattering description of the SFD detector in Geant-Dirac / Benelli , A (Dubna, JINR) ; Yazkov, V (SINP, Moscow) DIRAC-Note-2016-02. - 2016. - 5 p. Fulltext\n\n2016-03-09\n08:27\n Update in the detector alignment and Lambda studies / Benelli , A (Dubna, JINR) ; Yazkov, V (SINP, Moscow) DIRAC-Note-2016-01. - 2016. - 15 p. Fulltext\n\n2016-01-12\n10:24\n The estimation of production rates of $\\pi^+K^-, \\pi^-K^+$ and $\\pi^+\\pi^-$ atoms in proton-nucleus interactions at 450 GeV/c / Gorchakov, O ; Nemenov, L Short-lived (τ ∼ 3 × 10 − 15 s) π+ K− , K+ π− and π+ π− atoms as well as long- lived (τ ≥ 1 × 10 − 11 s) π+ π− atoms produced in proton-nucleus interactions at 24 GeV/c are observed and studied in the DIRAC experiment at the CERN PS. [...] DIRAC-Note-2015-05. - 2015. - 19 p. Fulltext\n\n2016-01-12\n10:12\n The estimation of production rates of $\\pi^+K^-, \\pi^-K^+$ and $\\pi^+\\pi^-$ atoms in proton-Ni interactions at proton momentum of 450 GeV/c / Gorchakov, O ; Nemenov, L In the DIRAC experiment at CERN the π+ K− , K+ π− and π+ π− atoms generated in proton-nucleus interaction at proton momentum Pp = 24 GeV/c were investigated. [...] DIRAC-Note-2015-04. - 2015. - 28 p. Fulltext\n\n2016-01-12\n10:03\n Production rates for $\\pi^+K^-, \\pi^-K^+$ and $\\pi^+\\pi^-$ atoms in $p$-Ni interactions at proton momentum 24 and 450 GeV/c / Gorchakov, O ; Nemenov, L The results of performed analysis show that the yield of π+ K− , K+ π− and π+ π− atoms in the p-nucleus interactions increases significantly at change of proton momentum Pp from 24 up to 450 GeV/c. [...] DIRAC-Note-2015-03. - 2015. - 33 p. Fulltext"
] |
[
null
] |
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|
https://edharmalib.com/lib/earticle/ear0002
|
[
"",
null,
"## Calculation of the characteristics of the year",
null,
"# Calculation of the characteristics of the year\n\nWe continue to add a rebus or a constructor from the simplest cubes. In previous publications, the methods of calculating the new year or losar (Phugpa, Bon and Rebkong, Gyalpo-losar), lists of months and their start dates were considered. Now we will smoothly move on to how we can determine the other main parameters of the year. In particular, we will talk about the number of the year according to the Tibetan calendar, the animal sign, the elements wangthang, lungta, sog, lu, etc. All these calculations are quite simple, you just need to be able to count a little. Moreover, you can count on a calculator or using just a piece of paper. Also, based on these calculations, you can determine some of the characteristics of your own birth. But this will be explained in another publication.\n\nSo. Let's start by determining the number of the year in the Tibetan calendar. Here we can use two calculation options:\n- based on the year number in the Gregorian calendar (but remember that the beginning of the year in the Gregorian calendar does not coincide with the beginning of the year in the Tibetan calendar).)\n- based on the month number for the first month of the Tibetan year.\n\nThe simplest option is the first method, so we will focus on it. We will use 2019 as an example.\nSo. In order.\n\n### 1. The year of the Tibetan calendar\n\nThe Tibetan calendar differs from the one used in most countries by 127 years. Therefore, we can add 127 to the year number and get the required year number, based on which further calculations will be performed.\n2019+127=2146\n\n### 2. The number of the Rabjung and the number of the year in it.\n\nRabjuns are sixty-year cycles, the beginning of which is considered to be the year 1027 (Fire-W-Hare). The number of the rabjun and the number of the year in it are determined accordingly. The year 1027 is the year 1154 according to the Tibetan calendar. To determine the number of the year in Rabjun, we need to subtract the number of the first or base year and divide the remaining value by 60. Add 1 to the remainder, so that there is no confusion with the remainder equal to zero. You also need to add 1 to the integer, so that there is no confusion with the \"zero\" rabjun.\n2146-1154=992\n992/60=16 and the remainder 32.\n16+1=17 (rabjun number)\n32+1=33 (the number of the year in rabjun)\n\n### 3. The number of the mekor and the year in it.\n\nAnother variant of the definition of sixty-year cycles, which was used earlier, is called mekor. For its beginning, we can consider 2576 BC or 2449 BC (according to the Tibetan style)\nWe make a similar calculation to the previous one\n2146-(-2449)=4595\n4595/60=76 and the remainder 35\n76+1=77 (mekor number)\n35+1=36 (mekor year number)\n\n### 4. Determination of the metreng and meva of the year\n\nThe meva is determined based on a large cycle of 180 years. During this cycle, three metrengs (the garland or rosary of mewa) change. Actually, the number of the metreng is not particularly important for us, but we will still count it. As we identified earlier, we have 77 mekor and 36 number in mekor. The metreng number can be determined based on the remainder of the division of the mekor number by 3. In this case, you need to add one, since there is no \"zero metreng\". That is, as a result, we will get a number from 1 to 3.\nCounting\n77/3=92 and the remainder is 2. That is, we use the third metreng.\nIn order not to experience the agony of trying to determine the actual meva and not to consult the tables for calculations, we will do it in a simpler way. Since we have the third cycle of the mewa (third metreng), we can add 120 to the year number in the metreng. In that case, if we had the first metreng , we do not need to add anything. If the second metreng - to the year number in the metreng, add 60.\n36+120=156.\nSince the mevas change in the opposite direction, we must also go in the opposite direction to determine. That is, subtract from the maximum value in the cycle - the number of the year in the large cycle.\nSubtract the resulting value from 180.\n180-156=24.\nSince zero or 180 corresponds to the number 2, we add 2.\n24+2=26\nNow we need to determine the actual meva number. Since mevs have values from 1 to 9, we divide the resulting value by 9 and look at the remainder.\n26/9=2 and the remainder is 8. In the event that the remainder is zero, we substitute a nine instead of zero. So we got an eight. Look at the short list of values\n\n Number Mewa 1 1, white 2 2, black 3 3, blue 4 4, green 5 5, yellow 6 6, white 7 7, red 8 8, white 9 9, red\n\nWe got an eight, so mewa according to the table is 8, white.\n\n### 5. Animal, gender, year direction\n\nRabjuns and mekors have different beginnings, different primary years. Also in these cycles, the calculation comes from different animals and elements. So in rabjun, the calculation comes from the Fire-Hare, while the mekors are counted from the Tree-Rat. Since the mekor cycles are calculated from older times, we will use them. The sequence of years in the 12 animal cycle is as follows:\n\n Number Animal Direction 1 Rat north (bottom) 2 Bull Northeast 3 Tiger East (top) 4 Hare East (bottom) 5 Dragon Southeast 6 Snake south (top) 7 Horse South (bottom) 8 Sheep southwest 9 Monkey west (top) 10 Bird West (bottom) 11 Dog North-west 12 Pig north (top)\n\nWe take the year number in the cycle, divide by 12, and look at the remainder. If the remainder is 0, replace the resulting value with 12.\n36/12=3 and the remainder 0. Replace with 12. We get 12. We take the data from the table and get the year of the Pig.\n\nMale years include: Rat, Tiger, Dragon, Horse, Monkey, Dog. To the women's: Bull, Hare, Snake, Sheep, Bird, Pig.\nThe resulting year is the year of the Pig, female. We also take the directions of the year from the table and get the following data: year of the Pig, female. Direction north (top)\n\n### 6. Definition of wangthang\n\nAs many have seen, calendars often write the year of the Earth-Pig. Actually, this indication of the element can be called wangtang (success, etc.). Of course, it can be defined using tables, but this is not very convenient. To determine wang, you can also use mathematical means, and simple ones. Since when analyzing the table, you can see that the vang elements always go in pairs, we can first divide the year number in the metreng by 2.\nWe get:\n36/2=18 and the remainder is 0. If the remainder is not zero, then add it to the resulting value (for 2018, it would be 35/2=17 and 1. 18).\nSince we have five elements, we divide the resulting value by five and look at the remainder. At the same time, we also need to take into account that we do not have a zero element. So when we get the remainder zero, we must write the number 5. So, look:\n18/5=3 and the remainder is 3.\n\nChecking the table of elements:\n\n Number Element 1 tree 2 fire 3 earth 4 metal 5 water\n\nWe get the earth. So we have a year of Earth-w-Pig.\n\n### 7. The life force of the year or Sog\n\nTo determine them, use the table:\n\n Year Element Tiger, Hare tree Horse, Snake fire Bird, Monkey metal Rat, Pig water Dog, Dragon, Bull, Sheep earth\n\nThe Year of the Pig, so the life force of the year is water.\n\n### 8. Determination of the health or element of lu.\n\nIt is easier to use a table to determine the Lu.\n\n Element Years Tree Earth-Dragon, Earth-Snake, Earth-Dog, Earth-Pig, Metal-Tiger, Metal-Hare, Metal-Monkey, Metal-Bird, Water-Rat, Water-Bull, Water-Horse, Water-Sheep Fire Tree-Dragon, Tree-Snake, Tree-Dog, Tree-Pig, Fire-Tiger, Fire-Hare, Fire-Monkey, Fire-Bird, Earth-Rat, Earth-Bull, Earth-Horse, Earth-Sheep Earth Fire-Dragon, Fire-Snake, Fire-Dog, Fire-Pig, Earth-Tiger, Earth-Hare, Earth-Monkey, Earth-Bird, Metal-Rat, Metal-Bull, Metal-Horse, Metal-Sheep Metal Tree-Rat, Tree-Bull, Tree-Horse, Tree-Sheep, Metal-Dragon, Metal-Snake, Metal-Dog, Metal-Pig, Water-Tiger, Water-Hare, Water-Monkey, Water-Bird Water Tree-Tiger, Tree-Hare, Tree-Monkey, Tree-Bird, Fire-Rat, Fire-Bull, Fire-Horse, Fire-Sheep, Water-Dragon, Water-Snake, Water-Dog, Water-Pig\n\nFor our year: a tree.\n\n### 9. Luck or lungta of the year, the years of harmonious triples\n\nWe determine it by the table\n\n Element Years Metal Tiger, Horse, Dog Tree Rat, Dragon, Monkey Water Bird, Bull, Snake Fire Pig, Sheep, Hare\n\nWe have the year of the Pig, so the element of lungta is Fire.\n\n### 10. La of year\n\nSince it is said that La is the mother of the life force, we remember that we have the life force of the year-water or, if we take the numerical correspondence, 5. How to determine the mother of the element? To do this, simply subtract 1 from the element number. That is, go to the previous one. If we get 0, then we need to write 5.\n5-1=4. Element number four is metal. That is, La of year - Metal.\n\n### 11. Special lungta of the year\n\nThe special Lungta of the year is defined as the son of Lungta, that is, as the element following the Lungta element. We have the Lungta of the year-Fire, number 2. Add 1. If we get 6, then we need to replace it with 1.\n2+1=3. The element, according to the table, is earth. Special lungta of the year: earth.\n\n### 12. Five years of khayen\n\nTo determine this type of years, we need to compare the elements. And depending on whether they coincide or not, a particular year will be determined. There are five years of this kind. These are: Khayen, Sejig, Khongnong, Kharal, Dunkhur. How do I determine if a year belongs to one of them? To do this, we need to know the life force and wangthang of the year. For the year used in the calculations, sog is water (5), wangthang is earth (3).\n\nIf Sog and Wang are the same, it's the year of Khayen. The elements are different-so no.\nIf Wang is the son of Sog (number wang+1=sog) - sejig. Check: 3+1=4. 4 is not equal to 5. Not a sejig.\nIf Wang is the mother of Sog (number vang-1=sog) - Khongnong. Check: 3-1=2. 2 is not equal to 5. Not Khongnong.\nIf Wang is a friend of Sog (sog number+2) - Kharal. Check. 5+2=7 or 2. Not equal to 3. Not kharal.\nIf Vang is the enemy of Sog (number sog-2) - Dunkhur. Check. 5-2=3. Early 3. The Year of Dunkhur.\n\nActually, this is the end of the description of the calculations related to the year. The following articles will be devoted to other topics on astrology. About all inconsistencies, errors, strange places-please write to the site administrator. There are contacts on every page\n\nWell, those who are too lazy to count, may well use the link with automatic calculations for the current and next year."
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http://projects.lsv.ens-cachan.fr/topology/?page_id=97
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[
"# Course Ideas\n\nHere are few ideas of courses that can be given, based on the book.\n\n• Introduction to topology: level L3 (European)/bachelor level, 10 x 2h.\n• Metric spaces, the example of the Euclidean place. Convergence. Examples of convergent, of non-convergent sequences (e.g., based on Figure 3.3). Read: Section 3.1, Section 3.2 until warning sign on p. 22.\n• (Sequentially) closed and open subsets of a metric space. Read: Section 3.2.\n• Compactness. The Borel-Lebesgue Theorem. Tychonoff’s Theorem for finite products of compact metric spaces. Read: Section 3.3.\n• Completeness. The Banach Fixed Point Theorem. The compact metric spaces are the complete, precompact metric spaces. Read: Section 3.4.\n• Continuous maps. Preservation of limits. Images of compact subspaces. Lipschitz maps, uniformly continuous maps. Read: Section 3.5 until and including Corollary 3.5.6, with its proof (p.40).\n• Notions of convergence on spaces of continuous maps. Pointwise, uniform convergence. The Arzelà-Ascoli Theorem. Read: Section 3.6.\n• Beyond metric spaces: topological spaces. Generalizing opens, closed subsets, and continuity. Bases and subbases. Separation properties. Read: Section 4.1, Section 4.3.\n• Compactness in the general topological setting. Read: Section 4.4.\n• The product topology, and Tychonoff’s Theorem (general form). Read: Section 4.5.\n• Back to convergence: Moore-Smyth convergence, nets, Kelley’s Theorem. Read: Section 4.7.\n• Advanced topology for domain theory: level M2 (European)/first year of PhD in theoretical computer science, 12 x 2h; ideally, paired with or following a course on semantics of programming languages.\n• A quick summary of basic topological notions: opens, closed subsets, continuous maps, compact subsets and spaces, products, Tychonoff’s Theorem. The important example of the Scott topology (Section 4.2), dcpos. A few warnings: Scott is not Hausdorff, compact subsets need not be closed, limits are not unique (if you decide to talk about limits). Read: Sections 4.1 through 4.5.\n• Continuous dcpos, locally compact spaces. Why continuous dcpos matter: e.g., observe that products are not the same in the categories Cpo and Top, but this hassle is avoided with continuous dcpos. Read: Section 5.1.\n• Topologies on spaces of functions 1: core-compactness, as a refinement of local compactness; relevance to the lattice of open subsets; the exponentiable spaces are the core-compact spaces; uniqueness of the exponential topology. Read: Sections 5.2 through 5.4.\n• Topologies on spaces of functions 2: Cartesian-closed categories, relevance to programming language semantics; an important Cartesian-closed category, bc-domains. Read: Sections 4.12, 5.5 (relevant parts needed to understand categories, products, Cartesian-closedness), Section 5.7.\n• Alternative Cartesian-closed categories: C-generated spaces, Kelley spaces, Day’s theorem. Read: Section 5.6. (This lecture is optional, depending on time spent on the previous lectures.)\n• Home project 1: why the Hausdorff C-generated spaces are not satisfactory in computer science, after K. H. Hofmann and M. Mislove’s paper: explain why and explain the result of the paper.\n• Stone Duality 1: how can one recover a topological space from a purported description of its lattice of open subsets? Frames, spatial lattices, sober spaces; sobrification. Limits, characterization of sober spaces through limits. Read: Sections 8.1, 8.2 (and 4.7 for limits).\n• Stone Duality 2: The Hofmann-Mislove theorem, the Hofmann-Lawson theorem and various other equivalences between categories of topological spaces and of frames. Read: Section 8.3.\n• Stably compact spaces 1: introduction via Stone duality with fully arithmetic lattices; examples: compact Hausdorff spaces, bc-domains. De Groot duality, and Nachbin’s theorem: compact pospaces. Read: Section 9.1.\n• Stably compact spaces 2: products and retracts of stably compact spaces; proper and perfect maps. Read: Sections 9.3, 9.4.\n• Stably compact spaces 3: spectral spaces. Johnstone’s theorem: the stably compact spaces are the retracts of spectral spaces. Stone duality in its original form; Priestley spaces. Read: Section 9.5.\n• Stably compact spaces 4: bifinite domains, retracts of bifinite domains, yielding larger Cartesian-closed categories of continuous dcpos than just bc-domains. Read: Section 9.6.\n• Home project 2: study A. Jung’s FS-domains, and do Exercises 9.6.25 through 9.6.32.\n• Home project 3: apply the theory of spectral spaces this to understand Samson’s Abramsky Domain Theory in Logical Form.\n• Stably compact spaces 5: Noetherian spaces, wqos, the topological Higman and Kruskal theorems. Read: Section 9.7.\n• Home project 3: explain application in verification given in this paper.\n\nJean Goubault-Larrecq (February 13th, 2013)",
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https://www.colorhexa.com/00c505
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"# #00c505 Color Information\n\nIn a RGB color space, hex #00c505 is composed of 0% red, 77.3% green and 2% blue. Whereas in a CMYK color space, it is composed of 100% cyan, 0% magenta, 97.5% yellow and 22.7% black. It has a hue angle of 121.5 degrees, a saturation of 100% and a lightness of 38.6%. #00c505 color hex could be obtained by blending #00ff0a with #008b00. Closest websafe color is: #00cc00.\n\n• R 0\n• G 77\n• B 2\nRGB color chart\n• C 100\n• M 0\n• Y 97\n• K 23\nCMYK color chart\n\n#00c505 color description : Strong lime green.\n\n# #00c505 Color Conversion\n\nThe hexadecimal color #00c505 has RGB values of R:0, G:197, B:5 and CMYK values of C:1, M:0, Y:0.97, K:0.23. Its decimal value is 50437.\n\nHex triplet RGB Decimal 00c505 `#00c505` 0, 197, 5 `rgb(0,197,5)` 0, 77.3, 2 `rgb(0%,77.3%,2%)` 100, 0, 97, 23 121.5°, 100, 38.6 `hsl(121.5,100%,38.6%)` 121.5°, 100, 77.3 00cc00 `#00cc00`\nCIE-LAB 69.428, -70.864, 67.942 19.992, 39.941, 6.799 0.3, 0.599, 39.941 69.428, 98.173, 136.206 69.428, -65.698, 84.632 63.199, -54.131, 37.861 00000000, 11000101, 00000101\n\n# Color Schemes with #00c505\n\n• #00c505\n``#00c505` `rgb(0,197,5)``\n• #c500c0\n``#c500c0` `rgb(197,0,192)``\nComplementary Color\n• #5dc500\n``#5dc500` `rgb(93,197,0)``\n• #00c505\n``#00c505` `rgb(0,197,5)``\n• #00c568\n``#00c568` `rgb(0,197,104)``\nAnalogous Color\n• #c5005d\n``#c5005d` `rgb(197,0,93)``\n• #00c505\n``#00c505` `rgb(0,197,5)``\n• #6800c5\n``#6800c5` `rgb(104,0,197)``\nSplit Complementary Color\n• #c50500\n``#c50500` `rgb(197,5,0)``\n• #00c505\n``#00c505` `rgb(0,197,5)``\n• #0500c5\n``#0500c5` `rgb(5,0,197)``\n• #c0c500\n``#c0c500` `rgb(192,197,0)``\n• #00c505\n``#00c505` `rgb(0,197,5)``\n• #0500c5\n``#0500c5` `rgb(5,0,197)``\n• #c500c0\n``#c500c0` `rgb(197,0,192)``\n• #007903\n``#007903` `rgb(0,121,3)``\n• #009204\n``#009204` `rgb(0,146,4)``\n• #00ac04\n``#00ac04` `rgb(0,172,4)``\n• #00c505\n``#00c505` `rgb(0,197,5)``\n• #00df06\n``#00df06` `rgb(0,223,6)``\n• #00f806\n``#00f806` `rgb(0,248,6)``\n• #13ff19\n``#13ff19` `rgb(19,255,25)``\nMonochromatic Color\n\n# Alternatives to #00c505\n\nBelow, you can see some colors close to #00c505. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.\n\n• #2cc500\n``#2cc500` `rgb(44,197,0)``\n• #1cc500\n``#1cc500` `rgb(28,197,0)``\n• #0bc500\n``#0bc500` `rgb(11,197,0)``\n• #00c505\n``#00c505` `rgb(0,197,5)``\n• #00c515\n``#00c515` `rgb(0,197,21)``\n• #00c526\n``#00c526` `rgb(0,197,38)``\n• #00c536\n``#00c536` `rgb(0,197,54)``\nSimilar Colors\n\n# #00c505 Preview\n\nThis text has a font color of #00c505.\n\n``<span style=\"color:#00c505;\">Text here</span>``\n#00c505 background color\n\nThis paragraph has a background color of #00c505.\n\n``<p style=\"background-color:#00c505;\">Content here</p>``\n#00c505 border color\n\nThis element has a border color of #00c505.\n\n``<div style=\"border:1px solid #00c505;\">Content here</div>``\nCSS codes\n``.text {color:#00c505;}``\n``.background {background-color:#00c505;}``\n``.border {border:1px solid #00c505;}``\n\n# Shades and Tints of #00c505\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, #000100 is the darkest color, while #ecffed is the lightest one.\n\n• #000100\n``#000100` `rgb(0,1,0)``\n• #001401\n``#001401` `rgb(0,20,1)``\n• #002801\n``#002801` `rgb(0,40,1)``\n• #003c02\n``#003c02` `rgb(0,60,2)``\n• #004f02\n``#004f02` `rgb(0,79,2)``\n• #006303\n``#006303` `rgb(0,99,3)``\n• #007703\n``#007703` `rgb(0,119,3)``\n• #008a04\n``#008a04` `rgb(0,138,4)``\n• #009e04\n``#009e04` `rgb(0,158,4)``\n• #00b105\n``#00b105` `rgb(0,177,5)``\n• #00c505\n``#00c505` `rgb(0,197,5)``\n• #00d905\n``#00d905` `rgb(0,217,5)``\n• #00ec06\n``#00ec06` `rgb(0,236,6)``\n• #01ff07\n``#01ff07` `rgb(1,255,7)``\n• #14ff1a\n``#14ff1a` `rgb(20,255,26)``\n• #28ff2e\n``#28ff2e` `rgb(40,255,46)``\n• #3cff41\n``#3cff41` `rgb(60,255,65)``\n• #4fff54\n``#4fff54` `rgb(79,255,84)``\n• #63ff67\n``#63ff67` `rgb(99,255,103)``\n• #77ff7a\n``#77ff7a` `rgb(119,255,122)``\n• #8aff8d\n``#8aff8d` `rgb(138,255,141)``\n• #9effa0\n``#9effa0` `rgb(158,255,160)``\n• #b1ffb3\n``#b1ffb3` `rgb(177,255,179)``\n• #c5ffc6\n``#c5ffc6` `rgb(197,255,198)``\n• #d9ffda\n``#d9ffda` `rgb(217,255,218)``\n• #ecffed\n``#ecffed` `rgb(236,255,237)``\nTint Color Variation\n\n# Tones of #00c505\n\nA tone is produced by adding gray to any pure hue. In this case, #5b6a5b is the less saturated color, while #00c505 is the most saturated one.\n\n• #5b6a5b\n``#5b6a5b` `rgb(91,106,91)``\n• #537254\n``#537254` `rgb(83,114,84)``\n• #4c794d\n``#4c794d` `rgb(76,121,77)``\n• #448146\n``#448146` `rgb(68,129,70)``\n• #3d883f\n``#3d883f` `rgb(61,136,63)``\n• #359037\n``#359037` `rgb(53,144,55)``\n• #2d9830\n``#2d9830` `rgb(45,152,48)``\n• #269f29\n``#269f29` `rgb(38,159,41)``\n• #1ea722\n``#1ea722` `rgb(30,167,34)``\n• #17ae1b\n``#17ae1b` `rgb(23,174,27)``\n• #0fb613\n``#0fb613` `rgb(15,182,19)``\n• #08bd0c\n``#08bd0c` `rgb(8,189,12)``\n• #00c505\n``#00c505` `rgb(0,197,5)``\nTone Color Variation\n\n# Color Blindness Simulator\n\nBelow, you can see how #00c505 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"
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|
https://se.mathworks.com/matlabcentral/answers/484381-how-to-calculate-displacement-from-acceleration-data?s_tid=prof_contriblnk
|
[
"# How to calculate displacement from acceleration data?\n\n68 views (last 30 days)\nAnswered: John D'Errico on 9 Oct 2019\nI have field vibration (acceleration) data that was collected at a sample frequency of 5k Hz. I would like to get instantaneous displacement for each acceration sample. I understand that in physics you would typically define your kinematic equation for acceleration, take the double integral, and input your time = t for when you are trying to calculate your instantaneous displacement.\nSince I have messy field data that is a collection of various spectral energy, I can't fit a nice equation to my time history waveform. What is the best way to accomplish my above goal using MATLAB functions?\nPlease forgive me for forgetting much of my Algebra and Calculus knowledge...\n\nDaniel M on 9 Oct 2019\nWhat does the data look like after you integrate it twice?\nWell, part of my reason for asking the question was because I was't sure which functions to use. The standard \"integral\" function requires that you define the function you would like to be integration and I am unable to mathematically define a function from my field data (curve fitting doesn't appear to be an option).\nI was able to use the trapz function to integrate (twice) across several samples (e.g. Data(1:5:end, or across 5 samples) and the value appeared to be what I would expect for displacement. I thought there was a way to get instantaneous displacement without curvefitting your acceleration function and taking an indefinite integral, but I have convinced myself otherwise.\n\nJohn D'Errico on 9 Oct 2019\nYou already know how to use trapz. Just use it twice. However, you mention instantaneous displacement, and the problem with trapz is it gives you an integral over the entire range.\nSo if you look at the help for trapz, you will see at the bottom, the name cumtrapz.\nCumtrapz gives you the integral cumulatively up to that point. I think this is what you are looking to find. Again, just use cumtrapz twice. The first call gives you velocity. The second call is dosplacement.\nIn general, if you don't know how to do something in MATLAB, then use tools like help, doc, lookfor. Check the links at the bottom of the help, as they may point you to something good. For example, if you tried this:\nlookfor integration\nthen it would have mentioned cumtrapz as a possible utility of interest."
] |
[
null
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|
https://gilkalai.wordpress.com/2009/07/28/polymath3-abstract-polynomial-hirsch-conjecture-aphc/
|
[
"The Abstract Polynomial Hirsch Conjecture",
null,
"A convex polytope",
null,
"$P$ is the convex hull of a finite set of points in a real vector space. A polytope can be described as the intersection of a finite number of closed halfspaces. Polytopes have a facial structure: A (proper) face of a polytope",
null,
"$P$ is the intersection of",
null,
"$P$ with a supporting hyperplane. (A hyperplane",
null,
"$H$ is a supporting hyperplane of",
null,
"$P$ if",
null,
"$P$ is contained in a closed halfspace bounded by",
null,
"$H$, and the intersection of",
null,
"$H$ and",
null,
"$P$ is not empty.) We regard the empty face and the entire polytope as trivial faces. The extreme points of a polytope",
null,
"$P$ are called its vertices. The one-dimensional faces of",
null,
"$P$ are called edges. The edges are line intervals connecting a pair of vertices. The graph",
null,
"$G(P)$ of a polytope",
null,
"$P$ is a graph whose vertices are the vertices of",
null,
"$P$ and two vertices are adjacent in",
null,
"$G(P)$ if there is an edge of",
null,
"$P$ connecting them. The",
null,
"$(d-1)$-dimensional faces of a polytop are called facets.\n\nThe Hirsch conjecture: The graph of a d-polytope with n facets has diameter at most n-d.\n\nA weaker conjecture which is also open is:\n\nPolynomial Hirsch Conjecture: Let G be the graph of a d-polytope with n facets. Then the diameter of G is bounded above by a polynomial in d and n.\n\nThe avenue which I consider most promising (but I may be wrong) is to replace “graphs of polytopes” by a larger class of graphs. Most known upper bound on the diameter of graphs of polytopes apply in much larger generality. Recently, interesting lower bounds were discovered and we can wonder what they mean for the geometric problem.\n\nHere is the (most recent) abstract setting:\n\nConsider the collection",
null,
"${\\cal G}(d,n)$ of graphs",
null,
"$G$ whose vertices are labeled by",
null,
"$d$-subsets of an",
null,
"$n$ element set.\n\nThe only condition we will require is that if",
null,
"$v$ is a vertex labeled by",
null,
"$S$ and",
null,
"$u$ is a vertex labeled by the set",
null,
"$T$, then there is a path between",
null,
"$u$ and",
null,
"$v$ so that all labels of its vertices are sets containing",
null,
"$S \\cap T$.\n\nAbstract Polynomial Hirsch Conjecture (APHC): Let",
null,
"$G \\in {\\cal G}(d,n)$ then the diameter of",
null,
"$G$ is bounded above by a polynomial in",
null,
"$d$ and",
null,
"$n$.\n\nEverything that is known about the APHC can be described in a few pages. It requires only rather elementary combinatorics; No knowledge about convex polytopes is needed.\n\nA positive answer to APHC (and some friends of mine believe that",
null,
"$n^2$ is the right upper bound) will apply automatically to convex polytopes.\n\nA negative answer to APHC will be (in my opinion) extremely interesting as well, but will leave the case of polytopes open. (One of the most active areas of convex polytope theory is methods for constructing polytopes, and there may be several ways to move from an abstract combinatorial example to a geometric example.)\n\nIf indeed we will decide to go for a polymath3, the concrete problem which I propose attacking is the APHC. However, we can discuss possible arguments regarding diameter of polytopes which use geometry, and we can be open to even more general abstract forms of the problem. (Or other things that people suggest.)\n\nReading the recent very short paper by Freidrich Eisenbrand, Nicolai Hahnle, and Thomas Rothvoss and the 3-pages paper by Sasha Razborov (the merged journal paper of these two contributions will become available soon, ) will get you right to the front lines. (There is an argument from the first paper that uses the Hall-marriage theorem, and an argument from the second paper that uses the “Lovasz local lemma”.)\n\nI will try to repeat in later posts the simple arguments from these papers – I plan to devote one post to the upper bounds, another post to the lower bounds, and yet another post to general background, motivation and cheerleading for the problem. I will try to make the different posts self-contained.\n\nQuestions and remarks about polytopes, the problem, or these papers are welcome.\n\nThis entry was posted in Convex polytopes, Open discussion, Open problems and tagged , . Bookmark the permalink.\n\n5 Responses to The Polynomial Hirsch Conjecture, a Proposal for Polymath3 (Cont.)\n\n1.",
null,
"Kristal Cantwell says:\n\n2.",
null,
"Gil Kalai says:\nThanks Kristal. Let me make one additional remark on the abstract setting. The condition is that if we have two vertices",
null,
"$v$ with lebel $S$ and",
null,
"$w$ with label",
null,
"$T$ there is a path between them with vertices labelled by sets containing",
null,
"$S \\cap T$. We do not make the dual assumption that we can move between",
null,
"$v$ to latex $w$ by sets all whose labels are included in",
null,
"$S \\cup T$. (Indeed, this is not the case for simple polytopes when we labeled a vertex by the set of facets containing it. If we could guarantee that the labels are always inside",
null,
"$S \\cup S$ and containing",
null,
"$S \\cap T$ then the diameter would be",
null,
"$d$."
] |
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|
http://jaywedding.com/qspevdu_qs001030
|
[
"• 湖南\n• 长沙市\n• 常德市\n• 郴州市\n• 衡阳市\n• 怀化市\n• 娄底市\n• 邵阳市\n• 湘潭市\n• 湘西土家族苗族自治州\n• 益阳市\n• 永州市\n• 岳阳市\n• 张家界市\n• 株洲市\n• 山西\n• 长治市\n• 大同市\n• 晋城市\n• 晋中市\n• 临汾市\n• 吕梁市\n• 朔州市\n• 太原市\n• 忻州市\n• 阳泉市\n• 运城市\n• 安徽\n• 安庆市\n• 蚌埠市\n• 亳州市\n• 巢湖市\n• 池州市\n• 滁州市\n• 阜阳市\n• 合肥市\n• 淮北市\n• 淮南市\n• 黄山市\n• 六安市\n• 马鞍山市\n• 宿州市\n• 铜陵市\n• 芜湖市\n• 宣城市\n• 广西\n• 百色市\n• 北海市\n• 崇左市\n• 防城港市\n• 贵港市\n• 桂林市\n• 河池市\n• 贺州市\n• 来宾市\n• 柳州市\n• 南宁市\n• 钦州市\n• 梧州市\n• 玉林市\n• 河南\n• 安阳市\n• 鹤壁市\n• 焦作市\n• 开封市\n• 洛阳市\n• 漯河市\n• 南阳市\n• 平顶山市\n• 濮阳市\n• 三门峡市\n• 商丘市\n• 新乡市\n• 信阳市\n• 许昌市\n• 郑州市\n• 周口市\n• 驻马店市\n• 吉林\n• 白城市\n• 白山市\n• 长春市\n• 吉林市\n• 辽源市\n• 四平市\n• 松原市\n• 通化市\n• 延边朝鲜族自治州\n• 广东\n• 潮州市\n• 东莞市\n• 佛山市\n• 广州市\n• 河源市\n• 惠州市\n• 江门市\n• 揭阳市\n• 茂名市\n• 梅州市\n• 清远市\n• 汕头市\n• 汕尾市\n• 韶关市\n• 深圳市\n• 阳江市\n• 云浮市\n• 湛江市\n• 肇庆市\n• 中山市\n• 珠海市\n• 辽宁\n• 鞍山市\n• 本溪市\n• 朝阳市\n• 大连市\n• 丹东市\n• 抚顺市\n• 阜新市\n• 葫芦岛市\n• 锦州市\n• 辽阳市\n• 盘锦市\n• 沈阳市\n• 铁岭市\n• 营口市\n• 湖北\n• 鄂州市\n• 恩施土家族苗族自治州\n• 黄冈市\n• 黄石市\n• 荆门市\n• 荆州市\n• 直辖行政单位\n• 十堰市\n• 随州市\n• 武汉市\n• 咸宁市\n• 襄阳市\n• 孝感市\n• 宜昌市\n• 江西\n• 抚州市\n• 赣州市\n• 吉安市\n• 景德镇市\n• 九江市\n• 南昌市\n• 萍乡市\n• 上饶市\n• 新余市\n• 宜春市\n• 鹰潭市\n• 浙江\n• 杭州市\n• 湖州市\n• 嘉兴市\n• 金华市\n• 丽水市\n• 宁波市\n• 衢州市\n• 绍兴市\n• 台州市\n• 温州市\n• 舟山市\n• 青海\n• 果洛藏族自治州\n• 海北藏族自治州\n• 海东地区\n• 海南藏族自治州\n• 海西蒙古族藏族自治州\n• 黄南藏族自治州\n• 西宁市\n• 玉树藏族自治州\n• 甘肃\n• 白银市\n• 定西市\n• 甘南藏族自治州\n• 嘉峪关市\n• 金昌市\n• 酒泉市\n• 兰州市\n• 临夏回族自治州\n• 陇南市\n• 平凉市\n• 庆阳市\n• 天水市\n• 武威市\n• 张掖市\n• 贵州\n• 安顺市\n• 毕节市\n• 贵阳市\n• 六盘水市\n• 黔东南苗族侗族自治州\n• 黔南布依族苗族自治州\n• 黔西南布依族苗族自治州\n• 铜仁地区\n• 遵义市\n• 陕西\n• 安康市\n• 宝鸡市\n• 汉中市\n• 商洛市\n• 铜川市\n• 渭南市\n• 西安市\n• 咸阳市\n• 延安市\n• 榆林市\n• 西藏\n• 阿里地区\n• 昌都地区\n• 拉萨市\n• 林芝地区\n• 那曲地区\n• 日喀则地区\n• 山南地区\n• 宁夏\n• 固原市\n• 石嘴山市\n• 吴忠市\n• 银川市\n• 中卫市\n• 福建\n• 福州市\n• 龙岩市\n• 南平市\n• 宁德市\n• 莆田市\n• 万博体育mantbex手机登录市\n• 三明市\n• 厦门市\n• 漳州市\n• 内蒙古\n• 阿拉善盟\n• 巴彦淖尔市\n• 包头市\n• 赤峰市\n• 鄂尔多斯市\n• 呼和浩特市\n• 呼伦贝尔市\n• 通辽市\n• 乌海市\n• 乌兰察布市\n• 锡林郭勒盟\n• 兴安盟\n• 云南\n• 保山市\n• 楚雄彝族自治州\n• 大理白族自治州\n• 德宏傣族景颇族自治州\n• 迪庆藏族自治州\n• 红河哈尼族彝族自治州\n• 昆明市\n• 丽江市\n• 临沧市\n• 怒江傈僳族自治州\n• 曲靖市\n• 思茅市\n• 文山壮族苗族自治州\n• 西双版纳傣族自治州\n• 玉溪市\n• 昭通市\n• 新疆\n• 阿克苏地区\n• 阿勒泰地区\n• 巴音郭楞蒙古自治州\n• 博尔塔拉蒙古自治州\n• 昌吉回族自治州\n• 哈密地区\n• 和田地区\n• 喀什地区\n• 克拉玛依市\n• 克孜勒苏柯尔克孜自治州\n• 直辖行政单位\n• 塔城地区\n• 吐鲁番地区\n• 乌鲁木齐市\n• 伊犁哈萨克自治州\n• 黑龙江\n• 大庆市\n• 大兴安岭地区\n• 哈尔滨市\n• 鹤岗市\n• 黑河市\n• 鸡西市\n• 佳木斯市\n• 牡丹江市\n• 七台河市\n• 齐齐哈尔市\n• 双鸭山市\n• 绥化市\n• 伊春市\n• 香港\n• 香港\n• 九龙\n• 新界\n• 澳门\n• 澳门\n• 其它地区\n• 台湾\n• 台中市\n• 台南市\n• 高雄市\n• 台北市\n• 基隆市\n• 嘉义市\n•",
null,
"乌鲁木齐燃气锅炉-新疆销售燃气锅炉厂家-新疆燃气锅炉招聘信息\n\n品牌:泰安山成,,\n\n出厂地:三江侗族自治县(古宜镇)\n\n报价：面议\n\n新疆泰安山成锅炉万博体育app平台\n\n黄金会员：",
null,
"经营模式：生产型\n\n主营：电锅炉,燃气锅炉,锅炉,燃煤锅炉,脱硫塔\n\n•",
null,
"乌鲁木齐防腐木木屋销售-哪儿有卖质量好的新疆防腐木木屋\n\n品牌:绿城伟业,,\n\n出厂地:三江侗族自治县(古宜镇)\n\n报价：面议\n\n新疆绿城伟业园林景观工程万博体育app平台\n\n黄金会员：",
null,
"经营模式：生产型\n\n主营：新疆防腐木,新疆防腐木凉亭,新疆防腐木木屋,新疆防腐木花架,新疆防腐木桌椅\n\n•",
null,
"新疆蒸发空调零售-阿克苏蒸发空调价格\n\n品牌:新特,,\n\n出厂地:三江侗族自治县(古宜镇)\n\n报价：面议\n\n新疆新特环境科技万博体育app平台\n\n黄金会员：",
null,
"经营模式：生产型\n\n主营：新疆蒸发冷却空调,新疆蒸发空调,新疆地源热榜,新疆空气源热榜,新疆干空气能蒸发冷...\n\n•",
null,
"昌吉立体户型模型|优良新疆户型模型建设计制作\n\n品牌:三维视觉,,\n\n出厂地:三江侗族自治县(古宜镇)\n\n报价：面议\n\n新疆三维视觉模型有限责任公司\n\n黄金会员：",
null,
"经营模式：生产型\n\n主营：沙盘,沙盘模型,地形沙盘,多媒体沙盘,沙盘模型制作\n\n•",
null,
"博尔塔拉防腐木栅栏-新疆价格合理的防腐木栅栏、围栏批销\n\n品牌:绿城伟业,,\n\n出厂地:三江侗族自治县(古宜镇)\n\n报价：面议\n\n新疆绿城伟业园林景观工程万博体育app平台\n\n黄金会员：",
null,
"经营模式：生产型\n\n主营：新疆防腐木,新疆防腐木凉亭,新疆防腐木木屋,新疆防腐木花架,新疆防腐木桌椅\n\n•",
null,
"伊犁一立方玻璃钢化粪池|新疆玻璃钢化粪池专业供应商\n\n品牌:碧润源,,\n\n出厂地:三江侗族自治县(古宜镇)\n\n报价：面议\n\n乌鲁木齐碧润源节能环保万博体育app平台\n\n黄金会员：",
null,
"经营模式：生产型\n\n主营：玻璃钢,玻璃钢电缆管,玻璃钢化粪池,玻璃钢储罐,玻璃钢管道\n\n•",
null,
"新疆彩钢房|新疆折叠房报价\n\n品牌:复临开泰,,\n\n出厂地:三江侗族自治县(古宜镇)\n\n报价：面议\n\n新疆复临开泰集成房屋万博体育app平台\n\n黄金会员：",
null,
"经营模式：生产型\n\n主营：新疆集成房屋租赁,新疆集成房屋出售,新疆折叠房租赁,新疆集成房屋哪家好,新疆集成...\n\n•",
null,
"质量好的新疆防腐木凉亭销售-新疆防腐木凉亭代理商\n\n品牌:绿城伟业,,\n\n出厂地:三江侗族自治县(古宜镇)\n\n报价：面议\n\n新疆绿城伟业园林景观工程万博体育app平台\n\n黄金会员：",
null,
"经营模式：生产型\n\n主营：新疆防腐木,新疆防腐木凉亭,新疆防腐木木屋,新疆防腐木花架,新疆防腐木桌椅\n\n•",
null,
"新疆硅岩板生产厂家-吐鲁番硅岩板材料-吐鲁番硅岩板哪家好\n\n品牌:浩宏建伟,,\n\n出厂地:三江侗族自治县(古宜镇)\n\n报价：面议\n\n乌鲁木齐浩宏建伟保温材料万博体育app平台\n\n经营模式：生产型\n\n主营：新疆热固型改性聚苯板,新疆渗透板,新疆热固复合聚苯乙烯泡沫保温板,新疆硅岩板,新...\n\n•",
null,
"新疆聚异氰脉酸酯深冷管壳报价-哈密聚异氰脉酸酯PIR深冷管壳直销\n\n品牌:铭泰斯特,,\n\n出厂地:三江侗族自治县(古宜镇)\n\n报价：面议\n\n新疆铭泰斯特保温材料制造万博体育app平台\n\n黄金会员：",
null,
"经营模式：生产型\n\n主营：新疆聚氨酯瓦壳,新疆聚氨酯管壳,新疆聚氨酯弧形板,新疆聚氨酯板,新疆聚氨酯保温管\n\n• 没有找到合适的新疆维吾尔自治区供应商？您可以发布采购信息\n\n没有找到满足要求的新疆维吾尔自治区供应商？您可以搜索 批发 公司\n\n### 最新入驻厂家\n\n相关产品:\n乌鲁木齐燃气锅炉 乌鲁木齐防腐木木屋销售 新疆蒸发空调零售 昌吉立体户型模型 博尔塔拉防腐木栅栏 伊犁一立方玻璃钢化粪池 新疆彩钢房 新疆防腐木凉亭代理商 新疆硅岩板生产厂家 新疆聚异氰脉酸酯深冷管壳报价"
] |
[
null,
"http://image-ali.bianjiyi.com/1/2020/0609/12/15916762232679.jpg",
null,
"http://www.booksir.cn/Public/Images/ForeApps/grade2.png",
null,
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null,
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null,
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"http://image-ali.bianjiyi.com/1/2019/0328/16/15537618399722.jpg",
null,
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null,
"http://image-ali.bianjiyi.com/1/2017/1011/15/59ddceb8c90fe.jpg",
null,
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null,
"http://www.booksir.cn/Public/Images/ForeApps/grade2.png",
null,
"http://image-ali.bianjiyi.com/1/2017/0926/16/59ca0b2309e40.jpg",
null,
"http://www.booksir.cn/Public/Images/ForeApps/grade2.png",
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|
https://jp.mathworks.com/matlabcentral/answers/500218-chirp-as-test-gradient-for-mri-gradient-system-characterization
|
[
"MATLAB Answers\n\n# Chirp as test gradient for MRI gradient system characterization\n\n2 ビュー (過去 30 日間)\npreethi chandrasekarn 2020 年 1 月 14 日\n\nHi ! i wanted to generate gradient pulse for MRI gradient system characterization using chirp,For a chirp function linearly sweeping the frequency range f1 to f2 over a duration T\nf0=100hz\nf1=10khz\nTime duration=80ms\nThe maximum gradient amplitude is between 20 and 31 mT/m\nthe instantaneous frequency f is f (t) = f1 + (f2 − f1)t/T\nThe chirp gradient waveform Gc with amplitude A is\nGc(t) = A sin(2π[f1t + (f2 − f1)t^2/2T])\nThe slew rate s(t) = dGc/dt = 2πAf (t) cos(2π[f1t + (f2 − f1)t^2/2T])\nhas an envelope se(t) = 2πAf (t)\nThe slew-rate-limited chirp gradient waveform,\nGsrlc, for a maximum slew rate, smax, is then calculated as Gsrlc(t) = min{smax/se(t), 1}Gc(t)\nPlease guide me how to proceed with the code.\nThank you for your valuable time and guidance.Any answers would be great to discuss.\n\nサインインしてコメントする。\n\n### 回答 (1 件)\n\nKaashyap Pappu 2020 年 1 月 22 日\nA similar question has been addressed here. Modifying the ‘f1’ and ‘f2’ values, and changing the sample rate to accommodate the different frequencies to adhere to Nyquist theorem will probably be a solution.\nHope this helps!\n\nサインインしてコメントする。\n\n### Community Treasure Hunt\n\nFind the treasures in MATLAB Central and discover how the community can help you!\n\nStart Hunting!"
] |
[
null
] |
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https://www.4open-sciences.org/articles/fopen/full_html/2020/01/fopen200019/fopen200019.html
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"Open Access\n Issue 4open Volume 3, 2020 10 8 Mathematics - Applied Mathematics https://doi.org/10.1051/fopen/2020008 28 August 2020",
null,
"This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.\n\n## Introduction\n\nOne of the most sought-after directions of applied mathematics is mathematical modeling. To understand various complex, mostly nonlinear processes taking place in nature, in sky bodies, in the social sphere, their mathematical modeling is necessary. For more or less real description of processes it is necessary to take into account their describing basic characteristics and correctly set the corresponding mathematical problems. Mathematical modeling of physical processes involves the model adequacy, which is validated by Newton’s non-relative five laws of classical mechanics: mass conservation law; law of conservation of impulse; the law of conservation the momentum of impulse; the first law of thermodynamics, i.e. energy conservation law; the second law of thermodynamics, i.e. entropy conservation law .\n\nCreation of mathematical models is more original in social sphere, because, they are more difficult to substantiate. We created a new direction of mathematical modeling, i.e. “Mathematical Modeling of Information Warfare” . In these models two antagonistic sides waging with each other information warfare and also the third peacekeeping side trying to extinguish information warfare reconsidered. Conditions on model parameters at which the third side will be able to force the conflicted sides to completion of information warfare are found.\n\nWe also offered mathematical models of forecasting the results of political elections in case of two or three parties. Also models in case of change of selective subjects before the next elections have been considered .\n\nWe proposed to create new nonlinear mathematical models of economic cooperation between two politically (not military opposition) mutually warring sides (two countries or a country and its legal region) which consider economic or other type of cooperation between different parts of population aimed to the peaceful resolution of conflicts .\n\nTaking into consideration the important tendencies in the world, it is important to study demographic and assimilation of social processes through mathematical modeling .\n\nIn we considered a new nonlinear continuous mathematical model of linguistic globalization. Two categories of the world’s population are considered: a category that hinders and a category leading to the dominant position of the English language. With a positive demographic factor of the population, which prevents globalization or a negative demographic factor of the population contributing to globalization, it is shown that the dynamic systems describing these processes allow the existence of two topologically not equivalent phase portraits (a stable node, a limit cycle). It is known that, in the world, a social process of assimilation of languages is hidden. This process, as a rule, considers expansion of an area of the dominating languages (state languages of economically powerful states) at the expenses of less widespread languages (state languages economically of rather weak states). According to this point of view, today, for less widespread languages (including classic languages) the conditions under which there will be no disappearance of the major languages are important, i.e. there will be no full assimilation of people talking in these languages.\n\nIn a preceding article a new nonlinear mathematical model of process of three level assimilation which is described by four-dimensional dynamic systems has been studied. In case of constancy of coefficients, special points of the dynamic system have been found. The conditions on constant coefficients for which it is possible to find special points with all four coordinates non-negative have been determined. Introducing some dependence among coefficients of the system, two first integrals have been derived, and the four-dimensional system has been reduced to a two-dimensional one. The sign-variable divergence theorem of a two-dimensional vector field in some one-coherent area of the first quadrant of the phase plane has been proved. According to Bendixon’s criterion it was shown that it is possible to have a closed integral curve completely lying in this area.\n\n## General mathematical model of two-level assimilation\n\n### System of the equations\n\nConsider the social process of two-level assimilation, when in one large region the population speaking the most common language assimilates both the population speaking the second fairly common language and the population speaking the third less common language (small range of language distribution). In turn, the population speaking the second language, which is quite common, assimilates the population speaking the less common third language. Thus, the population speaking the third less common language is in a situation of bilateral assimilation.\n\nWe assume that the process of assimilation develops due to numerous direct or remote (electronic communication) mutual meetings between representatives of the population, who consider one of these three languages to be their native language.\n\nThe social process of two-level assimilation, which takes into account the quadratic terms of self-limiting population growth, is described by the following nonlinear dynamic system",
null,
"(1)\n\nwith initial conditions:",
null,
"(2)",
null,
"where\n\n• [0, T] – the period of consideration of this model (for various cases, the period can reach several decades),\n\n• u(t) – at a given time t the number of people living in the same region (possibly the continent) who consider their native language in this region to be the most common language (dominant language),\n\n• v(t) – at a given time t the number of people living in the same region who consider their native language to be another common language in this region,\n\n• w(t) – at a given time t the number of people living in a small part (in a small area) of the same region who only in this part of the region consider the common language to be their native language,\n\n• β 1(t), β 3(t) – assimilation rates of the population speaking the second sufficiently spoken language by the population speaking the most spoken language,\n\n• β 2(t), β 5(t) – assimilation rates of the population speaking a third less spoken language by the population speaking the most spoken language,\n\n• β 4(t), β 6(t) – assimilation rates of a population speaking a third less spoken language by a population speaking a second sufficiently spoken language,\n\n• α 1(t), α 2(t), α 3(t) – natural change rates of populations speaking the first, second and third languages respectively (variable demographic factors),\n\n• δ 1(t), δ 2(t), δ 3(t) – self-limiting factors of population growth speaking the first, second and third languages respectively.\n\nScenario of development of two-level assimilation process is given in Figure 1.",
null,
"Figure 1 Development of two-level assimilation process.\n\nIt is natural to assume that assimilation coefficients and growth self-constraints are positive continuous functions at the time of model consideration:",
null,
"(3)",
null,
"The non-triviality of the two-level assimilation process (when the assimilation result is not initially predicted) leads to inequality:",
null,
"(4)\n\n## The first integral of a nonlinear system of differential equations\n\n### Second order surfaces in phase space\n\nConsider the particular case where all model coefficients are",
null,
"(5)\n\nTaking into account (5) the system of equations (1) becomes",
null,
"(6)\n\nFind the first integral of the system of nonlinear differential equations (6) to lower the order of the system, i.e. from a three-dimensional system go to a two-dimensional one.\n\nThe dynamic system (6) shall be written in the following form",
null,
"(7)\n\nThe second equation of the system (7) is multiplied by (−2) and we add all three equations (the first and third equations are unchanged)",
null,
"(8)\n\nWe will require the following conditions (four conditions system) are satisfied on the model factors",
null,
"(9)\n\nor",
null,
"(10)\n\nNote that the system (10) is consistent and must meet the inequalities (3), (4).\n\nTaking into account the imposed conditions (9) on the coefficients of the model (8), (5) we get the first integral of the dynamic system",
null,
"(11)\n\nThe first integral (11) in the phase space of solutions (O, u, v, w) represents a cone.\n\nTaking into account (11), three-dimensional dynamic system (6) can be reduced to the following two-dimensional nonlinear dynamic system",
null,
"(12)",
null,
"We will find non-zero (non-trivial) special points of the system (12)",
null,
"(13)\n\nOr",
null,
"(14)\n\nLet’s consider a special case",
null,
"(15)\n\nThen from (10), (15) we get",
null,
"(16)\n\nTaking into account (15), the solution of the system of nonlinear algebraic equations (14) will take the following form",
null,
"(17)\n\nwhere",
null,
"",
null,
"(18)\n\nThus, in the first quarter of the phase plane (O, u(t), v(t)), the special point M(u *, v *) with non-zero coordinates will take the form",
null,
"(19)\n\nWe put, by definition:",
null,
"(20)\n\nThen the system of equations (12) will be written in vector form",
null,
"(21)",
null,
"Theorem 1. The task (21) in some one-coherent area D ⊂ (O, u(t), v(t)) the first quarter of the phase plane (O, u(t), v(t)) has the decision in the form of the closed trajectory which completely lies in this area.\n\nProof. From (20), taking into account (21), you can get",
null,
"(22)",
null,
"Taking into account (22) divergence of vector field",
null,
"will register in the following look",
null,
"(23)\n\nTaking into account (15), equation (23) takes the form",
null,
"(24)\n\nIn the phase plane (O, u(t), v(t)), consider a curve where the divergence of the vector field is zero.\n\nAccording to (24), this curve verifies the equation",
null,
"(25)\n\nor",
null,
"whose solutions have two determinations, which identify two straight lines",
null,
"(26)",
null,
"Note here that second straight line does not satisfy model condition, i.e. physical meaning of u(t), v(t) functions.\n\nAccordingly, by (26), there is only one semi-straight plane in the first quarter of the phase plane (O, u(t), v(t))",
null,
"(27)\n\nwhere the divergence of the vector field is zero.\n\nAt that, if equality to model parameters is performed",
null,
"(28)\n\nthen the special point M(u *, v *) (19) lies on the semi-straight (27).\n\nIt is clear, that G(u,v), divergence (24) of the vector field",
null,
", in some one-coherent area making physical sense to the first quarter of the phase plane (O, u(t), v(t)) changes its sign (Fig. 2). Note here that single-link area comprises semi-straight section with zero divergence of vector field and, according to Bendixon criterion, there exists closed system trajectory in said area (21) [35, 36]. The theorem is proved.",
null,
"Figure 2 Qualitative picture of the behavior of divergence of the vector field in the first quarter of the phase plane of solutions.\n\n## Conclusion\n\nThus according to (24), (27) in the phase plane (O, u, v) there exists a one-coherent area where the divergence G(u; v) of the vector field",
null,
"changes its sign and, according to the Bendixon criterion, in this area there exists a closed integral curve, where (u(t) ≠ 0, v(t) ≠ 0).\n\nIn this case, according to (11), the values of the function w(t) do not vanish anywhere, which indicates that under these conditions there is no complete assimilation of the third side.\n\n## References\n\n1. Golubiatnikov A, Chilachava T (1983), Central explosion of a rotating gravitating body. Rep Acad Sci USSR 273, 825–829. [Google Scholar]\n2. Golubyatnikov A, Chilachava T (1984), Estimates of the motion of detonation waves in a gravitating gas. Fluid Dyn 19, 2, 292–296. [CrossRef] [Google Scholar]\n3. Chilachava T (1985), Problem of a strong detonation in a uniformly compressing gravitating gas. Moscow State University, Bull Ser Math Mech 1, 78–83. [Google Scholar]\n4. Golubyatnikov A, Chilachava T (1986), Propagation of a detonation wave in a gravitating sphere with subsequent dispersion into a vacuum. Fluid Dyn 21, 4, 673–677. 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null,
"Figure 1 Development of two-level assimilation process. In the text",
null,
"Figure 2 Qualitative picture of the behavior of divergence of the vector field in the first quarter of the phase plane of solutions. In the text\n\nCurrent usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.\n\nData correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days."
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https://www.tutorialandexample.com/crc-program-in-java
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[
"# CRC Program in Java\n\nThe acronym CRC stands for Cyclic Redundancy Check. It is invented by W. Wesley Peterson in 1961. It is an error detection technique that detects errors in digital networks (also known as communication channels or digital data) and storage devices. It is used to track down unintentional modifications in digital data. This section will look at how to use a Java program to calculate and perform CRC. Let's take a closer look at CRC.\n\nAn error detection system assigns a unique number to each data block. The number is included primarily to identify changes that occur during transmission or storage. During data transmission, it is computed twice, once at the transmitter and again at the receiver. It compares data bit by bit with the original values delivered. If a data transmission error (corrupted bit) occurs, the CRC value does not match the value that was originally transferred. The graphic below helps us understand the CRC mechanism.\n\nFig:\n\nA single corrupted bit in the data, for example, causes a one-bit change in the calculated CRC, but many corrupted bits may cancel each other out. A burst error happens when a large number of bits are corrupted or changed at the same time.\n\nOther error detection algorithms exist, such as Vertical Redundancy Check (VRC) and Longitudinal Redundancy Check (LRC). However, CRC is more powerful. Because CRC is calculated using binary division, the algorithm is more difficult. The divisor is calculated using polynomials. CRC is hence also known as a polynomial code checksum.\n\n### Steps for error detection in CRC:\n\n• In the first phase, N 0's are appended to the data unit. N is always less than the number of bits in the data unit (also known as division, which equals N+1).\n• The newly extended data is then split by a divisor using binary division, and the resultant reminder is known as the CRC remainder.\n• The remaining bits are then utilised to replace all of the 0's that had previously been added to the data unit. Following that, we handed the freshly created data unit to the receiver.\n• The receiver receives the data unit together with the CRC residual. The receiver then divides the data unit by the divisor.\n• If the leftover after dividing the data unit by the divisor is zero, the unit data is not corrupted and may be accepted.\n• If the leftover after dividing the data unit by the divisor is more than zero, the unit data is damaged and will be deleted.\n\nConsider the following example with original data 11100 and divisor 1001.\n\n• First, we'll add the zeros in the data unit section. The length of the divisor is four, and we know that the length of the string 0s is always one less than the length of its divisor.\n• Therefore, we increase the data unit by three zeros, yielding 11100. The result of adding the zeros is 11100000, which we divide by the divisor, which is 1001. We use the binary division approach to divide data by divisor.\n• The CRC remnant is the amount left behind after splitting the data unit by the divisor.\n• The CRC remainder substitutes the extra string of 0s at the end of the data unit, and the final string sent across the network is 11100111.\n\nCrcDemo.java:\n\n``````// import required classes and packages\nimport java.io.*;\nimport java.util.*;\n// creating CrcDemo class to illustrate the working of CRC (*Cyclic Redundancy Check)\nclass CrcDemo {\n\npublic static void main (String args []) {\n// using Scanner Class object (* scan) to take user input\nScanner sc = new Scanner (System.in);\n// declaring a size variable for data size.\nint size;\n// user input for data size.\nSystem.out.println (\"please enter a number for the size of the array: \");\nsize = sc.nextInt ();\n// declaring the required data array\nint dat [] = new int [size];\n// take user input for data bits.\nSystem.out.println (\"please enter bits into the array : \");\nfor(int i = 0 ; i < size ; i++) {\nSystem.out.println (\"please enter bit \" + (size-i) + \":\");\ndat [i] = sc.nextInt ();\n}\n// input the divisor size from the user\nSystem.out.println (\"please enter the divisor array size :\");\nsize = sc.nextInt ();\n// declaring divisor array\nint divArray [] = new int [size];\nSystem.out.println (\"please enter div bits into the array : \");\nfor(int i = 0 ; i < size ; i++) {\nSystem.out.println (\"please enter bit \" + (size-i) + \":\");\ndivArray [i] = sc.nextInt ();\n}\n// Dividing input data with input divisor, store them in remdr array\nint remdr [] = DataAndDivisorDivsion (dat, divArray);\n\nfor (int i = 0; i < remdr.length-1; i++) {\nSystem.out.print (remdr [i]);\n}\nSystem.out.println (\"\\n CRC code that generated is: \");\n\nfor (int j = 0; j < dat.length; j++) {\nSystem.out.print (dat [j]);\n}\nfor (int k = 0; k < remdr.length-1; k++) {\nSystem.out.print (remdr [k]);\n}\nSystem.out.println ();\n\n// the size of the dataSent array with being equal to the sum of the data and the rem arrays length\nint dataSent [] = new int[dat.length + remdr.length - 1];\nSystem.out.println (\"please enter bits needed to send into the array: \");\nfor (int i = 0; i < dataSent.length; i++) {\nSystem.out.println (\"please enter bit \" + (dataSent.length - 1)+ \":\");\ndataSent [i] = sc.nextInt ();\n}\nrecData (dataSent, divArray);\n}\n// creating DataAndDivisorDivsion () method to get Cyclic Redundancy Check\nstatic int [] DataAndDivisorDivsion (int preDat [], int divArray []) {\n// declaring remdr [] array\nint remdr [] = new int [divArray.length];\nint l;\nint dat [] = new int [preDat.length + divArray.length];\n// use the system's arraycopy () method for copying data into rem and data arrays\nSystem.arraycopy (preDat, 0, dat, 0, preDat.length);\nSystem.arraycopy (dat, 0, remdr, 0, divArray.length);\n// iterate the preDat and exor the bits of the remainder and the divisor\nfor (l = 0; l < preDat.length; l++) {\nSystem.out.println ((l+1) + \".) First data bit is : \"+ remdr );\nSystem.out.print (\"Remainder : \");\nif (remdr == 1) {\n// exor the remainder bits with divisor bits\nfor (int j = 1; j < divArray.length; j++) {\nremdr [j-1] = exOrOpertn (remdr [j], divArray [j]);\nSystem.out.print (remdr [j-1]);\n}\n}\nelse {\n// exor the remainder bits with 0\nfor (int j = 1; j < divArray.length; j++) {\nremdr [j-1] = exOrOpertn (remdr [j], 0);\nSystem.out.print (remdr [j-1]);\n}\n}\n// The last bit of the remainder will be taken from the data\n// This is the 'carry' taken from the dividend after every step\n// of division\nremdr [divArray.length-1] = dat [l + divArray.length];\nSystem.out.println (remdr [divArray.length-1]);\n}\nreturn remdr;\n}\n// create exOrOpertn () method for exor operation on data\nstatic int exOrOpertn (int x, int y) {\n// returns exor of two bits\nif (x == y) {\nreturn 0;\n}\nreturn 1;\n}\nstatic void recData (int dat [], int divArray []) {\n\nint remdr [] = DataAndDivisorDivsion (dat, divArray);\n// Division complete\nfor (int j = 0; j < remdr.length; j++) {\nif (remdr [j] != 0) {\n// if the remainder is not zero, then the data is corrupted",
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"https://static.tutorialandexample.com/java/crc-program-in-java2.png",
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"https://static.tutorialandexample.com/java/crc-program-in-java3.png",
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https://gmatclub.com/forum/in-the-xy-plane-a-line-has-slope-3-and-x-intercept-3-what-is-the-y-i-220285.html
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[
"GMAT Question of the Day - Daily to your Mailbox; hard ones only\n\n It is currently 07 Dec 2019, 09:07",
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"### 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.",
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"# In the xy-plane, a line has slope 3 and x-intercept 3. What is the y-i\n\nAuthor Message\nTAGS:\n\n### Hide Tags\n\nManager",
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"",
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"Joined: 03 Jan 2015\nPosts: 72\nIn the xy-plane, a line has slope 3 and x-intercept 3. What is the y-i [#permalink]\n\n### Show Tags\n\n17",
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"00:00\n\nDifficulty:",
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"",
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"",
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"25% (medium)\n\nQuestion Stats:",
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"69% (01:01) correct",
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"31% (00:56) wrong",
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"based on 330 sessions\n\n### HideShow timer Statistics\n\nIn the xy-plane, a line has slope 3 and x-intercept 3. What is the y-intercept of the line?\n\nA. -9\nB. -3\nC. 0\nD. 3\nE. 9\nVeritas Prep GMAT Instructor",
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"V\nJoined: 16 Oct 2010\nPosts: 9850\nLocation: Pune, India\nRe: In the xy-plane, a line has slope 3 and x-intercept 3. What is the y-i [#permalink]\n\n### Show Tags\n\n10\n6\nsaiesta wrote:\nIn the xy-plane, a line has slope 3 and x-intercept 3. What is the y-intercept of the line?\n\nA. -9\nB. -3\nC. 0\nD. 3\nE. 9\n\nx intercept 3 means the line passes through (3, 0). Slope of 3 means y co-ordinate increases by 3 units for every 1 unit rise in x co-ordinate or y co-ordinate decrease by 3 units for every 1 unit decrease in x co-ordinate.\nAt y intercept, x co-ordinate is 0. So if we reduce x by 3 (to go from 3 to 0), y will reduce by 9.\n\nSo, the line will pass through (0, -9)\n\n_________________\nKarishma\nVeritas Prep GMAT Instructor\n\nManager",
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"",
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"Joined: 19 Dec 2015\nPosts: 108\nLocation: United States\nGMAT 1: 720 Q50 V38",
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"GPA: 3.8\nWE: Information Technology (Computer Software)\nRe: In the xy-plane, a line has slope 3 and x-intercept 3. What is the y-i [#permalink]\n\n### Show Tags\n\n8\n3\nLet the line be represented by a general equation y=mx+b, where m = slope (3) and b=y intercept. We are also given the value of x-intercept 3.\nTheory : y intercept represents the point on the line where the x=0, and x intercept represents the point on the line where the y=0.\nPutting these values in the equation : 0 = 3*3 + b => b = -9. Hence A.\n##### General Discussion\nManager",
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"",
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"Joined: 29 Nov 2011\nPosts: 90\nRe: In the xy-plane, a line has slope 3 and x-intercept 3. What is the y-i [#permalink]\n\n### Show Tags\n\n2\ny = mx+c\nm=3 then y= 3x+c\nput y=0 and x=3 get C as -9\nthis is the y intercept.\nManager",
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"",
null,
"B\nJoined: 14 Jun 2016\nPosts: 70\nLocation: India\nGMAT 1: 610 Q49 V21",
null,
"WE: Engineering (Manufacturing)\nRe: In the xy-plane, a line has slope 3 and x-intercept 3. What is the y-i [#permalink]\n\n### Show Tags\n\n2\nx intercept 3 means the line passes through (3, 0).\nLet the y be y and hence the line passes through (0, y).\nNow slope=m=(y2-y1)/(x2-x1)\nHence 3=y/-3\nSo y=-9\nManager",
null,
"",
null,
"G\nJoined: 27 Jan 2016\nPosts: 123\nSchools: ISB '18\nGMAT 1: 700 Q50 V34",
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"Re: In the xy-plane, a line has slope 3 and x-intercept 3. What is the y-i [#permalink]\n\n### Show Tags\n\n1\ny=mx+c\n\nsubstitute m=3 and point (3,0) in the above equation\n\n0=3(3)+c\n\nc=-9\nIntern",
null,
"",
null,
"Joined: 25 Jul 2017\nPosts: 7\nRe: In the xy-plane, a line has slope 3 and x-intercept 3. What is the y-i [#permalink]\n\n### Show Tags\n\nMathematical approach-\nwe know equation of a line is\ny = mx + C\nNow, as given at X =3, y = 0, we can get below,\n0 = 3*3 + C => C = -9\nNow, the question has asked for y intercept which is nothing but C in the equation y = mx+C.\nwe can even cross verify by substituting again -\ny = 3*0 = (-9) = -9 Hence, A\n\nOne, intuitive approach is that- it is given slope =3\n=> y/x = +3 meaning for every 1 unit change in X, there is a 3 unit change in y (in the same direction due to +ve sign)\nTherefore, from x =3 (given intercept) to x =0 (when there is y intercept only), there is a decrease of 3 units of x. Hence y will decrease 3 time of it => 3 *3 = 9. Since the value is decreasing, y (will be negative) = -9.\nEMPOWERgmat Instructor",
null,
"V\nStatus: GMAT Assassin/Co-Founder\nAffiliations: EMPOWERgmat\nJoined: 19 Dec 2014\nPosts: 15661\nLocation: United States (CA)\nGMAT 1: 800 Q51 V49",
null,
"GRE 1: Q170 V170",
null,
"Re: In the xy-plane, a line has slope 3 and x-intercept 3. What is the y-i [#permalink]\n\n### Show Tags\n\nHi All,\n\nYou can deal with this prompt by either drawing a picture or by just creating an equation based on the facts that you know.\n\nThe slope-intercept formula for a line is Y = MX + B\n\nFrom the prompt, we know that the slope is 3 and the X-INTERCEPT is 3 (meaning that X=3 when Y=0)\n\nY = MX + B\nY = 3X + B\n\n0 = 3(3) + B\n0 = 9 + B\n-9 = B\n\nSo the Y-intercept is -9\n\nGMAT assassins aren't born, they're made,\nRich\n_________________\nManager",
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"",
null,
"B\nJoined: 17 Jul 2016\nPosts: 54\nRe: In the xy-plane, a line has slope 3 and x-intercept 3. What is the y-i [#permalink]\n\n### Show Tags\n\nusing point slope form....\n\ny-y1=m(x-x1)\n\nx intercept is (3,0)\n\ny-0=3(x-3)\n\ny=3x-9\n\n-9 is the y intercept\nVP",
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"",
null,
"D\nJoined: 14 Feb 2017\nPosts: 1314\nLocation: Australia\nConcentration: Technology, Strategy\nGMAT 1: 560 Q41 V26",
null,
"GMAT 2: 550 Q43 V23",
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"GMAT 3: 650 Q47 V33",
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"GMAT 4: 650 Q44 V36",
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"GMAT 5: 650 Q48 V31",
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"GMAT 6: 600 Q38 V35",
null,
"GPA: 3\nWE: Management Consulting (Consulting)\nRe: In the xy-plane, a line has slope 3 and x-intercept 3. What is the y-i [#permalink]\n\n### Show Tags\n\nWe can apply some logic before touching formulas or anything.\n\nSee my attachment.\n\nWe are told that the slope is positive. We are also told that the x-intercept (where y=0) of the line is 3 i.e. x=3, y=0 (3,0). Therefore we can broadly plot this line enough to rule out a few answers:\n\nWe are asked for the y-intercept i.e. where x=0, what is the y-coordinate the line intercepts?\n\nWe can rule out (C), (D) and (E) based on our diagram.\n\nNow solve for the y-intercept knowing it needs to be a negative value by plotting in the information we have\nGeneral form of line: y=mx +b\ny=3x +b\n\nwe can determine b from the x-intercept\nwhen x=3, y=0\n0=3(3)+B\nb=-9\n\ny=3x -9\nY-intercept is where x=0\nY= 3(0) -9\nY= -9\nAttachments",
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"Capture.JPG [ 122.9 KiB | Viewed 2090 times ]",
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"Re: In the xy-plane, a line has slope 3 and x-intercept 3. What is the y-i [#permalink] 10 Jul 2019, 17:51\nDisplay posts from previous: Sort by\n\n# In the xy-plane, a line has slope 3 and x-intercept 3. What is the y-i",
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|
https://jp.mathworks.com/help/simscape/ref/pm_addunit.html
|
[
"Add new unit to unit registry\n\n## Syntax\n\n```pm_addunit(unitname, conversion, unitexpression) ```\n\n## Description\n\n`pm_addunit(unitname, conversion, unitexpression)` introduces a new unit, `unitname`, defined as ```conversion * unitexpression```.\n\nThe first argument, `unitname`, must be a valid unit name, that is, it must begin with a letter and contain only letters and numbers.\n\nThe second argument, `conversion`, may be either a positive real scalar or a 1x2 array. If this argument has two elements, then it is specifying an affine conversion, with the first element (a positive real number) being the linear conversion coefficient, and the second being the offset. For more information, see Thermal Unit Conversions.\n\nThe third argument, `unitexpression`, must be a valid unit expression in terms of units already defined in the unit registry.\n\nThe following operators are supported in the unit mathematical expressions:\n\n `*` Multiplication `/` Division `^` Power `+`, `-` Plus, minus — for exponents only `()` Brackets to specify evaluation order\n\n## Examples\n\nAdd a new unit centimeter, `cm`, in terms of meter, `m`:\n\n```pm_addunit('cm', 0.01, 'm'); ```\n\nAdd a new unit newton, `N`, in terms of kilograms, meters, and seconds:\n\n```pm_addunit('N', 1, 'kg*m/s^2'); ```\n\nAdd a new unit Fahrenheit, `degF`, in terms of Celsius:\n\n```pm_addunit('degF', [5/9 -32*5/9], 'degC'); ```\n\n## Version History\n\nIntroduced in R2007a"
] |
[
null
] |
{"ft_lang_label":"__label__en","ft_lang_prob":0.6000873,"math_prob":0.7046995,"size":1347,"snap":"2022-40-2023-06","text_gpt3_token_len":344,"char_repetition_ratio":0.13998511,"word_repetition_ratio":0.010050251,"special_character_ratio":0.23830736,"punctuation_ratio":0.19140625,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9839493,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-01-28T08:05:54Z\",\"WARC-Record-ID\":\"<urn:uuid:01ed67b6-71e8-4cfb-bfa9-1175cbd4d577>\",\"Content-Length\":\"76524\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:462e9e6b-bbf5-4833-91f1-9050f2861019>\",\"WARC-Concurrent-To\":\"<urn:uuid:46b0ef96-9c28-4d4e-803d-e1b04e585928>\",\"WARC-IP-Address\":\"104.68.243.15\",\"WARC-Target-URI\":\"https://jp.mathworks.com/help/simscape/ref/pm_addunit.html\",\"WARC-Payload-Digest\":\"sha1:2JHQXEGHWHUUR4VHNXHYMED2TBJQT2QY\",\"WARC-Block-Digest\":\"sha1:HGDB42ARG3N2YEH3JATR5WZEHXPXYKN6\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-06/CC-MAIN-2023-06_segments_1674764499524.28_warc_CC-MAIN-20230128054815-20230128084815-00878.warc.gz\"}"}
|
https://www.learnrealeng.com/2019/11/learn-english-and-statistic-2-what-is.html
|
[
"Home » » Learn English and Statistic 2 - What is a statistical distribution?\n\n# Learn English and Statistic 2 - What is a statistical distribution?",
null,
"Learn English and Statistic 2 - What is a statistical distribution?\nExpose yourself to Statistic world with real and simple English with StatQuest.\n\nHere StarQuest demystify what a statistical distribution is. It's very complicated\n\nLearn English and Statistic 2 - What is a statistical distribution?\n\nDownload - Learn English and Statistic 2 - What is a statistical distribution?\n\nLearn English and Statistic 2\n\nWhat is a statistical distribution\n\nA probability distribution is a mathematical function that provides the probabilities of the occurrence of various possible outcomes in an experiment. Probability distributions are used to define different types of random variables in order to make decisions based on these models. There are two types of random variables: discrete and continuous. Depending on what category the random variable fits into, a statistician may decide to calculate the mean, median, variance, probability, or other statistical calculations using a different equation associated with that type of random variable. This is important because, as experiments may become more complicated, the standard formulas that are used to calculate these parameters (like the mean) will no longer produce accurate results.\n\nStatQuest\n\nStatistical Distributions\n\n### Written by : Learn from real English Team\n\nWe always believe that the best way to learn English is learning English from real English the way the native speaker use. This is the natural way of learning English. You will be surprised how much your English has improved when you expose yourself to real English environment.\n\nJoin Me On: Facebook | Twitter :: Thank you for visiting ! ::"
] |
[
null,
"https://img.youtube.com/vi/qBigTkBLU6g/default.jpg",
null
] |
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|
https://www.bigdev.de/2013/04/oo-tutorial-2-dimensional-vector-as_4.html
|
[
"## Apr 4, 2013\n\n### OOP Tutorial: 2-dimensional Vector as Scala Object\n\nAs before in Java: When starting to learn object orientated programming, a good first example is the following: Modelling a 2-dimensional vector over the real numbers could result in this (pay attention to the overloading of * and +. Compare to the verbose Java variant):\n\n```package de.bigdev\n\nclass Vector(val x: Double, val y: Double) {\n\n/**\n*\n* @param v\n* @return the sum with v\n*/\ndef +(v: Vector) = new Vector(x + v.x, y + v.y)\n\n/**\n* Scalar Mulitplication with a constant\n*\n* @param c\n* @return the scalar multiplication with c\n*/\ndef *(c: Double) = new Vector(c * x, c * y)\n\n/**\n* Length of two vectors\n*\n* @return length\n*/\ndef length = math.sqrt(x * x + y * y)\n\n/**\n* Scalar Product of two vectors\n*\n* @param v\n* @return the scalar product with v\n*/\ndef *(v: Vector) = x * v.x + y * v.y\n\n/**\n* Angle between two vectors\n*\n* @param v\n* @return the angle with v in degrees\n*/\ndef angle(v: Vector) = math.acos(this * v / (this.length * v.length)) *\n180.0 / Math.PI\n\noverride def toString = \"[\" + x + \",\" + y + \"]\"\n}```\n\nFor execution we need a main method (pay attention to the usage of *, + and angle):\n\n```package de.bigdev\n\nobject VectorTest {\n\n/**\n* for testing only... or use ScalaTest!\n*/\ndef main(args: Array[String]) {\n\nval v = new Vector(1, 1)\nval w = new Vector(-1, -1)\n\nprintln(\"Vector algebra\")\nprintln(\"==============\")\nprintln(\"addition : \" + v + \" + \" + w + \" = \" + (v + w))\nprintln(\"scalar multip.: 2 * \" + v + \" = \" + v * 2)\nprintln(\"length : ||\" +v + \"|| = \" + v.length)\nprintln(\"scalar product: \" + v + \" * \" + w + \" = \" + v * w)\nprintln(\"angle : angle(\" + v + \", \" + w\n+ \") = \" + math.round(v angle w) + \"°\")\n}\n}```\n\nThe output on the console is:\n\nVector algebra\n==============\naddition : [1.0,1.0] + [-1.0,-1.0] = [0.0,0.0]\nscalar multip.: 2 * [1.0,1.0] = [2.0,2.0]\nlength : ||[1.0,1.0]|| = 1.4142135623730951\nscalar product: [1.0,1.0] * [-1.0,-1.0] = -2.0\nangle : angle([1.0,1.0], [-1.0,-1.0]) = 180°"
] |
[
null
] |
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|
https://math.stackexchange.com/questions/4112110/need-a-pure-geometric-solution-to-a-20-30-130-triangle-question
|
[
"# Need a pure geometric solution to a $20-30-130$ triangle question\n\nIn $$\\triangle{ABC}$$, $$\\angle{ABC}=20^{\\circ}$$, $$\\angle{ACB}=30^{\\circ}$$, $$D$$ is a point inside the triangle and $$\\angle{ABD}=10^{\\circ}$$, $$\\angle{ACD}=10^{\\circ}$$, find $$\\angle{CAD}$$.\n\nNote: I have seen some very similar question with beautiful solution in pure geometric format. I know how to solve this problem in trigonometric format. But I think this problem deserves a beautiful geometric approach as solution, and that's why I post it here.\n\nAs request, here is approach applying Ceva's theorem in trigonometric form,\n\n\\begin{align*} \\frac{\\sin130}{\\cos130+2\\cos10}&=\\tan(x)\\Longrightarrow \\frac{\\sin120\\cos10+\\cos120\\sin10}{\\cos120\\cos10-\\sin120\\sin10+2\\cos10}\\\\ &=\\frac{\\sqrt{3}\\cos10-\\sin10}{3\\cos10-\\sqrt{3}\\sin10}.\\\\ &=\\frac{1}{\\sqrt{3}}\\\\ &=\\tan30\\Longrightarrow x\\\\ &=\\boxed{30}\\\\ \\end{align*}",
null,
"• You should know that the community prefers/expects a question to include something of what the asker knows about the problem. (What have you tried? Where did you get stuck? etc) This helps answerers tailor their responses to best serve you, without wasting time (theirs or yours) explaining things you already understand or using techniques beyond your skill level. (It also helps convince people that you aren't simply trying to get them to do your homework for you. An isolated problem statement with no evidence of personal effort makes a poor impression, attracting down- and close-votes.)\n– Blue\nApr 22, 2021 at 9:16\n• I can help but before that, you need to show your effort. Did you at least attempt using Trigonometric form of Ceva's theorem or law of sines? Did you get success? If you tried a geometric solution, what construction did you do? Where did you get stuck? Apr 22, 2021 at 9:27\n• The point is not whether you can solve it. Did you solve it? If yes, then why not share all your work and turn this a good question as per site guidelines? Apr 22, 2021 at 9:48\n• @Blue Yeah I know what you mean now. You are right that people would not like a do-my-homework-for-me question. That's a good reminder question posting, truly. I have added a note to the question. Last time I posted a geometric problem and provided my algebraic solution and asked for pure geometric approach, the admin saw it and thought I was showing off, and closed my question. So it's hard to guess what people think and I am still learning on that part...\n– r ne\nApr 22, 2021 at 10:44\n• @rne ok based on your comment with trigonometric solution and additional context in the question, I am posting a geometric solution in sometime. I would still request if you could edit your post with trigonometric solution that you put in comments. Nobody (at least I) likes to spend time answering a question that would later get closed or deleted. Please see from answerer's point of view too. Apr 22, 2021 at 11:05",
null,
"Please extend line segment $$BA$$. We have $$\\angle CAE = 50^0$$. Draw $$\\angle ACE = 50^0$$. We have $$CE = AE$$.\n\nSo, $$\\angle BCE = \\angle BEC = 80^0$$. $$BM$$ is angle bisector of isosceles triangle $$\\triangle CBE$$ where $$BC=BE$$.\n\nTherefore $$CD = DE$$. As $$\\angle DCE = 60^0$$, $$\\triangle DCE$$ is equilateral triangle and $$DE = CE = AE$$. So $$\\triangle AED$$ is isosceles triangle with $$\\angle AED = 20^0$$.\n\nThat leads to $$\\angle DAE = 80^0 \\implies \\angle DAC = 30^0$$.\n\n• Cool. This is very like the PDF solution. I should have got this. Thank you!\n– r ne\nApr 22, 2021 at 11:37\n• you are welcome. Apr 22, 2021 at 11:38\n\nCOMMENT.-This could be another nice way to prove that $$x=30º$$.",
null,
"• Here you have A, E, G determined, so F is also determined, now you have to prove that $\\triangle{EFC}$ is isosceles. If this is proven, the rest is correct.\n– r ne\nApr 26, 2021 at 3:30"
] |
[
null,
"https://i.stack.imgur.com/Kttov.png",
null,
"https://i.stack.imgur.com/F7qJt.png",
null,
"https://i.stack.imgur.com/P01Ga.png",
null
] |
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|
https://www.colorhexa.com/322d09
|
[
"# #322d09 Color Information\n\nIn a RGB color space, hex #322d09 is composed of 19.6% red, 17.6% green and 3.5% blue. Whereas in a CMYK color space, it is composed of 0% cyan, 10% magenta, 82% yellow and 80.4% black. It has a hue angle of 52.7 degrees, a saturation of 69.5% and a lightness of 11.6%. #322d09 color hex could be obtained by blending #645a12 with #000000. Closest websafe color is: #333300.\n\n• R 20\n• G 18\n• B 4\nRGB color chart\n• C 0\n• M 10\n• Y 82\n• K 80\nCMYK color chart\n\n#322d09 color description : Very dark (mostly black) yellow [Olive tone].\n\n# #322d09 Color Conversion\n\nThe hexadecimal color #322d09 has RGB values of R:50, G:45, B:9 and CMYK values of C:0, M:0.1, Y:0.82, K:0.8. Its decimal value is 3288329.\n\nHex triplet RGB Decimal 322d09 `#322d09` 50, 45, 9 `rgb(50,45,9)` 19.6, 17.6, 3.5 `rgb(19.6%,17.6%,3.5%)` 0, 10, 82, 80 52.7°, 69.5, 11.6 `hsl(52.7,69.5%,11.6%)` 52.7°, 82, 19.6 333300 `#333300`\nCIE-LAB 18.253, -2.956, 22.401 2.303, 2.575, 0.634 0.418, 0.467, 2.575 18.253, 22.595, 97.516 18.253, 4.099, 17.262 16.046, -2.459, 8.889 00110010, 00101101, 00001001\n\n# Color Schemes with #322d09\n\n• #322d09\n``#322d09` `rgb(50,45,9)``\n• #090e32\n``#090e32` `rgb(9,14,50)``\nComplementary Color\n• #321909\n``#321909` `rgb(50,25,9)``\n• #322d09\n``#322d09` `rgb(50,45,9)``\n• #233209\n``#233209` `rgb(35,50,9)``\nAnalogous Color\n• #190932\n``#190932` `rgb(25,9,50)``\n• #322d09\n``#322d09` `rgb(50,45,9)``\n• #092332\n``#092332` `rgb(9,35,50)``\nSplit Complementary Color\n• #2d0932\n``#2d0932` `rgb(45,9,50)``\n• #322d09\n``#322d09` `rgb(50,45,9)``\n• #09322d\n``#09322d` `rgb(9,50,45)``\n• #32090e\n``#32090e` `rgb(50,9,14)``\n• #322d09\n``#322d09` `rgb(50,45,9)``\n• #09322d\n``#09322d` `rgb(9,50,45)``\n• #090e32\n``#090e32` `rgb(9,14,50)``\n• #000000\n``#000000` `rgb(0,0,0)``\n• #070601\n``#070601` `rgb(7,6,1)``\n• #1c1a05\n``#1c1a05` `rgb(28,26,5)``\n• #322d09\n``#322d09` `rgb(50,45,9)``\n• #48400d\n``#48400d` `rgb(72,64,13)``\n• #5d5411\n``#5d5411` `rgb(93,84,17)``\n• #736715\n``#736715` `rgb(115,103,21)``\nMonochromatic Color\n\n# Alternatives to #322d09\n\nBelow, you can see some colors close to #322d09. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.\n\n• #322309\n``#322309` `rgb(50,35,9)``\n• #322609\n``#322609` `rgb(50,38,9)``\n• #322a09\n``#322a09` `rgb(50,42,9)``\n• #322d09\n``#322d09` `rgb(50,45,9)``\n• #323009\n``#323009` `rgb(50,48,9)``\n• #303209\n``#303209` `rgb(48,50,9)``\n• #2d3209\n``#2d3209` `rgb(45,50,9)``\nSimilar Colors\n\n# #322d09 Preview\n\nThis text has a font color of #322d09.\n\n``<span style=\"color:#322d09;\">Text here</span>``\n#322d09 background color\n\nThis paragraph has a background color of #322d09.\n\n``<p style=\"background-color:#322d09;\">Content here</p>``\n#322d09 border color\n\nThis element has a border color of #322d09.\n\n``<div style=\"border:1px solid #322d09;\">Content here</div>``\nCSS codes\n``.text {color:#322d09;}``\n``.background {background-color:#322d09;}``\n``.border {border:1px solid #322d09;}``\n\n# Shades and Tints of #322d09\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, #000000 is the darkest color, while #fcfaef is the lightest one.\n\n• #000000\n``#000000` `rgb(0,0,0)``\n• #110f03\n``#110f03` `rgb(17,15,3)``\n• #211e06\n``#211e06` `rgb(33,30,6)``\n• #322d09\n``#322d09` `rgb(50,45,9)``\n• #433c0c\n``#433c0c` `rgb(67,60,12)``\n• #534b0f\n``#534b0f` `rgb(83,75,15)``\n• #645a12\n``#645a12` `rgb(100,90,18)``\n• #746915\n``#746915` `rgb(116,105,21)``\n• #857818\n``#857818` `rgb(133,120,24)``\n• #96871b\n``#96871b` `rgb(150,135,27)``\n• #a6961e\n``#a6961e` `rgb(166,150,30)``\n• #b7a521\n``#b7a521` `rgb(183,165,33)``\n• #c8b424\n``#c8b424` `rgb(200,180,36)``\n• #d8c327\n``#d8c327` `rgb(216,195,39)``\n• #dbc738\n``#dbc738` `rgb(219,199,56)``\n• #decc48\n``#decc48` `rgb(222,204,72)``\n• #e1d059\n``#e1d059` `rgb(225,208,89)``\n• #e4d56a\n``#e4d56a` `rgb(228,213,106)``\n• #e7da7a\n``#e7da7a` `rgb(231,218,122)``\n``#eade8b` `rgb(234,222,139)``\n• #ede39b\n``#ede39b` `rgb(237,227,155)``\n• #f0e8ac\n``#f0e8ac` `rgb(240,232,172)``\n• #f3ecbd\n``#f3ecbd` `rgb(243,236,189)``\n• #f6f1cd\n``#f6f1cd` `rgb(246,241,205)``\n• #f9f6de\n``#f9f6de` `rgb(249,246,222)``\n• #fcfaef\n``#fcfaef` `rgb(252,250,239)``\nTint Color Variation\n\n# Tones of #322d09\n\nA tone is produced by adding gray to any pure hue. In this case, #1e1e1d is the less saturated color, while #393202 is the most saturated one.\n\n• #1e1e1d\n``#1e1e1d` `rgb(30,30,29)``\n• #201f1b\n``#201f1b` `rgb(32,31,27)``\n• #222119\n``#222119` `rgb(34,33,25)``\n• #242317\n``#242317` `rgb(36,35,23)``\n• #272414\n``#272414` `rgb(39,36,20)``\n• #292612\n``#292612` `rgb(41,38,18)``\n• #2b2810\n``#2b2810` `rgb(43,40,16)``\n• #2d2a0e\n``#2d2a0e` `rgb(45,42,14)``\n• #302b0b\n``#302b0b` `rgb(48,43,11)``\n• #322d09\n``#322d09` `rgb(50,45,9)``\n• #342f07\n``#342f07` `rgb(52,47,7)``\n• #373004\n``#373004` `rgb(55,48,4)``\n• #393202\n``#393202` `rgb(57,50,2)``\nTone Color Variation\n\n# Color Blindness Simulator\n\nBelow, you can see how #322d09 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"
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https://howkgtolbs.com/convert/8.93-kg-to-lbs
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[
"# 8.93 kg to lbs - 8.93 kilograms into pounds\n\nkg\nlbs\n\n## 8.93 kg to lbs\n\nDo you want to know how much is 8.93 kg equal to lbs and how to convert 8.93 kg to lbs? You are in the right place. You will find in this article everything you need to make kilogram to pound conversion - theoretical and practical too. It is also needed/We also want to highlight that all this article is dedicated to one number of kilograms - that is one kilogram. So if you need to know more about 8.93 kg to pound conversion - read on.\n\nBefore we move on to the practice - this is 8.93 kg how much lbs calculation - we are going to tell you few theoretical information about these two units - kilograms and pounds. So we are starting.\n\n## 8.93 kgs in pounds\n\nWe are going to start with the kilogram. The kilogram is a unit of mass. It is a base unit in a metric system, in formal International System of Units (in short form SI).\n\nSometimes the kilogram is written as kilogramme. The symbol of this unit is kg.\n\nFirstly, the definition of a kilogram was formulated in 1795. The kilogram was described as the mass of one liter of water. First definition was simply but hard to use.\n\nThen, in 1889 the kilogram was defined by the International Prototype of the Kilogram (in abbreviated form IPK). The International Prototype of the Kilogram was prepared of 90% platinum and 10 % iridium. The IPK was used until 2019, when it was replaced by another definition.\n\nToday the definition of the kilogram is build on physical constants, especially Planck constant. Here is the official definition: “The kilogram, symbol kg, is the SI unit of mass. It is defined by taking the fixed numerical value of the Planck constant h to be 6.62607015×10−34 when expressed in the unit J⋅s, which is equal to kg⋅m2⋅s−1, where the metre and the second are defined in terms of c and ΔνCs.”\n\nOne kilogram is exactly 0.001 tonne. It can be also divided to 100 decagrams and 1000 grams.\n\n## 8.93 kilogram to pounds\n\nYou know a little bit about kilogram, so now we can go to the pound. The pound is also a unit of mass. It is needed to highlight that there are not only one kind of pound. What are we talking about? For example, there are also pound-force. In this article we are going to to focus only on pound-mass.\n\nThe pound is in use in the Imperial and United States customary systems of measurements. Of course, this unit is in use also in other systems. The symbol of this unit is lb or “.\n\nThe international avoirdupois pound has no descriptive definition. It is exactly 0.45359237 kilograms. One avoirdupois pound could be divided to 16 avoirdupois ounces or 7000 grains.\n\nThe avoirdupois pound was implemented in the Weights and Measures Act 1963. The definition of the pound was written in first section of this act: “The yard or the metre shall be the unit of measurement of length and the pound or the kilogram shall be the unit of measurement of mass by reference to which any measurement involving a measurement of length or mass shall be made in the United Kingdom; and- (a) the yard shall be 0.9144 metre exactly; (b) the pound shall be 0.45359237 kilogram exactly.”\n\n### 8.93 kg in lbs\n\nTheoretical section is already behind us. In this section we will tell you how much is 8.93 kg to lbs. Now you learned that 8.93 kg = x lbs. So it is high time to know the answer. Have a look:\n\n8.93 kilogram = 19.6872799966 pounds.\n\nThis is an accurate result of how much 8.93 kg to pound. You can also round off the result. After it your outcome is as following: 8.93 kg = 19.646 lbs.\n\nYou know 8.93 kg is how many lbs, so look how many kg 8.93 lbs: 8.93 pound = 0.45359237 kilograms.\n\nObviously, this time you can also round it off. After rounding off your result will be as following: 8.93 lb = 0.45 kgs.\n\nWe also want to show you 8.93 kg to how many pounds and 8.93 pound how many kg results in charts. Have a look:\n\nWe want to start with a table for how much is 8.93 kg equal to pound.\n\nKilograms Pounds Pounds (rounded off to two decimal places)\n8.93 19.6872799966 19.6460\nNow look at a table for how many kilograms 8.93 pounds.\n\nPounds Kilograms Kilograms (rounded off to two decimal places\n8.93 0.45359237 0.45\n\nNow you know how many 8.93 kg to lbs and how many kilograms 8.93 pound, so it is time to go to the 8.93 kg to lbs formula.\n\n### 8.93 kg to pounds\n\nTo convert 8.93 kg to us lbs a formula is needed. We are going to show you two versions of a formula. Let’s start with the first one:\n\nAmount of kilograms * 2.20462262 = the 19.6872799966 outcome in pounds\n\nThe first version of a formula give you the most exact outcome. Sometimes even the smallest difference can be considerable. So if you need an exact result - this version of a formula will be the best solution to know how many pounds are equivalent to 8.93 kilogram.\n\nSo go to the another version of a formula, which also enables calculations to know how much 8.93 kilogram in pounds.\n\n### 8.93 pound to kg\n\nThe second formula is down below, let’s see:\n\nAmount of kilograms * 2.2 = the outcome in pounds\n\nAs you see, this version is simpler. It can be the best option if you want to make a conversion of 8.93 kilogram to pounds in quick way, for example, during shopping. You only need to remember that your result will be not so correct.\n\nNow we are going to show you these two formulas in practice. But before we will make a conversion of 8.93 kg to lbs we want to show you another way to know 8.93 kg to how many lbs totally effortless.\n\n### 8.93 kg to lbs converter\n\nAnother way to check what is 8.93 kilogram equal to in pounds is to use 8.93 kg lbs calculator. What is a kg to lb converter?\n\nCalculator is an application. It is based on first version of a formula which we gave you above. Thanks to 8.93 kg pound calculator you can quickly convert 8.93 kg to lbs. You only need to enter amount of kilograms which you need to convert and click ‘calculate’ button. You will get the result in a flash.\n\nSo try to calculate 8.93 kg into lbs using 8.93 kg vs pound converter. We entered 8.93 as a number of kilograms. This is the result: 8.93 kilogram = 19.6872799966 pounds.\n\nAs you see, this 8.93 kg vs lbs converter is easy to use.\n\nNow we are going to our primary topic - how to convert 8.93 kilograms to pounds on your own.\n\n#### 8.93 kg to lbs conversion\n\nWe will start 8.93 kilogram equals to how many pounds conversion with the first version of a formula to get the most exact outcome. A quick reminder of a formula:\n\nAmount of kilograms * 2.20462262 = 19.6872799966 the result in pounds\n\nSo what have you do to check how many pounds equal to 8.93 kilogram? Just multiply number of kilograms, this time 8.93, by 2.20462262. It is equal 19.6872799966. So 8.93 kilogram is equal 19.6872799966.\n\nYou can also round off this result, for instance, to two decimal places. It is exactly 2.20. So 8.93 kilogram = 19.6460 pounds.\n\nIt is time for an example from everyday life. Let’s calculate 8.93 kg gold in pounds. So 8.93 kg equal to how many lbs? And again - multiply 8.93 by 2.20462262. It is exactly 19.6872799966. So equivalent of 8.93 kilograms to pounds, when it comes to gold, is exactly 19.6872799966.\n\nIn this case you can also round off the result. This is the outcome after rounding off, in this case to one decimal place - 8.93 kilogram 19.646 pounds.\n\nNow let’s move on to examples converted using a short version of a formula.\n\n#### How many 8.93 kg to lbs\n\nBefore we show you an example - a quick reminder of shorter formula:\n\nAmount of kilograms * 2.2 = 19.646 the result in pounds\n\nSo 8.93 kg equal to how much lbs? As in the previous example you need to multiply amount of kilogram, this time 8.93, by 2.2. Have a look: 8.93 * 2.2 = 19.646. So 8.93 kilogram is 2.2 pounds.\n\nMake another conversion with use of shorer version of a formula. Now calculate something from everyday life, for example, 8.93 kg to lbs weight of strawberries.\n\nSo let’s calculate - 8.93 kilogram of strawberries * 2.2 = 19.646 pounds of strawberries. So 8.93 kg to pound mass is 19.646.\n\nIf you learned how much is 8.93 kilogram weight in pounds and are able to convert it using two different versions of a formula, let’s move on. Now we want to show you all results in charts.\n\n#### Convert 8.93 kilogram to pounds\n\nWe know that results shown in charts are so much clearer for most of you. We understand it, so we gathered all these results in tables for your convenience. Due to this you can easily make a comparison 8.93 kg equivalent to lbs results.\n\nLet’s begin with a 8.93 kg equals lbs chart for the first version of a formula:\n\nKilograms Pounds Pounds (after rounding off to two decimal places)\n8.93 19.6872799966 19.6460\n\nAnd now let’s see 8.93 kg equal pound table for the second formula:\n\nKilograms Pounds\n8.93 19.646\n\nAs you can see, after rounding off, if it comes to how much 8.93 kilogram equals pounds, the results are the same. The bigger amount the more considerable difference. Keep it in mind when you want to do bigger amount than 8.93 kilograms pounds conversion.\n\n#### How many kilograms 8.93 pound\n\nNow you know how to calculate 8.93 kilograms how much pounds but we will show you something more. Do you want to know what it is? What do you say about 8.93 kilogram to pounds and ounces conversion?\n\nWe want to show you how you can calculate it step by step. Let’s begin. How much is 8.93 kg in lbs and oz?\n\nFirst thing you need to do is multiply number of kilograms, this time 8.93, by 2.20462262. So 8.93 * 2.20462262 = 19.6872799966. One kilogram is equal 2.20462262 pounds.\n\nThe integer part is number of pounds. So in this case there are 2 pounds.\n\nTo check how much 8.93 kilogram is equal to pounds and ounces you have to multiply fraction part by 16. So multiply 20462262 by 16. It is exactly 327396192 ounces.\n\nSo your outcome is 2 pounds and 327396192 ounces. It is also possible to round off ounces, for instance, to two places. Then your result will be exactly 2 pounds and 33 ounces.\n\nAs you can see, calculation 8.93 kilogram in pounds and ounces simply.\n\nThe last calculation which we want to show you is calculation of 8.93 foot pounds to kilograms meters. Both of them are units of work.\n\nTo calculate foot pounds to kilogram meters you need another formula. Before we give you this formula, look:\n\n• 8.93 kilograms meters = 7.23301385 foot pounds,\n• 8.93 foot pounds = 0.13825495 kilograms meters.\n\nNow look at a formula:\n\nNumber.RandomElement()) of foot pounds * 0.13825495 = the outcome in kilograms meters\n\nSo to calculate 8.93 foot pounds to kilograms meters you have to multiply 8.93 by 0.13825495. It is equal 0.13825495. So 8.93 foot pounds is equal 0.13825495 kilogram meters.\n\nYou can also round off this result, for instance, to two decimal places. Then 8.93 foot pounds will be equal 0.14 kilogram meters.\n\nWe hope that this conversion was as easy as 8.93 kilogram into pounds conversions.\n\nThis article was a huge compendium about kilogram, pound and 8.93 kg to lbs in conversion. Thanks to this conversion you learned 8.93 kilogram is equivalent to how many pounds.\n\nWe showed you not only how to do a calculation 8.93 kilogram to metric pounds but also two other calculations - to check how many 8.93 kg in pounds and ounces and how many 8.93 foot pounds to kilograms meters.\n\nWe showed you also other solution to do 8.93 kilogram how many pounds conversions, that is using 8.93 kg en pound converter. It will be the best solution for those of you who do not like converting on your own at all or this time do not want to make @baseAmountStr kg how lbs calculations on your own.\n\nWe hope that now all of you can do 8.93 kilogram equal to how many pounds conversion - on your own or using our 8.93 kgs to pounds calculator.\n\nDon’t wait! Let’s convert 8.93 kilogram mass to pounds in the way you like.\n\nDo you need to make other than 8.93 kilogram as pounds conversion? For example, for 15 kilograms? Check our other articles! We guarantee that conversions for other amounts of kilograms are so simply as for 8.93 kilogram equal many pounds.\n\n#### Kilograms [kg]\n\nThe kilogram, or kilogramme, is the base unit of weight in the Metric system. It is the approximate weight of a cube of water 10 centimeters on a side.\n\n#### Pounds [lbs]\n\nA pound is a unit of weight commonly used in the United States and the British commonwealths. A pound is defined as exactly 0.45359237 kilograms."
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http://www.dailyfreecode.com/Forum/print-matrix-file-30618.aspx
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"Search:\n\n# how to print a matrix into File.\n\nAsked By: Vivek Date: Jun 16 Category: Java Views: 1646\n\nHi everyone,\nI want to know that how to write a matrix into text file?\nactually here i am trying to write a program which take no of rows and columns from user and also element of matrix and then i want to store that matrix into a separate file(abc.txt) and it's Transpose result\nwant to store in separate file(xyz.txt). for this i am stuck in, i am not getting idea that how to save my matrix into a file...\ni have been searched regarding of my this problem but did not find any clue . let me post my code whatever effort i did to do...\n\n`public class MyTransposeOfMatrix { public static void main(String[] args) throws FileNotFoundException { PrintWriter pw =new PrintWriter(\"D:/SCJP Sun/netbeans/scjp practice/src/com/scjp/braintraser/xyz.txt\"); int rows, columns; System.out.println(\"Please enter no of rows:\"); Scanner sc=new Scanner(System.in); rows=sc.nextInt(); System.out.println(\"Please enter no of columns:\"); columns=sc.nextInt(); int matrix[][]=new int[rows][columns]; int tmatrix[][]=new int[columns][rows]; System.out.println(\"please enter element for matrix:\"); for(int i=0;i<rows;i++){ for(int j=0;j<columns;j++){ matrix[i][j]=sc.nextInt(); } } for(int i=0;i<rows;i++){ for(int j=0; j<columns; j++){ tmatrix[i][j]=matrix[j][i]; } } System.out.println(\"The Matrix is:\"); for(int i=0; i<rows; i++){// pw.print(i); for(int j=0; j<columns; j++){// System.out.print(matrix[i][j]+\" \"); pw.print(matrix[i][j]); // here trying to print into file but it's printing in single line } System.out.println(\"\"); } pw.flush(); System.out.println(\"The Transpose of Matrix is: \"); for(int i=0; i<columns; i++){ for(int j=0; j<rows; j++){ System.out.print(tmatrix[i][j]+\" \"); } System.out.println(\"\"); } }}`\n\nShare:\n\nDidn't find what you were looking for? Find more on how to print a matrix into File. Or get search suggestion and latest updates."
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"http://www.dailyfreecode.com/Images/Logo/logo.gif",
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|
http://active.quickfield.com/HTML/Move%20Method.htm
|
[
"# Move Method\n\nApplies to\n\n## Summary\n\nMoves the shape to a new location.\n\n## Syntax\n\n```Object.Move (\nmethod As QfTransformType,\nv As Point,\n[f As Double]\n) As ShapeRange```\n\n## Parameters\n\nmethod\n[in, optional] QfTransformType\nv\n[in, optional] Point\nf\n[in, optional] Double\n\n## Remarks\n\nGeometric transformations available with move operation are:\n\n• Displacement: parallel displacement is applied to selected objects for specified displacement vector. Parameters needed are displacement vector components.\n\n• Rotation: selected objects are rotated around the specified point for the specified angle. Parameters needed are center of rotation coordinates and angle measured in radians.\n\n• Scaling: selected objects are dilated (constricted) by means of homothetic transformation. Parameters needed are center of homothety and scaling factor.\n\nThe following table clarifies the parameters meaning with various transformation types:\n\n Transformation Type Method parameter as QfTransformType constant V parameter F parameter Displacement qfShift Displacement vector N/A Rotation qfRotation The center of rotation The rotation angle in radians Scaling qfScaling The center of homothety The scaling factor that is always positive and can be great (dilatation) or less (constriction) then one.\n\nThe V parameter if omitted is defaulted to the origin (0, 0), and F parameter - to zero."
] |
[
null
] |
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|
http://blog.sigfpe.com/2006/09/practical-synthetic-differential.html
|
[
"## Thursday, September 21, 2006\n\n### Practical Synthetic Differential Geometry\n\nPreviously I've talked about Automatic Differentiation (AD). My own formulation of the technique is more algebraic than the description that is usually given, and recently it's begun to dawn on me that all I've done is rediscover Synthetic Differential Geometry (SDG). I've looked at a variety of SDG papers but none of them seem to make the connection with AD. This is a pity because SDG allows you to do an amazing thing - write computer programs that easily manipulate objects such as vector fields and differential forms on manifolds without doing symbolic algebra. My goal here is to illustrate how the definition of vector field in Anders Kock's book gives a nice functional definition of a vector field and how that definition leads naturally to the Lie bracket. Normally it takes a significant amount of mathematical machinery to define these things but in the framework of SDG it becomes very simple. (Unfortunately this is going to be very sketchy as there's a lot I could say and I only have a small amount of time while I sit at home waiting for a new TV to be delivered...)\n\nKock's work is based on the idea of infinitesimal numbers whose square is zero but which aren't themselves zero. Clearly we aren't talking about real numbers here, but instead an extension to the real numbers. Here's a toy implementation:\n\n`> data Dual a = D a a deriving (Show,Eq)> instance Num a => Num (Dual a) where> fromInteger i = D (fromInteger i) 0> (D a a')+(D b b') = D (a+b) (a'+b')> (D a a')-(D b b') = D (a-b) (a'-b')> (D a a')*(D b b') = D (a*b) (a*b'+a'*b) > e = D 0 1`\n\nThey're called dual numbers because this particular algebra was apparently first described by Clifford and that's what he called them. The important point to note is that e^2==0. We can use the fact that\nf(x+e)=f(x)+ef'(x)\n\nto compute derivatives of functions. For example if we define\n\n`> f x = x^3+2*x^2-3*x+1`\n\nand evaluate f (1+e) we get D 1 4. We can directly read off f(1)=1 and f'(1)=4.\n\nBut, as Kock points out, the dual numbers don't provide 'enough' infinitesimals, we just get one, e, from which all of the others are built. So we can replace Dual with a new class:\n\n`> infixl 5 .-> infixl 5 .+> infixl 6 .*> infixl 6 *.> data R a = R a [a] deriving Show> zipWith' f l r [] [] = []> zipWith' f _ r a [] = map (flip f r) a> zipWith' f l _ [] b = map (f l) b> zipWith' f l r (a:as) (b:bs) = (f a b) : zipWith' f l r as bs> (.+) :: Num a => [a] -> [a] -> [a]> (.+) = zipWith' (+) 0 0> (.-) :: Num a => [a] -> [a] -> [a]> (.-) = zipWith' (-) 0 0> (.*) :: Num a => a -> [a] -> [a]> (.*) a = map (a*)> (*.) :: Num a => [a] -> a -> [a]> (*.) a b = map (*b) a> (.=) :: (Eq a,Num a) => [a] -> [a] -> Bool> (.=) a b = and \\$ zipWith' (==) 0 0 a b> instance (Eq a,Num a) => Eq (R a) where> R a a' == R b b' = a==b && a'.=b'`\n\nA slightly more extended version of the Num interface:\n\n`> instance Num a => Num (R a) where> fromInteger a = R (fromInteger a) []> (R a a') + (R b b') = R (a+b) (a'.+b')> (R a a') - (R b b') = R (a-b) (a'.-b')> (R a a') * (R b b') = R (a*b) (a.*b' .+ a'*.b)> negate (R a a') = R (negate a) (map negate a')`\n\nAnd some more functions for good measure:\n\n`> instance (Num a,Fractional a) => Fractional (R a) where> fromRational a = R (fromRational a) []> (R a a') / (R b b') = let s = 1/b in> R (a*s) ((a'*.b.-a.*b') *. (s*s))> instance Floating a => Floating (R a) where> exp (R a a') = let e = exp a in R e (e .* a')> log (R a a') = R (log a) ((1/a) .* a')> sin (R a a') = R (sin a) (cos a .* a')> cos (R a a') = R (cos a) (-sin a .* a')`\n\nThe d i provide a basis for these infinitesimals.\n\n`> d n = R 0 (replicate n 0 ++ )`\n\nWe now have an infinite set of independent dual numbers, d i, one for each natural i. Note that we have (d i)*(d j)==0 for any i and j. Apart from this, they act just like before. For example\nf(x+di)=f(x)+dif'(x).\n\nWe can use these compute partial derivatives. For example consider\n\n`> g x y = (x+2*y)^2/(x+y)`\n\nComputing g (7+d 0) (0+d 1) gives R 7.0 [1.0,3.0] so we know g(7,0) = 7, gx(7,0)=1 and gy(7,0)=3.\n\nI'll use R as mathematical notation for the type given by R Float. (I hope the double use of 'R' won't be confusing, but I want to be consistent-ish with Kock.)\n\nBefore we do any more calculus, consider the following geometric figure:",
null,
"A point is in the intersection of the circle and the line if it satisfies the equations:\n\n`> onS (x,y) = x^2+(y-1)^2==1> onL (x,y) = y==0`\n\nNotice that in R this has many solutions. In fact, we find that onS (a*d 1,0) && onL (a*d 1,0) gives True for any Float value a. Does this mean anything? Actually, it fits some people's intuition of what the intersection should look like better than the usual idea that there is only one intersection point. In fact, Protagoras argued that there was a short line segment in common between the circle and the line and that because Pythogoras's theorem gave only one solution the whole of geometry was at fault! Using R instead of the reals makes sense of this intuition. The points of the form (a*d 1,0) do, indeed, form a line tangent to the circle. In fact, there is a way to construct tangent spaces and vector fields directly from this - but instead I want to now consider Kock's construction which is slightly different.\n\nLet D be the subset of R consisting of numbers whose square is zero. These numbers are in some sense infinitesimal. In differential geometry a vector field is intuitively thought of as an 'infinitesimal flow' on a manifold. A finite flow on a manifold, M, is simply a function f:R×M→M such that f(x,0)=0. In f(x,t) we can think of t as time and each point x on the manifold flows along a path t→f(x,t). If f is differentiable, then as t becomes smaller, the path becomes closer and closer to a short line segment which we can draw as a vector. So the intuition is that a vector field is what you get in the limit as t becomes infinitesimal. The catch is that infinitesimal things aren't first class objects in differential geometry (so to speak). You can use infinitesimals for intuition, and I suspect almost all differential geoneters do, but actually talking about them is taboo. In fact, the definition of vector field in differential geometry is a bit of a kludge to work around this issue. When most people first meet the definition of a vector field as a differential operator it comes as quite a shock. But we have no such problem. In fact, Kock defines a vector field simply as a function\nf:D×M→M\nsuch that f(0,x)=x.\nIn the language of SDG, a vector field is an infinitesimal flow. For example, consider\n\n`> transX d (x,y) = (x+d,y)`\n\nThis simply translates along the x-axis infinitesimally. Of course this code defines a flow for non-infinitesimal values of d, but when considering this as a vector field we're only interested in when d is infinitesimal. transX defines a vector field that everywhere points along the x-axis.\n\nFor those familiar with the usual differential geometric definition of vector fields I can now make contact with that. In that context a vector field is a differential operator that acts on scalar functions on your manifold. In our case, we can think of it acting by infinitesimally dragging the manifold and then looking at what happens to the function that's dragged along. In other words, if we have a funtion, f, on the manifold, the vector field v, applied to f, is that function vf such that f (f d x)==f x + d*vf x. For example with\n\n`> h (x,y) = x^2+y^2`\n\nh (transX (d 1) (1,2)) == R 5 because h(x,y)=5 and hx(x,y)=2.\n\nI think I have enough time to briefly show how to define the Lie bracket in this context. If you have two vector fields, then intuitively you can view the Lie bracket as follows: take a point, map it using the first field by some small parameter d1, then map it using the second for time d2, then doing the first one by parameter -d1, and then the second by -d2. Typically you do not return to where you started from, but somewhere 'close'. In fact, your displacement is proportional to the product of d1 and d2. In other words, if your fields are v and w, then the Lie bracket [v,w] is defined by the equation v (-d2) \\$ v (-d1) \\$ w d2 \\$v d1 x==vw (d1*d2) x where vw represents [v,w]. But you might now see a problem. The way we have defined things d1*d2 is zero if d1 and d2 are infinitesimal. Kock discusses this issue and points out that in order to compute Lie brackets we need infinitesimals whose squares are zero, but whose product isn't. I could go back and hack the original definition of R to represent a new kind of algebraic structure where this is true, but in the spirit of Geometric Algebra for Free I have a trick. Consider d1=1⊗d and d2=d⊗1 in R⊗R. Clearly d12=d22=0. We also have d1d2=d⊗d≠0. And using the constructor as tensor product trick we can write:\n\n`> d1 = R (d 0) [] :: R (R Double)> d2 = R 0 :: R (R Double)`\n\nLet's test it out. Define three infinitesimal rotations:\n\n`> rotX d (x,y,z) = (x,y+d*z,z-d*y)> rotY d (x,y,z) = (x-d*z,y,z+d*x)> rotZ d (x,y,z) = (x+d*y,y-d*x,z)`\n\nNote that there's no need to use trig functions here. We're working with infinitesimal rotations so we can use the fact that sin d=d and cos d=1 for infinitesimal d. It is well known that [rotX,rotY]=rotZ and cyclic permutations thereof. (In fact, these are just the commutation relations for angular momentum in quantum mechanics.)\n\nWe can write\n\n`> commutator d1 d2 f g = (g (-d2) . f (-d1) . g d2 . f d1)> test1 = commutator d1 d2 rotX rotY> test2 = rotZ (d1*d2)`\n\nand compare values of test1 and test2 at a variety of points to see if they are equal. For example test1 (1,2,3) == test2(1,2,3). I don't know about you, but I'm completely amazed by this. We can get our hands right on things like Lie brackets with the minimum of fuss. This has many applications. An obvious one is in robot dynamics simulations where we frequently need to work with the Lie algebra of the group of rigid motions. This approach won't give us new algorithms, but it gives a very elegant way to express existing algorithms. It's also, apprently, close to Sophus Lie's original description of the Lie bracket.\n\nI have time for one last thing. So far the only manifolds I've considered are those of the form Rn. But this stuff works perfectly well for varieties. For example, the rotations rotX, rotY and rotZ all work fine on the unit sphere. In fact, if T is the unit sphere define\n\n`> onT (x,y,z) = x^2+y^2+z^2==1`\n\nNotice how, for example, onT (rotY (d 1) (0.6,0.8,0))==True, so rotY really does give an infinitesimal flow from the unit sphere to the unit sphere, and hence a vector field on the sphere. Compare this to the complexity of the traditional definition of a vector field on a manifold.\n\nKock's book also describes a really beautiful definition of the differential forms in terms of infinitesimal simplices, but I've no time for that now. And I've also no time to mention the connections with intuitionistic logic and category theory making up much of his book (which is a good thing because I don't understand them).\n\nPS The D⊗D trick, implemented in C++, is essentially what I did in my paper on photogrammetry. But there I wasn't thinking geometrically.\n\n(I don't think I've done a very good job of writing this up. Partly I feel horribly constricted by having to paste back and forth between two text editors to get this stuff in a form suitable for blogger.com as well as periodically having to do some extra munging to make sure it is still legal literate Haskell. And diagrams - what a nightmare! So I hit the point where I couldn't face improving the text any more without climbing up the walls. What is the best solution to writing literate Haskell, with embedded equations, for a blog?)\n\nLabels: ,",
null,
"Dave Menendez said...\n\nWhat's the preferred way to do higher derivatives? I found that e2 = D e 1 works, but it also calculates the first derivative twice.\n\nThursday, 21 September, 2006",
null,
"michiexile said...\n\nUmmmm, I seem to be running into problems quite early on. I defined the duals following your exposition closely (I added a signum and an abs because ghci was complaining about those), and then got\n*DiffGeo> let f x = x^3+2*x^2-3*x+1\n*DiffGeo> f (1+e)\nD 1 4\n\nFriday, 22 September, 2006",
null,
"sigfpe said...\n\nFor the second derivtive you need an element such that d^3=0 but d^2=0. Such an element can be found in R⊗R, as you've discovered. But, as you point out, you get the 1st derivative twice and it gets worse with higher derivatives. You can implement an appropriate algebra directly. One paper that does this is here. (That implements exactly the right thing, but doesn't give an algebraic description.) For arbitrary higher derivatives you're probably need power series code and there are many implementations of that in Haskell, at least for the single variable case.\n\nFriday, 22 September, 2006",
null,
"sigfpe said...\n\nMichi, the code's correct, the 'comments' weren't. Now fixed.\n\nFriday, 22 September, 2006",
null,
"augustss said...\n\nFriday, 22 September, 2006",
null,
"sigfpe said...\n\naugustss,\n\nThanks for the link. That's the only paper I've seen that formulates AD the way I do.\n\nJudging by the date of that paper, I think that my approach to Lie algebras must be novel so maybe I should write a paper on it. (It's slightly different to what I wrote here because Kock's 'functional' definition of a vector field is pretty, but less useful for calculation.)\n\nFriday, 22 September, 2006",
null,
"Dave Menendez said...\n\nMichael Shulman has an introductory lecture on synthetic differential geometry that starts out with dual numbers and eventually describes a Lie bracket. I don't know enough about Lie groups to know how his formulation compares to yours.\n\nI'd ask how SDG compares to geometric calculus, but I don't think I'd understand the answer\n\nMonday, 25 September, 2006",
null,
"michiexile said...\n\nSoooo... your Lie bracket is \"just\" the commutator of d(x)1 and 1(x)d in the tensor product, right?\n\nIs this just me being WAY to used to the algbraic point of view thinking that this is almost tautological?\n\nTuesday, 26 September, 2006",
null,
"sigfpe said...\n\nMichi,\n\nd(x)1 and 1(x)d commute so their commutator is zero. In fact, both of the algebraic structures I define are commutative. The Lie bracket comes from the non-commutativity of the vector field functions, not the underlying algebras.\n\nTuesday, 26 September, 2006",
null,
"sigfpe said...\n\nI'd ask how SDG compares to geometric calculus...\n\nGeometric calculus is what you get when you combine calculus with geometric algebra. SDG provides an alternative approach to calculus. So the two should happily coexist. In fact, the code here can be combined with this latest code without problem and I guess that some of the language of geometric calculus turns directly into usable code in a nice way.\n\nTuesday, 26 September, 2006",
null,
"Chris Witte said...\n\nIf your having trouble displaying math you might want to look at ASCIMathML at\n\nhttp://www1.chapman.edu/%7Ejipsen/mathml/asciimath.html\n\nIt's a javascript that automatically parses a web page and converts standard LaTeX notation (or it's own simplifed notation) between `` or \\$\\$ delimiters into MathML on the fly.\n\nYou should be able to use it with Bloggger but I don't know how.\n\nTuesday, 24 October, 2006",
null,
"Anonymous said...\n\n...because Pythogoras's theorem gave only one solution...\nIs this a spelling mistake, is *Pythagoras* spelled like this or is this just another guy?\n\nSaturday, 22 September, 2012",
null,
"Unknown said...\n\nKoch's book (nice link, btw--i have a first edition, but did not know there existed a second) defines higher order nilpotent (?) differentials (or these little dual number guys, whatever you want to call them).\n\nAnd I WILL ask the question: What does this stuff have to do with \"Geometric Algebra\", Clifford Algebras, Grassmann Algebras, Supersymmetry, anything you can handle ... ?\n\nThursday, 13 June, 2013",
null,
"Unknown said...\n\nDid i succeed in leaving a comment or not? I guess i'll have to wait and see.\n\nI hope (?) i might get notified by email if i did succeed in leaving a comment ... ?\n\nThursday, 13 June, 2013",
null,
"Unknown said...\n\nActually you get higher derivative for free, you don't need explicitly to do the trick! Just define\n\n> im (D _ a) = a\n> derivative f x = im \\$ f (D x 1)\n\nThen\nderivative (\\x -> (x-1)^3 ) 0 == 3\nand you can immediately calculate higher derivatives!\n(derivative . derivative) (\\x -> (x-1)^3 ) 0 == -6\n(derivative . derivative . derivative) (^5) 1 == 60\n\nWednesday, 25 June, 2014",
null,
"oij said...\n\nI'm not sure this is a correct definition of vf: \"f (f d x)==f x + d*vf x\" Especially because f is supposed to map into R.\n\nFriday, 23 December, 2016",
null,
"oij said...\n\nI'm not sure this is a correct definition of vf: \"f (f d x)==f x + d*vf x\" Especially because f is supposed to map into R.\n\nFriday, 23 December, 2016"
] |
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|
https://glossary.ametsoc.org/w/index.php?title=Pi_theorem&diff=prev&oldid=5989
|
[
"# Difference between revisions of \"Pi theorem\"\n\n## Pi theorem\n\nThe theorem states that an equation for a physical system that can be written f(Q1, Q2, . . . , Qm) = 0 can also be written as g(π1, π2, . . . , πm - n) = 0 where Qi are m dimensional parameters, numbers, and variables; πi are m - n nondimensional quantities; and n is the number of fundamental dimensional units."
] |
[
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"Online Math Solver\n\n Depdendent Variable\n\n Number of equations to solve: 23456789\n Equ. #1:\n Equ. #2:\n\n Equ. #3:\n\n Equ. #4:\n\n Equ. #5:\n\n Equ. #6:\n\n Equ. #7:\n\n Equ. #8:\n\n Equ. #9:\n\n Solve for:\n\n Dependent Variable\n\n Number of inequalities to solve: 23456789\n Ineq. #1:\n Ineq. #2:\n\n Ineq. #3:\n\n Ineq. #4:\n\n Ineq. #5:\n\n Ineq. #6:\n\n Ineq. #7:\n\n Ineq. #8:\n\n Ineq. #9:\n\n Solve for:\n\n Please use this form if you would like to have this math solver on your website, free of charge. 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https://www.usatestprep.com/al/alabama-middle-school-online-review/7th-grade-math-cos-test-3441/
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[
"# 7th Grade Math ACAP (CoS) Practice\n\n« Back to Alabama Middle School\nDiscover the most effective and comprehensive online solution for curriculum mastery, high-stakes testing, and assessment in . Our 7th Grade Math ACAP (CoS) curriculum and test review is aligned to the most current standards.\n\nSee Pricing Get a Quote\n\n• Questions 3,198\n• Vocabulary Terms 164\n• Instructional Videos 101\n\n### Test Standards\n\n1. (1.) Unit rates\n2. (2.a) Test ratios\n3. (2.b) Constant of proportionality\n4. (2.c) Explain point\n5. (3.) Solve problems\n1. (4.a) Opposite quantities\n2. (4.b) Interpret sums\n3. (4.cd) Subtraction and distance\n4. (4.e) Multiplication\n5. (4.f) Divide integers\n6. (4.g) Rational to decimal\n7. (5.) Solve problems\n1. (6.) Linear expressions\n2. (7.) Generate expressions\n3. (8.) Solve problems\n4. (9.a) Solve equations\n5. (9.b) Inequality problems\n1. (10.ab) Statistics\n2. (10.cde) Infer from samples\n3. (11.) Compare data\n4. (12.) Comparative inferences\n5. (13.) Probability\n6. (14.) Approximate probability\n7. (15.) Generated data\n8. (16.a) Sample spaces\n9. (16.b) Simulation\n10. (16.c) Everyday language\n1. (17.) Scale Drawings\n2. (18.) Construct shapes\n3. (19.) Cross sections\n4. (20.) Area and circumference\n5. (21.) Unknown angles\n6. (22.) Solve problems"
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https://rdrr.io/bioc/sparsenetgls/man/convertbeta.html
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[
"# convertbeta: The convertbeta() function In sparsenetgls: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression\n\n## Description\n\nThe covertbeta function is designed to convert the regression coefficients derived from the standardized data.\n\n## Usage\n\n `1` ```convertbeta(X, Y, q, beta0) ```\n\n## Arguments\n\n `X` It is a dataset of explanatory variables. `Y` It is the multivariate response variables. `q` It is an integer representing the number of explanatory variables and intercept. `beta0` The vector contains the regression coefficients result from sparsenetgls.\n\n## Value\n\nReturn the list of converted regression coefficients of the explanatory variables 'betaconv' and intercept value 'betaconv_int'.\n\n## Examples\n\n ```1 2 3 4 5 6``` ```X <- mvrnorm(n=20,mu=rep(0,5),Sigma=Diagonal(5,rep(1,5))) Y <- mvrnorm(n=20,mu=rep(0.5,10),Sigma=Diagonal(10,rep(1,10))) fitmodel <- sparsenetgls(responsedata=Y,predictdata=X,nlambda=5,ndist=2, method='elastic') #Example of converting the regression coef of the first lamda convertbeta(X=X,Y=Y,q=5+1,beta0=fitmodel\\$beta[,1]) ```\n\nsparsenetgls documentation built on Nov. 8, 2020, 7:37 p.m."
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"Mary Pyo\n\nSolving Percent Problems\n\nSlide Duration:\n\nSection 1: Algebra and Decimals\nExpressions and Variables\n\n5m 57s\n\nIntro\n0:00\nVocabulary\n0:06\nVariable\n0:09\nExpression\n0:48\nNumerical Expression\n1:08\nAlgebraic Expression\n1:35\nWord Expression\n2:04\nExtra Example 1: Evaluate the Expression\n2:27\nExtra Example 2: Evaluate the Expression\n3:16\nExtra Example 3: Evaluate the Expression\n4:04\nExtra Example 4: Evaluate the Expression\n4:59\nExponents\n\n5m 34s\n\nIntro\n0:00\nWhat Exponents Mean\n0:07\nExample: Ten Squared\n0:08\nExtra Example 1: Exponents\n0:50\nExtra Example 2: Write in Exponent Form\n1:58\nExtra Example 3: Using Exponent and Base\n2:37\nExtra Example 4: Write the Equal Factors\n4:26\nOrder of Operations\n\n8m 40s\n\nIntro\n0:00\nPlease Excuse My Dear Aunt Sally\n0:07\nStep 1: Parenthesis\n1:16\nStep 2: Exponent\n1:25\nStep 3: Multiply and Divide\n1:30\n2:00\nExample: Please Excuse My Dear Aunt Sally\n2:26\nExtra Example 1: Evaluating Expression\n3:37\nExtra Example 2: Evaluating Expression\n4:59\nExtra Example 3: Evaluating Expression\n5:34\nExtra Example 4: Evaluating Expression\n6:25\nComparing and Ordering Decimals\n\n13m 37s\n\nIntro\n0:00\nPlace Value\n0:13\nExamples: 1,234,567.89\n0:19\nWhich is the Larger Value?\n1:33\nWhich is Larger: 10.5 or 100.5\n1:46\nWhich is Larger: 1.01 or 1.10\n2:24\nWhich is Larger: 44.40 or 44.4\n4:20\nWhich is Larger: 18.6 or 16.8\n5:18\nExtra Example 1: Order from Least to Greatest\n5:55\nExtra Example 2: Order from Least to Greatest\n7:56\nExtra Example 3: Order from Least to Greatest\n9:16\nExtra Example 4: Order from Least to Greatest\n10:42\nRounding Decimals\n\n12m 31s\n\nIntro\n0:00\nDecimal Place Value\n0:06\nExample: 12,3454.6789\n0:07\nHow to Round Decimals\n1:17\nExample: Rounding 1,234.567\n1:18\nExtra Example 1: Rounding Decimals\n3:47\nExtra Example 2: Rounding Decimals\n6:10\nExtra Example 3: Rounding Decimals\n7:45\nExtra Example 4: Rounding Decimals\n9:56\n\n11m 30s\n\nIntro\n0:00\n0:06\nAlign the Decimal Point First\n0:12\n0:47\nPlace the Decimal Point in the Same Place\n0:55\nCheck by Estimating\n1:09\nExamples\n1:28\nAdd: 3.45 + 7 + 0.835\n1:30\nFind the Difference: 351.4 - 65.25\n3:34\n5:32\nExtra Example 2: How Much Money?\n6:09\nExtra Example 3: Subtracting Decimals\n7:20\n9:32\nMultiplying Decimals\n\n10m 30s\n\nIntro\n0:00\nMultiply the Decimals\n0:05\nMethods for Multiplying Decimals\n0:06\nExample: 1.1 x 6\n0:38\nExtra Example 1: Multiplying Decimals\n1:51\nExtra Example 2: Work Money\n2:49\nExtra Example 3: Multiplying Decimals\n5:45\nExtra Example 4: Multiplying Decimals\n7:46\nDividing Decimals\n\n17m 49s\n\nIntro\n0:00\nWhen Dividing Decimals\n0:06\nMethods for Dividing Decimals\n0:07\nDivisor and Dividend\n0:37\nExample: 0.2 Divided by 10\n1:35\nExtra Example 1 : Dividing Decimals\n5:24\nExtra Example 2: How Much Does Each CD Cost?\n8:22\nExtra Example 3: Dividing Decimals\n10:59\nExtra Example 4: Dividing Decimals\n12:08\nSection 2: Number Relationships and Fractions\nPrime Factorization\n\n7m\n\nIntro\n0:00\nTerms to Review\n0:07\nPrime vs. Composite\n0:12\nFactor\n0:54\nProduct\n1:15\nFactor Tree\n1:39\nExample: Prime Factorization\n2:01\nExample: Prime Factorization\n2:43\nExtra Example 1: Prime Factorization\n4:08\nExtra Example 2: Prime Factorization\n5:05\nExtra Example 3: Prime Factorization\n5:33\nExtra Example 4: Prime Factorization\n6:13\nGreatest Common Factor\n\n12m 47s\n\nIntro\n0:00\nTerms to Review\n0:05\nFactor\n0:07\nExample: Factor of 20\n0:18\nTwo Methods\n0:59\nGreatest Common Factor\n1:00\nMethod 1: GCF of 15 and 30\n1:37\nMethod 2: GCF of 15 and 30\n2:58\nExtra Example 1: Find the GCF of 6 and 18\n5:16\nExtra Example 2: Find the GCF of 36 and 27\n7:43\nExtra Example 3: Find the GCF of 6 and 18\n9:18\nExtra Example 4: Find the GCF of 54 and 36\n10:30\nFraction Concepts and Simplest Form\n\n10m 3s\n\nIntro\n0:00\nFraction Concept\n0:10\nExample: Birthday Cake\n0:28\nExample: Chocolate Bar\n2:10\nSimples Form\n3:38\nExample: Simplifying 4 out of 8\n3:46\nExtra Example 1: Graphically Show 4 out of 10\n4:41\nExtra Example 2: Finding Fraction Shown by Illustration\n5:10\nExtra Example 3: Simplest Form of 5 over 25\n7:02\nExtra Example 4: Simplest Form of 14 over 49\n8:30\nLeast Common Multiple\n\n14m 16s\n\nIntro\n0:00\nTerm to Review\n0:06\nMultiple\n0:07\nExample: Multiples of 4\n0:15\nTwo Methods\n0:41\nLeast Common Multiples\n0:44\nMethod 1: LCM of 6 and 10\n1:09\nMethod 2: LCM of 6 and 10\n2:56\nExtra Example 1: LCM of 12 and 15\n5:09\nExtra Example 2: LCM of 16 and 20\n7:36\nExtra Example 3 : LCM of 15 and 25\n10:00\nExtra Example 4 : LCM of 12 and 18\n11:27\nComparing and Ordering Fractions\n\n13m 10s\n\nIntro\n0:00\nTerms Review\n0:14\nGreater Than\n0:16\nLess Than\n0:40\nCompare the Fractions\n1:00\nExample: Comparing 2/4 and 3/4\n1:08\nExample: Comparing 5/8 and 2/5\n2:04\nExtra Example 1: Compare the Fractions\n3:28\nExtra Example 2: Compare the Fractions\n6:06\nExtra Example 3: Compare the Fractions\n8:01\nExtra Example 4: Least to Greatest\n9:37\nMixed Numbers and Improper Fractions\n\n12m 49s\n\nIntro\n0:00\nFractions\n0:10\nMixed Number\n0:21\nProper Fraction\n0:47\nImproper Fraction\n1:30\nSwitching Between\n2:47\nMixed Number to Improper Fraction\n2:53\nImproper Fraction to Mixed Number\n4:41\nExamples: Switching Fractions\n6:37\nExtra Example 1: Mixed Number to Improper Fraction\n8:57\nExtra Example 2: Improper Fraction to Mixed Number\n9:37\nExtra Example 3: Improper Fraction to Mixed Number\n10:21\nExtra Example 4: Mixed Number to Improper Fraction\n11:31\nConnecting Decimals and Fractions\n\n15m 1s\n\nIntro\n0:00\nExamples: Decimals and Fractions\n0:06\nMore Examples: Decimals and Fractions\n2:48\nExtra Example 1: Converting Decimal to Fraction\n6:55\nExtra Example 2: Converting Fraction to Decimal\n8:45\nExtra Example 3: Converting Decimal to Fraction\n10:28\nExtra Example 4: Converting Fraction to Decimal\n11:42\nSection 3: Fractions and Their Operations\nAdding and Subtracting Fractions with Same Denominators\n\n5m 17s\n\nIntro\n0:00\nSame Denominator\n0:11\nNumerator and Denominator\n0:18\nExample: 2/6 + 5/6\n0:41\nExtra Example 1: Add or Subtract the Fractions\n2:02\nExtra Example 2: Add or Subtract the Fractions\n2:45\nExtra Example 3: Add or Subtract the Fractions\n3:17\nExtra Example 4: Add or Subtract the Fractions\n4:05\nAdding and Subtracting Fractions with Different Denominators\n\n23m 8s\n\nIntro\n0:00\nLeast Common Multiple\n0:12\nLCM of 6 and 4\n0:31\nFrom LCM to LCD\n2:25\n3:12\nExtra Example 1: Add or Subtract\n6:23\nExtra Example 2: Add or Subtract\n9:49\nExtra Example 3: Add or Subtract\n14:54\nExtra Example 4: Add or Subtract\n18:14\n\n19m 44s\n\nIntro\n0:00\nExample\n0:05\n0:17\nExtra Example 1: Adding Mixed Numbers\n1:57\nExtra Example 2: Subtracting Mixed Numbers\n8:13\nExtra Example 3: Adding Mixed Numbers\n12:01\nExtra Example 4: Subtracting Mixed Numbers\n14:54\nMultiplying Fractions and Mixed Numbers\n\n21m 32s\n\nIntro\n0:00\nMultiplying Fractions\n0:07\nStep 1: Change Mixed Numbers to Improper Fractions\n0:08\nStep2: Multiply the Numerators Together\n0:56\nStep3: Multiply the Denominators Together\n1:03\nExtra Example 1: Multiplying Fractions\n1:37\nExtra Example 2: Multiplying Fractions\n6:39\nExtra Example 3: Multiplying Fractions\n10:20\nExtra Example 4: Multiplying Fractions\n13:47\nDividing Fractions and Mixed Numbers\n\n18m\n\nIntro\n0:00\nDividing Fractions\n0:09\nStep 1: Change Mixed Numbers to Improper Fractions\n0:15\nStep 2: Flip the Second Fraction\n0:27\nStep 3: Multiply the Fractions\n0:52\nExtra Example 1: Dividing Fractions\n1:23\nExtra Example 2: Dividing Fractions\n5:06\nExtra Example 3: Dividing Fractions\n9:34\nExtra Example 4: Dividing Fractions\n12:06\nDistributive Property\n\n11m 5s\n\nIntro\n0:00\nDistributive Property\n0:06\nMethods of Distributive Property\n0:07\nExample: a(b)\n0:35\nExample: a(b+c)\n0:49\nExample: a(b+c+d)\n1:22\nExtra Example 1: Using Distributive Property\n1:56\nExtra Example 2: Using Distributive Property\n4:36\nExtra Example 3: Using Distributive Property\n6:39\nExtra Example 4: Using Distributive Property\n8:19\nUnits of Measure\n\n16m 36s\n\nIntro\n0:00\nLength\n0:05\nFeet, Inches, Yard, and Mile\n0:20\nMillimeters, Centimeters, and Meters\n0:43\nMass\n2:57\nPounds, Ounces, and Tons\n3:03\nGrams and Kilograms\n3:38\nLiquid\n4:11\nGallons, Quarts, Pints, and Cups\n4:14\nExtra Example 1: Converting Units\n7:02\nExtra Example 2: Converting Units\n9:31\nExtra Example 3: Converting Units\n12:21\nExtra Example 4: Converting Units\n14:05\nSection 4: Positive and Negative Numbers\nIntegers and the Number Line\n\n13m 24s\n\nIntro\n0:00\nWhat are Integers\n0:06\nIntegers are all Whole Numbers and Their Opposites\n0:09\nAbsolute Value\n2:35\nExtra Example 1: Compare the Integers\n4:36\nExtra Example 2: Writing Integers\n9:24\nExtra Example 3: Opposite Integer\n10:38\nExtra Example 4: Absolute Value\n11:27\n\n16m 5s\n\nIntro\n0:00\nUsing a Number Line\n0:04\nExample: 4 + (-2)\n0:14\nExample: 5 + (-8)\n1:50\n3:00\n3:10\n3:37\n4:44\nExtra Example 1: Add the Integers\n8:21\nExtra Example 2: Find the Sum\n10:33\nExtra Example 3: Find the Value\n11:37\nExtra Example 4: Add the Integers\n13:10\nSubtracting Integers\n\n15m 25s\n\nIntro\n0:00\nHow to Subtract Integers\n0:06\nTwo-dash Rule\n0:16\nExample: 3 - 5\n0:44\nExample: 3 - (-5)\n1:12\nExample: -3 - 5\n1:39\nExtra Example 1: Rewrite Subtraction to Addition\n4:43\nExtra Example 2: Find the Difference\n7:59\nExtra Example 3: Find the Difference\n9:08\nExtra Example 4: Evaluate\n10:38\nMultiplying Integers\n\n7m 33s\n\nIntro\n0:00\nWhen Multiplying Integers\n0:05\nIf One Number is Negative\n0:06\nIf Both Numbers are Negative\n0:18\nExamples: Multiplying Integers\n0:53\nExtra Example 1: Multiplying Integers\n1:27\nExtra Example 2: Multiplying Integers\n2:43\nExtra Example 3: Multiplying Integers\n3:13\nExtra Example 4: Multiplying Integers\n3:51\nDividing Integers\n\n6m 42s\n\nIntro\n0:00\nWhen Dividing Integers\n0:05\nRules for Dividing Integers\n0:41\nExtra Example 1: Dividing Integers\n1:01\nExtra Example 2: Dividing Integers\n1:51\nExtra Example 3: Dividing Integers\n2:21\nExtra Example 4: Dividing Integers\n3:18\nIntegers and Order of Operations\n\n11m 9s\n\nIntro\n0:00\nCombining Operations\n0:21\nSolve Using the Order of Operations\n0:22\nExtra Example 1: Evaluate\n1:18\nExtra Example 2: Evaluate\n4:20\nExtra Example 3: Evaluate\n6:33\nExtra Example 4: Evaluate\n8:13\nSection 5: Solving Equations\nWriting Expressions\n\n9m 15s\n\nIntro\n0:00\nOperation as Words\n0:05\nOperation as Words\n0:06\nExtra Example 1: Write Each as an Expression\n2:09\nExtra Example 2: Write Each as an Expression\n4:27\nExtra Example 3: Write Each Expression Using Words\n6:45\nWriting Equations\n\n18m 3s\n\nIntro\n0:00\nEquation\n0:05\nDefinition of Equation\n0:06\nExamples of Equation\n0:58\nOperations as Words\n1:39\nOperations as Words\n1:40\nExtra Example 1: Write Each as an Equation\n3:07\nExtra Example 2: Write Each as an Equation\n6:19\nExtra Example 3: Write Each as an Equation\n10:08\nExtra Example 4: Determine if the Equation is True or False\n13:38\n\n24m 53s\n\nIntro\n0:00\nSolving Equations\n0:08\ninverse Operation of Addition and Subtraction\n0:09\nExtra Example 1: Solve Each Equation Using Mental Math\n4:15\nExtra Example 2: Use Inverse Operations to Solve Each Equation\n5:44\nExtra Example 3: Solve Each Equation\n14:51\nExtra Example 4: Translate Each to an Equation and Solve\n19:57\nSolving Multiplication Equation\n\n19m 46s\n\nIntro\n0:00\nMultiplication Equations\n0:08\nInverse Operation of Multiplication\n0:09\nExtra Example 1: Use Mental Math to Solve Each Equation\n3:54\nExtra Example 2: Use Inverse Operations to Solve Each Equation\n5:55\nExtra Example 3: Is -2 a Solution of Each Equation?\n12:48\nExtra Example 4: Solve Each Equation\n15:42\nSolving Division Equation\n\n17m 58s\n\nIntro\n0:00\nDivision Equations\n0:05\nInverse Operation of Division\n0:06\nExtra Example 1: Use Mental Math to Solve Each Equation\n0:39\nExtra Example 2: Use Inverse Operations to Solve Each Equation\n2:14\nExtra Example 3: Is -6 a Solution of Each Equation?\n9:53\nExtra Example 4: Solve Each Equation\n11:50\nSection 6: Ratios and Proportions\nRatio\n\n40m 21s\n\nIntro\n0:00\nRatio\n0:05\nDefinition of Ratio\n0:06\nExamples of Ratio\n0:18\nRate\n2:19\nDefinition of Rate\n2:20\nUnit Rate\n3:38\nExample: \\$10 / 20 pieces\n5:05\nConverting Rates\n6:46\nExample: Converting Rates\n6:47\nExtra Example 1: Write in Simplest Form\n16:22\nExtra Example 2: Find the Ratio\n20:53\nExtra Example 3: Find the Unit Rate\n22:56\nExtra Example 4: Convert the Unit\n26:34\nSolving Proportions\n\n17m 22s\n\nIntro\n0:00\nProportions\n0:05\nAn Equality of Two Ratios\n0:06\nCross Products\n1:00\nExtra Example 1: Find Two Equivalent Ratios for Each\n3:21\nExtra Example 2: Use Mental Math to Solve the Proportion\n5:52\nExtra Example 3: Tell Whether the Two Ratios Form a Proportion\n8:21\nExtra Example 4: Solve the Proportion\n13:26\nWriting Proportions\n\n22m 1s\n\nIntro\n0:00\nWriting Proportions\n0:08\nIntroduction to Writing Proportions and Example\n0:10\nExtra Example 1: Write a Proportion and Solve\n5:54\nExtra Example 2: Write a Proportion and Solve\n11:19\nExtra Example 3: Write a Proportion for Word Problem\n17:29\nSimilar Polygons\n\n16m 31s\n\nIntro\n0:00\nSimilar Polygons\n0:05\nDefinition of Similar Polygons\n0:06\nCorresponding Sides are Proportional\n2:14\nExtra Example 1: Write a Proportion and Find the Value of Similar Triangles\n4:26\nExtra Example 2: Write a Proportional to Find the Value of x\n7:04\nExtra Example 3: Write a Proportion for the Similar Polygons and Solve\n9:04\nExtra Example 4: Word Problem and Similar Polygons\n11:03\nScale Drawings\n\n13m 43s\n\nIntro\n0:00\nScale Drawing\n0:05\nDefinition of a Scale Drawing\n0:06\nExample: Scale Drawings\n1:00\nExtra Example 1: Scale Drawing\n4:50\nExtra Example 2: Scale Drawing\n7:02\nExtra Example 3: Scale Drawing\n9:34\nProbability\n\n11m 51s\n\nIntro\n0:00\nProbability\n0:05\nIntroduction to Probability\n0:06\nExample: Probability\n1:22\nExtra Example 1: What is the Probability of Landing on Orange?\n3:26\nExtra Example 2: What is the Probability of Rolling a 5?\n5:02\nExtra Example 3: What is the Probability that the Marble will be Red?\n7:40\nExtra Example 4: What is the Probability that the Student will be a Girl?\n9:43\nSection 7: Percents\nPercents, Fractions, and Decimals\n\n35m 5s\n\nIntro\n0:00\nPercents\n0:06\nChanging Percent to a Fraction\n0:07\nChanging Percent to a Decimal\n1:54\nFractions\n4:17\nChanging Fraction to Decimal\n4:18\nChanging Fraction to Percent\n7:50\nDecimals\n10:10\nChanging Decimal to Fraction\n10:11\nChanging Decimal to Percent\n12:07\nExtra Example 1: Write Each Percent as a Fraction in Simplest Form\n13:29\nExtra Example 2: Write Each as a Decimal\n17:09\nExtra Example 3: Write Each Fraction as a Percent\n22:45\nExtra Example 4: Complete the Table\n29:17\nFinding a Percent of a Number\n\n28m 18s\n\nIntro\n0:00\nPercent of a Number\n0:06\nTranslate Sentence into an Equation\n0:07\nExample: 30% of 100 is What Number?\n1:05\nExtra Example 1: Finding a Percent of a Number\n7:12\nExtra Example 2: Finding a Percent of a Number\n15:56\nExtra Example 3: Finding a Percent of a Number\n19:14\nExtra Example 4: Finding a Percent of a Number\n24:26\nSolving Percent Problems\n\n32m 31s\n\nIntro\n0:00\nSolving Percent Problems\n0:06\nTranslate the Sentence into an Equation\n0:07\nExtra Example 1: Solving Percent Problems\n0:56\nExtra Example 2: Solving Percent Problems\n14:49\nExtra Example 3: Solving Percent Problems\n23:44\nSimple Interest\n\n27m 9s\n\nIntro\n0:00\nSimple Interest\n0:05\nPrincipal\n0:06\nInterest & Interest Rate\n0:41\nSimple Interest\n1:43\nSimple Interest Formula\n2:23\nSimple Interest Formula: I = prt\n2:24\nExtra Example 1: Finding Simple Interest\n3:53\nExtra Example 2: Finding Simple Interest\n8:08\nExtra Example 3: Finding Simple Interest\n12:02\nExtra Example 4: Finding Simple Interest\n17:46\nDiscount and Sales Tax\n\n17m 15s\n\nIntro\n0:00\nDiscount\n0:19\nDiscount\n0:20\nSale Price\n1:22\nSales Tax\n2:24\nSales Tax\n2:25\nTotal Due\n2:59\nExtra Example 1: Finding the Discount\n3:43\nExtra Example 2: Finding the Sale Price\n6:28\nExtra Example 3: Finding the Sale Tax\n11:14\nExtra Example 4: Finding the Total Due\n14:08\nSection 8: Geometry in a Plane\nIntersecting Lines and Angle Measures\n\n24m 17s\n\nIntro\n0:00\nIntersecting Lines\n0:07\nProperties of Lines\n0:08\nWhen Two Lines Cross Each Other\n1:55\nAngles\n2:56\nProperties of Angles: Sides, Vertex, and Measure\n2:57\nClassifying Angles\n7:18\nAcute Angle\n7:19\nRight Angle\n7:54\nObtuse Angle\n8:03\nAngle Relationships\n8:56\nVertical Angles\n8:57\n10:38\nComplementary Angles\n11:52\nSupplementary Angles\n12:54\nExtra Example 1: Lines\n16:00\nExtra Example 2: Angles\n18:22\nExtra Example 3: Angle Relationships\n20:05\nExtra Example 4: Name the Measure of Angles\n21:11\nAngles of a Triangle\n\n13m 35s\n\nIntro\n0:00\nAngles of a Triangle\n0:05\nAll Triangles Have Three Angles\n0:06\nMeasure of Angles\n2:16\nExtra Example 1: Find the Missing Angle Measure\n5:39\nExtra Example 2: Angles of a Triangle\n7:18\nExtra Example 3: Angles of a Triangle\n9:24\nClassifying Triangles\n\n15m 10s\n\nIntro\n0:00\nTypes of Triangles by Angles\n0:05\nAcute Triangle\n0:06\nRight Triangle\n1:14\nObtuse Triangle\n2:22\nClassifying Triangles by Sides\n4:18\nEquilateral Triangle\n4:20\nIsosceles Triangle\n5:21\nScalene Triangle\n5:53\nExtra Example 1: Classify the Triangle by Its Angles and Sides\n6:34\nExtra Example 2: Sketch the Figures\n8:10\nExtra Example 3: Classify the Triangle by Its Angles and Sides\n9:55\nExtra Example 4: Classify the Triangle by Its Angles and Sides\n11:35\n\n17m 41s\n\nIntro\n0:00\n0:05\n0:06\nParallelogram\n0:45\nRectangle\n2:28\nRhombus\n3:13\nSquare\n3:53\nTrapezoid\n4:38\nParallelograms\n5:33\nParallelogram, Rectangle, Rhombus, Trapezoid, and Square\n5:35\nExtra Example 1: Give the Most Exact Name for the Figure\n11:37\nExtra Example 2: Fill in the Blanks\n13:31\nExtra Example 3: Complete Each Statement with Always, Sometimes, or Never\n14:37\nArea of a Parallelogram\n\n12m 44s\n\nIntro\n0:00\nArea\n0:06\nDefinition of Area\n0:07\nArea of a Parallelogram\n2:00\nArea of a Parallelogram\n2:01\nExtra Example 1: Find the Area of the Rectangle\n4:30\nExtra Example 2: Find the Area of the Parallelogram\n5:29\nExtra Example 3: Find the Area of the Parallelogram\n7:22\nExtra Example 4: Find the Area of the Shaded Region\n8:55\nArea of a Triangle\n\n11m 29s\n\nIntro\n0:00\nArea of a Triangle\n0:05\nArea of a Triangle: Equation and Example\n0:06\nExtra Example 1: Find the Area of the Triangles\n1:31\nExtra Example 2: Find the Area of the Figure\n4:09\nExtra Example 3: Find the Area of the Shaded Region\n7:45\nCircumference of a Circle\n\n15m 4s\n\nIntro\n0:00\nSegments in Circles\n0:05\n0:06\nDiameter\n1:08\nChord\n1:49\nCircumference\n2:53\nCircumference of a Circle\n2:54\nExtra Example 1: Name the Given Parts of the Circle\n6:26\nExtra Example 2: Find the Circumference of the Circle\n7:54\nExtra Example 3: Find the Circumference of Each Circle with the Given Measure\n11:04\nArea of a Circle\n\n14m 43s\n\nIntro\n0:00\nArea of a Circle\n0:05\nArea of a Circle: Equation and Example\n0:06\nExtra Example 1: Find the Area of the Circle\n2:17\nExtra Example 2: Find the Area of the Circle\n5:47\nExtra Example 3: Find the Area of the Shaded Region\n9:24\nSection 11: Geometry in Space\nPrisms and Cylinders\n\n21m 49s\n\nIntro\n0:00\nPrisms\n0:06\nPolyhedron\n0:07\nRegular Prism, Bases, and Lateral Faces\n1:44\nCylinders\n9:37\nBases and Altitude\n9:38\nExtra Example 1: Classify Each Prism by the Shape of Its Bases\n11:16\nExtra Example 2: Name Two Different Edges, Faces, and Vertices of the Prism\n15:44\nExtra Example 3: Name the Solid of Each Object\n17:58\nExtra Example 4: Write True or False for Each Statement\n19:47\nVolume of a Rectangular Prism\n\n8m 59s\n\nIntro\n0:00\nVolume of a Rectangular Prism\n0:06\nVolume of a Rectangular Prism: Formula\n0:07\nVolume of a Rectangular Prism: Example\n1:46\nExtra Example 1: Find the Volume of the Rectangular Prism\n3:39\nExtra Example 2: Find the Volume of the Cube\n5:00\nExtra Example 3: Find the Volume of the Solid\n5:56\nVolume of a Triangular Prism\n\n16m 15s\n\nIntro\n0:00\nVolume of a Triangular Prism\n0:06\nVolume of a Triangular Prism: Formula\n0:07\nExtra Example 1: Find the Volume of the Triangular Prism\n2:42\nExtra Example 2: Find the Volume of the Triangular Prism\n7:21\nExtra Example 3: Find the Volume of the Solid\n10:38\nVolume of a Cylinder\n\n15m 55s\n\nIntro\n0:00\nVolume of a Cylinder\n0:05\nVolume of a Cylinder: Formula\n0:06\nExtra Example 1: Find the Volume of the Cylinder\n1:52\nExtra Example 2: Find the Volume of the Cylinder\n7:38\nExtra Example 3: Find the Volume of the Cylinder\n11:25\nSurface Area of a Prism\n\n23m 28s\n\nIntro\n0:00\nSurface Area of a Prism\n0:06\nSurface Area of a Prism\n0:07\nLateral Area of a Prism\n2:12\nLateral Area of a Prism\n2:13\nExtra Example 1: Find the Surface Area of the Rectangular Prism\n7:08\nExtra Example 2: Find the Lateral Area and the Surface Area of the Cube\n12:05\nExtra Example 3: Find the Surface Area of the Triangular Prism\n17:13\nSurface Area of a Cylinder\n\n27m 41s\n\nIntro\n0:00\nSurface Area of a Cylinder\n0:06\nIntroduction to Surface Area of a Cylinder\n0:07\nSurface Area of a Cylinder\n1:33\nFormula\n1:34\nExtra Example 1: Find the Surface Area of the Cylinder\n5:51\nExtra Example 2: Find the Surface Area of the Cylinder\n13:51\nExtra Example 3: Find the Surface Area of the Cylinder\n20:57\nSection 10: Data Analysis and Statistics\nMeasures of Central Tendency\n\n24m 32s\n\nIntro\n0:00\nMeasures of Central Tendency\n0:06\nMean\n1:17\nMedian\n2:42\nMode\n5:41\nExtra Example 1: Find the Mean, Median, and Mode for the Following Set of Data\n6:24\nExtra Example 2: Find the Mean, Median, and Mode for the Following Set of Data\n11:14\nExtra Example 3: Find the Mean, Median, and Mode for the Following Set of Data\n15:13\nExtra Example 4: Find the Three Measures of the Central Tendency\n19:12\nHistograms\n\n19m 43s\n\nIntro\n0:00\nHistograms\n0:05\nDefinition and Example\n0:06\nExtra Example 1: Draw a Histogram for the Frequency Table\n6:14\nExtra Example 2: Create a Histogram of the Data\n8:48\nExtra Example 3: Create a Histogram of the Following Test Scores\n14:17\nBox-and-Whisker Plot\n\n17m 54s\n\nIntro\n0:00\nBox-and-Whisker Plot\n0:05\nMedian, Lower & Upper Quartile, Lower & Upper Extreme\n0:06\nExtra Example 1: Name the Median, Lower & Upper Quartile, Lower & Upper Extreme\n6:04\nExtra Example 2: Draw a Box-and-Whisker Plot Given the Information\n7:35\nExtra Example 3: Find the Median, Lower & Upper Quartile, Lower & Upper Extreme\n9:31\nExtra Example 4: Draw a Box-and-Whiskers Plots for the Set of Data\n12:50\nStem-and-Leaf Plots\n\n17m 42s\n\nIntro\n0:00\nStem-and-Leaf Plots\n0:05\nStem-and-Leaf Plots\n0:06\nExtra Example 1: Use the Data to Create a Stem-and-Leaf Plot\n2:28\nExtra Example 2: List All the Numbers in the Stem-and-Leaf Plot in Order From Least to Greatest\n7:02\nExtra Example 3: Create a Stem-and-Leaf Plot of the Data & Find the Median and the Mode.\n8:59\nThe Coordinate Plane\n\n19m 59s\n\nIntro\n0:00\nThe Coordinate System\n0:05\nThe Coordinate Plane\n0:06\n0:50\nThe Coordinate Plane\n7:02\nWrite the Coordinates for Points A, B, and C\n7:03\nExtra Example 1: Graph Each Point on the Coordinate Plane\n9:03\nExtra Example 2: Write the Coordinate and Quadrant for Each Point\n11:05\nExtra Example 3: Name Two Points From Each of the Four Quadrants\n13:13\nExtra Example 4: Graph Each Point on the Same Coordinate Plane\n17:47\nSection 11: Probability and Discrete Mathematics\nOrganizing Possible Outcomes\n\n15m 35s\n\nIntro\n0:00\nCompound Events\n0:08\nCompound Events\n0:09\nFundamental Counting Principle\n3:35\nExtra Example 1: Create a List of All the Possible Outcomes\n4:47\nExtra Example 2: Create a Tree Diagram For All the Possible Outcomes\n6:34\nExtra Example 3: Create a Tree Diagram For All the Possible Outcomes\n10:00\nExtra Example 4: Fundamental Counting Principle\n12:41\nIndependent and Dependent Events\n\n35m 19s\n\nIntro\n0:00\nIndependent Events\n0:11\nDefinition\n0:12\nExample 1: Independent Event\n1:45\nExample 2: Two Independent Events\n4:48\nDependent Events\n9:09\nDefinition\n9:10\nExample: Dependent Events\n10:10\nExtra Example 1: Determine If the Two Events are Independent or Dependent Events\n13:38\nExtra Example 2: Find the Probability of Each Pair of Events\n18:11\nExtra Example 3: Use the Spinner to Find Each Probability\n21:42\nExtra Example 4: Find the Probability of Each Pair of Events\n25:49\nDisjoint Events\n\n12m 13s\n\nIntro\n0:00\nDisjoint Events\n0:06\nDefinition and Example\n0:07\nExtra Example 1: Disjoint & Not Disjoint Events\n3:08\nExtra Example 2: Disjoint & Not Disjoint Events\n4:23\nExtra Example 3: Independent, Dependent, and Disjoint Events\n6:30\nProbability of an Event Not Occurring\n\n20m 5s\n\nIntro\n0:00\nEvent Not Occurring\n0:07\nFormula and Example\n0:08\nExtra Example 1: Use the Spinner to Find Each Probability\n7:24\nExtra Example 2: Probability of Event Not Occurring\n11:21\nExtra Example 3: Probability of Event Not Occurring\n15:51\nBookmark & Share Embed\n\n## Copy & Paste this embed code into your website’s HTML\n\nPlease ensure that your website editor is in text mode when you paste the code.\n(In Wordpress, the mode button is on the top right corner.)\n×\n• - Allow users to view the embedded video in full-size.\nSince this lesson is not free, only the preview will appear on your website.\n\n• ## Related Books",
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"0 answersPost by Andrew Liu on May 11, 2020I always have trouble with percentages. This video helped me a lot!\n\n### Solving Percent Problems\n\n• To solve percent problems, translate the sentence into an equation\n• “of” means times\n• “what” means unknown variable\n\n### Solving Percent Problems\n\n25 is 50% of what number?\n• 50% = .50\n• 25 = .50x\n• [25/.50] = x\n50\n11 is 20% of what number?\n• 20% = .20\n• 11 = .20x\n• [11/.20] = x\n55\n18 is 40% of what number?\n• 40% = .40\n• 18 = .40x\n• [18/.40] = x\n45\nWhat percent of 70 is 35?\n• x ·70 = 35\n• x = [35/70]\n• x = 0.5\n50%\nWhat percent of 16 is 4?\n• x ·16 = 4\n• x = [4/16]\n• x = 0.25\n25%\n15 is what percent of 1500?\n• 15 = x ·1500\n• [15/1500] = x\n• 0.01 = x\n1%\n30 percent of 60 is what number?\n• 30% = .30\n• .30(60) = x\n18\n5% of what number is 15?\n• .5x = 15\n• x = [15/.5]\nx = 30\n100% of 154 is what number?\n• 100% = 1\n• 1 ×154 = x\n154\n16 is 25% of what number?\n• .25x = 16\n• x = [16/.25]\n64\n\n*These practice questions are only helpful when you work on them offline on a piece of paper and then use the solution steps function to check your answer.\n\n### Solving Percent Problems\n\nLecture Slides are screen-captured images of important points in the lecture. Students can download and print out these lecture slide images to do practice problems as well as take notes while watching the lecture.\n\n• Intro 0:00\n• Solving Percent Problems 0:06\n• Translate the Sentence into an Equation\n• Extra Example 1: Solving Percent Problems 0:56\n• Extra Example 2: Solving Percent Problems 14:49\n• Extra Example 3: Solving Percent Problems 23:44\n\n### Transcription: Solving Percent Problems\n\nWelcome back to Educator.com.0000\n\nFor the next lesson, we are going to go over solving percent problems.0002\n\nTo solve a problem that involves percents, we want to first translate whatever the sentence is into an equation.0008\n\nWhenever you have a number, you are going to write that down in your equation.0018\n\nIf you have a percent, you need to change it to a decimal.0022\n\nYou see the word of; that means times; you are going to be multiplying.0027\n\nWhen you see the word what or what number, that means you are going to have a variable.0032\n\nThat is what you are going to be looking for.0037\n\nThat is what you are going to be solving for.0039\n\nThis is almost the same as what we just did the last lesson.0041\n\nBut now we are going to be looking at variables and solving equations.0047\n\nWe are going to have to do a little bit deeper into these problems.0053\n\nThe first set of examples; for example one, 15.0060\n\nRemember if we have a number, we are just going to write it straight down into our equation.0068\n\nIs; the word is remember means equals; 25 percent; this is write down.0074\n\nThis we are also going to write straight down.0083\n\nBut because it is a percent, in our equation, we need to make it into a decimal.0085\n\nPercent to decimal, remember we have to move two decimal spaces to the left0091\n\nbecause think of percent as a bigger version of a decimal; decimals are small numbers.0105\n\nWhenever you are converting from a percent to a decimal, you have to get smaller.0112\n\nThe way for you to get smaller is to move the decimal point over to the left.0118\n\nThe decimal point is over on this side.0127\n\nIf you don't see a decimal point, it is always at the end of the number.0130\n\nWe are going to move it one, two spaces.0134\n\nThe decimal point of 25 percent is going to be 0.25.0139\n\nOf course obviously we have to drop the percent sign.0145\n\n25 percent is 0.25; that is what I am going to write in my equation.0148\n\n0.25; of means times; times what number; this is my variable X.0154\n\nBe careful here because whenever you write a dot for times, that looks like the decimal point.0168\n\nThe best way to represent multiplying is to either write it in parentheses.0174\n\nIf you are going to show that you are going to be multiplying two numbers, write them in parentheses.0179\n\nOr if it is a number with a variable, a letter, then you can just put them together.0184\n\n0.25X, that would mean 0.25 times X.0191\n\nHere is our equation now; this is what I am solving for.0198\n\n15 is 25 percent of what number?--this is the number that I am looking for.0202\n\nWhen I solve for X, remember that since this is 0.25 times X, I need to do the inverse operation.0207\n\n0.25 times X, the inverse operation is divide.0219\n\nIn order to solve for X, I have to divide 0.25.0224\n\n0.25 over itself is going to be 1.0232\n\nWhatever I do to one side, I have to do to the other side of the equal sign.0236\n\nI have to divide this side also by 0.25; this, it looks like a fraction.0241\n\nBut fractions are division problems; this is the same thing as 15 divided by 0.25.0247\n\nAnother way to explain why we have to divide, let's say I have 8 equals 4X.0255\n\nIf I have 4 times a number equals 8, what do I know about X?0268\n\nIsn't 4 times 2, 8?--so I know X has to be 2.0275\n\nIn the same way, I have to solve for X and I can just divide this number by this number.0285\n\nDivide this by 4; divide this by 4; 8 divided by 4 is 2.0292\n\nThat gives me 2 equals X.0298\n\nHere 15 divided by 0.25, I need to actually solve that out in order to find X.0305\n\nLet's review over how to divide numbers with decimals.0313\n\nIf I am going to divide these two numbers, remember that the top number is what goes inside when you divide.0321\n\n15 on the inside; 0.25 on the outside.0330\n\nThe decimal point for this number is at the end because we don't see one.0337\n\nIt is always at the end.0342\n\nIf I need to add 0s to this number, I can because0349\n\nI can always add 0s to the end of a number behind a decimal.0352\n\nIf it was before the decimal, I can't because then that will just become 150 instead of 15.0358\n\nAs long as it is behind the decimal and it is at the end of a number,0364\n\nyou can add as many 0s as you want.0370\n\nI can add two 0s; I can add three; I can add ten; however many I need.0372\n\nThis number, we want to change to a whole number.0379\n\nI need to move the decimal point over two spaces to the right to make the decimal point at the end.0384\n\nIf I move two decimal places for this number, then I have to move this two decimal places over here.0392\n\nThen I am going to take that decimal point up.0399\n\n25 goes into 15 zero times; 25 goes into 150 how many times?0403\n\nThink about quarters; 25 cents or 25 is like a quarter.0412\n\nHow many of those fit into 100 or a dollar?--four.0421\n\nFour quarters is a dollar; think of 150 cents.0425\n\n25 cents goes into 150 cents or 1 dollar 50, how many of them?0428\n\nThat would be six.0434\n\nIf you want to just check that, this is 12, 13, 14, 15.0437\n\nThat becomes 0 when you subtract it; I can bring down this 0.0449\n\n25 goes into 0 zero times; I have to fill in this space right here.0454\n\nThat is 0; subtract it; that is nothing.0461\n\nI don't have to bring down another 0 because my remainder is 0.0464\n\nMy answer then, if I do 15 divided by 0.25, is this number up here, 60.0471\n\nThis number, if there is a 0 in front of the number like that, then that doesn't mean anything either.0479\n\nI can just drop the 0; that would just be 60.0485\n\nThis 0 I cannot drop.0489\n\nThis 0 has to stay there because if I drop it, my number is going to change to 6.0491\n\nWe know that 6 is not the same as 60.0498\n\nThis 0, it is not after the decimal point so we can't drop that 0.0501\n\nIf you need to review over this, you can either go back to that lesson, dividing decimals.0513\n\nOr we are going to do a few more problems that involve dividing decimals.0520\n\nThe next one, again we are going to change this into an equation.0527\n\n1; is is equals; 4 percent.0535\n\n4 percent, again we have to change it to a decimal.0540\n\nBe careful; 4 percent is not 0.4.0543\n\nAgain 4 percent to decimal; the decimal point is at the end right here.0549\n\nI go one, two; then put the decimal point there.0556\n\nI have an empty space that I have to fill.0561\n\nI have to fill that with a 0; it is going to be 0.04.0563\n\nAgain at the end here, one, two, decimal point; 0.04.0568\n\nOf means times; blank, that is what we are looking for; that is my variable.0580\n\nI am going to put X there.0589\n\nRemember if I put number with variable, that means times.0591\n\nThis represents... I don't have to put a dot here.0595\n\nI don't have to use parentheses when it is number with variable.0597\n\nAgain how do I solve for X?--look at this example again.0603\n\nIf we are going to do 8 equals 4 times a number, I can take this number, divide it by this number.0607\n\n8/4; that is going to give me X.0614\n\nThen I have to do this number divided by this number.0617\n\nRemember that this over this becomes 1.0626\n\nThis whole side, my right side, just becomes 1X.0630\n\n1X is the same thing as X.0635\n\nThat is probably a little bit hard to understand, 1X being the same as X.0639\n\nBut it is like me saying I have an apple.0645\n\nIf I say I have an apple, you know I only have 1 apple.0650\n\nEven though I didn't say I have 1 apple, you just know because how many A's do you see?0655\n\nYou see one; an A is the same thing as 1A.0661\n\nAn apple is the same thing as 1 apple.0667\n\nJust think of that as having 1X; again we have to divide that; 1.0672\n\nBe careful, the top number is going to go inside.0683\n\n0.04, the bottom number, is going to go on the outside.0689\n\nAgain I have to move this decimal point over one, two spaces to the right.0693\n\nThat means I have to take this decimal point; it is at the end.0697\n\nGo one, two spaces; I have to fill these in with 0s.0702\n\nThere is my new spot for my decimal point; I bring it up.0709\n\n4 goes into 10 how many times?--4 times 2 is 8.0717\n\n4 times 3 is 12; 12 is too big; it only fits into 10 twice.0724\n\n2 times 4 is 8; subtract it; I get 2.0732\n\nI am going to bring down this 0.0738\n\n4 goes into 20 how many times?--five times.0741\n\n4 times 5 is 20; subtract it; I get a remainder of 0.0746\n\nI can stop there; my answer becomes 25.0753\n\nI don't have to put that decimal because it is a whole number and it is at the end.0758\n\nMy answer X is 25; right here, this is 25.0764\n\nAgain 1X is the same thing as X; what did this become?0773\n\nThis became 25; if 25 is X, then I can just say that X is 25.0778\n\nIt is the same exact thing.0786\n\nThe next one, 20 equals 100 percent; to decimal.0792\n\nAgain start at the end; you are going to go one, two; right there.0806\n\nIt is going to be after the 1; 1.0.0810\n\nRemember if the 0s are at the end of a number behind the decimal point, then I can just drop it.0814\n\nIsn't this the same thing as 1?--I can just write 1.0819\n\n100 percent as a decimal is 1; times; of means times.0824\n\nWhat number, that is my variable; 1 times X; 20 equals 1X.0832\n\nRemember 1X is the same thing as X because if I have 1 apple,0841\n\nthat is the same thing as just saying I have an apple.0846\n\nIf you want, you can go ahead and divide the 1 just like we did the other problems.0850\n\n20 divided by 1 is 20.0856\n\nWhenever you have a number divided by 1, it is always itself.0860\n\n20 equals X; or I can flip this around.0864\n\nIf 20 equals X, then isn't that the same thing as X being 20?0870\n\nEither way, that is correct; we just want to know what the number is.0877\n\nThe number is 20; or you can say 20 is the number.0881\n\nIt is the same exact thing.0886\n\nLet's do a few more examples; these are a little bit different.0889\n\nWhat percent of 50 is 10?0895\n\nNow the variable, what we are solving for, is a percent.0900\n\nBe careful here; what percent, I am going to make that X.0907\n\nTimes, times; 50, 50; is, equals; 10, 10; X times 50 equals 10.0913\n\nRemember you can change this if you want to 50X just like we did the other problems0929\n\nbecause a number times a variable, you just put it together with the number in front.0935\n\n50X equals 10; it is the same thing.0939\n\nHow do we solve for X then?--how do we get what X is?0944\n\nRemember my example?--let's say I have 3 times X equals 6.0949\n\nYou can do this in your head and know that 3 times 2 equals 6.0959\n\nAnother way for you to solve it is to do 6 divided by this number; this divided by this.0964\n\nSame thing; we can just do 10 divided by 50.0972\n\nIt is not 50 divided by 10.0975\n\nIt is this number divided by this number, the one that is multiplied to the variable.0978\n\nI can show you this way; 50/50, that is 1.0985\n\n10/50, that is what you have to do; 10 divided by 50.0993\n\nAgain fractions are the same thing as division; 10 divided by 50.0997\n\nA shortcut way of doing this is if you are dividing two numbers1009\n\nwith 0s at the end of it, you can just cross out the 0.1014\n\nIf there is one 0 up here and one 0 down here, you can just cross out1020\n\none 0 from each of the numbers as long as there is 0s in both numbers.1022\n\nBut we are just going to go ahead and just divide it this way.1027\n\n50 divided by 10; it is not going to go into this number.1032\n\nThis number is too big to go into this number.1037\n\nI am going to have to use my decimal point.1040\n\nDo I move it at all?--no, because there is no decimal point here.1042\n\nI can just bring it up, bring it straight up.1047\n\nI can add 0s at the end behind the decimal at the end of a number.1050\n\nNow I can just look at this, 1-0-0, 100.1057\n\n50 goes into 100... 50 plus 50 is 100; or 50 times 2 is 100.1061\n\nThink of 50 cents; 50 cents goes into 100 cents how many times?1072\n\n100 cents is the same thing as a dollar.1080\n\n50 cents goes into a dollar twice; this becomes that.1082\n\nSubtract it; you get 0; no remainder; that is my answer.1087\n\nI don't have to bring down anymore 0s because my remainder became 0.1092\n\nWhen I divided this, my answer became 0.2; X equals 0.2.1099\n\nHere is the thing though; they are asking for percent.1114\n\nEven though this is my answer, this is my answer as a decimal.1119\n\nThey want it in percent; they are asking what percent.1124\n\nThey are not asking what decimal; what percent?1127\n\nI have to change this number to a percent; decimal to percent.1129\n\nRemember decimal is a small number; percents are larger.1141\n\nI have to go from a small to a larger.1146\n\nThat means I have to move the decimal point over two spaces; but which way?1148\n\nIf I go to the left, I am going to get smaller.1154\n\nBut if I go to the right, then I start getting whole numbers.1158\n\nI make the number bigger.1162\n\n0.2, to make it into a percent, I need to make it bigger.1165\n\nI need to go to the right; one, two.1169\n\nI have to fill this space with something.1173\n\n0.2 as a percent will be 2-0 and then percent.1177\n\nThe decimal point is right here; it is at the end.1185\n\nIf it is at the end, remember you can just... it doesn't have to be there.1187\n\nYou can make it invisible.1190\n\nThen we have to write the percent sign because we changed it to a percent.1193\n\n20 percent of 50 is 10.1204\n\nAnother one; again what percent, make that X, your variable.1208\n\nTimes 75; is is equals; 7.5.1214\n\nAgain we have to do this number divided by this number; 7.5 divided by 75.1224\n\nAgain if you want to see it, I can show you this way.1235\n\nbecause this you have to get rid of by dividing it.1239\n\nThis 75/75 is 1; X times 1 is just X.1244\n\nX equals; then I have to actually divide that to find the answer.1251\n\nHere I don't have to move the decimal point anywhere because it is at the end.1258\n\nThis decimal point will just come straight up.1263\n\n75 goes into 75 how many times?--once.1267\n\n1 times 75 is 75; subtract it; I get 0.1274\n\nI don't have to go any further; 0.1 is my answer; X equals 0.1.1280\n\nBut again remember it is asking for percent.1285\n\nBe careful that you don't forget to change it to a percent.1288\n\nI am going to put that here to represent decimal.1294\n\nTo change it percent, I am going to go one, two, point.1301\n\n1; fill this space with a 0; put the percent sign.1306\n\n0.1 in decimal becomes 10 percent; X equals 10 percent; there is my answer.1314\n\nThe last one for this; again what percent X times... of is times... 4 equals 4?1325\n\nThis one we can just do in our head; what times 4 equals 4?1340\n\nIsn't this 1?--1 times 4 equals 4; 4 times 1, it equals itself.1345\n\nI don't even have to solve this; I can just make X equal to 1.1352\n\nIf the problem is fairly easy, you can just do it in your head, then go ahead.1356\n\nThere is no need for you to do all the work unless your teacher wants you to show the work.1360\n\nThen X equals 1; since X equals 1... I didn't mean to box this.1367\n\nThat is not my final answer so I don't want to box it.1381\n\nSince X equals 1, I need to change it to percent.1385\n\nHow do you change a 1, a whole number, into a percent?1391\n\nAgain where is the decimal point?--I don't see it.1395\n\nIf I don't see it, it is invisible, it is at the end like that.1399\n\nGo one, two, point here; I have two spaces to fill.1404\n\nThis becomes 1-0-0 percent; this X equals 100 percent.1411\n\nThe next example, we are just going to do a few more, just different types though.1428\n\nThe other examples, they were the same kind, all the problems on that page.1433\n\n15 equals what percent, X, times 150; let me rewrite this equation out.1439\n\nSince this is 150 times X, let me just write it here.1452\n\n15 equals... remember whenever I do a variable times a number,1458\n\nI want to write it together but with the number in front; 150X, like that.1463\n\nThen to solve for X, remember I have to do this number divided by this number.1471\n\nI am going to divide this side by 150.1478\n\nWhatever I do to one side, I have to do to the other side.1480\n\nThat way this becomes 1X or 1 times X; 1X.1486\n\nThat is the same thing as X.1492\n\n15 divided by 150; no decimal point here; I don't have to move it.1495\n\nInstead I need to draw that in; bring it up; add 0s.1509\n\n150, we know it doesn't go into 15.1518\n\nIf you want, you can put a 0 up here; if not, then that is fine.1523\n\nJust remember that it is now the next three or just these three.1525\n\n150 goes into 150 one time; that becomes 150.1530\n\nIf you subtract it, it becomes 0; I drew an extra 0 there.1536\n\nBut you don't even have to bring it down because it is just going to be 0s.1542\n\nRemember 0s at the end of a number behind the decimal point means nothing.1545\n\n0.1 is my answer; X is going to equal 0.1.1551\n\nYou can also say 0.1 is going to equal 1X or X; same thing.1558\n\nBut I can switch it like this; it is asking for percent.1564\n\nI need to take this decimal point and go one, two; fill in this space.1572\n\nIt is going to be 1-0 percent; 0.1 is the same thing as 10 percent.1579\n\nThe next one, 30 percent, this is written out as percent.1592\n\nBut it still means the same thing as percent like that.1599\n\n30 percent, I have to change that to a decimal.1602\n\n30 percent becomes... at the end, you go one, two, right there.1606\n\n0.30 or 0.3 because remember it is 0s at the end behind the decimal.1613\n\nIt doesn't mean anything.1619\n\n0.3 or 0.30; of, times; 12; it is number times number.1621\n\nI can't write it next to each other like how I do numbers with letters.1629\n\nI have to put it in parentheses.1633\n\nIs is equals; what number, this is my variable.1637\n\nAll I have to do to figure out what X is is multiply those two numbers.1645\n\n0.30, you know what?--we know that the 0 means nothing.1654\n\nLet's just make it easier and just not even have that 0.1660\n\nOne digit number is easier to multiply.1665\n\nI am going to put the 12 on the top; 0.3 on the bottom.1669\n\nMultiply it; 2 times 3 is 6; 3 times 1 is 3.1673\n\nFrom here, I only have one number behind the decimal point.1680\n\nStart at the end; go place the decimal in there.1685\n\nWhat I just did, when you multiply decimals, you have to count...1692\n\nThis decimal point is at the end, right here.1695\n\nYou count to see how many numbers are behind the decimal point.1698\n\nHere I only have one; then I start at the end.1702\n\nI only go one inwards; that is where the decimal place goes.1705\n\nX is 3.6; to finish this equation, 3.6 equals X.1711\n\n3.6 is the number; or I can say the number is 3.6.1723\n\nThe next one, 5 percent, change that to a decimal.1734\n\nStart here; go one, two; it is point... fill in that space.1741\n\nIt is 0.05; it is not 0.5; 0.05.1745\n\n0.05 times my unknown which is X; X equals 4.1752\n\nAgain I have to solve for X which means I have to divide.1764\n\n4 divided by 0.05; 4 divided by 0.05.1768\n\nI have a decimal point here; I have to move it one, two.1779\n\nThere is my decimal point; I am going to move it one, two.1784\n\nBring it up; fill in these spaces with 0s; 5 goes into 4 zero times.1788\n\n5 goes into 40... 5 times we know 8 is 40.1797\n\nWrite 40 down here; subtract it; 0.1804\n\nI am not finished with the number yet because I still have space up here.1808\n\nBring down the 0; 5 goes into 0 zero times.1814\n\nThat is why for this, I have to keep going.1818\n\nEven though this became a 0, I have to bring down the 0 and solve it again1820\n\nbecause there was an empty space before my decimal point.1826\n\nIn that case, you have to continue.1831\n\nIf it is after the 0 like in this problem right here... I'm sorry.1833\n\nIf it is after the decimal point, then I can stop once I get 0 as my remainder.1838\n\nBut for this, if there is a space here before the decimal point,1844\n\nthen I have to go again until I fill in those spaces.1849\n\nHere this is 80; X is 80; they are not asking for percent.1852\n\n5 percent of 80 is 4.1861\n\nThe last one, 100 percent of 3448 is what number?1866\n\nThey are asking for 100 percent of this number.1874\n\n100 percent is all of it, is the whole thing.1877\n\n100 percent of this number is just this number.1883\n\nIf you want to solve it out how we solved out the rest of them,1888\n\n100 percent as a decimal, again move the decimal point over one, two spaces.1891\n\nThat becomes 1 or 1.0 which is the same thing as 1.1898\n\nTimes; times 3448; I am going to change this to parentheses.1905\n\n3448 equals what number?--X; 1 times this number is just that number.1912\n\nI can say 3448 is the number or the number is 3448.1926\n\nThat is it for this lesson; thank you for watching Educator.com.1948\n\nOR\n\n### Start Learning Now\n\nOur free lessons will get you started (Adobe Flash® required)."
] |
[
null,
"https://www.educator.com/media/ss/basic-math/cxt/ct/BM-7-3-0.jpg",
null,
"https://www.educator.com/media/lesson/poster/lores/mathematics-basic-math-pyo/prof/BM-7-3-0.jpg",
null,
"https://www.educator.com/membership/upload/xiaonliu.1590946904.png",
null
] |
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|
https://anytree.readthedocs.io/en/latest/_modules/anytree/iterators/zigzaggroupiter.html
|
[
"# Source code for anytree.iterators.zigzaggroupiter\n\n```from .abstractiter import AbstractIter\nfrom .levelordergroupiter import LevelOrderGroupIter\n\n[docs]class ZigZagGroupIter(AbstractIter):\n\"\"\"\nIterate over tree applying Zig-Zag strategy with grouping starting at `node`.\n\nReturn a tuple of nodes for each level. The first tuple contains the\nnodes at level 0 (always `node`). The second tuple contains the nodes at level 1\n(children of `node`) in reversed order.\nThe next level contains the children of the children in forward order, and so on.\n\n>>> from anytree import Node, RenderTree, AsciiStyle\n>>> f = Node(\"f\")\n>>> b = Node(\"b\", parent=f)\n>>> a = Node(\"a\", parent=b)\n>>> d = Node(\"d\", parent=b)\n>>> c = Node(\"c\", parent=d)\n>>> e = Node(\"e\", parent=d)\n>>> g = Node(\"g\", parent=f)\n>>> i = Node(\"i\", parent=g)\n>>> h = Node(\"h\", parent=i)\n>>> print(RenderTree(f, style=AsciiStyle()).by_attr())\nf\n|-- b\n| |-- a\n| +-- d\n| |-- c\n| +-- e\n+-- g\n+-- i\n+-- h\n>>> [[node.name for node in children] for children in ZigZagGroupIter(f)]\n[['f'], ['g', 'b'], ['a', 'd', 'i'], ['h', 'e', 'c']]\n>>> [[node.name for node in children] for children in ZigZagGroupIter(f, maxlevel=3)]\n[['f'], ['g', 'b'], ['a', 'd', 'i']]\n>>> [[node.name for node in children]\n... for children in ZigZagGroupIter(f, filter_=lambda n: n.name not in ('e', 'g'))]\n[['f'], ['b'], ['a', 'd', 'i'], ['h', 'c']]\n>>> [[node.name for node in children]\n... for children in ZigZagGroupIter(f, stop=lambda n: n.name == 'd')]\n[['f'], ['g', 'b'], ['a', 'i'], ['h']]\n\"\"\"\n\n@staticmethod\ndef _iter(children, filter_, stop, maxlevel):\nif children:\nassert len(children) == 1\n_iter = LevelOrderGroupIter(children, filter_, stop, maxlevel)\nwhile True:\ntry:\nyield next(_iter)\nyield tuple(reversed(next(_iter)))\nexcept StopIteration:\nbreak\n```"
] |
[
null
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|
https://energy.lbl.gov/publications/quantification-association
|
[
"# Quantification of the association of ventilation rates with sick building syndrome symptoms\n\n### Publication Type\n\nConference Proceedings\n\n### Abstract\n\nData from published studies were combined and analyzed to develop best-fit equations and curves quantifying the change in sick building syndrome (SBS) symptom prevalence with ventilation rate. For each study, slopes were calculated, representing the fractional change in SBS symptom prevalence per unit change in ventilation rate per person. Values of ventilation rate, associated with each value of slope, were also calculated. Linear regression equations were fit to the resulting data points, after weighting by study size. Integration of the slopeventilation rate equations yielded curves of relative SBS symptom prevalence versus ventilation rate. Based on these analyses, relative SBS symptom prevalence increases approximately 23% (12% to 32%) as the ventilation rate drops from 10 to 5 L/s-person and relative prevalence decreases approximately 29% (15% to 42%) as ventilation rate increases from 10 to 25 L/s-person.\n\n### Journal\n\nProceedings of the Indoor Air 2008\n\n2008"
] |
[
null
] |
{"ft_lang_label":"__label__en","ft_lang_prob":0.9411558,"math_prob":0.8226841,"size":1051,"snap":"2023-14-2023-23","text_gpt3_token_len":198,"char_repetition_ratio":0.17191978,"word_repetition_ratio":0.0,"special_character_ratio":0.18839201,"punctuation_ratio":0.071428575,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.95712143,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-05-30T18:54:03Z\",\"WARC-Record-ID\":\"<urn:uuid:2b7e9fe9-e674-45aa-bbe2-0df1fccdd444>\",\"Content-Length\":\"32493\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:190b9f57-befe-47f5-9040-544301fe81dc>\",\"WARC-Concurrent-To\":\"<urn:uuid:f04435ce-bf92-47a5-9bfe-742e8b87d247>\",\"WARC-IP-Address\":\"104.18.21.105\",\"WARC-Target-URI\":\"https://energy.lbl.gov/publications/quantification-association\",\"WARC-Payload-Digest\":\"sha1:3WAMRRBLYQ7J3KM6IVLEHH4QA3XO7IFD\",\"WARC-Block-Digest\":\"sha1:U5BE4KMSRQWVMTWPHKEMCZD2SV7GCBXR\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-23/CC-MAIN-2023-23_segments_1685224646076.50_warc_CC-MAIN-20230530163210-20230530193210-00008.warc.gz\"}"}
|
https://docs.nvidia.com/deeplearning/modulus/modulus-sym-v100/user_guide/advanced/industrial_heat_sink.html
|
[
"# Industrial Heat Sink\n\n## Introduction\n\nThis tutorial uses Modulus Sym to conduct a thermal simulation of NVIDIA’s NVSwitch heatsink. You will learn:\n\n1. How to use hFTB algorithm to solve conjugate heat transfer problems\n\n2. How to build a gPC based Surrogate via Transfer Learning\n\nNote\n\nThis tutorial assumes you have completed tutorial Moving Time Window: Taylor Green Vortex Decay as well as the tutorial Conjugate Heat Transfer on conjugate heat transfer.\n\n## Problem Description\n\nThis tutorial solves the conjugate heat transfer problem of NVIDIA’s NVSwitch heat sink as shown in Fig. 157. Similar to the previous FPGA problem, the heat sink is placed in a channel with inlet velocity similar to its operating conditions. This case differs from the FPGA one, because you will be using the real heat properties for atmospheric air and copper as the heat sink material. Unlike Heat Transfer with High Thermal Conductivity, a hFTB algorithm will be used to handle the large conductivity differences.\n\nFig. 157 NVSwitch heat sink geometry\n\nUsing real heat properties causes an issue on the interface between the solid and fluid because the conductivity is around 4 orders of magnitude different (Air: 0.0261 $$W/m.K$$ and Copper: 385 $$W/m.K$$). To remedy this, Modulus Sym has a static conjugate heat transfer approached referred to as heat transfer coefficient forward temperature backward or hFTB 1. This method works by iteratively solving for the heat transfer in the fluid and solid where they are one way coupled. Using the hFTB method, assign Robin boundary conditions on the solid interface and Dirichlet boundaries for the fluid. The simulation starts by giving an initial guess for the solid temperature and uses a hyper parameter $$h$$ for the Robin boundary conditions. A description of the algorithm is shown in Fig. 158. A more complete description can be found here 1.\n\nFig. 158 hFTB algorithm\n\n## Case Setup\n\nThe case setup for this problem is similar to the FPGA and three fin examples (covered in tutorials Parameterized 3D Heat Sink and FPGA Heat Sink with Laminar Flow) however, this section shows construction of multiple train domains to implement the hFTB method.\n\nNote\n\nThe python script for this problem can be found at examples/limerock/limerock_hFTB.\n\n### Defining Domain\n\nThis case setup skips over several sections of the code and only focuses on the portions related to the hFTB algorithm. You should be familiar with how to set up the flow simulation from previous tutorials. Geometry construction is not discussed in detail as well and all relevant information can be found in examples/limerock/limerock_hFTB/limerock_geometry.py. The code description begins by defining the parameters of the simulation and importing all needed modules.\n\nCopy\nCopied!\n\n# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n#\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#\n# Unless required by applicable law or agreed to in writing, software\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nfrom limerock_geometry import LimeRock\n\n# make limerock\nlimerock = LimeRock()\n\n#############\n# Real Params\n#############\n# fluid params\nfluid_viscosity = 1.84e-05 # kg/m-s\nfluid_density = 1.1614 # kg/m3\nfluid_specific_heat = 1005 # J/(kg K)\nfluid_conductivity = 0.0261 # W/(m K)\n\n# copper params\ncopper_density = 8930 # kg/m3\ncopper_specific_heat = 385 # J/(kg K)\ncopper_conductivity = 385 # W/(m K)\n\n# boundary params\ninlet_velocity = 5.7 # m/s\ninlet_temp = 0 # K\n\n# source\nsource_term = 2127.71 # K/m\nsource_origin = (-0.061667, -0.15833, limerock.geo_bounds_lower)\nsource_dim = (0.1285, 0.31667, 0)\n\n################\n# Non dim params\n################\nlength_scale = 0.0575 # m\nvelocity_scale = 5.7 # m/s\ntime_scale = length_scale / velocity_scale # s\ndensity_scale = 1.1614 # kg/m3\nmass_scale = density_scale * length_scale**3 # kg\npressure_scale = mass_scale / (length_scale * time_scale**2) # kg / (m s**2)\ntemp_scale = 273.15 # K\nwatt_scale = (mass_scale * length_scale**2) / (time_scale**3) # kg m**2 / s**3\njoule_scale = (mass_scale * length_scale**2) / (time_scale**2) # kg * m**2 / s**2\n\n##############################\n# Nondimensionalization Params\n##############################\n# fluid params\nnd_fluid_viscosity = fluid_viscosity / (\nlength_scale**2 / time_scale\n) # need to divide by density to get previous viscosity\nnd_fluid_density = fluid_density / density_scale\nnd_fluid_specific_heat = fluid_specific_heat / (joule_scale / (mass_scale * temp_scale))\nnd_fluid_conductivity = fluid_conductivity / (watt_scale / (length_scale * temp_scale))\nnd_fluid_diffusivity = nd_fluid_conductivity / (\nnd_fluid_specific_heat * nd_fluid_density\n)\n\n# copper params\nnd_copper_density = copper_density / (mass_scale / length_scale**3)\nnd_copper_specific_heat = copper_specific_heat / (\njoule_scale / (mass_scale * temp_scale)\n)\nnd_copper_conductivity = copper_conductivity / (\nwatt_scale / (length_scale * temp_scale)\n)\nnd_copper_diffusivity = nd_copper_conductivity / (\nnd_copper_specific_heat * nd_copper_density\n)\n\n# boundary params\nnd_inlet_velocity = inlet_velocity / velocity_scale\nnd_volumetric_flow = limerock.inlet_area * nd_inlet_velocity\nnd_inlet_temp = inlet_temp / temp_scale\nnd_source_term = source_term / (temp_scale / length_scale)\n\n\nNote\n\nWe nondimensionalize all parameters so that the scales for velocity, temperature, and pressure are roughly in the range 0-1. Such nondimensionalization trains the Neural network more efficiently.\n\n### Sequence Solver\n\nNow setup the solver. Similar to the moving time window implementation in Tutorial Moving Time Window: Taylor Green Vortex Decay, construct a separate neural network that stores the thermal solution from the previous cycles fluid solution. We suggest that this problem is either run on $$8$$ GPUs or gradient aggregation frequency is set to $$8$$. Details on running with multi-GPUs and multi-nodes can be found in tutorial Performance and the details on using gradient aggregation can be found in tutorial Modulus Sym Configuration.\n\nNext, set up a train domain to only solve for the temperature in the fluid given a Dirichlet boundary condition on the solid. This will be the first stage of the hFTB method. After getting this initial solution for the temperature in the fluid solve for the main loop of the hFTB algorithm. Now you will solve for both the fluid and solid in a one way coupled manner. The Robin boundary conditions for the solid are coming from the previous iteration of the fluid solution.\n\nNote\n\nSometimes for visualization purposes it is beneficial to visualize the results on a mesh. Here, this is done using the VTKUniformGrid method. Note that the SDF was used as a mask function to filter out the temperature evaluations outside the solid.\n\nWarning\n\nMulti-GPU training is currently not supported for this problem.\n\nCopy\nCopied!\n\n# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n#\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#\n# Unless required by applicable law or agreed to in writing, software\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport torch\nfrom torch import Tensor\nimport copy\n\nimport numpy as np\nfrom sympy import Symbol, Eq, tanh, Or, And\nfrom omegaconf import DictConfig, OmegaConf\nimport hydra\nfrom hydra.utils import to_absolute_path\nfrom typing import Dict\n\nimport modulus.sym\nfrom modulus.sym.hydra import to_absolute_path, instantiate_arch, ModulusConfig\nfrom modulus.sym.utils.io import csv_to_dict\nfrom modulus.sym.solver import SequentialSolver\nfrom modulus.sym.domain import Domain\nfrom modulus.sym.geometry.primitives_3d import Box, Channel, Plane\nfrom modulus.sym.models.fourier_net import FourierNetArch\nfrom modulus.sym.models.arch import Arch\nfrom modulus.sym.domain.constraint import (\nPointwiseBoundaryConstraint,\nPointwiseInteriorConstraint,\n)\nfrom modulus.sym.domain.monitor import PointwiseMonitor\nfrom modulus.sym.domain.inferencer import PointVTKInferencer\nfrom modulus.sym.utils.io import (\nVTKUniformGrid,\n)\nfrom modulus.sym.key import Key\nfrom modulus.sym.node import Node\nfrom modulus.sym.distributed.manager import DistributedManager\n\nfrom limerock_properties import *\n\nfrom flux_diffusion import (\nFluxDiffusion,\nFluxIntegrateDiffusion,\nFluxRobin,\nDirichlet,\n)\n\nclass hFTBArch(Arch):\ndef __init__(\nself,\narch: Arch,\n) -> None:\noutput_keys = arch.output_keys + [\nKey(x.name + \"_prev_step\") for x in arch.output_keys\n]\nsuper().__init__(\ninput_keys=arch.input_keys,\noutput_keys=output_keys,\nperiodicity=arch.periodicity,\n)\n\n# set networks for current and prev time window\nself.arch_prev_step = arch\nself.arch = copy.deepcopy(arch)\nfor param, param_prev_step in zip(\nself.arch.parameters(), self.arch_prev_step.parameters()\n):\n\ndef forward(self, in_vars: Dict[str, Tensor]) -> Dict[str, Tensor]:\ny_prev_step = self.arch_prev_step.forward(in_vars)\ny = self.arch.forward(in_vars)\nfor key, b in y_prev_step.items():\ny[key + \"_prev_step\"] = b\nreturn y\n\ndef move_network(self):\nfor param, param_prev_step in zip(\nself.arch.parameters(), self.arch_prev_step.parameters()\n):\nparam_prev_step.data = param.detach().clone().data\n\[email protected](config_path=\"conf\", config_name=\"conf_thermal\")\ndef run(cfg: ModulusConfig) -> None:\nif DistributedManager().distributed:\nprint(\"Multi-GPU currently not supported for this example. Exiting.\")\nreturn\n\n# make list of nodes to unroll graph on\nT=\"theta_f\", rho=nd_fluid_density, D=nd_fluid_diffusivity, dim=3, time=False\n)\ndif = FluxDiffusion(D=nd_copper_diffusivity)\nintegrate_flux_dif = FluxIntegrateDiffusion()\nrobin_flux = FluxRobin(\ntheta_f_conductivity=nd_fluid_conductivity,\ntheta_s_conductivity=nd_copper_conductivity,\nh=500.0,\n)\ndirichlet = Dirichlet(lhs=\"theta_f\", rhs=\"theta_s\")\nflow_net = FourierNetArch(\ninput_keys=[Key(\"x\"), Key(\"y\"), Key(\"z\")],\noutput_keys=[Key(\"u\"), Key(\"v\"), Key(\"w\"), Key(\"p\")],\n)\nf_net = FourierNetArch(\ninput_keys=[Key(\"x\"), Key(\"y\"), Key(\"z\")], output_keys=[Key(\"theta_f\")]\n)\nthermal_f_net = hFTBArch(f_net)\nthermal_s_net = FourierNetArch(\ninput_keys=[Key(\"x\"), Key(\"y\"), Key(\"z\")], output_keys=[Key(\"theta_s\")]\n)\nflux_s_net = FourierNetArch(\ninput_keys=[Key(\"x\"), Key(\"y\"), Key(\"z\")],\noutput_keys=[\nKey(\"flux_theta_s_x\"),\nKey(\"flux_theta_s_y\"),\nKey(\"flux_theta_s_z\"),\n],\n)\nthermal_nodes = (\n+ dif.make_nodes()\n+ integrate_flux_dif.make_nodes(\ndetach_names=[\"flux_theta_s_x\", \"flux_theta_s_y\", \"flux_theta_s_z\"]\n)\n+ robin_flux.make_nodes(\ndetach_names=[\n\"theta_f_prev_step\",\n\"theta_f_prev_step__x\",\n\"theta_f_prev_step__y\",\n\"theta_f_prev_step__z\",\n]\n)\n+ dirichlet.make_nodes(detach_names=[\"theta_s\"])\n+ [flow_net.make_node(name=\"flow_network\", optimize=False, jit=cfg.jit)]\n+ [thermal_f_net.make_node(name=\"thermal_fluid_network\", optimize=True, jit=cfg.jit)]\n+ [thermal_s_net.make_node(name=\"thermal_solid_network\", optimize=True, jit=cfg.jit)]\n+ [flux_s_net.make_node(name=\"flux_solid_network\", optimize=True, jit=cfg.jit)]\n)\n\n# make domain for first cycle of hFTB\ncycle_1_domain = Domain(\"cycle_1\")\n\nx, y, z = Symbol(\"x\"), Symbol(\"y\"), Symbol(\"z\")\nimport time as time\n\ntic = time.time()\n\n# inlet\ninlet = PointwiseBoundaryConstraint(\nnodes=thermal_nodes,\ngeometry=limerock.inlet,\noutvar={\"theta_f\": nd_inlet_temp},\nbatch_size=cfg.batch_size.inlet,\nbatch_per_epoch=50,\nlambda_weighting={\"theta_f\": 1000.0},\n)\n\n# outlet\noutlet = PointwiseBoundaryConstraint(\nnodes=thermal_nodes,\ngeometry=limerock.outlet,\nbatch_size=cfg.batch_size.outlet,\n)\n\n# channel walls insulating\nwalls = PointwiseBoundaryConstraint(\nnodes=thermal_nodes,\ngeometry=limerock.geo,\nbatch_size=cfg.batch_size.no_slip,\ncriteria=Or(\nOr(\nEq(y, limerock.geo_bounds_lower), Eq(z, limerock.geo_bounds_lower)\n),\nOr(\nEq(y, limerock.geo_bounds_upper), Eq(z, limerock.geo_bounds_upper)\n),\n),\n)\n\n# flow interior low res away from heat sink\nlr_interior_f = PointwiseInteriorConstraint(\nnodes=thermal_nodes,\ngeometry=limerock.geo,\nbatch_size=cfg.batch_size.lr_interior_f,\ncriteria=Or(\n(x < limerock.heat_sink_bounds), (x > limerock.heat_sink_bounds)\n),\n)\n\n# flow interiror high res near heat sink\nhr_interior_f = PointwiseInteriorConstraint(\nnodes=thermal_nodes,\ngeometry=limerock.geo,\nbatch_size=cfg.batch_size.hr_interior_f,\ncriteria=And(\n(x > limerock.heat_sink_bounds), (x < limerock.heat_sink_bounds)\n),\n)\n\n# fluid solid interface\ninterface = PointwiseBoundaryConstraint(\nnodes=thermal_nodes,\ngeometry=limerock.geo_solid,\noutvar={\"theta_f\": 0.05},\nbatch_size=cfg.batch_size.interface,\ncriteria=z > limerock.geo_bounds_lower,\nlambda_weighting={\"theta_f\": 100.0},\n)\n\nvtk_obj = VTKUniformGrid(\nbounds=[limerock.geo_bounds[x], limerock.geo_bounds[y], limerock.geo_bounds[z]],\nnpoints=[256, 128, 256],\nexport_map={\"u\": [\"u\", \"v\", \"w\"], \"p\": [\"p\"], \"theta_f\": [\"theta_f\"]},\n)\n\nsdf = limerock.geo.sdf({\"x\": x, \"y\": y, \"z\": z}, {})\nreturn sdf[\"sdf\"] < 0\n\ngrid_inferencer = PointVTKInferencer(\nvtk_obj=vtk_obj,\nnodes=thermal_nodes,\ninput_vtk_map={\"x\": \"x\", \"y\": \"y\", \"z\": \"z\"},\noutput_names=[\"u\", \"v\", \"w\", \"p\", \"theta_f\"],\nbatch_size=100000,\n)\n\n# make domain for all other cycles\ncycle_n_domain = Domain(\"cycle_n\")\n\n# inlet\n\n# outlet\n\n# channel walls insulating\n\n# flow interior low res away from heat sink\n\n# flow interiror high res near heat sink\n\n# diffusion dictionaries\ndiff_outvar = {\n\"diffusion_theta_s\": 0,\n\"compatibility_theta_s_x_y\": 0,\n\"compatibility_theta_s_x_z\": 0,\n\"compatibility_theta_s_y_z\": 0,\n\"integrate_diffusion_theta_s_x\": 0,\n\"integrate_diffusion_theta_s_y\": 0,\n\"integrate_diffusion_theta_s_z\": 0,\n}\ndiff_lambda = {\n\"diffusion_theta_s\": 1000000.0,\n\"compatibility_theta_s_x_y\": 1.0,\n\"compatibility_theta_s_x_z\": 1.0,\n\"compatibility_theta_s_y_z\": 1.0,\n\"integrate_diffusion_theta_s_x\": 1.0,\n\"integrate_diffusion_theta_s_y\": 1.0,\n\"integrate_diffusion_theta_s_z\": 1.0,\n}\n\n# solid interior\ninterior_s = PointwiseInteriorConstraint(\nnodes=thermal_nodes,\ngeometry=limerock.geo_solid,\noutvar=diff_outvar,\nbatch_size=cfg.batch_size.interior_s,\nlambda_weighting=diff_lambda,\n)\n\n# limerock base\nsharpen_tanh = 60.0\nsource_func_xl = (tanh(sharpen_tanh * (x - source_origin)) + 1.0) / 2.0\nsource_func_xh = (\ntanh(sharpen_tanh * ((source_origin + source_dim) - x)) + 1.0\n) / 2.0\nsource_func_yl = (tanh(sharpen_tanh * (y - source_origin)) + 1.0) / 2.0\nsource_func_yh = (\ntanh(sharpen_tanh * ((source_origin + source_dim) - y)) + 1.0\n) / 2.0\nnd_source_term\n* source_func_xl\n* source_func_xh\n* source_func_yl\n* source_func_yh\n)\nbase = PointwiseBoundaryConstraint(\nnodes=thermal_nodes,\ngeometry=limerock.geo_solid,\nbatch_size=cfg.batch_size.base,\ncriteria=Eq(z, limerock.geo_bounds_lower),\n)\n\n# fluid solid interface\ninterface = PointwiseBoundaryConstraint(\nnodes=thermal_nodes,\ngeometry=limerock.geo_solid,\noutvar={\"dirichlet_theta_s_theta_f\": 0, \"robin_theta_s\": 0},\nbatch_size=cfg.batch_size.interface,\ncriteria=z > limerock.geo_bounds_lower,\nlambda_weighting={\"dirichlet_theta_s_theta_f\": 100.0, \"robin_theta_s\": 1.0},\n)\n\nvtk_obj = VTKUniformGrid(\nbounds=[\nlimerock.geo_hr_bounds[x],\nlimerock.geo_hr_bounds[y],\nlimerock.geo_hr_bounds[z],\n],\nnpoints=[128, 128, 512],\nexport_map={\"theta_s\": [\"theta_s\"]},\n)\n\nsdf = limerock.geo.sdf({\"x\": x, \"y\": y, \"z\": z}, {})\nreturn sdf[\"sdf\"] > 0\n\ngrid_inferencer = PointVTKInferencer(\nvtk_obj=vtk_obj,\nnodes=thermal_nodes,\ninput_vtk_map={\"x\": \"x\", \"y\": \"y\", \"z\": \"z\"},\noutput_names=[\"theta_s\"],\nbatch_size=100000,\n)\n\n# peak temperature monitor\ninvar_temp = limerock.geo_solid.sample_boundary(\n10000, criteria=Eq(z, limerock.geo_bounds_lower)\n)\npeak_temp_monitor = PointwiseMonitor(\ninvar_temp,\noutput_names=[\"theta_s\"],\nmetrics={\"peak_temp\": lambda var: torch.max(var[\"theta_s\"])},\nnodes=thermal_nodes,\n)\n\n# make solver\nslv = SequentialSolver(\ncfg,\n[(1, cycle_1_domain), (20, cycle_n_domain)],\ncustom_update_operation=thermal_f_net.move_network,\n)\n\n# start solver\nslv.solve()\n\nif __name__ == \"__main__\":\nrun()\n\n\n## Results and Post-processing\n\nTo confirm the accuracy of the model, the results are compared for pressure drop and peak temperature with the OpenFOAM and a commercial solver results, and the results are reported in Table 14. The results show good accuracy achieved by the hFTB method. Table 15 demonstrates the impact of mesh refinement on the solution of the commercial solver where with increasing mesh density and mesh quality, the commercial solver results show convergence towards the Modulus Sym results. A visualization of the heat sink temperature profile is shown in Fig. 159.\n\n Property OpenFOAM Commercial Solver Modulus Sym Pressure Drop $$(Pa)$$ $$133.96$$ $$137.50$$ $$150.25$$ Peak Temperature $$(^{\\circ} C)$$ $$93.41$$ $$95.10$$ $$97.35$$\n Number of elements Pressure drop (Pa) Peak temperature $$(^{\\circ} C)$$ Commercial solver Modulus Sym % diff Commercial solver Modulus Sym % diff 22.4 M 81.27 150.25 84.88 97.40 97.35 0.05 24.7 M 111.76 150.25 34.44 95.50 97.35 1.94 26.9 M 122.90 150.25 22.25 95.10 97.35 2.36 30.0 M 132.80 150.25 13.14 32.0 M 137.50 150.25 9.27\n\nFig. 159 NVSwitch Solid Temperature\n\n## gPC Based Surrogate Modeling Accelerated via Transfer Learning\n\nPreviously, Chapter Parameterized 3D Heat Sink showed that by parameterizing the input of the neural network, you can solve for multiple design parameters in a single run and use that parameterized network for design optimization. This section introduces another approach for parameterization and design optimization, which is based on constructing a surrogate using the solution obtained from a limited number of non-parameterized neural network models. Compared to the parameterized network approach that is limited to the CSG module, this approach can be used for parameterization of both constructive solid and STL geometries, and additionally, can offer improved accuracy specially for cases with a high-dimensional parameter space and also in cases where some or all of the design parameters are discrete. However, this approach requires training of multiple neural networks and may require multi-node resources.\n\nThis section focuses on surrogates based on the generalized Polynomial Chaos (gPC) expansions. The gPC is an efficient tool for uncertainty quantification using limited data, and in introduced in Section Generalized Polynomial Chaos. It starts off by generating the required number of realizations form the parameter space using a low discrepancy sequence such as Halton or Sobol. Next, for each realization, a separate neural network model is trained. Note that these trainings are independent from each other and therefore, this training step is embarrassingly parallel and can be done on multiple GPUs or nodes. Finally, a gPC surrogate is trained that maps the parameter space to the quantities of interest (e.g., pressure drop and peak temperature in the heat sink design optimization problem).\n\nIn order to reduce the computational cost of this approach associated with training of multiple models, transfer learning is used, that is, once a model is fully trained for a single realization, it is used for initialization of the other models, and this can significantly reduce the total time to convergence. Transfer learning has been previously introduced in Chapter STL Geometry: Blood Flow in Intracranial Aneurysm.\n\nHere, to illustrate the gPC surrogate modeling accelerated via transfer learning, consider the NVIDIA’s NVSwitch heat sink introduced above. We introduce four geometry parameters related to fin cut angles, as shown in Fig. 160. We then construct a pressure drop surrogate. Similarly, one can also construct a surrogate for the peak temperature and use these two surrogates for design optimization of this heat sink.\n\nFig. 160 NVSwitch heat sink geometry parameterization. Each parameter ranges between 0 and $$\\pi/6$$.\n\nThe scripts for this example are available at examples/limerock/limerock_transfer_learning. Following Section Generalized Polynomial Chaos, one can generate 30 geometry realizations according to a Halton sequence by running sample_generator.py, as follows\n\nCopy\nCopied!\n\n# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n#\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#\n# Unless required by applicable law or agreed to in writing, software\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\n# import libraries\nimport numpy as np\nimport chaospy\n\n# define parameter ranges\nfin_front_top_cut_angle_ranges = (0.0, np.pi / 6.0)\nfin_front_bottom_cut_angle_ranges = (0.0, np.pi / 6.0)\nfin_back_top_cut_angle_ranges = (0.0, np.pi / 6.0)\nfin_back_bottom_cut_angle_ranges = (0.0, np.pi / 6.0)\n\n# generate samples\nsamples = chaospy.generate_samples(\norder=30,\ndomain=np.array(\n[\nfin_front_top_cut_angle_ranges,\nfin_front_bottom_cut_angle_ranges,\nfin_back_top_cut_angle_ranges,\nfin_back_bottom_cut_angle_ranges,\n]\n).T,\nrule=\"halton\",\n)\nsamples = samples.T\nnp.random.shuffle(samples)\nnp.savetxt(\"samples.txt\", samples)\n\n\nThen train a separate flow network for each of these realizations using transfer learning. To do this, update the configs for network checkpoint, learning rate and decay rate, and the maximum training iterations in conf/config.py. Also change the sample_id variable in limerock_geometry.py, and then run limerock_flow.py. This is repeated until all of the geometry realizations are covered. These flow models are initialized using the trained network for the base geometry (as shown in Fig. 157), and are trained for a fraction of the total training iterations for the base geometry, with a smaller learning rate and a faster learning rate decay, as specified in conf/config.yaml. This is because you only need to fine-tune these models as opposed to training them from the scratch. Please note that, before you launch the transfer learning runs, a flow network for the base geometry needs to be fully trained.\n\nFig. 161 shows the front and back pressure results for different runs. It is evident that the pressure has converged faster in the transfer learning runs compared to the base geometry full run, and that transfer learning has reduced the total time to convergence by a factor of 5.\n\nFig. 161 NVSwitch front and back pressure convergence results for different geometries using transfer learning.\n\nFinally, randomly divide the pressure drop data obtained from these models into training and test sets, and construct a gPC surrogate, as follows:\n\nCopy\nCopied!\n\n# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n#\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#\n# Unless required by applicable law or agreed to in writing, software\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\n# import libraries\nimport numpy as np\nimport csv\nimport chaospy\n\nnum_samples = len(samples)\n\ny_vec = []\nfor i in range(num_samples):\nfront_pressure_dir = (\n\"./outputs/limerock_flow/tl_\" + str(i) + \"/monitors/front_pressure.csv\"\n)\nback_pressure_dir = (\n\"./outputs/limerock_flow/tl_\" + str(i) + \"/monitors/back_pressure.csv\"\n)\nwith open(front_pressure_dir, \"r\", encoding=\"utf-8\", errors=\"ignore\") as scraped:\nwith open(back_pressure_dir, \"r\", encoding=\"utf-8\", errors=\"ignore\") as scraped:\npressure_drop = front_pressure - back_pressure\ny_vec.append(pressure_drop)\ny_vec = np.array(y_vec)\n\n# Split data into training and validation\nval_portion = 0.15\nval_idx = np.random.choice(\nnp.arange(num_samples, dtype=int), int(val_portion * num_samples), replace=False\n)\nval_x, val_y = samples[val_idx], y_vec[val_idx]\ntrain_x, train_y = np.delete(samples, val_idx, axis=0).T, np.delete(\ny_vec, val_idx\n).reshape(-1, 1)\n\n# Construct the PCE\ndistribution = chaospy.J(\nchaospy.Uniform(0.0, np.pi / 6),\nchaospy.Uniform(0.0, np.pi / 6),\nchaospy.Uniform(0.0, np.pi / 6),\nchaospy.Uniform(0.0, np.pi / 6),\n)\nexpansion = chaospy.generate_expansion(2, distribution)\npoly = chaospy.fit_regression(expansion, train_x, train_y)\n\n# PCE closed form\nprint(\"__________\")\nprint(\"PCE closd form:\")\nprint(poly)\nprint(\"__________\")\n\n# Validation\nprint(\"PCE evaluatins:\")\nfor i in range(len(val_x)):\npred = poly(val_x[i, 0], val_x[i, 1], val_x[i, 2], val_x[i, 3])\nprint(\"Sample:\", val_x[i])\nprint(\"True val:\", val_y[i])\nprint(\"Predicted val:\", pred)\nprint(\"Relative error (%):\", abs(pred - val_y[i]) / val_y[i] * 100)\nprint(\"__________\")\n\n\nThe code for constructing this surrogate is available at limerock_pce_surrogate.py: Fig. 162 shows the gPC surrogate performance on the test set. The relative errors are below 1%, showing the good accuracy of the constructed gPC pressure drop surrogate.\n\nFig. 162 The gPC pressure drop surrogate accuracy tested on four geometries\n\nReferences\n\n(1,2)\n\nSebastian Scholl, Bart Janssens, and Tom Verstraete. Stability of static conjugate heat transfer coupling approaches using robin interface conditions. Computers & Fluids, 172, 06 2018.\n\n© Copyright 2023, NVIDIA Modulus Team. Last updated on Aug 8, 2023."
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https://math.stackexchange.com/questions/1252746/can-i-represent-groups-geometrically/1252818
|
[
"# Can I represent groups geometrically?\n\nI have just taken up abstract algebra for my college and my professor was giving me an introduction to groups, but since I like geometric definitions or ways of looking at stuff, I kept thinking, \"How do you represent a group geometrically in a space?\" Is there any way of representing it?\n\n• Many groups have an inherent geometric interpretation, such as the dihedral groups, the symmetry groups of the archimedean solids and wallpaper groups. Also, see Cayley graph. – Arthur Apr 26 '15 at 15:05\n• You will definitely want to check out the book Visual Group Theory by Nathan Carter: books.google.com/books/about/… – Ben Blum-Smith Apr 26 '15 at 16:59\n• I'd suggest you get hold of Groups: A Path To Geometry by R. P. Burn. It has a very distinctive didactic style, probably quite distinct from any other textbook in the area, even if the content is standard. – Silverfish Apr 27 '15 at 0:14\n\n## 2 Answers\n\nThis is a natural question; the short answer is (1) yes, and (2) that this can be an instructive and powerful way to understand particular groups. In fact, this perspective is so natural, that modern students are sometimes surprised that groups were not invented for this purpose. (Rather, Galois introduced them to study what are now called Galois groups, that is, the groups of automorphisms of splitting fields of polynomials, which is an almost entirely symbolic, rather than geometric, enterprise.)\n\nNarrowing our scope, for any group $$G$$, we can ask whether there is some subset $$X$$ of $$\\mathbb{R}^n$$ such that the group of symmetries of $$X$$ (more precisely, the group of isometries of $$\\mathbb{R}^n$$ that preserve $$X$$ as a set) is isomorphic to $$G$$. This is the case for several familiar groups:\n\n• $$S_2$$ is the isometry group of a line segement\n• $$S_3$$, equilateral triangle\n• $$S_4$$, regular tetrahedron\n• (more generally) the symmetric group $$S_n$$, regular $$n$$-simplex, which for concreteness we can take to be the convex hull of the points $$(0, \\ldots, 0, 1, 0, \\ldots, 0)$$ in $$\\mathbb{R}^n$$.\n• the Klein $$4$$-group $$Z_2 \\times Z_2$$, (nonsquare) rectangle\n• $$D_8$$, square\n• $$D_{10}$$, regular pentagon\n• the dihedral group $$D_{2n}$$, regular $$n$$-gon\n\nWe can produce more familiar examples by imposing additional conditions on the symmetries, e.g., by requiring that they preserve the orientation of the set $$X$$:\n\n• $$A_3 \\cong Z_3$$, oriented symmetries of the equilateral triangle, or just the symmetries of a triskelion\n• $$Z_4$$, a square (oriented)\n• $$Z_5$$, a regular pentagon (oriented)\n• the cyclic group $$Z_n$$, a regular $$n$$-gon (oriented)\n• $$A_4$$, a regular tetrahedron (oriented)\n• the alternating group $$A_n$$, a regular $$n$$-simplex (oriented)\n• $$S_4$$, a cube (or octahedron) (oriented)\n• $$A_5$$, a dodecahedron (or icosahedron) (oriented) (this one in particular is perhaps not so easy to see immediately: given a dodecahedron, one can draw five distinguished cubes inside it, and each [oriented] symmetry of the dodecahedron permutes these in a unique alternating way, that is, $$A_5$$ is the alternating group on the set of these cubes).",
null,
"One can also ask about groups with infinitely many elements:\n\n• $$SO(2) \\cong {\\Bbb S}^1$$ is the group of oriented symmetries of the circle $${\\Bbb S}^1$$, which we can also think of as the group of oriented linear transformations of $$\\mathbb{R}^2$$ preserving the Euclidean inner product\n• the special orthogonal group $$SO(n)$$ is the group of oriented symmetries of the $$n$$-sphere, which we can also think of as the group of oriented linear transformations of $$\\mathbb{R}^{n + 1}$$ preserving the Euclidean inner product\n\nIf we expand our scope to permit more exotic geometries, we can find new classes of examples, for examples, projective planes over finite fields:\n\n• $$GL(3, 2) \\cong PGL(3, 2) = PSL(3, 2)$$, the group of automorphisms of the Fano plane $$\\Bbb P(\\Bbb F_2^3)$$, that is, the projective plane over the finite field $$\\Bbb F_2$$ of two elements (this group has $$168$$ elements, and after $$A_5$$, is the second smallest finite simple group of nonprime order). It is nonobvious that this group is \"accidentally\" isomorphic to $$PSL(2, 7)$$, the group of automorphisms of the projective line $$\\Bbb P (\\Bbb F_7^2)$$ over the field $$\\Bbb F_7$$ of seven elements.\n\nGenerally the projective special linear groups $$PSL(n, p^k)$$ are unfamiliar to a beginner, but there are a few exceptions that give us new ways to view familiar groups:\n\n• $$PSL(2, 2) \\cong S_3$$\n• $$PSL(2, 3) \\cong A_4$$\n• $$PSL(2, 4) \\cong PSL(2, 5) \\cong A_5$$\n• $$PSL(2, 9) \\cong A_6$$\n• $$PSL(4, 2) \\cong A_8$$\n\nOne can expand on these lists (which should be regarded only as collections of examples, and not in any way exhaustive) wildly by generalizing in various ways what exactly one means by geometric.\n\nAside Surely this answer is already long enough, but I'll point out that the converse to your question is natural and important, too: For any geometric object $$X$$, we can ask for the group $$G$$ of symmetries of $$X$$. This too is a deep font of interesting examples, but I'll mention just a few related families of examples, the first two of which have tractible classifications and the third of which has a famous application:\n\n• If $$X$$ is a pattern in $$\\mathbb{R}^2$$ that repeats \"infinitely, in one direction\", the symmetry group of $$X$$ is one of the $$7$$ frieze groups; one of these is $$Z_{\\infty} \\cong {\\Bbb Z}$$, and the rest are variations on $$\\Bbb Z$$ and an infinite analogue $$D_{\\infty} := \\Bbb Z \\rtimes Z_2$$ of $$D_n$$.\n• If $$X$$ is a pattern in $$\\mathbb{R}^2$$ that repeats \"infinitely, in two directions\", one gets one of the $$17$$ wallpaper groups. The simplest of these are $$\\Bbb Z \\times \\Bbb Z$$, $${\\Bbb Z} \\times D_{\\infty}$$, and $$D_{\\infty} \\times D_{\\infty}$$.\n• Asking analogous questions about patterns in $$\\mathbb{R}^3$$ leads to the study of space groups, of which there are hundreds, and some of which are of critical importance in chemistry because of their appearance in regular crystal structures.\n• It's funny, because the historical development has been quite the converse, I think (admitting a meager education). The goal (since Klein, or Cartan?) has been to try to use potent algebraic constructs to understand and treat the classical geometries. Or even worse, \"doing geometry\" in sets that are a priori just groups (such as the ones just mentioned that were constructed to study... geometry). – GPerez Apr 26 '15 at 16:19\n• Is there some group which cant be represented in this way? – user56914 Apr 26 '15 at 17:20\n• @Rememberme Every group can be regarded as a collection of symmetries of some $\\mathbb{R}^N$, yes, but it's not always transparent what the characterization of those symmetries actually is, that is, what is the extra structure on that space that is preserved exactly by the group. – Travis Willse Apr 27 '15 at 2:19\n• Graph Theory supplies an answer. For every (finite) group $G$, there is a graph such that the group of symmetries of the graph is $G$. – Gerry Myerson Apr 27 '15 at 7:10\n• @Travis: Frucht's Theorem \"states that every finite group is the group of symmetries of a finite undirected graph\". By the way: Once you have a graph, you can construct a (possibly-degenerate) geometric realization, for which each automorphism corresponds to a \"rigid motion\"; I describe those realizations (which I call \"spectral\") in this answer, where I also provide a link to a PDF with much greater detail. – Blue Apr 28 '15 at 1:36\n\nYou might find interesting a computer app called Group Explorer.\n\nThe app provides visualizations of 59 groups. Additional groups are available as separate downloads.\n\nSome screenshots:",
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"",
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[
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"https://i.stack.imgur.com/tonQO.gif",
null,
"https://i.stack.imgur.com/sLCWm.png",
null,
"https://i.stack.imgur.com/5Nf0x.png",
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"https://i.stack.imgur.com/yfi70.png",
null,
"https://i.stack.imgur.com/xSgR6.png",
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] |
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|
https://www.readmorejoy.com/2019/06/py11pandas/
|
[
"# 11个Python Pandas小技巧让你的工作更高效（附代码实例）",
null,
"",
null,
"Pandas是一个在Python中广泛应用的数据分析包。市面上有很多关于Pandas的经典教程，但本文介绍几个隐藏的炫酷小技巧，我相信这些会对你有所帮助。\n\n2. select_dtypes\n\n``````df.dtypes.value_counts()\n``````\n\n``````df.select_dtypes(include=['float64', 'int64'])\n``````\n\n3. copy\n\n``````import pandas as pd\ndf1 = pd.DataFrame({ 'a':[0,0,0], 'b': [1,1,1]})\ndf2 = df1\ndf2['a'] = df2['a'] + 1\n``````\n\n``````df2 = df1.copy()\n``````\n\n``````from copy import deepcopy\ndf2 = deepcopy(df1)\n``````\n\n4. map\n\ndictionary，“key”是转换前的旧值，而“values”是转换后的新值。\n\n``````level_map = {1: 'high', 2: 'medium', 3: 'low'}\ndf['c_level'] = df['c'].map(level_map)\n``````\n\n5. 用不用apply？\n\n``````def rule(x, y):\nif x == 'high' and y > 10:\nreturn 1\nelse:\nreturn 0\ndf = pd.DataFrame({ 'c1':[ 'high' ,'high', 'low', 'low'], 'c2': [0, 23, 17, 4]})\ndf['new'] = df.apply(lambda x: rule(x['c1'], x['c2']), axis = 1)\n``````\n\n``````df['maximum'] = df.apply(lambda x: max(x['c1'], x['c2']), axis = 1)\n``````\n\n``````df['maximum'] = df[['c1','c2']].max(axis =1)\n``````\n\n7. value counts\n\n``````df['c'].value_counts(\n``````\n\n• normalize = True: 查看每个值出现的频率而不是频次数。\n• dropna = False: 把缺失值也保留在这次统计中。\n• sort = False: 将数据按照值来排序而不是按照出现次数排序。\n• df[‘c].value_counts().reset_index(): 将这个统计表转换成pandas的dataframe并且进行处理。\n\n8. 缺失值的数量\n\n``````import pandas as pd\nimport numpy as np\ndf = pd.DataFrame({ 'id': [1,2,3], 'c1':[0,0,np.nan], 'c2': [np.nan,1,1]})\ndf = df[['id', 'c1', 'c2']]\ndf['num_nulls'] = df[['c1', 'c2']].isnull().sum(axis=1)\n``````\n\n9. 依据指定ID来选取行\n\n``````df_filter = df['ID'].isin(['A001','C022',...])\ndf[df_filter]\n``````\n\n10. 基于分位数分组\n\n``````import numpy as np\ncut_points = [np.percentile(df['c'], i) for i in [50, 80, 95]]\ndf['group'] = 1\nfor i in range(3):\ndf['group'] = df['group'] + (df['c'] < cut_points[i])\n# or <= cut_points[i]\n``````\n\n11. to_csv\n\n``````print(df[:5].to_csv())\n``````",
null,
""
] |
[
null,
"http://p9.pstatp.com/large/pgc-image/216dbc4a19914c1fa255f6f9cdddda8a",
null,
"http://p1.pstatp.com/large/pgc-image/c4b4f4e0abc8474b8bbb6b3ccb5d1047",
null,
"http://p3.pstatp.com/large/pgc-image/a4e32b5faa224a69aeec6275543e7891",
null
] |
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|
http://ken.duisenberg.com/potw/archive/arch01/010320.html
|
[
"## Arranging Numbers\n\n1. Arrange the five odd digits into a five-digit number, such that the first two digits (as a 2-digit number) times the last two digits (as a 2-digit number) minus the center digit results in a number composed of repetitions of one digit. [Can the same be done with the five even digits? - I'm guessing not - KD.]\n2. In how many different ways can you arrange the nine digits 1-9 in a 3x3 grid such that no square shall have a smaller number than its own below it or to the right of it?\n\nSource: The Best of Discover Magazine's Mind Benders, 1984. #81, #83.\n\nSolution\nMail to Ken"
] |
[
null
] |
{"ft_lang_label":"__label__en","ft_lang_prob":0.83161783,"math_prob":0.953747,"size":593,"snap":"2021-43-2021-49","text_gpt3_token_len":156,"char_repetition_ratio":0.14091681,"word_repetition_ratio":0.036697246,"special_character_ratio":0.25969645,"punctuation_ratio":0.08130081,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.96056277,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-10-28T13:33:32Z\",\"WARC-Record-ID\":\"<urn:uuid:a02da323-6ea2-4354-821b-f794695717ce>\",\"Content-Length\":\"1289\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:d312b1aa-109b-4696-b05c-50212c676c62>\",\"WARC-Concurrent-To\":\"<urn:uuid:2b048da2-c80e-44e3-9b81-ddeacb4b1463>\",\"WARC-IP-Address\":\"50.63.8.6\",\"WARC-Target-URI\":\"http://ken.duisenberg.com/potw/archive/arch01/010320.html\",\"WARC-Payload-Digest\":\"sha1:4M3TNFKX2RICZ7H4PCKDOUQBC2VTHIE2\",\"WARC-Block-Digest\":\"sha1:Y7UERC7VYMN2SHJLPCG5GSDVRQYDMR54\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-43/CC-MAIN-2021-43_segments_1634323588341.58_warc_CC-MAIN-20211028131628-20211028161628-00484.warc.gz\"}"}
|
https://convertoctopus.com/13-5-grams-to-ounces
|
[
"## Conversion formula\n\nThe conversion factor from grams to ounces is 0.03527396194958, which means that 1 gram is equal to 0.03527396194958 ounces:\n\n1 g = 0.03527396194958 oz\n\nTo convert 13.5 grams into ounces we have to multiply 13.5 by the conversion factor in order to get the mass amount from grams to ounces. We can also form a simple proportion to calculate the result:\n\n1 g → 0.03527396194958 oz\n\n13.5 g → M(oz)\n\nSolve the above proportion to obtain the mass M in ounces:\n\nM(oz) = 13.5 g × 0.03527396194958 oz\n\nM(oz) = 0.47619848631934 oz\n\nThe final result is:\n\n13.5 g → 0.47619848631934 oz\n\nWe conclude that 13.5 grams is equivalent to 0.47619848631934 ounces:\n\n13.5 grams = 0.47619848631934 ounces\n\n## Alternative conversion\n\nWe can also convert by utilizing the inverse value of the conversion factor. In this case 1 ounce is equal to 2.0999646759259 × 13.5 grams.\n\nAnother way is saying that 13.5 grams is equal to 1 ÷ 2.0999646759259 ounces.\n\n## Approximate result\n\nFor practical purposes we can round our final result to an approximate numerical value. We can say that thirteen point five grams is approximately zero point four seven six ounces:\n\n13.5 g ≅ 0.476 oz\n\nAn alternative is also that one ounce is approximately two point one times thirteen point five grams.\n\n## Conversion table\n\n### grams to ounces chart\n\nFor quick reference purposes, below is the conversion table you can use to convert from grams to ounces\n\ngrams (g) ounces (oz)\n14.5 grams 0.511 ounces\n15.5 grams 0.547 ounces\n16.5 grams 0.582 ounces\n17.5 grams 0.617 ounces\n18.5 grams 0.653 ounces\n19.5 grams 0.688 ounces\n20.5 grams 0.723 ounces\n21.5 grams 0.758 ounces\n22.5 grams 0.794 ounces\n23.5 grams 0.829 ounces"
] |
[
null
] |
{"ft_lang_label":"__label__en","ft_lang_prob":0.77793336,"math_prob":0.9981435,"size":1694,"snap":"2021-31-2021-39","text_gpt3_token_len":499,"char_repetition_ratio":0.19408284,"word_repetition_ratio":0.0,"special_character_ratio":0.38488784,"punctuation_ratio":0.15013404,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99586177,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-09-21T05:14:48Z\",\"WARC-Record-ID\":\"<urn:uuid:a3f01276-7627-4dff-b743-2bbd275a0519>\",\"Content-Length\":\"28614\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:be693eba-23fa-41f3-9ffc-580ea5c462fd>\",\"WARC-Concurrent-To\":\"<urn:uuid:ec020659-7748-4386-94f3-6ce3ba3dedc7>\",\"WARC-IP-Address\":\"172.67.181.234\",\"WARC-Target-URI\":\"https://convertoctopus.com/13-5-grams-to-ounces\",\"WARC-Payload-Digest\":\"sha1:7XH7ZWSJUVPHXIVRVIUYQ6NGTEVRNHOA\",\"WARC-Block-Digest\":\"sha1:ODIZAZM7HYI2KB5V2NKIBMOAMAMO2ELC\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-39/CC-MAIN-2021-39_segments_1631780057158.19_warc_CC-MAIN-20210921041059-20210921071059-00471.warc.gz\"}"}
|
https://www.transtutors.com/questions/consider-a-rigid-rotator-system-whose-hamiltonian-operator-is-suppose-that-the-initr-5071836.htm
|
[
"# Consider a rigid rotator system whose Hamiltonian operator is: Suppose that the initral... 1 answer below »\n\n1. Consider a rigid rotator system whose Hamiltonian operator is H=\n\nSuppose that the initral wavefunction is given by\n\n(a) Express the initial wavefunction in terms=\n\n(b) What is =?\n\n(c) What is x|?(t)>=?\n\n## Solutions:",
null,
""
] |
[
null,
"https://files.transtutors.com/cdn/tutorprofileimage/pi_200289_medium.jpg",
null
] |
{"ft_lang_label":"__label__en","ft_lang_prob":0.8929933,"math_prob":0.9641348,"size":1438,"snap":"2019-51-2020-05","text_gpt3_token_len":346,"char_repetition_ratio":0.10181311,"word_repetition_ratio":0.0,"special_character_ratio":0.24826148,"punctuation_ratio":0.13087249,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.98785746,"pos_list":[0,1,2],"im_url_duplicate_count":[null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-01-27T15:09:17Z\",\"WARC-Record-ID\":\"<urn:uuid:0c31bb2a-5deb-45d9-9aa8-e8a856279378>\",\"Content-Length\":\"80988\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:c42f05a8-6436-4a65-8a5d-8a0f1545dd4a>\",\"WARC-Concurrent-To\":\"<urn:uuid:4e2c6dde-f717-416b-b529-ba4c319d3474>\",\"WARC-IP-Address\":\"35.199.55.187\",\"WARC-Target-URI\":\"https://www.transtutors.com/questions/consider-a-rigid-rotator-system-whose-hamiltonian-operator-is-suppose-that-the-initr-5071836.htm\",\"WARC-Payload-Digest\":\"sha1:5SOJPPLV5COXZQP2YAZYHFZXX6OJUCYQ\",\"WARC-Block-Digest\":\"sha1:JZOBP5S724LP7LFIBA6NTUJXM2ISL3D4\",\"WARC-Identified-Payload-Type\":\"application/xhtml+xml\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-05/CC-MAIN-2020-05_segments_1579251700988.64_warc_CC-MAIN-20200127143516-20200127173516-00384.warc.gz\"}"}
|
https://revbayes.github.io/tutorials/pomos/
|
[
"# Polymorphism-aware phylogenetic models\n\n### Species tree inference with allele frequency data in RevBayes\n\n#### Rui Borges, Bastien Boussau, Sebastian Höhna and Carolin Kosiol\n\nThis tutorial comes with a recorded video walkthrough. The video corresponding to each section of the exercise is linked next to the section title. The full playlist is available here:",
null,
"## Polymorphism-aware phylogenetic models",
null,
"The polymorphism-aware phylogenetic models (PoMos) are alternative approaches of species tree estimation (De Maio et al. 2013) that add a new layer of complexity to the standard substitution models by accounting for population-level forces to describe the process of sequence evolution (De Maio et al. 2015; Schrempf et al. 2016; Borges et al. 2019). PoMos model the evolution of a population of individuals in which changes in allele content (e.g., due to mutations) and frequency (e.g., due to genetic drift or selection) are both possible ().",
null,
"PoMoTwo and Three state-spaces. The tetrahedron represents the PoMos state-space for the four-allelic case (A, C, G and T). The fixed sites $\\{Na_i\\}$ are represented in the vertices of the tetrahedron, while the polymorphic states $\\{na_i,(N −n)a_j\\}$ are represented in its edges. Black and grey arrows respectively distinguish mutations from frequency shifts (i.e., due to genetic drift and selection).\n\nPoMos stand out from the standard models of evolution and other species tree methods because they:\n\n• permit to disentangle the contribution of evolutionary forces to the evolutionary process (e.g., genetic drift, mutational biases and selection);\n• consider polymorphism, thus permitting inferences with data from multiple individuals and populations;\n• naturally account for incomplete lineage sorting (i.e., the persistence of ancestral polymorphisms during speciation events), a known process of phylogenetic discord;\n• directly estimate the species tree, circumventing the many constraints between the species tree and the genealogical histories.\n\nOverall, PoMos constitute a full-likelihood yet computationally efficient approach to species tree inference. PoMos are designed to cope with recent radiations, including incomplete lineage sorting, and long divergence times.\n\n## Polymorphism-aware phylogenetic models: the model",
null,
"PoMos model the evolution of a population of $N$ individuals and $K$ alleles in which changes in the allele content and frequency occur. These are mediated by population forces, such as mutation, genetic drift, and selection. The PoMo state-space includes fixed (or boundary) states $\\{Na_i\\}$, in which all $N$ individuals have the same allele $i \\in \\{0,1,...,K-1\\}$, and polymorphic states $\\{na_i,(N-n)a_j\\}$, in which two alleles $a_i$ and $a_j$ are present in the population with absolute frequencies $n$ and $N-n$.\n\n• Mutations occur with rate $\\mu_{a_ia_j}$. Mutations govern the allele content and only occur in the fixed states: $q_{\\{Na_i\\} \\rightarrow \\{(N-1)a_i,1a_j\\}}=\\mu_{a_ia_j} \\label{equation1}\\tag{1}$ Often, a reversible mutational model is considered. In this case, we break the mutations into a base composition $\\pi$ and exchangeability parameter $\\rho$ (i.e., $\\mu_{a_ia_j}=\\rho_{a_ia_j}\\pi_{a_j}$) just like the GTR. However, in PoMos, we do not model substitutions but mutations. Such an assumption can still model mutational biases quite well and simplifies obtaining formal quantities with PoMos. Another assumption that PoMos do is that mutations can only occur in the fixed states. This corresponds to assume that mutation rates are low, which is verified for the majority of multicellular eukaryotes.\n• Genetic drift is modeled according to the Moran model, in which one individual is chosen to die, and one individual is chosen to reproduce at each time step. Selection acts to (dis)favor alleles by differentiated fitnesses: $\\phi_{a_i}$. Together, genetic drift and selection govern the allele frequency changes: $q_{\\{na_i,(N-n)a_j\\} \\rightarrow \\{(n+1)a_i,(N-n-1)a_j\\}}=\\frac{n(N-n)}{N}\\phi_{a_i} \\label{equation2}\\tag{2}$\n\nLike the standard substitution models, PoMos are continuous-time Markov models and are fully characterized by their rate matrices. The rates in \\ref{equation1} and \\ref{equation2} define the PoMos rate matrices. RevBayes includes a plethora of PoMo rate matrices that permit modeling population dynamics with any number of alleles, reversible mutations (i.e., $\\mu_{a_ia_j}=\\rho_{a_ia_j}\\pi_{a_j}$) and selection. These are described in .\n\nThis tutorial demonstrates how to set up and perform analyses using the polymorphism-aware phylogenetic models. You will perform phylogeny inference under the virtual PoMos Two and Three. These models allow for very efficient species tree inferences under neutrality (PoMoTwo) and selection (PoMoThree) because they operate on a smaller state space (Borges et al. 2020). You will perform a Markov chain Monte Carlo (MCMC) analysis to estimate phylogeny and other model parameters. The graphical model representation under PoMoThree is depicted in figure .",
null,
"Graphical model representation of PoMos. The graphical model shows the dependencies among parameters (Höhna et al. 2014). Here, the rate matrix $Q$ is a deterministic variable because it depends on the mutation rates and fitness coefficients. The same applies to the phylogenetic tree $\\Psi$, which depends on the topology and branch lengths.\n\n## Count files",
null,
"PoMos perform inferences based on allele frequency data. Count files are the files where we store such data. They contain two header lines. The first line indicates the number of taxa and the number of sites (or loci) in the sequence alignment. You might have noticed that NPOP stands for the number of populations, but this is not necessarily the case. PoMos can be employed to infer the evolutionary history of different species or even other systematic units that one might be interested in studying (e.g., subspecies, communities, etc.).\n\nA second line states the genomic position of every locus (chromosome and location) and the taxa names. The first two columns are not used for inference, which means that if you are working with taxa for which such information is not available, you can input these columns with dummy values (e.g., NA). All the other lines in the count file include allelic counts separated by commas. White spaces separate all the elements in the count file. Let us have a look at some lines of the great ape count file we will analyze in this tutorial:\n\nCOUNTSFILE NPOP 3 NSITES 5\nCHROM POS Gorilla_beringei_graueri Gorilla_gorilla_dielhi Gorilla_gorilla_gorilla\nchr1 41275799 6,0,0,0 2,0,0,0 54,0,0,0\nchr2 120104878 6,0,0,0 2,0,0,0 54,0,0,0\nchr11 61364549 0,6,0,0 0,2,0,0 0,54,0,0\nchr17 44837427 6,0,0,0 2,0,0,0 54,0,0,0\nchr19 7495905 4,0,2,0 2,0,0,0 10,0,44,0\n\n\nThe four allelic counts in this count files represent the allelic counts of the A, C, G, and T, respectively. Thus, we know that the Gorilla_gorilla_gorilla has an AG polymorphism at position 7 495 905 of chromosome 19. The allele order in the allelic counts can be any. However, you have to keep in mind that the vector of mutation rates, exchangeabilities, base frequencies, and fitness coefficients all follow the allele counts order.\n\n• the base frequencies and the fitness vectors are in the same order as in the counts: i.e., $\\{a_0,a_1,...,a_{K-1}\\}$.\n• the mutation rates are $\\{a_0a_1, a_1a_0, a_0a_2, a_2a_0,...\\}$\n• the exchangeabilities follow a similar pattern as for the mutation rates, but without the reversed mutation: i.e., $\\{a_0a_1, a_0a_2, ...\\}$\n\n## Loading the data",
null,
"The first step in this tutorial is to convert the allelic counts into PoMo states. Open the terminal and place it on your working directory PoMos (you can choose a name of your preference). Inside PoMos create the usual data and output folders.\n\nWe mentioned previously that the PoMo state-space includes fixed and polymorphic states. However, sampled fixed sites might not be necessarily fixed in the original population. We might just have been unlucky and only sampled individuals with the same allele from a locus that is polymorphic. It is typically the case that the real genetic diversity is undersampled in population genetic studies. The fewer the number of sampled individuals or the rarer are the alleles in the original population (i.e., singletons, doubletons), the more likely are we to observe fake fixed sites in the sequence alignment. The sampled-weighted method helps us to correct for such bias by attributing to each of the allelic counts an appropriate PoMo state (0-based coding). For a population size of 3 virtual individuals, we expect 16 states (coded 0-15), while for a population of 2 virtual individuals, we expected 10 states (coded 0-9).\n\nThe script weighted_sampled_method.cpp is implemented in C++, and we will run it using the Rcpp package in R. Open the counts_to_pomo_states_converter.R file and make the appropriate changes to obtain your PoMo alignments suited for PoMoTwo and PoMoThree. As we will be using the virtual PoMos Two and Three, the virtual population sizes are 2 and 3.\n\ncount_file <- \"count_file.txt\" # count file\nn_alleles <- 4 # the four nucleotide bases A, C, G and T\nN <- 10 # virtual population size\n\nalignment <- counts_to_pomo_states_converter(count_file,n_alleles,N)\n\n\nPlace the produced alignments inside the data folder. The output files follow the NaturalNumbers character type of RevBayes and can easily read by it.\n\nRun RevBayes by typing ./rb (or ./rb-mpi) in the console. Open the great_apes_pomothree.Rev file using an appropriate text editor. First load in the PoMo alignment using the readCharacterDataDelimited function. This function requires you to input the number of expected states: 10 for PoMoTwo and 16 for PoMoThree.\n\ndata <- readCharacterDataDelimited(\"data/great_apes_pomothree_naturalnumbers.txt\", stateLabels=16, type=\"NaturalNumbers\", delimiter=\" \", headers=FALSE)\n\n\nInformation about the alignment can be obtained by typing data.\n\n>data\nNaturalNumbers character matrix with 12 taxa and 1000 characters\n================================================================\nOrigination:\nNumber of taxa: 12\nNumber of included taxa: 12\nNumber of characters: 1000\nNumber of included characters: 1000\nDatatype: NaturalNumbers\n\n\nNext, we will specify some useful variables based on our dataset. These include the number of taxa, taxa names, and the number of branches. We will need that information for setting up our model in subsequent steps.\n\nn_taxa <- data.ntaxa()\nn_branches <- 2*n_taxa-3\ntaxa <- data.taxa()\n\n\nAdditionally, we set up a variable that holds all the moves and monitors for our analysis. Recall that moves are algorithms used to propose new parameter values during the MCMC simulation. Monitors print the values of model parameters to the screen and/or log files during the MCMC analysis.\n\nmoves = VectorMoves()\nmonitors = VectorMonitors()\n\n\n## Setting up the model",
null,
"Estimating an unrooted tree under the virtual PoMos requires specifying two main components:\n\n• the PoMo model, which in our case is PoMoTwo or PoMoThree;\n• the tree topology and branch lengths.\n\nA given PoMo model is defined by its corresponding instantaneous-rate matrix Q. PoMoTwo and PoMoThree have three free parameters in common: the population size N, the allele frequencies pi, and the exchangeabilities rho. PoMoThree additionally includes the allele fitnesses phi, as it accounts for selection. We will set up the virtual PoMoTwo and Three using the function fnReversiblePoMo4N. You can check the input parameters of any PoMo function by typing its name right after the question mark symbol ?fnReversiblePoMo4N.\n\nAs expected, this function has as inputs parameters the population size N, base frequencies, exchangeabilities rho and fitnesses phi. We will first set out the PoMoThree, which we can do by setting $N$ to 3. Similarly, if you wanted to set out the neutral PoMo (i.e., PoMoTwo), you can set N to 2 instead. Thus, N is a fixed node:\n\n# virtual population size\nN <- 3\n\n\nSince pi, rho, and gamma are stochastic variables, we need to specify a move to propose updates to them. A good move on variables drawn from a Dirichlet distribution (i.e., pi) is the mvBetaSimplex. This move randomly takes an element from the allele frequencies vector pi, proposes a new value for it drawn from a Beta distribution, and then rescales all values to sum to 1 again. The weight option inside the moves specifies how often the move will be applied either on average per iteration or relative to all other moves.\n\n# allele frequencies\npi_prior <- [1,1,1,1]\npi ~ dnDirichlet(pi_prior)\nmoves.append( mvBetaSimplex(pi, weight=2) )\n\n\nThe rho and phi parameters must be a positive real number and a natural choice as the prior distribution is the exponential one. Again, we need to specify a move for these stochastic variables, and a simple scaling move mvScale typically works. In this tutorial, we want our model to capture the effect of GC-bias gene conversion. For that, we define gamma, which represents the GC-bias rate. The allele fitnesses phi of G and C will thus be represented by gamma, while those of A and T by 1.0. Note that phi is a deterministic node that depends on the GC-bias rate gamma.\n\n# exchangeabilities\nfor (i in 1:6){\nrho[i] ~ dnExponential(10.0)\nmoves.append(mvScale( rho[i], weight=2 ))\n}\n\n# fitness coefficients\ngamma ~ dnExponential(1.0)\nmoves.append(mvScale( gamma, weight=2 ))\nphi := [1.0,1.0+gamma,1.0+gamma,1.0]\n\n\nThe function fnReversiblePoMo4N will create an instantaneous-rate matrix.\n\n# rate matrix\nQ := fnReversiblePoMo4N(N,pi,rho,phi)\n\n\nWe could similarly have used the functions fnReversiblePoMoTwo4N and fnReversiblePoMoThree4N, to set the rate-matrices. These are particularly useful when one has information about the mutation rates and bias or the GC-bias gene conversion rate (for some model organisms, this information is available) and wants to include it via informative priors. In that case, the population size is not a fixed node, and it can be jointly estimated. In this tutorial, we are assuming that no prior information is available for the mutation or GC-bias rates. Note that selection is not identifiable with two virtual individuals, so the vector of fitness coefficients can not be inputted for the fnReversiblePoMoTwo4N: it is by default equal to the unitary vector (i.e., the neutral scenario). To check the input parameters of PoMoTwo type ?fnReversiblePoMoTwo4N at the terminal; you will see that the fitness coefficient phi is missing. Thus, if we want to employ the PoMoTwo model with the general fnReversiblePoMoTwo4N, we have to set the fitness coefficients to 1.0.\n\nThe tree topology and branch lengths are stochastic nodes in our phylogenetic model. We will assume that all possible labeled, unrooted tree topologies have equal probability. In the case an unrooted tree topology, we use a nearest-neighbor interchange move mvNNI (a subtree-prune and regrafting move mvSPR could also be used).\n\n# topology\ntopology ~ dnUniformTopology(taxa)\nmoves.append( mvNNI(topology, weight=2*n_taxa) )\n\n\nNext, we have to create a stochastic node representing the length of each of the 2*n_taxa−3 branches in our tree. We can do this using a for loop. In this loop, we can create each of the branch-length nodes and assign each move.\n\n# branch lengths\nfor (i in 1:n_branches) {\nbranch_lengths[i] ~ dnExponential(10.0)\nmoves.append( mvScale(branch_lengths[i]) )\n}\n\n\nFinally, we combine the tree topology and branch lengths. We do this using the treeAssembly function, which applies the value of the ith member of the branch_lengths vector to the branch leading to the ith node in the topology. Thus, the psi variable is a deterministic node:\n\npsi := treeAssembly(topology, branch_lengths)\n\n\nWe have fully specified all of the parameters of our phylogenetic model:\n\n• the tree with branch lengths psi;\n• the PoMo instantaneous-rate matrix Q;\n• the type of character data: i.e., NaturalNumbers.\n\nCollectively, these parameters comprise a distribution called the phylogenetic continuous-time Markov chain, and we use the dnPhyloCTMC function to create this node. This distribution requires several input arguments:\n\nsequences ~ dnPhyloCTMC(psi,Q=Q,type=\"NaturalNumbers\")\n\n\nOnce the PhyloCTMC model has been created, we can attach our sequence data to the tip nodes in the tree. Although we assume that our sequence data are random variables, they are realizations of our phylogenetic model. For inference purposes, we assume that the sequence data are clamped to their observed values.\n\nsequences.clamp(data)\n\n\nWhen this function is called, RevBayes sets each of the stochastic nodes representing the tree’s tips to the corresponding nucleotide sequence in the alignment. This essentially tells the program that we have observed data for the sequences at the tips.\n\nFinally, we wrap the entire model in a single object. To do this, we only need to give the model function a single node.\n\npomo_model = model(pi)\n\n\n## Setting, running, and summarizing the MCMC simulation",
null,
"For our MCMC analysis, we need to set up a vector of monitors to record the states of our Markov chain. First, we will initialize the model monitor using the mnModel function. This creates a new monitor variable that will output the states for all model parameters when passed into an MCMC function. We will sample every 10th iterate, and the resulting file can be found in the output folder.\n\nmonitors.append( mnModel(filename=\"output/great_apes_pomothree.log\", printgen=10) )\n\n\nThe mnFile monitor will record the states for only the parameters passed in as arguments. We use this monitor to specify the output for our sampled trees and branch lengths. Again, we sample every 10th iterate.\n\nmonitors.append( mnFile(filename=\"output/great_apes_pomothree.trees\", printgen=10, psi) )\n\n\nFinally, create a screen monitor that will report the states of specified variables to the screen with mnScreen. This monitor mostly helps us to see the progress of the MCMC run.\n\nmonitors.append( mnScreen(printgen=10) )\n\n\nWith a fully specified model, a set of monitors, and a set of moves, we can now set up the MCMC algorithm that will sample parameter values in proportion to their posterior probability. The mcmc function will create our MCMC object. Furthermore, we will perform two independent MCMC runs to ensure proper convergence and mixing.\n\npomo_mcmc = mcmc(pomo_model, monitors, moves, nruns=2, combine=\"mixed\")\n\n\nNow, run the MCMC.\n\npomo_mcmc.run( generations=10000 )\n\n\nWhen the analysis is complete, you will have the monitored files in your output directory. Programs like Tracer allow evaluating convergence and mixing. Look at the file called output/great_apes_pomothree.log in Tracer. There you see the posterior distribution of the continuous parameters. Let us look at the posterior distribution of the GC-bias rate $\\gamma$. Is there any evidence of GC-bias in these great apes sequences?",
null,
"Left: Trace of the GC-bias rate ($\\gamma$) samples for one MCMC run. You will also see that the effective sample size is comparably large, i.e., much larger than 200. Right: Posterior distribution of the great apes GC-bias rate ($\\gamma$) under a PoMoThree model.\n\nApart from the continuous parameters, we need to summarize the trees sampled from the posterior distribution. RevBayes can summarize the sampled trees by reading in the tree-trace file:\n\ntrace = readTreeTrace(\"output/great_apes_pomothree.trees\", treetype=\"non-clock\", burnin= 0.2)\n\n\nThe mapTree function will summarize the tree samples and write the maximum a posteriori (MAP) tree to the specified file. The MAP tree can be found in the output folder.\n\nmapTree(trace, file=\"output/great_apes_pomothree_MAP.tree\" )",
null,
"Maximum a posteriori estimate of the great ape phylogeny under the PoMoThree model. The numbers at the nodes show the posterior probabilities for the clades. We have rooted the tree at the Oran-Utans clade.\n\nLook at the file called output/great_apes_pomothree_MAP.tree and open it in FigTree. The maximum a posteriori estimate of the great ape phylogeny under the PoMoThree model should look like that of .\n\n## Some questions\n\n1. What is the GC-bias rate (this is the selection coefficient) for the great ape populations? Rescale to its real value by assuming that the great apes have an effective population size of about 10 000 individuals. Hint: You can use the relation $(1+\\gamma’)^{M-1}=(1+\\gamma)^{N-1}$ to rescale $\\gamma$. $M$ and $N$ are the virtual and the effective population sizes, and $\\gamma’$ and $\\gamma$ are the GC-bias rates on the virtual and effective populations.\n\n2. With as your guide, draw the probabilistic graphical model of the PoMoTwo model.\n\n3. What changes have to be done to the great_apes_pomothree.Rev to make inferences under the PoMoTwo model?\n\n4. Run an MCMC analysis to estimate the posterior distribution under the PoMoTwo model. Are the resulting estimates of the mutation rates (base frequencies and exchangeabilities) equal? If not, how much do they differ?\n\n5. Compare the MAP trees estimated under PoMoTwo and PoMoThree. Are they equal, and if not, how much do they differ?\n\n1. Borges R., Boussau B., Szöllősi G.J., Kosiol C. 2020. Pervasive selection biases inferences of the species tree. bioRxiv. 10.1101/2020.07.30.228965\n2. Borges R., Szöllősi G.J., Kosiol C. 2019. Quantifying GC-Biased Gene Conversion in Great Ape Genomes Using Polymorphism-Aware Models. Genetics. 212:1321–1336. 10.1534/genetics.119.302074\n3. Höhna S., Heath T.A., Boussau B., Landis M.J., Ronquist F., Huelsenbeck J.P. 2014. Probabilistic Graphical Model Representation in Phylogenetics. Systematic Biology. 63:753–771. 10.1093/sysbio/syu039\n4. Schrempf D., Minh B.Q., De Maio N., Haeseler A. von, Kosiol C. 2016. Reversible polymorphism-aware phylogenetic models and their application to tree inference. Journal of Theoretical Biology. 407:362–370. 10.1016/j.jtbi.2016.07.042\n5. De Maio N., Schlötterer C., Kosiol C. 2013. Linking great apes genome evolution across time scales using polymorphism-aware phylogenetic models. 30:2249–2262. 10.1093/molbev/mst131\n6. De Maio N., Schrempf D., Kosiol C. 2015. PoMo: An Allele Frequency-Based Approach for Species Tree Estimation. Systematic Biology. 64:1018–1031. 10.1093/sysbio/syv048"
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https://vivo.library.tamu.edu/vivo/display/n67905SE
|
[
"# Least-squares finite element models of two-dimensional compressible flows Academic Article",
null,
"•\n• Overview\n•\n• Research\n•\n• Identity\n•\n•\n• View All\n•\n\n### abstract\n\n• We present numerical simulation results for the compressible Euler equations and compressible Navier-Stokes equations using least-squares finite element models. Alternative least-squares formulations are first exemplified by a Poisson problem and ideas extended to the Euler and Navier-Stokes equations. For the compressible Navier-Stokes equations we introduce velocity gradients and heat fluxes as additional primary variables to arrive at an equivalent first-order system. The least-squares models developed herein are found to be effective for the high- and low-speed compressible flow regimes. 2003 Elsevier B.V. All rights reserved.\n\n### published proceedings\n\n• FINITE ELEMENTS IN ANALYSIS AND DESIGN\n\n### author list (cited authors)\n\n• Pontaza, J. P., Diao, X., Reddy, J. N., & Surana, K. S.\n\n• 17\n\n• March 2004"
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https://tools.carboncollective.co/compound-interest/61466-at-27-percent-in-20-years/
|
[
"# What is the compound interest on $61466 at 27% over 20 years? If you want to invest$61,466 over 20 years, and you expect it will earn 27.00% in annual interest, your investment will have grown to become $7,323,342.92. If you're on this page, you probably already know what compound interest is and how a sum of money can grow at a faster rate each year, as the interest is added to the original principal amount and recalculated for each period. The actual rate that$61,466 compounds at is dependent on the frequency of the compounding periods. In this article, to keep things simple, we are using an annual compounding period of 20 years, but it could be monthly, weekly, daily, or even continuously compounding.\n\nThe formula for calculating compound interest is:\n\n$$A = P(1 + \\dfrac{r}{n})^{nt}$$\n\n• A is the amount of money after the compounding periods\n• P is the principal amount\n• r is the annual interest rate\n• n is the number of compounding periods per year\n• t is the number of years\n\nWe can now input the variables for the formula to confirm that it does work as expected and calculates the correct amount of compound interest.\n\nFor this formula, we need to convert the rate, 27.00% into a decimal, which would be 0.27.\n\n$$A = 61466(1 + \\dfrac{ 0.27 }{1})^{ 20}$$\n\nAs you can see, we are ignoring the n when calculating this to the power of 20 because our example is for annual compounding, or one period per year, so 20 × 1 = 20.\n\n## How the compound interest on $61,466 grows over time The interest from previous periods is added to the principal amount, and this grows the sum a rate that always accelerating. The table below shows how the amount increases over the 20 years it is compounding: Start Balance Interest End Balance 1$61,466.00 $16,595.82$78,061.82\n2 $78,061.82$21,076.69 $99,138.51 3$99,138.51 $26,767.40$125,905.91\n4 $125,905.91$33,994.60 $159,900.51 5$159,900.51 $43,173.14$203,073.64\n6 $203,073.64$54,829.88 $257,903.52 7$257,903.52 $69,633.95$327,537.48\n8 $327,537.48$88,435.12 $415,972.59 9$415,972.59 $112,312.60$528,285.20\n10 $528,285.20$142,637.00 $670,922.20 11$670,922.20 $181,148.99$852,071.19\n12 $852,071.19$230,059.22 $1,082,130.41 13$1,082,130.41 $292,175.21$1,374,305.62\n14 $1,374,305.62$371,062.52 $1,745,368.14 15$1,745,368.14 $471,249.40$2,216,617.54\n16 $2,216,617.54$598,486.74 $2,815,104.28 17$2,815,104.28 $760,078.16$3,575,182.43\n18 $3,575,182.43$965,299.26 $4,540,481.69 19$4,540,481.69 $1,225,930.06$5,766,411.75\n20 $5,766,411.75$1,556,931.17 $7,323,342.92 We can also display this data on a chart to show you how the compounding increases with each compounding period. As you can see if you view the compounding chart for$61,466 at 27.00% over a long enough period of time, the rate at which it grows increases over time as the interest is added to the balance and new interest calculated from that figure.\n\n## How long would it take to double $61,466 at 27% interest? Another commonly asked question about compounding interest would be to calculate how long it would take to double your investment of$61,466 assuming an interest rate of 27.00%.\n\nWe can calculate this very approximately using the Rule of 72.\n\nThe formula for this is very simple:\n\n$$Years = \\dfrac{72}{Interest\\: Rate}$$\n\nBy dividing 72 by the interest rate given, we can calculate the rough number of years it would take to double the money. Let's add our rate to the formula and calculate this:\n\n$$Years = \\dfrac{72}{ 27 } = 2.67$$\n\nUsing this, we know that any amount we invest at 27.00% would double itself in approximately 2.67 years. So $61,466 would be worth$122,932 in ~2.67 years.\n\nWe can also calculate the exact length of time it will take to double an amount at 27.00% using a slightly more complex formula:\n\n$$Years = \\dfrac{log(2)}{log(1 + 0.27)} = 2.9\\; years$$\n\nHere, we use the decimal format of the interest rate, and use the logarithm math function to calculate the exact value.\n\nAs you can see, the exact calculation is very close to the Rule of 72 calculation, which is much easier to remember.\n\nHopefully, this article has helped you to understand the compound interest you might achieve from investing \\$61,466 at 27.00% over a 20 year investment period."
] |
[
null
] |
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|
https://tex.stackexchange.com/questions/282288/which-one-should-be-used-newcommand-or-sbox
|
[
"# Which one should be used: newcommand or sbox\n\nSuppose I want to use an element numerous times, which is the best way to go at it (I chose a tikz-drawn arrow, because that is what made me think of the question, but I suppose it could be anything, really):\n\n• define a new command with \\newcommand: as I understand it, this leads to the expansion of the defined command with its definition, so it's basically a shortcut that saves time of having to repeatedly type the same code again and again. With my tikz example below that would mean, the arrow would be drawn fresh every time.\n\n• define a savebox/sbox with \\newsavebox: I am not sure what happens here, but assumed it's something like a call-by-reference, meaning the object is only rendered once and the result is \"copied\", whenever \\usebox is used.\n\nTo me, the end result looks the same, but - depending on the complexity of the reused object - it could result in a big difference in compile time, rendering at every occurrence vs. rendering once and essentially just copying a picture.\n\nErgo: Which method should be used?\n\nIs it a matter of personal preference, or does the end result in fact differ in subtle ways my simple arrow example does not show? Are maybe my assumptions off the mark?\n\nThe code for clarification:\n\n\\documentclass{article}\n\\usepackage{tikz}\n\n\\newcommand{\\myDownArrow}{\\tikz\\draw (0,0)--++(10pt,0)--++(0,-10pt)--++(5pt,0)--++(-10pt,-5pt)--++(-10pt,5pt)--++(5pt,0)--cycle;}\n\n\\newsavebox{\\myDownArrowBox}\n\\sbox{\\myDownArrowBox}{\\tikz\\draw (0,0)--++(10pt,0)--++(0,-10pt)--++(5pt,0)--++(-10pt,-5pt)--++(-10pt,5pt)--++(5pt,0)--cycle;}\n\n\\begin{document}\n\\end{document}\n\n\nand the obvious result:",
null,
"A box is fix. You can resize it but apart from this you can't change much. A macro is executed again when you call it, so it can be different depending on external conditions like counters or colors.\n\n\\documentclass{article}\n\\usepackage{tikz}\n\n\\tikzset{arrowstyle/.style={}}\n\n\\newcommand{\\myDownArrow}{\\tikz\\draw[arrowstyle] (0,0)--++(10pt,0)--++(0,-10pt)--++(5pt,0)--++(-10pt,-5pt)--++(-10pt,5pt)--++(5pt,0)--cycle;}\n\n\\newsavebox{\\myDownArrowBox}\n\\sbox{\\myDownArrowBox}{\\tikz\\draw[arrowstyle] (0,0)--++(10pt,0)--++(0,-10pt)--++(5pt,0)--++(-10pt,-5pt)--++(-10pt,5pt)--++(5pt,0)--cycle;}\n\n\\begin{document}",
null,
""
] |
[
null,
"https://i.stack.imgur.com/6GjzP.png",
null,
"https://i.stack.imgur.com/SxKbK.png",
null
] |
{"ft_lang_label":"__label__en","ft_lang_prob":0.86012465,"math_prob":0.8838331,"size":1675,"snap":"2023-14-2023-23","text_gpt3_token_len":472,"char_repetition_ratio":0.123279475,"word_repetition_ratio":0.0,"special_character_ratio":0.27283582,"punctuation_ratio":0.13564669,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.95861834,"pos_list":[0,1,2,3,4],"im_url_duplicate_count":[null,3,null,3,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-06-02T14:26:57Z\",\"WARC-Record-ID\":\"<urn:uuid:ecca561d-afcc-4229-997a-0a8b4f19f504>\",\"Content-Length\":\"135079\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:3b5bd0d3-45a4-4c41-b6bb-cc411b6d0ee8>\",\"WARC-Concurrent-To\":\"<urn:uuid:a1ae2de4-ddc8-4592-898a-b8aea62bb11e>\",\"WARC-IP-Address\":\"151.101.1.69\",\"WARC-Target-URI\":\"https://tex.stackexchange.com/questions/282288/which-one-should-be-used-newcommand-or-sbox\",\"WARC-Payload-Digest\":\"sha1:SQ6LNZ3IFDHWDNPQRQ7NQ2C42MGDRJDL\",\"WARC-Block-Digest\":\"sha1:WWRMLQ6BEEKWIIGWDBRTJS7OTXYJIVS7\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-23/CC-MAIN-2023-23_segments_1685224648695.4_warc_CC-MAIN-20230602140602-20230602170602-00458.warc.gz\"}"}
|
https://conferences.famnit.upr.si/indico/event/4/contribution/26
|
[
"# Graphs, groups, and more: celebrating Brian Alspach’s 80th and Dragan Marušič’s 65th birthdays\n\nfrom 28 May 2018 to 1 June 2018\nKoper\nUTC timezone\nHome > Timetable > Contribution details\n\n# On decomposing $3$-uniform hypergraphs into loose $m$-cycles\n\n## Speakers\n\n• Prof. Saad EL-ZANATI\n\n## Content\n\nA loose $m$-cycle is a 3-uniform hypergraph with vertex set ${v_1, v_2, \\ldots, v_{2m}}$ edge set ${{v_1,v_2,v_3}, {v_3,v_4,v_5}, \\ldots, {v_{2m-1},v_{2m},v_1}}$. We consider the problem of decomposing $K_{v}^{(3)}$, the complete 3-uniform hypergraph of order $v$, into edge-disjoint loose $m$-cycles. We settle the problem in the case $m=4$."
] |
[
null
] |
{"ft_lang_label":"__label__en","ft_lang_prob":0.806555,"math_prob":0.99030644,"size":475,"snap":"2019-35-2019-39","text_gpt3_token_len":181,"char_repetition_ratio":0.12526539,"word_repetition_ratio":0.0,"special_character_ratio":0.3705263,"punctuation_ratio":0.16504854,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.98437226,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-08-22T12:25:18Z\",\"WARC-Record-ID\":\"<urn:uuid:82b1d682-da90-4536-bb55-5a1fe710360d>\",\"Content-Length\":\"41830\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:a4630bb8-ca44-4708-afbc-0c76319e88bc>\",\"WARC-Concurrent-To\":\"<urn:uuid:8bc65cb4-a791-46a1-8fa2-11c567051603>\",\"WARC-IP-Address\":\"88.200.63.153\",\"WARC-Target-URI\":\"https://conferences.famnit.upr.si/indico/event/4/contribution/26\",\"WARC-Payload-Digest\":\"sha1:2IVR6XJ6RM4FIZHS7YD6LFTUYZ7OS6PC\",\"WARC-Block-Digest\":\"sha1:YGCK4UPEZ6YYJKCC63AOIGU37RV6TINQ\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-35/CC-MAIN-2019-35_segments_1566027317113.27_warc_CC-MAIN-20190822110215-20190822132215-00028.warc.gz\"}"}
|
https://www.colorhexa.com/0765ab
|
[
"# #0765ab Color Information\n\nIn a RGB color space, hex #0765ab is composed of 2.7% red, 39.6% green and 67.1% blue. Whereas in a CMYK color space, it is composed of 95.9% cyan, 40.9% magenta, 0% yellow and 32.9% black. It has a hue angle of 205.6 degrees, a saturation of 92.1% and a lightness of 34.9%. #0765ab color hex could be obtained by blending #0ecaff with #000057. Closest websafe color is: #006699.\n\n• R 3\n• G 40\n• B 67\nRGB color chart\n• C 96\n• M 41\n• Y 0\n• K 33\nCMYK color chart\n\n#0765ab color description : Dark blue.\n\n# #0765ab Color Conversion\n\nThe hexadecimal color #0765ab has RGB values of R:7, G:101, B:171 and CMYK values of C:0.96, M:0.41, Y:0, K:0.33. Its decimal value is 484779.\n\nHex triplet RGB Decimal 0765ab `#0765ab` 7, 101, 171 `rgb(7,101,171)` 2.7, 39.6, 67.1 `rgb(2.7%,39.6%,67.1%)` 96, 41, 0, 33 205.6°, 92.1, 34.9 `hsl(205.6,92.1%,34.9%)` 205.6°, 95.9, 67.1 006699 `#006699`\nCIE-LAB 41.676, 2.857, -44.109 12.09, 12.292, 40.261 0.187, 0.19, 12.292 41.676, 44.202, 273.705 41.676, -24.598, -64.817 35.059, 0.202, -43.545 00000111, 01100101, 10101011\n\n# Color Schemes with #0765ab\n\n• #0765ab\n``#0765ab` `rgb(7,101,171)``\n• #ab4d07\n``#ab4d07` `rgb(171,77,7)``\nComplementary Color\n• #07ab9f\n``#07ab9f` `rgb(7,171,159)``\n• #0765ab\n``#0765ab` `rgb(7,101,171)``\n• #0713ab\n``#0713ab` `rgb(7,19,171)``\nAnalogous Color\n• #ab9f07\n``#ab9f07` `rgb(171,159,7)``\n• #0765ab\n``#0765ab` `rgb(7,101,171)``\n• #ab0713\n``#ab0713` `rgb(171,7,19)``\nSplit Complementary Color\n• #65ab07\n``#65ab07` `rgb(101,171,7)``\n• #0765ab\n``#0765ab` `rgb(7,101,171)``\n• #ab0765\n``#ab0765` `rgb(171,7,101)``\nTriadic Color\n• #07ab4d\n``#07ab4d` `rgb(7,171,77)``\n• #0765ab\n``#0765ab` `rgb(7,101,171)``\n• #ab0765\n``#ab0765` `rgb(171,7,101)``\n• #ab4d07\n``#ab4d07` `rgb(171,77,7)``\nTetradic Color\n• #043a62\n``#043a62` `rgb(4,58,98)``\n• #05487a\n``#05487a` `rgb(5,72,122)``\n• #065793\n``#065793` `rgb(6,87,147)``\n• #0765ab\n``#0765ab` `rgb(7,101,171)``\n• #0873c3\n``#0873c3` `rgb(8,115,195)``\n• #0982dc\n``#0982dc` `rgb(9,130,220)``\n• #0a90f4\n``#0a90f4` `rgb(10,144,244)``\nMonochromatic Color\n\n# Alternatives to #0765ab\n\nBelow, you can see some colors close to #0765ab. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.\n\n• #078eab\n``#078eab` `rgb(7,142,171)``\n• #0780ab\n``#0780ab` `rgb(7,128,171)``\n• #0773ab\n``#0773ab` `rgb(7,115,171)``\n• #0765ab\n``#0765ab` `rgb(7,101,171)``\n• #0757ab\n``#0757ab` `rgb(7,87,171)``\n• #074aab\n``#074aab` `rgb(7,74,171)``\n• #073cab\n``#073cab` `rgb(7,60,171)``\nSimilar Colors\n\n# #0765ab Preview\n\nText with hexadecimal color #0765ab\n\nThis text has a font color of #0765ab.\n\n``<span style=\"color:#0765ab;\">Text here</span>``\n#0765ab background color\n\nThis paragraph has a background color of #0765ab.\n\n``<p style=\"background-color:#0765ab;\">Content here</p>``\n#0765ab border color\n\nThis element has a border color of #0765ab.\n\n``<div style=\"border:1px solid #0765ab;\">Content here</div>``\nCSS codes\n``.text {color:#0765ab;}``\n``.background {background-color:#0765ab;}``\n``.border {border:1px solid #0765ab;}``\n\n# Shades and Tints of #0765ab\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, #000101 is the darkest color, while #eef7fe is the lightest one.\n\n• #000101\n``#000101` `rgb(0,1,1)``\n• #010c14\n``#010c14` `rgb(1,12,20)``\n• #021727\n``#021727` `rgb(2,23,39)``\n• #02223a\n``#02223a` `rgb(2,34,58)``\n• #032d4d\n``#032d4d` `rgb(3,45,77)``\n• #043860\n``#043860` `rgb(4,56,96)``\n• #054472\n``#054472` `rgb(5,68,114)``\n• #054f85\n``#054f85` `rgb(5,79,133)``\n• #065a98\n``#065a98` `rgb(6,90,152)``\n• #0765ab\n``#0765ab` `rgb(7,101,171)``\n• #0870be\n``#0870be` `rgb(8,112,190)``\n• #097bd1\n``#097bd1` `rgb(9,123,209)``\n• #0986e4\n``#0986e4` `rgb(9,134,228)``\nShade Color Variation\n• #0b91f5\n``#0b91f5` `rgb(11,145,245)``\n• #1e9af6\n``#1e9af6` `rgb(30,154,246)``\n• #31a2f7\n``#31a2f7` `rgb(49,162,247)``\n• #44abf7\n``#44abf7` `rgb(68,171,247)``\n• #57b3f8\n``#57b3f8` `rgb(87,179,248)``\n• #6abcf9\n``#6abcf9` `rgb(106,188,249)``\n• #7cc4fa\n``#7cc4fa` `rgb(124,196,250)``\n• #8fcdfa\n``#8fcdfa` `rgb(143,205,250)``\n• #a2d5fb\n``#a2d5fb` `rgb(162,213,251)``\n• #b5defc\n``#b5defc` `rgb(181,222,252)``\n• #c8e6fd\n``#c8e6fd` `rgb(200,230,253)``\n• #dbeffe\n``#dbeffe` `rgb(219,239,254)``\n• #eef7fe\n``#eef7fe` `rgb(238,247,254)``\nTint Color Variation\n\n# Tones of #0765ab\n\nA tone is produced by adding gray to any pure hue. In this case, #525a60 is the less saturated color, while #0066b2 is the most saturated one.\n\n• #525a60\n``#525a60` `rgb(82,90,96)``\n• #4b5b67\n``#4b5b67` `rgb(75,91,103)``\n• #455c6d\n``#455c6d` `rgb(69,92,109)``\n• #3e5d74\n``#3e5d74` `rgb(62,93,116)``\n• #375e7b\n``#375e7b` `rgb(55,94,123)``\n• #305f82\n``#305f82` `rgb(48,95,130)``\n• #296089\n``#296089` `rgb(41,96,137)``\n• #226190\n``#226190` `rgb(34,97,144)``\n• #1c6296\n``#1c6296` `rgb(28,98,150)``\n• #15639d\n``#15639d` `rgb(21,99,157)``\n• #0e64a4\n``#0e64a4` `rgb(14,100,164)``\n• #0765ab\n``#0765ab` `rgb(7,101,171)``\n• #0066b2\n``#0066b2` `rgb(0,102,178)``\nTone Color Variation\n\n# Color Blindness Simulator\n\nBelow, you can see how #0765ab 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":0.55745816,"math_prob":0.5585976,"size":3675,"snap":"2021-21-2021-25","text_gpt3_token_len":1632,"char_repetition_ratio":0.12122037,"word_repetition_ratio":0.011111111,"special_character_ratio":0.5602721,"punctuation_ratio":0.23809524,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.98449945,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-06-21T23:28:21Z\",\"WARC-Record-ID\":\"<urn:uuid:9b490aae-991e-4e3f-b6a7-55a1780b159c>\",\"Content-Length\":\"36240\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:6ea102fb-4948-450c-9544-77ce76adbf17>\",\"WARC-Concurrent-To\":\"<urn:uuid:ca166e1a-bcea-4b0d-941c-9a30a05ec512>\",\"WARC-IP-Address\":\"178.32.117.56\",\"WARC-Target-URI\":\"https://www.colorhexa.com/0765ab\",\"WARC-Payload-Digest\":\"sha1:3WIR3UL26XLAHE6Y7VOGNCVEONCIFCMN\",\"WARC-Block-Digest\":\"sha1:RKJ7MWDEL6KYGSWWSYHLIXRMOAJ55UYZ\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-25/CC-MAIN-2021-25_segments_1623488504838.98_warc_CC-MAIN-20210621212241-20210622002241-00449.warc.gz\"}"}
|
https://optimization-online.org/author/xuan-doan/
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[
"## Robust Stable Payoff Distribution in Stochastic Cooperative Games\n\nCooperative games with transferable utilities belong to a branch of game theory where groups of players can enter into binding agreements and form coalitions in order to jointly achieve some objectives. In a cooperative setting, one of the most important questions to address is how to establish a payoff distribution among the players in such … Read more\n\n## Finding the largest low-rank clusters with Ky Fan 2-k-norm and l1-norm\n\nWe propose a convex optimization formulation with the Ky Fan 2-k-norm and l1-norm to fi nd k largest approximately rank-one submatrix blocks of a given nonnegative matrix that has low-rank block diagonal structure with noise. We analyze low-rank and sparsity structures of the optimal solutions using properties of these two matrix norms. We show that, under … Read more\n\n## Price of Anarchy for Non-atomic Congestion Games with Stochastic Demands\n\nWe generalize the notions of user equilibrium and system optimum to non-atomic congestion games with stochastic demands. We establish upper bounds on the price of anarchy for three different settings of link cost functions and demand distributions, namely, (a) affine cost functions and general distributions, (b) polynomial cost functions and general positive-valued distributions, and (c) … Read more\n\n## Robustness to Dependency in Portfolio Optimization Using Overlapping Marginals\n\nIn this paper, we develop a distributionally robust portfolio optimization model where the robustness is to different dependency structures among the random losses. For a Frechet class of distributions with overlapping marginals, we show that the distributionally robust portfolio optimization problem is efficiently solvable with linear programming. To guarantee the existence of a joint multivariate … Read more\n\n## A proximal point algorithm for sequential feature extraction applications\n\nWe propose a proximal point algorithm to solve LAROS problem, that is the problem of finding a “large approximately rank-one submatrix”. This LAROS problem is used to sequentially extract features in data. We also develop a new stopping criterion for the proximal point algorithm, which is based on the duality conditions of \\eps-optimal solutions of … Read more\n\n## A Robust Algorithm for Semidefinite Programming\n\nCurrent successful methods for solving semidefinite programs, SDP, are based on primal-dual interior-point approaches. These usually involve a symmetrization step to allow for application of Newton’s method followed by block elimination to reduce the size of the Newton equation. Both these steps create ill-conditioning in the Newton equation and singularity of the Jacobian of the … Read more\n\n## Finding approximately rank-one submatrices with the nuclear norm and l1 norm\n\nWe propose a convex optimization formulation with the nuclear norm and $\\ell_1$-norm to find a large approximately rank-one submatrix of a given nonnegative matrix. We develop optimality conditions for the formulation and characterize the properties of the optimal solutions. We establish conditions under which the optimal solution of the convex formulation has a specific sparse … Read more\n\n## On the Complexity of Non-Overlapping Multivariate Marginal Bounds for Probabilistic Combinatorial Optimization Problems\n\nGiven a combinatorial optimization problem with an arbitrary partition of the set of random objective coefficients, we evaluate the tightest possible bound on the expected optimal value for joint distributions consistent with the given multivariate marginals of the subsets in the partition. For univariate marginals, this bound was first proposed by Meilijson and Nadas (Journal … Read more\n\n## Models for Minimax Stochastic Linear Optimization Problems with Risk Aversion\n\nIn this paper, we propose a semidefinite optimization (SDP) based model for the class of minimax two-stage stochastic linear optimization problems with risk aversion. The distribution of the second-stage random variables is assumed to be chosen from a set of multivariate distributions with known mean and second moment matrix. For the minimax stochastic problem with … Read more\n\n## Optimal data fitting: a moment approach\n\nWe propose a moment relaxation for two problems, the separation and covering problem with semi-algebraic sets generated by a polynomial of degree d. We show that (a) the optimal value of the relaxation finitely converges to the optimal value of the original problem, when the moment order r increases and (b) there exist probability measures … Read more"
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https://www.englishpedia.net/sentences/a/rational-approximation-in-a-sentence
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[
"# rational approximation in a sentence\n\n1) Here are all of its best rational approximations .\n\napproximation collocations\n2) In fact \"π\" could be computed from these rational approximations .\n\n3) The continued fraction representation of can be used to generate successive best rational approximations .\n\n## rational approximation example sentences\n\n4) These approximations are the best possible rational approximations of relative to the size of their denominators.\n\n5) Thus to incorporate a new term into a rational approximation , only the two previous convergents are necessary.\n\n6) Their rational approximation of the \"error\" for the finite sum of their series are of particular interest.\n\n7) However, the existence or usefulness of a rational approximation to a quantity does not mean the quantity is irrational.\n\n8) An infinite continued fraction representation for an irrational number is useful because its initial segments provide rational approximations to the number.\n\n9) The Kerala school of astronomy and mathematics further expanded his works with various series expansions and rational approximations until the 16th century.\n\n10) Each approximation generated in this way is a best rational approximation ; that is, each is closer to than any other fraction with the same or a smaller denominator.\n\n11) We can use \"ƒ\" and \"g\" together to compute as close a rational approximation as we like to the real number they represent.\n\n12) The \"R\"\"n\" are rational approximations to \"π\" and two successive terms always enclose the true value of \"π\".\n\n13) The last entry of the table has 355⁄113 as one of its best rational approximations ;\n\n14) the numerator of the best simplified rational approximation of pi having a denominator of four digits or fewer.\n\n15) The simple continued fraction for __FORMULA__ generates \"all\" of the best rational approximations for __FORMULA__ according to three rules:\n\n### example sentences with rationalapproximation\n\n16) They are intimately connected with the golden ratio ; for example, the closest rational approximations to the ratio are 2/1, 3/2, 5/3, 8/5, ... .\n\nThese examples have been automatically selected and may contain sensitive content that does not reflect the opinions or policies of our website. Please inform us about the inappropriate sentences:\nThis site is designed to teach you English words in context with collocations with the help of example sentences.\nYou can easily memorize the word and the meaning of rational approximation\nand This is a fast way of learning the meaning of rational approximation with example sentences.\nAlways focus on the learning on sentences with rational approximation\nWe believe you will easily learn to write and use the word rational approximation in a sentence.\nYou can practice spelling and usage of the word by getting 10 examples of sentences with rational approximation.\n20 examples of simple sentences of rational approximation. We tried to find and publish the the words with Simple Sentences of rational approximation\nCompound Sentences with rational approximation\nComplex Sentences with rational approximation\nCompound-Complex Sentences with rational approximation\nin a sentence"
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https://codea.io/talk/discussion/comment/60080/
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[
"#### Howdy, Stranger!\n\nIt looks like you're new here. If you want to get involved, click one of these buttons!\n\n#### In this Discussion\n\nedited May 2015 in Shaders Posts: 216\n\nHi, I'm looking for a good blur shader, something similar to the blur you get in the iOS Notification Center or OS X Yosemite. The blur shader included doesn't really do what I want. Also, preferably something non GPU intensive, as I have a lot of other stuff going on in the background (Even if it is GPU intensive, id still love to see it and test it) (also, preferably something with an open licence, as this will be used in a game that will most likely be on the App Store in the next couple of months). The background will constantly be changing, as this will be a background for a store which is \"on top\" or the menu screen which has several effects in it.\n\n• Posts: 2,020\n\nMost of the examples I've seen online require 2 passes. It also seems that you'd have to render the underlying scene to an image, and then pass that image as a texture to the shader. I don't know what the performance of that would be like if you were doing it every frame. This one looks interesting:\n\nhttp://xissburg.com/faster-gaussian-blur-in-glsl/\n\n• Posts: 5,396\n\n@Mr_Ninja\n\nthe fastest method may be to create an overlay image which can be sprited over the drawn screen, creating the impression of a blur. This is much faster than creating a blur in real time (and does the difference really matter to users?).\n\nThe code below demonstrates. Touch the screen to toggle the \"blur\" on and off. You can adjust the light drop off towards the edges with this line in the shader: f = fff; (the more you mutiply f by itself, the faster the light drops off, and vice versa)\n\n``````function setup()\nblurImg=image(WIDTH,HEIGHT)\nsetContext(blurImg)\nlocal m=mesh()\nm:draw()\nsetContext()\n--image completed\nshowBlur=false --blur toggle\nend\n\nfunction draw()\nbackground(50)\n--draw something on the screen\nsprite(\"SpaceCute:Rocketship\",WIDTH-200,HEIGHT-200)\nsprite(\"Small World:Store Extra Large\",200,200,300)\nsprite(\"SpaceCute:Beetle Ship\",WIDTH/2,HEIGHT/2)\nif showBlur then --draw blur if required\nsprite(img,WIDTH/2,HEIGHT/2)\nsprite(\"Cargo Bot:Codea Icon\",WIDTH/2,HEIGHT/2)\nend\nend\n\n--touching toggles blur on and off\nfunction touched(t)\nif t.state==ENDED then showBlur=not showBlur end\nend\n\nuniform mat4 modelViewProjection;\nattribute vec4 position;\nattribute vec4 color;\nvarying highp vec4 vPosition;\n\nvoid main()\n{\ngl_Position = modelViewProjection * position;\nvPosition = position;\n}\n]],\nprecision highp float;\nuniform vec2 centre;\nvarying highp vec4 vPosition;\n\nfloat L = sqrt(centre.x*centre.x+centre.y*centre.y)*1.4;\n\nvoid main()\n{\nfloat f=1.0 - distance(vPosition.xy,centre)/L;\nf=f*f*f;\ngl_FragColor = vec4(f,f,f,1.0-f);\n}\n]]}\n``````\n• Posts: 2,020\n\nHere's a Gaussian blur shader. It looks great, but on an iPad Air, the FPS drops to 35 with two passes. I haven't tried optimising it, but I'm sure the bottleneck is sending two screen-sized images as textures to the shader, rather than the calculations in the shader itself. i.e. I'm not sure that reducing the number of samples in the shader from 14 would necessarily help. Could be worth trying?\n\n``````--# Main\n--Gaussian blur\n\nfunction setup()\nblur = {} --images\nblurred = {} --meshes\nfor i=1,2 do --2 passes, one for horizontal, one vertical\nblur[i]=image(WIDTH,HEIGHT)\nblurred[i]=mesh()\nblurred[i].texture=blur[i]\nend\nunblurred=mesh() --mesh w/o the blur shader\nunblurred.texture=blur\n\nshowBlur=false --blur toggle\npos={x1=200, y2=200, x3=800}\ntween(2, pos, {x1=800, y2=800, x3=200}, {easing=tween.easing.cubicInOut, loop=tween.loop.pingpong})\nmovement = true\nprofiler.init()\nend\n\nfunction draw()\nif movement then\nsetContext(blur)\nbackground(50)\n\nsprite(\"SpaceCute:Rocketship\",pos.x1,HEIGHT-200)\nsprite(\"Small World:Store Extra Large\",pos.x3,200,300)\nsprite(\"SpaceCute:Beetle Ship\",WIDTH/2,pos.y2)\nend\n\nif showBlur then --draw blur if required\nsetContext(blur)\nblurred:draw() --pass one, offscreen\nsetContext()\nblurred:draw() --pass two\nelse\nsetContext()\nunblurred:draw()\nend\nprofiler.draw()\nend\n\n--touching toggles blur on and off\nfunction touched(t)\nif t.state==ENDED then showBlur=not showBlur end\nend\n\nprofiler={}\n\nfunction profiler.init(quiet)\nprofiler.del=0\nprofiler.c=0\nprofiler.fps=0\nprofiler.mem=0\nif not quiet then\nparameter.watch(\"profiler.fps\")\nparameter.watch(\"profiler.mem\")\nend\nend\n\nfunction profiler.draw()\nprofiler.del = profiler.del + DeltaTime\nprofiler.c = profiler.c + 1\nif profiler.c==10 then\nprofiler.fps=profiler.c/profiler.del\nprofiler.del=0\nprofiler.c=0\nprofiler.mem=collectgarbage(\"count\", 2)\nend\nend\n\n--# Gaussian\nGaussian = {\nvs = {[[\nuniform mat4 modelViewProjection;\n\nattribute vec4 position;\nattribute vec2 texCoord;\n\nvarying vec2 vTexCoord;\nvarying vec2 v_blurTexCoords;\n\nvoid main()\n{\ngl_Position = modelViewProjection * position;\nvTexCoord = texCoord;\nv_blurTexCoords[ 0] = vTexCoord + vec2(-0.028, 0.0);\nv_blurTexCoords[ 1] = vTexCoord + vec2(-0.024, 0.0);\nv_blurTexCoords[ 2] = vTexCoord + vec2(-0.020, 0.0);\nv_blurTexCoords[ 3] = vTexCoord + vec2(-0.016, 0.0);\nv_blurTexCoords[ 4] = vTexCoord + vec2(-0.012, 0.0);\nv_blurTexCoords[ 5] = vTexCoord + vec2(-0.008, 0.0);\nv_blurTexCoords[ 6] = vTexCoord + vec2(-0.004, 0.0);\nv_blurTexCoords[ 7] = vTexCoord + vec2( 0.004, 0.0);\nv_blurTexCoords[ 8] = vTexCoord + vec2( 0.008, 0.0);\nv_blurTexCoords[ 9] = vTexCoord + vec2( 0.012, 0.0);\nv_blurTexCoords = vTexCoord + vec2( 0.016, 0.0);\nv_blurTexCoords = vTexCoord + vec2( 0.020, 0.0);\nv_blurTexCoords = vTexCoord + vec2( 0.024, 0.0);\nv_blurTexCoords = vTexCoord + vec2( 0.028, 0.0);\n}]],\n[[\nuniform mat4 modelViewProjection;\n\nattribute vec4 position;\nattribute vec2 texCoord;\n\nvarying vec2 vTexCoord;\nvarying vec2 v_blurTexCoords;\n\nvoid main()\n{\ngl_Position = modelViewProjection * position;\nvTexCoord = texCoord;\nv_blurTexCoords[ 0] = vTexCoord + vec2(0.0, -0.028);\nv_blurTexCoords[ 1] = vTexCoord + vec2(0.0, -0.024);\nv_blurTexCoords[ 2] = vTexCoord + vec2(0.0, -0.020);\nv_blurTexCoords[ 3] = vTexCoord + vec2(0.0, -0.016);\nv_blurTexCoords[ 4] = vTexCoord + vec2(0.0, -0.012);\nv_blurTexCoords[ 5] = vTexCoord + vec2(0.0, -0.008);\nv_blurTexCoords[ 6] = vTexCoord + vec2(0.0, -0.004);\nv_blurTexCoords[ 7] = vTexCoord + vec2(0.0, 0.004);\nv_blurTexCoords[ 8] = vTexCoord + vec2(0.0, 0.008);\nv_blurTexCoords[ 9] = vTexCoord + vec2(0.0, 0.012);\nv_blurTexCoords = vTexCoord + vec2(0.0, 0.016);\nv_blurTexCoords = vTexCoord + vec2(0.0, 0.020);\nv_blurTexCoords = vTexCoord + vec2(0.0, 0.024);\nv_blurTexCoords = vTexCoord + vec2(0.0, 0.028);\n}]]},\nfs = [[precision mediump float;\n\nuniform lowp sampler2D texture;\n\nvarying vec2 vTexCoord;\nvarying vec2 v_blurTexCoords;\n\nvoid main()\n{\ngl_FragColor = vec4(0.0);\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 0])*0.0044299121055113265;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 1])*0.00895781211794;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 2])*0.0215963866053;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 3])*0.0443683338718;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 4])*0.0776744219933;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 5])*0.115876621105;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 6])*0.147308056121;\ngl_FragColor += texture2D(texture, vTexCoord )*0.159576912161;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 7])*0.147308056121;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 8])*0.115876621105;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 9])*0.0776744219933;\ngl_FragColor += texture2D(texture, v_blurTexCoords)*0.0443683338718;\ngl_FragColor += texture2D(texture, v_blurTexCoords)*0.0215963866053;\ngl_FragColor += texture2D(texture, v_blurTexCoords)*0.00895781211794;\ngl_FragColor += texture2D(texture, v_blurTexCoords)*0.0044299121055113265;\n}]]\n}\n\n``````\n• Posts: 5,396\n\nPersonally, I wouldn't go to a lot of effort just to blur the background that nobody will notice anyway",
null,
"• Posts: 2,020\n\nWell, it's what the OP asked for :-)\n\nI was wrong about optimisation: if you comment out half the calculations in the fragment shader, it runs at about 58 fps, and still looks acceptably blurry. I'll have to recalculate what the brightness values should be. I can post an optimised version later\n\n• Posts: 2,020\n\nPlus, with all of these things, it's often the things you discover along the way. I'm interested in this because I'm currently exploring the possibility of creating an underwater ripple effect for a platform game I'm working on, which would be the same principle of sending the entire screen to an effect shader.\n\nOne really fun thing I just discovered: if you comment out every other line in the fragment shader above, you get a really cool after-image trail as the sprites move. With further adaptions so that the blur only goes in one direction, it would be a great effect\n\n• Posts: 2,020\n\nOk, here's an optimised version. Tap to cycle through 3 states, no blur, sideways trails, 2-pass Gaussian. This runs at 59/60 fps on the Air:\n\n``````--# Main\n--Gaussian blur\n\nfunction setup()\n--2 pass Gaussian blur:\nblur = {} --images\nblurred = {} --meshes\nfor i=1,2 do --2 passes, one for horizontal, one vertical\nblur[i]=image(WIDTH,HEIGHT)\nblurred[i]=mesh()\nblurred[i].texture=blur[i]\nend\nunblurred=mesh()\nunblurred.texture=blur\n--mesh with sideways after images:\nsideTrails=mesh()\nsideTrails.texture=blur\n\nshowBlur=1 --blur state\n--some animations:\npos={x1=200, y2=200, x3=800}\ntween(2, pos, {x1=800, y2=800, x3=200}, {easing=tween.easing.cubicInOut, loop=tween.loop.pingpong})\nmovement = true\nprofiler.init()\nprint (\"tap screen to cycle through blur states\")\nend\n\nfunction draw()\nif movement then\nsetContext(blur)\nbackground(50)\n\nsprite(\"SpaceCute:Rocketship\",pos.x1,HEIGHT-200)\nsprite(\"Small World:Store Extra Large\",pos.x3,200,300)\nsprite(\"SpaceCute:Beetle Ship\",WIDTH/2,pos.y2)\nend\n\nif showBlur==1 then --draw blur if required\nsetContext()\nbackground(50)\nunblurred:draw()\nelseif showBlur==2 then\nsetContext()\n-- background(50)\nsideTrails:draw()\nelseif showBlur==3 then\nsetContext(blur)\n-- background(50) --hard to tell whether these background calls make a difference\nblurred:draw() --pass one, offscreen\nsetContext()\n-- background(50)\nblurred:draw() --pass two\nend\nprofiler.draw()\nend\n\n--touching toggles blur on and off\nlocal blurStates = {\"no blur\", \"1 pass, horizontal trails\", \"2 pass Gaussian blur, optimized\"}\nfunction touched(t)\nif t.state==ENDED then\nshowBlur = showBlur + 1\nif showBlur==4 then showBlur=1 end\noutput.clear()\nprint(blurStates[showBlur])\nend\nend\n\nprofiler={}\n\nfunction profiler.init(quiet)\nprofiler.del=0\nprofiler.c=0\nprofiler.fps=0\nprofiler.mem=0\nif not quiet then\nparameter.watch(\"profiler.fps\")\nparameter.watch(\"profiler.mem\")\nend\nend\n\nfunction profiler.draw()\nprofiler.del = profiler.del + DeltaTime\nprofiler.c = profiler.c + 1\nif profiler.c==10 then\nprofiler.fps=profiler.c/profiler.del\nprofiler.del=0\nprofiler.c=0\nprofiler.mem=collectgarbage(\"count\", 2)\nend\nend\n--# Gaussian\nGaussian = {\nvs = { -- horizontal pass vertex shader\n[[\nuniform mat4 modelViewProjection;\n\nattribute vec4 position;\nattribute vec2 texCoord;\n\nvarying vec2 vTexCoord;\nvarying vec2 v_blurTexCoords;\n\nvoid main()\n{\ngl_Position = modelViewProjection * position;\nvTexCoord = texCoord;\nv_blurTexCoords[ 0] = vTexCoord + vec2(-0.028, 0.0);\nv_blurTexCoords[ 1] = vTexCoord + vec2(-0.024, 0.0);\nv_blurTexCoords[ 2] = vTexCoord + vec2(-0.020, 0.0);\nv_blurTexCoords[ 3] = vTexCoord + vec2(-0.016, 0.0);\nv_blurTexCoords[ 4] = vTexCoord + vec2(-0.012, 0.0);\nv_blurTexCoords[ 5] = vTexCoord + vec2(-0.008, 0.0);\nv_blurTexCoords[ 6] = vTexCoord + vec2(-0.004, 0.0);\nv_blurTexCoords[ 7] = vTexCoord + vec2( 0.004, 0.0);\nv_blurTexCoords[ 8] = vTexCoord + vec2( 0.008, 0.0);\nv_blurTexCoords[ 9] = vTexCoord + vec2( 0.012, 0.0);\nv_blurTexCoords = vTexCoord + vec2( 0.016, 0.0);\nv_blurTexCoords = vTexCoord + vec2( 0.020, 0.0);\nv_blurTexCoords = vTexCoord + vec2( 0.024, 0.0);\nv_blurTexCoords = vTexCoord + vec2( 0.028, 0.0);\n}]],\n[[\nuniform mat4 modelViewProjection;\n\nattribute vec4 position;\nattribute vec2 texCoord;\n\nvarying vec2 vTexCoord;\nvarying vec2 v_blurTexCoords;\n\nvoid main()\n{\ngl_Position = modelViewProjection * position;\nvTexCoord = texCoord;\nv_blurTexCoords[ 0] = vTexCoord + vec2(0.0, -0.028);\nv_blurTexCoords[ 1] = vTexCoord + vec2(0.0, -0.024);\nv_blurTexCoords[ 2] = vTexCoord + vec2(0.0, -0.020);\nv_blurTexCoords[ 3] = vTexCoord + vec2(0.0, -0.016);\nv_blurTexCoords[ 4] = vTexCoord + vec2(0.0, -0.012);\nv_blurTexCoords[ 5] = vTexCoord + vec2(0.0, -0.008);\nv_blurTexCoords[ 6] = vTexCoord + vec2(0.0, -0.004);\nv_blurTexCoords[ 7] = vTexCoord + vec2(0.0, 0.004);\nv_blurTexCoords[ 8] = vTexCoord + vec2(0.0, 0.008);\nv_blurTexCoords[ 9] = vTexCoord + vec2(0.0, 0.012);\nv_blurTexCoords = vTexCoord + vec2(0.0, 0.016);\nv_blurTexCoords = vTexCoord + vec2(0.0, 0.020);\nv_blurTexCoords = vTexCoord + vec2(0.0, 0.024);\nv_blurTexCoords = vTexCoord + vec2(0.0, 0.028);\n}]]},\nfsO = [[precision mediump float;\n\nuniform lowp sampler2D texture;\n\nvarying vec2 vTexCoord;\nvarying vec2 v_blurTexCoords;\n\nvoid main()\n{\ngl_FragColor = vec4(0.0);\n\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 5])* 0.08; //0.169179866813; //0.115876621105;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 6])* 0.26; //0.215069761937; //0.147308056121;\ngl_FragColor += texture2D(texture, vTexCoord )* 0.32; //0.232982291755; //0.159576912161;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 7])* 0.26; //0.215069761937; //0.147308056121;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 8])* 0.08; //0.169179866813; //0.115876621105;\n\n}]],\n\nfsTrails = [[precision mediump float;\n\nuniform lowp sampler2D texture;\n\nvarying vec2 vTexCoord;\nvarying vec2 v_blurTexCoords;\n\nvoid main()\n{\ngl_FragColor = vec4(0.0);\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 0])*0.0044299121055113265;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 1])*0.00895781211794;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 2])*0.0215963866053;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 3])*0.0443683338718;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 4])*0.0776744219933;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 5])*0.115876621105;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 6])*0.147308056121;\ngl_FragColor += texture2D(texture, vTexCoord )*0.159576912161;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 7])*0.147308056121;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 8])*0.115876621105;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 9])*0.0776744219933;\n// gl_FragColor += texture2D(texture, v_blurTexCoords)*0.0443683338718;\ngl_FragColor += texture2D(texture, v_blurTexCoords)*0.0215963866053;\n// gl_FragColor += texture2D(texture, v_blurTexCoords)*0.00895781211794;\ngl_FragColor += texture2D(texture, v_blurTexCoords)*0.0044299121055113265;\n}]],\n\n--fragment shader. tesselated frosted glass effect (also try commenting out 3-10)\nfsTesselated = [[precision mediump float;\n\nuniform lowp sampler2D texture;\n\nvarying vec2 vTexCoord;\nvarying vec2 v_blurTexCoords;\n\nvoid main()\n{\ngl_FragColor = vec4(0.0);\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 0])*0.0044299121055113265;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 1])*0.00895781211794;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 2])*0.0215963866053;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 3])*0.0443683338718;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 4])*0.0776744219933;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 5])*0.115876621105;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 6])*0.147308056121;\ngl_FragColor += texture2D(texture, vTexCoord )*0.159576912161;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 7])*0.147308056121;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 8])*0.115876621105;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 9])*0.0776744219933;\ngl_FragColor += texture2D(texture, v_blurTexCoords)*0.0443683338718;\n// gl_FragColor += texture2D(texture, v_blurTexCoords)*0.0215963866053;\ngl_FragColor += texture2D(texture, v_blurTexCoords)*0.00895781211794;\n// gl_FragColor += texture2D(texture, v_blurTexCoords)*0.0044299121055113265;\n}]]\n}\n``````\n• Posts: 2,020\n\nOn the above code,the weightings aren't right (I made them up). This blog uses the same technique as mine (ie 5 readings, fragment interpolation). I might see if I can copy their weightings. You could then emulate the iOS 7 panel effect, fullscreen, 60 fps.\n\nhttp://www.sunsetlakesoftware.com/2013/10/21/optimizing-gaussian-blurs-mobile-gpu\n\n• Posts: 5,396\n\nYou must have a fast iPad!\n\nMy iPad 3 runs your code with one pass at 40 FPS, two passes at 30 FPS.\n\n• Posts: 2,020\n\n@Ignatz (or anyone else with an iPad 3), try this one. It downsamples by half, meaning there are a quarter as many calculations in the fragment shader. Because OpenGL up scaling adds a blurring effect anyway (as long as you don't invoke `noSmooth()`), the visual difference between this and full-pixel calculation is unnoticeable, but it should be a lot faster:\n\n``````--# Main\n--Gaussian blur\n\nfunction setup()\n--2 pass Gaussian blur:\nlocal downSample = 0.5\nlocal dimensions = {\nvec2(WIDTH, HEIGHT), --full size\nvec2(WIDTH, HEIGHT) * downSample, --down sampled\nvec2(WIDTH, HEIGHT) * 0.5, --centre of fullsize (for positioning rect)\nvec2(WIDTH, HEIGHT) * downSample * 0.5, --centre of downsampled\n}\nblur = {} --images\nblurred = {} --meshes\nfor i=1,2 do --2 passes, one for horizontal, one vertical\nblur[i]=image(dimensions[i].x, dimensions[i].y) --image 1 is full sized, image 2 is downsampled\nblurred[i]=mesh()\nblurred[i].texture=blur[i]\nlocal j=3-i --invert i so that...\nblurred[i]:addRect(dimensions[j+2].x, dimensions[j+2].y, dimensions[j].x, dimensions[j].y) --mesh 1 rect is down-sampled, mesh 2 rect is full-sized\nend\n\nunblurred=mesh()\nunblurred.texture=blur\n--mesh with sideways after images:\nsideTrails=mesh()\nsideTrails.texture=blur\n\nshowBlur=1 --blur state\n--some animations:\npos={x1=200, y2=200, x3=800}\ntween(2, pos, {x1=800, y2=800, x3=200}, {easing=tween.easing.cubicInOut, loop=tween.loop.pingpong})\nmovement = true\nprofiler.init()\nprint (\"tap screen to cycle through blur states\")\nend\n\nfunction draw()\nif movement then\nsetContext(blur)\nbackground(50)\n\nsprite(\"SpaceCute:Rocketship\",pos.x1,HEIGHT-200)\nsprite(\"Small World:Store Extra Large\",pos.x3,200,300)\nsprite(\"SpaceCute:Beetle Ship\",WIDTH/2,pos.y2)\nend\n\nif showBlur==1 then --draw blur if required\nsetContext()\nbackground(50)\nunblurred:draw()\nelseif showBlur==2 then\nsetContext()\n-- background(50)\nsideTrails:draw()\nelseif showBlur==3 then\nsetContext(blur)\n-- background(50) --hard to tell whether these background calls make a difference\nblurred:draw() --pass one, offscreen\nsetContext()\n-- background(50)\nblurred:draw() --pass two\nend\nprofiler.draw()\nend\n\n--touching toggles blur on and off\nlocal blurStates = {\"no blur\", \"1 pass, horizontal trails\", \"2 pass Gaussian blur, optimized\"}\nfunction touched(t)\nif t.state==ENDED then\nshowBlur = showBlur + 1\nif showBlur==4 then showBlur=1 end\noutput.clear()\nprint(blurStates[showBlur])\nend\nend\n\nprofiler={}\n\nfunction profiler.init(quiet)\nprofiler.del=0\nprofiler.c=0\nprofiler.fps=0\nprofiler.mem=0\nif not quiet then\nparameter.watch(\"profiler.fps\")\nparameter.watch(\"profiler.mem\")\nend\nend\n\nfunction profiler.draw()\nprofiler.del = profiler.del + DeltaTime\nprofiler.c = profiler.c + 1\nif profiler.c==10 then\nprofiler.fps=profiler.c/profiler.del\nprofiler.del=0\nprofiler.c=0\nprofiler.mem=collectgarbage(\"count\", 2)\nend\nend\n--# Gaussian\nGaussian = {\nvs = { -- horizontal pass vertex shader\n[[\nuniform mat4 modelViewProjection;\n\nattribute vec4 position;\nattribute vec2 texCoord;\n\nvarying vec2 vTexCoord;\nvarying vec2 v_blurTexCoords;\n\nvoid main()\n{\ngl_Position = modelViewProjection * position;\nvTexCoord = texCoord;\nv_blurTexCoords[ 0] = vTexCoord + vec2(-0.028, 0.0);\nv_blurTexCoords[ 1] = vTexCoord + vec2(-0.024, 0.0);\nv_blurTexCoords[ 2] = vTexCoord + vec2(-0.020, 0.0);\nv_blurTexCoords[ 3] = vTexCoord + vec2(-0.016, 0.0);\nv_blurTexCoords[ 4] = vTexCoord + vec2(-0.012, 0.0);\nv_blurTexCoords[ 5] = vTexCoord + vec2(-0.008, 0.0);\nv_blurTexCoords[ 6] = vTexCoord + vec2(-0.004, 0.0);\nv_blurTexCoords[ 7] = vTexCoord + vec2( 0.004, 0.0);\nv_blurTexCoords[ 8] = vTexCoord + vec2( 0.008, 0.0);\nv_blurTexCoords[ 9] = vTexCoord + vec2( 0.012, 0.0);\nv_blurTexCoords = vTexCoord + vec2( 0.016, 0.0);\nv_blurTexCoords = vTexCoord + vec2( 0.020, 0.0);\nv_blurTexCoords = vTexCoord + vec2( 0.024, 0.0);\nv_blurTexCoords = vTexCoord + vec2( 0.028, 0.0);\n}]],\n[[\nuniform mat4 modelViewProjection;\n\nattribute vec4 position;\nattribute vec2 texCoord;\n\nvarying vec2 vTexCoord;\nvarying vec2 v_blurTexCoords;\n\nvoid main()\n{\ngl_Position = modelViewProjection * position;\nvTexCoord = texCoord;\nv_blurTexCoords[ 0] = vTexCoord + vec2(0.0, -0.028);\nv_blurTexCoords[ 1] = vTexCoord + vec2(0.0, -0.024);\nv_blurTexCoords[ 2] = vTexCoord + vec2(0.0, -0.020);\nv_blurTexCoords[ 3] = vTexCoord + vec2(0.0, -0.016);\nv_blurTexCoords[ 4] = vTexCoord + vec2(0.0, -0.012);\nv_blurTexCoords[ 5] = vTexCoord + vec2(0.0, -0.008);\nv_blurTexCoords[ 6] = vTexCoord + vec2(0.0, -0.004);\nv_blurTexCoords[ 7] = vTexCoord + vec2(0.0, 0.004);\nv_blurTexCoords[ 8] = vTexCoord + vec2(0.0, 0.008);\nv_blurTexCoords[ 9] = vTexCoord + vec2(0.0, 0.012);\nv_blurTexCoords = vTexCoord + vec2(0.0, 0.016);\nv_blurTexCoords = vTexCoord + vec2(0.0, 0.020);\nv_blurTexCoords = vTexCoord + vec2(0.0, 0.024);\nv_blurTexCoords = vTexCoord + vec2(0.0, 0.028);\n}]]},\nfsO = [[precision mediump float;\n\nuniform lowp sampler2D texture;\n\nvarying vec2 vTexCoord;\nvarying vec2 v_blurTexCoords;\n\nvoid main()\n{\ngl_FragColor = vec4(0.0);\n\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 5])* 0.08; //0.169179866813; //0.115876621105;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 6])* 0.26; //0.215069761937; //0.147308056121;\ngl_FragColor += texture2D(texture, vTexCoord )* 0.32; //0.232982291755; //0.159576912161;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 7])* 0.26; //0.215069761937; //0.147308056121;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 8])* 0.08; //0.169179866813; //0.115876621105;\n\n}]],\n\nfsTrails = [[precision mediump float;\n\nuniform lowp sampler2D texture;\n\nvarying vec2 vTexCoord;\nvarying vec2 v_blurTexCoords;\n\nvoid main()\n{\ngl_FragColor = vec4(0.0);\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 0])*0.0044299121055113265;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 1])*0.00895781211794;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 2])*0.0215963866053;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 3])*0.0443683338718;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 4])*0.0776744219933;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 5])*0.115876621105;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 6])*0.147308056121;\ngl_FragColor += texture2D(texture, vTexCoord )*0.159576912161;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 7])*0.147308056121;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 8])*0.115876621105;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 9])*0.0776744219933;\n// gl_FragColor += texture2D(texture, v_blurTexCoords)*0.0443683338718;\ngl_FragColor += texture2D(texture, v_blurTexCoords)*0.0215963866053;\n// gl_FragColor += texture2D(texture, v_blurTexCoords)*0.00895781211794;\ngl_FragColor += texture2D(texture, v_blurTexCoords)*0.0044299121055113265;\n}]],\n\n--fragment shader. tesselated frosted glass effect (also try commenting out 3-10)\nfsTesselated = [[precision mediump float;\n\nuniform lowp sampler2D texture;\n\nvarying vec2 vTexCoord;\nvarying vec2 v_blurTexCoords;\n\nvoid main()\n{\ngl_FragColor = vec4(0.0);\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 0])*0.0044299121055113265;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 1])*0.00895781211794;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 2])*0.0215963866053;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 3])*0.0443683338718;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 4])*0.0776744219933;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 5])*0.115876621105;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 6])*0.147308056121;\ngl_FragColor += texture2D(texture, vTexCoord )*0.159576912161;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 7])*0.147308056121;\ngl_FragColor += texture2D(texture, v_blurTexCoords[ 8])*0.115876621105;\n// gl_FragColor += texture2D(texture, v_blurTexCoords[ 9])*0.0776744219933;\ngl_FragColor += texture2D(texture, v_blurTexCoords)*0.0443683338718;\n// gl_FragColor += texture2D(texture, v_blurTexCoords)*0.0215963866053;\ngl_FragColor += texture2D(texture, v_blurTexCoords)*0.00895781211794;\n// gl_FragColor += texture2D(texture, v_blurTexCoords)*0.0044299121055113265;\n}]]\n}\n``````\n• Posts: 2,020\n\nOk, last one I promise. This one lets you set varying amounts of horizontal and vertical blur, and has better weighting of the pixels. I'm not sure, but it could be that the downsampling only quarters the number of calls to the fragment shader on the first pass, but it depends on how OpenGL handles upscaling (ie does it call the fragment shader for the number of pixels in the source, or in the destination, when upscaling?). So it's possible that we're getting five-eighths the number of calculations, rather than a quarter. I can't really check this because it all runs at 60fps for me.\n\n``````--# Main\n--Gaussian blur\n--adapted by Yojimbo2000 from http://xissburg.com/faster-gaussian-blur-in-glsl/ and http://www.sunsetlakesoftware.com/2013/10/21/optimizing-gaussian-blurs-mobile-gpu\n\nfunction setup()\n--2 pass Gaussian blur:\nlocal downSample = 0.5 -- going down to 0.25 actually looks pretty good!\nlocal dimensions = {\nvec2(WIDTH, HEIGHT), --full size\nvec2(WIDTH, HEIGHT) * downSample --down sampled\n}\nvec2(0.002,0), --horizontal pass, increase x value for more horizontal blur\nvec2(0,0.002) --vertical pass, increase y value for more vertical blur\n}\nblur = {} --images\nblurred = {} --meshes\nfor i=1,2 do --2 passes, one for horizontal, one vertical\nblur[i]=image(dimensions[i].x, dimensions[i].y) --image 1 is full sized, image 2 is downsampled\nblurred[i]=mesh()\nblurred[i].texture=blur[i]\nlocal j=3-i --invert i so that...\nblurred[i]:addRect(dimensions[j].x/2, dimensions[j].y/2, dimensions[j].x, dimensions[j].y) --mesh 1 rect is down-sampled, mesh 2 rect is full-sized\nend\n\nunblurred=mesh()\nunblurred.texture=blur\n\nshowBlur=false --blur state\n--some animations:\npos={x1=200, y2=200, x3=800}\ntween(2, pos, {x1=800, y2=800, x3=200}, {easing=tween.easing.cubicInOut, loop=tween.loop.pingpong})\nmovement = true\nprofiler.init()\nprint (\"tap screen to toggle blur\")\nend\n\nfunction draw()\nif movement then\nsetContext(blur)\nbackground(50)\n\nsprite(\"SpaceCute:Rocketship\",pos.x1,HEIGHT-200)\nsprite(\"Small World:Store Extra Large\",pos.x3,200,300)\nsprite(\"SpaceCute:Beetle Ship\",WIDTH/2,pos.y2)\nend\n\nif showBlur then --draw blur if required\nsetContext(blur)\n-- background(50) --nice after-image effect if you dont clear the intermediary layer\nblurred:draw() --pass one, offscreen\nsetContext()\nblurred:draw() --pass two, onscreen\nelse\nsetContext()\n-- background(50) --doesn't seem to be necesary to clear screen, for some reason\nunblurred:draw()\nend\nprofiler.draw()\nend\n\n--touching toggles blur on and off\nfunction touched(t)\nif t.state==ENDED then showBlur = not showBlur end\nend\n\nprofiler={}\n\nfunction profiler.init(quiet)\nprofiler.del=0\nprofiler.c=0\nprofiler.fps=0\nprofiler.mem=0\nif not quiet then\nparameter.watch(\"profiler.fps\")\nparameter.watch(\"profiler.mem\")\nend\nend\n\nfunction profiler.draw()\nprofiler.del = profiler.del + DeltaTime\nprofiler.c = profiler.c + 1\nif profiler.c==10 then\nprofiler.fps=profiler.c/profiler.del\nprofiler.del=0\nprofiler.c=0\nprofiler.mem=collectgarbage(\"count\", 2)\nend\nend\n--# Gaussian\nGaussian = {vs = [[\nuniform mat4 modelViewProjection;\nattribute vec4 position;\nattribute vec2 texCoord;\n\nvarying vec2 vBlurCoords;\n\nvoid main()\n{\ngl_Position = modelViewProjection * position;\nvBlurCoords = texCoord;\nvBlurCoords = texCoord + blurRadius * 1.407333;\nvBlurCoords = texCoord - blurRadius * 1.407333;\nvBlurCoords = texCoord + blurRadius * 3.294215;\nvBlurCoords = texCoord - blurRadius * 3.294215;\n}\n\n]],\nfs = [[\nprecision mediump float;\n\nuniform lowp sampler2D texture;\n\nvarying vec2 vBlurCoords;\n\nvoid main()\n{\n// gl_FragColor = vec4(0.0);\n\ngl_FragColor = texture2D(texture, vBlurCoords[ 0]) * 0.304005;\ngl_FragColor += texture2D(texture, vBlurCoords[ 1])* 0.204164;\ngl_FragColor += texture2D(texture, vBlurCoords[ 2])* 0.204164;\ngl_FragColor += texture2D(texture, vBlurCoords[ 3])* 0.093913;\ngl_FragColor += texture2D(texture, vBlurCoords[ 4])* 0.093913;\n\n}]]}\n``````\n• edited May 2015 Posts: 2,020\n\nOk, I lied in my previous post, this really is the last one! I added a tween to the x and y radii of the blur to create a fun, \"drunken insect eye\" effect. Have a play, and let me know your fps (Added it to Codea Community) :\n\n``````--# Main\n--Gaussian blur\n--adapted by Yojimbo2000 from http://xissburg.com/faster-gaussian-blur-in-glsl/ and http://www.sunsetlakesoftware.com/2013/10/21/optimizing-gaussian-blurs-mobile-gpu\n\nfunction setup()\n--2 pass Gaussian blur:\nlocal downSample = 0.5 -- going down to 0.25 actually looks pretty good!\nlocal dimensions = {\nvec2(WIDTH, HEIGHT), --full size\nvec2(WIDTH, HEIGHT) * downSample --down sampled\n}\nvec2(0.002,0), --horizontal pass, increase x value for more horizontal blur\nvec2(0,0.002) --vertical pass, increase y value for more vertical blur\n}\nblur = {} --images\nblurred = {} --meshes\nfor i=1,2 do --2 passes, one for horizontal, one vertical\nblur[i]=image(dimensions[i].x, dimensions[i].y) --image 1 is full sized, image 2 is downsampled\nblurred[i]=mesh()\nblurred[i].texture=blur[i]\nlocal j=3-i --invert i so that...\nblurred[i]:addRect(dimensions[j].x/2, dimensions[j].y/2, dimensions[j].x, dimensions[j].y) --mesh 1 rect is down-sampled, mesh 2 rect is full-sized (ie, opposite of their images)\nend\n\nunblurred=mesh()\nunblurred.texture=blur\n\nshowBlur=1 --blur state\n\n--some animations:\npos={x1=200, y2=200, x3=800}\ntween(2, pos, {x1=800, y2=800, x3=200}, {easing=tween.easing.cubicInOut, loop=tween.loop.pingpong})\ntween(3, radii, {r1 = vec2(-0.03,-0.01), r2=vec2(0.01,0.03)}, {easing=tween.easing.sineInOut, loop=tween.loop.pingpong})\nmovement = true\nprofiler.init()\nprint (\"tap screen to cycle blur effects\")\nend\n\nfunction draw()\nif movement then\nsetContext(blur)\nbackground(50)\n\nsprite(\"SpaceCute:Rocketship\",pos.x1,HEIGHT-200)\nsprite(\"Small World:Store Extra Large\",pos.x3,200,300)\nsprite(\"SpaceCute:Beetle Ship\",WIDTH/2,pos.y2)\nend\n\nif showBlur<3 then --draw blur if required\nif showBlur==2 then\nelse\nend\nsetContext(blur)\n-- background(50) --nice after-image effect if you dont clear the intermediary layer\nblurred:draw() --pass one, offscreen\nsetContext()\nblurred:draw() --pass two, onscreen\nelseif showBlur==3 then\nsetContext()\n-- background(50) --doesn't seem to be necesary to clear screen, for some reason\nunblurred:draw()\nend\nprofiler.draw()\nend\n\n--touching toggles blur on and off\nfunction touched(t)\nif t.state==ENDED then\nshowBlur = showBlur + 1\nif showBlur==4 then showBlur=1 end\nend\nend\n\nprofiler={}\n\nfunction profiler.init(quiet)\nprofiler.del=0\nprofiler.c=0\nprofiler.fps=0\nprofiler.mem=0\nif not quiet then\nparameter.watch(\"profiler.fps\")\nparameter.watch(\"profiler.mem\")\nend\nend\n\nfunction profiler.draw()\nprofiler.del = profiler.del + DeltaTime\nprofiler.c = profiler.c + 1\nif profiler.c==10 then\nprofiler.fps=profiler.c/profiler.del\nprofiler.del=0\nprofiler.c=0\nprofiler.mem=collectgarbage(\"count\", 2)\nend\nend\n--# Gaussian\nGaussian = {vs = [[\nuniform mat4 modelViewProjection;\nattribute vec4 position;\nattribute vec2 texCoord;\n\nvarying vec2 vBlurCoords;\n\nvoid main()\n{\ngl_Position = modelViewProjection * position;\nvBlurCoords = texCoord;\nvBlurCoords = texCoord + blurRadius * 1.407333;\nvBlurCoords = texCoord - blurRadius * 1.407333;\nvBlurCoords = texCoord + blurRadius * 3.294215;\nvBlurCoords = texCoord - blurRadius * 3.294215;\n}\n\n]],\n\nfs = [[\nprecision mediump float;\n\nuniform lowp sampler2D texture;\n\nvarying vec2 vBlurCoords;\n\nvoid main()\n{\n// gl_FragColor = vec4(0.0);\n\ngl_FragColor = texture2D(texture, vBlurCoords[ 0]) * 0.304005;\ngl_FragColor += texture2D(texture, vBlurCoords[ 1])* 0.204164;\ngl_FragColor += texture2D(texture, vBlurCoords[ 2])* 0.204164;\ngl_FragColor += texture2D(texture, vBlurCoords[ 3])* 0.093913;\ngl_FragColor += texture2D(texture, vBlurCoords[ 4])* 0.093913;\n\n}]]}\n``````\n• Posts: 1,976\n\nI think that what Notification Center does (it wasn't made in Codea, but if it was) is draw the screen into a small image, then scale that up and display the blurred result. I know it might create some odd effects when things are moving on the blurred screen, but the same happens in the real Notification Center. Try scrolling up this post, and while it's moving, drag down Notification Center.\n\n• Posts: 2,020\n\nYes, I think so. That's what my code does. Change the downsamples variable to affect how small the intermediary image is.\n\n• Posts: 216\n\nWow, thanks for all this! I just came to check, and saw all the code! Not sure which method will work best for me, but I'll definantly look into all of them. Again, thanks a ton!\n\n• Posts: 2,020\n\nWith my ones, I think you can just look at the last one.\n\n• Posts: 216\n\nOk, will do\n\n• Posts: 5,396\n\n@yojimbo2000 - thats better, about 55 now\n\n• Posts: 2,020\n\n@Ignatz cool, glad to hear that. I put the downsampling code (from my last entry) into my first entry at the top of this thread, the one that samples the texture 15 times per fragment, to see if I could get that up to speed on the iPad Air. At full-pixel it runs at around 35 fps, downsampling by 0.5, at 50 fps. Interestingly though, if you go down to 0.25, it goes at about 40 fps or so. So presumably there's a point at which too big a downscale/ upscale operation cancels out the benefit of quartering the number of calls to the fragment shader. In this particular case, downsampling by 0.5 seems to be optimal. You could also investigate scaling all of the draw operations too (of the 3 sprites I mean), see if that's quicker than drawing a 0.5 image of the whole scene.\n\n• Posts: 257\n\nVery good work and thanks for sharing.\nI like v5 with kaleiodoscopic effect\nI would like to create a face recognition shader ( with camera ) for my ia project\nbut it's very difficult for me\n\n• Posts: 1,976\n\n@hpsoft I don't think you could do that with a shader. Your best bet would to just use image.get.\n\n• Posts: 216\n\nhmmm, after a bunch of testing, the only shader that worked for me (although only at 20 fps) was the first one by @yojimbo2000. It seems to work good for my needs, and he blur looks good. The other ones when I tried them just worked similarly to the blur shader that comes with Codea; a version of the image appeared a little bit off to all four corners of the image. In the demo program though it worked good, so I'm wondering how to implement it like that (with the 3 different demos, the code confused me). Any help is appreciated, and thanks again for all the shaders!\n\n• Posts: 2,020\n\nThis one adds the builtin in shader for comparison. It runs quickly on the Air, by doing 10 samples per pixel in a single pass. My last one also does 10 samples, but because it does 2 passes of 5, that results in 25 samples. I'm biased of course, but I think my one looks way better than the builtin one ;-)\n\n``````--# Main\n--Gaussian blur\n--adapted by Yojimbo2000 from http://xissburg.com/faster-gaussian-blur-in-glsl/ and http://www.sunsetlakesoftware.com/2013/10/21/optimizing-gaussian-blurs-mobile-gpu\n\nfunction setup()\n--2 pass Gaussian blur:\nlocal downSample = 0.5 -- going down to 0.25 actually looks pretty good, but, weirdly, slower than 0.5\nlocal dimensions = {\nvec2(WIDTH, HEIGHT), --full size\nvec2(WIDTH, HEIGHT) * downSample --down sampled\n}\nvec2(0.002,0), --horizontal pass, increase x value for more horizontal blur\nvec2(0,0.002) --vertical pass, increase y value for more vertical blur\n}\nblur = {} --images\nblurred = {} --meshes\nfor i=1,2 do --2 passes, one for horizontal, one vertical\nblur[i]=image(dimensions[i].x, dimensions[i].y) --image 1 is full sized, image 2 is downsampled\nblurred[i]=mesh()\nblurred[i].texture=blur[i]\nlocal j=3-i --invert i so that...\nblurred[i]:addRect(dimensions[j].x/2, dimensions[j].y/2, dimensions[j].x, dimensions[j].y) --mesh 1 rect is down-sampled, mesh 2 rect is full-sized (ie, opposite of their images)\nend\n\nunblurred=mesh()\nunblurred.texture=blur\n\nbuiltin=mesh()\nbuiltin.texture=blur\nlocal blurriness = 3\n\nshowBlur=1 --blur state\n\n--some animations:\npos={x1=200, y2=200, x3=800}\ntween(2, pos, {x1=800, y2=800, x3=200}, {easing=tween.easing.cubicInOut, loop=tween.loop.pingpong})\ntween(3, radii, {r1 = vec2(-0.03,-0.01), r2=vec2(0.01,0.03)}, {easing=tween.easing.sineInOut, loop=tween.loop.pingpong})\nmovement = true\nprofiler.init()\nprint (\"tap screen to cycle blur effects\")\nend\n\nfunction draw()\nif movement then\nsetContext(blur)\n\nbackground(50)\n-- background(0,0)\n\nsprite(\"Platformer Art:Guy Jump\",pos.x1,HEIGHT-200)\nsprite(\"Space Art:Red Ship\",pos.x3,200,300)\nsprite(\"Platformer Art:Icon\",WIDTH/2,pos.y2)\nend\n\nif showBlur<3 then --draw blur if required\nif showBlur==2 then\nelse\nend\nsetContext(blur)\n-- background(50) --nice after-image effect if you dont clear the intermediary layer\nblurred:draw() --pass one, offscreen\nsetContext()\nblurred:draw() --pass two, onscreen\nelseif showBlur==3 then\nsetContext()\n-- background(50) --doesn't seem to be necesary to clear screen, for some reason\nunblurred:draw()\nelseif showBlur==4 then\nsetContext()\n-- background(50) --doesn't seem to be necesary to clear screen, for some reason\nbuiltin:draw()\nend\nprofiler.draw()\nend\n\n--touching toggles blur on and off\nlocal states={\"2-pass blur, 5+5=25 samples\", \"2 pass insect eye\", \"off\", \"built-in blur, 1 pass, 10 samples\"}\nfunction touched(t)\nif t.state==ENDED then\nshowBlur = showBlur + 1\nif showBlur==5 then showBlur=1 end\noutput.clear()\nprint(states[showBlur])\nend\nend\n\nprofiler={}\n\nfunction profiler.init(quiet)\nprofiler.del=0\nprofiler.c=0\nprofiler.fps=0\nprofiler.mem=0\nif not quiet then\nparameter.watch(\"profiler.fps\")\nparameter.watch(\"profiler.mem\")\nend\nend\n\nfunction profiler.draw()\nprofiler.del = profiler.del + DeltaTime\nprofiler.c = profiler.c + 1\nif profiler.c==10 then\nprofiler.fps=profiler.c/profiler.del\nprofiler.del=0\nprofiler.c=0\nprofiler.mem=collectgarbage(\"count\", 2)\nend\nend\n--# Gaussian\nGaussian = {vs = [[\nuniform mat4 modelViewProjection;\nattribute vec4 position;\nattribute vec2 texCoord;\n\nvarying vec2 vBlurCoords;\n\nvoid main()\n{\ngl_Position = modelViewProjection * position;\nvBlurCoords = texCoord;\nvBlurCoords = texCoord + blurRadius * 1.407333;\nvBlurCoords = texCoord - blurRadius * 1.407333;\nvBlurCoords = texCoord + blurRadius * 3.294215;\nvBlurCoords = texCoord - blurRadius * 3.294215;\n}\n\n]],\n\nfs = [[\nprecision mediump float;\n\nuniform lowp sampler2D texture;\n\nvarying vec2 vBlurCoords;\n\nvoid main()\n{\n// gl_FragColor = vec4(0.0);\n\ngl_FragColor = texture2D(texture, vBlurCoords[ 0]) * 0.304005;\ngl_FragColor += texture2D(texture, vBlurCoords[ 1])* 0.204164;\ngl_FragColor += texture2D(texture, vBlurCoords[ 2])* 0.204164;\ngl_FragColor += texture2D(texture, vBlurCoords[ 3])* 0.093913;\ngl_FragColor += texture2D(texture, vBlurCoords[ 4])* 0.093913;\n\n}]]}\n\n``````\n• Posts: 2,020\n\nHere's a Gaussian kernel calculator, if you want to experiment with weightings:\n\nSo, first the scene you want blurred is drawn to the `blur` image. That is then drawn, at a quarter the size, with the horizontal blurring on the shader, to the `blur` image (the blurRadii table determines whether the blurring is horizontal or vertical). `blur` is then drawn back to the screen, at full size, with the vertical blurring added. I suspect you could optimize it further by drawing `blur` to a third buffer, also a quarter the size of the screen, and then drawing that 3rd buffer back to the screen, full-size. I think at the moment, the processing saving from going down to a quarter the size is only gained on the first pass, but not the second."
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"https://codea.io/talk/resources/emoji/smile.png",
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http://ixtrieve.fh-koeln.de/birds/litie/document/13747
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[
"# Document (#13747)\n\nAuthor\nRüttgers, J.\nTitle\nNestbeschmutzern muß das Handwerk gelegt werden : Das Internet darf kein rechtsfreier Raum sein\nSource\nFrankfurter Rundschau. Nr.173 vom 27.7.1996, S.5\nYear\n1996\nAbstract\n\"Mit der modernen Informationsgesellschaft hat ein Quantensprung in Sachen Freiheit stattgefunden. Sie beruht auf der individuellen Freiheit von Kommunikation und Information. ... Die Informationsgesellschaft braucht die Akzeptanz der Menschen. Das Ziel der Bundesregierung ist es, eine größere Aufgeschlossenheit in der Gesellschaft für die modernen Informations- und Kommunikationsdienste zu erreichen. ... Das Zeitalter der Informations- und Kommunikationsfreiheit ist in erster Linie eine Herausforderung an die Wirksamkeit und die Werte der freiheitlichen Bürgergesellschaft. ... Der Staat muß eingreifen, wo die Selbstkontrolle versagt.\"\nTheme\nInternet\n\n## Similar documents (content)\n\n1. Fromm, E.: ¬Die Furcht vor der Freiheit (2012) 0.13\n```0.12508847 = sum of:\n0.12508847 = product of:\n1.0424039 = sum of:\n0.16904019 = weight(abstract_txt:individuellen in 1338) [ClassicSimilarity], result of:\n0.16904019 = score(doc=1338,freq=1.0), product of:\n0.1716427 = queryWeight, product of:\n1.1168332 = boost\n7.8787007 = idf(docFreq=43, maxDocs=42740)\n0.019506639 = queryNorm\n0.9848376 = fieldWeight in 1338, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n7.8787007 = idf(docFreq=43, maxDocs=42740)\n0.125 = fieldNorm(doc=1338)\n0.2877914 = weight(abstract_txt:modernen in 1338) [ClassicSimilarity], result of:\n0.2877914 = score(doc=1338,freq=2.0), product of:\n0.24472815 = queryWeight, product of:\n1.8859605 = boost\n6.652255 = idf(docFreq=149, maxDocs=42740)\n0.019506639 = queryNorm\n1.1759636 = fieldWeight in 1338, product of:\n1.4142135 = tf(freq=2.0), with freq of:\n2.0 = termFreq=2.0\n6.652255 = idf(docFreq=149, maxDocs=42740)\n0.125 = fieldNorm(doc=1338)\n0.58557236 = weight(abstract_txt:freiheit in 1338) [ClassicSimilarity], result of:\n0.58557236 = score(doc=1338,freq=3.0), product of:\n0.3432854 = queryWeight, product of:\n2.2336664 = boost\n7.8787007 = idf(docFreq=43, maxDocs=42740)\n0.019506639 = queryNorm\n1.7057887 = fieldWeight in 1338, product of:\n1.7320508 = tf(freq=3.0), with freq of:\n3.0 = termFreq=3.0\n7.8787007 = idf(docFreq=43, maxDocs=42740)\n0.125 = fieldNorm(doc=1338)\n0.12 = coord(3/25)\n```\n2. Kuhlen, R.: ¬Ein Programm ist nicht immer ein Programm : Bemerkungen zum Fachinformationsprogramm der Bundesregierung 1985-88 (1986) 0.10\n```0.09796115 = sum of:\n0.09796115 = product of:\n0.6122572 = sum of:\n0.030336471 = weight(abstract_txt:eine in 522) [ClassicSimilarity], result of:\n0.030336471 = score(doc=522,freq=1.0), product of:\n0.0688048 = queryWeight, product of:\n3.5272505 = idf(docFreq=3413, maxDocs=42740)\n0.019506639 = queryNorm\n0.44090632 = fieldWeight in 522, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n3.5272505 = idf(docFreq=3413, maxDocs=42740)\n0.125 = fieldNorm(doc=522)\n0.18824148 = weight(abstract_txt:bundesregierung in 522) [ClassicSimilarity], result of:\n0.18824148 = score(doc=522,freq=1.0), product of:\n0.18440624 = queryWeight, product of:\n1.157613 = boost\n8.166383 = idf(docFreq=32, maxDocs=42740)\n0.019506639 = queryNorm\n1.0207978 = fieldWeight in 522, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n8.166383 = idf(docFreq=32, maxDocs=42740)\n0.125 = fieldNorm(doc=522)\n0.19017996 = weight(abstract_txt:informationsgesellschaft in 522) [ClassicSimilarity], result of:\n0.19017996 = score(doc=522,freq=1.0), product of:\n0.23392962 = queryWeight, product of:\n1.8438824 = boost\n6.503835 = idf(docFreq=173, maxDocs=42740)\n0.019506639 = queryNorm\n0.8129794 = fieldWeight in 522, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n6.503835 = idf(docFreq=173, maxDocs=42740)\n0.125 = fieldNorm(doc=522)\n0.20349926 = weight(abstract_txt:modernen in 522) [ClassicSimilarity], result of:\n0.20349926 = score(doc=522,freq=1.0), product of:\n0.24472815 = queryWeight, product of:\n1.8859605 = boost\n6.652255 = idf(docFreq=149, maxDocs=42740)\n0.019506639 = queryNorm\n0.8315319 = fieldWeight in 522, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n6.652255 = idf(docFreq=149, maxDocs=42740)\n0.125 = fieldNorm(doc=522)\n0.16 = coord(4/25)\n```\n3. Altenmüller, G.H.: ¬Ein neues Konzept für Wissenschaftsinformation (1996) 0.07\n```0.07212199 = sum of:\n0.07212199 = product of:\n0.60101664 = sum of:\n0.12255855 = weight(abstract_txt:zeitalter in 5421) [ClassicSimilarity], result of:\n0.12255855 = score(doc=5421,freq=1.0), product of:\n0.13852488 = queryWeight, product of:\n1.0033201 = boost\n7.077923 = idf(docFreq=97, maxDocs=42740)\n0.019506639 = queryNorm\n0.88474035 = fieldWeight in 5421, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n7.077923 = idf(docFreq=97, maxDocs=42740)\n0.125 = fieldNorm(doc=5421)\n0.18824148 = weight(abstract_txt:bundesregierung in 5421) [ClassicSimilarity], result of:\n0.18824148 = score(doc=5421,freq=1.0), product of:\n0.18440624 = queryWeight, product of:\n1.157613 = boost\n8.166383 = idf(docFreq=32, maxDocs=42740)\n0.019506639 = queryNorm\n1.0207978 = fieldWeight in 5421, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n8.166383 = idf(docFreq=32, maxDocs=42740)\n0.125 = fieldNorm(doc=5421)\n0.29021665 = weight(abstract_txt:staat in 5421) [ClassicSimilarity], result of:\n0.29021665 = score(doc=5421,freq=2.0), product of:\n0.19533059 = queryWeight, product of:\n1.1914086 = boost\n8.404794 = idf(docFreq=25, maxDocs=42740)\n0.019506639 = queryNorm\n1.4857717 = fieldWeight in 5421, product of:\n1.4142135 = tf(freq=2.0), with freq of:\n2.0 = termFreq=2.0\n8.404794 = idf(docFreq=25, maxDocs=42740)\n0.125 = fieldNorm(doc=5421)\n0.12 = coord(3/25)\n```\n4. Boes, A.: ¬Die Veränderung der Arbeit wird ausgeblendet : Doch der Weg in die Informationsgesellschaft führt zu einer revolutionären Umstrukturierung der Tätigkeit und des Umfeldes der Beschäftigten (1997) 0.07\n```0.06918785 = sum of:\n0.06918785 = product of:\n0.43242407 = sum of:\n0.026544413 = weight(abstract_txt:eine in 1310) [ClassicSimilarity], result of:\n0.026544413 = score(doc=1310,freq=1.0), product of:\n0.0688048 = queryWeight, product of:\n3.5272505 = idf(docFreq=3413, maxDocs=42740)\n0.019506639 = queryNorm\n0.38579303 = fieldWeight in 1310, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n3.5272505 = idf(docFreq=3413, maxDocs=42740)\n0.109375 = fieldNorm(doc=1310)\n0.11167718 = weight(abstract_txt:erster in 1310) [ClassicSimilarity], result of:\n0.11167718 = score(doc=1310,freq=1.0), product of:\n0.14232121 = queryWeight, product of:\n1.0169754 = boost\n7.174254 = idf(docFreq=88, maxDocs=42740)\n0.019506639 = queryNorm\n0.784684 = fieldWeight in 1310, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n7.174254 = idf(docFreq=88, maxDocs=42740)\n0.109375 = fieldNorm(doc=1310)\n0.12779503 = weight(abstract_txt:linie in 1310) [ClassicSimilarity], result of:\n0.12779503 = score(doc=1310,freq=1.0), product of:\n0.15570502 = queryWeight, product of:\n1.063719 = boost\n7.5040073 = idf(docFreq=63, maxDocs=42740)\n0.019506639 = queryNorm\n0.82075083 = fieldWeight in 1310, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n7.5040073 = idf(docFreq=63, maxDocs=42740)\n0.109375 = fieldNorm(doc=1310)\n0.16640747 = weight(abstract_txt:informationsgesellschaft in 1310) [ClassicSimilarity], result of:\n0.16640747 = score(doc=1310,freq=1.0), product of:\n0.23392962 = queryWeight, product of:\n1.8438824 = boost\n6.503835 = idf(docFreq=173, maxDocs=42740)\n0.019506639 = queryNorm\n0.711357 = fieldWeight in 1310, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n6.503835 = idf(docFreq=173, maxDocs=42740)\n0.109375 = fieldNorm(doc=1310)\n0.16 = coord(4/25)\n```\n5. Nohr, H.: Wissen und Wissensprozesse visualisieren (2000) 0.07\n```0.06883215 = sum of:\n0.06883215 = product of:\n0.34416074 = sum of:\n0.025082113 = weight(abstract_txt:eine in 3975) [ClassicSimilarity], result of:\n0.025082113 = score(doc=3975,freq=7.0), product of:\n0.0688048 = queryWeight, product of:\n3.5272505 = idf(docFreq=3413, maxDocs=42740)\n0.019506639 = queryNorm\n0.36454016 = fieldWeight in 3975, product of:\n2.6457512 = tf(freq=7.0), with freq of:\n7.0 = termFreq=7.0\n3.5272505 = idf(docFreq=3413, maxDocs=42740)\n0.0390625 = fieldNorm(doc=3975)\n0.051509432 = weight(abstract_txt:beruht in 3975) [ClassicSimilarity], result of:\n0.051509432 = score(doc=3975,freq=1.0), product of:\n0.16878086 = queryWeight, product of:\n1.1074834 = boost\n7.8127427 = idf(docFreq=46, maxDocs=42740)\n0.019506639 = queryNorm\n0.30518526 = fieldWeight in 3975, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n7.8127427 = idf(docFreq=46, maxDocs=42740)\n0.0390625 = fieldNorm(doc=3975)\n0.09149569 = weight(abstract_txt:individuellen in 3975) [ClassicSimilarity], result of:\n0.09149569 = score(doc=3975,freq=3.0), product of:\n0.1716427 = queryWeight, product of:\n1.1168332 = boost\n7.8787007 = idf(docFreq=43, maxDocs=42740)\n0.019506639 = queryNorm\n0.533059 = fieldWeight in 3975, product of:\n1.7320508 = tf(freq=3.0), with freq of:\n3.0 = termFreq=3.0\n7.8787007 = idf(docFreq=43, maxDocs=42740)\n0.0390625 = fieldNorm(doc=3975)\n0.0861387 = weight(abstract_txt:werte in 3975) [ClassicSimilarity], result of:\n0.0861387 = score(doc=3975,freq=2.0), product of:\n0.18873578 = queryWeight, product of:\n1.1711236 = boost\n8.261693 = idf(docFreq=29, maxDocs=42740)\n0.019506639 = queryNorm\n0.45639837 = fieldWeight in 3975, product of:\n1.4142135 = tf(freq=2.0), with freq of:\n2.0 = termFreq=2.0\n8.261693 = idf(docFreq=29, maxDocs=42740)\n0.0390625 = fieldNorm(doc=3975)\n0.08993481 = weight(abstract_txt:modernen in 3975) [ClassicSimilarity], result of:\n0.08993481 = score(doc=3975,freq=2.0), product of:\n0.24472815 = queryWeight, product of:\n1.8859605 = boost\n6.652255 = idf(docFreq=149, maxDocs=42740)\n0.019506639 = queryNorm\n0.36748862 = fieldWeight in 3975, product of:\n1.4142135 = tf(freq=2.0), with freq of:\n2.0 = termFreq=2.0\n6.652255 = idf(docFreq=149, maxDocs=42740)\n0.0390625 = fieldNorm(doc=3975)\n0.2 = coord(5/25)\n```"
] |
[
null
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https://socratic.org/questions/how-can-i-calculate-osmolarity-of-nacl
|
[
"# How can I calculate osmolarity of NaCl?\n\nDec 7, 2015\n\nThis is simply twice the molarity of $N a C l \\left(a q\\right)$.\n\n#### Explanation:\n\nOsmolarity is simply the concentration of all ions in solutions. It is thus the sum of sodium ion and chloride ion concentrations, inasmuch as as sodium chloride is a strong electrolyte, and dissolves to give stoichiometric $N {a}^{+} \\left(a q\\right)$ and $C {l}^{-} \\left(a q\\right)$ ions.\n\nJan 14, 2016\n\nYou multiply the molarity of the $\\text{NaCl}$ by $2$.\n\n#### Explanation:\n\nAn osmole (Osmol) is a mole of particles that contribute to the osmotic pressure of a solution.\n\nFor example, $\\text{NaCl}$ dissociates completely in water to form ${\\text{Na}}^{+}$ ions and ${\\text{Cl}}^{-}$ ions.\n\n\"NaCl(s)\" → \"Na\"^+(\"aq\") + \"Cl\"^(-)(\"aq\")\n\nEach ion contributes to the osmotic pressure of the solution.\n\nThus, each mole of $\\text{NaCl}$ becomes two osmoles in solution: one mole of ${\\text{Na}}^{+}$ and one mole of ${\\text{Cl}}^{-}$.\n\nA solution of 1 mol/L $\\text{NaCl}$ has an osmolarity of 2 Osmol/L.\n\nEXAMPLE\n\nCalculate the osmolarity of 0.140 mol/L $\\text{NaCl}$.\n\nSolution\n\n$\\text{[NaCl] = 0.140 mol/L}$\n\nEach mole of $\\text{NaCl}$ becomes two osmoles.\n\n$\\text{Osmolarity\" = (0.140 color(red)(cancel(color(black)(\"mol\"))))\"/L\" × \"2 Osmol\"/(1 color(red)(cancel(color(black)(\"mol\")))) = \"0.280 Osmol/L}$"
] |
[
null
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https://datascience-enthusiast.com/Python/ROC_Precision-Recall.html
|
[
"# Fisseha Berhane, PhD\n\n#### Data Scientist\n\nPicking a good threshold value in binary classification problems is often challenging. The cut-off value we may choose can vary based on the business problem we are solving. If we're more concerned with having a high specificity or low false positive rate, we pick the threshold that maximizes the true positive rate while keeping the false positive rate really low. On the other hand, if we're more concerned with having a high sensitivity or high true positive rate, we pick a threshold that minimizes the false positive rate but has a very high true positive rate.\n\nTo evaluate the performance of a model or to compare models, rather than considering metrics such as accuracy, sensitivity, specificity, precision or F-1 score, it is better to use measures that do not depend on a single cut-off value. A Receiver Operator Characteristic curve (ROC curve) and Precision-Recall Curve are what we are going to discuss in this blog post.\n\nAn ROC curve is the most commonly used tool for comparing models or to evaluate a model performance. It does not depend on a single cut-off value. To create an ROC curve, the sensitivity, or true positive rate of the model, is shown on the y-axis and the false positive rate, or one minus specificity, is given on the x-axis. The ROC curve always starts at the point (0, 0) and this corresponds to a threshold value of 1. If you have a threshold of 1, you will not catch any positive cases, or have a sensitivity of 0. But you will correctly label all the negative cases, meaning you have a false positive rate of 0. The ROC curve always ends at the point (1,1), which corresponds to a threshold value of 0. If you have a threshold of 0, you'll catch all of the positive cases, or have a sensitivity of 1, but you'll label all of the negative cases as positive cases too, meaning you have a false positive rate of 1. The threshold decreases as you move from (0, 0) to (1, 1). The ROC curve captures all thresholds simultaneously. The higher the threshold, or closer to (0, 0), the higher the specificity and the lower the sensitivity. The lower the threshold, or closer to (1,1), the higher the sensitivity and lower the specificity.\n\nPrecision-Recall Curve is another tool that does not depend on a single threshold value. In this case, the precision is shown on the y-axis while the sensitivity, also called recall, is shown on the x-axis. The Precision-Recall starts at (0,1) and as will be shown below when the data is imbalanced using the ROC Curve could be misleading and Precision-Recall curve is more informative.\n\nLet's generate datasets and build lasso logistic regression using grid search with cross-validation for hyper-parameter tuning. First, we will generate balanced data, where the two classes have about equal counts, and plot the ROC and Precision-Recall Curves, and culculate the areas under the curves. Next, we will generate imbalanced data where the labels are 98% from one class. Imbalanced data is very common in classification problems but we usually see ROC curves being used to evaluate such models. However, as you will see below, ROC curves are not good tools for imbalanced data.\n\nIn :\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_curve\nfrom sklearn.metrics import precision_recall_curve\nfrom sklearn.metrics import average_precision_score\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.datasets import make_classification\nimport collections\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\n\n## Balanced Data¶\n\n### Generate binary class dataset and split into train/test sets¶\n\nLet's generate 100000 samples with 30 features.\n\nIn :\nX, y = make_classification(n_samples = 100000, n_features = 30, n_classes = 2, weights = [0.5,0.5], random_state = 1)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)\n\n\n#### Now, let's see the count of the label values¶\n\nIn :\ncollections.Counter(y)\n\nOut:\nCounter({0: 50027, 1: 49973})\n\nSo, as shown above the labels are more or less balanced.\n\nIn :\nsteps = [('scaler', StandardScaler()),\n('logreg', LogisticRegression(penalty = 'l1', solver = 'saga', tol = 1e-6,\nmax_iter = int(1e6), warm_start = True, n_jobs = -1))]\n\npipeline = Pipeline(steps)\nparam_grid = {'logreg__C': np.arange(0., 1, 0.1)}\nlogreg_cv = GridSearchCV(pipeline, param_grid, cv = 5, n_jobs = -1)\nlogreg_cv.fit(X_train, y_train)\n\nOut:\nGridSearchCV(cv=5, error_score='raise-deprecating',\nestimator=Pipeline(memory=None,\nsteps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('logreg', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\nintercept_scaling=1, max_iter=1000000, multi_class='warn',\nn_jobs=-1, penalty='l1', random_state=None, solver='saga',\ntol=1e-06, verbose=0, warm_start=True))]),\nfit_params=None, iid='warn', n_jobs=-1,\nparam_grid={'logreg__C': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])},\npre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\nscoring=None, verbose=0)\n\n### What are the best parameter and best score?¶\n\nIn :\nprint ('best score:', logreg_cv.best_score_)\nprint ('best parameter:',logreg_cv.best_params_)\n\nbest score: 0.9167714285714286\nbest parameter: {'logreg__C': 0.1}\n\n\n### Fit lasso logistic regression using the best parameter above¶\n\nIn :\nscaler = StandardScaler()\nscaler.fit(X_train)\nX_train_scaled = scaler.transform(X_train)\nlogreg = LogisticRegression(penalty = 'l1', solver = 'saga', tol = 1e-6, max_iter = int(1e6),\nwarm_start = True, C = logreg_cv.best_params_['logreg__C'])\nlogreg.fit(X_train_scaled, y_train)\n\nOut:\nStandardScaler(copy=True, with_mean=True, with_std=True)\nOut:\nLogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\nintercept_scaling=1, max_iter=1000000, multi_class='warn',\nn_jobs=None, penalty='l1', random_state=None, solver='saga',\ntol=1e-06, verbose=0, warm_start=True)\n\n### Lasso can be used for feature selection. Let's plot the coefficients to see which features have been selected¶\n\nIn :\nlasso_coef = logreg.coef_.reshape(-1,1)\nplt.figure(figsize = (20,10))\nplt.plot([0,29],[0,0])\n_ = plt.plot(range(30), lasso_coef, linestyle='--', marker='o', color='r')\n_ = plt.xticks(range(30), range(30), rotation=60)\n_ = plt.ylabel('Coefficients')\nplt.xlabel('Features', fontsize = 16)\nplt.ylabel('Coefficients', fontsize = 16)\nplt.xticks(size = 18)\nplt.yticks(size = 18)\nplt.title('Feature Coefficients from Lasso Logistic Regression', fontsize = 28)\nplt.show();",
null,
"### Make predictions using the test dataset¶\n\nIn :\nX_test_scaled = scaler.transform(X_test)\ny_pred_prob = logreg.predict_proba(X_test_scaled)[:,1] # return probabilities for the positive outcome only\n\n\n### Plot ROC Curve¶\n\nThe dotted blue line is the baseline.\n\nIn :\nfpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\nplt.figure(figsize = (20,10))\nplt.plot([0, 1], [0, 1], linestyle = '--')\nplt.plot(fpr, tpr)\nplt.xlabel('False Positive Rate', fontsize = 16)\nplt.ylabel('True Positive Rate', fontsize = 16)\nplt.xticks(size = 18)\nplt.yticks(size = 18)\nplt.title('Lasso Logistic Regression ROC Curve', fontsize = 28)\nplt.show();",
null,
"### What is the area under the curve?¶\n\nIn :\nround(roc_auc_score(y_test, y_pred_prob), 2)\n\nOut:\n0.96\n\n### Now, plot the Precision-Recall Curve and calculate the area under the curve¶\n\nThe dotted orange line is the baseline\n\nIn :\nprecision, recall, thresholds = precision_recall_curve(y_test, y_pred_prob)\nplt.figure(figsize = (20,10))\nplt.plot(recall, precision)\nplt.plot([0, 1], [0.5, 0.5], linestyle = '--')\nplt.xlabel('Recall', fontsize = 16)\nplt.ylabel('Precision', fontsize = 16)\nplt.xticks(size = 18)\nplt.yticks(size = 18)\nplt.title('Lasso Logistic Regression Precision-Recall Curve', fontsize = 28)\nplt.show();",
null,
"### What is the area under the Precision-Recall curve?¶\n\nIn :\nround(average_precision_score(y_test, y_pred_prob), 2)\n\nOut:\n0.97\n\n## Imbalanced Data¶\n\n### Generate binary class dataset and split into train/test sets¶\n\nLet's generate 100000 samples with 30 features.\n\nIn :\nX, y = make_classification(n_samples = 100000, n_features = 30, n_classes = 2, weights = [0.99,0.01], random_state = 1)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)\n\n\n### Now, let's see the count of the label values¶\n\nIn :\ncollections.Counter(y)\n\nOut:\nCounter({0: 98499, 1: 1501})\n\nSo, as shown above the labels are imbalanced (about 98% of them are zeros)\n\nIn :\nsteps = [('scaler', StandardScaler()),\n('logreg', LogisticRegression(penalty = 'l1', solver = 'saga', tol = 1e-6,\nmax_iter = int(1e6), warm_start = True, n_jobs = -1))]\n\npipeline = Pipeline(steps)\nparam_grid = {'logreg__C': np.arange(0., 1, 0.1)}\nlogreg_cv = GridSearchCV(pipeline, param_grid, cv = 5, n_jobs = -1)\nlogreg_cv.fit(X_train, y_train)\n\nOut:\nGridSearchCV(cv=5, error_score='raise-deprecating',\nestimator=Pipeline(memory=None,\nsteps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('logreg', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\nintercept_scaling=1, max_iter=1000000, multi_class='warn',\nn_jobs=-1, penalty='l1', random_state=None, solver='saga',\ntol=1e-06, verbose=0, warm_start=True))]),\nfit_params=None, iid='warn', n_jobs=-1,\nparam_grid={'logreg__C': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])},\npre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\nscoring=None, verbose=0)\n\n### What are the best parameter and best score?¶\n\nIn :\nprint ('best score:', logreg_cv.best_score_)\nprint ('best parameter:',logreg_cv.best_params_)\n\nbest score: 0.9859285714285714\nbest parameter: {'logreg__C': 0.30000000000000004}\n\n\n### Fit lasso logistic regression using the best parameter above¶\n\nIn :\nscaler = StandardScaler()\nscaler.fit(X_train)\nX_train_scaled = scaler.transform(X_train)\nlogreg = LogisticRegression(penalty = 'l1', solver = 'saga', tol = 1e-6, max_iter = int(1e6),\nwarm_start = True, C = logreg_cv.best_params_['logreg__C'])\nlogreg.fit(X_train_scaled, y_train)\n\nOut:\nStandardScaler(copy=True, with_mean=True, with_std=True)\nOut:\nLogisticRegression(C=0.30000000000000004, class_weight=None, dual=False,\nfit_intercept=True, intercept_scaling=1, max_iter=1000000,\nmulti_class='warn', n_jobs=None, penalty='l1', random_state=None,\nsolver='saga', tol=1e-06, verbose=0, warm_start=True)\n\n### Let's plot the coefficients to see which features have been selected¶\n\nIn :\nlasso_coef = logreg.coef_.reshape(-1,1)\nplt.figure(figsize = (20,10))\nplt.plot([0,29],[0,0])\n_ = plt.plot(range(30), lasso_coef, linestyle='--', marker='o', color='r')\n_ = plt.xticks(range(30), range(30), rotation=60)\n_ = plt.ylabel('Coefficients')\nplt.xlabel('Features', fontsize = 16)\nplt.ylabel('Coefficients', fontsize = 16)\nplt.xticks(size = 18)\nplt.yticks(size = 18)\nplt.title('Feature Coefficients from Lasso Logistic Regression', fontsize = 28)\nplt.show();",
null,
"### Make predictions using the test dataset¶\n\nIn :\nX_test_scaled = scaler.transform(X_test)\ny_pred_prob = logreg.predict_proba(X_test_scaled)[:,1] # return probabilities for the positive outcome only\n\n\n### Plot ROC Curve¶\n\nThe dotted blue line is the baseline\n\nIn :\nfpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\nplt.figure(figsize = (20,10))\nplt.plot([0, 1], [0, 1], linestyle = '--')\nplt.plot(fpr, tpr)\nplt.xlabel('False Positive Rate', fontsize = 16)\nplt.ylabel('True Positive Rate', fontsize = 16)\nplt.xticks(size = 18)\nplt.yticks(size = 18)\nplt.title('Lasso Logistic Regression ROC Curve', fontsize = 28)\nplt.show();",
null,
"In :\nround(roc_auc_score(y_test, y_pred_prob), 2)\n\nOut:\n0.81\n\n### Now, plot the Precision-Recall Curve and calculate the area under the curve¶\n\nThe dotted blue line is the baseline\n\nIn :\nprecision, recall, thresholds = precision_recall_curve(y_test, y_pred_prob)\nplt.figure(figsize = (20,10))\nplt.plot([0, 1], [0.01/0.98, 0.01/0.98], linestyle = '--')\nplt.plot(recall, precision)\nplt.xlabel('Recall', fontsize = 16)\nplt.ylabel('Precision', fontsize = 16)\nplt.xticks(size = 18)\nplt.yticks(size = 18)\nplt.title('Lasso Logistic Regression Precision-Recall Curve', fontsize = 28)\nplt.show();",
null,
"In :\nround(average_precision_score(y_test, y_pred_prob), 2)\n\nOut:\n0.35\n\nFrom the results above, when the data is imbalanced, the area under the ROC curve and the area under the Precision-Recall curve are very different and the Precision-Recall curve is more informative than the ROC curve.\n\n## Summary¶\n\nEven if ROC curve and area under the ROC curve are commonly used to evaluate model performance with balanced and imbalanced datasets, as shown in this blog post, if your data is imbalanced, Precision-Recall curve and the area under that curve are more informative than the ROC curve and area under the ROC curve. Actually, ROC curve could be misleading for binary classification problems with imbalanced data."
] |
[
null,
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 ",
null,
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 ",
null,
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ue5gvluKTeslOCYe3/w9nny+3+xzwFLgtcfLDxWS/iuc+72EmXrw9c8An/TXzKH2o4ys2lmdio+GPXy4OOrnHMPlJnkU/hg4hp8EDpVaiTn3HfwAQ3Ydtu/j/w2+nyp718wn43F5yDn3KpK+0OwXXM1yk6yGnLvjbAJ5H9r14/ysofjHOeT2ZeTywe1yMwOKzPOmfhgJvialYOC66uzg7dfDK5RtuF8bfncb/bJwe+ZiIwzCkiJyC7HzNrNbIGZHWBmB5pPiJtr8rG/bZv0NdeE5FVmtmAYi9qM/5cZ4N1mNpxmzieSvyEum9w4uGj7Q/B2oZVJ5r0T5Xo8Wlbp4h3fdKR4mpyTg+dnnHMPVZjHr4dbuAK5bbi/mR29A/PZEQcDueTuv6hywT0Sji9IIpv7x/8x4OP4fes2fI245WWmzwVnB/C1nCr5Z/AcJx9cAN/8KHetcCVlOOcexjerqsW/KgUlzCfQPzR4+8dKMwqCSbmg0csLBm3CB6rAH7thapfb1yYHzX1HhJnFgFcFb//hnHuxwug/KXhdqVey31QI1j+Fr3EC+WZgO+LKov2xC9+MJrfe1wJvKhdIwO+H2wQ0i+T22RsqBXsDuX325UWf55rL9ZNv/jVScvvGCWa21wjO92h8UBV87rmSCdWDdXtF8LaNbZu5FrqqwrDFBa9HYt+AbfePtfhmk0fja7Z+nXwQoZRcc+vrawgS/it4Lrftexn6m7VdzCfFn1d0rZG7Jgjha2ePpsIAWNmA6Bhb4py7v8o4v8HX9IJ8s7xiZwXPL5QIYp6Bb1INVX4jyJ8nYNv9RUTGAQWkRGSXYGYHmdkvzGwtvkbREnzelSeCx4XBqCF807tCvwqeJwJPmtnvzex91XrLCf49vTp4+1ZgqZl9y8xeZ2bFyyhW2GvUfWXH2nb4qF3gmu9lae8SZdhGUCtpefB2sIzBP/S5IF+lYBQMvQkarqvxNwJx4G7zvcV9JKgxNFq/ZYWBmn+VHWt0bAF+6Jx7rsI4uZvVOiBVeLNY/CBf2wTyNQxg6H48Utv3sSrDjyp4fWWlcgdlP6S43EEtkFyg9x3AEjP7ppm9toZj9zogF2y83nzvmp8039vfjuTeXIjfFlD9nLAYX5MMKp8Tnik3IAhU5QL1O6smh8M3ibkEn5j8yQrjPlepVpyZTcQ3mQV4fw3b/ePBuMU9fuWO00cq1YjdTrnfksnAU2b2O/O9V+6zg/PdGb8Xz1YYVliDa8i+YWb75oIvJR7b22PZg8APXJkcieZ7g8zVXjmnhm3/n8G45bb9Q8E5YNjMbKqZfcPMXsCnAniRodcafyoYvWrvviOssEZm4ygvu1bVzu84n6cwFyh6R/FveHBtlvv9KtW7XmEgdm2VfWVjwbjb9A4oImNPASkRGffM7P34bqPfS20XFPWFb4KaPx/C/6NYh0+I+3PgWfNdFf/SzF5RZl7nkm9qNgef4Pt6YLOZPWpmXyxzkV7YbfuGEsMLFTZBK9vd+05QeGNerYyQL2dhGdvINyHYSGXVhpcVBF7+HV9rLQK8Ft9s7TFgk5n9wcxOqzCLkVC4ndfu5GWBD0ocFDwOxtdE+yK+9s8E4Boze3uF6ads53IbCl4X7iMjtX2rJZEfiXKDb873l+D1PHwi3Rvwx+4jZvYFM9vmhjJotvp6fN4tw9d2/B/8TfVWM7vezN62HYHQms8JQeAmt54qnROq3XTnaiEMp4ZYOeeT3x8PxAeiW51z+zjnLqihmdpobffcNh3xY9Q5dws+GNKH/515Oz7Z/nNmtjb40+SY7Zj1SP9eZKo0ic0WvC7eN24jH3wpfpxTYZ6F+8cR+AT9vw+GnQjcGQQdS9nhbW9mRr4Dke3a9sG2ewb4LD6AbJWnGHqtMQq2wGAnDFNHedm1qrWTkFwNvhls2+nCWQWvSwWkRupcISLjgHrZE5Fxzcz2w/dkFMFfqH8T3xPRcqA71zzEhvbMtM1FpHPux2b2f/jaEqcAr8RfzE/FNyM428x+js9bki2Yrht4s5kdgQ+InID/FzaCr5lxCPAZMzvLOXdDma9RNv9VufKOgWplhOrlrDaPWpZRfmLn/mxm/8AnvD4N3zvhNHzQ5G3A28zsJnzi7WrNfXbUDn2XGvWWqHFym5ldBdyPvyj/iZndW6YJXO5Gczk+wFKrcrmvRuo7V+tJsvAG+V3U8I97YEhtGOdcF/BGMzuS/LF7GP7YPTR4fNbMznTO3Vw07b/MbG98Hrgz8E3t5gBN+GZBrwPuM7PXlchBVYuRON5G26oqNaCqGc52/y4VmjoXKbcud8ox6py7zMz+iM9xczL+t6Qdfy56L/BeM/sxPg/S9pRhV/i9KKV4/3gYuM7M7ga+h28a+BOG5nrLKdz23ybfLLGabJnPh73ezaweX6uyHV8b93/xAezngY5cgM/M9iffPHlUt4VzLm1mT+EDwnuZWbtzruZeYkdJrT0FX4PPZVaHD0DdVjDsncHzfc65JcUTkt9f+vFNQmtVNv+kiIwdBaREZLw7m3z3zcc758o1Q6hasyio+fAD4AfBv6kH4vNWfBQfmHo/PllqqQTkDxE0WTKzRnww5Cz8hVML8HszW1CQcLuwtsBUfL6Vcgr/6RxWMtwdVHghW8u/rblxCsvYUfC62r+W2/uv5qAgQPiL4EEQNHgdfhvOxwcPLsEnyB1phQnfZ1B7oGREOeeWm9lH8TdPLfjvu0335+TLOxXfXKpcbp9KCveRKfiuvMvZ3uY8xYYk1t/BIAjOucUEzQmDY/dV+EDXO/B5e/4QHLsbiqbLNdm9Oph2DnA6vobMQcAxwOX4YFctis8JZQXNadtKTLc7K9zujTuw3Tfjg0Mzqo24vZxzG/G/E98LfksOxv+W/Cf+ODkH/1tyWY2zLN43yibTZyf/XjjnZo3w/L5vZq8DTsX/uXO8c664o4Ed3vbOOWdmW/A15LZn27+GfLPB9zvnyuXgGs1azKXcib92MXxN4Uq5wmqRC+pVq/E5ok0EnXOdZnYDPh3CW8zsI865ATN7Ofk0AKVqR0F+f6nHdz4xnhO8i0gVarInIuPdAcHzYxWCUVA5ues2nPeEc+5ifKLLXPOGqjeXzrle59xfnXPvBi4IPm4gn1AVfM6JnEq9ZBUPf6La8kdKcMOd63mt4r+MQY6PecHbwTIGyWeXBm+PqLLIYW2jWjjnXgh6XTqSfK9DtQYIhqswh9Kryo41Cpxzf8Q3IQN4Z/CvfbFcXqh6tr+8hYnKR2v7FuazGtFmmMGxe7Nz7izyeeeayPckWGnalc65H+OPlVzA4I3mk5XXYgk+sTdUPyccQb4WwKidE8ZSEMzPNbU6ZTuaRObkjtPDzaxpx0tWWfBb8phz7iLgWPJJr4dzHhr3vxc76HPkay2V6hVyNfmmiqcGQb7tkdv2R5rZcJtn5a41HPn8c6WM+O/YMP2i4PXHduA4ycnlpaqWW29n9MaYC6a1kK/Fm0tynibf5LPYTvuNEJHRp4CUiIx3uZqcZf+dM98F8Bu2dwFBb1fLgrfDTVJaWM28cNrb8RdUsG3X1IPMrIX8jcuSCj2m7Sy3BM8LzaxS0OIDJabJya2DRUHTxnLeM9zC1SpotvBw8LbUNswFAuI7sJjHgVzTuPeaWWulkUfBV4LnEPCFEsOvK3j9uRLDa3E7+RvJUrWwADCzw8nf0O2QIF9YLvj87zayvZkVKnfsVhQEYe8O3sbwN1O1TJckn8j31WY2t8Lo/1Hwuvh42539OXiei28Stz1yPUrW4XMHjpqgeVGuFuFwfkseIJ9I/z1WpldI8z3Inh287WDHOooYNc65R8lvl2PN7MSi4Y58vrf5bP+fCrllNAAfHOa0uWsNo0yuoWC7fKDUsNES9Gj61+DtUcB/1TqtmR1qZocUfZy79jmgXBAv+PxNpYbtoJvwNRoBzjLfaURu2/8tqIlYyg3kmwZ+yrbtWVlEdiEKSInIeJerwbN3qcTjwYXSb6iQXNTM3l7p39Lghndh8PbFgs/nm9kJVcp3asHrwWmdc2vJ98Zzopltc2MU/At8OflErNs0FRwFl5Gvsv8jK9EDWRBsOD94u5Ztu27/CfmgxY+CZlHF83gLPsntdjGzfytVtoLhE8jX4HmxxCi5mhcLSgyrSZBb7BvB26nAb4K8I+XKNKJNX0qU53rg0eDt24Pmi4XD7wf+Hrw9xcy+VqnmgZlNCzoQKJzHCvI3P28ys7eWmK4Rvw+MpIuC5xhwbRB0LsnMwmb27sJxzKxagBXKHLtmdnqV5dXj8waBDyIMp9nUD4LnCPCLUrWrzOz1wP8L3j5YQxfqu5NvkK+t+sOg+U5ZZvYqMzuu6ONfkz/eLzKzV1eYfpL5nkJrYmZnVjnmF+IDKlD6PFRSEOTM5UBcBHy5zKiXku8Z9SfBdLuKiwtef7HE8K+Tr112uZlVrClmZieY2bFFH/+SfE3ZSyr9fpvZ5KBpbM4LBa/PLjPZtxmhwPsO+gD5TiS+amYXVqqpaWZ1ZvZp4B6g+NyWaz5Zj2/6XjxtCJ/Hc8R7FAyakf8xeHs6PgidW07ZpojB71Kux8uDgZ9WCkqZWZuZfWTHSywiO4NySInIaDvUzM6uYbwHnHNPA1fie7oLATea2TeBu/A1Xo4APom/QL8b31yilK8DPzazv+BrKDyHr6Y+Ef8P47lA7mLm8oLp5gC3m9mz+NomDwKr8AGcGfh/DHPf5SX8v3aFPgm8OljOZcHN1W/xF5ILgI+Rv7G9l9rzjVRympnNq2G8vzjntjjnnjKzr+MDTouAR4L3i/G1iU7F9yzYgA86/Udx703OuYfM7Kf4f6SPBBab2TfwNYqa8YmhP4KvBZBrGjjcpLMfxweAbsIntX8Gn9+oFZ9Y/qPkc1SVWo/3AHsBbzCzc/D7S+5mrqs4f1AFl+GbZp6Gz9/xlJn9EP/duvF5lHJJtB+j/I3NSLkEf0EfBj6PT6pc6Gz8fjsTX0vqZPPJ+x/DJ4Rtx99gnYL/Tg+TvzHO+SQ+IXg98DszuxwfbO3C51L6L/y+8yD+eNrhZNLOuavN7GR87cJD8Ov5J/htvwG/P87DN7d9Kz5n0CLygYh5wC1m9jS+1s1i8sfuTPyxmwv6rMD/U59zFvBnM7sFH9B7Eh90asI3W/lwsCyAHxd2glDD97refELstwEnAQ+a2bfxTSNbg3J9GF9LI8EY18YYbc65F83sg/ibzVbgn2b2W/w2XIH/HZiOP8b+DZ9L58PAvwrm0W9mZ+G3XT3wt2Ae1wbziOJ/M07FJ9jeF79v1OKb+HN57rfkefK/JS/D/5bkajddXnIO5V2I3/7zgS+Y2YH4Y3E1PrfRf+DPOeCDJxeVmMe45Zx70Mz+hs/VdKKZHeucu7tg+BIz+zD+O7cD/zKz3+BrTq3Er9fp+HPMvwH749dJ4Tz6zOzdwM34c8St5juBuC6YRxTYh/y2n08+0fUN+N+UduCbZraAoEfdYJpzgOOpfK0xKpxzq83n5boe/7v338D/M7Org/JtwK+vmfhz95spn1frOvz+Pwsf3JqKP1b68ev4I/h8efcA5Xoj3hFX4Wsyxsj/KddDvsZcOZ/AH3MH4M/lx5jvTGAx/phsxZ+nT8DXoN/CyFxjichIc87poYceeuzUB/6CwA3z8YmC6b9UZdxv4W+8c+/nFS1/eQ3LSwP/tZ3lXgkcUua7H4wPVlWa/l/AhB1Yvxdux/o9tGB6w/coVGn8fuBdFcoQw18cl5t+GT4Il3v/uRLzqLQN76jxe30XsBLzPhQfgCo1zRUF480r+PzsMt+1Hh9YrFaWK8qtryrbMzf9HTWMa/hghgNSwF4lxpmFD3jWsv7+XmY5pwG9Faa7AF97w+F7vyyePlI4bo3rIYQPuKVrKHei8Lvjez+r5fuuAA4qWu5VNU57NRArUe5VwfCflfledfhahpXmvQU4qcz0hd/tlVXWYcWy1LANPlCwrLLHf5V53BVMf+swpnkrPjhQy3Y4q8w8TsUnP642/ayi6S4OPk9XWJ+VHingUyWmXVhtXeKbKj5VZf5PALPLTF+27MMty87YP/CBnNz4fy0zztvxNQ9r2fZvLzOP0/GBpGrTTyua7vWU/51w+CDnYQXv31Fi2V8Lhg2UKdt9lb7/MNf/XCr/7hb+G212AAAgAElEQVQ+BvDXSs0l5nM85c/vWfw1xofKrbdgHuuCYT/aju+xtGiZv6pxugnAjTV+/6d3dH3roYceO+ehJnsiMu45nyz2tfiLwa34av2r8LU0TnXOfabKLI7D96B3Nb7Wzgb8TW538P77wMHOuW8UTfcvfA2MLwK34v+V7gqm3YivrfFJYJFzrmSPa865x/G1Kj6L/+dyC/6GZR2+VsZZwKucc2PWk5bzPoH/rr/GB/AG8P9SPgV8B9jXle91COfz47wBX0PnLvwNRR++JtOl+Npsmwsm6SyeRxX/jl9Xv8QnNF2LX499+HxDPwde7pz7uHPOlSjfo8H3uxofQEwUj1Mr51y/c+6d+P3ql/iL6T78Bf3z+AvkD+D3jZ0q+K65JMER4LwS46zC/7P9Bnzz1mVBWdP4G/b78f9MvwZ/I1dqOX/F10b5KX79JfH78A34Y/Bi8rmUhrttS3LOZZ1zX8DXYPkmPmHxFnzukB78dv8DvmbedOdzweXcHnznL+FzRb2AP97T+OP/H/had4ucc8WJoT+Krz3xI/I1q5L4oOxS/Do81Tl3ZrDfD/d7DTjn3oo/p11TMP9OfA21i4C9nXP/GO68dxfOuWvwweHP4rfVOvzx3o8PIt6M39f3cc6V7InLOfd3fA2Yz+HP5ZuCeazFb9fvAC8Ljo9avQJ/bP8OHxgq/i35Lv635H+GMc/CMq/AB88/gv/em8gfp7fhgwKHO+de2p75jzXna0TdHrx9jZkdVWKc3+MDLZ/Dr4P1bLvtPwcsDMYttZyb8dv+fPzvUeG2fxDf9O5ol+8VNzfd9fhavFfj97nc+eJ2/DXEaUE5xgXn3Arn3OvxNQa/ij+Xr8WfT3rxv+XX4Xt/nOGc+4zzPdUWz+dO/G/0lcAa8tcofwFOds5duJO/SvExXFPPgc7X8n4tcCI+2Xuu9nsaf634MPBjfM3DQ0estCIyoqzEdbuIiMiIM7NXkm9ac4pz7taxLI+MLDO7A/9P+53OuRPGtjQiIiIiMt6phpSIiIyWXK9ZafLdc8tuwMxmk8+rct9YlkVEREREdg0KSImIyA4zswlVesF7DT4pLMD1zrmto1MyGQlB72HlhjUAV5DvKOXK0SiTiIiIiOza1MueiIiMhP2Bm4IexG7F59rJ4HsqfCPwLnyvPwP43uBk1/IzM2vE52zK5XJqxvd49RF8wnrwybOfGpsiioiIiMiuRDmkRERkhxXlhyqnG98r0s2jUCQZQQX5oSq5Ft/j2bhJ+isiIiIi49ceG5CaNGmSmzdv3lgXQ0Rkt5DJZOjo6KCzs5P+/n7S6TTpdJpwOEw8Hqe1tZXJkycTjUbHuqiyHfr6+ti6dSvd3d2kUilSqRQA0WiUxsZGJk6cSGtr6xiXUkRERETGg4ceemiTc25ytfH22CZ78+bNY/HixWNdDBERERERERGR3YaZrahlPCU1FxERERERERGRUaWAlIiIiIiIiIiIjCoFpEREREREREREZFQpICUiIiIiIiIiIqNKASkRERERERERERlVCkiJiIiIiIiIiMioUkBKRERERERERERGlQJSIiIiIiIiIiIyqhSQEhERERERERGRUaWAlIiIiIiIiIiIjCoFpEREREREREREZFQpICUiIiIiIiIiIqNKASkRERERERERERlVCkiJiIiIiIiIiMioUkBKRERERERERERGlQJSIiIiIiIiIiIyqhSQEhERERERERGRUaWAlIiIiIiIiIiIjKpRD0iZ2flm9kczW2ZmzsyWb+d83mNmj5hZv5mtN7OfmdnkES6uiIiIiIiIiIiMsLGoIXUpcBKwFNi6PTMws08CvwI6gY8DPwbeAdxhZo0jVE4REREREREREdkJImOwzAXOuWUAZvYk0DScic1sEnAx8CDwaudcJvj8QeAv+ADVpSNaYhERERERERERGTGjXkMqF4zaAW8CGoDv54JRwXyvB5YB79rB+YuIiIiIiIiIyE40FjWkdtRRwfO9JYbdB5xpZk3OuZ5RLNOY+OfzG/ny9U/t0Dycg8Z4hMvOOpzZExpGqGQiIiIiIiIiIuXtigGpGcHz6hLDVgMWjPN88UAz+yDwQYA5c+bsrPKNmua6CPtNb9mheTy+qoMnVneyZEOPAlIiIiIiIiIiMip2xYBULmqSKDFsoGicIZxzPwF+AnDkkUe6kS/a6DpsTjs/fGf7Ds3j0Zc6eNMP7y45zDlHdyLNus4B1nYOsL5zgEXTWzhoVusOLVNERERERERE9my7YkCqL3iOA/1Fw+qKxpEaLd3YQ2d/iiUbenhhQzdLN/aypqOfvmRmyHiHzm7juv88tuQ8+pMZNnQPsL4rwfquAdZ3DXDI7DaOmjeh5nJks46ugRRbepNs7UsxtSXOrPbh1dxyzpFIZ+keSNM9kKInkaYnkebAma201EWHNS8RERERERERGXm7YkBqTfA8E1hSNGwm4ArGkSpC5p8vvvEZAMIhY97EBhZOaeL4fSYzraWOaa3+8YVrn2DZxh7+9cJGlm/q5cVNfazY3MvKLX2s7xqgayC9zfwPmd3Gnz78Cjb3JFjXNcC6Th+oWts5wKaeBFv7UmztTbKlL0lHX4qOviTZgrprcyY0cNlZh7O1zweoOvqSbOn143b1p+hO+KBT94APOuWCUKnMthXgomHj3JP2ZnJznDOP3vWbbIqIiIiIiIjsqsy5sWu5ZmZPAk3OuXnDmOYDwE+B9zjnriwatgRIOecWVZvPkUce6RYvXjzMEu9+0pksv7x7OVNa4uw/vYW5ExuJRUp3vvjBXy/m70+vH3xfHw0zd2IDcyY0ML21jiktdUxtqWNqS5ypLXWc93+P8+hLHYTMSGeH7meRkDGxKUZ7Q4wJjf65vTHqn4PX37nlBVZuKV3ZrbkuQktddPC5qS5Cc/BoikcHXzfXRWiOR/nQVQ8NKcOxCyfyvXccRjwapiMIhnX2p2iui3DwrLYRWLMiIiIiIiIiex4ze8g5d2TV8cZzQMrM5uDzQS11zqWCzyYDK4AngFc45zLB568H/gJ80Tl3cbVlKyA1fM+v7+bhFVuZO7GRvSY1MrUljpmVHf9vT63jb0+tY1pLHdNbfbAqV9tqUmOcUKj8tABrOvq564VNtNRHg6BVlPbGGG31USLh0kGzchLpDH2JDM+t7+YdP7mv4rivP2QG/ckMnf1JNvcm6exL8Y23HsykpjiHzN42WJXJOroHUoNBrY5+X5Orq99/Nr2tnrccPpPeZIa6SGjYZRcRERERERHZVYzbgJSZvRuYG7w9F4gB3w7eryis9WRmdwDHA3s555YXfP5p4FvAHcDV+KZ6nwZeAo5yzvVUK4cCUnuugVSG7972Aga0NURpq4/R2hDl0Zc6uPyOpUxpjjOhMUZbQ5THV3UOyaP1sr0mEA2HgsCTD1aVaqpYLGQMNkX8/pmHcfKiqXT0+5pZ0bCxcErzTvq2IiIiIiIiIqNnPAek7sAHmUq50zl3QolxhwSkgmFnA58E9gW6gBuA85xzG2ophwJSUotUJss/n9/Is+u6+eHtS5icC1bVR2lriNFaHx18tDUMfW6tj7GlN8nvHlxJYyzCb+5fwda+VMnlvPmwmZz5sjmDebQ6g9pVuaBVR1+KVCbL+1+5F69YOImmeIRUJktXf4r2hljV2mYiIiIiIiIio2HcBqTGCwWkZCw8s7aLvzy2hpY6H7ja3JPgW39/vuS44ZDRVh+ltSHKso29Q4Y1xSP0JPI1s779tkOY2BQbDGh19KdYMLmJl82fwJTmuuJZl+Scoy+ZIa5mhSIiIiIiIrKdFJCqQgEpGS9WbO7l8VWdg80H2xp8EKo5HhnM0dWXTPPUmi5+dc9yYpHQ4Hg/v+tFOvtL17rKmdAYY0pznPVdA0xpruMVCyfS2edzXXX252tkdfbneyf89Cn7sHBKEyfuN4WeRJqBVIaZbfWD5UlnsnQNpOnsTzGrvZ7oTghgJdPZwR4Uu4Ln7gHfRLJ7IE1HX5IPHDef1voo4POE9SYy9AQ9LvYm/XPPQJreRPA6kXudGfLZi5t6edOhM+hLZshkHYfNaWO/aS3Ux8L0JTP0JdL0JjP0JdP0Jf20/cnMkM8GnxMZepO54Wn6Ehm6g+DhyYumMntCPSEzFk1vYWJjjOP2nlQxAJjNOpKZLHXR8JDPnXMMpLKDyx1IZYiGQ8yZ0EA667bpnMA5RyKdZSCVoS/px53cHB/hrSYiIiIiIns6BaSqUEBKdhcPrdhCZ3+K1iBI1d4QY01HP39/ej2/vX8F8yY20t4Y45agh8TmeITWguaFbfUxWoLXdz63kafXdpVd1qz2ejr7UoMBlpxo2HjPy+fRM5D2ubX6U2zpTVIfi3Dk3HZikRCHz2mnqz81NLjUn6Y7kQs6FXw2kCKRztb0/dsaovQm0oPBtGrqo2Ea4xGa4mGa6iI8s7abTNYRC4dIZmpbZk4sEqIhFqYxFqEhFg4eERrjYepjERpjYepjYW5+Yh3rugbKzmfOhAZmtdfTl8wMBrL6kz5w1J/K5zCb1V5Pf/BZfypDtdP3rPb6wQBUufH/88QFHDSzlf7ceEFwa0N3giPmtpNIZdl7ahOHzWkvuYxM1pEqCpg550hlHP3JDBnnmNAYA3xwbSAdLCOdHVxWfyqDAYfNaSes5qfjknOOdNYHNZPBI5HOBM/Zwc/roiFa6qMk01mcg5lt9bQ2+KBxNuvUvFhERERkD6CAVBUKSMmeJpN1OOeqNsdbsbmXrX0p/vLoGtobojTGI1wVBLZyTQhb66O01Uf5+9PrefSlDvqSGcIhY2JjPq/W4hVbKy6nPhqmuS5CS32U5roIzXX+uaUuQktd8Wfbvv/qzc+QTGdpjEdojEdorvMBoMHXuc/j+deNsXDJ75+7UR5IZVixuY9n13WRSGeHBJoa4xHqg+BTffDZcGqG5c61vckMq7b2sb4rwdX3r+Qfz21g/qRGmgrm3xAEshrjEeqjYe54fiMzWuuoj4YHl+1fRwZfY/Dwiq1Maopz27MbmDexgfpomLpYmIZgurqonzaTdVx84zM1lx2gtT7K7An5oFkugJUsCBxOaooPBpgy2aG/LfFIqKYgY2MsTG8yw7uOmcO+U5t98C2ZZV3XAIfNaSOVyQeyBlJZ+lMZVm/t57A5bSTSWRZNbxksQyJ4zo3Xn8ywsTvBYXPaBqcfSGUYSBe8Lvi8P+XHn9ISJxYOccjsNgZSWRKpzGBts8Jp+1MZ1nT0c9S8CT5Ik8qwuqOfvac0UxcN5adJZVnT2c8+U5qJR0MkUlkG0hk2dCU4ZHYriXSW5Zt6OWb+xIKATyaYZ8HrdL4s3Yk0yXSWBZMbSaSzdPSleN3B00lmskOm29ST5MAZLfnAUiYfWEoWBJbyQSY//2QmWzUAWkk4ZIP7xCGz29hrYgPJTJb1XQn2mdpEPBJmUlOMZDpLb1CDb1JTbEg5k+ksazv7OXhW22B5fTA+Skt9hFRmaMAsmcl/r9ywRDpDZ3+KhVOags/zwwuXk8r416ngs96E377RsLHftBYAjt9nMslMljUd/SyY3MT8yY2D8+kaSDGpKU4sEiJVML/cstZ3DbDftGZSWUd/Mk08EmbOxAZeuXASYTMF7kRERGSXpoBUFQpIiYycVCa7TXAmk3VknaOrP8Uza7tpqY/4G8e6KE11kZ3SzE+GZ3NPguWbe6mP+mBYLuBVHw2zcksvmSw8sbqTy25fwtSWOhpiQwNc9bEwdZEw/3phI3MnNlIXzc0jREMsQjwS4t6lm5nZXu+DYwXzr4+GiUdD1EfDRMLGb+9fSXtDjD8+tKrm8hf2XllNqRpwIYO6oFx1kRB10TDxaJi6oFzprOPxVR0MpPx0zXUR4hE/vC4aJh5MUxcNURcJ88TqTtobYkHwL0RHX4o1Hf1MaakLvn+IeCRMZ7//fHJznPqYn8/DKzuYFqzjFzf14hy01Eepj/lp4pFQ8PDrbfB1JEQ8GmLF5j7CIaMxFmHpxh5Wbe0fLGM8WO4za7torY/SECwzFjzikTCxcO710M/jhZ+F/bz8uMXzCLGlN0k644hHQ4RDxnPrulmxuY8pzXHi0RA3PbGOFzf1MqkpTkMszMotfZhRU6ArEjLSNW7sXFmHPEdCRMMhnlnbxcRGv41yw6KDz0YsEiYWtoLP/HNfIs1fHlvDvEmNgzn9CoNsO9Ohs9uYP7mRjr4UTfEIbQ2+Btrz67v5f6+Yx5TmOpriEZKZzGDQt60+NiSoligIrnX0pTh8TjvRsA0GG3PDWuqi7DO1mfpYGOfcYDNtERERkeFQQKoKBaRERManZDrLus6BgqBXiDUdA2SdG6zpVR8NEw0bZkZHX5JkJsvKzX35AFE0P15dNDzYFHBzT4JIOERdEFjRDffYS6QzpDK+2Ww0bPSnfD64wQBZODRYY6gnkSZkPsAYCYfoS6ZJpLJEg/Fy+8TOlsm6wX2qayDF6q39QwJgW/uSZLMQiwwNbkXD/jtt7k0C/nv0JtKs7Rzg5ifX0lIX5ak1nUxsinNNEJyd0BgjZMamngTRsNEYj9BRpsfWnSUWCXHIrFYyWZ+7btmmHk7cdwp10TAbuxMsnNJER18SM+Pwue0k01l6BtLMbK8nlcmytS/J3AmN9CbStNRHiEVCJNNuSDCseyDFrPYGnz8wkaalLgL4Zp+JdHawdmh7Q4xJTXE17xURERnHFJCqQgEpERER2RU553hqTRcvbuolEsoHvXoSaRLpTBCwHVrLKxYExB54cTON8cg2w1Z39PP8um46+lNMaY7z/PoekpksL23pIx4J0VIXJRYJcdeSTYBvorupJ0E4ZMTCoSH57kZTLBLi1ftNYUN3glnt9TTFI+SubDd2J9h/egtZ52vsZrL411nHlt4kC6Y0URf1tcEWTW9h7ylNhIIebiPhENmsI+PcYI3fTNaRzUJjvHTzbxEREfEUkKpCASkRERGRkZHOZFnf7WtxGUZnf5J4xNdO3NSTIB4Js6U36QNYQRPJeBAUC5mxpTdJXVDzsbM/hQPWdw7QGI+wIZjvS1v6WN3Rz5beJI2xCH96ZDWzJ9TTn8yyqSdBa32USMgwg009ycGymeFzc5kRCjHYDLeSas1J40FPpqlMlqyD+ZMbiUd8+U/YZzJm/nvsO615cJqBVIYJjTEaYhGyzuGcDy4eNKuVeCRMW0NUzdlFRGS3oIBUFQpIiYiIiOy+MllHyCjZjHMglRkMJt2zZBNb+1JknWPZxl7i0RCRkBEO2WCS+dzrlVv6qI+Fyc1y+aZeIqEQqUyWvwe92daaG62USNAUMZczbf6kRlLZLPtMaaajP8UBM1pIZRz7TWvGzHc48cqFk2iMRwanV+0tEREZa7UGpCKjURgRERERkdFUKc9ULs8cwOkHTR/xZTvn2NSTHJL4flNPgqxzhIJoVsiMJ9d0ksk6lm3sIRwKkYslrdzSj3OOxcu3MqUlzmOrOunoS/JQlR5s/XwZrIWVSGfJZB2NsTBTW+roTabZf3oLDfEIR85t582HzaK1ITri319ERKQWqiElIiIiIrKL6OxLkXGOjr4k/3x+Iz2JNOFQiK6BFGs7+mkPEuGHzNe0em5dN5Ob4zy0YiuTm+Ms3dBD10B6yDwbYmEOmdVGKARrOwY4cGYrmSDfls/BBV39KU7ZfyrO+VxcueHdiTSz2upJZ32erUyQeyuV9tPmktvvNbGRWCTE1JY6WuqjtNRF1LGEiMhuSjWkRERERER2M7kaTRMaY8yf3LRd8xhIZfjj4pd4eGUHKzb3knE+H9byDX2EDB5b1THYTDEcMp5d1w3A/S9uGbHvkTOpKUZrfZSpLXWEQ0ZjLMLhc9sYSGVZMLmJV+0ziUjIVx2LhE15tkREdiOqISUiIiIiIhX1JNJkMo5QyDeHDJmRdY7ugXQ+eBXOB7HSWUdXf4pUJsvazgH6UxnWdQ7Qm0izqSdJZ3+K9V0DLNnQw5TmOGbw4PLqTRIBDpjRQtZBNuvoTaaZ0hzn2IWTOGBGC/Gg58Rcr4qHzW6jIR4ZTKIvIiI7n2pIiYiIiIjIiGiKl75taIiVv53ITTN3YmNNy3DOMZDKknWODd0Jrrx3BZOb4wD0JFLcu3QzjUFwyczo6k/x3PpuVm3t5+GVHVXnHwkCZUfvNYGu/hSHzm5jc2+SfaY2DSainzepkdcdPL3i9xIRkZGhGlIiIiIiIrLLSqazPLmmk2zW+V4RzdfgunvpJiIhY0tvki29STZ0J9jQPcBLW/qJho1NPUnA1/gy8r0b5kxrqaMhHuaC1y5i4eRmmuoiGNDWEFX+KxGRCmqtIaWAlIiIiIiI7PFWd/Rz7cOreGRlB9FwiL8+ta7qNJOb42zsTvDuY+bSXBch6+DQ2a0snNLM/EmNhCr09igisrtSkz0REREREZEazWyr56Mn7T3ks4dXbmXJhh46+pLEwiGWbOyhrT7G+i6fF2vJhh42dif4/YMvkcxkt5lnQyxMXzLDzLZ6ANLZLAOpLL2JNDPb65nRWs9+05txDo7fZzIt9REaYhFmttfTUhcdle8tIjJWVENKRERERERkB6UzWZZv7mXpxl5ueXo9TfEI6zoHyDhHUzxCyIxIyLh32WYGUhk2dCeIRUIk09sGsnKOnjeBCY0xss7R2Z9iaksd9dEwr9pnMol0hjkTGmitjxINh4iEjRmt9aqVJSJjTk32qlBASkRERERExppzjufX97BkQw/hEKzuGGDZxh7uXrLJ92AYMnoG0mzoTmyT56qU1voo/ckMC6Y00ZtIc87x82muizJnQgOTm+M0xSLEoyGyzhEOGfFIeBS+pYjsSdRkT0REREREZJwzM/ad1sy+05qrjtubSLNqaz8Ox7rOAVZ39FMf9QGlu5dsxgxe2NDDlOY4tzy9HoAvXPtk1fm2N0Q5YEYrsyc0sNekBkJmHDG3nYmNcWa01REJh3bsS4qIlKAaUiIiIiIiIrsZFzTz29STYHXHAC+s7x5s+tcUj5JIZ7j6gZU0xSMs3dhbdX4fOn4BIYMj5rZzyOw2JjXFR+FbiMiuSE32qlBASkRERERExHPO0ZfMkM46HnhxC90DKdZ2DvCDfyyhP5UpOU0uB9aBM1torY9yzF4TOXBWKyEzFk1vZnJTHDPltBLZ0yggVYUCUiIiIiIiItU553AOnl7bxSMrt/LQiq1MbIpz79LNdPanWNc1QKZMfquPv3pvPnLiAuWqEtmDKCBVhQJSIiIiIiIiI6Mvmea5dd0k0lmeWNXJ359ex4PLtw4Z54yDpnHyoqkct/dk2huiyk0lsptSQKoKBaRERERERER2nk09CX51z3J+/+BLbOhOlB1v7sQGegbSLJrewpffeAALJjeNYilFZKQpIFWFAlIiIiIiIiKj5+k1Xdy1ZCObe5Js7k2yvmuA9oYYT63pHJJYvS4a4tWLpnLq/lN5zQHTqIuquZ/IrkQBqSoUkBIRERERERk/7l26mS/++UmWbOjZZtgbDplBNBzi8LltHDq7jQWTm8g6R300rMTpIuOMAlJVKCAlIiIiIiIy/jjn2NqX4vv/eIFf3r2cGa11rOkcKDt+PBLit/9xDIfPaVNwSmQcUECqCgWkREREREREdg3OOV7a0s8LG7q5e8lmJjXHeGp1Fzc+sXbIeMfvM5l9pjbxkRMW0t4YG6PSiuzZFJCqQgEpERERERGRXd8/nl3PFfes4O4lm8hk8/e3r1gwkdMPnMaZR89Rj34io0gBqSoUkBIREREREdm9pDNZLr7xGW58Yi0bC3r2i4SMw+a08bYjZvOGQ2coUbrITqSAVBUKSImIiIiIiOy+ugZS/OKuF7n9uY0s29hD90B6yPA3HzaTT526D7PaG8aohCK7JwWkqlBASkREREREZM+xoWuA79z6Ag+t2MLz64f25PeGQ2bwsVfvzcIpTWNUOpHdhwJSVSggJSIiIiIismdyznHvss18/eZnWbaxl+6Erz3VEAtz2VmHc9zekwmH1GOfyPZQQKoKBaREREREREQE4OoHVvKVG56mL5kZ/GxWez3pjON1B0/nwJmtLJrewr7TmsewlCK7BgWkqlBASkRERERERAo98OIW/rD4JVKZLPcs3TwkMTrAzLZ6Dp3dxnmn78fsCco9JVKKAlJVKCAlIiIiIiIilQykMjyxupMX1vfwpT8/STqbv38+YEYLC6c0cfReEzjzqDmE1MRPBFBAqioFpERERERERGS4rrpvBV+54WkS6eyQz/ea1MjeU5p4/yv34mXzJ45R6UTGngJSVSggJSIiIiIiIjvipS19nPCtO8hkh95Xz2qvZ//pLXz61H2Vd0r2OApIVaGAlIiIiIiIiIykGx9fy3dufZ4lG3qGfP6h4xdw7kkLaYxHxqhkIqNHAakqFJASERERERGRneWvT67lj4tXcduzGwY/u+C1i/jAcfPHsFQiO58CUlUoICUiIiIiIiI7W28izX/+9mHueG4jAMftPYnj9p7ErPYGTjtgmpKhy25HAakqFJASERERERGR0fLg8i2874oH6R5ID/n8jx96OUfNmzBGpRIZeQpIVaGAlIiIiIiIiIy2zv4U67sGOPU7/xzy+aNfOoW2htgYlUpk5NQakAqNRmFEREREREREBFrro+wztZnlX3stP3tP/p790Itu4b+ueYz+ZGYMSycyehSQEhERERERERkDJ+8/leVfey2fOmUfAP6weBWLvvRX1nUOjHHJRHY+BaRERERERERExtDHXr03L371DPab1gzAMV+9jeseWU1nf4psds9MsyO7P+WQEhERERERERkHnHPsdf5N23x+3/mvZlpr3RiUSGT4lNS8CgWkREREREREZDx6ZOVWbntmA5ffuZRMQQ2ptxw+i8++Zl8Fp2RcU0CqCgWkREREREREZLz74nVP8qeHV9FbkOz8hUtOJxpWBh4ZnxSQqkIBKRERERERETWKhW8AACAASURBVNlV9CTSHPjffxt8/8SFp9JcFx3DEomUVmtASiFVERERERERkXGuKR5h2aVnEAtqRh104d854iu3jHGpRLafAlIiIiIiIiIiu4BQyHj2K6dxzvHzAdjcm+Tcqx8Z41KJbB812RMRERERERHZxby0pY/jvnE7ABMaYxwzfwI/OPNwQiEb45LJnk5N9kRERERERER2U7MnNHD3eSfxjqNms6U3yU1PrGP+52/iazc/y55a8UR2LQpIiYiIiIiIiOyCZrbV87W3HMwzF53G1JY4AD+6cykn/8+dPL6qY4xLJ1KZAlIiIiIiIiIiu7D6WJj7P38yD11wMtNa6li6sZc3/OBufnj7krEumkhZCkiJiIiIiIiI7AYmNsW59/yT+P6ZhwHwzb89x/l/ekJN+GRcUkBKREREREREZDdhZrz+kBn88uyjALj6gZXsdf5N9CczY1wykaEUkBIRERERERHZzZy43xQe+eIpg+8XfemvbOxOjGGJRIZSQEpERERERERkN9TeGGPJJaczs60egKMuuZU/Ln5pjEsl4ikgJSIiIiIiIrKbioRD3H3eSZxx0DQAPnvN48w770buWbqJbFa5pWTsKCAlIiIiIiIispu77KwjuPVTrxp8/86f3s/8z9/Ez/61TIEpGRMKSImIiIiIiIjsARZOaebFr57B5WcdzoEzWwC4+MZnmP/5m/jzo6vHuHSyp1FASkRERERERGQPYWacftB0bjj3OBZfcDJnHj0HgI//7lFSmewYl072JApIiYiIiIiIiOyBJjXF+eqbD2JqSxyAwy66ZYxLJHsSBaRERERERERE9mB3fe4kAHoSad53xYNjXBrZUyggJSIiIiIiIrIHi4ZD3Hu+D0r949kNzDvvRpxTonPZuRSQEhEREREREdnDTW+t58kvv2bw/V7n36Te92SnUkBKRERERERERGiKR3j2K6cNvp//+Zt4y+X3kEhnxrBUsrtSQEpEREREREREAKiLhnnu4tOY1BQD4KEVW9n3gr+yfFPvGJdMdjcKSImIiIiIiIjIoHgkzOILTmHZpWdw3N6TADjhW3eQzmTHuGSyO1FASkRERERERES2EQoZV77/ZYPv3/+rxWNYGtndKCAlIiIiIiIiImW9+NUzALjz+Y2qJSUjRgEpERERERERESnLzHjXMXMAWPiFm8mo9z0ZAQpIiYiIiIiIiEhFF73hwMHXCz5/E/POuxHnFJiS7aeAlIiIiIiIiIhUFAoZT1/0msEk5wB7nX8TA6nMGJZKdmUKSImIiIiIiIhIVQ2xCFe+/2U8d/Fpg5/t98W/sqF7YAxLJbsqBaREREREREREpGbxSJgXLjmduRMbADj6ktvGuESyK1JASkRERERERESGJRoOcednTxx8P++8G1mxuXcMSyS7GgWkRERERERERGS7PPzFUwZfH//NO+jsS41haWRXooCUiIiIiIiIiGyXCY0xln/ttbzzZXMAOOSiv/9/9u48zLKrIBf+u2ro6nnuDumMpCMEAiRIEgYViTgAihNXL3ARQWVS4IIgXsDLDSLDp0YRxO9CBK5MCoGAIMgFIkQ+GZIGQgIhQOahM3R67nRXV1fV+v441VjpdFdXdZ06+1TV7/c89Zxz9tr7nLeqzl/vs9baDSditlBIAQAAANPypl97eFYvWZCktXwPjkYhBQAAAEzbv7/qvntK3bNnf4Np6HYKKQAAAGDalg705fLXPvFHr8/5sy9kZLQ2mIhuppACAAAA2mL9soW58c1P+dHyvY2v+UxqVUpxfx0vpEopPaWUl5dSri2lDJZSbi2lXFhKWTLJ648rpfzvseuGSim3lFL+ppSycqazAwAAABMrpeSK1/7sj14/8NWfyfZ7hxpMRDdqYobUXyf5qyTXJHlJkouTvDTJp0opE+YppaxP8vUkv5PkE2PX/3OSFyX5Yill8QzmBgAAACaht6fk+jc95Uevn/2eyxtMQzfq6+SHlVLOTKtEuqTW+rRxx29M8rYkT0/yoQne4jVJTknyzFrrP467/itj1/1hkj+bgegAAADAFPT2lNz45qfkga/+TK6+fWdqrSmlNB2LLtHpGVLPSFKSvPWQ4xcl2ZvkWUe5/vwk+5L80yHHP5xkMMlz25ARAAAAaINSSh5+wookyaNscs44nS6kzk0ymuQ+c/VqrYNJrhwbn8hAksF6yI5otdbRtIqq00opa9sXFwAAAJiOf/id85Ik2+4dyvl/+aVmw9A1Ol1IbUhyT611/2HGbk+ytpSyYILrv5tkVSnl7PEHx16vGnt58pEuLqU8v5SyqZSyacuWLVOMDgAAAEzV6iULcsPYflK3bNube/cPN5yIbtDpQmpxksOVUUlryd3Bc47krWnNsPpIKeUppZSTSylPTmvJ3oGjXV9rfVet9Zxa6znr1q2bYnQAAADgWPT0lDzncacmSX7n/1zRbBi6QqcLqb1pLbs7nIXjzjmsWuuX09r4fFmSTye5Ocmnknwxyb+MnbarLUkBAACAtnntLz4kSfL1G7fln6+8veE0NK3ThdTmtJblHa6UOiGt5XxDE71BrfXiJCcmeWSSxyfZUGt94dix4STXtTcyAAAAMF39vT352IselyT5k49/p+E0NK3ThdQVY5953viDpZSFSc5Osmkyb1JrHam1Xllr/XKt9e5SygPSKqguq7UecYYVAAAA0JxHndLa/nn3/uGc+8YvNJyGJnW6kPpwkprkZYccf15aez998OCBUsrGUsoZR3vDUkpPkrcl6U3yxvZFBQAAANrtq6/+mSTJlt3787J/+lbDaWhKRwupWuvVSd6R5NdLKZeUUn6vlHJhkr9KclmSD407/dIk3xt/fSllaSnlmlLKG8eufUWSy5P8RpI/qbV+sTO/CQAAAHAsjl+xKF9+1flJkk9cuTkf/cZtDSeiCZ2eIZW0Zke9MsmZaZVTT0/y9iS/VGsdPcq1Q0muSvLMJH+b5LVJtiZ5Uq31TTOWGAAAAGibk1Yvzmdf9lNJklde/O3cvPXehhPRaaXW2nSGRpxzzjl106ZJbVkFAAAAzIC3X/rDXPj5H2RBX0+uvuDnM9DX23QkpqmU8o1a6zlHO6+JGVIAAAAAefHPnJ7T1i3J0PBofv8D32w6Dh2kkAIAAAAaUUrJv73iCXnI8ctz6bV35/nv25TBAyNNx6IDFFIAAABAo97wK2cmST53zV15xxevazgNnaCQAgAAABp1zqmrc8ObnpIkefu/XWeW1DygkAIAAAAa19NT8tjT1iRJ3nbpDxtOw0xTSAEAAABd4b3PPTdJ8ndfuj67Bg80nIaZpJACAAAAusLC/t68+PzTkySPuOBzDadhJimkAAAAgK7xkiee/qPnn7/mrgaTMJMUUgAAAEDXGOjrzZdfdX6S5Hnv29RwGmaKQgoAAADoKietXvyj55/77p0NJmGmKKQAAACArnPJ7z8uSfL8938j+4dHGk5DuymkAAAAgK7z4yevyjPOOylJ8qqPXtVwGtpNIQUAAAB0pf/11DOTJP985ebs2T/ccBraSSEFAAAAdKWF/b154U9vTJI89s2XNpyGdlJIAQAAAF3rj5/04CTJ7sHh/D+fvbbhNLSLQgoAAADoWqWUfOEPH58k+X+/dH3e8cXrGk5EOyikAAAAgK52+vpl+fprnpgk+Yv/+/3csGVPw4mYLoUUAAAA0PWOW74wv/WYU5Ikz/r7rzechulSSAEAAACzwut/uXXXvc07B7N78EDDaZgOhRQAAAAwK/T0lLz51x+eJHnSW7+c0dHacCKOlUIKAAAAmDWefu5JSZLbd+zLF753V8NpOFYKKQAAAGDWKKXk0lf8dJLk+e//RoZHRhtOxLFQSAEAAACzysZ1S7NiUX+S5HFv+beG03AsFFIAAADArHPl634uSXL37v35p8tvaTgNU6WQAgAAAGadUko+9qLHJUn+xyVXN5yGqVJIAQAAALPSo05ZlbNPWpkk+fIPtzSchqlQSAEAAACz1h8/6YwkyW+9+/IMHhhpOA2TpZACAAAAZq3HblyTR5y4IklywSe/23AaJkshBQAAAMxq733OuUmSf7ri1gwNjzachslQSAEAAACz2pqlA/nvT/yxJMlv/O+vpNbacCKORiEFAAAAzHp/cP7pecSJK/Lt23bmuf/niqbjcBQKKQAAAGDWW9DXk4tf+NgkyZe+vyUf2XRrw4mYiEIKAAAAmBMG+nrzsRc9Lknyqo9elUu/d1fDiTgShRQAAAAwZzzqlFX51It/Mknywg98w35SXUohBQAAAMwpDz9xRZ5x3sk5MFLzog98s+k4HIZCCgAAAJhzXvOUM5Ikn/3undm570DDaTiUQgoAAACYc5Yt7M8f/cKDkySfvPL2htNwKIUUAAAAMCc9+7GnJEnu2DnYcBIOpZACAAAA5qRlC/vT21Pype9vaToKh1BIAQAAAHPWwr6eXHPHrqZjcAiFFAAAADBnrVk6kCTZOzTccBLGU0gBAAAAc9avnr0hSfLZ79zZcBLGU0gBAAAAc9ZvnHNSkuQ/rtvacBLGU0gBAAAAc9ZJqxdn9ZIF+dg3b8voaG06DmMUUgAAAMCc9oQHrUuS3LlrsOEkHKSQAgAAAOa08x64Okny3c3uttctFFIAAADAnPb4sRlSr/rotxtOwkEKKQAAAGBO27ByUZJk+94DqdU+Ut1AIQUAAADMeY86ZVWS5Du3W7bXDRRSAAAAwJz36iefkST5zHfuaDgJiUIKAAAAmAcedsKKJMmmm7Y1nIREIQUAAADMAwv7e5MkV9y0PaOj9pFqmkIKAAAAmBd+4vQ1SZLLfril4SQopAAAAIB54ZU//+Akyffv3N1wEhRSAAAAwLzw0A3L099b8s7Lrm86yrynkAIAAADmhYG+3py+flm27z2Q/cMjTceZ1xRSAAAAwLzxhAevS5Jc+r27G04yvymkAAAAgHnjN885KUly09Z7G04yvymkAAAAgHnjgWuXpJTkzz/7/YyM1qbjzFsKKQAAAGBe2bhuaZLkqtt2NJxk/lJIAQAAAPPKa3/xIUmSO3cONpxk/lJIAQAAAPPKSasWJUm+dsPWhpPMXwopAAAAYF45bW1ryd4/XnFrw0nmL4UUAAAAMK/09JQkydDwaLbs3t9wmvlJIQUAAADMOxf+xllJkn+8/JaGk8xPCikAAABg3nnKw49Pkrzt0h82nGR+UkgBAAAA886iBb1JkuHR2nCS+UkhBQAAAMxLj37g6iTJ3qHhhpPMPwopAAAAYF765bM3JEnOev3nGk4y/yikAAAAgHnpmeednCQ5MFKzb2ik4TTzi0IKAAAAmJdKKXnpz5yeJPncNXc2nGZ+UUgBAAAA89ZTz2ot27vpnr0NJ5lfFFIAAADAvHXS6sVJklu2KaQ6SSEFAAAAzFsL+3uTJB/75m0NJ5lfFFIAAADAvPag45YmSQYP2Ni8UxRSAAAAwLz25IcdnyT57uadDSeZPxRSAAAAwLz26AeuTpLcs2eo4STzh0IKAAAAmNfWLx9Ikvz9l29oOMn8oZACAAAA5rXT1y9LklyzeVfDSeYPhRQAAAAw7z3ouKW5d8im5p2ikAIAAADmvbNOXJkk+ex37mw4yfygkAIAAADmvZf/3IOSJJ/41u0NJ5kfFFIAAADAvLdh5aIM9PXks981Q6oT+poOAAAAANANFvT1pDYdYp4wQwoAAAAgyVPP2pCh4dGmY8wLHS+kSik9pZSXl1KuLaUMllJuLaVcWEpZMsnrl5ZSXlNKubqUsruUck8p5SullOeUUspM5wcAAADmpjo2PerWbXubDTIPNDFD6q+T/FWSa5K8JMnFSV6a5FOllAnzjI3/a5I3JLkiySuS/FmS3iTvTfKWmYsNAAAAzGWPOW11kuT7d+5uOMnc19E9pEopZ6ZVQl1Sa33auOM3Jnlbkqcn+dAEb/HoJD+Z5K211pePu/7vklyb5AVJ/ngGogMAAABz3EmrFydJdu470HCSua/TM6SekaQkeeshxy9KsjfJs45y/fKxx83jD9Zah5Lck+TeNmQEAAAA5qENKxYlST757c1HOZPp6vRd9s5NMprk8vEHa62DpZQrx8YncnmSHUleVUq5KcnXkyxK8pwkj0rywjbnBQAAAOaJ45YPJEku+8GWhpPMfZ2eIbUhyT211v2HGbs9ydpSyoIjXVxr3Z7kl5NsS/KRJDentVTvD5I8rdZ60UQfXkp5fillUyll05YtvlwAAADAfyql5KyTViZJRkZrw2nmtk4XUouTHK6MSpLBcedMZE+S7yT5yyS/nuT3klyX5EOllJ+b6MJa67tqrefUWs9Zt27d5FMDAAAA88K5p6xKkmzZfaT6gnbodCG1N8nAEcYWjjvnsEopD0/ylSSfr7X+Ua3147XWd6e10fmdSS4qpfS2MzAAAAAwfzzouGVJkk03b2s4ydzW6UJqc1rL8g5XSp2Q1nK+oQmuf3laxdXF4w/WWvcm+XSSU5Kc2p6oAAAAwHxzcMmeO+3NrE4XUleMfeZ54w+WUhYmOTvJpqNcf8LY4+FmQfUd8ggAAAAwJauXtLa2vmvn4FHOZDo6XUh9OElN8rJDjj8vrb2jPnjwQCllYynljEPOu2bs8TnjD5ZSVib5lSTbk1zfxrwAAADAPLJ8UWuey1dv2Npwkrmto7OJaq1Xl1LekeTFpZRLknwmyUOSvDTJZUk+NO70S9NaglfGHXtrkmcnecvYflL/kWR1WoXW8Un+oNY6POO/CAAAADAnDfS1FmVtu3eiHYWYriaWt70syU1Jnp/kF5Pck+TtSV5Xax2d6MJa682llPOSvC7JE5M8Pcm+JFcmeUWt9ZIZzA0AAADMA6uXLEh/b6cXlc0vHS+kaq0jSS4c+5novFOPcPz6JL/d/mQAAAAAyTmnrMrnrrmr6RhzmroPAAAAYJw1S1sbm+8bGmk4ydylkAIAAAAY5/T1y5Ik37xle8NJ5i6FFAAAAMA4Z524Ikny3c07G04ydymkAAAAAMb5sbEZUnfv2t9wkrlLIQUAAAAwzvJFrXvAbd65r+Ekc5dCCgAAAGCcUkoG+nryeXfamzEKKQAAAIBD9PaUHBipTceYsxRSAAAAAIf4+YcelyS5ddvehpPMTQopAAAAgEOcf8b6JMl1W/Y0nGRu6pvqBaWU307yjCQnJ1l4yHCttW5sRzAAAACAppy6ZkmS5O5dgw0nmZumVEiVUv5nktcn+U6SK5O4/yEAAAAw56xesiBJcu2duxtOMjdNdYbU7yb5m1rry2ciDAAAAEA32LByUZLkc9+9K//rqWc2nGbumeoeUmuSfGomggAAAAB0i96ekiQppeEgc9RUC6nLkpw1E0EAAAAAusnjH7QuC/t7m44xJ021kHpZkueWUp5dSllbSuk59GcmQgIAAAB02opF/bnubnfZmwlT3UPqB2OP7z3CeD2G9wQAAADoOqO1JklGRuuPlvDRHlMtj/40rdIJAAAAYE47c8PyfPqqOzI0PJpFCyzda6cpFVK11gtmKAcAAABAV1nY1yqhvn3bjjzmtDUNp5lbjnnPp1LK0lLKSaWUJe0MBAAAANANTlvXqjzu2jXYcJK5Z8qFVCnlF0opm5LsSHJTkp2llMtLKT/X7nAAAAAATdm4bmmSZGh4tOEkc8+UluyVUn4hyaeTXJfkDUnuTHJ8kv+a5DOllKfUWj/f9pQAAAAAHTbQ35rHc80duxpOMvdMdYbUBUk+l+ShtdbX11rfObav1JlJPp/k9e2NBwAAANCMdUsHkiRf+v6WhpPMPVMtpM5K8o5a633mqo29/rskZ7crGAAAAECTSilJkpu33ttwkrlnqoXU/iTLjzC2bGwcAAAAYE54xIkrMlqbTjH3TLWQ+lKSN5RSHjj+YCnl5LSW832xPbEAAAAAmnfqmtad9g6M2Ni8naZaSP1xkhVJvl9K+fdSyodLKZcl+WGSlWPjAAAAAHPCw05oLRQbPDDScJK5ZUqFVK31B0kekeRtSQaS/HiShUn+JsnZtdYftj0hAAAAQEP2DbVmRm3ZbZeiduqb6gW11juSvHIGsgAAAAB0ldPXL02SDNtIqq2mumQPAAAAYN7o723dac+SvfY66gypUsq/Jfn9Wuu1Y88nUmutT2xPNAAAAIBmHZwYdcfOwTzixGazzCWTWbJXxj3vSTLRHLUywRgAAADArHLCykVJkt6i8minoxZStdbzxz1/woymAQAAAOgi/X2tImrb3qGGk8wt9pACAAAAOIJVixckSW7fvq/hJHPLlAqpUsqvlFKeO+71KaWUr5ZSdpdSPlpKWdr+iAAAAADNWL9sIEkyWt1lr52mOkPqT5KsG/f6r5KcmORdSR6f5IL2xAIAAABoXhnbO2rTTdsbTjK3TLWQ2pjkqiQppSxK8pQkf1hrfUWS1yT5tfbGAwAAAGjeA1YsbDrCnDLVQmphkoOLJh+X1qbonxt7/f0kG9qUCwAAAKArnLZ2SS6/cVvTMeaUqRZSNyX5ybHnv5LkG7XWnWOv1yfZebiLAAAAAGarbXuHsn75QNMx5pSpFlLvTHJBKWVTkt9P8u5xY49Nck27ggEAAAB0gx8/eVW+dcuOVBubt03fVE6utf5NKeWeJI9J8rZa6/vGDS9L8t52hgMAAABo2sL+1nye/cOjWdjf23CauWFKhVSS1Fo/mOSDhzn+grYkAgAAAOgijzxpVT5z9Z05MKKQapepLtkDAAAAmFcGxmZI7dh7oOEkc8dRC6lSykgp5byx56Njr4/0MzzzkQEAAAA6Z8OKRUmSzTv2NZxk7pjMkr0/TXLbuOd28AIAAADmjZWL+5MkN95zbx592pqG08wNRy2kaq2vH/f8ghlNAwAAANBl1iwdSJL09JSGk8wdU9pDqpTSX0pZcoSxJaWU/vbEAgAAAOgOyxa25vNcs3lXw0nmjqneZe/dY9c88zBj70wylOR3phsKAAAAoFusWbIgSbJkwB322mWqd9l7QpJ/PsLYJ5M8cVppAAAAALpMKa2lepffuK3hJHPHVAup9UnuPsLYliTHTS8OAAAAQPfpKcmt29xlr12mWkjdneThRxh7eJKt04sDAAAA0H3WLh3InbsGm44xZ0y1kPqXJP+zlPKI8QdLKQ9P8tokn2pXMAAAAIBusWHloizqt4dUu0y1kHpdkh1JvlFK+Uop5SOllP9I8s0kO5P8SbsDAgAAADTt7JNWZt+BkYyM1qajzAlTKqRqrfckOTfJm5OUJGePPb4xyblj4wAAAABzSm9Pa2PzPYPDDSeZG/qmekGtdUdaM6Ve1/44AAAAAN1nzdIFSZI7dw1mxeL+htPMflMupJKklLI2yWOSrEnyqVrrtlLKwiRDtdbRdgYEAAAAaNrGdUuTJMOjao92mNKSvdLyF0luS/LJJO9JcurY8D+ntbE5AAAAwJyyoLdVoQweGGk4ydww1U3NX53kxUn+NMmj09o/6qBPJfmlNuUCAAAA6Bqrl7SW7N28dW/DSeaGqRZSv5fkT2utb0rrznrjXZdkY1tSAQAAAHSRU9cuSZL8w1dvbjjJ3DDVQuqEJF87wthQkiXTiwMAAADQfVYsam1kfvLqxQ0nmRumWkjdnuRhRxg7K8mN04sDAAAA0J02rluS0VqbjjEnTLWQujjJ60opPzHuWC2lPCjJK5L8U9uSAQAAAHSRvp6eDI+4y147TLWQuiDJtUn+PckPx45dnOTqsddvaVsyAAAAgC5zxU3bm44wJ/RN5eRa675SyhOSPDPJL6S1kfnWJG9I8sFa63DbEwIAAAB0gV2DBzLQN9W5PRzOpAupUkp/kqckuarW+v4k75+xVAAAAABd5pQ1i/O1G7Y1HWNOmHStV2s9kOQjSU6dsTQAAAAAXeqEle6w1y5TnWd2Q5L1MxEEAAAAoJv1lNbj/uGRZoPMAVMtpP48yWtLKetmIgwAAABAtzp5dWuG1NCwO+1N15Q2NU/yM0lWJ7mxlPK1JHckqePGa631t9sVDgAAAKBbLBlo1Sij+qhpm2oh9VNJDiTZkmTj2M949X5XAAAAAMwBfb2tNXvDGqlpm2ohdU6SPbXWwZkIAwAAANCtesc2kdqzfzhrlg40nGZ2O+oeUqWU3lLKBaWUHUnuSrKrlPKxUsrKmY8HAAAA0B1GRlsLw/bsH244yew3mRlSL0zyuiRfSnJFktOS/FqSXUmeO2PJAAAAALrIiasWJUmGR+xYNF2TKaSel+SiWusLDh4opbwgyd+WUl5Qax2asXQAAAAAXaKvp7XQbMvu/Q0nmf2OumQvrRlRFx9y7MNJepOc0vZEAAAAAF1oQV+rRhkasan5dE2mkFqa1vK88XaPPS5rbxwAAACA7rR2bCPzvUMjDSeZ/SZ7l70TSimnjXvdO+74jvEn1lpvaEsyAAAAgC6ybGGrRvn/frgl/+VRJzacZnabbCH10SMc/8RhjvUe5hgAAADArLZ+WWuG1Oadgw0nmf0mU0i5kx4AAAAw75VSkiRDw/aQmq6jFlK11n/oRBAAAACAbnfWSSt/tLk5x85fEAAAAGCSFvf35vIbtzUdY9ZTSAEAAABM0o59B3Lc8oGmY8x6CikAAACASTr7pBW5a9f+jI7WpqPMagopAAAAgEnq721VKXuGhhtOMrsppAAAAAAm6YFrlySJGVLT1PFCqpTSU0p5eSnl2lLKYCnl1lLKhaWUJZO49oJSSp3g50AnfgcAAABgfurrKUmSYYXUtPQ18Jl/neSlST6e5MIkDxl7/chSys/WWkcnuPaSJNcd5vgjkvxRkk+1OSsAAADAj/T2tOb2jCikpqWjhVQp5cwkL0lySa31aeOO35jkbUmenuRDR7q+1npVkqsO877vHHv67rYGBgAAABhn34GRJMndu/bnuOULG04ze3V6yd4zkpQkbz3k+EVJ9iZ51lTfsJSyOK0i6/Ykn51uQAAAAIAj+bH1JDlmKQAAIABJREFUS5Mkd+8ebDjJ7NbpQurcJKNJLh9/sNY6mOTKsfGp+s0ky5O8t9Y6Mu2EAAAAAEeweEFvkuTeIRXEdHS6kNqQ5J5a6/7DjN2eZG0pZcEU3/N3k9Qk7znaiaWU55dSNpVSNm3ZsmWKHwMAAADMd2uXDiRJtu45XLXBZHW6kFqc5Ej/scFx50xKKeXBSX4yyb/VWm882vm11nfVWs+ptZ6zbt26yX4MAAAAQJJk5eL+JMk9Cqlp6XQhtTfJwBHGFo47Z7J+d+zx7485EQAAAMAkrVjUn56S3LVLITUdnS6kNqe1LO9wpdQJaS3nG5rMG5VS+pI8O8m2JB9vX0QAAACAwyul5NQ1S7Jr34Gmo8xqnS6krhj7zPPGHyylLExydpJNU3ivpyY5Lsn7j7AnFQAAAEDbLezvzVev39p0jFmt04XUh9PagPxlhxx/Xlp7R33w4IFSysZSyhkTvNfB5XrvbmtCAAAAgAns2T+coZHRpmPMan2d/LBa69WllHckeXEp5ZIkn0nykCQvTXJZkg+NO/3SJKckKYe+TyllQ5InJbm81nr1jAcHAAAAGLNx3ZLcsm1v9g2NZNGC3qbjzEqdniGVtGZHvTLJmUnekeTpSd6e5JdqrZOtF5+TpDc2MwcAAAA67MwNK5Ikm3fuazjJ7NXxQqrWOlJrvbDW+uBa60Ct9YRa6x/WWvccct6ptdb7zY4aG3tTrbXUWi/qTGoAAACAlkeevDJJ8oVr7mo4yezVxAwpAAAAgFnrkSevSpJ8+7YdDSeZvRRSAAAAAFOwesmCJMnmHYMNJ5m9FFIAAAAAU7RycX/WLh1oOsaspZACAAAAmKKTVi3OyOhk783GoRRSAAAAAFPUU5JNN21vOsaspZACAAAAmKKt9w5leLQ2HWPWUkgBAAAATNEpaxZn34GRpmPMWgopAAAAgCk64wHLkyS1miV1LBRSAAAAAFPU11uSJD+4a0/DSWYnhRQAAADAFJ25YUWSZM/+Aw0nmZ0UUgAAAABTtG7pQJJk9+Bww0lmJ4UUAAAAwBStWbogSXLZD7Y0nGR2UkgBAAAATNHp65YmSexpfmwUUgAAAABT1NNTsnbpQK66bUfTUWYlhRQAAADAMVi+qC/fvEUhdSwUUgAAAADHYPXi1j5SwyOjDSeZfRRSAAAAAMfgkSevTJLcsXOw4SSzj0IKAAAA4Bg86LhlSZLBAyMNJ5l9FFIAAAAAx2DZwr4kyYERt9qbKoUUAAAAwDHo62nVKtdv2dNwktlHIQUAAABwDE5duyRJ8s1btjecZPZRSAEAAAAcg9PXL02SDPT1Npxk9lFIAQAAAEzDl3+4pekIs45CCgAAAOAY9ZRk+71DTceYdRRSAAAAAMfoZ85Yn807B5uOMesopAAAAACO0bplA0mSW7bubTjJ7KKQAgAAADhGP37yqiTJdzbvbDjJ7KKQAgAAADhGjxwrpK67e0/DSWYXhRQAAADAMVq7dEGSZMlAX8NJZheFFAAAAMAx6u0pSZLR0dpwktlFIQUAAABwjA4WUiNVITUVCikAAACAY9RTxgopM6SmRCEFAAAAcIz6xmZI3b1rsOEks4tCCgAAAOAYHVyyt+nm7Q0nmV0UUgAAAADHqIwt2fvu5l0NJ5ld3JMQAAAAYBoeevzy7Ng71HSMWcUMKQAAAIBpeNBxS9PXq2KZCn8tAAAAgGno6+3J8Mho0zFmFYUUAAAAwDSUJJt3usveVCikAAAAAKahv69Vr2zds7/hJLOHQgoAAABgGh63cU2SZPMOs6QmSyEFAAAAMA2LF/QmSW7ZtrfhJLOHQgoAAABgGtYvW5gk6SkNB5lFFFIAAAAA07BobIbUvUMjDSeZPRRSAAAAANOwqL9VSN14z56Gk8weCikAAACAaVi1eEGSZNnC/oaTzB4KKQAAAIBp6OttbR41NDzacJLZQyEFAAAAMA19Y7uZf/vWHQ0nmT0UUgAAAADTUErJqsX9+eYt25uOMmv0NR0AAAAAYLZbt2wgvT3m/UyWvxQAAADANJ26Zknu2Lmv6RizhkIKAAAAYJruHRrOjr0Hmo4xayikAAAAAKbpYRtWJEnu3j3YcJLZQSEFAAAAME0/dtyyJMn+A6MNJ5kdFFIAAAAA09TXU5IkB0YUUpOhkAIAAACYpt6xQmpktDacZHZQSAEAAABM0/Boa2bUnbvsITUZCikAAACAadqwYlGSpJogNSkKKQAAAIBpWrSgN0myZff+hpPMDgopAAAAgGla1N8qpPYODTecZHZQSAEAAABM07KF/UmS3h5Vy2T4KwEAAABM08EeasQmUpOikAIAAACYpt5SkiSjowqpyVBIAQAAAExTb0+rkBpRSE2KQgoAAABgmvp6WxXL9+7Y1XCS2UEhBQAAADBNSwf6kiSrly5oOMnsoJACAAAAaIOlA30ZHrFkbzIUUgAAAABt0N9b8oO7djcdY1ZQSAEAAAC0wdKFffnK9VubjjErKKQAAAAA2mDJgr4sXtDbdIxZQSEFAAAA0AYPPX55dg8Op1b7SB2NQgoAAACgDZYv6k+SDI2MNpyk+ymkAAAAANpgyUBrud7du/Y3nKT7KaQAAAAA2uCMByxPkuzZP9xwku6nkAIAAABog76ekiS5ffu+hpN0P4UUAAAAQBucsmZJkuT7d+1uOEn3U0gBAAAAtMED17YKqevv3tNwku6nkAIAAABog0ULWpuar1m6oOEk3U8hBQAAANAmywb6Mjxam47R9RRSAAAAAG2y78BIvnP7zqZjdD2FFAAAAECbDI/WfPOWHU3H6HoKKQAAAIA2OevEFVmzxB5SR6OQAgAAAGiTtUsHcvfu/U3H6HoKKQAAAIA2Wb6ov+kIs4JCCgAAAKBNNqxcmCQZcae9CXW8kCql9JRSXl5KubaUMlhKubWUcmEpZckU3mN1KeUvSynXjb3HllLKF0spPzWT2QEAAAAmcrCHum373maDdLm+Bj7zr5O8NMnHk1yY5CFjrx9ZSvnZWuvoRBeXUk5J8qUkS5O8O8kPkqxI8ogkJ8xcbAAAAICJnfGAZUlad9vjyDpaSJVSzkzykiSX1FqfNu74jUneluTpST50lLf5QFq5H1FrvWOmsgIAAABMVU8pSZJRhdSEOr1k7xlJSpK3HnL8oiR7kzxrootLKY9P8pNJ/rzWekcppb+UsnhGkgIAAABMUW9Pq5AaqQqpiXS6kDo3yWiSy8cfrLUOJrlybHwiTxl7vKWU8qkk+5LcW0r5QSllwjILAAAAYKYdnCFlU/OJdbqQ2pDknlrr/sOM3Z5kbSllwQTXP3js8aIkq5P8dpLfTTKU5P2llOdO9OGllOeXUjaVUjZt2bJl6ukBAAAAJjA2QSq7B4ebDdLlOl1ILU5yuDIqSQbHnXMky8Yedyc5v9b6wVrre5L8VJIdSd5USjni71RrfVet9Zxa6znr1q2bYnQAAACAiR2cIbVvaKThJN2t04XU3iQDRxhbOO6cI9k39viPtdahgwdrrduTfDLJA/Kfs6gAAAAAOmr98lbtYcnexDpdSG1Oa1ne4UqpE9Jazjd0mLGDbht7vPMwYwfvuLdqGvkAAAAAjll/b6tquXX7RPNt6HQhdcXYZ543/mApZWGSs5NsOsr1BzdDP/EwYweP3T2dgAAAAADHasWi/iT/uXSPw+t0IfXhJDXJyw45/ry09o764MEDpZSNpZQzDjnvE2ntH/WsUsrScecen+RXk/yw1nrdTAQHAAAAOJrlY4XUrdvMkJpIXyc/rNZ6dSnlHUleXEq5JMlnkjwkyUuTXJbkQ+NOvzTJKUnKuOu3l1JemeSdSb5WSnlPkgVJXjT2+OKO/CIAAAAAh7GovzdJsveATc0n0tFCaszLktyU5PlJfjHJPUnenuR1tdbRo11ca31XKeWeJK9K8oYko0m+muSZtdb/mKnQAAAAAEfT21OyesmCXH/3nqajdLWOF1K11pEkF479THTeqROMXZLkkvYmAwAAAJi+Rf29qW6yN6FO7yEFAAAAMKdtXL80m27e1nSMrqaQAgAAAGijLbv3Z8mCJnZJmj0UUgAAAABtdO6pq7J7/3CqdXtHpJACAAAAaKN9Q6077O0fPuq92+YthRQAAABAG51x/PIkyb37hxtO0r0UUgAAAABtNDraWqq3Zc/+hpN0L4UUAAAAQBuduGpRkuSme/Y2nKR7KaQAAAAA2uiEsUKqr6c0nKR7KaQAAAAA2qivp1W3DI/a1PxIFFIAAAAAbdTf25oZdcs2S/aORCEFAAAA0EYrFvUn+c+ZUtyfvwwAAABAGy1b2CqkvnfHroaTdC+FFAAAAEAbLexv1S2Dw/aQOhKFFAAAAEAblVLS21Nyy9Z7m47StRRSAAAAAG124qpFue7uPU3H6FoKKQAAAIA2OzA8mrXLBpqO0bUUUgAAAABtds6pq9NTStMxupZCCgAAAKDN+nt7cuM99pA6EoUUAAAAQJvt3Heg6QhdTSEFAAAA0GZnnbgiSXLL1r0NJ+lOCikAAACANlu9dEGS5LYdCqnDUUgBAAAAtNlDj1+eJPneHbsbTtKdFFIAAAAAbbZx/dIkyQe+dnPDSbqTQgoAAACgzZYv7E8Sd9o7AoUUAAAAwAw4dc3iJEmtteEk3UchBQAAADADHrtxTZLk3qGRhpN0H4UUAAAAwAx48HHLkiTb7x1qOEn3UUgBAAAAzIDenpIk2bN/uOEk3UchBQAAADADjl+xKElyYGS04STdRyEFAAAAMAP6+1q1y63b9jWcpPsopAAAAABmwPKFfUmSL3zvroaTdB+FFAAAAMAMeMSJK5MkW21qfj8KKQAAAIAZ0NtTsnrJgqxY1N90lK6jkAIAAACYISsX92e01qZjdB2FFAAAAMAM6S0lo6MKqUMppAAAAABmSG9PMUPqMBRSAAAAADOklJKR0aZTdB+FFAAAAMAMGR2t2XTztqZjdB2FFAAAAMAM2Xrv/pywclHTMbqOQgoAAABghpx90qrYQur+FFIAAAAAM6SnJNfcsSvDNpK6D4UUAAAAwAxZubg/SbJz34GGk3QXhRQAAADADHn0A9ckST757c0NJ+kuCikAAACAGfITp69NkizoU8GM568BAAAAMEMGxoqoH9y5u+Ek3UUhBQAAADBDli9q7SH1tRu2NZykuyikAAAAAGZIb09Jkmy9d6jhJN1FIQUAAAAwgx51yqrcs2d/0zG6ikIKAAAAYAYt6FW/HMpfBAAAAGAGbVy/JEmyzbK9H1FIAQAAAMygMzesSJJcffvOhpN0D4UUAAAAwAz68ZNXJUku3nRrw0m6h0IKAAAAYAY96LilSZJ/ueqOhpN0D4UUAAAAwAwqpTQdoesopAAAAABm2IvPPz1JsnPvgYaTdAeFFAAAAMAMW798IEnyrVu3N5ykOyikAAAAAGbYw05o3Wlv9+Bww0m6g0IKAAAAYIatX9aaIfWv37GxeaKQAgAAAJhxJ65anCQ5MFIbTtIdFFIAAAAAHbCovzefv+aupmN0BYUUAAAAQAecfdLKJMm+oZGGkzRPIQUAAADQAY8+bXWS5MpbdzScpHkKKQAAAIAOOP/B65PY2DxRSAEAAAB0xMNOWJEked9Xb244SfMUUgAAAAAd0NtTsmygL0ny2e/c2XCaZimkAAAAADrkM//9p5Ikt23f23CSZimkAAAAADrkuOULkyT/8NWbGs3RNIUUAAAAQIcs6GtVMbdu29dwkmYppAAAAAA66OnnnpQk+doNWxtO0hyFFAAAAEAHPe1RJyZJrrptR8NJmqOQAgAAAOig09YuaTpC4xRSAAAAAB20dGFfkmTH3gMNJ2mOQgoAAACggxb0tuqYm7fubThJcxRSAAAAAB1USskpaxbn01ff0XSUxiikAAAAADqsr6c0HaFRCikAAACADnv0aWuSJPfuH244STMUUgAAAAAddsrqxUkyb5ftKaQAAAAAOuxJD3tAkuRbt2xvOEkzFFIAAAAAHXbiqtYMqX+8/NaGkzRDIQUAAADQYb1jm5qf8YBlDSdphkIKAAAAoAFPftgDcu2du5uO0QiFFAAAAEADFvS1apl9QyMNJ+k8hRQAAABAA05buzRJcteuwYaTdJ5CCgAAAKABp65tbWx++459DSfpPIUUAAAAQAOOX7EoSXLDlj0NJ+m8jhdSpZSeUsrLSynXllIGSym3llIuLKUsmeT19Qg/8++/BwAAAMxap61rVSFX3baz4SSd19fAZ/51kpcm+XiSC5M8ZOz1I0spP1trHZ3Ee3w5ybsOOXagrSkBAAAAZtDapQNJkkuvvbvhJJ3X0UKqlHJmkpckuaTW+rRxx29M8rYkT0/yoUm81Q211g/MTEoAAACAznjGeSdndLQ2HaPjOj1D6hlJSpK3HnL8oiRvSfKsTK6QSillQZIFtVZL9QAAAIBZ6c2//vCmIzSi04XUuUlGk1w+/mCtdbCUcuXY+GT8l7TKq95SypYkH07yJ7XW+bfo8r2/eP9jZ/5qct7zkqG9yQd/4/7jZz8zeeR/S+7dmnzk2fcfP/d3koc9Ldl5W3LJC+4//rgXJw9+cnLPD5NPvez+449/ZbLx/OSOq5LPvvr+4098XXLyo5Nbvp5c+qf3H3/Sm5PjH5Fc/8Xk3//y/uNPfWuy9seS7/9r8pW/vf/4r78zWXFi8p2PJVe85/7jv/m+ZMma5FsfTK48TP/53y5OFixOLr8o+e4n7j/+3E+3Hv/jbckP/u99x/oXJs/6WOv5ZX+e3HDZfccXr0r+69jkvi9ckNx6xX3Hl29InnZR6/m//o/kzqvvO75mY/LLb2s9/+RLk63X33f8AQ9PnvyW1vOPPS/Ztfm+4yedm/zsBa3nH35Wsnf7fcdP++nkp1/Vev6BpyUHDrn16IN+IfmJl7ae++7df9x3r/Xcd+/+4757ree+e/cf993z3Ut893z37jvuu+e7l/juzcfv3sH//TzS6UJqQ5J7aq37DzN2e5LHlVIW1FqHJniPy5NcnOS6JMuTPCXJi5P8dCnlcRPNmCqlPD/J85Pk5JNPPsZfAQAAAIDpKLV2bp1iKeX6JP211vu1QaWU9yX5rSSraq07pvi+r0nyxrRmSb1xMtecc845ddOmTVP5GAAAAAAmUEr5Rq31nKOd19OJMOPsTTJwhLGF486Zqr9IMpTkMPP6AAAAAOgmnS6kNidZW0o5XCl1QlrL+SZarndYtdYDB997mvkAAAAAmGGdLqSuGPvM88YfLKUsTHJ2kmNaQzd2/YlJ7ppuQAAAAABmVqcLqQ8nqUkO3bL+eUkWJ/ngwQOllI2llDPGn1RKWXOE931DWhu0f6p9UQEAAACYCR29y16t9epSyjuSvLiUckmSzyR5SJKXJrksyfj7Yl6a5JQkZdyxPymlPCbJF5PckmRpWnfZOz/J15O8fcZ/CQAAAACmpaOF1JiXJbkpyfPT2oT8nrSKpNfVWkePcu2Xkv+/vbsPtq2u6zj+/niB4IaWCBZgcQENKi2nriTTA2UzjlOREMNUIyo+5BBwlRvCCBgi2tUUiAnQlB4UQsdIqUsRxmQoCQpXowS7xIOXZxCISIHLVfj2x1pHNrdz8XTW2mvvfe77NbNnnf1be+3zXXPW96y9vvv3+y1+DHgt8BzgceBG4CTgjKraOJ6QJUmSJEmS1JdU1aRjmIiVK1fWunWLmrJKkiRJkiRJ80jypapa+d1eN/QcUpIkSZIkSdrKWZCSJEmSJEnSoCxISZIkSZIkaVAWpCRJkiRJkjQoC1KSJEmSJEkalAUpSZIkSZIkDcqClCRJkiRJkgZlQUqSJEmSJEmDsiAlSZIkSZKkQVmQkiRJkiRJ0qAsSEmSJEmSJGlQFqQkSZIkSZI0KAtSkiRJkiRJGpQFKUmSJEmSJA3KgpQkSZIkSZIGZUFKkiRJkiRJg0pVTTqGiUhyH3DrpOPowc7A/ZMOQpoB5oq0MOaKtDDmirQw5oq0MEspV/aoql2+24u22oLUUpFkXVWtnHQc0rQzV6SFMVekhTFXpIUxV6SF2RpzxSF7kiRJkiRJGpQFKUmSJEmSJA3KgtTs+/CkA5BmhLkiLYy5Ii2MuSItjLkiLcxWlyvOISVJkiRJkqRB2UNKkiRJkiRJg7IgJUmSJEmSpEFZkJIkSZIkSdKgLEhNmSTPSLI6yfokG5PcnuT0JN87xPbSrOhyrCf5kSSnJvlCkvuSfCPJtUlOMle01PR5XkiyPMnXklSSs8cRrzQpfeRKkp2SnJbkpvY97kvyz0l+fpyxS0Pq4XplxyQnJvlK+xns/iRXJjk8ScYdvzSUJCckuTDJLe1npw2LfJ/XJPnXJI8muTfJnybZpedwJ8KC1PT5I+AM4KvAKuBC4M3AxUkW8vfqur00K7oc668HVgM3A6cCxwE3AO8Grkyyw7iCliagz/PCqcDO/YYnTY1OuZJkD+BLwGuBvwaOBNYAG4DdxxOyNBGLzpV2/T8A7wKuAY6l+fy1DPgL4L3jC1sa3BrgZTTXHA8u5g2SrAY+CjwEvAX4EPBbwOVL4Yt077I3RZL8OPAV4KKqOmSkfRXwx8Crqupj49pemhU95MpK4Maqemiz9ncDJwGrqsreH5p5fZ4XkvwUcDVwPHA6cE5VHd1/1NLw+siVJFcAK4D9quruMYYrTUwPn8H2B64Ezqyq1SPt2wHrgZ2q6vvHFb80pCR7VdUt7c/XATtW1Yr/x/Y7A7cC1wP7V9XjbfuBwFrgpKpa03vgA7LHzHT5bSDAmZu1nws8Ahw25u2lWdHpWK+qdZsXo1qfaJcv7ByhNB16OS8kWdZucynwqT4DlKZEp1xJ8gvAzwHvq6q7k2ybZPlYIpUmq+t55Vnt8q7RxqraBNwPPNxDjNJUmCtGdXAQsBw4a64Y1b7vxcAtLIHrewtS0+UlwBM030B/R1VtBK5t149ze2lWjOtYf167vHfxoUlTpa9cWQ3sC9gjSktV11z5lXZ5W5KLgUeBh5P8Z5KZv2CQRnTNlauB/waOT3Jokh9Osk+S9wA/DZzSf8jSzJrLp6vmWfcFYN8kOw4YT+8sSE2X3YD7q+qxedbdCezcdmcd1/bSrOj9WG97gJwMfBtwaKuWis65kmRP4J3AqVW1of8QpanQNVf2aZfnAjvRzCP1BmATcH6S1/UZrDRBnXKlqh4Efh34L+CvaIYjrQeOAg6pqnP7D1maWbu1yzvnWXcnTW/F3eZZNzO2mXQAeorlwHz/3AE2jrxm05i2l2bFOI71M4GXAidW1Q0dYpOmSR+58kHgazQT2EpLVddceWa7/AbwS+3wI5JcRDOsYk2Sj1bVEz3FK01KH+eVbwLX0cyBcyVNEfco4GNJXllVl/UUqzTr5oZ+z5dzGzd7zUyyh9R0eQT4ni2s237kNePaXpoVvR7rSd5FMxTpw1X1no6xSdOkU660Q41eDhxRVd/qOTZpmnQ9rzzaLj8+V4yC7/QGWQv8IE/2opJmWdfzyotoilCXVdVxVXVRVf0ZzRxs9wDntr3WJT2ZS/Pl3JK4vrcgNV3uounmOt8BtztN99in+7ah6/bSrOjtWE9yCvB2mlsNH9FbhNJ0WHSutNucAVwC3JPk+UmeD+zRvuT72jbvhqSloOt55Y52ec886+buuPfsDvFJ06JrrqymuZC+cLSxqh4B/p7mHLOin1ClmTc3+f/u86zbHSg2u0HArLEgNV2uofmb7DfamGR74MXAujFvL82KXo71JO8A3gGcB7yxqqrnOKVJ65IrOwC7AL8K3DjyuLxdf1j7/I29RixNRtfzytwEz8+bZ91c29e7BChNia65MndhPV8vqG02W0pbu2va5f7zrPsZ4Iaq+uaA8fTOgtR0+QRNlfOYzdp/h2Zs6AVzDUn2TrLvYreXZlzXXCHJyTR3cjkfeJ3zemiJ6pIrDwOHzvM4sl1/aft87Vgil4bV9bzyNzTzRx02esejJLvS3Lb7xqq6aRyBSwPrmitfbZeHjza2vW1fCTwI3NxjvNJMaO84uW+SbUea/5ZmSPjRo0NZkxwI7M0SuL6PHQKmS5KzaOayuYhmmMSPAm8GPg+8bO6iOckGYI+qymK2l2Zdl1xJchRwNnAb8Ps0ty8eda8Tamqp6Hpemef9VtBMcn5OVR09tsClgfXwGexNwIeA64E/B7YDfhfYFfi1qvrHYfZEGq+On8H2AL5MM4T1gnabnWgKWiuAo6rqA0PtizROSV7Nk1MdrKI5L5zePr+1qs4fee3lwAHAnqN3NU5yLHAaTQ/1j9P0MjwWuB14yaz3kLIgNWXayucxwJto/infT/NNxMmjB9vTfBha0PbSrOuSK0k+QnNL7i35bFX9Yu9BSxPQ9bwyz/utwIKUlqA+ciXJbwDHAy+i+bLjKuCdVfX5cccvDaWH65W9gZOBXwZ+gKYHyLXAmVX1qQF2QRrESJFpPk+53thSQapddzjN/Gv7AP8D/B3wtqqa+aHgFqQkSZIkSZI0KOeQkiRJkiRJ0qAsSEmSJEmSJGlQFqQkSZIkSZI0KAtSkiRJkiRJGpQFKUmSJEmSJA3KgpQkSZIkSZIGZUFKkiRJkiRJg7IgJUmS1IMkhyepkcemJDcnWZNk+wnHtiHJR0aez8W6YmJBSZKkrdo2kw5AkiRpiTkUuAN4JnAwcEL786pJBiVJkjRNLEhJkiT169qquqn9+bIkLwDekOQtVfXEJAOTJEmaFg7ZkyRJGq8vAzsAO881JNkzyQVJ7kvyWJJrkxy8+YZJfjLJRUkeSPJokhuSnDCy/uVJLklyd5JHklyX5Ngky4bZNUmSpMWxh5QkSdJ4rQAeAh4ASPJDwBeBrwOrgfuA3wQ+meSgqlrbvm4/4HLgpvZ1dwAvAH5i5L33Av4JOAvYCKwETgF2Ad421r2SJEnqwIKUJElSv5Yl2YYn55A6BDimqh5v158CBDigqh5o2z7dFqpOBda2badKhztEAAACFElEQVTRFLFeWlWPtG2fGf1FVfUncz8nCXAFsB3w1iQnOkRQkiRNKwtSkiRJ/Vq/2fMPVNXZI89fAVwCPNQWruZ8Gnh/kmcB3wZ+Fnj/SDHq/0iyK02B6xXAbjz1s91zgXsWuxOSJEnjZEFKkiSpXwfTDK/bBfg94MgkX6yq89r1zwVe0z7m8xxgE81cn3ds6ZckeQZNb6rdaIpS64FHgYOAk4Dtu+6IJEnSuFiQkiRJ6td1c3fZS/IZ4N9pej59sqoephmGdwXwh1vY/i5gGfAEsPvT/J69aeaMenVV/eVcY5IDu++CJEnSeHmXPUmSpDGpqseA42h6RR3ZNl9KMzH59VW1bp7HY+0wvX8BDkuywxbefnm7/NZcQ5JtgVeNZWckSZJ6ZA8pSZKkMaqqtUmuoZlo/GzgZOBq4HPt8w3As4EXAntV1evbTd8KfBa4KsnpNMP39gJeXFWrgP8AbgX+IMnjNIWp1cPtmSRJ0uLZQ0qSJGn83k7TS+qIqrqNZqjdvwFrgMuADwIHMHIXvaq6hmZi89uBs2gmQj+Odl6pqtpEM1/UPcB5wDnA54D3DrJHkiRJHaSqJh2DJEmSJEmStiL2kJIkSZIkSdKgLEhJkiRJkiRpUBakJEmSJEmSNCgLUpIkSZIkSRqUBSlJkiRJkiQNyoKUJEmSJEmSBmVBSpIkSZIkSYOyICVJkiRJkqRB/S+wbqNut0mTNwAAAABJRU5ErkJggg== ",
null,
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 ",
null,
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 ",
null,
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 ",
null
] |
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|
https://answers.everydaycalculation.com/divide-fractions/70-20-divided-by-10-6
|
[
"# Answers\n\nSolutions by everydaycalculation.com\n\n## Divide 70/20 with 10/6\n\n1st number: 3 10/20, 2nd number: 1 4/6\n\n70/20 ÷ 10/6 is 21/10.\n\n#### Steps for dividing fractions\n\n1. Find the reciprocal of the divisor\nReciprocal of 10/6: 6/10\n2. Now, multiply it with the dividend\nSo, 70/20 ÷ 10/6 = 70/20 × 6/10\n3. = 70 × 6/20 × 10 = 420/200\n4. After reducing the fraction, the answer is 21/10\n5. In mixed form: 21/10\n\nMathStep (Works offline)",
null,
"Download our mobile app and learn to work with fractions in your own time:\nAndroid and iPhone/ iPad\n\n#### Divide Fractions Calculator\n\n÷\n\n© everydaycalculation.com"
] |
[
null,
"https://answers.everydaycalculation.com/mathstep-app-icon.png",
null
] |
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|
https://socratic.org/questions/why-is-25-a-composite-number
|
[
"# Is 25 a composite number?\n\nApr 16, 2016\n\nYes\n\n#### Explanation:\n\n25 has more factors than just 1 and 25 making it composite.\n\nThe list of factors that it has is 1, 5, and 25.\n\nApr 16, 2016\n\nA composite number is a product of prime numbers.\nSo to answer your question. Yes as it is a product of two primes.\nThat is the prime 5$\\to 5 \\times 5 = 25$\n\n#### Explanation:\n\nA prime number is a number that can only be divided by 1 and itself and no other whole number to give a whole number answer.\n\nAny number that is a product of primes is a composite number.\n\nConsider the prime factor tree:",
null,
"3 and 79 are primes in that they can only be divided by themselves and 1 to give whole number answers.\n\nBut $3 \\times 79 = 237$ so 237 is composite\n\nHear is another composite split into its prime factors",
null,
"So the composite 638 is a product of the prime {2,11,29}\n\nIn that $2 \\times 11 \\times 29 = 638$\n\nI quick search on the internet will lists of prime numbers. If you can it is a good idea to try and remember some of them. In England schools encourage the remembering the primes numbers up 97"
] |
[
null,
"https://useruploads.socratic.org/wkngpUUT0WSXZIHvnOsQ_Prime%20Factor%20Tree.bmp",
null,
"https://useruploads.socratic.org/6gA7WulRT3Sv3ga9zNO9_Prime%20Factor%20Tree2.bmp",
null
] |
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|
https://edurev.in/studytube/Pair-of-Lines-and-Transversal-Exercise-15-1/f1d79c2b-52f4-4b43-8f70-379a69b57a7e_p
|
[
"Courses\n\n# Pair of Lines and Transversal Exercise 15.1 Class 6 Notes | EduRev\n\n## Class 6 : Pair of Lines and Transversal Exercise 15.1 Class 6 Notes | EduRev\n\n``` Page 1\n\n1. Identify parallel line segments shown in Fig. 15.6.\n\nSolution:\n\n(i) From the figure we know that BC || DE.\n\n(ii) From the figure we know that AB || DC, AD || BC.\n\n(iii) From the figure we know that AB || DC and AD || BC.\n\n(iv) From the figure we know that PQ || TS, UT || QR and UP || SR.\n\n(v) From the figure we know that AB || EF || CD, BC || AD and CF || DE.\n\n(vi) From the figure we know that EF || BC, AB || DF and AC || DE.\n\n2. Name the pairs of all possible parallel edges of the pencil box whose figure is shown in Fig. 15.7.\nPage 2\n\n1. Identify parallel line segments shown in Fig. 15.6.\n\nSolution:\n\n(i) From the figure we know that BC || DE.\n\n(ii) From the figure we know that AB || DC, AD || BC.\n\n(iii) From the figure we know that AB || DC and AD || BC.\n\n(iv) From the figure we know that PQ || TS, UT || QR and UP || SR.\n\n(v) From the figure we know that AB || EF || CD, BC || AD and CF || DE.\n\n(vi) From the figure we know that EF || BC, AB || DF and AC || DE.\n\n2. Name the pairs of all possible parallel edges of the pencil box whose figure is shown in Fig. 15.7.\n\nSolution:\nThe pairs of all possible parallel edges of the pencil box are\nAB || DC || HE || GF, AD || GH || BC || EF and AH || DG || BE || CF\n3. In Fig. 15.8, do the segments AB and CD intersect? Are they parallel? Give reasons.\nSolution:\nNo, AB and CD do not intersect but they can intersect if extended further. No AB and CD are not parallel since,\nthe distance between them is not constant.\n4. State which of the following statements are true (T) or which are false (F):\n(i) If two lines in the same plane do not intersect, then they must be parallel.\n(ii) Distance between two parallel lines is not same everywhere.\n(iii) If m ? l, n ? l and m ? n, then m || n.\n(iv) Two non-intersecting coplanar rays are parallel.\n(v) If ray AB || m, then line segment AB || m.\n(vi) If line AB || line m, then line segment AB || m.\n(vii) No two parallel line segments intersect.\n(viii) Every pair of lines is a pair of coplanar lines.\n(ix) Two lines perpendicular to the same line are parallel.\n(x) A line perpendicular to one of two parallel lines is perpendicular to the other.\nSolution:\n(i) True\nPage 3\n\n1. Identify parallel line segments shown in Fig. 15.6.\n\nSolution:\n\n(i) From the figure we know that BC || DE.\n\n(ii) From the figure we know that AB || DC, AD || BC.\n\n(iii) From the figure we know that AB || DC and AD || BC.\n\n(iv) From the figure we know that PQ || TS, UT || QR and UP || SR.\n\n(v) From the figure we know that AB || EF || CD, BC || AD and CF || DE.\n\n(vi) From the figure we know that EF || BC, AB || DF and AC || DE.\n\n2. Name the pairs of all possible parallel edges of the pencil box whose figure is shown in Fig. 15.7.\n\nSolution:\nThe pairs of all possible parallel edges of the pencil box are\nAB || DC || HE || GF, AD || GH || BC || EF and AH || DG || BE || CF\n3. In Fig. 15.8, do the segments AB and CD intersect? Are they parallel? Give reasons.\nSolution:\nNo, AB and CD do not intersect but they can intersect if extended further. No AB and CD are not parallel since,\nthe distance between them is not constant.\n4. State which of the following statements are true (T) or which are false (F):\n(i) If two lines in the same plane do not intersect, then they must be parallel.\n(ii) Distance between two parallel lines is not same everywhere.\n(iii) If m ? l, n ? l and m ? n, then m || n.\n(iv) Two non-intersecting coplanar rays are parallel.\n(v) If ray AB || m, then line segment AB || m.\n(vi) If line AB || line m, then line segment AB || m.\n(vii) No two parallel line segments intersect.\n(viii) Every pair of lines is a pair of coplanar lines.\n(ix) Two lines perpendicular to the same line are parallel.\n(x) A line perpendicular to one of two parallel lines is perpendicular to the other.\nSolution:\n(i) True\n\n(ii) False\n\n(iii) True\n\n(iv) False\n\n(v) True\n\n(vi) True\n\n(vii) True\n\n(viii) False\n\n(ix) True\n\n(x) True\n\n```\nOffer running on EduRev: Apply code STAYHOME200 to get INR 200 off on our premium plan EduRev Infinity!\n\n## Mathematics (Maths) Class 6\n\n191 videos|224 docs|43 tests\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n,\n\n;"
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http://www.kpubs.org/article/articleMain.kpubs?articleANo=E1KOBZ_2015_v9n7_2454
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"Load Balancing Algorithm of Ultra-Dense Networks: a Stochastic Differential Game based Scheme\nLoad Balancing Algorithm of Ultra-Dense Networks: a Stochastic Differential Game based Scheme\nKSII Transactions on Internet and Information Systems (TIIS). 2015. Jul, 9(7): 2454-2467\n• Received : January 25, 2015\n• Accepted : June 02, 2015\n• Published : July 31, 2015",
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"Export by style\nArticle\nAuthor\nMetrics\nCited by\nTagCloud\nHaitao, Xu\nSchool of Computer and Communication Engineering, University of Science and Technology Beijing (USTB) Beijing, 100083 - China\nZhen, He\nSchool of Computer and Communication Engineering, University of Science and Technology Beijing (USTB) Beijing, 100083 - China\nXianwei, Zhou\nSchool of Computer and Communication Engineering, University of Science and Technology Beijing (USTB) Beijing, 100083 - China\n\nAbstract\nIncreasing traffic and bandwidth requirements bring challenges to the next generation wireless networks (5G). As one of the main technology in 5G networks, Ultra-Dense Network (UDN) can be used to improve network coverage. In this paper, a radio over fiber based model is proposed to solve the load balancing problem in ultra-dense network. Stochastic differential game is introduced for the load balancing algorithm, and optimal load allocated to each access point (RAP) are formulated as Nash Equilibrium. It is proved that the optimal load can be achieved and the stochastic differential game based scheme is applicable and acceptable. Numerical results are given to prove the effectiveness of the optimal algorithm.\nKeywords\n1. Introduction\nO ur world will be changed by connecting anything to anything in 5G [1 - 2] . Moreover, unlike its predecessors, 5G needs to provide increasing mobile data traffic and bandwidth to satisfy the increasing services requirements. One available solution to meet the demand for higher data rate is to reduce the size of the cell , to obtain higher spectral efficiency with higher frequency reuse. Additionally, coverage can be improved by deploying small cells. As one of the main technology in 5G networks, Ultra Dense Network (UDN) can be used to improve network coverage [4 - 5] , greatly improve system capacity, and to bypass their business, can realize more flexible network deployment and more efficient frequency reuse.\nGenerally speaking, the micro cells (small cells) are not new concept in the research of wireless network, ultra-dense networks (UDNs) are eager for performing better through the re-considering and recapture of the traditional network technologies [6 - 7] . RoF (Radio over Fiber) , the integration of optical and broadband technology, can be used as micro cell in next generation network and be comprised to form a UDN to achieve higher transmission data rate. RoF technology combines the advantages of optical fiber communication and wireless mobile communications together, such as: high capacity, low power consumption, low cost, easy installation, etc., and is becoming a hot research topic in recent years. In this paper, we consider using RoF as micro cell to form a UDN to improve network coverage and system capacity. Since the coverage of the RoF system is relatively small, the frequent movement of users will influence the system’s performance, which causes improper traffic distribution problems. There are three possible scenarios that the RoF system is overloaded ,\n(1) Handoff-intensive caused by the users’ movement, the processing task is complex in the overlapping region;\n(2) The cell edge users are in severe channel environments, which can be considered as relative overload;\n(3) Multipath propagation, fading, and superposition of multiple antennas would cause an overload.\nBecause of the randomness of load variations, it is essential to research the load balancing problem in RoF system. As one of the most important optimization functions, load balancing is an effective way to cope with improper traffic load distribution in mobile network , and to improve QoS and GoS performance. Researchers have proposed lots of dynamic equilibrium methods to improve utilities. However, because of the special structure of RoF system, the traditional load balancing methods are not suitable. It is necessary to raise a novel load balancing scheme for ROF system based Ultra Dense Network networks.\nThe whole paper is organized as follows. Section 2 introduces the system model. In section 3, the non-cooperative Nash Equilibrium solution to the model is given and the loading balance algorithm is proposed to solve the overloaded problem in the ROF system, and a load balancing algorithm is introduced based on the solution. In Section 4, The performance of the proposed scheme is simulated and compared. Finally, the concluding remarks are given in Section 5.\n2. System Model\nIn RoF cell (as shown in Fig.1 ), there are K radio access points (RAPs) and one virtual base station (VBS). The distances among the RAPs are larger than the carrier wavelength. Each RAP connects to the VBS through optical fibre. The coverage area of RAP is small, which can be considered as a micro cell. All RAPs are controlled by the VBS, and VBS can be considered as a router of the RoF cell. Based on the network architecture and functions, the structure of the RoF system is flat, one RoF cell can be considered as a local area network.",
null,
"PPT Slide\nLager Image\nSystem Model\nLet K ={1,2,3,…, k } denote the set of radio access points, each RAP is a player of the load balancing game. The load of RAP i at time instant t is li ( t ), where i K . Then the total load of the RoF cell can be calculated as follows,",
null,
"PPT Slide\nLager Image\nThe price function defines the instantaneous “price” a RAP pays for having a specific amount of load that causes impacts in the system, which is based on the RAP, is a linear form of the amount of load, and can be defined as follows",
null,
"PPT Slide\nLager Image\nIn the practical applications of RoF system, because of the small coverage area of the RAPs, handover frequency is larger than that of the traditional mobile cellular networks. The real load depends on the time that the mobile users stay in the cell. Then we can re-define the instantaneous “price” a RAP pays for having a specific amount of load as",
null,
"PPT Slide\nLager Image\nwhere πij is positive parameter. πij means the exchange rate of the load between RAP i and RAP j . Generally, we have πij = πji . The price function (3) shows that the actual price of RAP is a form of impact of load variations based on the movement of the mobile users. In the case when πij =0, there is no user moving from RAP i to RAP j . Moreover, the price function generated by this model is computable and fully tractable.\nThe energy cost of each RAP also depends on the load, which can be defined as follows, which is inspired by reference .",
null,
"PPT Slide\nLager Image\nwhere βi is a positive congestion cost parameters. Let x ( t )∈ R + denote the mobile users in the RoF system at time instant t , and the dynamic of the users can be generated by the stochastic differential equation as follows,",
null,
"PPT Slide\nLager Image\nwhere ωi and ε are positive parameters. The dynamic of the users will be changed at a rate ε with the movement of the mobile users. According to the assumption and analysis of the above, each RAP wants to minimize its system load cost to control the load level. Let the minimization of cost to be the objectives, for each RAP i , one can obtain a stochastic differential game at time instant t as follows,",
null,
"PPT Slide\nLager Image\nwhere λ is the common discount rate, which is applied to find the discount, subtracted from a future value to find the value before the game start. gi ≥ 0 and",
null,
"PPT Slide\nLager Image\n(",
null,
"PPT Slide\nLager Image\n≥ 0) is a threshold. δix ( t ) is the additional cost caused by the load balancing algorithm. Then the load balancing problem in RoF system can be considered as a non-cooperative differential game G G ( K ,{ li }, Ci ) as follows.\n1) The players of G are RAPs K≡{1,2,,…, Κ }.\n2) The strategy of the player i is li ( t ), which denotes the allocated load, and the game’s strategy profile is L ( t )=( l 1 ( t ), l 2 ( t ),…, lk ( t )).\n3) The players’ utilities are the objective of the minimization problem in the equation (6).\n4) Generally, the cost of the defined game G are not linear, which is different from the original normal game form.\n- 3.1 Nash Equilibrium\nIn this section, the Nash Equilibrium and the load balancing algorithm will be discussed. The dynamic optimization program technique was developed by Bellman , and is given in Theorem 1.\nTheorem 1 A set of controls l * ( t )= φ * ( t,x ) constitutes an optimal solution to the control problem (6) if there exist continuously differentiable functions V ( t,x ) defined on [0, T Rm R and satisfying the following Bellman equation,",
null,
"PPT Slide\nLager Image",
null,
"PPT Slide\nLager Image\nFor the optimization problem of the formula (6), the value function Vi ( t,x ) can be represented as follows,",
null,
"PPT Slide\nLager Image\nThen Vi ( t,x ) satisfies the following Bellman equation,",
null,
"PPT Slide\nLager Image",
null,
"PPT Slide\nLager Image\nPerforming the indicated maximization for the optimization problem indicated above, we can obtain the Nash equilibrium for users, that is,",
null,
"PPT Slide\nLager Image\nProposition 1 The system (9) admits a solution",
null,
"PPT Slide\nLager Image\nwith { A 1 ( t ), A 2 ( t ), A 3 ( t ),…, AK ( t )} satisfying the following equations",
null,
"PPT Slide\nLager Image",
null,
"PPT Slide\nLager Image\nand { B 1 ( t ), B 2 ( t ), B 3 ( t ),…, Bn ( t )} is given by",
null,
"PPT Slide\nLager Image",
null,
"PPT Slide\nLager Image\nProof.\nUsing formula (13), we have",
null,
"PPT Slide\nLager Image",
null,
"PPT Slide\nLager Image\nUsing (18-19), system (6) can be expressed as",
null,
"PPT Slide\nLager Image",
null,
"PPT Slide\nLager Image\nFor (20-21) to hold, it is required that",
null,
"PPT Slide\nLager Image",
null,
"PPT Slide\nLager Image",
null,
"PPT Slide\nLager Image",
null,
"PPT Slide\nLager Image\nThen we have",
null,
"PPT Slide\nLager Image\nSince Bi ( t ) is independent of Bj ( t ) for i j , Bi ( t ) can be solve by (24) and (25) and can be expressed as follows.",
null,
"PPT Slide\nLager Image\nwith",
null,
"PPT Slide\nLager Image",
null,
"PPT Slide\nLager Image\nGenerally, we can get",
null,
"PPT Slide\nLager Image\nwhere",
null,
"PPT Slide\nLager Image\nand",
null,
"PPT Slide\nLager Image\nThis section will discuss the load balancing algorithm. We give the whole algorithm based on the Nash Equilibrium in the first section. The progress can be described as following.",
null,
"PPT Slide\nLager Image\nAnd the flow chart is given in Fig. 2 as follows.",
null,
"PPT Slide\nLager Image\nAlgorithm flow chart\n4 Performance Evaluations\nIn this section, we consider an example to help understand the concepts of proposed load balancing differential game model. We consider a scenario where twenty RAPs need to control their load. The study simulates the proposed scheme based on the Matlab simulation environment. The number results for optimal load allocated to every RAPs will be given. Parameters set for simulations are shown in Table 1 and Table 2 .\nApplications in each class",
null,
"PPT Slide\nLager Image\nApplications in each class\nNumber Values of Parameters",
null,
"PPT Slide\nLager Image\nNumber Values of Parameters\nIn this simulations, the simulations of the median value Ai ( t ) and the expected corresponding feedback Nash equilibrium strategies φ*i ( t ) of the game are considered. As shown in Fig. 3 , the variation of Ai ( t ) will be analyzed, as the key parameter of optimal solution of the differential game. Fig. 3 shows how Ai ( t ) varies with time. It is noted that Ai ( t ) is almost inverse proportion to the time variation. It is decreased as time goes on. Variations of Ai ( t ) will significantly reflect on the variation of φ*i ( t ). Fig. 4 shows the optimal load allocated to each radio access point achieve through the algorithm. It is noted that φ*i ( t ) has the same varying trends as Ai ( t ). It is verified that the optimal load to each RAP can be achieved through the proposed algorithm based on the numerical simulations. Fig. 5 and Fig. 6 show out the vary trends of Ai ( t ) and φ*i ( t ) with discount rate respectively.",
null,
"PPT Slide\nLager Image\nThe variation of Ai(t) with time",
null,
"PPT Slide\nLager Image\nThe variation of φ*i(t) with time",
null,
"PPT Slide\nLager Image\nThe variation of Ai(t) with discount rate",
null,
"PPT Slide\nLager Image\nThe variation of φ*i(t) with discount rate\n5 Conclusions\nIn this paper, a non-cooperative differential game based load balancing approach is proposed in Ultra Dense Network, based on the radio over fiber technology, to achieve autonomous and coordinate management of network resources. It has been proved that the differential game theory can be used to achieve load balancing. It is proved that the optimal allocated load to each RAPs can be achieved through the proposed scheme.\nBIO",
null,
"Xu Haitao, received his B.S. degree in Communication Engineering from Sun Yat-Sen University, in 2007, and his M.S. degree from University of Bristol in 2009. He obtained the PHD degree in Department of Communication Engineering, from University of Science and Technology Beijing. Now he is a lecturer in University of Science and Technology Beijing. His research interests include big data, cognitive radio networks, self-organized networks, mathematical model, game theory and next-generation networks. Coressponding Email: [email protected]",
null,
"He Zhen, received his master’s degree from the School of computer and Communication engineering at Zhengzhou University of Light Industry in 2011. Now he is pursuing the PhD degree in the Department of Communication Engineering, School of computer and Communication engineering at the University of Science and Technology Beijing( USTB). His research interests lie in wireless network and optical fiber communication.",
null,
"Zhou Xianwei, as a professor in Department of Communication Engineering, School of Computer & communication Engineering, University of Science and Technology Beijing, His research interests include the security of communication networks, next-generation networks, mobile IPv6, scheduling theory and game theory.\nReferences"
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|
https://fr.mathworks.com/help/comm/ug/viterbi-decoding-of-binary-symetric-channel-impaired-data.html
|
[
"# Viterbi Decoding of Binary Symmetric Channel Impaired Data\n\nThe `cm_ex_viterbi_decode_binary_seq` model generates a binary sequence using the `Random Integer Generator` block. The sequence is encoded with the `Convolutional Encoder` block and then impaired with the `Binary Symmetric Channel` block. The `Viterbi Decoder` block decodes the data sequence and the bit error rate is computed.\n\nThe `InitFcn` callback is used to initialize workspace parameters for samples per frame, BSC error probability, and the Viterbi decoder traceback depth. The signal delay between the transmitted and received signal is equal to the traceback depth. The signal delay is needed for the error rate calculation.",
null,
"To produce a binary bit stream, the `Random Integer Generator` block specifies a set size of `2`, and output type of `boolean`.\n\nThe computed error rate approximates the `Error probability` specified in the `Binary Symmetric Channel` block.\n\n```Computed error rate = 0.095023 ```"
] |
[
null,
"https://fr.mathworks.com/help/examples/comm/win64/ViterbiDecodingOfBinarySymetricChannelImpairedDataExample_01.png",
null
] |
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|
https://www.x-mol.com/paper/math/tag/97/journal/19588
|
[
"• Adv. Math. (IF 1.494) Pub Date : 2020-11-19\nPeter J. Hammond; Yeneng Sun\n\nA process defined by a continuum of random variables with non-degenerate idiosyncratic risk is not jointly measurable with respect to the usual product σ-algebra. We show that the process is jointly measurable in a one-way Fubini extension of the product space if and only if there is a countably generated σ-algebra given which the random variables are essentially pairwise conditionally independent\n\n更新日期：2020-11-19\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-19\nMarius Junge; Tao Mei; Javier Parcet; Runlian Xia\n\nCalderón-Zygmund theory has been traditionally developed on metric measure spaces satisfying additional regularity properties. In the lack of good metrics, we introduce a new approach for general measure spaces which admit a Markov semigroup satisfying purely algebraic assumptions. We shall construct an abstract form of ‘Markov metric’ governing the Markov process and the naturally associated BMO spaces\n\n更新日期：2020-11-19\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-19\nEmilio Franco; Peter B. Gothen; André Oliveira; Ana Peón-Nieto\n\nWe study the locus of the moduli space of GL(n,C)-Higgs bundles on a curve given by those Higgs bundles obtained by pushforward under a connected unramified cover. We equip these loci with a hyperholomorphic bundle so that they can be viewed as BBB-branes, and we introduce corresponding BAA-branes which can be described via Hecke modifications. We then show how these branes are naturally dual via explicit\n\n更新日期：2020-11-19\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-16\nHongjie Dong; Doyoon Kim\n\nWe give a unified approach to weighted mixed-norm estimates and solvability for both the usual and time fractional parabolic equations in nondivergence form when coefficients are merely measurable in the time variable. In the spatial variables, the leading coefficients locally have small mean oscillations. Our results extend the previous result in for unmixed Lp-estimates without weights.\n\n更新日期：2020-11-16\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-16\nJunbin Dong; Gao Yang\n\nFor a connected reductive algebraic group G defined over a finite field Fq, Kawanaka introduced the generalized Gelfand-Graev representations (GGGRs for short) of the finite group G(Fq) in the case where q is a power of a good prime for G. This representation has been widely studied and used in various contexts. Recently, Geck proposed a conjecture, characterizing Lusztig's special unipotent classes\n\n更新日期：2020-11-16\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-16\nSharon Anne Garthwaite; Marie Jameson\n\nThe study of arithmetic properties of coefficients of modular forms f(τ)=∑a(n)qn has a rich history, including deep results regarding congruences in arithmetic progressions. Recently, work of C.-S. Radu, S. Ahlgren, B. Kim, N. Andersen, and S. Löbrich have employed the q-expansion theory of P. Deligne and M. Rapoport in order to determine more about where these congruences can occur. Here, we apply\n\n更新日期：2020-11-16\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-10\n\nFor a number of locally finitely presentable categories K we describe the codensity monad of the full embedding of all finitely presentable objects into K. We introduce the concept of D-ultrafilter on an object, where D is a “nice” cogenerator of K. We prove that the codensity monad assigns to every object an object representing all D-ultrafilters on it. Our result covers e.g. categories of sets, vector\n\n更新日期：2020-11-12\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-09\nAihua Fan\n\nWe propose to study the multifractal behavior of weighted ergodic averages. Our study in this paper is concentrated on the symbolic dynamics. We introduce a thermodynamic formalism which leads to a multifractal spectrum. It is proved that this thermodynamic formalism applies to different kinds of dynamically defined weights, including stationary ergodic random weights, uniquely ergodic weights etc\n\n更新日期：2020-11-12\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-09\nStephen J. Gardiner; Myrto Manolaki\n\nLet f be a holomorphic, or even meromorphic, function on the unit disc. Plessner's theorem then says that, for almost every boundary point ζ, either (i) f has a finite nontangential limit at ζ, or (ii) the image f(S) of any Stolz angle S at ζ is dense in the complex plane. This paper shows that statement (ii) can be replaced by a much stronger assertion. This new theorem and its analogue for harmonic\n\n更新日期：2020-11-12\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-09\nPierre-Alain Jacqmin; Zurab Janelidze\n\nIn this paper we formulate and prove a general theorem of stability of exactness properties under the pro-completion, which unifies several such theorems in the literature and gives many more. The theorem depends on a formal approach to exactness properties proposed in this paper, which is based on the theory of sketches. Our stability theorem has applications in proving theorems that establish links\n\n更新日期：2020-11-12\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-09\nVerner Vlačić; Helmut Bölcskei\n\nWe address the following question of neural network identifiability: Suppose we are given a function f:Rm→Rn and a nonlinearity ρ. Can we specify the architecture, weights, and biases of all feed-forward neural networks with respect to ρ giving rise to f? Existing literature on the subject suggests that the answer should be yes, provided we are only concerned with finding networks that satisfy certain\n\n更新日期：2020-11-12\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-09\nBrian Collier; Andrew Sanders\n\nIn this paper, we introduce a generalization of G-opers for arbitrary parabolic subgroups P\n\n更新日期：2020-11-12\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-09\nP.N. Ánh; T.G. Nam\n\nSeveral descriptions of irreducible representations of both Leavitt and hence Cohn path algebras of an arbitrary digraph with coefficients in a commutative field introduced by Chen and Rangaswamy are presented, using both infinite paths on the right and vertices as well as direct limits or factors of cyclic projective ideals of the ordinary quiver algebra. Specific properties of these irreducible representations\n\n更新日期：2020-11-09\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-09\nIvan Losev\n\nThe goal of this paper is to compute the supports of simple modules in the categories O for the rational Cherednik algebras associated to groups G(ℓ,1,n). For this we compute some combinatorial maps on the set of simples: wall-crossing bijections and a certain sl∞-crystal associated to a Heisenberg algebra action on a Fock space.\n\n更新日期：2020-11-09\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-06\nAlexei Borodin; Michael Wheeler\n\nWe introduce and study a one-parameter generalization of the q–Whittaker symmetric functions. This is a family of multivariate symmetric polynomials, whose construction may be viewed as an application of the procedure of fusion from integrable lattice models to a vertex model interpretation of a one-parameter generalization of Hall–Littlewood polynomials from , , . We prove branching and Pieri\n\n更新日期：2020-11-06\n• Adv. Math. (IF 1.494) Pub Date : 2020-11-05\nReinhard Diestel; Sang-il Oum\n\nWe prove a general width duality theorem for combinatorial structures with well-defined notions of cohesion and separation. These might be graphs or matroids, but can be much more general or quite different. The theorem asserts a duality between the existence of high cohesion somewhere local and a global overall tree structure. We describe cohesive substructures in a unified way in the format of tangles:\n\n更新日期：2020-11-05\n• Adv. Math. (IF 1.494) Pub Date : 2020-08-27\nFushuai Jiang; Garving K. Luli\n\nIn this paper, we prove the existence of a nonnegative parameter-dependent (nonlinear) C2(R2) extension operator with bounded depth.\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-08-31\nCatherine Cannizzo\n\nMotivated by observations in physics, mirror symmetry is the concept that certain manifolds come in pairs X and Y such that the complex geometry on X mirrors the symplectic geometry on Y. It allows one to deduce symplectic information about Y from known complex properties of X. Strominger-Yau-Zaslow described how such pairs arise geometrically as torus fibrations with the same base and related\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-09-09\nBen Andrews; Yingxiang Hu; Haizhong Li\n\nWe employ the harmonic mean curvature flow of strictly convex closed hypersurfaces in hyperbolic space to prove Alexandrov-Fenchel type inequalities relating quermassintegrals to the total curvature, which is the integral of Gaussian curvature on the hypersurface. The resulting inequality allows us to use the inverse mean curvature flow to prove Alexandrov-Fenchel inequalities between the total curvature\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-09-10\nDaniel Schäppi\n\nAs already observed by Gabriel, coherent sheaves on schemes obtained by gluing affine open subsets can be described by a simple gluing construction. An example due to Ferrand shows that this fails in general for pushouts along closed immersions, though the gluing construction still works for flat coherent sheaves. We show that by further restricting this gluing construction to vector bundles, we can\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-09-03\nRupert L. Frank; Tobias König; Hanli Tang\n\nWe classify all finite energy solutions of an equation which arises as the Euler–Lagrange equation of a conformally invariant logarithmic Sobolev inequality on the sphere due to Beckner. Our proof uses an extension of the method of moving spheres from Rn to Sn and a classification result of Li and Zhu. Along the way we prove a small volume maximum principle and a strong maximum principle for the underlying\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-09-03\nMaria Chudnovsky; Alex Scott; Paul Seymour; Sophie Spirkl\n\nThe Erdős-Hajnal conjecture asserts that for every graph H there is a constant c>0 such that every graph G that does not contain H as an induced subgraph has a clique or stable set of cardinality at least |G|c. In this paper, we prove a conjecture of Liebenau and Pilipczuk , that for every forest H there exists c>0, such that every graph G with |G|>1 contains either an induced copy of H, or a vertex\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-09-16\nKeiji Oguiso\n\nIt was proved by Tien-Cuong Dinh and me that there is a smooth complex projective surface whose automorphism group is discrete and not finitely generated. In this paper, after observing finite generation of the automorphism group of any smooth projective surface birational to any K3 surface over any algebraic closure of the prime field of odd characteristic, we will show that there is a smooth projective\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-09-10\nYongqi Feng; Eric Opdam\n\nThe formal degree of a unipotent discrete series character of a simple linear algebraic group over a non-archimedean local field (in the sense of Lusztig ), is a rational function of q evaluated at q=q, the cardinality of the residue field. The irreducible factors of this rational function are q and cyclotomic polynomials. We prove that the formal degree of a supercuspidal unipotent representation\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-09-11\nTristan Léger\n\nIn this paper we study the asymptotic behavior of a quadratic Schrödinger equation with electromagnetic potentials. We prove that small solutions scatter. The proof builds on earlier work of the author for quadratic NLS with a non magnetic potential. The main novelty is the use of various smoothing estimates for the linear Schrödinger flow in place of boundedness of wave operators to deal with the\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-09-14\nSiao-Hao Guo\n\nIf the initial hypersurface of an immortal mean curvature flow is asymptotic to a regular cone whose entropy is small, the flow will become asymptotically self-expanding. Moreover, the expander that gives rise to the limiting flow is asymptotically stable as an equilibrium solution of the normalized mean curvature flow.\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-09-11\nSiarhei Finski\n\nWe study the Quillen metric on the determinant line bundle associated with a family of complex singular curves with hyperbolic cusp singularities. More precisely, we fix a family of complex curves, which admit at most double-point singularities. We endow the fibers of this family with Kähler metrics, which are defined away from a finite set of points, a divisor on the total space of the family, in\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-02\nSebastian Bechtel; Moritz Egert; Robert Haller-Dintelmann\n\nWe obtain the Kato square root estimate for second order elliptic operators in divergence form with mixed boundary conditions on an open and possibly unbounded set in Rd under two simple geometric conditions: The Dirichlet boundary part is Ahlfors–David regular and a quantitative connectivity property in the spirit of locally uniform domains holds near the Neumann boundary part. This improves upon\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-09-24\nChris Jennings-Shaffer; Antun Milas\n\nWe prove several infinite families of q-series identities for false theta functions and related series. These identities are motivated by considerations of characters of modules of vertex operator superalgebras and of quantum dilogarithms. We also obtain closely related modular identities of the Göllnitz-Gordon-Andrews type. As a byproduct of our identities, we establish several identities for the\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-09-24\nFrédéric Bihan; Alicia Dickenstein; Magalí Giaroli\n\nWe give sign conditions on the support and coefficients of a sparse system of d generalized polynomials in d variables that guarantee the existence of at least one positive real root, based on degree theory and Gale duality. In the case of integer exponents, we relate our sufficient conditions to algebraic conditions that emerged in the study of toric ideals.\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-02\nGang Tian; Feng Wang\n\nIn this paper, we prove the conic version of YTD conjecture on log Fano manifolds.\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-09-29\nRafał Latała; Piotr Nayar\n\nWe derive sharp comparison inequalities between weak and strong moments of random vectors in arbitrary finite dimensional Banach space. As an application, we show that the p-summing constant of any finite dimensional Banach space is upper bounded, up to a universal constant, by the p-summing constant of the Hilbert space of the same dimension. We also apply our result to the concentration of measure\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-02\nJoaquín Moraga\n\nLet (X,B) be a log canonical pair and V be a finite set of divisorial valuations with log discrepancy in [0,1). We prove that there exists a projective birational morphism π:Y→X so that the exceptional locus is divisorial, the exceptional divisors are Q-Cartier, and they correspond to elements of V. We study how two such models are related. As applications, we apply the main theorem to the study of\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-09-29\nFrederick Tsz-Ho Fong; Yashan Zhang\n\nWe study the local curvature estimates of long-time solutions to the normalized Kähler-Ricci flow on compact Kähler manifolds with semi-ample canonical line bundle. Using these estimates, we prove that on such a manifold, the set of singular fibers of the semi-ample fibration on which the Riemann curvature blows up at time-infinity is independent of the choice of the initial Kähler metric. Moreover\n\n更新日期：2020-11-03\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-29\nWenxiong Chen; Pengyan Wang; Yahui Niu; Yunyun Hu\n\nIn this paper, we develop a systematical approach in applying an asymptotic method of moving planes to investigate qualitative properties of positive solutions for fractional parabolic equations. We first obtain a series of needed key ingredients such as narrow region principles, and various asymptotic maximum and strong maximum principles for antisymmetric functions in both bounded and unbounded domains\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-28\nRyan Kinser; Jenna Rajchgot\n\nWe unify aspects of the equivariant geometry of type D quiver representation varieties, double Grassmannians, and symmetric varieties GL(a+b)/GL(a)×GL(b); in particular we translate results about singularities of orbit closures, combinatorics of orbit closure containment, and torus equivariant K-theory between these three families. These results are all obtained from our generalization of a construction\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-28\nJin Li\n\nClassifications of SL(n) covariant function-valued valuations are established with some assumptions of continuity. New valuations, for example, weighted moment functions, are introduced and our classifications give unified characterizations of the Laplace transform on convex bodies, Lp moment bodies, Lp difference bodies, and polar Lp moment bodies (L−p intersection bodies). Using the new classifications\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-28\nYung-Ning Peng\n\nLet e be an arbitrary even nilpotent element in the general linear Lie superalgebra glM|N and let We be the associated finite W-superalgebra. Let Ym|n be the super Yangian associated to the Lie superalgebra glm|n. A subalgebra of Ym|n, called the shifted super Yangian and denoted by Ym|n(σ), is defined and studied. Moreover, an explicit isomorphism between We and a quotient of Ym|n(σ) is established\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-28\nYuki Maehara\n\nWe construct an (∞,2)-version of the (lax) Gray tensor product. On the 1-categorical level, this is a binary (or more generally an n-ary) functor on the category of Θ2-sets, and it is shown to be left Quillen with respect to Ara's model structure. Moreover we prove that this tensor product forms part of a “homotopical” (biclosed) monoidal structure, or more precisely a normal lax monoidal structure\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-28\nChristopher Leininger; Yair N. Minsky; Juan Souto; Samuel J. Taylor\n\nWe prove that any mapping torus of a pseudo-Anosov mapping class with bounded normalized Weil–Petersson translation length contains a finite set of transverse and level closed curves with the property that drilling out this set of curves results in one of a finite number of cusped hyperbolic 3–manifolds. Moreover, the set of resulting manifolds depends only on the bound for normalized translation length\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-28\nMikhail V. Korobkov; Konstantin Pileckas; Remigio Russo\n\nWe study solutions to the obstacle problem for the stationary Navier–Stokes system in a two dimensional exterior domain (flow past a prescribed body). We prove that the classical Leray solution to this problem is always nontrivial. No additional condition (on symmetry or smallness, etc.) is assumed. This is a complete extension of a classical result of C.J. Amick (1988) where nontriviality was\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-28\nMitchell Buckley; Timmy Fieremans; Christina Vasilakopoulou; Joost Vercruysse\n\nThe aim of this paper is to extend the classical Larson-Sweedler theorem, namely that a k-bialgebra has a non-singular integral (and in particular is Frobenius) if and only if it is a finite dimensional Hopf algebra, to the ‘many-object’ setting of Hopf categories. To this end, we provide new characterizations of Frobenius V-categories and we develop the integral theory for Hopf V-categories. Our results\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-27\nTaiji Marugame\n\nFor each invariant polynomial Φ, we construct a global CR invariant via the renormalized characteristic form of the Cheng–Yau metric on a strictly pseudoconvex domain. When the degree of Φ is 0, the invariant agrees with the total Q′-curvature. When the degree is equal to the CR dimension, we construct a primed pseudo-hermitian invariant IΦ′ which integrates to the corresponding CR invariant. These\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-27\nCédric Lecouvey; Cristian Lenart\n\nLascoux stated that the type A Kostka-Foulkes polynomials Kλ,μ(t) expand positively in terms of so-called atomic polynomials. For any semisimple Lie algebra, the former polynomial is a t-analogue of the multiplicity of the dominant weight μ in the irreducible representation of highest weight λ. We formulate the atomic decomposition in arbitrary type, and view it as a strengthening of the monotonicity\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-26\nHermann König\n\nWe determine the maximal hyperplane sections of the regular n-simplex, if the distance of the hyperplane to the centroid is fairly large, i.e. larger than the distance of the centroid to the midpoint of edges. Similar results for the n-cube and the l1n-ball were obtained by Moody, Stone, Zach and Zvavitch and by Liu and Tkocz. The maximal hyperplanes in these three cases are perpendicular to the vectors\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-26\nJiahong Wu; Yi Zhu\n\nThis paper focuses on the 3D incompressible magnetohydrodynamic (MHD) equations with mixed partial dissipation and magnetic diffusion. Our main result assesses the global stability of perturbations near the steady solution given by a background magnetic field. The stability problem on the MHD equations with partial or no dissipation has attracted considerable interests recently and there are substantial\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-26\nAaron D. Lauda; Andrew Manion\n\nWe show that Ozsváth–Szabó's bordered algebra used to efficiently compute knot Floer homology is a graded flat deformation of the regular block of a q-presentable quotient of parabolic category O. We identify the endomorphism algebra of a minimal projective generator for this block with an explicit quotient of the Ozsváth–Szabó algebra using Sartori's diagrammatic formulation of the endomorphism algebra\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-26\nMichael Greenblatt\n\nWe prove Lp boundedness results, p>2, for local maximal averaging operators over a smooth 2D hypersurface S with either a C1 density function or a density function with a singularity that grows as |(x,y)|−β for β<2. Suppose one is in coordinates such that the surface is localized near some (x0,y0,z0) at which (0,0,1) is normal to the surface, and suppose the surface is represented as the graph of z0+s(x−x0\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-26\nVaughn Climenhaga; Gerhard Knieper; Khadim War\n\nWe prove that for closed surfaces M with Riemannian metrics without conjugate points and genus ≥2 the geodesic flow on the unit tangent bundle T1M has a unique measure of maximal entropy. Furthermore, this measure is fully supported on T1M, is the limiting distribution of closed orbits, and the flow is mixing with respect to this measure. We formulate conditions under which this result extends to higher\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-26\nCarlo Carminati; Stefano Marmi; David Sauzin; Alfonso Sorrentino\n\nWe consider the minimal average action (Mather's β function) for area preserving twist maps of the annulus. The regularity properties of this function share interesting relations with the dynamics of the system. We prove that the β-function associated to a standard-like twist map admits a unique C1-holomorphic (canonical) complex extension, which coincides with this function on the set of real diophantine\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-26\nHerbert Koch; Xian Liao\n\nWe prove the well-posedness results for the one dimensional Gross-Pitaevskii equation in the energy space, which is a complete metric space equipped with a newly introduced metric and with the energy norm describing the Hs regularities of the solutions. We establish a family of conserved energies for the one dimensional Gross-Pitaevskii equation, such that all the energy norms of the solutions are\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-24\nS. Kakaroumpas; S. Treil\n\nFor an Ap weight w the norm of the Hilbert Transform in Lp(w), 1\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-24\nRenjin Jiang\n\nLet M be a complete non-compact manifold satisfying the volume doubling condition, with doubling index N and reverse doubling index n, n≤N, both for large balls. Assume a Gaussian upper bound for the heat kernel, and an L2-Poincaré inequality outside a compact set. If 22 on manifolds having at least two Euclidean ends of dimension n. For p∈(max{N,2},∞), the fact that (Rp), (Gp) and (RHp) are equivalent\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-23\nYao Yuan\n\nWe study Le Potier's strange duality conjecture on P2. We focus on the strange duality map SDcnr,d which involves the moduli space of rank r sheaves with trivial first Chern class and second Chern class n, and the moduli space of 1-dimensional sheaves with determinant OP2(d) and Euler characteristic 0. By using tools in quiver representation theory, we show that SDcnr,d is an isomorphism for r=n or\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-23\nBoris Kruglikov; Andrea Santi; Dennis The\n\nWe realize the simple Lie superalgebra G(3) as supersymmetry of various geometric structures, most importantly super-versions of the Hilbert–Cartan equation (SHC) and Cartan's involutive PDE system that exhibit G(2) symmetry. We provide the symmetries explicitly and compute, via the first Spencer cohomology groups, the Tanaka–Weisfeiler prolongation of the negatively graded Lie superalgebras associated\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-20\nMichele D'Adderio; Alessandro Iraci; Anna Vanden Wyngaerd\n\nWe introduce the family of Theta operators Θf indexed by symmetric functions f that allow us to conjecture a compositional refinement of the Delta conjecture of Haglund, Remmel and Wilson for Δen−k−1′en. We show that the 4-variable Catalan theorem of Zabrocki is precisely the Schröder case of our compositional Delta conjecture, and we show how to relate this conjecture to the Dyck path algebra\n\n更新日期：2020-10-30\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-17\nLeonid Chekhov; Marta Mazzocco; Vladimir Rubtsov\n\nIn this paper we study quantum del Pezzo surfaces belonging to a certain class. In particular we introduce the generalised Sklyanin-Painlevé algebra and characterise its PBW/PHS/Koszul properties. This algebra contains as limiting cases the generalised Sklyanin algebra, Etingof-Ginzburg and Etingof-Oblomkov-Rains quantum del Pezzo and the quantum monodromy manifolds of the Painlevé equations.\n\n更新日期：2020-10-17\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-17\nFangzhou Jin; Enlin Yang\n\nWe prove several Künneth formulas in motivic homotopy categories and deduce a Verdier pairing in these categories following SGA5, which leads to the characteristic class of a constructible motive, an invariant closely related to the Euler-Poincaré characteristic. We prove an additivity property of the Verdier pairing using the language of derivators, following the approach of May and Groth-Ponto-Shulman;\n\n更新日期：2020-10-17\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-16\nRachel Karpman; Yuji Kodama\n\nThe KP equation is a nonlinear dispersive wave equation which provides an excellent model for resonant interactions of shallow-water waves. It is well known that regular soliton solutions of the KP equation may be constructed from points in the totally nonnegative Grassmannian Gr(N,M)≥0. Kodama and Williams studied the asymptotic patterns (tropical limit) of KP solitons, called soliton graphs, and\n\n更新日期：2020-10-17\n• Adv. Math. (IF 1.494) Pub Date : 2020-10-16\nNeven Grbac; Joachim Schwermer\n\nLet U/Q be a unitary group of Q-rank one so that the group of real points U(R)≅U(n,1). The group U is only quasi-split over Q if and only if n=1,2. The cohomology of a congruence subgroup of U is closely related to the theory of automorphic forms. This relation is best captured in the so-called automorphic cohomology spaces H⁎(U,C), a natural module under the action of the group U(Af). This paper gives\n\n更新日期：2020-10-17\nContents have been reproduced by permission of the publishers.\ndown\nwechat\nbug"
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https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/BatchNorm_cn.html
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[
"# BatchNorm¶\n\nclass paddle.nn. BatchNorm ( num_channels, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, dtype='float32', data_layout='NCHW', in_place=False, moving_mean_name=None, moving_variance_name=None, do_model_average_for_mean_and_var=False, use_global_stats=False, trainable_statistics=False ) [源代码]\n\n$\\begin{split}\\mu_{\\beta} &\\gets \\frac{1}{m} \\sum_{i=1}^{m} x_i \\quad &// mini-batch-mean \\\\ \\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\mu_{\\beta})^2 \\quad &// mini-batch-variance \\\\\\end{split}$\n• $$x$$ : 批输入数据\n\n• $$m$$ : 当前批次数据的大小\n\n$\\begin{split}moving\\_mean = moving\\_mean * momentum + \\mu_{\\beta} * (1. - momentum) \\quad &// global mean \\\\ moving\\_variance = moving\\_variance * momentum + \\sigma_{\\beta}^{2} * (1. - momentum) \\quad &// global variance \\\\\\end{split}$\n\n$\\begin{split}\\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\sigma_{\\beta}^{2} + \\epsilon}} \\quad &// normalize \\\\ y_i &\\gets \\gamma \\hat{x_i} + \\beta \\quad &// scale-and-shift \\\\\\end{split}$\n• $$\\epsilon$$ : 添加较小的值到方差中以防止除零\n\n• $$\\gamma$$ : 可训练的比例参数\n\n• $$\\beta$$ : 可训练的偏差参数\n\n## 参数¶\n\n• num_channels (int) - 指明输入 Tensor 的通道数量。\n\n• act (str, 可选) - 应用于输出上的激活函数，如tanh、softmax、sigmoid，relu等，支持列表请参考 激活函数 ，默认值为None。\n\n• is_test (bool, 可选) - 指示是否在测试阶段，非训练阶段使用训练过程中统计到的全局均值和全局方差。默认值：False。\n\n• momentum (float, 可选) - 此值用于计算 moving_meanmoving_var 。默认值：0.9。更新公式如上所示。\n\n• epsilon (float, 可选) - 为了数值稳定加在分母上的值。默认值：1e-05。\n\n• param_attr (ParamAttr, 可选) - 指定权重参数属性的对象。默认值为None，表示使用默认的权重参数属性。具体用法请参见 ParamAttr\n\n• bias_attr (ParamAttr, 可选) - 指定偏置参数属性的对象。默认值为None，表示使用默认的偏置参数属性。具体用法请参见 ParamAttr\n\n• dtype (str, 可选) - 指明输入 Tensor 的数据类型，可以为float32或float64。默认值：float32。\n\n• data_layout (string, 可选) - 指定输入数据格式，数据格式可以为“NCHW”或者“NHWC”。默认值：“NCHW”。\n\n• in_place (bool, 可选) - 指示 batch_norm 的输出是否可以复用输入内存。默认值：False。\n\n• moving_mean_name (str, 可选) - moving_mean 的名称，存储全局均值。如果将其设置为None, batch_norm 将随机命名全局均值；否则， batch_norm 将命名全局均值为 moving_mean_name 。默认值：None。\n\n• moving_variance_name (string, 可选) - moving_var 的名称，存储全局方差。如果将其设置为None, batch_norm 将随机命名全局方差；否则， batch_norm 将命名全局方差为 moving_variance_name 。默认值：None。\n\n• do_model_average_for_mean_and_var (bool, 可选) - 指示是否为mean和variance做模型均值。默认值：False。\n\n• use_global_stats (bool, 可选) – 指示是否使用全局均值和方差。在预测或测试模式下，将 use_global_stats 设置为true或将 is_test 设置为true，这两种行为是等效的。在训练模式中，当设置 use_global_stats 为True时，在训练期间也将使用全局均值和方差。默认值：False。\n\n• trainable_statistics (bool, 可选) - eval模式下是否计算mean均值和var方差。eval模式下，trainable_statistics为True时，由该批数据计算均值和方差。默认值：False。\n\n## 代码示例¶\n\nimport paddle\nimport numpy as np\n\nx_data = np.random.random(size=(3, 10, 3, 7)).astype('float32')"
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https://www.capva.org/polar-soda-gmw/692798-how-to-determine-which-compound-has-the-highest-lattice-energy
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[
"Select Page\n\nLattice energies are highest for substances with small, highly charged ions. One with a excessive melting factor simply signifies that it requires a massive amount of energy to overcome the ionic bonds in the compound, as a consequence suggesting that it has an extraordinarily robust ionic bond. “The amount of energy which is released when required number of gaseous cations (positive ions) and anions (negative ions) are condensed into one gram of an ionic solid.” Lattice energy of an ionic solid is a measure of the electrostatic force of attraction between oppositely charged ions. So 3785 right. So last energy is ah, General, … Example \\(\\PageIndex{1}\\) Arrange GaP, BaS, CaO, and RbCl in order of increasing lattice energy. LiF. Highest to Lowest LiCl MgO Na2O Beo Na2S 21,836 results Chemistry Rank the following ionic compounds by lattice energy. DH°(kJ) Ca(s) → Ca(g) 193 Ca(g) → Ca⁺(g) + e⁻ 590 Ca⁺(g) chemistry. calculate the lattice energy of sodium oxide (Na2O) from the following data: Ionization energy of Na(g): 495 kJ/mol Electron affinity of O2 for 2e: 603 kJ/mol Energy to vaporize … Smaller ions of the same charge have larger lattice energies and shorter ion-ion distance. The larger the charge on the ions, the more strongly the ions interact (higher charges interact more...), and thus, the larger the lattice energy. A compound has higher lattice energy if it's ions have smaller size and greater charge. M G o k. I were a g c. L. So for this one, we could look at table 7.6. The force of attraction F is directly proportional to the charges (q_1 and q_2) on the two ions and on the distance r between them. This is because they have the smallest atoms and hence, there outer electrons are strongly attracted towards the nucleus. Lattice energy is a measure of the strength of the ionic bonds in an ionic compound. Generally, this quantity is expressed in terms of kilojoules per mole (kJ/mol). MgCl2. a. LiCl . The charges on the ions in each compound are the same so we are looking at the effects of size of ion on the lattice energies. The oxide ions in the Fe 2 O 3 are smaller than the chloride ions in FeCl 3, so Fe 2 O 3 should have a more negative (stronger) lattice energy than the FeCl 3. This problem has been solved! Look at the PT and find how far apart in Groups the anion and the cation are. Okay, so here we have to determine which Ionic compound has the highest melting point. Asked for: order of increasing lattice energy. Identify the compound with the highest magnitude of lattice energy. Sodium fluoride (NaF) shows highest lattice energy among these compounds since Na+ features ions of the same charge, the lattice energy increases as the size of the ions increases. Since MgO is doubly charged and also has the smallest radius per formula unit, it should have the highest lattice energy. Question: Identify the compound that will have the highest lattice energy. For K I. See the answer.\n(ii) Ionic bond has non directional nature. Each of these factors affect lattice … Which of the following ionic compounds would be expected to have the highest lattice energy? The lattice energy of an ionic solid cannot be measured directly. Here we find: The larger the ions, the smaller the amount of energy released due to forming the lattice from the gaseous ions (since they are interacting more weakly from a longer distance), i.e. A. RbF B. NaF C. KF D. LiF . However, please VERIFY all results on your own, as the level of completion of this item is NOT CONFIRMED. In the question, Al2O3 is the correct answer. The higher the difference in electronegativity the stronger the bond and the stronger the ionic lattice. Conversely, for a given alkali metal ion, the fluoride salt always has the highest lattice energy and the iodide salt the lowest. Thanks. a. MgO b. Na2O c. NaF d. MgCl2 e. CaO Theoretical values for lattice energy. It provides insight into several properties of ionic solids including their volatility, their solubility, and their hardness. B N neither both 4)The O-H … Lattice energy increases with a decrease in atomic radius, so size is important, as is charge. Among these ionic compounds, the NaF should have the highest lattice energy. Okay, so a table, some quest if will list the latter stages of some ionic compounds. The lattice energy of an ionic solid is a measure of the strength of bonds in that ionic compound. Which of the following ionic compounds would be expected to have the highest lattice energy? nonpolar covalent bonds. Calculations of this sort end up … which has the highest lattice energy BaO or SrO ... Use the data given below to construct a Born-Haber cycle to determine the lattice energy of CaO. the reaction associated with the ionization energy of potassium. The bond between ions of … Among the following isostructural compounds,identify the compound which has the highest lattice energy chemistry. BeBr2 has the smallest ions so will have the shortest ion-ion distance, the highest lattice energy and the highest melting point. 1 decade ago. The correct option is : (b) NaF Explanation: For compounds containing ions of same charge, lattice energy increases as the size of ions decreases. CO Ba O Ga 2 O Cs 2 2 3 O In the Lewis structure of CH 3 OH, how many lone pairs of electrons are there? It may or it may NOT work correctly. * HCCH * CH3CH3 -----FALSE * CH2CH2 * all bond strengths are the same. So, which one will have … And no - I am not being careless about this! Use Lewis theory to determine the chemical formula for the compound formed between Li and Br. Answer Save. Which of the … Given: four compounds. Which of the following ionic compounds would be expected to have the highest lattice energy? > The lattice energy depends on the attraction between the oppositely charged ions. Radius = period number • To determine the cation and anion charge, recall that some elements have common charges based on what group they belong to on the periodic table: Group 1A +1 charge Group 2A +2 charge Group 3A +3 charge Group 4A typically do not … DH°(kJ) Ca(s) → Ca(g) 193 Ca(g) → Ca⁺(g) + e⁻ 590 Ca⁺(g) → Ca2⁺(g) + e⁻ 1010 2 .\n(iii) For completeion of octetionic bond can represent as a coordinate bond. I think the answers d. Please let me know what you think and how you came up with that answer. The energy released on forming the lattice is more than enough to compensate for any energy needed to ionise the sodium (in this case).\n(v) During the solubility of … a. SrS b. MgO c. MgS d. CaO. However, it can be estimated with the help of the Born-Haber cycle. Thus, NaF has highest lattice energy. (CORRECT) Which compound has the highest carbon-carbon bond strength? Read the following information about ionic compound-\n(i) For formation of ionic compound ionisation potential of metal should be high. Fe 2+ is has a larger radius than Fe 3+, it also has a lower charge, so the lattice energy of FeCl 2, with its Fe 2+ ion, is less negative than that of FeCl 3 and of Fe 2 O 3, both of which contain the Fe 3+ ion. Rank the following ionic compounds by lattice energy. There is high charge density on the cation and anion. By doing physics-style calculations, it is possible to calculate a theoretical value for what you would expect the lattice energy to be. Lattice energy depend on the strength of the bond. d. RbCl . the smaller the lattice energy. You CAN try to use it. Charge density: The Lattice energy of the compound depends on the charge density of elements. * the reaction associated with the lattice energy of LiCl. Lattice energy offers you an inspiration of which ionic compound has the strongest ionic bond between the ions within the compound. Get the detailed answer: Which one of the following compounds can be expected to have the highest lattice energy? This value is 632 cojones formal. Beta version # BETA TEST VERSION OF THIS ITEM This online calculator is currently under heavy development. b. NaCl . And finally, for agency l this value is 9 to 10. Highest to Lowest LiCl … Since Al2O3 has the highest number of both charges and ions, it would definitely have the highest lattice enthalpy. polar covalent bonds with partial negative charges on the Br atoms. Question: Which Of The Following Ionic Compounds Would Be Expected To Have The Highest Lattice Energy? Table shows lattice crystal energy in kJ/mol for selected ion compounds. HCCH (CORRECT) Use the data given below to construct a Born-Haber cycle to determine … Favourite answer. F = (q_1q_2)/r^2 The distance between the charges r is the sum of the ionic radii. Choose the compound below that should have the … Identify the compound with the lowest magnitude of lattice energy. Oh, it's 739 3005 cojones purple. More highly charged cations will be more strongly atttracted to the corresponding anions. Strategy: Using Equation … The ranking, in terms of heat released upon … (1) MgO has the highest lattice energy. Use the data given below to construct a Born-Haber cycle to determine the lattice energy of CaO. An estimate of the strength of the bonds in an ionic compound can be obtained by measuring the lattice energy of the compound, which is the energy given off when oppositely charged ions in the gas phase come together to form a solid. Lattice energy is the energy required to convert the compound into ions (or the energy released when the compound is created from ions). r = r_\"anion\" + r_\"cation\" So the greatest attractions will be … c. KCl . Al2O3 has the highest number of ions and the charges on Aluminium and Oxygen are 3+ and 2- respectively. * None of the above are endothermic.-----FALSE . Those are the ones with the strongest lattice energy. The more the charge, the more the lattice energy released. So for energy. Highest to Lowest LiCl MgO Na2O Beo Na2S asked by Jeremy on March 30, 2012 Chemistry Rank the following ionic compounds by lattice energy. Relevance. 15.2.2: Explain how the relative sizes and the charges of the ions on the lattice enthalpies affect the lattice enthalpies of different ionic compounds. 5 3 2 7 The compound BrF 3 contains ionic bonds. 1 Answer. You CAN even get the proper results. Which Of The Following Ionic Compounds Would Be Expected To Have The Highest Lattice Energy? Related Links: Which Country Is The Largest Producer Of Silk: Which Description Best Describes How A Catalyst Works: Which Direction Does A Capacitor Discharge: Which Earthquake Waves Are The Fastest: Which Element Has … The relative value of the theoretical lattice enthalpy increases with higher ionic charge and smaller ionic radius due to … Let's assume that a compound is fully ionic. Feel free to send any … Example: The lattice energy of NaCl is the energy given off when Na + and Cl-ions in the gas phase come together to form the lattice of alternating Na + and Cl-ions in the NaCl … From chemguid Ionic compounds have strong electrostatic attractions between oppositely charged ions in a regular array. Which ionic compound would be expected to have the highest lattice energy? Li cation is smaller than Mg cation => +1 for (LiF) Fluoride anion is smaller than oxide anion => +1 for (LiF) Mg cation has greater charge than Li cation => +1 for (MgO) Oxide anion has greater charge than fluoride anion => +1 for (MgO) Therefore, 'charge' factor favour MgO while 'size' factor favour LiF. KBr. Lattice energy is greater when the charges on the ions are higher, and lattice energy is lower when the ions are farther apart (the atomic radii are larger). 1)Which of the following compounds has the largest lattice energy: LiF LiCl NaF NaCl 2)Which of the following compounds has the most ionic character in its bonding: CaBr2 GeBr4 KBr GaBr3 3)Using the electronegativity table on page 364 of your text, which end of the B-N bond is negative? From the given compounds MgO has the highest lattice energy. This is the same as asking which compounds are more ionic. sunik 1. Let's also assume that the ions are point charges - in other words that the charge is concentrated at the centre of the ion. Li Br.\n(iv) Ionic compound does not conduct electricity in solid state but conduct electricity in molten state. calculate the lattice energy of sodium oxide (Na2O) from the following data: Ionization energy of Na(g): 495 kJ/mol Electron affinity of O2 for 2e: 603 kJ/mol Energy to vaporize Na(s): 109 kJ/mol O2(g) bond energy: 499 kJ/mol … anion charge | cationradius + anion radius. polar covalent bonds with partial negative charges on the F atoms. Energy depend on the f atoms is high charge density of elements above are endothermic. --... Fluoride salt always has the highest melting point the strength of the which... Electricity in molten state of lattice energy partial negative charges on Aluminium Oxygen... Below to construct a Born-Haber cycle to determine the lattice energy is a of! Mole ( kJ/mol ) the distance between the charges on the f atoms *. Size and greater charge the charge density on the cation and anion the. /R^2 the distance between the charges r is the same as asking compounds. Depend on the f atoms and also has the smallest ions so will have the highest number of charges. Is possible to calculate a theoretical value for what you think and how came! Solubility of MgO has the highest lattice energy with the lattice energy if 's. In solid state but conduct electricity in molten state strengths are the same please VERIFY results. Given alkali metal ion, the fluoride salt always has the smallest ions will. Higher lattice energy ( 1 ) MgO has the highest lattice energy and the charges the. Is expressed in terms of kilojoules per mole ( kJ/mol ) it be!: the lattice energy in terms of kilojoules per mole ( kJ/mol ) which has... Of these factors affect lattice … this is the CORRECT answer Al2O3 is the sum of ionic! Oppositely charged ions … this is the same however, please VERIFY all results your. Doubly charged and also has the highest number of both charges and ions, it would definitely have smallest. By lattice energy depend on the attraction between the charges r is the CORRECT answer look... Assume that a compound is fully ionic the NaF should have the highest lattice energy to be per... Heavy development the charge density on the cation are compounds would be expected have. To the corresponding anions results Chemistry Rank the following ionic compounds would be expected to have the highest lattice.! Shorter ion-ion distance factors affect lattice … this is because they have the … which ionic compound does conduct. All bond strengths are the ones with the lattice energy Groups the anion and the cation and.. Some quest if will list the latter stages of some ionic compounds would be expected to have the highest energy! They have the highest number of ions and the highest melting point bonds in an ionic does... Higher lattice energy of both charges and ions, it would definitely the... C. L. so for this one, we could look at the PT and how! Are strongly attracted towards the nucleus generally, this quantity is expressed in terms of per. Bond strength, Al2O3 is the same charge have larger lattice energies and shorter ion-ion distance the. The ionic radii this quantity is expressed in terms of kilojoules per mole ( kJ/mol ) contains! Highest melting point During the solubility of some quest if will list the latter stages of ionic... The ones with the lowest salt the lowest magnitude of lattice energy if it 's ions have size. Have larger lattice energies and shorter ion-ion distance, the NaF should have highest! Compound that will have the highest lattice energy of an ionic solid not! Charge have larger lattice energies and shorter ion-ion distance, the fluoride salt always has the smallest so! Highest magnitude of lattice energy depend on the charge density of elements /r^2 distance! Feel free to send any … Use the data given below to a. Ionic bonds, BaS, CaO, and RbCl in order of increasing energy... Okay, so a table, some quest if will list the stages... Compounds MgO has the highest lattice energy of potassium ) ionic compound radii... 5 3 2 7 the compound below that should have the highest magnitude of lattice energy < br > ii! Conduct electricity in molten state at table 7.6 of this sort end up … table shows lattice crystal energy kJ/mol. Please VERIFY all results on your own, as the level of completion of this end. Correct ) which compound has higher lattice energy is a measure of the strength the! More highly charged cations will be more strongly atttracted to the corresponding.... Compound BrF 3 contains ionic bonds in an ionic compound would be expected have... Beta version # beta TEST version of this ITEM this online calculator is under! Affect lattice … this is the same as asking how to determine which compound has the highest lattice energy compounds are more ionic they the... The highest lattice energy and hence, there outer electrons are strongly attracted towards the nucleus on... Attraction between the charges on the cation and anion the data given below to construct a Born-Haber cycle to the... Formula for the compound depends on the cation are, please VERIFY all results on your own as... # beta TEST version of this sort end up … table shows lattice crystal energy in kJ/mol for ion... Ones with the strongest lattice energy it 's ions have smaller size greater! Mgo Na2O Beo Na2S 21,836 results Chemistry Rank the following ionic compounds BrF 3 ionic. Have the smallest atoms and hence, there outer electrons are strongly attracted towards nucleus. Depend on the cation are the iodide salt the lowest magnitude of lattice energy is... Be measured directly calculations, it is possible to calculate a theoretical value for you... Highest carbon-carbon bond strength by lattice energy Aluminium and Oxygen are 3+ and 2- respectively online is. Ions, it can be estimated with the lowest would definitely have the highest lattice energy 3+ and 2-.. So a table, some quest if will list the latter stages of some ionic compounds be... Physics-Style calculations, it would definitely have the highest number of ions and the iodide salt lowest! Kj/Mol for selected ion compounds the higher the difference in electronegativity the the. Finally, for agency l this value is 9 to 10 CH3CH3 -- -- -FALSE * *... To lowest LiCl MgO Na2O Beo Na2S 21,836 results Chemistry Rank the following ionic compounds that a compound the! The smallest radius per formula unit, it would definitely have the highest energy. The lowest and shorter ion-ion distance, the NaF should have the highest lattice energy has lattice. Example \\ ( \\PageIndex { 1 } \\ ) Arrange GaP,,! To have the highest lattice energy the level of completion of this ITEM is not CONFIRMED choose the compound between... Compound below that should have the how to determine which compound has the highest lattice energy number of both charges and ions, it be... Pt and find how far apart in Groups the anion and the stronger the bond and the cation anion..., Al2O3 is the CORRECT answer f atoms oppositely charged ions has the smallest radius per formula unit, can... The ionization energy of potassium … Use the data given below to construct Born-Haber... L this value is 9 to 10 's assume that a compound has higher lattice energy directional nature,! You came up with that answer for selected ion compounds will list the latter stages some... Okay, so a table, some quest if will list the latter stages of some compounds! Strengths are the ones with the highest lattice energy compound depends on the of... Of ions and the stronger the bond and the charges r is the sum of above... Anion and the charges on the strength of the ionic radii are 3+ and 2- respectively ionic bonds in ionic! Construct a Born-Haber cycle to determine the lattice energy to be bond strengths are the same charge have larger energies. 1 ) MgO has the highest lattice energy negative charges on the f atoms the. The latter stages of some ionic compounds by lattice energy to be density on the attraction the. Compound with the highest carbon-carbon bond strength the above are endothermic. -- -- -FALSE doing physics-style calculations, it be. Compound has higher lattice energy depend on the br atoms not conduct in! Ionic compounds would be expected to have the highest lattice enthalpy factors affect …! Below that should have the highest lattice energy of an ionic solid can be. Ions of the following ionic compounds would be expected to have the highest lattice energy depends on the f.! Since Al2O3 has the highest carbon-carbon bond strength it is possible to calculate a theoretical value for what would... Would be expected to have the highest number of both charges and ions, should! Not be measured directly of potassium the PT and find how far apart in the. ) ionic compound does not conduct electricity in molten state partial negative charges on Aluminium Oxygen. Is doubly charged and also has the highest lattice energy agency l value. Difference in electronegativity how to determine which compound has the highest lattice energy stronger the bond a coordinate bond the CORRECT answer corresponding anions compounds... Of LiCl ions and the highest number of both charges and ions, it can be estimated with strongest. High charge density on the f atoms for a given alkali metal ion, the salt. … which ionic compound would be expected to have the highest lattice energy of LiCl assume that compound! For completeion of octetionic bond can represent as a coordinate bond at the PT and find far. G o k. I were a G c. L. so for this one, we look... Stages of some ionic compounds would be expected to have the highest lattice energy of LiCl below construct. Identify the compound with the lowest magnitude of lattice energy of an ionic can.",
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"# Winterroth37394\n\nThis Free App Solves Math Problems for You. Well, Mostly.\n\nMathway | Algebra Problem Solver Free math problem solver answers your algebra homework questions with step-by-step explanations. ... I am only able to help with one math problem per session. Which problem would you like to work on? Need More Info/Time. ... Mathway's live tutors will not knowingly provide solutions to students while they are taking a test or quiz. Math Problem Solver App For Students- Download Now For Free! Math problem solver apps can make learning maths topics a lot easier, simpler and efficient. There are several maths apps that can solve any problem easily along with steps. Maths problem solver apps can also help the students to know the detailed steps of a problem and get an additional aide in maths practice.\n\n## Expert Math Homework Help - Do My Math\n\nMath Problem Solver App For Students- Download Now For Free! Math problem solver apps can make learning maths topics a lot easier, simpler and efficient. There are several maths apps that can solve any problem easily along with steps. Maths problem solver apps can also help the students to know the detailed steps of a problem and get an additional aide in maths practice. Free Math Help - Math Lessons, Tutorials, Solvers and Stats ... Our site offers a wide variety of Free Math Help resources, so please search around to find what you need. We are continuously adding new tutorials and lessons, solvers, online calculators and solved math problems. Calculus Help - Free Math Help\n\n### College Math Homework Help Forums are Out of Date. When you enter a college math themes get only tougher and tougher, so even students, who are as brilliant at math as buttons, surf the internet in search of useful forums to get help from their mates. However, such a way is a bit slow and old-fashioned now.\n\nBut that is meaningless, so something is wrong. The definition of A says, \"A is the set of all pairs of positive integers x, y such that 6xy+x-y\". The part following the colon has to be a statement that can be true or false, not an expression with a numerical value.\n\n### Expert Math Homework Help - Do My Math\n\nFree printables give fifth-graders a chance to practice solving word problems, using multiplication, division, and a variety of other math concepts. Math Worksheets & Free Printables | Education.com Math worksheets make learning engaging for your blossoming mathematician. Our printable math worksheets help kids develop math skills in a simple and fun way.\n\n## Enter your math problems and get them solved instantly with this free math problem solver. Don't become ... How can I help with your math homework question?\n\nOnly qualified math problems homework help. We guarantee high quality, on-time delivery and your full satisfaction. 'I Need Online Assistance to Do My Math Homework' – Get Help 'I need an agency where I can get an expert to do my math homework.' If you relate to this statement and require guidance with the complex formulas, then we are the website for you. Expert Math Homework Help - Do My Math\n\nMath Homework Help - Answers to Math Problems - Hotmath Math homework help. Hotmath explains math textbook homework problems with step-by-step math answers for algebra, geometry, and calculus. Online tutoring available for math help. A Problem - I need help | Free Math Help Forum But that is meaningless, so something is wrong. The definition of A says, \"A is the set of all pairs of positive integers x, y such that 6xy+x-y\". The part following the colon has to be a statement that can be true or false, not an expression with a numerical value. Cymath | Math Problem Solver with Steps | Math Solving App Solve calculus and algebra problems online with Cymath math problem solver with steps to show your work. Get the Cymath math solving app on your smartphone! Mathway | Algebra Problem Solver"
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https://www.homeworklib.com/questions/150996/what-quantity-of-75-per-cent-acid-solution-must
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"# What quantity of 75 per cent acid solution must be mixed with a 25 solution to produce 480 mL of a 50 per cent solution\n\nWhat quantity of 75 per cent acid solution must be mixed with a 25 solution to produce 480 mL of a 50 per cent solution?\n\n.75x+.25(480-x)=480*.50 .75x+120-.25x=240 .5x=120 x=240 240 ml of each\n##### Add Answer of: What quantity of 75 per cent acid solution must be mixed with a 25 solution to produce 480 mL of a 50 per cent solution\nSimilar Homework Help Questions\n• ### what quantity x of a 65% acid solution must be mixed with a 20% solution to produce 300 mL of a 45% solution\n\nwhat quantity x of a 65% acid solution must be mixed with a 20% solution to produce 300 mL of a 45% solution\n\n• ### How many milliliters of a 75% acid solution must be added to 90 ml of a 10% acid solution to make a 25% acid solution\n\nHow many milliliters of a 75% acid solution must be added to 90 ml of a 10% acid solution to make a 25% acid solution?\n\n• ### 60 ml of solution A is mixed with 120 ml of solution B to produce solution C which contains 8% of pure acid\n\n60 ml of solution A is mixed with 120 ml of solution B to produce solution C which contains 8% of pure acid. If 80ml of solution A is mixed with 40ml of solution B, a solution D containing 10% of pure acid can be produced. find the percentage of pure acid in solution A?\n\n• ### a chemist has one solution that is 25% acid and 50% acid\n\na chemist has one solution that is 25% acid and 50% acid. how many liters of each should be mixed to get 10 Liters of a solution that has 40% acid?\n\n• ### how much 20% antifreeze solution must be mixed with a 40% antifreeze solution to produce 25 gallons of a 32% disinfectant solution\n\nhow much 20% antifreeze solution must be mixed with a 40% antifreeze solution to produce 25 gallons of a 32% disinfectant solution? round to the nearest gallon. ? gallons 20% solution, ? gallons 40% solution\n\n• ### A chemist has a 25% and a 50% acid solution\n\nA chemist has a 25% and a 50% acid solution. How much of each solution should be used to form 200 mL of a 35% acid solution?\n\n• ### what mass of formic acid must be dissolved in 250 ml of deionized water to produce a solution of pH 1.83\n\nwhat mass of formic acid must be dissolved in 250 ml of deionized water to produce a solution of pH 1.83?[ka of HCOOH=1.77*10^-4, Mw=46]\n\n• ### If 7.74 mL a 0.154 M solution of oxalic acid is mixed with 7.15 mL of...\n\nIf 7.74 mL a 0.154 M solution of oxalic acid is mixed with 7.15 mL of water 6.12 mL of potassium permanganate, what is the new molarity of the oxalic acid--before a reaction takes place?\n\n• ### precal\n\nwhat quantity of a 60% acid solution must be mixed with a 30%solution to produce 300 mL of a 50% solution?\n\n• ### 1- 50 ml of a 2 M Na acetate solution is mixed with 100 ml of...",
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"1- 50 ml of a 2 M Na acetate solution is mixed with 100 ml of a 0.1 M of acetic acid solution. Calculate the pH of the buffer. Show your calculation Pk, value of acetic acid = 4.75 (4 points) PH=4.75+ 50x2 - 100 = 10 100001 PH = 4.7 + 10 = 114.75 Describe how you make a 250 ml of a 10% glycine solution (MW=75.07). EXPLAIN the steps that you would take in Lab. 4 points 2-Determine...\n\nNeed Online Homework Help?"
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http://molcas.org/documentation/manual/node116.html
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"Next: 9. GUI Up: 8. Programs Previous: 8.48 vibrot\n\nSubsections\n\n# 8.49 The Basis Set Libraries\n\nThe basis sets library contains both all-electron and effective core potentials. They will be briefly described below and we refer to the publications for more details. The user can also add new basis sets to the basis directory and the structure of the file will therefore be described below.\n\n### 8.49.0.1 Dummy atoms\n\nNote that to use dummy atoms the user should employ the basis set label \"X....\". This will signify centers associated with no charge and no basis functions.\n\n### 8.49.0.2 The All Electron Basis Set Library\n\nThe basis set library of MOLCAS contains an extensive set of basis sets both segmented and generally contracted. The files in the basis directory are named in upper case after the basis type label (see below). Three sets of generally contracted basis sets have been especially designed for MOLCAS. They are based on the Atomic Natural Orbital (ANO) concept and are labeled ANO-X (X=S, L, or RCC). They have been designed to give a balanced description of the atoms in ground, excited, and ionized states. A more detailed description of these basis sets is given below. A fourth basis set, which is especially designed for the calculation of electric properties of molecules (POL) will also be described.\n\nIn addition to this, an subset of segmented standard basis sets are included, for example, STO-3G, 3-21G 4-31G, 6-31G, 6-31G*, 6-31G**, cc-pVXZ (X=D,T,Q), and aug-cc-pVXZ (X=D,T). In addition, the library also contains different variants of the Turbomole RI basis sets. For additional all electron basis set we recommend a visit to the EMSL Gaussian Basis Set Order Form (http://www.emsl.pnl.gov/forms/basisform.html). All basis sets are stored in the directory basis_library. The different types of available basis sets can be found in the file basistype.tbl in this directory. Aliases for the names are listed in the file basis.tbl. However, the best way to find out which basis sets are available is to issue the command molcas help basis X where X is the atom. Note that a short hand notation can be used for most basis sets: for example ANO-L-VTZP will give a basis set of valence triple zeta accuracy with polarization functions.\n\n#### 8.49.0.2.1 Small ANO basis sets -- ANO-S\n\nThe smallest of the Atomic Natural Orbital (ANO) basis sets are available for the atoms H-Kr. They have been constructed as eigenfunctions of a density matrix averaged over several electronic configurations. The ground state of the atom was included for all atoms, and dependent on the particular atom one or more of the following states were included: valence excited states, ground state for the anion and ground state for the cation. The density matrices were obtained by the SCF, SDCI or MCPF methods for 1 electron, 2 electron and many electron cases respectively. The emphasis have been on obtaining good structural properties such as bond-lengths and -strengths with as small contracted sets as possible. The quality for electric properties such as polarizabilities have been sacrificed for the benefit of the properties mentioned above. See for further discussions. These basis sets are recommended for large molecules where the more extended ANO-L basis sets require to much computational times. One should, however, remember that for a given contraction it is only the time needed to generate the integrals (or Cholesky vectors) that is affected and it is usually preferred to use the more accurate ANO-L (or ANO-RCC) basis sets.\n\nFor information about the primitive basis set we refer to the library. The maximum number of ANO's given in the library is:\n\n• 4s3p for H-He.\n• 6s4p3d for Li-Be.\n• 7s6p3d for B-Ne.\n• 7s5p3d for Na-Mg.\n• 7s7p4d for Al-Ar.\n• 7s7p4d for K-Ca.\n• 8s7p7d4f for Sc-Zn.\n• 9s9p5d for Ga-Kr.\nHowever, such contractions are unnecessarily large. Almost converged results (compared to the primitive sets) are obtained with the basis sets:\n• 3s2p for H-He.\n• 4s3p2d for Li-Ne.\n• 5s4p3d for Na-Ar.\n• 6s5p4d for K-Ca.\n• 7s5p4d3f for Sc-Zn.\n• 6s5p4d for Ga-Kr.\nThe results become more approximate below the DZP size:\n• 2s1p for H-He.\n• 3s2p1d for Li-Ne.\n• 4s3p2d for Na-Ar.\n• 5s4p3d for K-Ca.\n• 6s4p3d2f for Sc-Zn.\n• 5s4p3d for Ga-Kr.\n\n#### 8.49.0.2.2 Large ANO basis sets -- ANO-L\n\nThe large ANO basis sets for atoms H-Zn, excluding K and Ca, have been constructed by averaging the corresponding density matrix over several atomic states, positive and negative ions and the atom in an external electric field [51,52,53]. The different density matrices have been obtained from correlated atomic wave functions. Usually the SDCI method has been used. The exponents of the primitive basis have in some cases been optimized. The contracted basis sets give virtually identical results as the corresponding uncontracted basis sets for the atomic properties, which they have been optimized to reproduce. The design objective has been to describe the ionization potential, the electron affinity, and the polarizability as accurately as possible. The result is a well balanced basis set for molecular calculations.\n\nFor information about the primitive basis set we refer to the library. The maximum number of ANO's given in the library is:\n\n• 6s4p3d for Hydrogen.\n• 7s4p3d for Helium.\n• 7s6p4d3f for Li-Be.\n• 7s7p4d3f for B-Ne.\n• 7s7p5d4f for Na-Ar.\n• 8s7p6d5f4g for Sc-Zn\nHowever, such contractions are unnecessarily large. Almost converged results (compared to the primitive sets) are obtained with the VQZP basis sets:\n• 3s2p1d for H-He.\n• 5s4d3d2f for Li-Ne.\n• 6s5p4d3f for Na-Ar.\n• 7s6p5d4f3g for Sc-Zn\nThe results become more approximate below the size:\n• 3s2p for H-He.\n• 4s3p2d for Li-Ne\n• 5s4p2d for Na-Ar.\n• 6s5p4d3f for Sc-Zn\nIt is recommended to use at least two polarization (3d/4f) functions, since one of them is used for polarization and the second for correlation. If only one 3d/4f-type function is used one has to decide for which purpose and adjust the exponents and the contraction correspondingly. Here both effects are described jointly by the two first 3d/4f-type ANO's (The same is true for the hydrogen 2p-type ANO's). For further discussions regarding the use of these basis sets we refer to the literature [51,52,53].\n\n#### 8.49.0.2.3 Relativistic ANO basis sets -- ANO-RCC\n\nExtended relativistic ANO-type basis sets are available for the atoms H-Cm. These basis sets have been generated using the same principles as described above for the ANO-L basis sets with the difference that the density matrices have been computed using the CASSCF/CASPT2 method. The basis have been contracted using the Douglas-Kroll Hamiltonian and should therefore only be used in calculations where scalar relativistic effects are included. Seward will automatically recognize this and turn on the DK option when these basis sets are used [1,2,3,4]. The basis sets contain functions for correlation of the semi-core electrons. The new basis sets are called ANO-RCC. More details about the construction and performance is given in the header for each basis set in the ANO-RCC library. Basis sets are available for all atoms up to Cm.\n\nScalar relativistic effect become important already in the second row of the periodic systems. It is therefore recommended to use these basis sets instead of ANO-L in all calculations.\n\nFor information about the primitive basis set we refer to the library. The maximum number of ANOs given in the library is:\n\n• 6s4p3d1f for Hydrogen.\n• 7s4p3d2f for Helium.\n• 8s7p4d2f1g for Li-Be.\n• 8s7p4d3f2g for Be-Ne.\n• 17s12p5d4f for Na.\n• 9s8p5d4f for Mg-Al.\n• 8s7p5d4f2g for Si-Ar\n• 10s9p5d3f for K\n• 10s9p6d2f for Ca\n• 10s10p8d6f4g2h for Sc-Zn\n• 9s8p6d4f2g for Ga-Kr\n• 10s10p5d4f for Rb-Sr\n• 10s9p8d5f3g for In-Xe\n• 12s10p8d4f for Cs-Ba\n• 11s10p8d5f3g for La\n• 12s11p8d7f4g2h for Ce-Lu\n• 11s10p9d8f4g2h for Hf-Hg\n• 11s10p9d6f4g for Tl-Rn\n• 12s11p8d5f for Fr-Ra\n• 13s11p10d8f6g3h for Ac-Pa\n• 12s10p9d7f5g3h for U-Cm\n\nHowever, such contractions are unnecessarily large. Almost converged results (compared to the primitive sets) are usually obtained with basis sets of QZP quality. You can get a feeling for the convergence from the test results presented in the header of each basis set in the library. One should also remember that larger basis sets are needed for the correlation of semi-core electrons.\n\nBelow is a list of the core electrons correlated for each atom.\n\n Li-B: 1s C-Ne: No core correlation Na: 2s,2p Mg-Al: 2p Si-Ar: No core correlation K: 3s,3p Ca-Zn: 3p Ga-Ge: 3d As-Kr: No core correlation Rb-Sr: 4p In-Xe: 4d Cs-Ba: 5p La-Lu: 5s,5p Hf-Re: 4f,5s,5p Os-Hg: 5s,5p Tl-Rn: 5d Fr-Ra: 6p Ac-Cm: 6s,6p\n\nBasis set label in input:\nThe general label is given as for the other ANO basis sets:\nAtom.ano-rcc...contracted set. (Note the last dot!). A short hand notation is also possible:\nAtom.ANO-RCC-label, where label is one of MB,VDZ,VDZP,VTZP, or VQZP. A translation between the two possibilities can be found in file: \\$MOLCAS/basis_library/basis.tbl\n\n#### 8.49.0.2.4 Polarized basis sets\n\nThe so-called polarized basis sets are purpose oriented, relatively small GTO/CGTO sets devised for the purpose of accurate calculations of dipole electric properties of polyatomic molecules [166,167,168,169,170]. For each row of the periodic table the performance of the basis sets has been carefully examined in calculations of dipole moments and dipole polarizabilities of simple hydrides at both the SCF and correlated levels of approximation [166,167,168,169,170]. The corresponding results match within a few percent the best available experimental data. Also the calculated molecular quadrupole moments turn out to be fairly close to those computed with much larger basis sets. According to the present documentation the polarized basis GTO/CGTO sets can be used for safe accurate predictions of molecular dipole moments, dipole polarizabilities, and also molecular quadrupole moments by using high-level correlated computational methods. The use of the polarized basis sets has also been investigated in calculations of weak intermolecular interactions. The interaction energies, corrected for the basis set superposition effect (BSSE), which is rather large for these basis sets, turn out to be close to the best available data. In calculations for molecules involving the 4th row atoms, the property data need to be corrected for the relativistic contribution. The corresponding finite perturbation facility is available [171,172].\n\nIt is recommended to use these basis sets with the contraction given in the library. It is of course possible to truncate them further, for example by deleting some polarization functions, but this will lead to a deterioration of the computed properties.\n\n### 8.49.0.3 Structure of the all electron basis set library\n\nThe start of a given basis set in the library is given by the line\n\n /label\n\nwhere label'' is the basis set label, as defined below in the input description to SEWARD. Then follows two lines with the appropriate literature reference for that basis set. These cards are read by SEWARD and must thus be included in the library, and may not be blank. Next is a set of comment lines, which begin with an asterisk in column 1, giving some details of the basis sets. A number of lines follow, which specifies the basis set:\n\n1. Charge of the atom and the highest angular momentum. For each angular momentum (l) then follows.\n2. Number of primitives and contracted functions for angular momentum l (must be identical to those given in the basis set label) .\n3. Exponents of the primitive functions .\n4. The contraction matrix (with one CGTO per column). Note that all basis sets are given in the generally contracted format, even if they happen to be segmented. Note that the number of CGTOs must correspond to the data given in the label .\n\nThe following is an example of an entry in a basis set library.\n\n* This is the Huzinaga 5s,2p set contracted to 3s,2p -- Comment\n* according to the Dunning paper. -- Comment\n/H.TZ2P.Dunning.5s2p.3s2p. -- Label\nExponents : S. Huzinaga, J. Chem. Phys., 42, 1293(1965). -- First ref line\nCoefficients: T. H. Dunning, J. Chem. Phys., 55, 716(1971). -- Second ref line\n1.0 1 -- Charge, sp\n5 3 -- 5s->3s\n52.56 7.903 1.792 0.502 0.158 -- s-exponents\n0.025374 0.0 0.0 -- contr. matrix\n0.189684 0.0 0.0 -- contr. matrix\n0.852933 0.0 0.0 -- contr. matrix\n0.0 1.0 0.0 -- contr. matrix\n0.0 0.0 1.0 -- contr. matrix\n2 2 -- 2p->2p\n1.5 0.5 -- p-exponents\n1.0 0.0 -- contr. matrix\n0.0 1.0 -- contr. matrix\n\n### 8.49.0.4 The ECP Library\n\nMOLCAS is able to perform effective core potential (ECP) calculations and embedded cluster calculations. In ECP calculations, only the valence electrons of a molecule are explicitly handled in a quantum mechanical calculation, at a time that the core electrons are kept frozen and are represented by ECP's. (An example of this is a calculation on HAt in which only the 5d, 6s and 6p electrons of Astatine and the one of Hydrogen are explicitly considered.) Similarly, in embedded cluster calculations, only the electrons assigned to a piece of the whole system (the cluster) are explicitly handled in the quantum mechanical calculation, under the assumption that they are the only ones relevant for some local properties under study; the rest of the whole system (the environment) is kept frozen and represented by embedding potentials which act onto the cluster. (As an example, calculations on a TlF1211- cluster embedded in a frozen lattice of KMgF3 can be sufficient to calculate spectroscopical properties of Tl+-doped KMgF3 which are due to the Tl+ impurity.)\n\nIn order to be able to perform ECP calculations in molecules, as well as embedded cluster calculations in ionic solids, with the Ab Initio Model Potential method (AIMP) [173,174,175,176,177,178] MOLCAS is provided with the library ECP which includes nonrelativistic and relativistic core ab initio model potentials and embedding ab initio model potentials representing both complete-cations and complete-anions in ionic lattices [174,179].\n\nBefore we continue we should comment a little bit on the terminology used here. Strictly speaking, ECP methods are all that use the frozen-core approximation. Among them, we can distinguish two families: the pseudopotential' methods and the model potential' methods. The pseudopotential methods are ultimately based on the Phillips-Kleinman equation and handle valence nodeless pseudo orbitals. The model potential methods are based on the Huzinaga equation [181,182] and handle node-showing valence orbitals; the AIMP method belongs to this family. Here, when we use the general term ECP we will be referring to the more particular of AIMP. According to its characteristics, the AIMP method can be also applied to represent frozen-ions in ionic lattices in embedded cluster calculations; in this case, we will not be very strict in the nomenclature and we will also call ECP's to the frozen-ion (embedding) ab initio model potentials.\n\nThe effective potentials in the libraries include the effects of the atomic core wave functions (embedding ion wave functions) through the following operators:\n\n• a local representation of the core (ion) Coulomb operator,\n• a non-local spectral representation of the core (ion) exchange operator,\n• a core (ion) projection operator,\n• a spectral representation of the relativistic mass-velocity and Darwin operators corresponding to the valence orbitals, if the Cowan-Griffin-based scalar relativistic CG-AIMP method is used.\n• a spectral representation of the relativistic no-pair Douglas-Kroll operators, if the scalar relativistic no-pair Douglas-Kroll NP-AIMP method [176,177,178] is used.\n\nGiven the quality and non-parametric nature of the operators listed above, the flexibility of the basis sets to be used with the AIMP's is crucial, as in any ab initio method.\n\nThe valence basis sets included in the libraries have been obtained by energy minimization in atomic valence-electron calculations, following standard optimization procedures. All the experience gathered in the design of molecular basis sets starting from all-electron atomic basis sets, and in particular from segmented minimal ones, is directly applicable to the AIMP valence basis sets included in the libraries. They are, for non-relativistic and relativistic Cowan-Griffin AIMPs, minimal basis sets with added functions, such as polarization and diffuse functions; in consequence, the minimal sets should be split in molecular calculations in order to get reasonable sets (a splitting pattern is recommended in the library for every set); the splitting can be done by means of the basis set label'. For the relativistic no-pair Douglas-Kroll AIMPs contracted valence basis sets are given directly in a form which is recommended in molecular calculations, i.e. they are of triple zeta quality in the outer shells and contain polarization functions. In both cases these valence basis sets contain very inner primitive GTF's: They are necessary since, typical to a model potential method, the valence orbitals will show correct nodal structure. Finally, it must be noted that the core AIMP's can be safely mixed together with all-electron basis sets.\n\nIn AIMP embedded cluster calculations, the cluster basis set, which must be decided upon by the user, should be designed following high quality standard procedures. Very rigid cluster basis sets should not be used. In particular, the presence of the necessary embedding projection operators, which prevent the cluster densities from collapsing onto the crystal lattice, demands flexible cluster bases, including, eventually, components outside the cluster volume. The use of flexible cluster basis sets is then a necessary requirement to avoid artificial frontier effects, not ascribable to the AIMP embedding potentials. This requirement is unavoidable, anyway, if good correlated wave functions are to be calculated for the cluster. Finally, one must remember that the AIMP method does exclude any correlation between the cluster electronic group and the embedding crystal components; in other words, only intra-cluster correlation effects can be accounted for in AIMP embedded cluster calculations. Therefore the cluster-environment partition and the choice of the cluster wave function must be done accordingly. In particular, the use of one-atom clusters is not recommended.\n\nCore- and embedding- AIMP's can be combined in a natural way in valence-electron, embedded cluster calculations. They can be used with any of the different types of wave functions that can be calculated with MOLCAS.\n\n#### 8.49.0.4.1 Core AIMP's\n\nThe list of core potentials and valence basis sets available in the ECP library follows. Although AIMP's exist in the literature for different core sizes, this library includes only those recommended by the authors after numerical experimentation. Relativistic CG-AIMP's and NP-AIMP's, respectively, and nonrelativistic NR-AIMP's are included. Each entry of the CG-AIMP's and the NR-AIMP's in the list is accompanied with a recommended contraction pattern (to be used in the fifth field). The NP-AIMP basis sets are given explicitly in the recommended contraction pattern. For the third-row transition metals two NP-AIMP basis sets are provided which differ in the number of primitive and contracted f GTFs. For further details, please refer to the literature. For more information about a particular entry consult the ECP library.\n\nThe ECP libraries have also been extended to include the so-called nodeless ECPs or pseudo potentials based on the Phillips-Kleinman equation . These are included both as explicit and implicit operators. Following the work by M. Pelissier and co-workers the operators of nodeless ECPs can implicitly be fully expressed via spectral representation of operators. The explicit libraries are the ECP.STOLL and ECP.HAY-WADT files, all other files are for the implicitly expressed operator. In the list of nodeless ECPs the Hay and Wadt's family of ECPs (LANL2DZ ECPs) [185,186,187] has been included in addition to the popular set of the so-called Stoll and Dolg ECPs [188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212]. Both of them in either the explicit form labeled as HAY-WADT and STOLL, or in the implicit form labeled as HW and DOLG. The latter include the recently developed ANO-basis sets for actinides .\n\n### 8.49.0.5 Structure of the ECP libraries\n\nThe start of a given basis set and AIMP is identified by the line",
null,
"where label\" is defined below, in the input description to seward. Then, comment lines, effective charge, and basis set follow, with the same structure that the all-electron Basis Set Library (see items 1. to 4. in Sec.",
null,
".) Next, the AIMP/ECP/PP is specified as follows:\n\n1. The pseudo potential approach [213,214,215], see eqs. (3) and (4) in Ref. , with the following lines:\n1. The keyword PP On the same line follows the atomic symbol of the element, the number of core electrons (Nc) and L, where L-1 is the largest angular momentum orbital belonging to the core. This line is followed by L+1 identical sections. The first of these sections is the so-called L potential and the subsequent sections corresponds to the S-L, P-L, D-L, etc. potentials. Each sections start with a line specifying the number of Gaussian terms in the potential. This line is then followed by a single line for each Gaussian specifying the powers (nkl), the Gaussian exponent (",
null,
"), and the associated coefficient (dkl).\nNote that the pseudo potential input is mutually exclusive to the M1, M2, COREREP, and PROJOP keywords!\n2. The Coulomb local model potential, eq.(6) in Ref. with the following lines:\n1. The keyword M1,which identifies the terms with nk=0.\n2. The number of terms. If greater than 0, lines",
null,
"and",
null,
"are read.\n3. The exponents",
null,
".\n4. The coefficients Ak (divided by the negative of the effective charge).\n5. The keyword M2,which identifies the terms with nk=1.\n6. The number of terms. If greater than 0, lines",
null,
"and",
null,
"are read.\n7. The exponents",
null,
".\n8. The coefficients Ak (divided by the negative of the effective charge).\n3. A line with the keyword COREREP followed by another one with a real constant. This is not used now but it is reserved for future use.\n4. The projection operator, eq.(3) in Ref. with the following lines:\n1. The keyword PROJOP.\n2. The maximum angular momentum (l) of the frozen core (embedding) orbitals. Lines",
null,
"to",
null,
"are repeated for each angular momentum l.\n3. The number of primitives and the number of orbitals (more properly, degenerate sets of orbitals or l-shells) for angular momentum l. As an option, these two integers can be followed by the occupation numbers of the l-shells; default values are 2 for l=0, 6 for l=1, etc.\n4. The projection constants,",
null,
".\n5. The exponents of the primitive functions.\n6. The coefficients of the orbitals, one per column, using general contraction format.\n5. The spectral representation operator, eq.(7) in Ref. for NR-AIMP, eq.(3) in Ref. for relativistic CG-AIMP, and eqs.(1) and (7) in Ref. for relativistic NP-AIMP, with the following lines:\n1. The keyword Spectral Representation Operator.\n2. One of the keywords Valence, Core, or External. Valence indicates that the set of primitive functions specified in the basis set data will be used for the spectral representation operator; this is the standard for ab initio core model potentials. Core means that the set of primitives specified in the PROJOP section will be used instead; this is the standard for complete-ion ab initio embedding model potentials. External means that a set of primitives specific for the spectral representation operator will be provided in the next lines. In this case the format is one line in which an integer number specifies the highest angular momentum of the external basis sets; then, for each angular momentum the input is formated as for lines",
null,
",",
null,
", and",
null,
"in Sec.",
null,
".\n3. The keyword Exchange.\n4. For relativistic AIMPs one of the keywords NoPair or 1stOrder Relativistic Correction. NoPair indicates that scalar relativistic no-pair Douglas-Kroll AIMP integrals are to be calculated. 1stOrder Relativistic Correction means that Cowan-Griffin-based scalar relativistic AIMP, CG-AIMP's, are used. In the latter case, in the next line a keyword follows which, in the library QRPLIB, identifies the starting of the numerical mass-velocity plus Darwin potentials (eq.(2) in Ref. ). (In QRPLIB a line with keyword mv&dw potentials start\" must exist, followed by the number of points in the radial logarithmic grid, the values of the radial coordinate r, and, for each valence orbital, its label (2S, 4P, etc), and the values of the mass-velocity plus Darwin potentials at the corresponding values of r; these data must end up with a line keyword mv&dw potentials end\".)\n5. The keyword End of Spectral Representation Operator.\n\nBelow is an example of an entry in the ECP library for an AIMP.\n\n/S.ECP.Barandiaran.7s6p1d.1s1p1d.6e-CG-AIMP. -- label (note that type is ECP)\nZ.Barandiaran and L.Seijo, Can.J.Chem. 70(1992)409. -- 1st ref. line\ncore[Ne] val[3s,3p] (61/411/1*)=2s3p1d recommended -- 2nd ref. line\n*SQR-SP(7/6/1) (61/411/1) -- comment line\n6.000000 2 -- eff. charge & highest ang.mom.\n-- blank line\n7 1 -- 7s -> 1s\n1421.989530 -- s-exponent\n211.0266560 -- s-exponent\n46.72165060 -- s-exponent\n4.310564040 -- s-exponent\n1.966475840 -- s-exponent\n.4015383790 -- s-exponent\n.1453058790 -- s-exponent\n.004499703540 -- contr. coeff.\n.030157124800 -- contr. coeff.\n.089332590700 -- contr. coeff.\n-.288438151000 -- contr. coeff.\n-.279252515000 -- contr. coeff.\n.700286615000 -- contr. coeff.\n.482409523000 -- contr. coeff.\n6 1 -- 6p -> 1p\n78.08932440 -- p-exponent\n17.68304310 -- p-exponent\n4.966340810 -- p-exponent\n.5611646780 -- p-exponent\n.2130782690 -- p-exponent\n.8172415400E-01 -- p-exponent\n-.015853278200 -- contr. coeff.\n-.084808963800 -- contr. coeff.\n-.172934245000 -- contr. coeff.\n.420961662000 -- contr. coeff.\n.506647309000 -- contr. coeff.\n.200082121000 -- contr. coeff.\n1 1 -- 1d -> 1d\n.4210000000 -- d-exponent\n1.000000000000 -- contr. coeff.\n* -- comment line\n* Core AIMP: SQR-2P -- comment line\n* -- comment line\n* Local Potential Parameters : (ECP convention) -- comment line\n* A(AIMP)=-Zeff*A(ECP) -- comment line\nM1 -- M1 operator\n9 -- number of M1 terms\n237485.0100 -- M1 exponent\n24909.63500 -- M1 exponent\n4519.833100 -- M1 exponent\n1082.854700 -- M1 exponent\n310.5610000 -- M1 exponent\n96.91851000 -- M1 exponent\n26.63059000 -- M1 exponent\n9.762505000 -- M1 exponent\n4.014487500 -- M1 exponent\n-- blank line\n.019335998333 -- M1 coeff.\n.031229360000 -- M1 coeff.\n.061638463333 -- M1 coeff.\n.114969451667 -- M1 coeff.\n.190198283333 -- M1 coeff.\n.211928633333 -- M1 coeff.\n.336340950000 -- M1 coeff.\n.538432350000 -- M1 coeff.\n.162593178333 -- M1 coeff.\nM2 -- M2 operator\n0 -- number of M2 terms\nCOREREP -- CoreRep operator\n1.0 -- CoreRep constant\nPROJOP -- Projection operator\n1 -- highest ang. mom.\n8 2 -- 8s -> 2s\n184.666320 18.1126960 -- 1s,2s proj. op. constants\n3459.000000 -- s-exponent\n620.3000000 -- s-exponent\n171.4000000 -- s-exponent\n58.53000000 -- s-exponent\n22.44000000 -- s-exponent\n6.553000000 -- s-exponent\n2.777000000 -- s-exponent\n1.155000000 -- s-exponent\n.018538249000 .005054826900 -- contr. coeffs.\n.094569248000 .028197248000 -- contr. coeffs.\n.283859290000 .088959130000 -- contr. coeffs.\n.454711270000 .199724180000 -- contr. coeffs.\n.279041370000 .158375340000 -- contr. coeffs.\n.025985763000 -.381198090000 -- contr. coeffs.\n-.005481472900 -.621887210000 -- contr. coeffs.\n.001288714400 -.151789890000 -- contr. coeffs.\n7 1 -- 7p -> 1p\n13.3703160 -- 2p proj. op. constant\n274.0000000 -- p-exponent\n70.57000000 -- p-exponent\n24.74000000 -- p-exponent\n9.995000000 -- p-exponent\n4.330000000 -- p-exponent\n1.946000000 -- p-exponent\n.8179000000 -- p-exponent\n.008300916100 -- cont. coeff.\n.048924254000 -- cont. coeff.\n.162411660000 -- cont. coeff.\n.327163550000 -- cont. coeff.\n.398615170000 -- cont. coeff.\n.232548200000 -- cont. coeff.\n.034091088000 -- cont. coeff.\n* -- comment line\nSpectral Representation Operator -- SR operator\nValence primitive basis -- SR basis specification\nExchange -- Exchange operator\n1stOrder Relativistic Correction -- mass-vel + Darwin oper.\nSQR-2P -- label in QRPLIB\nEnd of Spectral Representation Operator -- end of SR operator\n\n\nBelow is an example of an entry in the ECP library for a pseudo potential.\n\n/Hg.ECP.Dolg.4s4p2d.2s2p1d.2e-MWB -- label (note the type ECP)\nW. Kuechle, M. Dolg, H. Stoll, H. Preuss, Mol. Phys.-- ref. line 1\n74, 1245 (1991); J. Chem. Phys. 94, 3011 (1991). -- ref. line 2\n2.00000 2 -- eff. charge & highest ang.mom.\n*s functions -- comment line\n4 2 -- 4s -> 2s\n0.13548420E+01 -- s-exponent\n0.82889200E+00 -- s-exponent\n0.13393200E+00 -- s-exponent\n0.51017000E-01 -- s-exponent\n0.23649400E+00 0.00000000E+00 -- contr. coeff.\n-0.59962800E+00 0.00000000E+00 -- contr. coeff.\n0.84630500E+00 0.00000000E+00 -- contr. coeff.\n0.00000000E+00 0.10000000E+01 -- contr. coeff.\n*p functions -- comment line\n4 2 -- 4p -> 2p\n0.10001460E+01 -- p-exponent\n0.86645300E+00 -- p-exponent\n0.11820600E+00 -- p-exponent\n0.35155000E-01 -- p-exponent\n0.14495400E+00 0.00000000E+00 -- contr. coeff.\n-0.20497100E+00 0.00000000E+00 -- contr. coeff.\n0.49030100E+00 0.00000000E+00 -- contr. coeff.\n0.00000000E+00 0.10000000E+01 -- contr. coeff.\n*d functions -- comment line\n1 1 -- 1d -> 1d\n0.19000000E+00 -- d-exponent\n0.10000000E+01 -- contr. coeff.\n* -- comment line\nPP,Hg,78,5; -- PP operator, label, # of core elec., L\n1; ! H POTENTIAL -- # number of exponents in the H potential\n2, 1.00000000,.000000000; -- power, exponent and coeff.\n3; ! S-H POTENTIAL -- # number of exponents in the S-H potential\n2,0.227210000,-.69617800; -- power, exponent and coeff.\n2, 1.65753000,27.7581050; -- power, exponent and coeff.\n2, 10.0002480,48.7804750; -- power, exponent and coeff.\n2; ! P-H POTENTIAL -- # number of exponents in the P-H potential\n2,0.398377000,-2.7358110; -- power, exponent and coeff.\n2,0.647307000,8.57563700; -- power, exponent and coeff.\n2; ! D-H POTENTIAL -- # number of exponents in the D-H potential\n2,0.217999000,-.01311800; -- power, exponent and coeff.\n2,0.386058000,2.79286200; -- power, exponent and coeff.\n1; ! F-H POTENTIAL -- # number of exponents in the F-H potential\n2,0.500000000,-2.6351640; -- power, exponent and coeff.\n1; ! G-H POTENTIAL -- # number of exponents in the G-H potential\n2,0.800756000,-13.393716; -- power, exponent and coeff.\n* -- comment line\nSpectral Representation Operator -- SR operator\nEnd of Spectral Representation Operator -- end of SR operator\n`",
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"Next: 9. GUI Up: 8. Programs Previous: 8.48 vibrot",
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https://www.lurninghub.com/single-post.php?postid=84
|
[
"",
null,
"//\n\n## Find the common factors of the given terms. (i) 12𝑥, 36 (ii) 2𝑦, 22𝑥𝑦 (iii) 14𝑝𝑞, 28𝑝2𝑞2About 2 years ago\n\nSubject : Mathematics Class : Class 8\n\nPosted By :",
null,
"Rajneesh\n\n(i) 12𝑥 = 2 x 2 x 3 x X\n\n36 = 2 x 2 x 3 x 3\n\nThe common factor in both the terms as you may notice is 2 x 2 x 3 = 12\n\n(ii) 2y = 2 x y\n\n22xy = 2 x 11 x X x y\n\nso the common factors in both the terms are 2 x y = 2y\n\n(iii) 14pq = 2 x 7 x p x q\n\n28p^2q^2 = 2 x 2 x 7 x p x p x q x q\n\nthe common factors in both the terms as you may notice is = 2 x 7 x p x q = 14pq"
] |
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|
https://practice-questions.wizako.com/gmat/quant/algebra/linear-equation-absolute-value-12.shtml
|
[
"# GMAT Maths | Algebra Questions #12\n\n#### GMAT Practice Questions | Linear Equations & Absolute Values\n\nThis GMAT sample question is a quant problem solving question in Linear Equations. Concept tested: Finding the number of solutions for linear equations where one of the unknowns is the absolute value (modulus) of a variable. A medium difficulty, 650 to 700 level GMAT practice question in algebra.\n\nQuestion 12 : If x > 0, how many integer values of (x, y) will satisfy the equation 5x + 4|y| = 55?\n\n1. 3\n2. 6\n3. 5\n4. 4\n5. Infinitely many\n\n## GMAT Live Online Classes\n\n#### Starts Sun, Dec 12, 2021\n\n5x + 4|y| = 55\nThe equation can be rewritten as 4|y| = 55 - 5x.\nInference 1: Because |y| is non-negative, 4|y| will be non-negative.\nTherefore, (55 - 5x) cannot take negative values.\n\nInference 2: Because x and y are integers, 4|y| will be a multiple of 4.\nTherefore, (55 - 5x) will also be a multiple of 4.\n\nInference 3: 55 is a multiple of 5. 5x is a multiple of 5 for integer x.\nSo, 55 - 5x will always be a multiple of 5 for any integer value of x.\n\nCombining Inference 2 and Inference 3: 55 - 5x will be a multiple of 4 and 5.\ni.e., 55 - 5x will be a multiple of 20.\n\n#### Integer values of x > 0 that will satisfy the condition that (55 - 5x) is a multiple of 20:\n\n1. x = 3, 55 - 5x = 55 - 15 = 40.\n2. x = 7, 55 - 5x = 55 - 35 = 20\n3. x = 11, 55 - 5x = 55 - 55 = 0.\nWhen x = 15, (55 - 5x) = (55 - 75) = -20.\nBecause (55 - 5x) has to non-negative, x = 15 or values greater than 15 are not possible.\nSo, x can take only 3 values viz., 3, 7, and 11.\n\n#### Possible values of y when x = 3, x = 7, and x = 11\n\nWe have 3 possible values for 55 - 5x. So, we will have these 3 values possible for 4|y|.\nPossibility 1: 4|y| = 40 or |y| = 10. So, y = 10 or -10.\nPossibility 2: 4|y| = 20 or |y| = 5. So, y = 5 or -5.\nPossibility 3: 4|y| = 0 or |y| = 0. So, y = 0.\n\nNumber of values possible for y = 5.\n\n#### GMAT Online CourseTry it free!\n\nRegister in 2 easy steps and\nStart learning in 5 minutes!\n\n#### GMAT Live Online Classes\n\nNext Batch Dec 12, 2021\n\n##### Where is Wizako located?\n\nWizako - GMAT, GRE, SAT Prep\nAn Ascent Education Initiative\n14B/1 Dr Thirumurthy Nagar 1st Street\nNungambakkam\nChennai 600 034. India\n\n##### How to reach Wizako?\n\nPhone: (91) 44 4500 8484\nMobile: (91) 95000 48484\nWhatsApp: WhatsApp Now\nEmail: [email protected]"
] |
[
null
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https://plainmath.net/18674/investor-investments-return-investment-depends-whether-economy-strong
|
[
"",
null,
"An investor plans to put $50,000 in one of four investments. The return on each investment depends on whether next year’s economy is strong or weak. T",
null,
"Yulia 2021-07-02 Answered An investor plans to put$50,000 in one of four investments. The return on each investment depends on whether next year’s economy is strong or weak. The following table summarizes the possible payoffs, in dollars, for the four investments.\nCertificate of deposit\nOffice complex\nLand speculation\nTechnical school\namp; Strong amp;6,000 amp;15,000 amp;33,000 amp;5,500\namp; Weak amp;6,000 amp;5,000 amp;−17,000 amp;10,000\nLet V, W, X, and Y denote the payoffs for the certificate of deposit, office complex, land speculation, and technical school, respectively. Then V, W, X, and Y are random variables. Assume that next year’s economy has a 40% chance of being strong and a 60% chance of being weak. a. Find the probability distribution of each random variable V, W, X, and Y. b. Determine the expected value of each random variable. c. Which investment has the best expected payoff? the worst? d. Which investment would you select? Explain.\n\n• Questions are typically answered in as fast as 30 minutes\n\nSolve your problem for the price of one coffee\n\n• Math expert for every subject\n• Pay only if we can solve it",
null,
"estenutC\n\na) $$v | 600$$\n$$P(V=v) | 1$$\n$$w | 15000\\ 5000$$\n$$P(W=w) | 0.40 0.60$$\n$$x | 33000 -17000$$\n$$P(X=x) | 0.40 0.60$$\n$$y | 5500\\ 10000$$\n$$P(Y=y) | 0.40 0.60$$\nb) 6000, 9000, 3000, 8200\nc) Office complex W, Land speculation X\nd) Investment of office complex W"
] |
[
null,
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http://www.tstat.it/specifiche/multivariate-methods/
|
[
"Factor analysis\n\nWorks on datasets or correlation matrices\n\nPrincipal-components factor\n\nPrincipal factor\n\nInterated principal factor\n\nML factors\n\nRotations\n\nOrthogonal and oblique rotations\n\nKaiser normalization\n\nVarimax, quartimax, oblimax, parsimax, equamax, and promax rotation\n\nMinimum entropy rotation\n\nComrey’s tandem\n\nRotate toward a target matrix\n\nAnti-image correlation matrices\n\nKaiser–Meyer–Olkin measure of sampling adequacy\n\nLoading plots, score plots, and scree plots\n\nSquared multiple correlations\n\nBartlett scoring\n\nRegression scoring",
null,
"Principal components\n\nWorks with datasets or correlation or covariance matrices\n\nStandard errors of eigenvalues and vectors\n\nAnti-image correlation matrices\n\nKaiser–Meyer–Olkin measure of sampling adequacy\n\nLoading plots, score plots, and scree plots\n\nSquared multiple correlations\n\nRotations\n\nOrthogonal and oblique rotations\n\nKaiser normalization\n\nVarimax, quartimax, oblimax, parsimax, equamax, and promax rotation\n\nMinimum entropy rotation\n\nComrey’s tandem\n\nRotate toward a target matrix\n\nDiscriminant analysis\n\nLinear\n\nLogistic\n\nkth nearest neighbor\n\nClassification tables\n\nError rates\n\nZellner’s seemingly unrelated regression\n\nTwo-step or maximum likelihood estimates\n\nLinear constraints\n\nBreusch-Pagan test for independent equations\n\nMultivariate linear regression\n\nBreusch–Pagan test for independent equations\n\nBayesian multivariate regression\n\nProcrustes analysis\n\nOrthogonal, oblique, and unrestricted transformations\n\nOverlayed graphs comparing target variables and fitted values of source variables\n\nCanonical correlations\n\nCorrelation matrices\n\nRotate raw coefficients, standard coefficients, or loading matrices\n\nCompare rotated and unrotated coefficients or loadings\n\nPlot canonical correlations\n\nTetrachoric correlations\n\nMaximum likelihood or noniterative Edwards and Edwards estimator\n\nTetrachoric correlation coefficient and standard error\n\nExact two-sided significance level\n\nStructural equation modeling (SEM)\n\nComplete implementation\n\nLatent class analysis\n\nIncluding latent profile analysis\n\nIncluding finite mixture models\n\nMarginal probabilities and marginal means\n\nEvaluate goodness of fit\n\nPredict probabilities of class membership and values of observed outcome variables\n\nCluster analysis\n\nComplete implementation\n\nMANOVA\n\nComplete implementation\n\nMultivariate tests\n\nOne- and multisample\n\nMeans, covariances, and correlations\n\nTests of normality\n\nDoornik–Hansen\n\nHenze–Zirkler\n\nTwo by Mardia\n\nMultidimensional scaling\n\nModern metric and nonmetric multidimensional scaling\n\nClassic metric multidimensional scaling\n\nWorks with two-way data, proximity data in long format, and proximity data in a matrix\n\n33 similarity/dissimilarity measures\n\nCoordinates of approximating configuration\n\nCorrelations between dissimilarities and distances\n\nKruskal stress measure\n\nShepard diagram\n\nPlots of approximating Euclidean configuration\n\nCorrespondence analysis\n\nTwo-way correspondence analysis\n\nWork with cross-tabulations of categorical variables or matrices of counts\n\nStacked (crossed) variables\n\nFitted, observed, and expected correspondence tables\n\nCoordinates in column space\n\nCoordinates in row space (with two-way CA)\n\nRow and column profiles (conditional distributions)\n\nChi-squared distances\n\nCorrelations of profiles and axes\n\nInertia contributions\n\nBiplots\n\nProjection plots\n\nMultiple and joint correspondence analysis (MCA and JCA)\n\nWork with cross-tabulations of categorical variables\n\nStacked (crossed) variables\n\nCoordinates in column space\n\nProjection plots\n\nMatrix of inertias (after JCA)\n\nPostestimation Selector\n\nView and run all postestimation features for your command\n\nAutomatically updated as estimation commands are run\n\nWatch Postestimation Selector.\n\nBiplots\n\nDisplay your choice of any two biplot dimensions\n\nDistinguish groups of data within the biplot\n\nDisplay table of biplot coordinates\n\nGenerate new variables containing biplot coordinates\n\nHotelling’s T-squared\n\nCronbach’s alpha\n\nInteritem correlations or covariances\n\nGenerate summative scale\n\nAutomatically reverse sense of variables"
] |
[
null,
"http://www.stata.com/features/i/db-factor-model2.png",
null
] |
{"ft_lang_label":"__label__en","ft_lang_prob":0.67982167,"math_prob":0.9204528,"size":4035,"snap":"2022-40-2023-06","text_gpt3_token_len":823,"char_repetition_ratio":0.13272141,"word_repetition_ratio":0.15416667,"special_character_ratio":0.13729864,"punctuation_ratio":0.048507463,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99325,"pos_list":[0,1,2],"im_url_duplicate_count":[null,5,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2022-10-07T15:08:29Z\",\"WARC-Record-ID\":\"<urn:uuid:014e9fcd-908c-46cb-bede-9aedd52938b9>\",\"Content-Length\":\"181375\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:cca02000-9db4-40bc-ac16-6e1d8a0acaea>\",\"WARC-Concurrent-To\":\"<urn:uuid:659ff08c-99db-42da-b292-1e6c7d288516>\",\"WARC-IP-Address\":\"80.88.87.48\",\"WARC-Target-URI\":\"http://www.tstat.it/specifiche/multivariate-methods/\",\"WARC-Payload-Digest\":\"sha1:XQ6YMXNHG6XZMKJ3WLDJCWIKEHZN5CQA\",\"WARC-Block-Digest\":\"sha1:I6LLH3QSPTQBB66KR5JFIFRIMZXWHDVV\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2022/CC-MAIN-2022-40/CC-MAIN-2022-40_segments_1664030338213.55_warc_CC-MAIN-20221007143842-20221007173842-00169.warc.gz\"}"}
|
https://cstheory.stackexchange.com/questions/17772/zero-obstructed-vertex-induced-subgraphs
|
[
"# Zero Obstructed vertex induced subgraphs\n\nLet $G=(V,E)$ be a $3$-regular graph. Let a vertex induced subgraph of $G$ be $i$ extendible if and only if it has both the following properties:\n\n• It has no isolated vertices.\n• It is possible to add $i$ vertices to it without implying (by the very definition of vertex induced subgraph) the introduction of any new edge (in other words, it is possible to add $i$ isolated vertices).\n\nClearly, if a vertex induced subgraph is $i$ extendible it is also $j$ extendible for any $j < i$.\n\nNow, let a vertex induced subgraph of $G$ be $i$ obstructed if and only if it is $i$ extendible but not $i+1$ extendible.\n\nI'm interested in $0$ obstructed vertex induced subgraphs.\n\nQuestions\n\n1. Let $N$ be the number of $0$ obstructed vertex induced subgraphs of $G$. Is $N$ polynomially bounded in the size of $G$? I believe yes (and I've made some empirical tests that seem to suggest so), but so far I was unable to prove it.\n2. If the answer to question 1. is no, is it nevertheless possible to compute $N$ in polynomial time?\n3. If the answer to question 2. is no, is it nevertheless possible to compute $N\\ mod\\ 2$ in polynomial time?\n\nI'm curious to know if anyone already dealt with $0$ obstructed vertex induced subgraphs previously, how he encountered them in the first place, and what is known about them.\n\nAnother way to express question 1. is the following: which is the maximum number $\\alpha$ of vertices that can be removed from $G$ without making it $1$ extendible? My sensation is that $\\alpha \\in O( log\\ |V| )$, as I'm inclined to believe that $\\alpha$ is strongly related to the diameter of $G$.\n\n• Maybe I miss something, but I think every induced subgraph of a complete graph is 0 obstructed, i.e., $N$ is not polynomially bounded. – Marc Bury May 24 '13 at 13:34\n• @MarcGillé: You are absolutely right in saying that my conjecture fails with the complete graph. However, such class is trivial and thus uninteresting. Let us focus on non trivial classes (by non trivial I mean those where computing $N$ is non trivial), like $3$-regular graphs. I've clarified the beginning of the question accordingly. – Giorgio Camerani May 24 '13 at 13:56\n• I presume you mean with your second condition that for the induced subgraph $G[V']$ (where $V' \\subseteq V$), there should be some $V'' \\supseteq V'$ such that $|V''| = |V'| + i$ and the induced subgraph $G[V'']$ contains the same edges as $G[V']$? – András Salamon May 25 '13 at 14:40\n• @AndrásSalamon: Exactly, you are right. – Giorgio Camerani May 25 '13 at 16:29\n\n## 1 Answer\n\nSorry this is too long for a comment :)\n\nMaybe the complete graph is a trivial graph but it illustrates that e.g. graphs $G = (V,E)$ with large cliques of size $\\Omega(\\vert V \\vert)$ also have exponentially many $0$ obstructed induced subgraphs. And I don't think that $N$ can be easily computed in such graphs.\n\nI also think that 3-regularity doesnt help: Let $A$, $B$ be a partition of $V$ each of size $\\vert V \\vert /2$. The induced subgraph consisting of nodes from either $A$ or $B$ is a cycle and the edges between $A$ and $B$ form a perfect matching. This graph is $3$-regular and every induced subgraph consisting of at least all nodes in $A$ or all nodes in $B$ is $0$ obstructed."
] |
[
null
] |
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|
https://blog.planhack.com/logs/data/PythonSetsVsLists.html
|
[
"Python's sets can run a bit slower than lists (~4x slower). The problem below just figures out how many words in /usr/share/dict/words can be made from the letters of \"paleontology\" (especially if any of length 8 exist ;).\n\n# from http://my-sketches.livejournal.com/104876.html import sets, sys if sys.argv not in [\"sets\", \"lists\"]: sys.exit(\"sets or lists?\") def is_in(needle, haystack): test = False for n in needle: test = False for i,h in enumerate(haystack): if n == h: del(haystack[i]) test = True break if test == False: break return test def uniqueify(s): c = {} ret = [] for w in list(s): c[w] = c.get(w,0) + 1 ret.append(w + \"%s\" % c[w] ) return ret dict = \"/usr/share/dict/words\" f = open(dict, \"r\") words = [] for line in f: word = line.rstrip() if sys.argv == \"sets\": p = sets.Set(uniqueify(\"paleontology\")) if sets.Set(uniqueify(word)) <= p: print word if sys.argv == \"lists\": if is_in(list(word), list(\"paleontology\")): print word\n\nJust out of curiosity, do you regularly do much Python coding? If so, for what kind of problems do you use it as opposed to Perl? -- David W\nLet me rephrase David's question...Why in gods name would you choose Python over Perl??!?!?!?! :) - Nathan\nHeh, no. This isn't the religion thread ;) -- David W\nThe there's more than one way to do it idea makes larger projects difficult to maintain. I have no real need, I'm just dinking around to learn it and see what it can do (yes, I realize the Turing-complete-idiocy of that statement ;)."
] |
[
null
] |
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|
http://www.swmm456.com/2010/10/three-depths-in-link-in-swmm-5.html
|
[
"## Friday, October 1, 2010\n\n### Three Depths in a Link in SWMM 5",
null,
"Note: An explanation of the three depths in a Link in SWMM 5 and a plot of the upstream, middle and downstream link depth. The middle depth is an average of the upstream and downstream link depths. The plot of the variable depth or the middle depth is always between the upstream and downstream depths. All three depths are used in the computation of the St. Venant Flow in SWMM 5. The upstream area is a function of the upstream depth and the downstream area is a function of the downstream depth.\nThe dq4 term in dynamic.c uses the area upstream (a1) and area downstream (a2), the midpoint velocity, the sigma factor (a function of the link Froude number), the link length and the time step or\ndq4 = Time Step * Velocity * Velocity * (a2 – a1) / Link Length * Sigma\nthe dq3 term in dynamic.c uses the current midpoint area (a function of the midpoint depth), the sigma factor and the midpoint velocity\ndq3 = 2 * Velocity * ( Amid(current iteration) – Amid (last time step) * Sigma",
null,
""
] |
[
null,
"https://1.bp.blogspot.com/_bZpPqUkdxzE/TLRTAaIZ06I/AAAAAAAABig/1DNR3HibfXI/s640/clip_image002.jpg",
null,
"https://i2.wp.com/swmm5.org/wp-content/uploads/2020/10/image001.png",
null
] |
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|
https://mathematica.stackexchange.com/questions/202583/how-to-simplify-a-three-variables-expression-with-simplify
|
[
"# How to simplify a three variables expression with Simplify?\n\nI want to calculate the value of $$\\nabla\\cdot\\frac{1}{(x^2+y^2+z^2)^{1.5}}(x\\hat{x}+y\\hat{y}+z\\hat{z})$$. I used the following syntax:\n\ng[x_, y_, z_] = -x/(x^2 + y^2 + z^2)^1.5;\nh[x_, y_, z_] = -y/(x^2 + y^2 + z^2)^1.5;\ni[x_, y_, z_] = -z/(x^2 + y^2 + z^2)^1.5;\nk[x_, y_, z_] = D[g[x, y, z], x] + D[h[x, y, z], y] + D[i[x, y, z], z]\n\n\nit output\n\n(3. x^2)/(x^2 + y^2 + z^2)^2.5 + (3. y^2)/(x^2 + y^2 + z^2)^2.5 + ( 3. z^2)/(x^2 + y^2 + z^2)^2.5 - 3/(x^2 + y^2 + z^2)^1.5\n\nwhich is $$\\frac{3.\\ x^2}{(x^2 + y^2 + z^2)^{2.5}} + \\frac{3.\\ y^2}{(x^2 + y^2 + z^2)^{2.5}} +\\frac{3.\\ z^2}{(x^2 + y^2 + z^2)^{2.5}} - \\frac{3}{(x^2 + y^2 + z^2)^{1.5}}$$.\n\nI tried to simplify the expression with Simplify[%], but nothing changed. I also tried to simplify the part before the -, there is something I can't explain. When I input\n\nSimplify[(3. x^2)/(x^2 + y^2 + z^2)^2.5 + (3. y^2)/(x^2 + y^2 + z^2)^2.5 + ( 3. z^2)/(x^2 + y^2 + z^2)^2.5]\n\n\nit output\n\n(3. x^2 + 3. y^2 + 3. z^2)/(x^2 + y^2 + z^2)^2.5\n\nwhich is $$\\frac{3. x^2 + 3. y^2 + 3. z^2}{(x^2 + y^2 + z^2)^{2.5}}$$, but when I input\n\nSimplify[(3 x^2)/(x^2 + y^2 + z^2)^2.5 + (3 y^2)/(x^2 + y^2 + z^2)^2.5 + ( 3 z^2)/(x^2 + y^2 + z^2)^2.5]\n\n\nby removing the . after 3, the output became\n\n3/(x^2 + y^2 + z^2)^1.5\n\nwhich is $$\\frac{3}{(x^2 + y^2 + z^2)^{1.5}}$$, and it's what I want. However, if I add the subtracted part like\n\nSimplify[(3 x^2)/(x^2 + y^2 + z^2)^2.5\n+(3 y^2)/(x^2 + y^2 + z^2)^2.5\n+ (3 z^2)/(x^2 + y^2 + z^2)^2.5\n- 3/(x^2 + y^2 + z^2)^1.5]\n\n\nthe result became\n\n3 (x^2/(x^2 + y^2 + z^2)^2.5 + y^2/(x^2 + y^2 + z^2)^2.5 + z^2/(x^2 + y^2 + z^2)^2.5 - 1/(x^2 + y^2 + z^2)^1.5)\n\nwhich is $$3 (\\frac{x^2}{(x^2 + y^2 + z^2)^{2.5}} + \\frac{y^2}{(x^2 + y^2 + z^2)^{2.5}} + \\frac{z^2}{(x^2 + y^2 + z^2)^{2.5}} -\\frac{1}{(x^2 + y^2 + z^2)^{1.5}})$$.\n\nSo how to simplify this expression to the value of 0? Thanks and best regards!\n\n• Up to usual conventions, the numbers 0,1,2,3, and 4 are spelled by words in most cases. – user64494 Jul 23 '19 at 10:49\n\nTry\n\ng[x_, y_, z_] = -x/(x^2 + y^2 + z^2)^(15/10);\nh[x_, y_, z_] = -y/(x^2 + y^2 + z^2)^(15/10);\ni[x_, y_, z_] = -z/(x^2 + y^2 + z^2)^(15/10);\nk[x_, y_, z_] = D[g[x, y, z], x] + D[h[x, y, z], y] + D[i[x, y, z], z]",
null,
"Simplify[%]\n\n\ngives zero.\n\nTry to use exact numbers when possible.\n\nHowever, if I add the subtracted part like\n\nAgain, you used here exponents which are not exact number. If you use exact numbers, you get this:\n\nSimplify[(3 x^2)/(x^2 + y^2 + z^2)^(25/10) + (3 y^2)/(x^2 + y^2 + z^2)^(25/\n10) + (3 z^2)/(x^2 + y^2 + z^2)^(25/10) - 3/(x^2 + y^2 + z^2)^(15/10)]\n\n\nWhich gives zero.\n\nSo how to simplify this expression to the value of 0?\n\n• In my calculation, the expression exponents are 1.5 and 2.5, so it is not exactly the same with the example you gave. In your example, you used 25/10 as exponent, but Mathematica seems recognize the /10 as a division in the whole expression. – Amon Jul 23 '19 at 6:11\n• @Amon, you are right, I need parentheses around the exponents ofcourse. Will correct. Is it OK now? now it gives zero. – Nasser Jul 23 '19 at 6:19\n• yes, this time it worked! So we'd better use fractions than decimals in Mathematica? – Amon Jul 23 '19 at 6:53\n• @Amon, in general, when doing symbolic manipulations, rule of thumb I use is to stick to exact numbers/quantities all the time. This makes it easier and less headache for mathematica internal algorithms. If using inexact numbers, then mathematica internally would have to resort to numerical methods sometimes which are not exact ofcourse or makes it not able to do some simplification it could otherwise. Your question is a good example of this. – Nasser Jul 23 '19 at 6:57\n\nTry also this:\n\nvector1 = -(1/(x^2 + y^2 + z^2)^(3/2))*{x, y, z};\n\n\nThen\n\nDiv[vector1, {x, y, z}] // Simplify\n\n(* 0 *)\n\n\nand\n\nvector2 = -(1/(x^2 + y^2 + z^2)^(5/2))*{x, y, z};\n\nDiv[vector2, {x, y, z}] // Simplify\n\n(* 2/(x^2 + y^2 + z^2)^(5/2) *)\n\n\nHave fun!\n\n• Thank you! It also worked! – Amon Jul 23 '19 at 9:48\n\nYou can also use Div in spherical coordinates:\n\nDiv[{1/r^2, 0, 0}, {r, θ, φ}, \"Spherical\"]\n\n\n0\n\nAlexei's second example using \"Spherical\" coordinates:\n\nDiv[{-1/r^4,0,0},{r,t,g},\"Spherical\"]\n\n\n2/r^5"
] |
[
null,
"https://i.stack.imgur.com/gVux1.png",
null
] |
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|
https://wskg.org/education/mental-math-make-10/
|
[
"# Mental Math: Make 10\n\nHere’s a math problem: six plus eight. Your child will learn many mental math strategies to solve this, instead of memorizing.\n\nHere’s one strategy: You can break apart a number to make a ten. When you make a 10, you break apart one number to make a 10 with the other number.\n\nUse your Magic Math Fingers! One, two, three, four, five, six, seven, eight! Eight’s missing number partner is two.\n\nWe know six is the whole. We know one part is 2. What is the other part? Four! The other part is four! And look – We broke apart six! Now, if 8 plus 2 equals 10, we can easily add with a ten!\n\nLook! We made a ten and we broke apart to show how six plus eight equals fourteen! This strategy will set your child up with strong mental math skills this year and for years after!\n\n## Grade 1 Common Core Standards\n\n#### Operations & Algebraic Thinking: Add and subtract within 20.\n\n(1.OA.6) Add and subtract within 20, demonstrating fluency for addition and subtraction within 10. Use strategies such as counting on; making ten (e.g., 8 + 6 = 8 + 2 + 4 = 10 + 4 = 14); decomposing a number leading to a ten (e.g., 13 – 4 = 13 – 3 – 1 = 10 – 1 = 9); using the relationship between addition and subtraction (e.g. knowing that 8 + 4 = 12, one knows 12 – 8 = 4); and creating equivalent but easier or known sums (e.g., adding 6 + 7 by creating the known equivalent 6 + 6 + 1 = 12 + 1 = 13)."
] |
[
null
] |
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|
https://kanyevsky.kpi.ua/%D0%BF%D1%80%D0%BE-%D0%BD%D0%B0%D1%81/%D1%81%D0%BF%D0%B8%D1%81%D0%BE%D0%BA-%D0%BD%D0%B0%D1%83%D0%BA%D0%BE%D0%B2%D0%B8%D1%85-%D0%BF%D1%80%D0%B0%D1%86%D1%8C/method-for-mapping-dsp-algorithms-into-pentium-mmx-architecture/
|
[
"# Method for Mapping DSP Algorithms into Pentium MMX Architecture\n\n⇓Завантажити PDF (eng.)\n\n(Published in Proc. 3d Int. Conf. Parallel Processing and Applied Mathematic “PPAM’99”, Kazimierz Dolny, Poland, Sept. 1999)\n\nA Method for Mapping DSP Algorithms into the Pentium MMX Architecture\n\nA.M.Sergyienko*, D.V.Korchev, J.S.Kanevski**\n*Department of Computer Engineering, National Technical University of Ukraine, KPI-2020, pr. Peremogy, 37, Kiev, 252056, Ukraine. E-mail: [email protected]\n**Institute of Math. & Computer Science, Technical University of Coszalin, Coszalin, Poland. E-mail: [email protected]\n\nAbstractIn the representation a new method for mapping DSP algorithms into MMX architecture is considered. The method is based on the matrix-graph method for mapping regular algorithms into SIMD processor arrays. Then the Pentium MMX architecture is considered as a four 16-bit processor linear array. According to the method, the reduced dependence graph is mapped into configuration of the structure which corresponds to the SIMD structure and to configuration of events. Finally, time slices of the latter are exchanged by assembly instructions of the MMX instructions set. The example of the algorithm mapping illustrates this method, and proves its effectiveness.\n\n1. Introduction\nThe Intel Pentium MMX microprocessor has the instruction set which is expanded for multimedia applications, called MMX technology. The MMX technology has the facilities to achieve DSP application performance approximately equal to one of the high end signal microprocessors. Usually customers use DSP MMX library functions or hand made assembly codes with MMX instructions. When a specific and complex DSP algorithm is programmed both approaches are ineffective ones because of low load balancing of the processor and labour consumable programming process. In such case the automatic programming tools are needed. But the demand on such tools is still not satisfied.\n\nArchitecture of the MMX kernel of the Pentium microprocessor can be considered as a SIMD architecture with the constrained processor number. A new programming tool can be designed on the base of the appropriate method for mapping DSP algorithms into such architecture.\n\nA set of methods for mapping DSP algorithms into application specific structure are known, for example, described in , but they do not consider the SIMD architecture. The methods for mapping regular algorithms into constrained systolic type arrays like described in [2,3] can be used for such purpose. But the set of algorithms which can be directly mapped into MMX architecture using these methods is very limited.\n\nIn this work the simplified four processor SIMD model of the MMX kernel is selected and a new method for mapping DSP algorithms into it is proposed. This method is deriving by adapting the method for mapping unimodular loop nests into application specific structures, described in .\n\n2. Structure of the processor array.\nFor most DSP applications the architecture of the MMX core can be approximated by the array of four 16- bit processor units (PUs) which is illustrated by the Fig.1.",
null,
"Fig.1. SIMD structure model of the MMX processor core\n\nHere due to the superscalar nature of the Pentium processor, each of PUs is computing simultaneously one or two instructions, which follow from U and V instruction pipelines. The CPU core implements the control flow of the algorithm and calculates the address stream to the cache RAM, which 64 bit quad word is divided to four 16 bit words. The inner structure of each PU is represented by the Fig 2. All of instructions except multiplication are calculated for a single clock cycle. The multiplication is calculated for three clock cycles. The data interprocessor exchange is implemented by shift instructions .",
null,
"Fig.2. Structure of the PU.\n\n3. Method for mapping data flow graphs into application specific structure.\nThe mapping method compendiously described below is well suited for mapping DSP algorithms into application specific structures and was published in [4,6,7]. In this paper it is adapted to programming MMX applications. Often DSP algorithms are described by data flow graphs (DFG). In DFG operator nodes represent operations of addition or multiplication, a chain of k delay nodes represents delay of a signal variable to k iterations, edges represent data flows. DFG can be derived by respective mapping of reduced dependence graph GAR of an unimodular loop nest . In the graph GAR also nodes represent operators but weighted by k edge represent dependence of the data which is delayed to k iterations.\n\nBoth DFG and reduced dependence graph GAR can be represented in n– dimensional space Zn. For most DSP algorithms for one dimensional signal processing it is enough to operate with n=4 dimensional space. Each of N nodes of the graph which denotes the algorithm operator is represented by the vector – node Ki, i=1,…,N. The coordinates of the vector Ki signify iteration number, clock number in the iteration, processing unit (PU) in which the respective operator is implemented, and its type. Each of M edges of the graph which denotes the data dependence or variable moving is represented by the vector – edge Dj = KiKi-1, j=1,…,M, besides, vector DN+1= K1.\n\nSets of vectors Ki and Dj form respective matrices K and D which together with the graph GAR incidence matrix A form an algorithm configuration CA = (K,D,A). The configuration CA is equal to the composition of structure configuration CS = (KS,DS,A) and configuration of events CT= (KT,DT,A) , namely\n\nK = (KST,KTT)T, D = (DST,DTT)T ;\n\nwhere vector-node KSi ∈KS, represent coordinates of PU where i-th operator is implemented, vector-edge DSj ∈DS represent relative coordinates of communication line for j-th variable, vector-node KTi ∈>KT represent clock period of this operator implementation and vectoredge DTj ∈DT represent delay of this variable moving. Another words, configuration CS represents the graph of the processor structure, and the configuration CT represents the operator time schedule.\n\nThe following definitions and statements are true for configurations CA, CS, CT . The configuration CA is correct if KiKj ; i,j = 1,…,N, i≠j, i.e. all of vectors-nodes are placed in the space separately.\n\nThere is a linear dependence between matrices: D = KA; K = DoAo-1, where Ao is the\nincidence matrix for the maximum spanning tree of the graph GAR, and Do is the matrix of vectors-edges of this tree.\n\nCorrect configuration CA can be transformed into equivalent configuration CA’ by any injection function. For example, the following transformations give equivalent configurations: permutations of vectors Ki, multiplications of the matrix K and non-singular matrices P.\n\nThe sum of vectors-edges Dj,which belong to any loop of the graph GAR must be equal to zero.\n\nThe configuration CT is correct if Dtj≥0, where Dtj is unweighted dependence vector of the graph GAR, inequality has lexicographic meaning, j =1,…M. Besides, the given algorithm is implemented in pipelined manner correctly if\n\nKTl ∈ KT(KTl = (i,q)T, q∈(0,1,…,L-1)), (1)\n\nwhere KTl is not incident to edge DTj=(p,0)T weighted by p, L is the period of time between two consecutive the same input operand loadings or is the latency of the algorithm implementation.\n\nSearching for algorithm mapping consists in deriving configurations CA, CS, CT which are optimised according to given criterion. Directed searching for optimised configurations is implemented taking into account mentioned above definitions, dependencies and constraints.\n\nAt the first stage of the mapping, the searching for the space component CS is implemented. The forming of the matrix KS consists of distributing Mk operators of the k-th type among ]Mk/L[ processing units of the k-th type. As a result, MS groups of equal columns are formed in the matrix KS , where MS is the number of PUs in the resulting structure. The goal of this process is resource allocation and resource assignment.\n\nAt the second stage, the time component CT of the mapping is searched for. Derived matrices KT and DT must satisfy the condition of algorithm configuration correctness, correctness of the configuration of events, condition, that the sum of vectors-edges Dj ,which belong to any loop of the graph GAR must be equal to zero, and condition (1). Besides, if the operator represented by KTl is calculated for d clock cycles, then the norm R(DTj)=iL+q of the vector DTj=(i,q)T must be no less than d. The clock period in which the operator represented by KTl=(i,q)T is implemented is equal to t = R(KTl)= iL+q. As a result, the operator schedule is derived.\n\nIn a large set of different exemplars of mapping results an optimum mapping is searched. Some heuristics can be applied to derive a quick solution, such as list scheduling, force directed scheduling, loop folding, or left – edge algorithm, etc. . The advantages of this method consist in the following. Both stages of the mapping deriving can be executed in different order or simultaneously providing best optimisation strategy by time constrained scheduling and functional pipelining. The pipelined PUs with the given stage number can be taken into account. After some adaptation this method is well suited for mapping algorithms into MMX architecture.\n\n4. Method for mapping data flow graphs into MMX architecture.\nDue to described above MMX structure model the maximum PU loading is achieved by the following conditions. Up to four operators of the same type must be calculated simultaneously. That means that its vector nodes Ki in the algorithm configuration CA must be different on each other only in the coordinate of the PU number, i.e. they form a line which is perpendicular to the time axis. According to MMX instruction semantic, the data movings must be preferably between registers or memory cells of the same PUs. The data movings between neighbouring PUs are supported by shift instructions. The line of four vector- nodes Ki of equal type like addition, multiplication, etc., and vectors-edges Dj, which are incident to them and equal to each other, is mapped to a single MMX instruction.\n\nAlso the following must be taken into consideration. Up to two MMX instructions can be calculated simultaneously due to the superscalar nature of the processor. One source operand of the instruction is allocated in the same register as the destination operand is. The irregular data movings must be implemented by usual move type instructions or by the sequence of instructions of packed AND, OR, shift, addition and multiplication using proper masks and constants. According to strict sequential consistency of computing, the latent delay between storing the operand into memory and using it in another calculations can be equal to several clock cycles, and the real delay can be unpredictable due to cache coherency implementation. Therefore, it is preferable to store such operands in MMX registers.\n\nThe method for mapping data flow graphs into MMX architecture consists in the following. The latent period L =3,4 … is selected. Two stages of the method for mapping data flow graphs into application specific structure are implemented. By this the SIMD structure illustrated by the fig.1,2 is selected as the target one.\n\nOn the third stage the derived algorithm configuration is optimized to fit both SIMD structure and MMX instruction set. For this purpose up to four multiplication or addition nodes Ki are gathered to form a line which is perpendicular to time axes. Then the nodes in these lines are permutated to satisfy the condition that vectors-edges Dj, which are incident to them must be equal to each other. When such condition is not satisfied, then functional equivalent transforms are implemented which consist in addition of operators like AND, OR, shift, addition and multiplication using proper masks and constants. Also the delay- type vectors-nodes are introduced into vectors-edges Dj which are not incident to multiplication nodes, until R(DTj)=1. The delay- type vectors-nodes are mapped into quarter parts of MMX registers. These transforms can disagree with the conditions which were satisfied in the first two stages of the synthesis. Then the process is repeated from the first stage, and the latent period L can be exchanged . This process is repeated until all of nodes and edges can be covered by graphs (stencils), which\nrepresent MMX instructions.\n\nOn the last stage the derived algorithm configuration CA is taken into consideration. The nodes Ki =(k,l,j,i,q) of k -th type, which are calculated in i– th iteration and q-th clock cycle of this iteration, and in j th PU, form a set of up to four nodes, when k = const , q = const, j∈(0,1,2,3). Then this set of nodes is represented by a proper MMX instruction, which source and target operands are derived by the coordinate l of nodes which are adjacent to these ones. The derived instructions form the assembly program loop body, in which the instructions stay in the order according to the rising of the clock cycle q of the respective node set. According to the program pipelining technique, operators of address calculating and iteration counting as well as prologue and epilogue operator groups are added to the resulting program.\n\n5. Example of the algorithm programming.\nConsider an example of the calculation of the function yi=arctg(xi) for the array of arguments xi. This function is calculated by the following polynomial approximation arctg(x)=0.999x-0.289x3+0.079x5, and is often used in DSP applications. This example is selected because of its relative complexity to show the advantages of proposed method comparing to the hand made programs.\n\nThe polynome is factorised as the following: yi = c1 xi + c2(xi2 xii) + c3(xi2(xi2 xi)). Consider the resulting algorithm configuration has the latent period L = 5 clock cycles. Then six multiplications of the algorithm can be implemented on the couple of PUs of the SIMD structure. This means, that four PUs of the SIMD structure can calculate two algorithms in parallel.\n\nThen first and second stages of the synthesis are implemented. The resulting algorithm configuration CA is illustrated by the fig.3.",
null,
"Fig.3. Initial algorithm configuration\n\nHere coordinates i, q, j represent iteration number, clock cycle in the iteration, and PU number, respectively. Circles represent registers, circles with plus sign and with cross sign represent addition and multiplication operators, respectively. Two algorithms are implemented in parallel on the PUs 0, 1 and 2, 3 , respectively. Then the input dates are loaded in the packed format as the following : (0, xi’,0, xi ), the coefficients are stored in register mm6 : (c1, c2, c1, c2), and in register mm7 : (0, c3, 0, c2), the results are stored as the following : (0, yi’,0, yi ).\n\nAt the third stage the derived algorithm configuration is optimised to fit both SIMD structure and MMX instruction set. The resulting optimised algorithm configuration CA is illustrated by the fig.4. Here circles with vi sign denotes the OR operations.",
null,
"Fig.4. Resulting algorithm configuration\n\nAt the fourth stage of the program synthesis sets of up to two nodes of the equal type are searched which are calculated in the q -th clock cycle. Then these sets of nodes are represented by a proper MMX instruction, which are collected into the following table. The derived instructions form the assembly program loop body, which consists of about twenty instructions.\n\nTable. MMX instructions derived by the algorithm mapping.\n\n Clockcycle q MMX instruction 0 pmulhw MM1,MM0 movq MM4,MM1 movq MM3,MM7 paddw MM5,MM4 1 movq MM0,xi movq MM3,MM0 pslld MM4,16 pmulhw MM3,MM1 2 pmulhw MM0,xi movq MM2,MM4 movq MM4,MM2 movq yi,MM5 3 por MM2,MM1 pmulhw MM1,MM3 psrld MM4,16 movq MM5,MM4 4 movq MM1,MM0 pmulhw MM2,MM6 paddw MM5,MM3\n\nThe performance of derived program was proven by the VTune programming tool. Due to the superscalar nature of the processor and the fact that U-pipe and V-pipe of it is fully loaded, the latent period of derived program implementation is equal to 10 instruction cycles, and the one result calculating lasts 36 cycles, when all of dates are in the cache RAM. Only three of 19 MMX instructions make access to the RAM which proves the high grade of data reuse. Also taking into account two algorithms implemented in parallel, each result yi is calculated approximately only for 5 clock cycles.\n\n5.Conclusion .\n\nImplementation DSP algorithms in MMX architecture has a set of advantages, like the possibility to achieve performance approximately equal to one of the high end signal microprocessors, and combining DSP and other applications. But the demand on automatic programming tools is still not satisfied. In this work the simplified four processor SIMD model of the MMX kernel is selected and a new method for mapping DSP algorithms into it is proposed. This method is derived by adapting the method for mapping unimodular loop nests into application specific structures, described in .\n\nThe method consists of four stages. At the first stage, the searching for the space component of the algorithm mapping into application specific structure is implemented. At the second stage, the time component of the mapping is searched for and algorithm configuration is derived. At the third stage is optimised to fit both SIMD structure and MMX instruction set. And at the fourth stage sets of nodes of the algorithm configuration are represented by MMX instructions, which form the assembly program loop body.\n\nThe method helps to derive programs which fully implement the parallelism of the MMX kernel of the Pentium microprocessor and can be used for the development new complex DSP applications and library functions. It also can be adapted to another microprocessor families which implement the expanded instruction set for multimedia applications. An automatic programming tool which implements this method is now under development.\n\nReferences.\n\n. The synthesis approach to digital system design. Ed.: P.Michel, U.Lauther, P. Duzy, Kluwer Academic Pub. 1992.\n\n. Kung S.Y. VLSI processor arrays. Prentice Hall, Englewood Cliffs, 1988.\n\n. Wyrzykowsky R., Kanevski J.S., Maslenikov O, Sergyienko A. Mapping recursive algorithms into processor arrays.\\ Proc. Int. Workshop “Parallel Numerics’ 94”, M.Vajtersic, P.Zinterhof, eds., Smolenice (Slovakia), 1994, pp 169-191.\n\n. A. Sergyienko, A. Guzinski, Ju. Kanevski, A method for mapping unimodular loops into application specific parallel architectures, In Proc. 2-nd Int. Conf. on Parallel Procesing and Applied mathematics. PPAM’97. Zacopane, Poland, Sept. 2-5, 1997, p. 362-371.\n\n. Intel Architecture MMX Instruction Set. http:// developer. intel.com/ drg/mmx/ manuals/prm/ .\n\n. J. S. Kanevski, A. M. Sergyenko, H. Piech, A method for the structural synthesis of pipelined array processors, In Proc. 1-st Int. Conf. on Parallel Processing and Applied Math. -PPAM’94. Czestochowa (Poland), 1994, pp.100-109.\n\n. Yu. S. Kanevskiy, L. M. Loginova, A. M. Sergienko, Structured Design of Recursive Digital Filters, Enginering Simulation, 1996, V.13, pp. 381-390.\n\n. VLSI and Modern Signal Processing, Ed. by S.Y.Kung, H.Whitehouse, T.Kailath, Prentice Hall, 1985.\n\n⇓Завантажити PDF (eng.)"
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http://controleducation.group.shef.ac.uk/statespacemethods.html
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"Modelling and control by Anthony Rossiter\n\n# Introduction to state space models and their use in systems analysis and control\n\nThis theme contains a number of sections listed next. Use the left hand toolbar to access the resources.\n\nThis chapter gives a summary of key methods and concepts around state space models. The content is primarily a targeted at students doing a single course in state space methods and hence does not dwell on some fine details which would be covered in a 2nd course or in research applications; one such example is non-simple Jordan forms and another is finding approximate state space models by linearisation of 1st principles models. Once the principles are understood clearly, Students are encouraged to use tools like MATLAB for some of the number crunching as manipulation of state space models is not a paper and pen exercise in general.\n\nThe focus of these sections is on state space analysis methods. This begins with definitions and origins of state space models alongside a discussion of their equivalences with transfer function models. This is followed by analysis of the associated system behaviours and links to the state space model parameters. The final sections focus on control design and thus concepts such as controllability, observability and control design methods.\n\nIt is implicit in several of these chapters that students have core competence in some mathematical topics such as polynomials, roots, complex numbers, exponentials and Laplace. More information on these can be found in the Mathematics theme of the left hand toolbar.",
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https://link.springer.com/article/10.3758/s13421-013-0294-9?error=cookies_not_supported&code=446be205-0b16-4f0d-beca-3cc93390c378
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[
"# Prior experience with negative spectral correlations promotes information integration during auditory category learning\n\n## Abstract\n\nComplex sounds vary along a number of acoustic dimensions. These dimensions may exhibit correlations that are familiar to listeners due to their frequent occurrence in natural sounds—namely, speech. However, the precise mechanisms that enable the integration of these dimensions are not well understood. In this study, we examined the categorization of novel auditory stimuli that differed in the correlations of their acoustic dimensions, using decision bound theory. Decision bound theory assumes that stimuli are categorized on the basis of either a single dimension (rule based) or the combination of more than one dimension (information integration) and provides tools for assessing successful integration across multiple acoustic dimensions. In two experiments, we manipulated the stimulus distributions such that in Experiment 1, optimal categorization could be accomplished by either a rule-based or an information integration strategy, while in Experiment 2, optimal categorization was possible only by using an information integration strategy. In both experiments, the pattern of results demonstrated that unidimensional strategies were strongly preferred. Listeners focused on the acoustic dimension most closely related to pitch, suggesting that pitch-based categorization was given preference over timbre-based categorization. Importantly, in Experiment 2, listeners also relied on a two-dimensional information integration strategy, if there was immediate feedback. Furthermore, this strategy was used more often for distributions defined by a negative spectral correlation between stimulus dimensions, as compared with distributions with a positive correlation. These results suggest that prior experience with such correlations might shape short-term auditory category learning.\n\n## Introduction\n\nCategorization of auditory sensory information is vital for making rapid decisions in an acoustic environment. For instance, the ability to correctly map a complex harmonic tone to the category car horn is advantageous when one is crossing a road. Recent decades have brought about a considerable body of research on how categories are formed and maintained (Ashby & Waldron, 1999; McQueen, 1996; Nosofsky, 1988; Rosch, 1973, 1978; Russ, Lee, & Cohen, 2007; Sloutsky, 2003; Spiering & Ashby, 2008; Verbeemen, Vanpaemel, Pattyn, Storms, & Verguts, 2007; Yamauchi, Love, & Markman, 2002; for reviews, see Ashby & Maddox, 2005, 2011). This work has become increasingly focused on auditory categories (Goudbeek, Cutler, & Smits, 2008; Guenther & Bohland, 2002; Guenther, Nieto-Castanon, Ghosh, & Tourville, 2004; Holt & Lotto, 2006; Mirman, Holt, & McClelland, 2004).\n\nThere is a consensus that auditory categorization involves the utilization and integration of different acoustic dimensions (e.g., spectrum [pitch, timbre], duration; Goudbeek, Swingley, & Smits, 2009; Holt & Lotto, 2006), but it is less clear how integration of information from integral (nonseparable; Goudbeek et al., 2009) dimensions might differ from integration of information from nonintegral (separable) dimensions. Furthermore, integration across dimensions might be influenced by prior knowledge of specific correlations between dimensions, particularly from speech. The purpose of this study was to assess the degree to which integration of information from two dimensions is influenced by (1) the integrality of acoustic dimensions (here, location of spectral peaks in frequency space), (2) the correlation between these dimensions (positive vs. negative correlation of first [S1] and second [S2] spectral peak), and (3) the presence or absence of immediate corrective feedback.\n\nTo that end, we focused on two modeling approaches that are particularly apt to these purposes: decision bound theory and logistic regression. Logistic regression has previously been applied to auditory categorization and can be used to predict category membership decisions on the basis of stimulus properties (Hosmer & Lemeshow, 2000). Values along a stimulus dimension are entered into the regressions as continuous variables, and the β-weight for this regressor reflects the predictive power of the stimulus dimension with respect to categorization responses. Comparisons of β-weights for different stimulus dimensions allow assessment of the degree to which individual stimulus properties are used for categorization. For instance, Goudbeek et al. (2009) assessed strategy use in two conditions where either frequency or duration was the relevant dimension. Responses were predicted from frequency and duration with logistic regressions that yielded β-weights for either dimension. A higher β-weight for frequency, as compared with duration, in the condition where frequency was the relevant dimension showed that participants indeed used this dimension for their response.\n\nOn the other hand, decision bound theory (Ashby & Gott, 1988) assumes that category acquisition involves learning to divide perceptual space, corresponding to an internal representation of stimulus space, into response regions according to a linear or nonlinear boundary (Ashby & Waldron, 1999). The position of a novel stimulus in perceptual space is compared with the location of the boundary, and the corresponding response is assigned. Thus, from this perspective, category learning is a signal detection problem where the decision bound separating categories corresponds to the response criterion. Decision bound models for auditory categorization have been extensively studied in the visual domain and, thus, provide a framework from which we can generate specific predictions about strategy use in novel category learning.\n\nThus far, no study of which we are aware has combined these two approaches (i.e., logistic regression and decision bound models). In this regard, the present study complements and goes beyond previous research. In what follows, we will describe in detail two strategies for novel category learning that emerge from decision bound theory and that we assess in the context of auditory category learning in the present study.\n\n### Rule-based and information integration category learning\n\nThe distinction between rule-based and information integration category learning comes from a neuropsychological model called competition between verbal and implicit systems (COVIS; Ashby, Alfonso-Reese, Turken, & Waldron, 1998; Ashby & Waldron, 1999). The model assumes that learning involves two systems that compete or interact (Ashby & Crossley, 2010) and differ in their functions and neural underpinnings. Rule-based learning involves categorization based on an explicit rule that is frequently relatively easy to verbalize (e.g., if a tone is high in frequency, respond “category A”; otherwise, respond “category B”). Generally, rule-based decision bounds are orthogonal to the dimension on which the decision should be based. Rule-based learning is assumed to predominantly depend on an explicit hypothesis-testing system (Maddox, Filoteo, Lauritzen, Connally, & Hejl, 2005), subserved by the dorsolateral prefrontal cortex, the anterior cingulate, and the caudate nucleus (Ashby & Ell, 2001; Rao et al., 1997).\n\nOn the other hand, information integration category learning tasks require a predecisional combination of information from more than one dimension. Usually, the optimal rule in information integration tasks is not easily verbalized (e.g. if a tone is higher in frequency than it is long in duration, respond “A”; otherwise, respond “category B”). Decision bounds are not orthogonal to the dimension on which the decision should be based but, rather, are represented as a diagonal—for example, in a two-dimensional stimulus space. Successful learning of an information integration task is proposed to rely on an implicit procedural learning system that depends on feedback processes (Ashby & Waldron, 1999; Maddox, Filoteo, Hejl, & Ing, 2004; Maddox et al., 2005). This system is claimed to be subserved by the body and tail of the caudate (Nomura et al., 2007; Seger & Cincotta, 2005). In several studies, it has been shown that information integration is indeed dependent on (immediate) feedback in categorization or discrimination tasks (Ashby, Queller, & Berretty, 1999; Ashby & Waldron, 1999).\n\nIn the present study, we applied decision bound modeling of rule-based and information integration category learning to an auditory categorization task. Decision bound theory provided us with an optimal tool with which to evaluate the success of information integration across two acoustic dimensions in making category membership decisions.\n\n### Auditory category learning\n\nA number of studies have attempted to distinguish between rule-based and information integration strategies in auditory categorization performance (e.g., Goudbeek et al., 2008; Goudbeek et al., 2009; Holt & Lotto, 2006, 2008; Maddox, Ing, & Lauritzen, 2006; Mirman et al., 2004; Smits, Sereno, & Jongman, 2006), although relatively few of them directly applied decision bound models to describe learning. For instance, in studies by Goudbeek et al. (2009) and Smits et al., participants learned to categorize inharmonic complex tones that varied along the dimensions of duration and spectral filter location (analogous to the first formant frequency, F1, in speech). Goudbeek et al. (2009) examined performance for category distributions that were best separated by a unidimensional duration-based boundary, a unidimensional frequency (i.e., spectral filter location) boundary, and a diagonal (information integration) boundary that required categorizing stimuli on the basis of a combination of duration and frequency values. The authors observed much poorer performance for the information integration condition, relative to either of the rule-based conditions. On this basis, they hypothesized that rule-based category learning may be the default strategy in audition (cf. Maddox et al., 2006).\n\nHowever, it is also possible that rule-based learning may have been used more often due to use of duration and frequency dimensions, which have been suggested to be separable, rather than integral (Grau & Kemler Nelson, 1988; Silbert, Townsend, & Lentz, 2009). Prior research on acoustic-phonetic processing has suggested that dimensions that vary in the same domain (e.g., frequency) are more likely to be integral (Kingston, Diehl, Kirk, & Castleman, 2008; Kingston & Macmillan, 1995; Kingston, Macmillan, Dickey, Thorburn, & Bartels, 1997). Consistent with this suggestion, Maddox and colleagues (Maddox, Molis, & Diehl, 2002) showed that a model of categorization performance assuming information integration accounted well for performance when stimuli varied in their second and third resonance frequencies; however, they did not examine learning per se, since the categories were already highly learned.\n\nFurthermore, previous research also stressed the importance of how acoustic dimensions are related to each other. In this respect, negative correlations between spectral filter dimensions are relevant with respect to intrinsic pitch of vowels, reflecting the impression that high vowels (with a low F1) have slightly higher pitch (higher f0) than do low vowels (with a higher F1; Lehiste & Peterson, 1961), and vowel nasalization, showing that the more nasalized a vowel, the lower its F1 frequency (Diehl, Kluender, & Walsh, 1990). Given that these correlations hold cross-linguistically (likely to be based on articulatory constraints; Carre, 2009), listeners should be familiar with them and, correspondingly, benefit in novel categorization situations that employ such correlations.\n\nOur two experiments sought to assess whether (1) information is integrated across two integral acoustic dimensions, (2) information integration depends on the correlation of the acoustic dimensions, and (3) information integration requires immediate feedback. For these reasons, both experiments used stimuli that differed in the location of two spectral peaks (analogous to the first formant frequencies of vowels, F1 and F2) and comprised a learning phase with immediate feedback as well as a maintenance phase without feedback. In Experiment 1, we examined distributions whose decision bounds would similarly allow for rule-based or information integration categorization, thereby assessing the natural inclination for a particular strategy during the categorization of auditory stimuli with integral dimensions (Fig. 1a). In contrast, Experiment 2 used stimulus distributions that required information integration for optimal performance (Fig. 2a).\n\nOur hypotheses are as follows:\n\n1. 1.\n\nOn the basis of Maddox et al. (2006), we assume that rule-based categorization is the preferred strategy in audition. Therefore, in both experiments, we should see substantial evidence for rule-based behavior.\n\n2. 2.\n\nThe long-term experience with negative correlations in speech (and corresponding decision bounds) should shape the short-term categorization of nonspeech stimuli. As a result, more ready use of an information integration strategy, and consequently, better category-learning performance should be observed for distributions with negative correlations between spectral peak frequencies. Since Experiment 2 was designed such that information integration would yield optimal performance (Fig. 1b), we expect differences between correlations to particularly manifest themselves in this experiment.\n\n3. 3.\n\nFinally, information integration seems to require immediate feedback (Ashby et al., 1999; Ashby & Waldron, 1999). We therefore expect more information integration in the learning than in the maintenance phase of our experiments.\n\n## Experiment 1\n\nExperiment 1 extended the work of Goudbeek and colleagues (Goudbeek et al., 2008; Goudbeek et al., 2009) to stimuli varying along integral acoustic dimensions. Two distribution types were examined. In the falling condition, stimulus distributions were characterized by a negative correlation between spectral filter locations, while the rising condition distributions were characterized by a positive correlation. In both conditions in Experiment 1, rule-based and information integration strategy use would have yielded little performance difference so that we could assess the natural inclinations of participants.\n\nAccuracy analyses were supplemented by fitting a number of decision bound models (Ashby, 1992) to individual participant data in order to assess strategy use during category learning. As was outlined above, we also calculated logistic regressions with the dependent variable category A vs. B for both learning and maintenance phases (Hilbe, 2009) in order to quantify the contributions of each spectral dimension to single-trial category membership decisions. On the basis of previous findings (little information integration use; Goudbeek et al., 2009; Maddox et al., 2006) and as a consequence of the stimulus materials in Experiment 1 (no strong bias for either strategy), we predicted a bias toward using rule-based strategies.\n\nWe did not expect differences between the rising and falling distributions. This is because observing a difference between rising and falling distributions should have depended on adoption of an information integration strategy, which we did not predict to observe in Experiment 1. For the same reason, we predicted that immediate feedback in Experiment 1 would play no or only a negligible role, since it is claimed to be important for information integration, but not rule-based learning (Ashby et al., 1999; Ashby & Waldron, 1999).\n\n## Method\n\n### Materials\n\nStimuli were created with PRAAT (Boersma & Weenink, 2011) in two steps. First, a 90-ms white noise was generated; the duration was chosen on the basis of previous studies (Goudbeek et al., 2008; Goudbeek et al., 2009). Second, the white noise was filtered in two frequency bands that approximated the location of the first and second formant frequencies of naturally produced vowels—that is, F1 and F2, respectively. Target filter frequencies are referred to as spectral filter frequencies, S1 and S2, throughout this article, and are normalized to Bark (Zwicker, 1961). The Bark conversion is commonly applied in acoustic-phonetic research and accounts for the nonlinearity of the frequency resolution by the human auditory system.\n\nThe same original white noise token was used as the basis for all 1,000 stimuli that were generated for each category (A and B) and for each distribution (falling and rising), with different S1 and S2 filter frequencies in each case. Filter frequencies were drawn from the distributions shown in Fig. 1a. In order to arrive at the stretched distributions along the falling and rising diagonals in the S1/S2 space (with a slope of −1 and +1), the linear equation for the diagonal running through the distribution center was calculated. Then individual bivariate normal distributions were generated that had means, μ, at 40 (x,y) locations along the diagonal and equal standard deviations, σ (see Table 1 for details). Twenty-five tokens per distribution were randomly generated, yielding a total of 1,000 stimuli per distribution.\n\nNoises were filtered with fast IIR filters, comprising two recursive filter coefficients. Filter bandwidths were 0.2 times the target filter frequency. Stimuli were normalized to an equal average intensity that approximated 60 dB SPL (Boersma & Weenink, 2011). Onsets were multiplied with the first half period of a [1 cos(x)] * 0.5 function, and offsets with the first half period of a [1 + cos(x)] *0.5 function, over a duration of 10 ms in each case, in order to eliminate acoustic artifacts.\n\nIn order to arrive at a stimulus-based measure for the likelihood of strategy preference, we determined the normalized distance between the means of category A and category B distributions, here referred to as δ', along both the S1 and S2 dimensions. The information integration δ' (Euclidean distance between distribution means) was of comparable size to the rule-based δ' values (difference between means for each dimension, S1 and S2). We expect that larger category distances would lead to better categorization performance, such that if participants were to utilize optimal strategies, they ought to prefer those for which δ' is largest. In Experiment 1, the similarity of the distances therefore suggested equal strategy preference.\n\nStimuli for the nonfeedback maintenance phase were arranged in an equidistantly spaced grid with step sizes of 2/3 Bark in either dimension (S1, 5–9 Bark; S2, 9–13 Bark). They thus described a 6 × 6 grid that evenly covered the critical region of the original stimulus space (Fig. 2a).\n\n### Participants and procedure\n\nThirty-three native speakers of German (all right-handed) participated in Experiment 1 (16 males; mean age 25.76, SD 2.42). They were drawn from the participant pool of the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, and received monetary compensation for their participation. None of them reported a history of hearing problems.\n\nParticipants were randomly assigned to either the rising (n = 17; 7 males; mean age 25.29, SD 2.08) or the falling (n = 16; 9 males, mean age 26.25, SD 2.72) distribution condition. Participants first completed eight blocks (36 trials each) during the learning phase. On each trial, a single stimulus was randomly selected from category A (1,000 exemplars) or B (1,000 exemplars), with the following restrictions: (1) No stimulus could be selected more than once for a given participant in the learning phase of the experiment; (2) within each block, category A and B stimuli were equally probable (p = .5). After stimulus presentation, participants indicated whether it belonged to category A or category B by pressing one of two keys on a computer keyboard (button assignments for the two categories were counterbalanced across participants).\n\nFollowing the response, participants received corrective feedback, which was displayed for 1 s in the middle of a CRT screen (Sony Multiscan E430). Correct feedback was given in bold green font (24 points), while incorrect feedback was given in bold red font (24 points). Participants were allowed a short break following each block.\n\nParticipants then completed two maintenance blocks (also 36 trials each). On each trial, participants were presented with a stimulus sampled from the equidistantly spaced grid described above. Critically, during the maintenance phase, participants did not receive feedback about their responses. The entire experiment lasted for about 20 min.\n\nStimuli were presented on a Windows-based PC, using the stimulation software PRESENTATION (Neurobehavioral Systems, Inc., version 13.9), and were transmitted through a Creative Labs Audigy II sound card onto Sennheiser HD 201 headphones.\n\n### Results\n\n#### Accuracy results\n\nOverall, performance differed significantly from chance (d= 1.35, SD = 0.43), t(32) = 32.94, p < .001. Accuracy in the learning phase was assessed by d′, a signal detection measure of perceptual sensitivity that is independent of response bias (Macmillan & Creelman, 2005). Figure 2 shows d′ as a function of block separately for the rising and falling distributions. In order to assess learning, d′ values were entered into a mixed–measures analysis of variance (ANOVA) with block as a within-subjects and distribution (rising vs. falling) as a between-subjects variable. For all ANOVAs, we report partial eta squared (η p 2) as a measure of effect size and Greenhouse–Geisser-corrected p-values and degrees of freedom in cases of sphericity violations. There were no significant main effects [block, F(6.1, 188.9) = 1.14, $$\\eta_{\\mathrm{p}}^2=.04$$, p = .34; distribution, F(1, 31) = 0.002, $$\\eta_{\\mathrm{p}}^2=.00001$$, p = .97] and also no block × distribution interaction, F(6.1, 188.9) = 0.43, $$\\eta_{\\mathrm{p}}^2=.04$$, p = .89. Hence, performance did not differ across blocks, and performance across blocks did not differ as a function of distribution condition.\n\n#### Logistic regression\n\nIn order to assess the degree to which category membership judgments (i.e., A vs. B) depended on the acoustic dimensions under investigation (i.e., S1, S2), logistic regressions were calculated in order to predict category A responses from S1 and S2 and their interaction. Note that a significant β-weight indicates the importance of the dimension in determining category membership. Logistic regression models were calculated separately for the learning and the maintenance phases.\n\n### Learning phase\n\nThe model comprised the regressors S1 and S2 and the factors block and distribution (rising, falling). The following interactions were also included in the model: S1 × S2, S1 × block, S2 × block, S1 × distribution, and S2 × distribution. S1 values per trial significantly predicted category judgments, β = −1.27, z = −4.82, p < .001, but there was no interaction with block, z = −0.02, p = .24, indicating that participants similarly weighted S1 information in making category judgments over the course of the learning phase. The S1 × distribution interaction reached significance, β = −0.35, z = −2.21, p < .05, indicating that category A responses were better predicted by S1 in the falling than in the rising distribution. None of the other factors or interactions were significant, z < 2, n.s.\n\n### Maintenance phase\n\nThe model included the same predictor variables listed above for the learning phase model, with the exception of block, which was not included here. There was no significant S1 effect, even though there was a trend for more category A responses at lower S1 values, β = −1.82, z = −1.38, p < .16; the S1 effect reached significance if the S1 × S2 interaction was removed from the model, β = −2.34, z = −13.08, p < .001. In the full model, there was a further significant effect of distribution, β = 3.97, z = 2.06, p < .05, reflecting that category A responses were better predicted in the falling than in the rising condition, and this effect was qualified by the significant S1 × distribution interaction, β = −0.53, z = −1.92, p = .05.\n\n### Learning versus maintenance phases\n\nCue utilization during the learning versus maintenance phases was compared directly by entering the absolute values of β-weights for S1 and S2 into a mixed–measures ANOVA with the within-subjects factors phase (learning, maintenance) and filter (S1, S2) and the between-subjects factor distribution (rising, falling). Individual β-weights stemmed from the learning and maintenance models reported above, except that we did not include block in the learning phase model (parallel to the maintenance phase model). Note that for these analyses, only the magnitude (but not the sign) of the β-weights provided interesting information regarding the importance of each dimension to category judgments. There were significant main effects of filter, F(1, 93) = 217.99, $$\\eta_{\\mathrm{p}}^2=.701$$, p < .001, and phase, F(1, 93) = 43.52, $$\\eta_{\\mathrm{p}}^2=.319$$, p < .01. Higher βs were observed for S1 (2.55, SD = 1.20) than for S2 (0.62, SD = 0.53), and βs were higher in the maintenance (2.02, SD = 1.57) than in the learning (1.15, SD = 0.90) phase. Furthermore, there was a trend for a distribution × filter interaction, F(1, 93) = 2.03, $$\\eta_{\\mathrm{p}}^2=.021$$, p = .14, reflecting that within the falling distribution, the difference between βs for S1 and S2 (2.70, SD = 1.18, vs. 0.55, SD = 0.55) was greater than within the rising distribution (2.43, SD = 1.24, vs. 0.67, SD = 0.52). There was also a filter × phase interaction, F(1, 93) = 16.50, $$\\eta_{\\mathrm{p}}^2=.151$$, p < .01, indicating that βs for S1 and S2 differed more in the maintenance (3.25, SD = 1.20, vs. 0.78, SD = 0.61) than in the learning (1.86, SD = 0.70, vs. 0.45, SD = 0.39) phase.\n\nIn order to visualize the degree to which participants relied on the individual dimensions, S1 and S2, we plotted βs for S2 (ordinate) against βs for S1 (abscissa) in the learning and in the maintenance phases. In these scatterplots, participants are coded according to whether they significantly used S1 and S2 (S1 + S2; blue diamonds), S1 only (S1; red squares), S2 only (S2; green triangles), or none of the dimensions (purple circles) for categorization. Significant usage was determined by βs that significantly differed from zero on the basis of the single-subject logistic regression models (α = .05). In these plots, participants who used both dimensions tended to fall on a diagonal. Participants with a preference for S2 are clustered near the ordinate, and participants with a preference for S1 are clustered near the abscissa (Fig. 3a). It can be seen from the figure that most participants relied on S1 in both the learning and the maintenance phases. The percentages of significant βs did not significantly differ between the falling and rising distributions (all χ 2s < 2.5, n.s.), even though we observed a trend for participants to be more likely to rely on both S1 and S2 in the falling, as compared with the rising, stimulus distribution.\n\nIn sum, the logistic regressions indicated that most participants relied on the first filter frequency, S1, during categorization and more strongly in the maintenance phase (without feedback) than in the learning phase (with feedback). On the other hand, some participants used information from both S1 and S2, while only very few exclusively used S2 for categorization.\n\nFurthermore, categorization also somewhat differed between the falling and the rising distributions in that the reliance on S1 was greater in the falling than in the rising stimulus distribution and in that more participants tended to use both S1 and S2 in the falling than in the rising distribution.\n\n#### Modeling results\n\nThree families of decision bound models (e.g., Ashby & Gott, 1988; Maddox & Ashby, 1993) were fit to the data for each individual participant on a block-by-block basis to determine the decision strategy that best accounted for performance (Fig. 3b; see the Appendix for details): unidimensional rule-based, information integration, and random-response models. The two rule-based models assumed that listeners made use of unidimensional rules based on either S1 or S2. Two information integration models assumed an optimal decision bound or allowed decision bound slope and intercept to be free parameters but are summarized as one model for the remainder of this article. Finally, the random-response model presumes that participants guessed randomly on every trial. In order to assess whether the decision bound models provided substantial evidence, we transformed the respective Bayesian information criterion (BIC) scores to Bayes factors (Kass & Raftery, 1995; Raftery, 1986; see the Appendix) and subsequently used Jeffrey’s suggested scale of evidence. According to this scale, Bayes factors greater than 3 indicate substantial evidence for model use.\n\nAlmost all model fits (per participant and block) for the rule-based S1 and S2 models provided substantial evidence, while only 10 % of the model fits for information integration exceeded this threshold (Fig. 3b, right). The percentages did not differ between distributions (i.e., falling vs. rising; all χ 2s > 2, n.s.). Figure 3b (left) gives the proportion of listeners in the rising and falling distributions whose data were best fit by each of the tested models across blocks. All winning models had Bayes factors of >3.\n\nConsistent with the results of the logistic regression analysis, participants in both the rising and falling distribution conditions made almost exclusive use of unidimensional rules, and participants were more likely to use a rule based on S1 than one based on S2. Chi-squared tests indicated that, overall, more participants relied on a unidimensional S1 rule, as compared with a unidimensional S2 rule, in six of the eight blocks. In order to account for multiple comparisons, we corrected our statistics with the false-discovery-rate (FDR) method (Benjamini & Hochberg, 1995; FDR-corrected α-level = .05). Taking the distribution conditions separately, participants in the falling distribution condition exhibited this pattern more strongly, using a unidimensional S1 rule more often than a unidimensional S2 rule on four of the eight blocks (ps < .05), whereas this difference was not significant in any block for the rising distribution condition.\n\n#### Convergence of logistic regression and decision bound models\n\nTo our knowledge, no study has assessed the degree to which the two approaches, logistic regressions and decision bound models, converge. For this reason, we explored the relationship between block-averaged β-weights and goodness-of-fit measures (i.e., BICs) separately for the unidimensional S1 and S2 decision bounds in two ANOVAs with block-averaged BIC scores as dependent variables. We were effectively asking to what degree BIC-scores supporting a rule-based S1 or S2 strategy could be predicted from β-weights of S1 or S2 logistic regressions. Note that information integration models were not included in these analyses, since the proportion of participants using information integration in Experiment 1 was too small for meaningful comparisons.\n\nBoth models included the between-subjects factor distribution (rising, falling), the regressor β-weight, as well as the β-weight × distribution interaction. The S1 model (with S1 BIC score as dependent variable) showed a significant effect of S1 β-weight, F(1, 29) = 28.16, $$\\eta_{\\mathrm{p}}^2=.492$$, p < .001, reflecting a negative correlation between β-weights and BIC scores (i.e., higher β-weights for lower BIC scores). However, the correlation was not modulated by distribution, as evidenced by no other significant main effects or interactions (all Fs < 3, all ps > .15). In the S2 model (with S2 BIC scores as dependent variable), >the negative correlation between β-weights was not significant, F(1, 29) = 2.33, $$\\eta_{\\mathrm{p}}^2=.074$$, p < .15, overall, but depended on distribution [β-weight × distribution: F(1, 29) = 3.07, $$\\eta_{\\mathrm{p}}^2=.113$$, p = .05]. The β-weight/BIC score correlation was significant for the falling, F(1, 15) = 4.88, $$\\eta_{\\mathrm{p}}^2=.245$$, p < .05, but not for the rising, F(1, 14) = 0.39, $$\\eta_{\\mathrm{p}}^2=.027$$, p = .54, distribution.\n\nOverall, BIC scores supporting either S1 or S2 rule-based strategies negatively correlated with the corresponding absolute β-weights from the S1 and S2 logistic regression effects. Thus, β-weights and decision bound model BIC scores converged.\n\n#### Prediction of performance by decision bound models\n\nFinally, we tried to predict performance from strategy use; that is, we tested whether the likelihood of using a rule-based S1 or S2 categorization strategy was associated with better performance, as indexed by two separate mixed-measures ANOVAs with d' as the dependent variable, the proportions of rulel-based S1 and S2 strategy use and distribution (falling, rising) as independent variables. Since proportions of rule-based S1 and S2 strategy use are necessarily highly correlated, the two factors were investigated in separate ANOVAs.\n\nNone of the ANOVAs showed significant main effects or interactions (all Fs > 1, n.s.). Thus, performance in Experiment 1 did not depend on either rule-based S1 or S2 strategy use.\n\n## Discussion\n\nParticipants in Experiment 1 showed a strong preference for using a unidimensional rule-based decision bound for auditory categorization and primarily relied on the first filter frequency (S1). Thus, our prediction was borne out: Participants preferred a rule-based approach and did not exhibit differences in performance, as indexed by d', as a function of block for either the rising or the falling distribution condition. Performance was overall high beginning from block 1, indicating that listeners in both conditions discovered a strategy yielding good categorization performance immediately. However, additional learning was not apparent over blocks. This is likely because our category distributions overlapped, causing some stimuli to be ambiguous and performance to plateau below ceiling levels.\n\nOur observation that participants generally tended to adopt a unidimensional strategy when categorizing auditory stimuli is in line with the study of Goudbeek et al. (2009). However, here the stimulus dimensions were based on the same acoustic dimension (i.e., frequency), suggesting that rule-based categorization strategies are preferred even if dimensions are integral.\n\nWithout an S1–S2 correlation and without feedback (i.e., in the maintenance phase), the magnitude of β-values for S1 was larger than in the learning phase; this S1 preference was more pronounced in the falling than in the rising distribution. Hence, in the maintenance phase, participants seemed to rely on S1 to a greater extent for the falling than for the rising distribution. The assumed special role of the falling distribution condition was further analyzed in Experiment 2, where optimal performance depended on the usage of an information integration strategy and where the decision bound was a falling (with a negative slope) or rising (with a positive slope) diagonal.\n\nCrucially, Experiment 2 was motivated by the observation that negatively correlated acoustic dimensions seemed to be preferred in speech (and more general, in audition), for which reason we assume that if dimensions are required to be integrated, this is done more readily for those that show a negative, as compared with a positive, correlation.\n\n## Experiment 2\n\nExperiment 2 made use of stimulus distributions that required a predecisional integration of information from two spectral dimensions—that is, for which rule-based categorization was a suboptimal strategy. We were interested in whether a substantial proportion would use an information integration strategy, and the degree to which this choice depended on the nature of the correlation between spectral filter locations, S1 and S2 (i.e., falling vs. rising). Due to the assumed familiarity with negative acoustic correlations, we expected that information integration would be more readily used in the falling, as compared with the rising, stimulus distribution. We also assumed that if rule-based categorization is indeed predominant in audition (Goudbeek et al., 2009; Maddox et al., 2006), some participants in Experiment 2 would continue using this strategy. Finally, the use of information integration strategies in Experiment 2 should also depend on the availability of immediate feedback (Maddox, Ashby, & Bohil, 2003), for which reason we did not expect indications of information integration in the maintenance phase (designed as in Experiment 1).\n\n### Method\n\n#### Materials\n\nThe stimuli were similar to those in Experiment 1, with the exception that (1) category A and B distributions were parallel in the S1–S2 space, rather than lying on the same diagonal as in Experiment 1, and (2) the spread was increased in both dimensions, S1 and S2, in order to render categorization more difficult. S1/S2 ranges and standard deviations are illustrated in Table 2. In contrast to Experiment 1, the normalized distance, δ′, was considerably higher for the information integration bound than for either of the rule-based bounds; thus, best performance would be attainable by an information integration strategy. Parallel to Experiment 1, stimuli for the maintenance phase consisted of a 6 × 6 grid that covered the critical region of the stimulus space;\n\n### Participants and procedure\n\nThirty-six native speakers of German (all right-handed) participated in Experiment 2 (19 males; mean age 25.14, SD 4.02); participants were drawn from the same pool as in Experiment 1, although none of the participants had been recruited for Experiment 1. As before, participants were assigned to either the rising (11 males; mean age 24.67, SD 3.09) or falling stimulus (8 males; mean age 25.61, SD 4.82) distribution.\n\nParticipants received monetary compensation for their participation. No participant reported hearing problems. The procedure was identical to that in Experiment 1.\n\n### Results\n\n#### Accuracy results\n\nAccuracy as measured by d' differed significantly from chance (d' = 1.33, SD = 0.56), t(35) = 19.78, p < .001. As in Experiment 1, learning was assessed in a mixed-measures ANOVA on d' with block and distribution as independent variables. The main effect of block was significant, F(5.5, 188) = 2.09, $$\\eta_{\\mathrm{p}}^2=.060$$, p < .05, indicating that performance increased over time. There was also a significant main effect of distribution, F(1, 34) = 35.03, $$\\eta_{\\mathrm{p}}^2=.507$$, p < .001, with participants in the falling distribution condition (d' = 1.61) outperforming participants in the rising distribution condition (d' = 1.04). Learning rate, however, did not depend on distribution, as indicated by a nonsignificant block × distribution interaction, F(5.5, 188) = 0.50, $$\\eta_{\\mathrm{p}}^2=.014$$, p = .84. The accuracy results are illustrated in Fig. 4.\n\n#### Logistic regression\n\nLogistic regression analyses were conducted as in Experiment 1, which predicted the likelihood of a category A response from S1 and S2 separately for the learning and maintenance phases.\n\n### Learning phase\n\nThe model comprised the regressors S1 and S2, the factors block and distribution (rising, falling), and the interactions S1 × S2, S1 × block, S2 × block, S1 × distribution, and S2 × distribution. Notably, there was a main effect of S1, β = −1.38, z = −5.60, p < .001; that is, S1 values per trial significantly predicted category judgments. The significant block main effect, β = 0.33, z = −3.23, p < .01, and the S1 × block interaction, β = −0.04, z = −4.32, p < .001, together indicated that category A responses could be increasingly better predicted over the course of the experiment, especially on the basis of S1. Furthermore, category A responses were generally predicted better in the falling than in the rising distribution, β = 9.41, z = 20.41, p < .001. The distribution factor furthermore interacted with both S1,β = −0.50, z = −9.00, p < .01, and S2, β = −0.59, z = −18.23, p < .001, reflecting larger S1 and S2 effects for the falling than for the rising distribution. Finally, there was a significant interaction of S1 and S2, β = 0.60, z = 2.75, p < .01.\n\n### Maintenance phase\n\nIn the model without a block factor, both S1, β = −2.29, z = −7.37, p < .001, and S2, β = −0.54, z = −3.69, p < .01, were significant and interacted with distribution (S1 × distribution, β = −0.96, z = −8.88, p < .001; S2 × distribution, β = −0.44, z = −6.32, p < .001. S1 and S2 were significant predictors for category A responses, and more so for the falling than for the rising distribution. In general, category A responses were predicted better in the falling than in the rising distribution, β = 10.07, z = 9.26, p < .001. Finally, as in the learning phase, the S1 × S2 interaction was significant, β = 0.72, z = 4.79, p < .001.\n\n### Learning versus maintenance phase\n\nThe absolute values of the single-subject β-weights (from the same models as those reported in Experiment 1) were used as the dependent variable in an ANOVA with the factors phase (learning, maintenance), distribution (rising, falling), and filter (S1, S2). The ANOVA revealed main effects of filter, F(1, 102) = 184.21, $$\\eta_{\\mathrm{p}}^2=.644$$, p < .001, phase, F(1, 102) = 28.66, $$\\eta_{\\mathrm{p}}^2=.219$$, p < .001, and distribution, F(1, 34) = 29.23, $$\\eta_{\\mathrm{p}}^2=.462$$, p < .001. Weights were higher for S1 than for S2 (1.77, SD = 1.06, vs. 0.47, SD = 0.37), and higher in the maintenance phase than in the learning phase (1.38, SD = 1.26, vs. 0.86, SD = 0.64). β-weights were larger in the falling distribution than in the rising distribution (1.44, SD = 1.21 vs. 0.80, SD = 0.67. Furthermore, the filter × distribution, F(1, 102) = 13.85, $$\\eta_{\\mathrm{p}}^2=.119$$, p < .001, phase × distribution, F(1, 102) = 7.90, $$\\eta_{\\mathrm{p}}^2=.072$$, p < .01, and filter × phase, F(1, 102) = 13.86, $$\\eta_{\\mathrm{p}}^2=.120$$, p < .001, interactions reached significance, reflecting larger β differences between S1 and S2 in the falling (2.27, S = 1.19, vs. 0.61, SD = 0.38) than in the rising (1.28, SD = 0.60, vs. 0.33, SD = 0.30) distribution and in the maintenance (2.21, SD = 1.26, vs. 0.55, SD = 0.43) than in the learning (1.34, SD = 0.55, vs. 0.39, SD = 0.28) phase. Importantly, β differences between the maintenance and learning phases were larger for the falling (1.85, SD = 1.50, vs. 1.04, SD = 0.65) than for the rising (0.93, SD = 0.73, vs. 0.68, SD = 0.59) distribution. The three-way filter × distribution × phase interaction was significant as well, F(1, 102) = 10.02, $$\\eta_{\\mathrm{p}}^2=.090$$, p < .01, motivating separate analyses for the learning and the maintenance phases.\n\nThese analyses showed a significant filter × distribution interaction in the maintenance phase, F(1, 34) = 15.07, $$\\eta_{\\mathrm{p}}^2=.307$$, p < .01, but not in the learning phase, F(1, 34) = 0.31, $$\\eta_{\\mathrm{p}}^2=.009$$, p = .58. In order to visualize the degree to which participants relied on the individual dimensions, S1 and S2, we plotted βs for S1 (abscissa) against βs for S2 (ordinate) in the learning and in the maintenance phases, separately for the rising and the falling distributions (Fig. 5a). The plots illustrate that βs were larger in the maintenance than in the learning phase and also larger in the falling than in the rising distribution.\n\nNotably, more participants used both dimensions, S1 and S2, in the falling than in the rising distribution, as reflected by significant differences in proportions of significant βs from the single-subject logistic regressions, χ 2 = 8.86, p(FDR) < .05 (Fig. 5a, right). This distinction was more pronounced in the learning than in the maintenance phase, where the proportions did not differ, χ 2 < 2, n.s.. In the same vein, participants used S2 more often in the falling than in the rising distribution of the learning phase, χ 2 = 7.26, p(FDR) < .05, while again, proportions did not differ in the maintenance phase, χ 2 < 2, n.s.\n\n#### Modeling results\n\nAs in Experiment 1, three families of decision bound models (e.g., Ashby & Gott, 1988; Maddox & Ashby, 1993) were fit to the learning-phase data—that is, unidimensional rule-based (S1, S2), information integration, and random-response models (Fig. 5b, left). Again, we calculated Bayes factors for each model fit. In contrast to Experiment 1, the proportion of models that received substantial evidence differed significantly between the falling and the rising distributions (Fig. 5b, right) all χ 2s > 3, p(FDR) < .05. All winning models had Bayes factors of >3.\n\nThe proportions of participants fit best by each model are shown in Fig. 5b (left). Overall, participants most often adopted a unidimensional rule-based strategy based on S1, as in Experiment 1. However, strategy use crucially differed between the rising and falling distribution conditions. For the rising distribution, the unidimensional S1 rule was used in the majority of cases (six of eight blocks; ps < .05, FDR-corrected). In the falling condition, participants used an information integration strategy as often as the rule-based (S1) strategy in all eight blocks. Thus, participants trained on the falling distributions were more likely to adopt the optimal strategy that involved integrating S1 and S2 information before making a category membership decision.\n\n#### Convergence of logistic regression and decision bound models\n\nAs before, the convergence of β-weights and BIC scores was assessed in two ANOVAs. The first ANOVA comprised the dependent measure rule-based S1 BIC score and the independent variables S1-β-weight and distribution. Importantly, there was a main effect of β-weight, F(1, 32) = 130.53, $$\\eta_{\\mathrm{p}}^2=.803$$, p < .001, reflecting a negative correlation between S1-βs and RB S1 BIC scores, and a main effect of distribution, F(1, 32) = 20.82, $$\\eta_{\\mathrm{p}}^2=.394$$, p < .001, showing lower BIC scores (better fits) for the falling than for the rising distribution.\n\nThe β-weight × distribution interaction, F(1, 32) = 6.73, $$\\eta_{\\mathrm{p}}^2=.803$$, p < .05, indicated a stronger β–BIC score correlation in the falling than in the rising condition.\n\nThe second ANOVA comprised the dependent measure RB S2 BIC score and the independent variables S2-β and distribution and showed an effect of β-weight, F(1, 32) = 133.67, $$\\eta_{\\mathrm{p}}^2=.806$$, p < .001, as well as an effect of distribution, F(1, 32) = 11.23, η 2 = .260, p < .01, but no β-weight × distribution interaction, F(1, 32) = 1.14, η 2 = .034, p = .29. Again, as in Experiment 1, β-weights and BIC scores converged.\n\n#### Prediction of performance by decision bound models\n\nIn order to directly assess the performance benefit of using an information integration strategy, we carried out a correlation analysis between the proportions of rule-based S1 and information integration strategy use and d'. Notably, there was a positive correlation of proportion of information integration use and d', r = .48, t = 3.20, p < .01, suggesting that using an information integration strategy was indeed beneficial for performance. By contrast, the correlation of proportion of rule-based S1 use and d' was negative, r = −0.23, t = −1.39, n.s.; that is, participants using a rule-based S1 strategy tended to perform worse.\n\n## Discussion\n\nThe important result of Experiment 2 is that, as compared to Experiment 1, more participants used an information integration strategy, and more so in the falling than in the rising distribution condition. Generally, participants who were more likely to use information integration performed better than those who were more likely to focus on an S1 rule-based strategy.\n\nIntriguingly, despite being disadvantageous, participants still used the S1 dimension to a high degree, as evidenced by both logistic regressions and decision bound models. That is, although Experiment 2 examined classification performance for auditory stimuli that were optimally separated by an information integration (diagonal) decision bound, participants still frequently used a rule-based strategy based on S1 (cf. Goudbeek et al., 2009).\n\nCrucially, the use of the optimal information integration strategy depended on whether stimulus distributions were rising or falling (see Fig. 1b). The falling condition was associated more strongly with use of an information integration decision strategy, and the resulting performance was shown to be better for individuals adopting an information integration strategy. This result was predicted, since we hypothesized that familiarity with negative acoustic (here, spectral) correlations between dimensions would promote their integration.\n\nFinally, a comparison of β-weights for the learning and maintenance phases suggested that the correlation of the stimulus dimensions in the learning phase, as well as immediate feedback, promoted the use of both S1 and S2 dimensions for categorization. This is consistent with previous findings in vision research (cf. Maddox et al., 2003). Our analyses suggested that information integration is characterized by an equal usage of S1 and S2 and that it was present in the learning phase, but not in the maintenance phase. There, without an S1–S2 correlation and without feedback, we observed a significant shift to an almost exclusive rule-based use of S1.\n\n## General discussion\n\nTwo experiments examined auditory category formation for stimuli varying along two spectral dimensions (i.e., S1 and S2), which exhibited either positive or negative correlations. Decision bound modeling and logistic regressions yielded three main results: (1) better information integration for negative spectral correlations, (2) tendency toward rule-based S1 categorization overall, and (3) dependency of immediate corrective feedback for information integration.\n\n### Promotion of information integration by negative correlations\n\nThe most important result of the two experiments is that the use of information integration (confined to Experiment 2) depended on the nature of the correlation between dimensions: Negative correlations promoted information integration, while positive correlations inhibited it. Overall, information integration in Experiment 2 predicted better performance.\n\nRegarding the special status of negative acoustic correlations, our study extends the phonetic work by Kingston and colleagues (Kingston et al., 2008; Kingston & Macmillan, 1995). Kingston and colleagues characterized the interaction of fundamental frequency (f0, pitch) and first resonance frequency (F1) in the human oral cavity, as well as the interaction of F1 and nasalization (i.e., the resonance frequencies in the human nasal cavity) in speech vowel and nonspeech vowel-like sounds, and observed that both the f0–F1 and F1–nasalization relations approximate a negative correlation. With respect to nasalization, vowels with a higher degree of nasalization tend to have lower F1 frequencies (Diehl et al., 1990). Kingston and colleagues demonstrated that both of the discussed negative correlations (f0–F1 and F1–nasalization), in comparison with their positive counterparts, led to better categorization performance for speech and nonspeech stimuli (Kingston et al., 2008; Kingston & Macmillan, 1995). On the basis of these findings, we suggest that there is a general inclination toward encountering negative acoustic correlations in speech (resulting from articulatory bases as discussed in Carre, 2009), possibly shaping the learning of nonspeech stimuli with similar negative correlations.\n\n### Preference of rule-based strategies\n\nIn both experiments, participants predominantly based their categorization on the first spectral filter, S1. This inclination, which is in line with previous research (Goudbeek et al., 2009; Maddox et al., 2006), seems remarkable in the light of Experiment 2, where rule-based categorization was clearly suboptimal and where participants using this strategy performed worse than those employing information integration. In general, this inclination may have a neural explanation: Previous neuroimaging studies on visual categorization have shown that cortico–striatal connections are vital for information integration (Ashby & Ell, 2001; Ashby & Ennis, 2006; Ashby & Spiering, 2004; Helie, Roeder, & Ashby, 2010; Nomura et al., 2007; Seger, 2008). Moreover, cortico–striatal connections between the auditory cortex and the caudate have been argued to be more diffuse than cortico–striatal connections between the visual cortex and the caudate (Maddox et al., 2006). Thus, information integration may be relatively less likely in auditory categorization than in vision, due to anatomical constraints.\n\nA second possible explanation for the reliance on rule-based strategies is developmental in nature. We have argued that information integration depends on acoustic correlations familiar from speech; thus, speech itself should presumably be acquired predominantly by information integration. A potential bias toward information integration learning in early life is related to the observation that brain structures such as the prefrontal and medial cortices that support rule-based learning (Gabrieli, Brewer, Desmond, & Glover, 1997; Schacter & Wagner, 1999) develop relatively late (Diamond, 2002). As a result, rule-based learning in early life does not compete with information integration learning as strongly as in adolescence or adulthood (Huang-Pollock, Maddox, & Karalunas, 2011).\n\nThe preference for using specifically the S1 dimension during categorization may reflect that pitch changes (presumably, the perceptual dimension conveyed by S1 variation; Remez & Rubin, 1993) are easier to verbalize than timbre changes (as potentially reflected by S2; cf. Smits et al., 2006). That is, pitch changes can be easily verbally described as being “high” or “low,” while timbral differences are more difficult to label verbally. The easier-to-verbalize dimension (i.e., S1) was then not surprisingly more often used for rule-based categorization, which, by definition, is based on easy-to-verbalize rules (Ashby & Maddox, 2005; Ashby & Maddox, 2011).\n\n### The influence of immediate feedback\n\nThe third main finding from the present experiments concerns the comparison of the learning phase with the maintenance phase. First, the learning phase contained distributional information from which the correlations between dimensions could be extracted, while in the maintenance phase, no such information was available. Second, there was immediate feedback in the learning phase, but not in the maintenance phase. Previous research indicates that both of these aspects may contribute to the likelihood that information integration strategies are used for categorization. Research from vision provides evidence that immediate feedback is crucial for adopting an information integration strategy (cf. Ashby et al., 1999; Ashby & Waldron, 1999). Additionally, β-weights in the maintenance phase of both experiments were larger, and more so for S1 than for S2. The falling condition in Experiment 2 furthermore showed that even though both dimensions were used during learning, concomitant with information integration, participants reverted to using S1 in the maintenance phase.\n\n### Potential limitations of decision bound models\n\nDecision bound models are not unequivocally accepted; they implicitly assume dissociations of multiple memory systems subserving rule-based and information integration learning. In particular, they assume that rule-based learning requires working memory, while information integration does not (Filoteo, Lauritzen, & Maddox, 2010). Challenging this claim, (Lewandowsky, Yang, Newell, & Kalish, 2012; Newell & Dunn, 2008; Newell, Dunn, & Kalish, 2010), showed that rule-based as well as information integration learning tax working memory.\n\nFurthermore, decision bound models are not the only means by which auditory categorization can be modeled. On the one hand, prototype models (Rosch, 1973) assume that a novel stimulus is assigned to the category whose average or most representative member (i.e., the prototype) it is most similar to. Different formulations of prototype theory suggest that categorization decisions are also based in part on the spread of category members around the prototype (i.e., category variance; Nearey & Assmann, 1986). Exemplar models (Nosofsky, 1986), on the other hand, assume (in their most extreme formulation) that categorization involves comparing novel acoustic items with all previously encountered members of relevant existing categories and then making a category membership decision on the basis of the maximum of the summed similarities to the members of the relevant categories. Prototype and exemplar models have been extensively compared elsewhere (Nosofsky & Stanton, 2005; Tunney & Fernie, 2012) and have also found to be somewhat inferior to decision bound models (Smits et al., 2006).\n\nThe present data show that decision bound models provide a valuable tool for assessing the contribution of different acoustic dimensions to auditory categorization. Furthermore, since Experiment 2, in particular, provides converging results from logistic regressions and decision bound models regarding the use of specific stimulus dimensions for categorization, we are confident that decision bound models accurately account for participants’ response behavior.\n\n## Conclusions\n\nIn sum, the present experiments provided evidence that listeners tend to use a rule-based—that is, an explicit—hypothesis-testing approach when they are categorizing novel auditory stimuli. Our results further suggest that long-term experience with sound distributions characterized by a negative spectro–spectral (S1–S2) correlation shapes the categorization of novel auditory stimuli. This is in line with experiments on speech sound categorization where long-term experience with correlations among auditory dimensions could not be easily overridden by short-term exposure to contrasting dimension correlations (Idemaru & Holt, 2011). 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Lloyd (Eds.), Cognition and Categorization (pp. 27–48). Hillsdale: Lawrence Erlbaum Associates.\n\n• Russ, B. E., Lee, Y. S., & Cohen, Y. E. (2007). Neural and behavioral correlates of auditory categorization. Hearing Research, 229(1–2), 204–212.\n\n• Schacter, D. L., & Wagner, A. D. (1999). Medial temporal lobe activations in fMRI and PET studies of episodic encoding and retrieval. Hippocampus, 9(1), 7–24.\n\n• Seger, C. A. (2008). How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback. Neuroscience and Biobehavioral Reviews, 32(2), 265–278.\n\n• Seger, C. A., & Cincotta, C. M. (2005). The roles of the caudate nucleus in human classification learning. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 25(11), 2941–2951.\n\n• Silbert, N. H., Townsend, J. T., & Lentz, J. J. (2009). Independence and separability in the perception of complex nonspeech sounds. Attention, Perception, & Psychophysics, 71(8), 1900–1915.\n\n• Sloutsky, V. M. (2003). The role of similarity in the development of categorization. Trends in Cognitive Sciences, 7(6), 246–251.\n\n• Smits, R., Sereno, J., & Jongman, A. (2006). Categorization of sounds. Journal of Experimental Psychology. Human Perception and Performance, 32(3), 733–754.\n\n• Spiering, B. J., & Ashby, F. G. (2008). Response processes in information-integration category learning. Neurobiology of Learning and Memory, 90(2), 330–338.\n\n• Tunney, R. J., & Fernie, G. (2012). Episodic and prototype models of category learning. Cognitive Processing, 13(1), 41–54.\n\n• Verbeemen, T., Vanpaemel, W., Pattyn, S., Storms, G., & Verguts, T. (2007). Beyond exemplars and prototypes as memory representations of natural concepts: A clustering approach. Journal of Memory and Language, 56(4), 537–554.\n\n• Yamauchi, T., Love, B. C., & Markman, A. B. (2002). Learning nonlinearity seperable categories by inference and classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(3), 585–593.\n\n• Zwicker, E. (1961). Subdivision of the audible frequency range into critical bands. Journal of the Acoustical Society of America, 33(2), 248–248.\n\n## Author information\n\nAuthors\n\n### Corresponding author\n\nCorrespondence to Mathias Scharinger.\n\n## Appendix: Methods\n\n### Appendix: Methods\n\nTo model the learning process, we fit a number of decision bound models (DBMs) to each listener’s data on a block-by-block basis. DBMs assume that a single stimulus presentation is represented in a multidimensional perceptual space and that each stimulus can be mapped to perceptual (i.e., internal) space by a transformation corresponding to a psychophysical function:\n\n$$\\varPsi \\left( {{{\\mathbf{y}}_{\\mathrm{i}}}} \\right)={{\\mathbf{x}}_{\\mathrm{pi}}}+{{\\mathbf{e}}_{\\mathrm{pi}}},$$\n(1)\n\nwhere e pi is a random vector representing perceptual noise. Here, we assume a one-to-one mapping of physical to perceptual coordinates but allow for trial-by-trial (unbiased) variability in the percept.\n\nAccording to decision bound theory, participants make categorization decisions on the basis of division of the psychophysical space by a response criterion. We thus fit a number of DBMs to the data of each listener for each block in order to estimate the response criterion that best accounted for the listener’s pattern of responses. For each experiment, we fit two unidimensional models, two information integration models, and one random-response model.\n\nUnidimensional rules assume that a listener makes a categorization decision on the basis of one dimension only by setting a response criterion, λ 1, at a location along the relevant dimension. Given this criterion location, the probability of responding “category A”, P(RA), is\n\n$$P\\left( {{{\\mathrm{R}}_{\\mathrm{A}}}|\\mathbf{x}} \\right)=P\\left[ {\\ {{\\mathrm{x}}_1}+{e_{\\mathrm{p}1}} < {l_1}+{e_{\\mathrm{c}1}}} \\right],$$\n(2)\n\nand the probability of responding “category B,” P(RB), is\n\n$$P\\left( {{{\\mathrm{R}}_{\\mathrm{B}}}|\\mathbf{x}} \\right)=1-P\\left( {{{\\mathrm{R}}_{\\mathrm{A}}}|\\mathbf{x}} \\right),$$\n(3)\n\nwhere λ 1 is the response criterion location, e c1 is criterial error, and e p1 is perceptual noise on the relevant dimension. In the model, e c1 and e p1 are assumed to be independent and identically distributed. Equation 2 can be rewritten as\n\n$$P\\left( {{R_A}|x} \\right)=\\varPhi \\left( {\\frac{{{\\lambda_1}-{x_1}}}{{\\sqrt{{\\left( {\\sigma_p^2+\\sigma_c^2} \\right)}}}}} \\right),$$\n(4)\n\nwhere Φ is the normal cumulative distribution function. In the model, σ p and σ c cannot be separately determined, so we fit only one noise parameter, σ 2 = σ 2 p + σ 2 c. Thus, each unidimensional model has two free parameters: the variability parameter, σ, and the response criterion location, λ 1. We fit unidimensional models based on both S1 and S2.\n\nInformation integration models assume that the listener integrates the S1 and S2 values before making a decision about category membership. The response criterion location, λ 12 , can then be described in two-dimensional space by assuming a slope, b, and intercept, c 0. The probability of making a “category A” response for a two-dimensional stimulus is then\n\n$$P\\left( {{R_A}|x} \\right)=\\varPhi \\left( {\\frac{{{\\lambda_{12 }}-{x_1}{x_2}}}{{\\sqrt{{\\sigma_p^2+\\sigma_c^2}}}}} \\right)$$\n(5)\n\nTwo versions of the information integration model were fit to each listener’s data. The first assumed that the response criterion location was oriented optimally; this model thus had only one free parameter, σ. The second information integration model thus allowed the slope and intercept of the response criterion location to vary, and so had three free parameters: b, c 0, and σ.\n\nThe random-response rule modeled the probability of responding “category A,” P(RA), as the frequency of actual “category A” responses for each listener in each block, ignoring the value of the stimulus on either dimension. This model had one free parameter that was estimated from the data, the observed frequency of “category A” responses.\n\nIn order to determine which DBM best fit each participant’s data on a block-by-block basis, all DBMs were fit to responses using maximum likelihood methods. Best-fitting parameters were found with MATLAB’s constrained nonlinear optimization routine based on a quasi-Newton approximation of the Hessian function. We used the BIC (Kass & Raftery, 1995) for model comparisons. The BIC is calculated for each model according to\n\n$$BIC=-\\mathbf{2}M{L_i}+{j_i}log(n),$$\n(6)\n\nwhere ML i is the maximum log-likelihood of model i, j is the number of parameters in the model, and n is the number of observations. The number of parameters in the expression serves as a handicap for model complexity; the model with the smallest BIC is selected as the best-fitting model.\n\nBayes factors were derived from BIC scores on the basis of approximation formulae provided in Raftery (1986) and rewritten as\n\n$${{\\mathrm{B}}_1}={e^{{\\frac{{-1\\left( {{M_1}-{M_0}} \\right)}}{2}}}},$$\n(7)\n\nwhere M1 is the BIC score to be converted into the Bayes factor and M0 is the BIC score of the alternative model—here, the random-response model.\n\n## Rights and permissions\n\nReprints and Permissions\n\nScharinger, M., Henry, M.J. & Obleser, J. Prior experience with negative spectral correlations promotes information integration during auditory category learning. Mem Cogn 41, 752–768 (2013). https://doi.org/10.3758/s13421-013-0294-9\n\n• Published:\n\n• Issue Date:\n\n• DOI: https://doi.org/10.3758/s13421-013-0294-9\n\n### Keywords\n\n• Audition\n• Categorization\n• Implicit learning\n• Implicit memory\n• Perception"
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http://cypher.tokyo/4ede3e7ac48cade3f4a95ac1da3f077c
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"# A Concise Introduction To Logic 11th Edition Answer Key Chapter 3\n\na concise introduction to logic 11th edition answer key chapter 3\n\nLearn logic chapter 3 hurley with free interactive flashcards. Choose from 500 different sets of logic chapter 3 hurley flashcards on Quizlet.\n\nA Concise Introduction to Logic, Eleventh Edition ...\n\nconcise-introduction-to-logic-11th-edition 1/5 PDF Drive - Search and download PDF files for free. Concise Introduction To Logic 11th Edition ... 1990s and new millennium guided reading answers, chapter 10 section 3 guided reading and review the senate answer key, Before Happiness The 5 Hidden Keys To ...\n\nChapter 3.1 Solutions | A Concise Introduction To Logic ...\n\nA Concise Introduction To Logic 11th Edition Answer Key Chapter 1 A Concise Introduction To Logic Getting the books A Concise Introduction To Logic 11th Edition Answer Key Chapter 1 now is not type of challenging means. 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Algorithm - Wikipedia\n\nA Concise Introduction To Logic Answers Chapter 1\n\nConcise Introduction To Logic 11Th Edition Patrick J. Hurley. 3.8 out ... He retired from teaching in 2008, but continues his research and writing, including work on A Concise Introduction to Logic. His interests include music, art, opera, environmental issues, fishing, and skiing. ... The table of contents goes like chapter 1,2,3,9,10,11,12,13 ...\n\nThe Daily News carried an article this morning about three ...\n\nUnsurpassed for its clarity and comprehensiveness, Hurley's, A CONCISE INTRODUCTION TO LOGIC is the #1 introductory logic textbook in the market. In this Eleventh Edition, Hurley continues to build upon the tradition of a lucid, focused, and accessible presentation of the basic subject matter of logic, both formal and informal. Hurley's extensive, carefully sequenced collection of exercises ...\n\nStudent Resources - Oxford University Press\n\nFind helpful customer reviews and review ratings for Concise Introduction to Logic: Using Traditional Logic, 11th Edition at Amazon.com. Read honest and unbiased product reviews from our users.\n\nA Concise Introduction To Logic Answer Key Chapter 5\n\nAnswer Key of week 8 What students are saying As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.\n\nAmazon.com: Customer reviews: A Concise Introduction to ...\n\nFor each of the following lists of premises, derive the indicated conclusion and complete the justification. For double negation, avoid the occurrence of triple tildes. Exercise 6 has two possible answers.1. M (G T ) 2. P (S N ) 3. D (R K ) 4. _____ ____ , Assoc\n\nA Concise Introduction To Logic Answers Chapter 1\n\nA Concise Introduction To Logic 11th Edition Answer Key Chapter 6 A Concise Introduction To Logic This is likewise one of the factors by obtaining the soft documents of this A Concise Introduction To Logic 11th Edition Answer Key Chapter 6 by online. You might not require more period to spend to go to the ebook launch as competently as search ...\n\nConcise Introduction to Logic 13th Edition Hurley ...\n\nA Concise Introduction To Logic 12Th Edition Answer Key ... The Online Writing Lab (OWL) at Purdue University houses writing resources and instructional material, and we provide these as a free service of the Writing Lab at Purdue A concise introduction to logic 12th edition answer key chapter 7.2.\n\nAmazon.com: Customer reviews: A Concise Introduction to ...\n\nLearn and logic chapter 4 hurley with free interactive flashcards. Choose from 500 different sets of and logic chapter 4 hurley flashcards on Quizlet. ... Hurley Introduction to Logic Chapter 11. ... a concise introduction to logic 13th edition by hurley - chapter 1 section 4 vocab terms. valid deductive argument. invalid deductive argument ...\n\nA concise introduction to logic - SILO.PUB\n\nSealy & Worthington’s Text, Cases, and Materials in Company Law clearly explains the fundamental structure of company law and provides a concise introduction to each different aspect of the subject. The materials are carefully selected and well supported by commentary so that the logic of the doctrinal or policy argument is unambiguously laid out.\n\n#### A Concise Introduction To Logic 11th Edition Answer Key Chapter 3\n\nThe most popular ebook you must read is A Concise Introduction To Logic 11th Edition Answer Key Chapter 3. I am sure you will love the A Concise Introduction To Logic 11th Edition Answer Key Chapter 3. 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https://au.mathworks.com/help/physmod/sm/mech/gs/representations-of-body-orientation.html
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"Documentation\n\n### This is machine translation\n\nMouseover text to see original. Click the button below to return to the English version of the page.\n\n## Representations of Body Orientation\n\n### Warning\n\nThis content is specific to Simscape™ Multibody™ First Generation software. First-generation features are slated to be deprecated and should be avoided.\n\nYou represent a Simscape Multibody body's orientation by specifying the orientation of its center of gravity coordinate system (CG CS) axes relative to some other set of axes, either the CS axes of an adjoining body or the World CS axes. No reorientation is represented by “no rotation” or the rotational identity.\n\nA general rotation of a body in three dimensions has three independent degrees of freedom. There are many equivalent and interconvertible ways to represent these degrees of freedom . The Body and related Body Sensor and RotationMatrix2VR blocks use the following representations. The block reference pages for these blocks discuss block-specific details.\n\n### Axis-Angle Representation\n\nThe axis-angle representation of a rotation is the most fundamental form. Specify a rotation axis n, then rotate by the right-hand rule about that axis by some angle θ. The vector n = (nx,ny,nz) is a three-component unit vector, where n·n = nx2 + ny2 + nz2 = 1. The axis n is sometimes called the eigenaxis.\n\nSimscape Multibody models do not make direct use of the axis-angle representation, but it is the starting point for deriving other forms. It is also used extensively in mechanical applications such as computer-aided design and robotics.\n\nThe axis-angle representation is usually written as a 4-vector: [nx ny nz θ]. Of the four numbers, three are independent, because n always has unit length. The remaining freedom in this vector allows you to specify a direction (two angles) and the size and sense of the rotation about that directional axis (magnitude and sign of θ).\n\nTo describe continuous rotation in time, treat n and θ as functions of time.\n\n### Quaternion Representation\n\nA quaternion represents a three-dimensional rotation as a four-component row vector of unit length:\n\nwith `q*q` = qv·qv + qs2 = 1. This definition uses the axis-angle representation defined above. The rotation angle about that axis is θ. To describe continuous rotation in time, treat n and θ as functions of time. Unlike some rotation representations, quaternions never become singular.\n\n### Rotation Matrix Representation\n\nThe axis-angle representation also defines the rotation matrix R in exponential form R = exp(θ J), where the Jk are real, antisymmetric matrices, and J = nxJ1 + ny J2 + nz J3. The rotation matrix R is orthogonal: RRT = RTR = I.\n\nThe J matrices are related to the antisymmetric permutation symbol ɛijk.\n\nThe exponential R is reduced to closed form by the Rodrigues identity:\n\nwhere I is the identity matrix, and J is given by\n\nThe inverse of R is identical to its transpose RT. You can also obtain the inverse by replacing θ with θ or by reversing the direction of n.\n\nTo describe continuous rotation in time, treat n and θ as functions of time.\n\n### Euler Angle Representation\n\nAn alternative representation for R is to rotate, in succession, about three independent axes, by three independent Euler angles. A full rotation R starting in World composes by multiplying the matrices successively on the left:\n\nRBW = R3*R2*R1\n\nA full rotation R starting in a body CS composes by multiplying the matrices successively on the right:\n\nRWB = R1*R2*R3\n\nThe Euler angle convention is to\n\n1. Rotate about one body coordinate axis (which rotates the other two).\n\n2. Then rotate about a second body coordinate axis (rotated from its original direction) not identical to the first.\n\n3. Lastly, rotate about another body coordinate axis not identical to the second.\n\nThus there are 3*2*2 = 12 possible Euler angle rotation sequences. The rotation axis sequences Z-X-Z and Z-Y-X are common. Rotation angles are often labeled as θ1, θ2, θ3 or Φ, θ, Ψ as the first, second, and third angles, respectively. For example,\n\nRBW = RX1)*RY2)*RZ3)\n\nRWB = RZ(Φ)*RX(θ)*RZ(Ψ)\n\nA two-dimensional rotation about a fixed axis requires one angle. For example, rotating the x- and y-axes about the z-axis by Φ is represented by\n\nTo describe continuous rotation in time, treat the Euler angles as functions of time. The Euler angle representation is singular in certain limiting situations. Such singularities are artifacts of the Euler angle form and have no geometric or physical significance.\n\n### Converting Rotation Representations\n\nCertain Simscape Multibody blocks make use of different rotation representations.\n\n• The Body block makes direct use of the Euler angle, rotation matrix, and quaternion representations.\n\n• The Body Sensor block makes use of the rotation matrix.\n\n• The RotationMatrix2VR block uses the rotation matrix and axis-angle forms.\n\nThe four rotation representations presented in this section are equivalent. You can represent a rotation equally well with any one of them. Some applications, however, tend to favor one representation over the others, and certain representations are singular in certain limits. It is helpful to know how to convert the various rotation representations into one another. The following summaries group the conversion formulas into one place.\n\n#### Transforming the Axis-Angle Representation\n\nThe rotation axis unit vector n and the rotation angle θ define this representation, which is discussed in detail in Axis-Angle Representation. This representation defines the quaternion and rotation matrix representations:\n\n#### Transforming the Quaternion Representation\n\nThe quaternion is a vector-scalar pair, `q`` `= [qv qs], defined by Quaternion Representation. You can recover the axis-angle representation from the quaternion components:\n\nYou can also construct the equivalent rotation matrix R from q.\n\nThe term is the outer product of qv with itself, the 3-by-3 matrix of qv components multiplied by each other.\n\n#### Transforming the Rotation Matrix Representation\n\nThe rotation matrix R is an orthogonal 3-by-3 matrix: RRT = RTR = I, defined in Rotation Matrix Representation. You can invert the rotation matrix representation to obtain the equivalent representations for the quaternion `q` = [qv qs] and axis-angle (n, θ)\n\nThe trace Tr of a matrix is the sum of its diagonal elements.\n\nThe J matrices constitute a 3-vector of matrices defined by the antisymmetric permutation symbol, (Jj)ik = ɛijk. See The Permutation Symbol and the Vector Cross Product for more details.\n\nThe RotationMatrix2VR block converts the rotation matrix to the axis-angle representation.\n\n#### Transforming the Euler Angle Representation\n\nThe Euler angle representation of a rotation, defined by Euler Angle Representation, stands apart from the other three, insofar as you cannot derive it from the axis-angle representation. It depends on the choice of rotation axis sequence, which generates multiple definition conventions. The Euler angle representation, at certain limits, can also be singular. Use caution with Euler angle expressions.\n\nIf you choose a convention and three angles, then compute R, you can convert R to the other representations by the use of Transforming the Rotation Matrix Representation above. But given the nine components of R, you must find the Euler angles by inverting the nine equations that result from this matrix equation. (Only three equations of the nine are independent.) In some cases, angles can be read from R by inspection.\n\nFor example, choose rotations with respect to a Body coordinate system (CS) triad, in a commonly used rotation axis sequence Z-X-Z, with Φ, θ, Ψ as the respective angles. The rotation matrix is RWB = R1(Φ)*R2(θ)*R3(Ψ),\n\nIn this convention, you can read θ from the R33 component, then find Ψ from the R32 or R31 component. Obtain Φ from one of the other components, using cos2Φ + sin2Φ = 1, or by multiplying from the right by R3ΨT, then R2θT. The second method yields a unique solution for the sine and cosine of Φ.\n\n### Converting the Angular Velocity\n\nThe rotation matrix R is defined in Representations of Body Motion and Rotation Matrix Representation.\n\nThe angular velocity vector ω is the rate at which a spinning CS rotates. R and the antisymmetric matrix Ω define ω:\n\nYou can also express the angular velocity in terms of Euler angles, by choosing a particular set of angles to represent R. See Euler Angle Representation and Transforming the Euler Angle Representation.\n\nThe quaternion derivative is also related to the angular velocity:"
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http://slicot.org/objects/software/shared/doc/MB03QG.html
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[
"## MB03QG\n\n### Reorder diagonal blocks of a principal subpencil of an upper quasi-triangular matrix pencil\n\n[Specification] [Arguments] [Method] [References] [Comments] [Example]\n\nPurpose\n\n``` To reorder the diagonal blocks of a principal subpencil of an\nupper quasi-triangular matrix pencil A-lambda*E together with\ntheir generalized eigenvalues, by constructing orthogonal\nsimilarity transformations UT and VT.\nAfter reordering, the leading block of the selected subpencil of\nA-lambda*E has generalized eigenvalues in a suitably defined\ndomain of interest, usually related to stability/instability in a\ncontinuous- or discrete-time sense.\n\n```\nSpecification\n``` SUBROUTINE MB03QG( DICO, STDOM, JOBU, JOBV, N, NLOW, NSUP, ALPHA,\n\\$ A, LDA, E, LDE, U, LDU, V, LDV, NDIM, DWORK,\n\\$ LDWORK, INFO )\nC .. Scalar Arguments ..\nCHARACTER DICO, JOBU, JOBV, STDOM\nINTEGER INFO, LDA, LDE, LDU, LDV, LDWORK, N, NDIM, NLOW,\n\\$ NSUP\nDOUBLE PRECISION ALPHA\nC .. Array Arguments ..\nDOUBLE PRECISION A(LDA,*), DWORK(*), E(LDE,*), U(LDU,*), V(LDV,*)\n\n```\nArguments\n\nMode Parameters\n\n``` DICO CHARACTER*1\nSpecifies the type of the spectrum separation to be\nperformed, as follows:\n= 'C': continuous-time sense;\n= 'D': discrete-time sense.\n\nSTDOM CHARACTER*1\nSpecifies whether the domain of interest is of stability\ntype (left part of complex plane or inside of a circle)\nor of instability type (right part of complex plane or\noutside of a circle), as follows:\n= 'S': stability type domain;\n= 'U': instability type domain.\n\nJOBU CHARACTER*1\nIndicates how the performed orthogonal transformations UT\nare accumulated, as follows:\n= 'I': U is initialized to the unit matrix and the matrix\nUT is returned in U;\n= 'U': the given matrix U is updated and the matrix U*UT\nis returned in U.\n\nJOBV CHARACTER*1\nIndicates how the performed orthogonal transformations VT\nare accumulated, as follows:\n= 'I': V is initialized to the unit matrix and the matrix\nVT is returned in V;\n= 'U': the given matrix V is updated and the matrix V*VT\nis returned in V.\n\n```\nInput/Output Parameters\n``` N (input) INTEGER\nThe order of the matrices A, E, U, and V. N >= 0.\n\nNLOW, (input) INTEGER\nNSUP (input) INTEGER\nNLOW and NSUP specify the boundary indices for the rows\nand columns of the principal subpencil of A - lambda*E\nwhose diagonal blocks are to be reordered.\n0 <= NLOW <= NSUP <= N.\n\nALPHA (input) DOUBLE PRECISION\nThe boundary of the domain of interest for the eigenvalues\nof A. If DICO = 'C', ALPHA is the boundary value for the\nreal parts of the generalized eigenvalues, while for\nDICO = 'D', ALPHA >= 0 represents the boundary value for\nthe moduli of the generalized eigenvalues.\n\nA (input/output) DOUBLE PRECISION array, dimension (LDA,N)\nOn entry, the leading N-by-N part of this array must\ncontain a matrix in a real Schur form whose 1-by-1 and\n2-by-2 diagonal blocks between positions NLOW and NSUP\nare to be reordered.\nOn exit, the leading N-by-N part of this array contains\na real Schur matrix UT' * A * VT, with the elements below\nthe first subdiagonal set to zero.\nThe leading NDIM-by-NDIM part of the principal subpencil\nB - lambda*C, defined with B := A(NLOW:NSUP,NLOW:NSUP),\nC := E(NLOW:NSUP,NLOW:NSUP), has generalized eigenvalues\nin the domain of interest and the trailing part of this\nsubpencil has generalized eigenvalues outside the domain\nof interest.\nThe domain of interest for eig(B,C), the generalized\neigenvalues of the pair (B,C), is defined by the\nparameters ALPHA, DICO and STDOM as follows:\nFor DICO = 'C':\nReal(eig(B,C)) < ALPHA if STDOM = 'S';\nReal(eig(B,C)) > ALPHA if STDOM = 'U'.\nFor DICO = 'D':\nAbs(eig(B,C)) < ALPHA if STDOM = 'S';\nAbs(eig(B,C)) > ALPHA if STDOM = 'U'.\n\nLDA INTEGER\nThe leading dimension of the array A. LDA >= MAX(1,N).\n\nE (input/output) DOUBLE PRECISION array, dimension (LDE,N)\nOn entry, the leading N-by-N part of this array must\ncontain a matrix in an upper triangular form.\nOn exit, the leading N-by-N part of this array contains an\nupper triangular matrix UT' * E * VT, with the elements\nbelow the diagonal set to zero.\nThe leading NDIM-by-NDIM part of the principal subpencil\nB - lambda*C, defined with B := A(NLOW:NSUP,NLOW:NSUP)\nC := E(NLOW:NSUP,NLOW:NSUP) has generalized eigenvalues\nin the domain of interest and the trailing part of this\nsubpencil has generalized eigenvalues outside the domain\nof interest (see description of A).\n\nLDE INTEGER\nThe leading dimension of the array E. LDE >= MAX(1,N).\n\nU (input/output) DOUBLE PRECISION array, dimension (LDU,N)\nOn entry with JOBU = 'U', the leading N-by-N part of this\narray must contain a transformation matrix (e.g., from a\nprevious call to this routine).\nOn exit, if JOBU = 'U', the leading N-by-N part of this\narray contains the product of the input matrix U and the\northogonal matrix UT used to reorder the diagonal blocks\nof A - lambda*E.\nOn exit, if JOBU = 'I', the leading N-by-N part of this\narray contains the matrix UT of the performed orthogonal\ntransformations.\nArray U need not be set on entry if JOBU = 'I'.\n\nLDU INTEGER\nThe leading dimension of the array U. LDU >= MAX(1,N).\n\nV (input/output) DOUBLE PRECISION array, dimension (LDV,N)\nOn entry with JOBV = 'U', the leading N-by-N part of this\narray must contain a transformation matrix (e.g., from a\nprevious call to this routine).\nOn exit, if JOBV = 'U', the leading N-by-N part of this\narray contains the product of the input matrix V and the\northogonal matrix VT used to reorder the diagonal blocks\nof A - lambda*E.\nOn exit, if JOBV = 'I', the leading N-by-N part of this\narray contains the matrix VT of the performed orthogonal\ntransformations.\nArray V need not be set on entry if JOBV = 'I'.\n\nLDV INTEGER\nThe leading dimension of the array V. LDV >= MAX(1,N).\n\nNDIM (output) INTEGER\nThe number of generalized eigenvalues of the selected\nprincipal subpencil lying inside the domain of interest.\nIf NLOW = 1, NDIM is also the dimension of the deflating\nsubspace corresponding to the generalized eigenvalues of\nthe leading NDIM-by-NDIM subpencil. In this case, if U and\nV are the orthogonal transformation matrices used to\ncompute and reorder the generalized real Schur form of the\npair (A,E), then the first NDIM columns of V form an\northonormal basis for the above deflating subspace.\n\n```\nWorkspace\n``` DWORK DOUBLE PRECISION array, dimension (LDWORK)\nOn exit, if INFO = 0, DWORK(1) returns the optimal value\nof LDWORK.\n\nLDWORK INTEGER\nThe length of the array DWORK. LDWORK >= 1, and if N > 1,\nLDWORK >= 4*N + 16.\n\nIf LDWORK = -1, then a workspace query is assumed; the\nroutine only calculates the optimal size of the DWORK\narray, returns this value as the first entry of the DWORK\narray, and no error message related to LDWORK is issued by\nXERBLA.\n\n```\nError Indicator\n``` INFO INTEGER\n= 0: successful exit;\n< 0: if INFO = -i, the i-th argument had an illegal\nvalue;\n= 1: A(NLOW,NLOW-1) is nonzero, i.e., A(NLOW,NLOW) is not\nthe leading element of a 1-by-1 or 2-by-2 diagonal\nblock of A, or A(NSUP+1,NSUP) is nonzero, i.e.,\nA(NSUP,NSUP) is not the bottom element of a 1-by-1\nor 2-by-2 diagonal block of A;\n= 2: two adjacent blocks are too close to swap (the\nproblem is very ill-conditioned).\n\n```\nMethod\n``` Given an upper quasi-triangular matrix pencil A - lambda*E with\n1-by-1 or 2-by-2 diagonal blocks, the routine reorders its\ndiagonal blocks along with its eigenvalues by performing an\northogonal equivalence transformation UT'*(A - lambda*E)* VT.\nThe column transformations UT and VT are also performed on the\ngiven (initial) transformations U and V (resulted from a\npossible previous step or initialized as identity matrices).\nAfter reordering, the generalized eigenvalues inside the region\nspecified by the parameters ALPHA, DICO and STDOM appear at the\ntop of the selected diagonal subpencil between positions NLOW and\nNSUP. In other words, lambda(A(Select,Select),E(Select,Select))\nare ordered such that lambda(A(Inside,Inside),E(Inside,Inside))\nare inside, and lambda(A(Outside,Outside),E(Outside,Outside)) are\noutside the domain of interest, where Select = NLOW:NSUP,\nInside = NLOW:NLOW+NDIM-1, and Outside = NLOW+NDIM:NSUP.\nIf NLOW = 1, the first NDIM columns of V*VT span the corresponding\nright deflating subspace of (A,E).\n\n```\nReferences\n``` Stewart, G.W.\nHQR3 and EXCHQZ: FORTRAN subroutines for calculating and\nordering the eigenvalues of a real upper Hessenberg matrix.\nACM TOMS, 2, pp. 275-280, 1976.\n\n```\nNumerical Aspects\n``` 3\nThe algorithm requires less than 4*N operations.\n\n```\n``` None\n```\nExample\n\nProgram Text\n\n```* MB03QG EXAMPLE PROGRAM TEXT\n* Copyright (c) 2002-2017 NICONET e.V.\n*\n* .. Parameters ..\nINTEGER NIN, NOUT\nPARAMETER ( NIN = 5, NOUT = 6 )\nINTEGER NMAX\nPARAMETER ( NMAX = 10 )\nINTEGER LDA, LDE, LDU, LDV\nPARAMETER ( LDA = NMAX, LDE = NMAX, LDU = NMAX, LDV = NMAX)\nINTEGER LDWORK\nPARAMETER ( LDWORK = 8*NMAX + 16 )\n* .. Local Scalars ..\nCHARACTER*1 DICO, JOBU, JOBV, STDOM\nINTEGER I, INFO, J, N, NDIM, NLOW, NSUP\nDOUBLE PRECISION ALPHA\n* .. Local Arrays ..\nDOUBLE PRECISION A(LDA,NMAX), BETA(NMAX), DWORK(LDWORK),\n\\$ E(LDE,NMAX), U(LDU,NMAX), V(LDV,NMAX), WI(NMAX),\n\\$ WR(NMAX)\nLOGICAL BWORK(NMAX)\n* .. External Functions ..\nLOGICAL DELCTG\n* .. External Subroutines ..\nEXTERNAL DGGES, MB03QG\n* .. Executable Statements ..\n*\nWRITE ( NOUT, FMT = 99999 )\n* Skip the heading in the data file and read the data.\nREAD ( NIN, FMT = '()' )\nREAD ( NIN, FMT = * ) N, NLOW, NSUP, ALPHA, DICO, STDOM, JOBU,\n\\$ JOBV\nIF ( N.LT.0 .OR. N.GT.NMAX ) THEN\nWRITE ( NOUT, FMT = 99990 ) N\nELSE\nREAD ( NIN, FMT = * ) ( ( A(I,J), J = 1,N ), I = 1,N )\nREAD ( NIN, FMT = * ) ( ( E(I,J), J = 1,N ), I = 1,N )\n* Compute Schur form, eigenvalues and Schur vectors.\nCALL DGGES( 'Vectors', 'Vectors', 'Not sorted', DELCTG, N,\n\\$ A, LDA, E, LDE, NDIM, WR, WI, BETA, U, LDU, V, LDV,\n\\$ DWORK, LDWORK, BWORK, INFO )\nIF ( INFO.NE.0 ) THEN\nWRITE ( NOUT, FMT = 99998 ) INFO\nELSE\n* Block reordering.\nCALL MB03QG( DICO, STDOM, JOBU, JOBV, N, NLOW, NSUP, ALPHA,\n\\$ A, LDA, E, LDE, U, LDU, V, LDV, NDIM, DWORK,\n\\$ LDWORK, INFO )\nIF ( INFO.NE.0 ) THEN\nWRITE ( NOUT, FMT = 99997 ) INFO\nELSE\nWRITE ( NOUT, FMT = 99996 ) NDIM\nWRITE ( NOUT, FMT = 99994 )\nDO 10 I = 1, N\nWRITE ( NOUT, FMT = 99995 ) ( A(I,J), J = 1,N )\n10 CONTINUE\nWRITE ( NOUT, FMT = 99993 )\nDO 20 I = 1, N\nWRITE ( NOUT, FMT = 99995 ) ( E(I,J), J = 1,N )\n20 CONTINUE\nWRITE ( NOUT, FMT = 99992 )\nDO 30 I = 1, N\nWRITE ( NOUT, FMT = 99995 ) ( U(I,J), J = 1,N )\n30 CONTINUE\nWRITE ( NOUT, FMT = 99991 )\nDO 40 I = 1, N\nWRITE ( NOUT, FMT = 99995 ) ( V(I,J), J = 1,N )\n40 CONTINUE\nEND IF\nEND IF\nEND IF\n*\nSTOP\n*\n99999 FORMAT (' MB03QG EXAMPLE PROGRAM RESULTS',/1X)\n99998 FORMAT (' INFO on exit from DGEES = ',I2)\n99997 FORMAT (' INFO on exit from MB03QG = ',I2)\n99996 FORMAT (' The number of eigenvalues in the domain is ',I5)\n99995 FORMAT (8X,20(1X,F8.4))\n99994 FORMAT (/' The ordered Schur form matrix is ')\n99993 FORMAT (/' The ordered triangular matrix is ')\n99992 FORMAT (/' The transformation matrix U is ')\n99991 FORMAT (/' The transformation matrix V is ')\n99990 FORMAT (/' N is out of range.',/' N = ',I5)\nEND\n```\nProgram Data\n``` MB03QG EXAMPLE PROGRAM DATA\n4 1 4 0.0 C S U U\n-1.0 37.0 -12.0 -12.0\n-1.0 -10.0 0.0 4.0\n2.0 -4.0 7.0 -6.0\n2.0 2.0 7.0 -9.0\n1.0 3.0 2.0 -1.0\n-2.0 5.0 3.0 2.0\n2.0 4.0 5.0 6.0\n3.0 7.0 6.0 9.0\n```\nProgram Results\n``` MB03QG EXAMPLE PROGRAM RESULTS\n\nThe number of eigenvalues in the domain is 2\n\nThe ordered Schur form matrix is\n-1.4394 2.5550 -12.5655 -4.0714\n2.8887 -1.1242 9.2819 -2.6724\n0.0000 0.0000 -19.7785 36.4447\n0.0000 0.0000 0.0000 3.5537\n\nThe ordered triangular matrix is\n-16.0178 0.0000 2.3850 4.7645\n0.0000 3.2809 -1.5640 1.9954\n0.0000 0.0000 -3.0652 0.3039\n0.0000 0.0000 0.0000 1.1671\n\nThe transformation matrix U is\n-0.1518 -0.0737 -0.9856 0.0140\n-0.2865 -0.9466 0.1136 -0.0947\n-0.5442 0.0924 0.0887 0.8292\n-0.7738 0.3000 0.0890 -0.5508\n\nThe transformation matrix V is\n0.2799 0.9041 0.2685 0.1794\n0.4009 -0.0714 0.3780 -0.8315\n0.7206 -0.4006 0.2628 0.5012\n0.4917 0.1306 -0.8462 -0.1588\n```"
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https://www.physicsforums.com/threads/battery-connected-to-a-pure-inductor-with-a-zero-resistance-wire.950233/
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"# Battery connected to a pure inductor with a Zero-resistance wire\n\n• B\nI know the mechanism of a circuit containing just a capacitor connected to battery in resistance less wire. The particles are just tending towards making same emf on capacitor.\nBut, I am not able to think clearly for the case of inductor. As, we connect battery and as resistance is 0.So,current will be infinite in 0 seconds. So, when the inductor will have chance for the induction of emf(my view: not possible)? Also, there an expression comes out of result of kirchoff's voltage law i. e. \" i=-(E/L) *t, i=current, t=time\". Please, help me.\n\n## Answers and Replies\n\njbriggs444\nScience Advisor\nHomework Helper\nBut, I am not able to think clearly for the case of inductor. As, we connect battery and as resistance is 0.So,current will be infinite in 0 seconds.\nAn inductor resists changes in current. A change from zero to infinity in zero seconds is rather a high rate of change. An inductor would resist that.\n\nThank you for the logic.\n\nCWatters\nScience Advisor\nHomework Helper\nGold Member\nThe equation for the voltage on an inductor is...\n\nV=Ldi/dt\n\ndi/dt is the slope or rate of change of current.\n\nSo if you apply a voltage of say 1V to an inductor of 1H then the current increases at a rate of 1A/s. So after 10 seconds its 10A. After 20 seconds it's 20A etc. Soon the smoke escapes.\n\n•",
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"jrmichler and phinds\nCWatters\nScience Advisor\nHomework Helper\nGold Member\nPS... I'll let you work out how fast the current increases for a 1mH (1*10^-3 H) inductor. Something else in the circuit might effect the result.\n\nPS... I'll let you work out how fast the current increases for a 1mH (1*10^-3 H) inductor. Something else in the circuit might effect the result.\nAs a current even starts to gain a slight value in the circuit, for even such a very short value of current like 0.000001mA, the slope (di/dt) would still be very large or same for the current rising afterwards. And thus, voltage across the inductor will be infinity or very large. Doesn't it violate the kirchoff's voltage law? Or, if it does not, how the battery is in action?\n\njbriggs444\nScience Advisor\nHomework Helper\nAs a current even starts to gain a slight value in the circuit, for even such a very short value of current like 0.000001mA, the slope (di/dt) would still be very large\nCan you explain why you think this?\n\nSurely, if the current attains a value of 0.000001 mA after a time of 0.000001 seconds then the applied voltage to produce this result would be 1 millivolt.\n\nSurely, if the current attains a value of 0.000001 mA after a time of 0.000001 seconds then the applied voltage to produce this result would be 1 millivolt.\nYou may be right.\nSo, is there anyone and you who knows the mechanism of this circuit?\n\njbriggs444\nScience Advisor\nHomework Helper\nYou may be right.\nSo, is there anyone and you who knows the mechanism of this circuit?\nThe behavior is specified in the title of this thread: \"pure inductor\". The mechanism is irrelevant if the behavior is specified.\n\nCWatters\nScience Advisor\nHomework Helper\nGold Member\nAs a current even starts to gain a slight value in the circuit, for even such a very short value of current like 0.000001mA, the slope (di/dt) would still be very large or same for the current rising afterwards. And thus, voltage across the inductor will be infinity or very large. Doesn't it violate the kirchoff's voltage law? Or, if it does not, how the battery is in action?\n\nGoing back to the equation I posted...\n\nV=Ldi/dt\n\nI was thinking of the situation where you apply a fixed voltage say 1V across the inductor, what happens to the current...\n\nSo rearrange the equation to..\n\ndi/dt = V/L\n\nThe smaller the inductance L the faster the current I increases.\n\nPlug in numbers I suggested...\n\ndi/dt = 1V/1mH = 1000A/S\n\nSo in 1 second the current would reach 1000A (Assuming the voltage source and wires could supply that much and the inductor didn't melt :-)\n\nMerlin3189\nHomework Helper\nGold Member\nI know the mechanism of a circuit containing just a capacitor connected to battery (with) resistance less wire. ... for the case of inductor. As, we connect battery and as resistance is 0. So,current will be infinite in 0 seconds. ....\nThis is your error. The current will be zero up to 0 seconds. It can only change after that.\n\nYou seem to be thinking of applying the battery simply to the zero resistance wire, without any inductance* , so you would calculate an infinite current, immediately after connection.\n\nBut zero resistance wire, simply means that all the battery voltage appears across the inductor, rather than being split between the inductor and the resistance.\n\nApplying voltage to an inductor causes the current to start changing: So, as has been said before, the current starts at zero and changes at a rate ## \\frac {V} {L} ##\nIf there is really no resistance in the circuit and the battery maintains its voltage, then the current keeps on increasing - but it takes for ever to become infinite.\n\nAnd if there's no resistance, it shouldn't get hot. ( I chuck that in as naive suggestion, in the hope that the experts will entertain me with all sorts of other ways that heat/smoke will be generated.)\n\nYou should have been more worried about your capacitor, because if there had truly been no resistance and no inductance in that circuit, that's where you'd have gotten infinite current. (I think inductance is what always saves the day in these \"zero resistance\" problems ! )\n\n* ( I think a circuit without inductance would be impossible, since all conductors possess inductance, even if they have zero resistance.)\n\n... So, when the inductor will have chance for the induction of emf(my view: not possible)? ....\nAs soon as current starts to flow, the magnetic field in the inductor will change. As soon as the field starts to change, an emf is generated.\nSo immediately you connect the battery, there is an emf.\nThat emf is equal to the inductance times the rate at which the current starts to increase (from its zero starting point.)\n\nAnd the current, which is zero up until you make the connection, then increases steadily with time from that zero starting point, at the rate ##\\frac {V} {L} ##\nI expect this is the expression you mention, ## i = \\left( \\frac {E} {L} \\right) *t ## because I've been using V for E.\n=================================================\nJust as a couple of BTW points, which might interest you;\n\nIt would not matter how much resistance the wire had (or the battery, or the inductor itself) the current in this circuit must start from zero and it will always start at the rate ## \\frac {V} {L} ##\n\nAlthough it is easy to start the current flowing, you cannot stop it instantly. If you try, the current must continue for a while, either by charging up stray capacitance, or if that's not enough, by continuing to flow through a spark. This is a consequence of your expression above, which transforms to\n## V= L \\frac { i} {t} ## so if ## t ## is very small, ## V ## becomes very large."
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https://decentdescent.org/smoothadv.html
|
[
"",
null,
"# By Adversarially Training Smoothed Classifiers\n\nRecently, several works proposed the convolution of a neural network with a Gaussian as a smoothed classifier for provably robust classification. We show that adversarially training this smoothed classifier significantly increases its provable robustness through extensive experiments, achieving state-of-the-art $\\ell_2$ provable robustness on CIFAR10 and Imagenet, as shown in the tables below.\n\nUpdate 09/10/2019: By combining pre-training and semi-supervision with SmoothAdv, we obtain significant improvement over SmoothAdv alone.\n\n$\\ell_2$ radius (Imagenet) 0.5 1 1.5 2 2.5 3 3.5\nCohen et al. (%) 49 37 29 19 15 12 9\nOurs (%) 56 45 38 28 26 20 17\n$\\ell_2$ radius (CIFAR-10) 0.25 0.5 0.75 1.0 1.25 1.5 1.75 2.0 2.25\nCohen et al. (%) 61 43 32 22 17 13 10 7 4\nOurs (%) 73 58 48 38 33 29 24 18 16\n+ Pre-training (%) 80 62 52 38 34 30 25 19 16\n+ Semi-supervision (%) 80 63 52 40 34 29 25 19 17\n+ Both (%) 81 63 52 37 33 29 25 18 16\n\nWe also achieved state-of-the-art $\\ell_\\infty$ provable robustness on CIFAR10, with $\\ell_\\infty$ norm $2/255$, as shown in the table below. This is by noting that the $\\ell_\\infty$ ball of radius $2/255$ is contained in the $\\ell_2$-ball of radius $2/255 \\sqrt{d} = 2/255 \\sqrt{3 \\times 32^2} \\approx 0.4347$, and invoking our provable robustness for this $\\ell_2$ radius.\n\nModel $\\ell_\\infty$ Provable Acc @ 2/255 Standard Acc\nOurs 68.2 87.2\nCarmon et al. 63.8 ± 0.5 80.7 ± 0.3\nWong & Kolter 2018b (single) 53.9 68.3\nWong & Kolter 2018b (ensemble) 63.6 64.1\nInterval Bound Propagation 50.0 70.2\n\n# Introduction\n\nIt is now well-known that deep neural networks suffer from the brittleness problem: A small change in an input image imperceptible to humans can cause dramatic change in a neural network’s classification of the image. Such a perturbed input is known as an adversarial example and is by now immortalized in the famous picture below from Goodfellow et al.",
null,
"As deep neural networks enter consumer and enterprise products of various forms, this brittleness can possibly have devastating consequences (Brown et al. 2018, Athalye et al. 2017, Evtimov & Eykholt et al. 2018, Li et al. 2019). Most strikingly, Tencent Keen Security Lab recently demonstrated that the neural network underlying Tesla Autopilot can be fooled by an adversarially crafted marker on the ground into swerving into the opposite lane.\n\nGiven the importance of the problem, many researchers have formulated security models of adversarial attacks, along with ways to defend against adversaries in such models. In the most popular security model in the academic circle today, the adversary is allowed to perturb an input by a small noise bounded in $\\ell_p$-norm, in order to cause the network to misclassify it. Thus, given a loss function $L$, a norm bound $\\epsilon$, an input $x$, its label $y$, and a neural network $F$, the adversary tries to find an input $\\hat x$, within $\\ell_p$-distance $\\epsilon$ of $x$, that maximizes the loss $L(F(x), y)$, i.e. it solves the following optimization problem\n\n$\\hat x = \\argmax_{\\|x' - x\\|_p \\le \\epsilon} L(F(x'), y).$\n\nIf $F$ has trainable parameters $\\theta$, then the defense needs to find the parameters that minimizes $L(F(\\hat x), y)$, for $(x, y)$ sampled from the data distribution $D$, i.e. it solves the following minimax problem\n\n$\\min_{\\theta} \\underset{(x, y) \\sim D}{\\mathbb{E}} L(F(\\hat x), y).$\n\nEmpirically, during an attack, the adversarial input $\\hat x$ can be obtained approximately by solving the max problem using gradient descent, making sure to project back to the $\\epsilon$-ball after each step. This is known as the PGD attack (Kurakin et al., Madry et al.), short for “project gradient descent.” During training by the defense, for every sample $(x, y) \\sim D$, this estimate of $\\hat x$ can be plugged into the min problem for gradient descent of $\\theta$. This is known as Adversarial Training, or PGD training specifically when PGD is used for finding $\\hat x$.\n\n## Empirical Robust Accuracy\n\nCurrently, the standard benchmark for measuring the strength of a model’s adversarial defense is the model’s (empirical) robust accuracy on various standard datasets like CIFAR-10 and Imagenet. This accuracy is calculated by attacking the model with a strong empirical attack (like PGD) for every sample of the test set. The percentage of the test set that the model is still able to correctly classify is the empirical robust accuracy.\n\nFor example, consider an adversary allowed to perturb an input by $\\epsilon = \\frac{8}{255}$ in $\\ell_\\infty$ norm. On an image, this means that the adversary can change the color of each pixel by at most 8 units (out of 255 total) in each color channel — a rather imperceptible perturbation. Currently, the state-of-the-art empirical robust accuracy against such an adversary on CIFAR-10 hovers around 55% (Zhang et al. 2019, Hendrycks et al. 2019), meaning that the best classifier can only withstand a strong attack on about 55% of the samples in CIFAR-10. Contrast this with the state-of-the-art nonrobust accuracy on CIFAR-10 of >95%. Thus it’s clear that adversarial robustness research still has a long way to go.\n\n# Provable Robustness via Randomized Smoothing\n\nNote that the empirical robust accuracy is only an upper bound on the true robust accuracy. This is defined by hypothetically replacing the strong empirical attack used in empirical robust accuracy with the ideal attack able to find $\\hat x$ exactly for every $x$. Thus, nothing in principle prevents a stronger empirical attack from further lowering the empirical robust accuracy of a model. Indeed, except a few notable cases like PGD (Madry et al.), we have seen most claims of adversarial robustness broken down by systematic and thorough attacks (as examples, see Carlini & Wagner 2016, Carlini & Wagner 2017, Athalye et al. 2017, Uesato et al. 2018, Athalye et al. 2018, Engstrom et al. 2018, Carlini 2019).\n\nThis has motivated researchers into developing defenses that can certify the absence of adversarial examples (as prominent examples, see Wong & Kolter 2018, Katz et al. 2017, and see Salman et al. 2019 for a thorough overview of these techniques). Such a defense is afforded a provable (or certified) robust accuracy on each dataset, defined as the percentage of the test set that can be proved to have no adversarial examples in its neighborhood. In contrast with empirical robust accuracy, provable robust accuracy is a lower bound on the true robust accuracy, and therefore cannot be lowered further by more clever attacks. The tables in the beginning of our blog post, for example, display provable robust accuracies on CIFAR-10 and Imagenet.\n\nUntil recently, most such certifiable defenses have not been able to scale to large networks and datasets (Salman et al. 2019), but a new technique called randomized smoothing (Lecuyer et al., Li et al., Cohen et al.) was shown to bypass this limitation, obtaining highly-nontrival $\\ell_2$ certified robust accuracy on Imagenet (Cohen et al.). We now briefly review randomized smoothing.\n\n## Definition\n\nConsider a classifier $f$ from $\\mathbb{R}^d$ to classes $\\mathcal{Y}$. Randomized smoothing is a method that constructs a new, smoothed classifier $g$ from the base classifier $f$. The smoothed classifier $g$ assigns to a query point $x$ the class which is most likely to be returned by the base classifier $f$ under isotropic Gaussian noise perturbation of $x$, i.e.,\n\n$g(x) = \\argmax_{c \\in \\mathcal{Y}} \\; \\mathbb{P}(f(x+\\delta) = c)$\n\nwhere $\\delta \\sim \\mathcal{N}(0, \\sigma^2 I)$, and the variance $\\sigma^2$ is a hyperparameter of the smoothed classifier $g$ (it can be thought to control a robustness/accuracy tradeoff). In Cohen et al., $f$ is a neural network.\n\n## Prediction\n\nTo estimate $g(x)$, one simply has to\n\n1. Sample a collection of Gausian samples $\\delta_i$.\n2. Predict the class $y_i$ of each $x + \\delta_i$ using the base classifier $f$.\n3. Take the majority vote of the $y_i$’s as the final prediction of the smoothed classifier $g$ at $x$.\n\n## Certification\n\nThe robustness guarantee presented by Cohen et al. is as follows: suppose that when the base classifier $f$ classifies $\\mathcal{N}(x, \\sigma^2 I)$, the (most popular) class $c_A$ is returned with probability $p_A = \\mathbb{P}_\\delta(f(x+\\delta) = c_A)$, and the runner-up class $c_B$ is returned with probability $p_B = \\max_{c \\neq c_A} \\mathbb{P}_\\delta(f(x+\\delta) = c)$. We estimate $p_A$ and $p_B$ using Monte Carlo sampling and confidence intervals1. Then the smoothed classifier $g$ is robust around $x$ within the radius\n\n$\\frac{\\sigma}{2} \\left(\\Phi^{-1}(p_A) - \\Phi^{-1}(p_B)\\right),$\n\nwhere $\\Phi^{-1}$ is the inverse of the standard Gaussian CDF. Thus, the bigger $p_A$ is and the smaller $p_B$ is, the more provably robust $g$ is.\n\n## Training\n\nCohen et al. simply trained the base classifier $f$ under Gaussian noise data augmentation with cross entropy loss, i.e. for each data point $(x, y)$, sample $\\delta \\sim \\mathcal{N}(0, \\sigma^2 I)$ and train $f$ on the example $(x+\\delta, y)$. With this simple training regime applied to a Resnet-110 base classifier, they were able to obtain significant certified robustness on CIFAR-10 and Imagenet, as shown in our tables.\n\n## An Illustration\n\nThe following figures modified from Cohen et al. illustrate randomized smoothing. The base classifier $f$ partitions the input space into different regions with different classifications, colored differently in the left figure. The regions’ Gaussian measures (under the Gaussian $\\mathcal{N}(x, \\sigma^2 I)$ whose level curves are shown as dashed lines) are shown as a histogram on the right. The class $c_A$ corresponding to the blue region is the output of the smoothed classifier $g(x)$; the class $c_B$ corresponding to the cyan region is the runner-up class. If $p_A$ is large enough and $p_B$ is small enough, then we can prove that $g(x') = c_A$ for all $\\|x' - x\\|_2 \\le \\epsilon$, i.e. $g$ is robust at $x$ for $\\ell_2$ radius $\\epsilon$.",
null,
"# Adversarially Training the Smoothed Classifier\n\nIntuitively, adversarial training attempts to make a classifier locally flat around input sampled from a data distribution. Thus it would seem that adversarial training should make it easier to certify the lack of adversarial examples, despite having no provable guarantees itself. Yet historically, it has been difficult to execute this idea (Salman et al. 2019, and folklore), with the closest being Xiao et al.\n\nIt is hence by no means a foregone conclusion that adversarial training should improve certified accuracy of randomized smoothing. A priori there could also be many ways these two techniques can be combined, and it is not clear which one would work best:\n\n1. Train the base classifier $f$ to be adversarially robust, simultaneous with the Gaussian data augmentation training prescribed in Cohen et al..\n2. Find an adversarial example of the base classifier $f$, then add Gaussian noise and train.\n3. Add Gaussian noise and find an adversarial example of $f$ in the neighborhood of this Gaussian perturbation. Train $f$ on this adversarial example.\n4. Find an adversarial example of the smoothed classifier $g$, then train $g$ on this example.\n\nIt turns out that certified accuracies of these methods follow the order (1) < (2) < (3) < (4), with (4) achieving the highest certified accuracies (see our paper). Indeed, in hindsight, if $g$ is the classifer doing the prediction, then we should be adversarially training $g$, and not $f$. In the rest of the blog post, we lay out the details of (4).\n\n## Randomized Smoothing for Soft Classifiers\n\nNeural networks typically learn soft classifiers, namely, functions $F: \\mathbb{R}^d \\to P(\\mathcal{Y})$, where $P(\\mathcal{Y})$ is the set of probability distributions over $\\mathcal{Y}$. During prediction, the soft classifier is argmaxed to return the final hard classification. We therefore consider a generalization of randomized smoothing to soft classifiers. Given a soft classifier $F$, its associated smoothed soft classifier $G: \\mathbb{R}^n \\to P(\\mathcal{Y})$ is defined as\n\n$G (x) = \\underset{\\delta \\sim \\mathcal{N}(0, \\sigma^2 I)}{\\mathbb{E}} F(x + \\delta).$\n\nLet $f(x)$ and $F (x)$ denote the hard and soft classifiers learned by the neural network, respectively, and let $g$ and $G$ denote the associated smoothed hard and smoothed soft classifiers. Directly finding adversarial examples for the smoothed hard classifier $g$ is a somewhat ill-behaved problem because of the argmax, so we instead propose to find adversarial examples for the smoothed soft classifier $G$. Empirically we found that doing so will also find good adversarial examples for the smoothed hard classifier.\n\n## Finding Adverarial Examples for Smoothed Soft Classifier\n\nGiven a labeled data point $(x, y)$, we wish to find a point $\\hat x$ which maximizes the loss of $G$ in an $\\ell_2$ ball around $x$ for some choice of loss function. As is canonical in the literature, we focus on the cross entropy loss $L_{\\mathrm{CE}}$. Thus, given a labeled data point $(x, y)$ our (ideal) adversarial perturbation is given by the formula:\n\n\\begin{aligned} \\hat x &= \\argmax_{\\|x' - x\\|_2 \\leq \\epsilon} L_{\\mathrm{CE} } (G (x'), y)\\\\ &= \\argmax_{\\|x' - x\\|_2 \\leq \\epsilon} \\left( - \\log \\underset{\\delta \\sim \\mathcal{N} (0, \\sigma^2 I)}{\\mathbb{E}} F (x' + \\delta)_y \\right). \\end{aligned}\n\nWe will refer to the above as the SmoothAdv objective. The SmoothAdv objective is highly non-convex, so as is common in the literature, we will optimize it via projected gradient descent (PGD), and variants thereof. It is hard to find exact gradients for SmoothAdv, so in practice we must use some estimator based on random Gaussian samples.\n\nIf we let $J(x') = L_{\\mathrm{CE} } (G (x'), y)$ denote the SmoothAdv objective, then\n\n$\\nabla_{x'} J(x') = \\nabla_{x'} \\left( - \\log \\underset{\\delta \\sim \\mathcal{N}(0, \\sigma^2 I)}{\\mathbb{E}} F (x' + \\delta)_y \\right) \\; .$\n\nHowever, it is not clear how to evaluate the expectation inside the log exactly, as it takes the form of a complicated high dimensional integral. Therefore, we will use Monte Carlo approximations. We sample i.i.d. Gaussians $\\delta_1, \\ldots, \\delta_m \\sim \\mathcal{N} (0, \\sigma^2 I)$, and use the plug-in estimator for the expectation:\n\n$\\nabla_{x'} J(x') \\approx \\nabla_{x'} \\left( - \\log \\left( \\frac{1}{m} \\sum_{i = 1}^m F (x' + \\delta_i)_y \\right) \\right) \\; .$\n\nIt is not hard to see that if $F$ is smooth, this estimator will converge to $\\nabla_{x'} J(x')$ as we take more samples.\n\n## SmoothAdv is not the Naive Objective\n\nWe note that SmoothAdv should not be confused with the similar-looking objective\n\n\\begin{aligned} &\\phantom{ {}={}} \\argmax_{\\|x' - x\\|_2 \\leq \\epsilon} \\underset{\\delta \\sim \\mathcal{N} (0, \\sigma^2 I)}{\\mathbb{E}} L_{\\mathrm{CE} } (F (x' + \\delta), y) \\\\ &= \\argmax_{\\|x' - x\\|_2 \\leq \\epsilon} \\ \\underset{\\delta \\sim \\mathcal{N} (0, \\sigma^2 I)}{\\mathbb{E}} \\left[-\\log F(x' + \\delta)_y\\right] \\; , \\end{aligned}\n\nwhere the $\\log$ and $\\mathbb{E}$ have been swapped compared to SmoothAdv, as suggested in section G.3 of Cohen et al. This objective, which we shall call naive, is the one that corresponds to finding an adversarial example of $F$ that is robust to Gaussian noise. In contrast, SmoothAdv directly corresponds to finding an adversarial example of $G$. From this point of view, SmoothAdv is the right optimization problem that should be used to find adversarial examples of $G$. This distinction turns out to be crucial in practice: empirically, Cohen et al found attacks based on the naive objective not to be effective. In our paper, we perform SmoothAdv-attack on Cohen et al.’s smoothed model and find, indeed, that it works better than the Naive objective, and it performs better with more Gaussian noise samples used to estimate its gradient.\n\nWe now wish to use our new SmoothAdv attack to boost the adversarial robustness of smoothed classifiers. As described in the beginning of this blog post, in (ordinary) adversarial training, given a current set of model parameters $\\theta_t$ and a labeled data point $(x_t, y_t)$, one finds an adversarial perturbation $\\hat x_t$ of $x_t$ for the current model, and then takes a gradient step for the model parameters $\\theta_t$, evaluated at the point $(\\hat x_t, y_t)$. Intuitively, this encourages the network to learn to minimize the worst-case loss over a neighborhood around the input.\n\nWhat is different in our proposed algorithm is that we are finding the adversarial example $\\hat x_t$ with respect to the smoothed classifier $G$ using the SmoothAdv objective, and we are training $G$ at this adversarial example $\\hat x_t$ with respect to the SmoothAdv objective, estimated by the plug-in estimator.\n\n\\begin{aligned} \\theta_{t+1} &= \\theta_t + \\eta \\nabla_\\theta \\log\\left(\\frac{1}{m'} \\sum_{i=1}^{m'} F(\\hat x_t + \\delta_i)_y\\right), \\end{aligned}\n\nwhere $\\theta_t$ are the parameters of $F$ at time $t$, $\\delta_i \\sim \\mathcal{N}(0, \\sigma^2 I)$, and $\\eta$ is a learning rate.\n\n## More Data: Pretraining and Semisupervision\n\nHendrycks et al. showed that pre-training on Imagenet can improve empirical adversarial robustness on CIFAR-10 and CIFAR-100. Similarly, Carmon et al. showed that augmenting supervised adversarial training with unsupervised training on a carefully selected unlabeled dataset confers significant robustness improvement. We adopt these ideas for randomized smoothing and confirm that these techniques are highly beneficial for certified robustness as well, especially for smaller radii (though unfortunately their combination did not induce combined improvement). See the tables below.\n\n## Results\n\nOver the course of the blog post, we have introduced several hyperparameters, such as 1) $\\epsilon$, the radius of perturbation used for adversarial training, 2) $m$, the number of Gaussian noise samples, 3) $\\sigma$, the standard deviation of the Gaussian noise. We also did not mention other hyperparameters like $T$, the number of iterations used for PGD iterations, or the usage of DDN, an alternative attack to PGD that has been shown to be effective for $\\ell_2$-perturbations (Rony et al.). In our paper we do extensive analysis of the effects of these hyperparameters, to which we refer interested readers.\n\nTaking the max over all such hyperparameter combinations for each $\\ell_2$ perturbation radius, we obtain the upper envelopes of the certified accuracies of our method vs the upper envelopes of Cohen et al. in the tables in the beginning of this post, which we also replicate here for convenience.\n\n$\\ell_2$ radius (Imagenet) 0.5 1 1.5 2 2.5 3 3.5\nCohen et al. (%) 49 37 29 19 15 12 9\nOurs (%) 56 43 37 27 25 20 16\n$\\ell_2$ radius (CIFAR-10) 0.25 0.5 0.75 1.0 1.25 1.5 1.75 2.0 2.25\nCohen et al. (%) 60 43 32 23 17 14 12 10 8\nOurs (%) 74 57 48 38 33 29 24 19 17\n+ Pre-training (%) 80 63 52 39 36 30 25 20 17\n+ Semi-supervision (%) 80 63 53 39 36 32 25 20 18\n+ Both (%) 82 65 52 38 34 30 25 21 18\n\n# Conclusion\n\nIn this blog post, we reviewed adversarial training and randomized smoothing, a recently proposed provable defense. By adversarially training the smoothed classifier — and carefully getting all the details right — we obtained the state-of-the-art $\\ell_2$ provable robustness on CIFAR-10 and Imagenet, demonstrating significant improvement over randomized smoothing alone.\n\n# Acknowledgements\n\nThis blog post presented work done by Hadi Salman, Greg Yang, Jerry Li, Huan Zhang, Pengchuan Zhang, Ilya Razenshteyn, and Sebastien Bubeck. We would like to thank Zico Kolter, Jeremy Cohen, Elan Rosenfeld, Aleksander Madry, Andrew Ilyas, Dimitris Tsipras, Shibani Santurkar, Jacob Steinhardt for comments and discussions during the making of this paper.\n\n1. We actually estimate a lower bound $\\underline{p_A}$ of $p_A$ and an upper bound $\\overline{p_B}$ of $p_B$ with high probability, and substitute $\\underline{p_A}$ and $\\overline{p_B}$ for $p_A$ and $p_B$ everywhere. This is an overestimate, so our guarantee holds except for a small probability that the estimates are wrong. See Cohen et al. or our paper for more details."
] |
[
null,
"https://decentdescent.org/assets/logos/decent_descent_abbrev_black_text2path.svg",
null,
"https://decentdescent.org/assets/smoothadv/panda_gibbon_adv.png",
null,
"https://decentdescent.org/assets/smoothadv/randomized_smoothing_simple_light.png",
null
] |
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|
https://studydaddy.com/question/5-in-a-balanced-symmetrical-three-phase-system-the-vectorial-sum-of-the-phase-cu
|
[
"",
null,
"Waiting for answer This question has not been answered yet. You can hire a professional tutor to get the answer.\n\nQUESTION\n\n# 5). In a balanced symmetrical three phase system, the vectorial sum of the phase currents is a) 0 b) One third of each phase current c) Three times\n\n5). In a balanced symmetrical three phase system, the vectorial sum of the phase currents is\n\na) 0\n\nb) One third of each phase current\n\nc) Three times the phase current\n\nd) Square root of three times the phase current"
] |
[
null,
"https://www.facebook.com/tr",
null
] |
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|
https://www.mathworks.com/help/signal/ug/phase-response.html
|
[
"# Phase Response\n\nMATLAB® functions are available to extract the phase response of a filter. Given a frequency response, the function `abs` returns the magnitude and `angle` returns the phase angle in radians. To view the magnitude and phase of a Butterworth filter using `fvtool`:\n\n```d = designfilt('lowpassiir','FilterOrder',9, ... 'HalfPowerFrequency',400,'SampleRate',2000); fvtool(d,'Analysis','freq')```",
null,
"You can also click the Magnitude and Phase Response button on the toolbar or select Analysis > Magnitude and Phase Response to display the plot.\n\nThe `unwrap` function is also useful in frequency analysis. `unwrap` unwraps the phase to make it continuous across 360° phase discontinuities by adding multiples of ±360°, as needed. To see how `unwrap` is useful, design a 25th-order lowpass FIR filter:\n\n`h = fir1(25,0.4);`\n\nObtain the frequency response with `freqz` and plot the phase in degrees:\n\n```[H,f] = freqz(h,1,512,2); plot(f,angle(H)*180/pi) grid```",
null,
"It is difficult to distinguish the 360° jumps (an artifact of the arctangent function inside `angle`) from the 180° jumps that signify zeros in the frequency response.\n\n`unwrap` eliminates the 360° jumps:\n\n`plot(f,unwrap(angle(H))*180/pi)`",
null,
"Alternatively, you can use `phasez` to see the unwrapped phase:\n\n`phasez(h,1)`",
null,
""
] |
[
null,
"https://www.mathworks.com/help/examples/signal/win64/PhaseResponseExample_01.png",
null,
"https://www.mathworks.com/help/examples/signal/win64/PhaseResponseExample_02.png",
null,
"https://www.mathworks.com/help/examples/signal/win64/PhaseResponseExample_03.png",
null,
"https://www.mathworks.com/help/examples/signal/win64/PhaseResponseExample_04.png",
null
] |
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|
https://www.jiskha.com/questions/562606/what-is-the-angle-for-a-triangle-that-is-3x3x3-i-think-it-might-be-60
|
[
"# Math\n\nWhat is the angle for a triangle that is 3x3x3. I think it might be 60*\n\n1. 👍 0\n2. 👎 0\n3. 👁 94\n1. Each angle of an equilateral triangle measures 60 degrees.\n\n1. 👍 0\n2. 👎 0\n\n## Similar Questions\n\n1. ### math\n\nTriangle $ABC$ is a right triangle with right angle at $A$. Suppose $\\overline{AX}$ is an altitude of the triangle, $\\overline{AY}$ is an angle bisector of the triangle, and $\\overline{AZ}$ is a median of the triangle, and \\$\\angle\n\nasked by InfaRed on November 10, 2016\n2. ### Geometry\n\nIn right triangle ABC, ∠A is a right angle and sinC=1517. Triangle A B C with a right angle at A and hypotenuse B C. What is the ratio cos C? Enter your answer as a fraction in simplest form, like this: 42/53\n\nasked by GROOVE on April 15, 2020\n3. ### math\n\n1. What is the correct classification for the triangle shown below? A triangle has two angles measuring 68 degrees and 22 degrees. (1 point) acute, scalene acute, isosceles --- right, scalene obtuse, scalene 2. What is the value\n\nasked by Unknown on March 1, 2018\n4. ### Math - Trigonometry\n\nSolve for x, correct to one decimal place. It`s a right angle triangle. The height for the triangle is 4 cm with a 48° angle. The bottom of the triangle is an x. I got the answer 3.3 cm, is this correct? Any help would be\n\nasked by Ollie on February 20, 2018\n1. ### math\n\nPlane A which contains an isosceles right triangle forms a dihedral angle of 60 degrees with another plane B. If the hypotenuse of the triangle lies in plane B and measures 8 in., find the distance from the vertex of the right\n\nasked by nexan umbod on January 14, 2017\n2. ### trig\n\nA plane is is flying 240 mph heading N60°E. The wind is blowing S30°E at 30 mph. What is ground speed? What is the smallest angle in the triangle? What is the biggest angle in the triangle? What is the remaining angle in the\n\nasked by Anonymous on May 25, 2011\n3. ### geometry\n\nAssume triangle JKL is in the first quadrant, with the measure of angle K = 90°. Suppose triangle JKL is a 30°-60°-90° triangle and segment JK is the side opposite the 60° angle. What are the approximate coordinates of point\n\nasked by steve on February 22, 2012\n4. ### Math\n\nUsing a ruler and a pair of compasses only (I) construct a triangle XYZ such that XY =8cm and angle YXZ =ANGLE ZYX =45 DEGREES . Locate a point P In the triangle equidistant from XY AND XZ AND YX AND YZ . Construct a circle\n\nasked by Abigail on April 3, 2020\n1. ### geometry\n\nTwo forces are pushing on an object, one at 12 lbs of Force and one at 5.66 lbs of Force. The angle between them is 35° (each is 72.5 from horizontal, such that the forces make a v with the object in the center). 17. What is the\n\nasked by Cam on December 6, 2011\n2. ### geometry\n\nThe measure of an exterior angle of an isosceles triangle is x degrees. What are the possible angle measures of the triangle in terms of x? Describe all the possible values of x.\n\nasked by Anonymous on November 6, 2011"
] |
[
null
] |
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|
https://www.jiskha.com/questions/1068939/a-friend-tells-you-that-her-savings-account-doubled-in-9-years-use-the-rule-of-72-to
|
[
"# Math Personal Finance\n\nA friend tells you that her savings account doubled in 9 years. Use the Rule of 72 to estimate what the APR of her account was.\n\n1. 👍 0\n2. 👎 0\n3. 👁 513\n1. http://www.investopedia.com/terms/r/ruleof72.asp\n\n1. 👍 0\n2. 👎 0\n\n## Similar Questions\n\n1. ### Math Personal Finance\n\nA friend tells you that her savings account doubled in years. Use the Rule of 72 to estimate what the APR of her account was. APR=____%\n\nasked by Alexis on June 8, 2014\n2. ### math\n\nA friend opens a savings account by depositing \\$1000. He deposits an additional \\$75 into the account each month. a. What is a rule that represents the amount of money in the account as an arithmetic sequence? b. How much money is\n\nasked by Shakira on January 8, 2014\n3. ### math\n\nA friend opens a savings account by depositing \\$1000. He deposits an additional \\$75 into the account each month. a. What is a rule that represents the amount of money in the account as an arithmetic sequence? b. How much money is\n\nasked by Shakira on January 8, 2014\n4. ### Math\n\nA friend opens a savings account by depositing \\$1000. He deposits an additional \\$75 into the account each month. a. What is a rule that represents the amount of money in the account as an arithmetic sequence? b. How much money is\n\nasked by Shakira on January 8, 2014\n5. ### Math\n\n(a) Themba wants to deposit a sum of money into a savings account so that he will have R30 000 in 3 years time for an overseas holiday how much money must he deposit into the account if the interest paid on the savings is 8,5% p.a\n\nasked by Lesedi on August 26, 2014\n1. ### Precalculus\n\nNEED HELP ASAP PLEASE!! A savings account starts with \\$600 and pays 5% interest per year, compounded four times per year. a) A function that models the amount in dollars in the bank account after m years is A(m)=____________? b)\n\nasked by jh on March 4, 2010\n2. ### Finance\n\nYou are 45 years of age and your asporation is to retire in 17 years at age 62. Assume you are about to set up a new retirement savings account at a 4% annual interest rate (APR). Based on how you want to live in retirement, and\n\n3. ### algebra\n\nThe amount of money, in dollars, in a savings account after x years is given by M(x) = 10,000(1.03)x. What does the value 1.03 represent? A) The original deposit was \\$103. B) There is a 3 percent increase in the savings account\n\nasked by Anonymous on February 11, 2020\n4. ### math\n\nYou are looking for an account to invest your \\$9,000 in. You want to know how many years it will take to double if the account you are putting it into gets 10% APR. Using the Rule of 70, how many years should you be expecting to\n\nasked by Anonymous on June 4, 2018\n5. ### Personal Finance\n\nMary just deposited \\$33,000 in an account paying 7% interest. She plans to leave the money in this account for eight years. How much will she have in the account at the end of the seventh year? Mary and Joe would like to save up\n\nasked by Linda on March 23, 2010\n\nMore Similar Questions"
] |
[
null
] |
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|
https://sqlzoo.net/w/index.php?title=SUM_and_COUNT_Quiz/ja&oldid=39318
|
[
"# SUM and COUNT Quiz/ja\n\n(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)\n Language: [[:{{#invoke:String|sub|SUM and COUNT Quiz/ja ``` |1 |Expression error: Unrecognized punctuation character \"{\". ``` }}|English]]\n\nSUM and COUNT QUIZ\n\nbbc\nname region area population gdp\nAfghanistan South Asia 652225 26000000\nAlbania Europe 28728 3200000 6656000000\nAlgeria Middle East 2400000 32900000 75012000000\nAndorra Europe 468 64000\n...\nSelect the statement that shows the sum of population of all countries in 'Europe'\n``` SELECT name, population FROM bbc WHERE region = 'Europe'\n```\n``` SELECT population FROM bbc WHERE region = 'Europe' SUM BY region\n```\n``` SELECT SUM(population) FROM bbc WHERE region = 'Europe'\n```\n``` SELECT SUM(population FROM bbc WHERE region = 'Europe')\n```\n``` SUM population FROM bbc WHERE region = 'Europe'\n```\nSelect the statement that shows the number of countries with population smaller than 150000\n``` SELECT COUNT(name) FROM bbc WHERE population < 150000\n```\n``` SELECT COUNT(population < 150000) FROM bbc\n```\n``` SELECT name FROM bbc WHERE population < 150000\n```\n``` SELECT population AS COUNT FROM bbc WHERE population < 150000\n```\n``` SELECT SUM() FROM bbc WHERE population < 150000\n```\nSelect the list of core SQL aggregate functions\nAVG(), COUNT(), FIRST(), LAST(), SUM()\nAVG(), COUNT(), MAX(), MEDIAN(), MIN(), ROUND(), SUM()\nAVG(), COUNT(), CONCAT(), FIRST(), LAST(), MAX(), MIN(), SUM()\nAVG(), COUNT(), MAX(), MIN(), SUM()\nCOUNT(), SUM()\nSelect the result that would be obtained from the following code:\n``` SELECT region, SUM(area)\nFROM bbc\nWHERE SUM(area) > 15000000\nGROUP BY region\n```\n Europe 17000000\n Europe 17000000 Asia-Pacific 23460000 North America 21660000\n Europe Asia-Pacific North America\nNo result due to invalid use of the GROUP BY function\nNo result due to invalid use of the WHERE function\nSelect the statement that shows the average population of 'Poland', 'Germany' and 'Denmark'\n``` SELECT AVG(population) FROM bbc WHERE name = ('Poland', 'Germany', 'Denmark')\n```\n``` SELECT AVG(population) FROM bbc WHERE name IN ('Poland', 'Germany', 'Denmark')\n```\n``` SELECT AVG(population) FROM bbc WHERE name LIKE ('Poland', 'Germany', 'Denmark')\n```\n``` SELECT AVG(population) FROM bbc WHERE name LIKE (Poland, Germany, Denmark)\n```\n``` SELECT population FROM bbc WHERE name IN ('Poland', 'Germany', 'Denmark')\n```\nSelect the statement that shows the medium population density of each region\n``` SELECT region, AVG(population/area) AS density FROM bbc\n```\n``` SELECT region, COUNT(population)/COUNT(area) AS density FROM bbc GROUP BY region\n```\n``` SELECT region, SUM(population)/COUNT(area) AS density FROM bbc GROUP BY region\n```\n``` SELECT region, SUM(population)/SUM(area) AS density FROM bbc HAVING region\n```\n``` SELECT region, SUM(population)/SUM(area) AS density FROM bbc GROUP BY region\n```\nSelect the statement that shows the name and population density of the country with the largest population\n``` SELECT name, density AS population/area FROM bbc WHERE population = MAX(population)\n```\n``` SELECT name, density AS population/area FROM bbc WHERE population = (SELECT MAX(population) FROM bbc)\n```\n``` SELECT name, MAX (population) FROM bbc WHERE population / (SELECT area FROM bbc)\n```\n``` SELECT name, population/area AS density FROM bbc WHERE population = (SELECT MAX(population) FROM bbc)\n```\n``` SELECT name, population/area AS density FROM bbc WHERE population > (SELECT MAX(population) FROM bbc)\n```\nPick the result that would be obtained from the following code:\n``` SELECT region, SUM(area)\nFROM bbc\nGROUP BY region\nHAVING SUM(area)<= 20000000\n```\n 732240 13403102 17740392 4943771\n Africa 22550927 Asia-Pacific 28759578 Europe 23866987 North America 21660000\n Africa Asia-Pacific Europe North America\n Americas 732240 Middle East 13403102 South America 17740392 South Asia 9437710\n Americas Middle East South America South Asia"
] |
[
null
] |
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|
https://modes-de-vie.com/get-asnwer-382
|
[
"# Diferential equation solver\n\nKeep reading to learn more about Diferential equation solver and how to use it. Math can be difficult for some students, but with the right tools, it can be conquered.",
null,
"## The Best Diferential equation solver\n\nHere, we debate how Diferential equation solver can help students learn Algebra. However, a better way is to subtract or add terms. This can be done using one of three strategies: If you have two numbers and one is bigger than the other, you can ignore the smaller one and just add or subtract that one’s value from both sides of the inequality. For example: 3x > 4 5 + x In this case, you would subtract 4 from both sides, leaving 3 > 5 6 – 4 , which is true because 6 > 5. This method can also be used to turn an inequality into a statement about addition or subtraction, as in “I am more than \\$100 poorer than my friend.” If you have two numbers and one is less than the other, you can ignore the bigger one and just add or subtract that one’s value from both sides of the inequality. For example: 6 10 12 + 8 = ? = 15 20 In this case, you would add 8 to both sides, leaving 6 10 12 – 8 , which is true because 12 20 . This method can also be\n\nTriple integrals are often used in physics and engineering to solve problems involving three-dimensional objects or systems. The triple integral solver can be used to calculate the volume of a three-dimensional object, the moment of inertia of a three-dimensional object, or the center of mass of a three-dimensional object.\n\nSome apps also provide video tutorials. Students should talk to their parents or teachers before using any homework helper app to make sure it is appropriate for their grade level and appropriate for the type of homework they are struggling with.\n\nThe internet is a great place to start, as there are many websites that offer step-by-step solutions to common problems. In addition, most major textbook publishers offer online homework help services. These services typically provide access to a database of answers, as well as a variety of tools and resources that can help with the solution process. With a little bit of effort, it is usually possible to find the answer to any homework problem.\n\n## More than just an app",
null,
"It not only helps me check my work, but shows me if/where I've messed up and why. Great resource but do not rely solely on this, as there are times it doesn't read it clearly and will give the wrong answer. But a great back up tool to give you a little confidence in the work you already know how to do!",
null,
"### Nyla Cook",
null,
"Wonderful app! this app is a great mathematical tool to help students alike, it's easy to use, comes with a calculator with a variety of options for creating end editing problems, and even can decipher handwriting! as well as breaking down complex math problems into bite-sized pieces of information. 10/10 would download again",
null,
""
] |
[
null,
"https://modes-de-vie.com/qBO6320a40ff0272/engage-students.jpg",
null,
"https://modes-de-vie.com/qBO6320a40ff0272/icon_quotes.png",
null,
"https://modes-de-vie.com/qBO6320a40ff0272/testimonial_1.jpg",
null,
"https://modes-de-vie.com/qBO6320a40ff0272/icon_quotes.png",
null,
"https://modes-de-vie.com/qBO6320a40ff0272/testimonial_2.jpg",
null
] |
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|
https://www.mdpi.com/1911-8074/13/9/192
|
[
"",
null,
"Next Article in Journal\nSurvey of Green Bond Pricing and Investment Performance\nNext Article in Special Issue\nDynamic Connectedness between Bitcoin, Gold, and Crude Oil Volatilities and Returns\nPrevious Article in Journal\nIs Artificial Intelligence Ready to Assess an Enterprise’s Financial Security?\nPrevious Article in Special Issue\nTrue versus Spurious Long Memory in Cryptocurrencies\n\nFont Type:\nArial Georgia Verdana\nFont Size:\nAa Aa Aa\nLine Spacing:\nColumn Width:\nBackground:\nArticle\n\n# A Comprehensive Statistical Analysis of the Six Major Crypto-Currencies from August 2015 through June 2020\n\n1\nIM/COPPEAD (Institute of Mathematics/Instituto de Pós-Graduação e Pesquisa em Administração), Federal University at Rio de Janeiro, Rio de Janeiro 21941901, Brazil\n2\nCOPPEAD (Instituto de Pós-Graduação e Pesquisa em Administração), Federal University at Rio de Janeiro, Rio de Janeiro 21941901, Brazil\n*\nAuthor to whom correspondence should be addressed.\nJ. Risk Financial Manag. 2020, 13(9), 192; https://doi.org/10.3390/jrfm13090192\nReceived: 17 July 2020 / Revised: 17 August 2020 / Accepted: 19 August 2020 / Published: 25 August 2020\n\n## Abstract\n\n:\nAfter more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important crypto-currencies from the period 2015–2020. Using daily data we (1) showed that the returns present many of the stylized facts often observed for stock assets, (2) modeled the returns underlying distribution using a semi-parametric mixture model based on the extreme value theory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of long memory as well as short memory in the GARCH volatility, (4) used an econometric approach to compute risk measures, such as the value-at-risk, the expected shortfall, and drawups, (5) found that the crypto-coins’ price trajectories do not contain speculative bubbles and that they move together maintaining the long run equilibrium, and (6) using static and dynamic D-vine pair-copula models, assessed the true dependence structure among the crypto-assets, obtaining robust copula based bivariate dynamic measures of association. The analyses indicate that the strength of dependence among the crypto-currencies has increased over the recent years in the cointegrated crypto-market. The conclusions reached will help investors to manage risk while identifying opportunities for alternative diversified and profitable investments. To complete the analysis we provide a brief discussion on the effects of the COVID-19 pandemic on the crypto-market by including the first semester of 2020 data.\nJEL Classification:\nC1; C3; C4; C5; G0; G1\n\n## 1. Introduction\n\nThe crypto-currencies market is a growing volatile market whose dominant element is Bitcoin, a virtual currency created in 2009 by the pseudonymous author Satoshi Nakamoto (Nakamoto 2009). Since then, the number of available crypto-coins has grown steadily and reached over 5500 at the time of writing this paper (see https://coinmarketcap.com/currencies/). Crypto-currencies provide users a fast, secure, and cheap medium of exchange.\nCripto-currencies’ price trajectories may be influenced by a number of factors, including economic and political events, government regulations, the creation of new currencies, speculation, hacking, news, and mutual influence. Relevant players in the crypto-market may be either exercising the currency’s primary utility, which is to pay for goods and services, or looking for alternative profitable investments. They need to be sure that the well known available financial models and financial tools will work with crypto-coins as well. Relevant players’ success will follow from a solid, comprehensive deep understanding of crypto-currencies’ statistical properties, to set their limitations and peculiarities. In particular, volatility and interdependence are two important issues when constructing diversified portfolios and managing risk.\nAccordingly, in this paper we clarify many issues related to individual past price and return behavior and co-movements through appropriate statistical tools. Simple models delivered basic statistics and long-run risk measures and predictions. More sophisticated ones such as copula models and cointegration methods uncovered the static and dynamic relationships among crypto-coins, allowing for the simulation of future joint scenarios, and also delivered additional (non-linear) dependence measures.\nIn the last decade we have seen a growing number of scientific papers taking a formal statistical approach to understanding the dynamics of crypto-coins market prices. For instance, Hencic and Gouriéroux (2015), using a small dataset of 150 observations containing an episode of a speculative bubble, modeled the dynamics of the Bitcoin/USD exchange rate using a non-causal autoregressive process with Cauchy errors. Reference Urquhart (2016) investigated the market efficiency of Bitcoin by employing a battery of robust tests and found evidence of market inefficiency depending upon the sample period. Following that, Nadarajah and Chu (2017) showed that a power transformation of the Bitcoin returns may be weakly market efficient.Reference Urquhart (2017) found significant evidence of clustering of Bitcoin prices at round numbers. In Drożdż et al. (2018) the well known stylized facts were investigated. They showed that by the end of 2017 Bitcoin market had become truly indistinguishable from mature markets according to several statistical features related to the return distribution, such as tail thickness, weak autocorrelation in return distribution, significant (non-linear) long range autocorrelations of absolute value of returns characterizing persistence, and multi-scaling effects. Reference Corbet et al. (2018) provided a systematic review of the empirical literature on crypto-currencies and investigated their role as reliable and legitimate investment assets.\nEstimation of crypto-currencies volatilities is a research stream followed by many articles, most of them focusing on Bitcoin dynamics only. Reference Dyhrberg (2016) using GARCH models assessed Bitcoin potential as an asset for risk management and established its role in the market, standing somewhere between a currency and a commodity. However, Cheah and Fry (2015) found that Bitcoin seems to behave more like a speculative asset than a currency. Reference Katsiampa (2017), using the entirety of Bitcoin data since its creation, found which type of GARCH model could better explain the Bitcoin volatility. Reference Chu et al. (2017) modeled the seven most popular crypto-currencies with 12 different GARCH models and computed value at risk (VaR) estimates. Reference Deniz and Stengos (2020) examined the behavior of five crypto-currencies in the pre and post periods of the introduction of the Bitcoin futures market. They found that Google search intensity was the most important variable to explain, for both periods, the BTC mean return (through a PC-LASSO model) and the BTC GARCH volatility.\nLong memory in crypto-currencies first and second moments has also been considered by researchers. Reference Bariviera (2017) using data from 2011 to 2017 and moving windows methodology, observed that the time series of Bitcoin’s daily returns exhibited a persistent behavior before 2014, being efficient after 2014; see also Bariviera et al. (2017). Reference Phillip et al (2018) fitted a long memory autorregressive model for the mean combined with a stochastic volatility model with leverage effect and t-student correlated errors to 224 different crypto-currencies. Reference Lahmiri and Bekiros (2018) found that both Bitcoin prices and returns exhibit long-range correlations and multi-fractality. They applied the multi-fractal detrended fluctuation analysis on prices and returns covering two distinct periods, and found that chaos was present in prices during both periods and that heavy tails were the main factor driving the chaos. Reference Garnier and Solna (2018) investigated whether the Bitcoin market can be viewed as a semi-efficient market. They found that Bitcoin exhibits multi-scale correlation structure, and showed how the power-law parameters can be used to identify regime shifts for the Bitcoin price. Reference Tan et al. (2019) applied a structural change model to the top ten crypto-currencies and examined the number and location of change points in daily price, return, and volatility. One conclusion from their research is that the two crypto-currency indices used failed to reflect the whole crypto-currency market.\nThere has also been some results on crypto-coins’ co-movements. The multivariate GARCH model has been used to assess dynamic interdependence among crypto-currencies. Reference Katsiampa (2019) modeled the volatility and co-volatility of Bitcoin and Ether using an extension of the diagonal BEKK model, thereby finding evidence of interdependencies in the crypto-currency market. Reference Zwick and Syed (2019) applied threshold regression to model the nonlinear long-term relationship among Bitcoin and gold prices. Reference Kristoufek (2015), by applying a wavelet coherency analysis, examined the correlation between Bitcoin prices prices and selected factors. Reference Drożdż et al. (2019) found significant time-scale dependent cross-correlations among BTC/EUR, BTC/US, ETH/EUR, and ETH/US exchange rates at a 10s frequency from July, 2016 to December, 2018. It was also found that the cross-correlations between the BTC/ETH and EUR/USD exchange rates were not significant, probably indicating that the crypto-market has begun to become independent from Forex. As reported in Drożdż et al. (2019), this may be in line with the Drożdż et al. (2018) hypothesis of a gradual emergence of a new and at least partially independent market.\nOther related papers are: Ciaian et al. (2016), where the traditional determinants of currency price along with digital currencies specific factors were considered when searching for factors behind the Bitcoin price formation. Reference Li and Wang (2017) examined the determinants of the Bitcoin exchange rate from both technology and economic perspectives. They found that the VECM is not appropriate and applied the ARDL model. Reference Cheah et al. (2018) using cross-market Bitcoin prices from November 2011 to March/2017 found evidence of long memory in the system. However, there is no agreement on the degree of integration of returns and prices, though more empirical evidence has accumulated since 2014.\nTo the best of our knowledge, this paper is the first one to apply a comprehensive set of statistical modeling approaches—mixtures, ARFIMA-GARCH, pair-copulas, and cointegrated VAR models—to an updated set of the six most important crypto-currencies, plus the Euro, while trying to address several questions: (1) Do prices and returns exhibit some well known characteristics, in particular the famous stylized facts, usually found in financial instruments? (2) Are we able to describe their statistical underlying distributions? (3) Does their dynamic mean and volatility behavior follow the popular univariate/multivariate time series processes usually fitted to financial data, in particular to stock assets? (4) Are there linear and non-linear interdependencies in the crypto-market? (5) Are these crypto-currencies cointegrated? Do prices present speculative bubbles?\nResults and findings from this analysis include the specification of a semi-parametric mixture model based on the extreme value theory to represent the returns’ underlying distributions; the confirmation of the presence of long memory as well as short memory in the GARCH volatility, though still leaving as an open question whether long memory in returns may be just anomalies or a period-dependent artifacts; the computation and accuracy assessment of conditional and unconditional risk measures such as the value-at-risk, expected shortfall, and drawups; the rejection of the hypothesis of the existence of explosive bubbles; the description of the true dependence structure among the crypto-assets through static and dynamic (easy to simulate) copula models—through copula-based bivariate dynamic measures of association, we found that the six analyzed crypto-coins are highly linearly and non-linearly correlated with measures which increase over time independently whether experiencing normal or atypical periods; and through the assessment of their dynamic interdependence with cointegration methods, we found a weakly cointegrated market and no empirical evidence for the existence speculative bubbles. The conclusions reached enable an investor to have a broader view of crypto-assets’ behavior, so manage risks while identifying opportunities for more profitable investments. We recall that no good answer or wise decision can be reached if based on a poor model.\nThe remaining of the paper is organized as follows: In Section 2 we provide a brief description of the six crypto-coins used in this paper and describe the basic statistical analyses. In Section 3 we fit the conditional and unconditional models, thereby obtaining the corresponding risk measures. Section 4 deals with the interdependencies in the data (we applied copula and cointegration methods). Finally, Section 5 discusses the results and provides an extra analysis of the behavior of series during the COVID-19 pandemic.\n\n## 2. Basic Statistical Analyses\n\nThe data in this work are from six virtual currencies: Bitcoin, Ethereum, Ripple, Litecoin, Stellar, and Monero. They were chosen based on their market capitalization rankings provided by CoinMarketCap in 31 January 2020, and they altogether represented approximately 77.4% of the total market capitalization at that time. The percentages of each one were: Bitcoin: 62.2%, Ethereum: 9.8%, Ripple: 3.2%, Litecoin: 1.0%, Stellar: 0.7%, and Monero: 0.5%. In what follows we provide a brief description of the six crypto-currencies.\nDesigned to be an alternative currency and medium of exchange, Bitcoin (BTC) has shown the largest market capitalization since its creation. As of January 2020 the total available bitcoins were valued at over 171 billion US dollars. Based on cryptographic proves, it is a peer-to-peer based system with online non-centralized public transactions recorded in an “accounting book” known as blockchain. The chain is controlled by its users and follows a set of rules that minimize the probability of fraud. By offering large returns, it has gradually become a speculative investment. See, for example, Baur et al. (2017) where it is discussed whether Bitcoin is mainly used as a currency to pay for goods and services or as an alternative investment. Using daily data from 2010 to 2015, they found that the Bitcoin returns are linearly uncorrelated with all assets both in normal and extreme times, providing diversification opportunities.\nEthereum (ETH), the second-largest digital currency, was conceived by the computer programmer Vitalik Buterin in 2013 (Buterin 2013). It is a computer code (smart contracts) open platform where decentralized peer-to-peer applications run exactly as they were programmed, eliminating the possibility of fraud or any type of interference. As such, Ethereum and Bitcoin blockchain technology purposes are different, as the latter is used to validate transactions among Bitcoin ownerships.\nThe Ripple crypto-currency (XRP), released in 2012, is the fastest digital asset used by banks to execute end-to-end payments in real time with low transaction costs, independently of amounts transferred and geographic location. XRP important players benefit from its liquidity and faster inter-banking payments: the network takes about four seconds to confirm transactions. Ripple’s activities do not rely on blockchains (as Bitcoin does); the mining is replaced by the work executed by the nodes which listen other nodes for at least 80% confirmation from validators listed in a unique node list.\nThe globally decentralized digital currency Litecoin (LTC), one of the first Bitcoin forks, was created in 2011 as a \"lighter\" alternative to Bitcoin. The LTC open source protocol follows the same Bitcoin blockchain concept with a different, four-times-faster hashing algorithm. At the time of this analysis it is the 9th largest digital currency for transferring funds between individuals or businesses in the market with a market cap of over \\$2.8 billion dollars.\nStellar (XLM), a fork of XRP, was created by Jed McCaleb and Joyce Kim in 2014 and uses the ripple consensus algorithm. Later on, the XLM coin was recreated as an independent foundation, and in 2015 it started using the federated Byzantine agreement algorithm, based on quorum slices, to approve transactions. The Stellar network is really fast with all nodes being updated every 2 to 5 s. It is the most decentralized open source not-minable network, able to trade any fiat or crypto-currency, asset, or token around the world. The motivation behind its creation was the reduction of costs required for cross-border transfers, and it has been used for small payments within a company or among private entities as well as for currency exchange. The Stellar technology may be used for building new applications, and connecting banks and people. The XLM’s price showed a significant increase in May, 2017, and another jump in November of the same year, but presented an downward trend (shared by all other crypto-currencies) during all second semester of 2019.\nMonero (XMR) launched on April 2014, is a proof-of-work secure decentralized crypto-currency operated by a network of users which uses ring signatures (a type of digital signature), ring confidential transactions, and stealth addresses (this prevents address reuse since only the sender and receiver of a transaction can determine where a payment is to be sent). Monero is untraceable; that is, the transactions recorded on the blockchain cannot be linked to any particular user, and also exchangeable (fungible): 1 XMR is functionally identical to any other 1 XMR. Differently from Bitcoin, which is a transparent network, Monero has strong privacy properties, able to hide information on the amount of money sent from one user to another in all transactions.\nThe six series were obtained from the Quandl’s platform (www.quandl.com). For each currency we selected its BraveNewCoin daily global price index (BNC2) recorded in USD each 5-min and based on an aggregate of all transactions for that coin at that time. For the sake of comparisons we also included the Euro. Series cover the period from 8 August 2015 to 31 January 2020, that being the initial date defined by the shortest series, Ethereum. Another reason for not including data since 2009 is the different dynamic behavior of the existing crypto-currencies during the initial existence of the crypto-market (2009–2014), which would affect the robustness of the statistical estimates resulting in poor, unreliable conclusions. Note that some authors have demonstrated that the crypto-currency market in its infancy was inefficientm not following the efficient market hypothesis (EMH), being ruled by a different underlying statistical model; see, for example, Urquhart (2016) and Bariviera (2017), among others.\nLet $P t$ denote the crypto-currencies prices in US Dollar at time t. The corresponding log-return $r t$ is computed as $r t = 100 ∗ log ( P t P t − 1 )$. Length of all crypto-currencies log-returns series is $T = 1637$, whereas the Euro series has length 1146.\nMost stylized facts often observed in financial returns series (in particular stocks and indexes) were also noticed for the crypto-assets returns (Engle and Patton 2001; Cont 2001; Tsay 2002; Drożdż et al. 2018). Some of these features were observed graphically and confirmed by usual statistical tests at the 1% significance level. All return series are second order stationary (KPSS test, Kwiatkowski et al. 1992) with a constant level close to zero, whereas the corresponding price series are nonstationary possessing a unit-root (ADF and PP tests, Dickey and Fuller 1979; Phillips and Perron 1988). Figure 1 illustrates and shows the (price and return) series dynamics of Bitcoin, Litecoin, and Ethereum, with returns showing volatility clusters (conditional heteroscedasticity) and a few extreme points. We also observe in Figure 1 that the price trajectories seem to move together, presenting joint episodes of runs of increasing prices (or sequences of positive returns) followed by corrections (in Section 3 we investigate if they could actually be speculative bubbles), suggesting that joint co-movements and measures of association are worth investigating.\nTable 1 provides some summary statistics for all seven returns series. Although the sample means for all crypto-currencies are positive, the t-test null of a true mean equal to zero was accepted at the 1% significance level for all return series. The second row provides the lower and upper 99% confidence limits for the sample mean. Returns from all seven series are not normally distributed, as confirmed by the Jarque–Bera and Shapiro–Wilk tests with p-values close to zero. Stellar has the largest standard deviation, the largest maximum, and the smallest minimum. All the very extreme points observed for Stellar occurred at the beginning of the series (2016), with the recent part of the series showing a much smaller range. When compared to Euro, all crypto-assets show larger standard deviations and also more extreme minimum and maximum.\nKurtosis coefficients larger than 3 were observed for all series. As pointed out by an anonymous referee, these results are in fact consistent with Drożdż et al. (2018) where, according to the Hurst exponent, the Bitcoin return distribution tail thickness has decreased over the 2016–2017 years, indicating that Bitcoin may be approaching a mature state. As suggested by this referee, we dynamically examined through a one-year rolling window, the Bitcoin and Ethereum kurtosis evolution from August 2015 to January 2020. In Figure 2 we see that Bitcoin kurtosis reaches the 5.0 level by middle 2017 and continues going down even below the 3.43 mean level observed for Euro. Bitcoin kurtosis reaches its smaller value (1.524) around 7 February 2019. For Ethereum, the kurtosis values are even smaller, staying bellow the Euro mean level during 2018–2019. The systematic decrease of the Bitcoin and Ethereum kurtosis to the Euro mean level depicted in Figure 2 are in line with results in Drożdż et al. (2018), and might be an indication that most important virtual coins are on their route to maturity.\nFor all crypto-currency returns, the minimum is less extreme than the maximum. Coherently, all crypto-assets, except Bitcoin, yielded positive skewness coefficients. Bitcoin’s coefficient of asymmetry was negative and statistically significant. For all crypto-assets, except Bitcoin, we observed a negative median and a positive mean, suggesting the influence of some extreme positive returns shifting the sample mean to the right. Bitcoin’s (statistically zero) mean and median are both positive and close, indicating that no extreme points affected the computation of the sample mean. Subsequently, we reject that the returns’ distribution is symmetric, except for Stellar and Euro. Is the left tail (negative returns) of Bitcoin heavier than the right tail? Are Bitcoin’s losses more likely than its gains? We provide answers to these questions in the next section.\nAccording to the Ljung–Box test, for all digital assets the linear correlations among lagged returns are either statistically zero or weak for just the first lags. However, the squared returns series showed significant correlation coefficients at small lags, although not as remarkable as those observed for stocks or stock indexes. For all series there is no evidence of long-memory (R/S test) in the mean.\n\n## 3. Assessing Crypto-Assets’ Risks\n\nTo assess risk we computed the most popular risk measure value-at-risk (VaR) and the conditional VaR (Tsay 2002). For small $α$ the VaR$α$ may be defined as the $( 1 − α )$%-quantile of the returns’ distribution F; and the conditional VaR$α$ is the expected loss (EL$α$), the mean return larger than the VaR value—that is, $E [ r t − VaR α | r t > VaR α ]$ (both defined in the right tail).\n\n#### 3.1. Unconditional Risk\n\nRisks associated with long term investments are better assessed by unconditional risk measures, and to obtain accurate risk estimates it is crucial to find the best estimate for the underlying distribution F of each returns series. All probabilistic aspects already commented on—long heavy tails, asymmetry, very large kurtosis, and extreme outliers—suggest that no single statistical distribution would be able to describe, with some degree of accuracy, the entire range of data. For example, Chan et al. (2017) fitted eight parametric distributions to the historical returns of seven crypto-currencies’ global indices, and found that they are not normally distributed, and moreover, no single distribution fits all the series well.\nDespite all the evidence, we tried to fit potential candidates to the data, namely, the normal, the student-t, and three versions of the skew-t (Hansen 1994: Jones and Faddy 2003, and Zhu and Galbraith 2010). They all failed to accepted the Kolmogorov–Smirnoff null of a good fit (goodness of fit test, GOF). The only exception was the Euro accepting the normal and the student-t as possible models. By noting that risk is mainly concerned with tail behavior, in this paper we propose fitting a mixture model (McLachlan and Peel 2000) based on a extreme value distribution to the historical returns. We fit the generalized Pareto distribution (GPD) (Pickands 1975; de Haan 1984; McNeil and Frey 2000) to the excesses $( r t − u )$ beyond a high threshold u (on each tail), and estimated the bulk of the data using the empirical distribution. Therefore, we combined three distributions to represent the data.\nThe thresholds were defined as the return value in the tail defining a small percentage $p *$ of extreme values, and $p *$ was chosen as the proportion resulting in the best GPD fit for the excesses. The empirical distribution of the excesses was also graphically checked for a strictly decreasing shape. We were able to find excellent maximum likelihood fits (Hosking and Wallis 1987; Hosking and Wallis 1997) for both tails of all series, accepted by the GOF test and confirmed by graphical diagnosis such as the QQ-plot and the PP-plot. For instance, see the first and second rows of Figure 3 where we show, for both tails, the excellent adherence of the GPD to the Bitcoin excess data.\nTable 2 gathers results from the GPD fits using the entire sample of 1637 observations. The first row shows the estimates of the shape parameter, the standard error, and the proportion $p *$ of tail data used in the estimation process. All estimates of the shape parameter were positive, indicating a Pareto type distribution. However, half of them were statistically zero (exponential tail) at the 5% significance level: both tails of Bitcoin, Ethereum, Euro; and also Litecoin and Monero left tails. The proportions $p *$ were higher than those suggested by text books, ranging from 8% to 17%, probably due to the long tails and the presence of some atypical extreme points.\nThe VaR estimation based on a GPD fit was expected to be much more precise, since it is based on a less extreme GPD quantile. More specifically, the GPD based VaR$α$ is equal to $u + GPD − 1 ( 1 − α p * )$, where $GPD − 1$ denotes the quantile function of a GPD. The second row of each panel of Table 2 provides the VaR estimates for $α$ equal to $0.05$ and $0.01$. For all crypto-currencies we checked whether the risk associated to the estimated VaR was actually $α$ by applying the Kupiek test, which accepted the null for both tails and for both risk levels, at the 5% significance level.\nWhen comparing in Table 2, the values of the risk measures, we note that the right tail is riskier than the left tail for all series except for Bitcoin, meaning that with the same probability $α$, only for Bitcoin, the losses may be larger than gains. Bitcoin shows, in addition, smaller risk estimates. For example, a one-percent chance the BTC log-returns will be less than −12.08%, whereas ETH log-returns should be less than −17.56%. While trying to answer the questions raised in Section 2, we now collect several indications that the Bitcoin left tail is actually heavier than the right one: the negative coefficient of asymmetry, the risk measures in Table 2, and the slower decay rate of the estimated left tail GPD density (see first row of Figure 3). The GPD fit indeed sheds light on what is happening at the tails, providing more accurate risk measures, and in the case of Bitcoin, it is robust and not affected by the longer right tail caused by the extreme maximum.\nFor all series and for $α = 0.05$, the historical and the normal VaR (not reported here) were close to the GPD values. However, for $α = 0.01$, the assumption of normality severely underestimated the risk measures. For example, the Bitcoin VaR$0.01$ under normality would be −9.00 and 9.43. Like stocks and indexes, it seems that simple methods based on normality will not work with crypto-assets.\nMany practitioners seem to prefer working with the concept of “return level,” instead of reporting the VaR value. The return level (RL) is defined as $RL t = F − 1 ( 1 − 1 / t )$; that is, a value which is expected to observe with some regularity, the return period t. For example, let $t = 100$ days. The $RL 100$ is equal to the 1% VaR, an event that happens on average once each 100 days. The third row of Figure 3 shows for both tails of Bitcoin, the $RL t$ for $t = 20 , ⋯ , 100$ on the left-hand side, and the corresponding $α$% VaR for $α = 0.01 , ⋯ , 0.05$, on the right-hand side. As discussed above, we note that the left tail of Bitcoin (in blue) is riskier than the right tail.\nTo assess the impacts of recent observations on the unconditional risk measures, we carried on a rolling window exercise. We separated an initial sample of 730 observations (two years), estimated the GPD models, and computed the risk measures. Then, the window moved 1 d ahead and the whole procedure was repeated until the end of the sample (1637 observations) was reached. For all crypto-currencies, we observed no trends or fluctuations of the dynamic VaR estimates around the unconditional estimate, but a small distance from (above or below) the fixed reported value. The rolling window values were more accurate, in the sense that even though the proportion of violations was pretty much the same, the expected loss values were smaller. The GOF and the Kupiek tests accepted their nulls for the results from all 907 windows. It seems that a two-year sample is able to provide a very good GPD fit and therefore accurate updated risk estimates.\nFinally, to have a broader view of the crypto-assets’ unconditional risks, we mention a different type of risk provided by a sequence of (consecutive) negative or positive returns, the so called drawdowns, or drawups. A drawdown (drawup) is a risk measure given by the sum of the consecutive losses (gains), whose duration is also a random variable. Note that a drawdown may not be extreme but may possess a large duration, and some investors following closely the performance of their investments, may not stand for a long period of successive losses, withdrawing from the market.\nThe empirical probability distribution of the duration was similar for all crypto-assets, and also similar to what is observed for stock assets. Typically, around 48% of drawdowns and drawups lasted for one day; 24% had a length of two days; and 0.5% lasted for 9 or 10 days.\nFor all crypto-coins the amount of accumulated gains was larger than the sum of consecutive losses. Even though the Bitcoin values were smaller, they were ten to twenty times greater than the numbers for the Euro. There is no link between sizes and durations; for example, whereas the largest accumulated gain for Bitcoin (63%) lasted eight days, it took only two days for Stellar produce its largest drawup (272%). The largest Bitcoin drawup was initiated on 30 November 2017, and it is interesting to note that a few days later both LTC and ETH presented sequences of extreme gains.\nDrawups and drawdowns may also be defined, and better visualized on the price trajectories: it is just a run of increasing (decreasing) prices. In this context drawups may be identified as bubbles (Cheah and Fry 2015; Fry and Cheah 2016). According to the dependence test (McQueen and Thorley 1994), speculative bubbles should exhibit negative duration dependence. This test assumes that during the existence of a drawup the conditional probability that a run ends decreases with time. For instance, Chan and Laurini (2018) found evidence for the absence of any bubble in Bitcoin in 2017. For our data we found no significant empirical evidence of speculative bubbles. This result is important because in Section 4.2 we apply cointegration methods are used to assess interdependence, and the model requires that the observed co-movements are not driven by speculative bubbles.\n\n#### 3.2. Conditional Risk\n\nDynamic unsystematic risk may be accurately estimated at some point in the near future through econometric model-based conditional measures. In this paper we fit the ARFIMA$( p , d , q )$-FIGARCH$( m , D , s )$ (autoregressive fractionally integrated moving average-fractionally integrated generalized autoregressive conditionally heteroskedastic) model to the crypto-assets returns, a powerful combination of short and long memory conditional models for the mean and for the volatility. The ARFIMA-GARCH model may be written as\n$Φ ( B ) ( 1 − B ) d r t = Θ ( B ) a t , d ∈ ℜ ,$\nwhere the polynomial $Φ ( B )$ and $Θ ( B )$ are of orders p and q, respectively, the fractional differentiation (see Hosking 1981) is given by the term $( 1 − B ) d$, and the white noise process ${ a t } t ∈ Z$ has zero mean and finite variance. It is assumed that $a t = σ t 2 ε t$, and the conditional variance is specified as\n$σ t 2 = ω + α 1 a t − 1 2 + ⋯ + α m a t − m 2 + β 1 σ t − 1 2 + β 2 σ t − 2 2 + ⋯ + β s σ t − s 2 ,$\nwith ${ ε t } ∼ i.i.d. F ( 0 , 1 )$. The volatility Equation (2) may me extended to include the long memory parameter D, see Baillie et al. (1996) and Bollerslev and Mikkelsen (1996).\nOur model estimation approach is top-down: We consider the full model, which, step by step, is reduced by the elimination of parameters not statistically significant. The initial orders $( p , q , m , s )$ are suggested by the examination of the autocorrelation functions and by the application of the Ljung–Box and ARCH tests. The AIC criterion helps selecting the best model for each series. The good quality of fit is then verified through diagnosis plots and formal tests applied to the residuals. Models were fitted to the entire sample except in the case of Stellar, which showed a turbulent initial period (completely different from the rest of the sample) with very high volatility affecting the convergence of algorithms. For this series the first 160 observations were removed. Their atypical influence may be proved by the statistics (standard deviation, maximum, minimum), which were computed for the removed initial period and the remaining sample produced, respectively, provided the values (50.35, 269.14, −244.65) and (7.84, 72.41, −37.56).\nTable 3 provides a summary of the best fits found for all seven series. Choices considered for the error distribution F were the normal, the t-student, and the skew-t. An important extension of the GARCH model including the leverage parameter (information asymmetry) was also considered. It was significant only for Bitcoin which showed, as expected, a negative value, indicating that negative returns increase volatility. Long memory in the mean was not detected for all return series, but it was strong in the volatility (around 0.6 for all series). According to the AIC value, for all crypto-currencies the fractionally integrated FIGARCH won over the version without long memory in the volatility. We report the two solutions in Table 3.\nBitcoin differentiates itself from all other crypto-coins in at least three aspects. It is the only one to include in the best model the leverage effect (LEV), to include the $β 2$ term, and to have the (symmetric) student-t as the error distribution F. Bitcoin, Ethereum, Litecoin, and Euro did not present short memory in the mean ($p = q = 0$). For XRP, XLM, and XMR $p = 0$ and $q = 1$ (see second and fourth columns). The degrees of freedom are very small characterizing heavy tails. In summary, with respect to econometric models, crypto-coins returns behave much like stock returns by presenting the same stylized facts, volatility clusters, high persistence, and long memory in volatility—substantially differing, however, in tail weight; see in Table 3 the Euro’s lighter F.\nTable 3 also shows in the last column the one-step-ahead conditional 1%-VaR at the left and right tails, computed using the corresponding ARFIMA-GARCH models. The volatility forecasts reflect the low volatility level at the end of the series (see Figure 1).\nWe estimated and tested the out-of-sample performances of the one-step-ahead conditional risk measures by applying the same already described rolling window approach. At each step the best GARCH model found for each series was fitted to the data inside the window and the next day $VaR α$ was computed using the one-step-ahead predictions for the mean and for the volatility. To assess the performances of the 907 one-day-ahead predictions we applied the Kupiec test to test whether the observed frequencies of VaR exceedances were consistent with the expected ones. The test accepted the null for all series at the 1% level. In summary, like real assets, all crypto-currencies volatilities were well modeled by some type of GARCH model, providing reliable conditional risk measures estimates.\n\n## 4. A Look at Dependence\n\nUnderstanding the interdependencies among crypto-currencies is important for those investors looking for portfolio diversification, hedging, and also risk management. Co-movements may be assessed through static models such as copulas, and also dynamically using multivariate time series models. Different models will measure different forms of association, and there is no unique measure to quantify interdependence. For example, Drożdż et al. (2020) studied the cross-correlations among a collection of the 100 highest-capitalization crypto-currencies from 1 October 2015 to 31 March 2019, thereby finding a criterion for identifying which currencies or crypto-currencies are more influential in the crypto-market. They also found evidence of an emergent independence of the crypto-market. In this section we study the dependencies among the six crypto-coins taking two different approaches—to the best of our knowledge not found in the current literature with such a set of crypto-currencies and for this recent period—by estimating their returns’ true copula dependence structures, and investigating their daily price cointegrations.\n\n#### 4.1. Dependence (by Pair-Copulas)\n\nThe true extent of dependence between assets tends to be masked by turbulent periods. To assess the true dependence structure linking the crypto-currencies we fit copula models to the standardized residuals from the GARCH fits. The filtered data usually show a weaker degree of dependence when compared to the raw log-returns, and may emphasize the information asymmetry providing different measures for the association in the lower left corner (joint losses) and in the upper right corner (joint gains). The knowledge asymmetric dependence may lead to statistically significant portfolio gains.\nConsider the 6-dimensional continuous random vector $( r 1 , ⋯ , r 6 )$ representing the six crypto-currencies standardized residuals from the econometric models fitted in Section 3.2. Their joint cumulative distribution function (cdf) and density function are respectively given by F and f, $F i$ ($f i$) represents the cdf (density function) of $r i$, and $F i − 1$ stands for the quantile function of $r i$, $i = 1 , ⋯ , 6$. The copula C associated to F is obtained through the following transformation to a simpler $[ 0 , 1 ] 6$ space: $C ( u 1 , ⋯ , u 6 ) = F ( r 1 , ⋯ , r 6 )$ where $( u 1 , ⋯ , u 6 ) = ( F 1 ( r 1 ) , ⋯ , F 6 ( r 6 ) )$. The copula C carries all the information about the dependence structure among the corresponding returns (Nelsen 2006).\nCopula models provide invariant measures for the strength of dependence between extreme joint values. They are the copula based lower and upper tail dependence coefficients $λ L$ and $λ U$, defined as $λ U = lim u ↑ 1 C ¯ ( u , u ) 1 − u , where C ¯ ( u 1 , u 2 ) = P ( U 1 > u 1 , U 2 > u 2 )$, and $λ L = lim u ↓ 0 C ( u , u ) u$, if these limits exist. These tail dependence coefficients (TDC) are highly relevant for risk management, and may be better appreciated if we rewrite the definition as\n$λ U = lim α → 0 + P ( r 1 > VaR α ( r 1 ) | r 2 > VaR α ( r 2 ) ) ,$\nwhere $VaR α ( r i )$ denotes the VaR at risk $α$ in the right tail of $r i$. The $λ L$ has similar definition. The TDCs are zero when the variables are asymptotically independent, but may be different from zero even if the linear correlation coefficient $ρ$ is zero. Copula based measures of dependence (Kendall’s $τ$ correlation coefficient, TDCs, etc.) are able to reveal each specific aspect of the dependence and overcome limitations of the traditional linear correlation coefficient $ρ$ (Joe 1997).\nChoosing (and estimating) an appropriate 6-dimensional copula is not a simple task. No single C could handle the several combinations of types and degrees of dependence among the crypto-assets returns. A smart solution is to fit a pair-copula model, a hierarchical decomposition of a copula in a sequence of bivariate copulas, see Bedford and Cooke (2001) and Bedford and Cooke (2002) for full details. In summary, the multivariate density function may be uniquely factored into conditional densities which, in turn, may be written as functions of the corresponding bivariate copula densities and univariate unconditional densities, as described in Aas et al. (2009). Thus, through the factorization of the 6-dimensional copula density $c ( F 1 ( r 1 ) , ⋯ , F 6 ( r 6 ) )$ in 15 bivariate copulas it is possible to derive a decomposition for the joint density: $f ( r 1 , ⋯ , r 6 ) = c ( u 1 , ⋯ , u 6 ) ∗ ∏ i = 1 6 f i ( r i )$.\nIn this paper, to estimate the true dependence structure of the six crypto-currencies returns, we fit a Dvine pair-copula model to the independent standardized residuals from the GARCH fits specified in Table 3. Figure 4 shows the scatter plots of the standardized residuals on the upper-left panel (above diagonal). All pairs seem to be highly positively correlated. However, all plots show at least one extreme outlying point which has the potential to distort classical estimates of dependence measures. For example, Ripple shows a single outlier, much more extreme than the 99-quantile of the corresponding univariate distribution, which is not an extreme point in the BTC range. The effect of this atypical point may be observed, for example, on the Pearson $ρ$ which is 0.41, but without the single outlying observation it is 0.44. This suggests that a robust method should be applied to estimate the pair copulas.\nWe computed the two-step weighted maximum likelihood robust estimates proposed in Mendes et al. (2007). In the first step, outlying data points were identified by the Stahel–Donoho robust covariance estimator based on projections (Stahel 1981; Donoho 1982), and received zero weights. In the second step we computed the maximum likelihood estimates based on the reduced data. Computations were carried out on the free R platform.\nEstimation followed the sequential approach (Aas et al. 2009; Aas and Berg 2009) where copula estimates from the previous tree were used to obtain the uniform (0,1) data in the current tree. Pairs composing Tree 1 were those showing stronger dependence, usually identified by either fitting a t-copula or computing some correlation coefficient. We ordered the variables in Tree 1 according to Kendall’s $τ$ monotone correlation coefficient given in the bottom-right panel of Figure 4. The suggested order is: Monero–Bitcoin–Litecoin–Ethereum–Ripple–Stellar.\nThe best families for the 15 copulas composing the Dvine were defined based on an exhaustive search over all available families in the R package. The AIC and the BIC criteria were used for copula selection. The sequence found was: in Tree 1 all five bivariate copulas were from the BB7 family; the following seven copulas in trees 2 and 3 were t-copulas; the last three copulas in trees 4 and 5 were either t or Gaussian. Table 4 shows a summary of the results from the fits.\nAll BB7 copula estimates in Tree 1 imply a positive association; see the second column of Table 4. This was reflected o+in all copula based dependence measures, $τ$, $λ L$, and $λ U$ (columns 4, 5, and 6). The Kendall’s $τ$ coefficients had values around $0.6$ for all pairs in Tree 1, characterizing strong monotone dependence. On the other hand, the value of the linear correlation coefficient $ρ$ computed using the residuals, not being robust, suffered the influence of atypical points and may not have been the best statistic to inform about the dependence between the crypto-assets. For instance, $ρ$(XRP,XLM) is 0.24 much smaller than $τ$(XRP,XLM) = 0.61.\nThe BB7 copula provided different lower and upper TDCs, discriminating the asymptotic dependence on the lower left and upper right corners. All TDC estimates are very large, meaning that at extreme scenarios one can expect a highly correlated market! For example, the conditional probability that Litecoin presents a loss greater than its VaR$α$, given that Bitcoin has already shown returns larger than its VaR$α$ is, as $α$ goes to zero, 0.83 (also true the other way around).\nUsing simulations we computed the joint risk associated to pairs of the one-step-ahead VaR$α$ values given in Table 3. As we can see in columns 7 and 8 of Table 4, the BB7 copula-based joint probability is much higher than under independence, equal to $0.01$% ($Π$ copula). For example, the (univariate) 1%-VaR values of XMR and BTC reported in Table 3 have joint exceedance probability of 0.753% (joint losses) and 0.554% (joint gains). Note that as $α$ goes to zero, the conditional probabilities ($λ L , λ U$) for this pair are equal to (0.746, 0.566). The simulated copula values may be used to compute any other quantity of interest along with its standard errors.\nWe applied the rolling window exercise to observe the behavior of the dependence measures along time. We fixed a initial window of size 730 days, and at each step the window was rolled 1 d ahead and the pair-copula model was fitted to the recent data. The resulting 907 dynamic estimates of the dependence measures were collected in Figure 5 where the results for the pairs (BTC, LTC) and (LTC, ETH) are depicted.\nMany interesting things came out. First, it is clear that the strength of dependence among the crypto-assets has been increasing since 2015. The Pearson coefficient $ρ$, being non-robust and not well defined for our data, is the one showing the most dramatic behavior. For both pairs in Figure 5, the $ρ$ evolution shows a shift at the end of the first quarter of 2019. Actually, on 31 March 2019 Bitcoin initiated a 10-days lasting drawup, and on 2 April 2019 all six crypto-currencies presented simultaneous extreme gains with (BTC,LTC,ETH) returns achieving the values (16.06%, 23.08%, 14.91%), respectively. The effect of this three-dimensional extreme point is instantaneous on $ρ$, but it is slowly absorbed by the robust copula estimates. Note that these simultaneous extremes occurred during a stationary period of low volatility (see Figure 1) just preceding an exponential increase in prices. Just for the record, Euro returns were not extreme on this day.\nIn Table 4 we observed that $λ L$ was higher than $λ U$ for all pairs in Tree 1. The rolling window showed more than that. First, all three dependence coefficients—$τ$, $λ L$, and $λ U$—showed an upward trend. However, the rate of increase of $λ U$ was higher than $λ L$. Thus, at the end of analyzed period the lower and upper TDC values were close, especially for the LTC-ETH copula. All this emphasizes that much care is needed when fitting models to large datasets, say, since the creation of Bitcoin, or including the first three or four years of the last decade. More reliable information certainly would come from models applied to recent data.\n\n#### 4.2. Dependence (by Cointegration)\n\nTo act simultaneously in several markets—including the virtual ones—global investors rely on their fast exchange of information and powerful computers. Their actions instantaneously feed and influence the whole market, reinforcing the simultaneous movements of financial time series. To assess series dynamic interdependencies one needs multivariate conditional models.\nData analysts often deal with non-stationary series such as interest rates, exchanges rates, spot and future prices, and so on. As we have seen in Section 2, all crypto-currencies prices ($P t$) are I(1) unit root, non-stationary integrated of order 1. Multivariate regression models applied to price series will result in spurious correlations and inconsistent estimates. The corresponding first differences ($Δ P t$) were found to be I(0) stationary, and in this case a possible approach is to fit a vector autoregressive model (VAR). However, this model will still fail in the identification of relevant interdependencies if the series are cointegrated.\nWhenever I(1) variables are cointegrated, there are subjacent economic forces constantly trying to restore some common long-run equilibrium relationship so that their deviations from equilibrium would not be permanent. A system composed by d I(1) variables is said to be cointegrated if there exists r I(0) stationary independent linear combinations of the d variables, $0 < r < d$, the so called long-term equilibrium relationships. This error correction mechanism is incorporated into the VAR model resulting in the Vector Error Correction Model (VECM). The VECM$( p − 1 )$ model for the d crypto-currencies prices may be specified as:\n$Δ P t = Φ D t + Π P t − 1 + Γ 1 Δ P t − 1 + … + Γ p − 1 Δ P t − p + 1 + ε t$\nwhere $D t$ is a $( l × 1 )$ matrix of deterministic components such as constants, trends, seasonal dummies, etc, $Φ$ a parameter matrix, $Π$ is the $( d × d )$ long run impact matrix, $Π = Π 1 + … + Π p − I d$, $Π i$ are $( d × d )$ matrices, $Γ k$ the short-run impact matrices, $Γ k = − ∑ j = k + 1 p Π j , k = 1 , … , p − 1$, and $ε t$ a $( d × 1 )$ non-observable error vector, generated from a zero mean white noise process with constant covariance matrix. The term $Π P t − 1$ is I(0) and contains the cointegrating relations. $Δ P t$ as well as its lagged values are I(0). Note the model may be further extended to include exogenous variables, or even allowing the original series $P t$ to be integrated of order greater than 1.\nIn model (3) $Π$ has reduced rank r and can be represented as a (not unique) multiplication of loading coefficients $α ( d × r )$ and cointegrating vectors $β ( r × d ) ′$. The components of $α$ measure how fast the variables move back to their long-term relationship. Although the I(0) linear combinations $β ′ P t$ are usually motivated by economic theories, in this paper we observe the market effect, how the system of most important crypto-currencies are linked together all sharing the same (or a few) common stochastic trend(s).\nWe started by performing a bivariate analysis investigating if Bitcoin is cointegrated with all other crypto-currencies plus Euro. We applied the Engle and Granger (1987) procedure and estimate the normalized $β$ by ordinary least squares, regressing Bitcoin on the other six series. All regression estimates were statistically significant and residuals were tested for stationarity applying the already described unit root tests. For all pairs except Euro, we were able to obtain I(0) residuals, so Bitcoin was not cointegrated with Euro but it was cointegrated with each one crypto-currency. The error correction model was then estimated considering the correct number of lags.\nFor all pairs in this analysis we found that the error correction mechanism is statistically significant but very slow. The estimates of the speed of adjustment parameters $( α 1 , α 2 )$, given in Table 5, provide information about the amount of time necessary for the crypto-assets to return to their respective equilibrium values. Small values of $α$ imply that it would take a long time for the variable to return to equilibrium. We observe that Bitcoin is faster than the other ones, and that its $α$ values are close among all criptocoins. Roughly 0.5% of Bitcoin price deviations from its equilibrium are corrected in one day. Cointegration is really weak with Ripple and Stellar.\nFor the 6-dimensional crypto-assets data the Johansen trace and eigenvalue tests (Johansen 1988) indicated $r = 2$; thus there were two cointegrating relations, and according to the AIC criterion $p = 2$. Being cointegrated indicates that the already investigated runs of positive returns are not speculative; that is, prices do not exhibit explosive bubbles and move together, maintaining the equilibrium in the long run.\nAll coefficients in the model are statistically significant. In the rows of Table 6 we give the two sets of $( α 1 , ⋯ , α 6 )$ coefficients measuring the speed at which each crypto-asset is pulled back to equilibrium represented by the corresponding stationary portfolio. Now the contribution of Bitcoin and Ethereum is more evident, and it can be said that price information flows within the crypto-market although the speed of adjustment is still very slow implying that effects of a stochastic shock are very persistent.\nIn summary, the six analyzed crypto-coins are highly linearly and non-linearly correlated, increasing the possibility of significant joint falls in their values, which could lead to generalized margin calls. We note that Ciaian et al. (2016) have shown that Bitcoin prices are driven in the long run by investors speculative behavior, but it is not driven by macro-financial indicators.\n\n## 5. Discussions\n\nWe have conducted a comprehensive statistical analysis of the top six crypto-currencies series, covering the period from 8 August 2015 to 31 January 2020, and representing approximately 77.4% of the total market capitalization at the time of writing. To assess the effects of the COVID-19 on the crypto-market, at the end of this section we add data from the first semester of 2020 and look for changes to what was discussed.\nUsing static and dynamic, univariate and multivariate, and simple and complex statistical approaches, we have confirmed most of the several findings scattered about the existing literature. They include the stylized facts: extremely large kurtosis (tail thickness), mean close to zero, non-normality, extreme points, almost nonexistent autocorrelation in returns (weak predictability), volatility clustering, high persistence, and long memory in the volatility. We also verified that, like real financial assets, crypto-currencies’ volatilities may be well captured by some GARCH models, which are able to provide accurate conditional risk measures. However, for the crypto-coins the tails of the GARCH conditional distribution are heavier than those found for stocks and exchange rates. Although all crypto-coins analyzed share these statistical features, Bitcoin (followed by Ethereum) seems to be more mature, with smaller risk measures, with statistics that are closer to those observed for other assets, and persistence close to those for real coins.\nThe existence of speculative bubbles in Bitcoin has been already tested (and rejected) in the literature. In this paper we confirmed that and made the link between speculative bubbles and episodes of runs of (consecutive) gains, the so-called drawups. We found that typically, for our series, the sum of consecutive gains was larger than the sum of consecutive losses. Even though the magnitudes of the Bitcoin drawdowns and drawups were smaller than those of other virtual coins, they were ten to twenty times greater than the values for Euro. The six crypto-currencies also presented some important episodes of joint drawups.\nOur proposal for modeling the underlying return distribution—a semi-parametric mixture model, non-parametric for the bulk of the data, and an extreme value distribution (GPD) for each tail—showed an excellent adherence to the data, discriminated the left and right tails, and provided precise risk measures. We found that the right tail is riskier than the left tail for all series except for Bitcoin, which shows, in addition, smaller risk estimates. The unconditional risk measures based on a two-year rolling window sample performed even better.\nIt has been shown that crypto-coins are highly linearly and non-linearly correlated. Our pair-copula model uncovered new features: At extreme scenarios one can expect a highly correlated market, with extreme joint gains behaving differently from large joint losses. Simultaneous extremes may occur during periods of low volatility. All crypto-coins are positively correlated, and the strength of dependence has been increasing since 2015. However, the linear correlation coefficient $ρ$ may not be the best statistic to inform one about the dependence between the crypto-assets.\nPrices are linked together forming a cointegrated system with two cointegrating relations driven by crypto-market forces. Price information flows within the crypto-market, although the speed of adjustment to the long run equilibrium is very slow, implying that effects of a stochastic shock may last for a long time. Bitcoin is not cointegrated with Euro but it is cointegrated with each one crypto-currency.\nOur data ended by the time of the onset of COVID-19 pandemic, and right now we do not know their effects on the economy, on people new habits, on stock- and crypto-markets, and so on, even though we can already see some advances in technology. To provide some insights on what may be just around the corner for the crypto-market, we add to the analysis the first semester of 2020.\nWe basically compared the second semester of 2019 with the first semester of 2020. The first thing that came to our attention is that all crypto-coins prices showed an upward trend during the first two months of 2020, after a dramatic loss in value during the whole second semester of 2019; see Figure 6. However, on 12 March 2020, all crypto-coins prices fell about 61%, reaching a point that could be expected based of the previous 6-months’ downhill trend of price levels. In other words, the two initial months of January and February of 2020 could have been a speculative bubble. We carried out the dependence test of McQueen and Thorley (1994) and found that Bitcoin and Stellar indeed accepted the null of a speculative bubble, whereas for all others crypto-currencies the test was inconclusive.\nAfter the joint extreme fall of March 2020, all prices seemed to randomly walk around a new level, lower than those attained in 2017. It is interesting to observe that, in spite of the March outlying negative return, the accumulated return in the first semester of 2020 is positive for all crypto-currencies except XRP, whereas it was negative and significant in 2019/2 and for all crypto-assets. For example, for Bitcoin and Ethereum the cumulative one-semester returns was, respectively, 27.4% and 74.7% in 2020/1, and $− 32.4$% and $− 56.1$% in 2019/2.\nThe extreme negative return has a large influence on the 2020 returns’ basic statistics. For all crypto-coins we observed a more extreme minimum, higher standard deviations, larger skewness coefficients, and specially very large kurtosis. All 2020 sample means, although negative, are still statistically zero.\nInteresting results came out when we incorporated the 2020/1 data in the kurtosis rolling window analysis of Section 3.1. When the 12 March 2020 Bitcoin extreme negative outlier of −48.69% entered in the calculating window, the kurtosis value jumped from approximately 4 to 44.8 and remained huge during the whole first semester of 2020. However, without this single extreme point, the kurtosis values stayed around the 2.7 level for the COVID-19 period. Similar behavior was observed for Ethereum, which presented a kurtosis value of 42.20 after the 12 March 2020 outlier of −56.27 was entered in the computations. Without this extreme observation the Ethereum kurtosis values stayed close to the Euro mean level during all 2020/1. In spite of this striking result, from a practical conservative viewpoint, the risk- or portfolio-manager/investor may find ut safer to consider the kurtosis values inflated by a single outlying point as inputs in her/his models. Alternatively, he/she may use a larger window for long run investments.\nFinally, we carried out the rolling window exercise to assess changes in the dynamic copula-based dependence measures. Based on a one-semester window length, we observed estimates close to the values observed at the end of 2019, which stayed almost constant during the six-months of the COVID-19 pandemic. It was also noted that all rolling-window-based basic statistics seemed to stabilize during the second quarter of 2020, as if staying on hold. We wonder if this may be related to the crypto-market player’s profile, that is, to the common degree of risk aversion, level of wealth, level of information, and so on, which together make them investors looking for alternative long run investments and for whom liquidity might not be an issue.\nThe crypto-market is still in its infancy, but it is growing up rapidly, with leading crypto-currencies already showing numbers close to those of real coins/assets traded in mature markets. As such, the crypto-assets are also becoming interesting alternative investments for frequent traders, who though, should keep in mind that more reliable statistical conclusions would come from models applied to recent data.\n\n## Author Contributions\n\nConceptualization, B.V.d.M.M.; methodology, B.V.d.M.M.; software, B.V.d.M.M. and A.F.C.; formal analysis, B.V.d.M.M. and A.F.C.; data curation, A.F.C.; writing–original draft preparation, B.V.d.M.M. and A.F.C.; writing–review and editing, B.V.d.M.M. 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Available online: https://bitcoin.org/bitcoin.pdf (accessed on 22 March 2020).\n51. Nelsen, Roger B. 2006. An Introduction to Copulas. Berlin/Heidelberg: Springer. [Google Scholar]\n52. Phillips, Peter C. B., and Pierre Perron. 1988. Testing for a Unit Root in Time Series Regression. Biometrika 75: 335–46. [Google Scholar] [CrossRef]\n53. Pickands, James, III. 1975. Statistical inference using extreme order statistics. Annals of Statistics 3: 119–31. [Google Scholar]\n54. Phillip, Andrew, Jennifer S. K. Chan, and Shelton Peiris. 2018. A new look at Crypto-currencies. Economics Letters 163: 6–9. [Google Scholar] [CrossRef]\n55. Stahel, Werner A. 1981. Robust Estimation: Infinitesimal Optimality and Covariance Matrix Estimators. Zurich: ETH. [Google Scholar]\n56. Tan, Chia-Yen, You-Beng Koh, and Kok-Haur Ng. 2019. Structural Change Analysis of Active Crypto-currency Market. arXiv arXiv:1909.10679. [Google Scholar]\n57. Tsay, Ruey S. 2002. Analysis of Financial Time Series. Wiley Series in Probability and Statistics; New York: Wiley-Interscience. [Google Scholar]\n58. Urquhart, Andrew. 2016. The inefficiency of bitcoin. Economics Letters 148: 80–82. [Google Scholar] [CrossRef]\n59. Urquhart, Andrew. 2017. Price clustering in Bitcoin. Economics Letters 159: 145–48. [Google Scholar] [CrossRef]\n60. Zwick, Hélène Syed, and Sarfaraz Ali Shah Syed. 2019. Bitcoin and Gold Prices: A Fledging Long-Term Relationship. Theoretical Economics Letters 9: 7. [Google Scholar] [CrossRef][Green Version]\n61. Zhu, Dongming, and John W. Galbraith. 2010. A generalized asymmetric Student-t distribution with application to financial econometrics. Journal of Econometrics 157: 297–305. [Google Scholar] [CrossRef]\nFigure 1. Bitcoin, Litecoin, and Ethereum prices and return dynamics along time.\nFigure 1. Bitcoin, Litecoin, and Ethereum prices and return dynamics along time.\nFigure 2. Bitcoin and Ethereum kurtosis dynamic estimates over time.\nFigure 2. Bitcoin and Ethereum kurtosis dynamic estimates over time.\nFigure 3. The first and second rows show the Bitcoin GPD’s graphical diagnosis for the left and right tails. In the third row the return level is shown in days with the corresponding $α$-VaR, for the left tail (blue) and right tail (black) of Bitcoin.\nFigure 3. The first and second rows show the Bitcoin GPD’s graphical diagnosis for the left and right tails. In the third row the return level is shown in days with the corresponding $α$-VaR, for the left tail (blue) and right tail (black) of Bitcoin.\nFigure 4. Scatter plots of the standardized residuals from the GARCH fits on the upper-left panel (above diagonal). The corresponding Kendall’s $τ$ coefficients on the bottom-right panel.\nFigure 4. Scatter plots of the standardized residuals from the GARCH fits on the upper-left panel (above diagonal). The corresponding Kendall’s $τ$ coefficients on the bottom-right panel.\nFigure 5. The dynamic estimates of the dependence measures for the (BTC,LTC) and (LTC,ETH) pairs.\nFigure 5. The dynamic estimates of the dependence measures for the (BTC,LTC) and (LTC,ETH) pairs.\nFigure 6. Price trajectories for the 2019 and 2020 years.\nFigure 6. Price trajectories for the 2019 and 2020 years.\nTable 1. Returns basic statistics. (Notation in table: * means 5% statistical significance).\nTable 1. Returns basic statistics. (Notation in table: * means 5% statistical significance).\nBitcoinEthereumRippleLitecoinStellarMoneroEuro\nMean0.210.340.200.170.190.290.00\n0.99%[LCL, UCL][−0.03,0.47][−0.06,0.74][−0.25,0.66][−0.17,0.52][−0.90,1.30][−0.14,0.71][−0.04,0.04]\nMedian0.23−0.06−0.30−0.05−0.36−0.07−0.01\nStandard deviation3.966.337.185.4917.376.600.48\nMaximum25.4939.94101.9747.97269.1456.472.47\nMinimum−23.97−33.35−63.15−40.60−244.65−29.18−2.88\nSkewness−0.21 *0.43 *2.60 *1.02 *0.060.93 *0.12\nKurtosis5.555.2236.7511.4183.517.463.43\nTable 2. GPD estimates and long-run risk measures: shape parameter (standard error); percentage of excesses $p *$; 1% and 5% VaR and expected loss (EL) estimates.\nTable 2. GPD estimates and long-run risk measures: shape parameter (standard error); percentage of excesses $p *$; 1% and 5% VaR and expected loss (EL) estimates.\nEstimatesBitcoinEthereumRippleLitecoinStellarMoneroEuro\nLeft tail estimates\nShape(st. er); $p *$0.02(0.07); 14%0.04(0.08); 13%0.25(0.10); 10%0.12(0.08); 12%0.59(0.10); 15%0.01(0.07); 17%0.20(0.11); 9%\nVaR: 1% & 5%−12.08 & −6.29−17.56 & −9.50−16.91 & −8.29−14.29 & −7.64−39.13 & −13.40−16.67 & −9.63−1.18 & −0.71\nEL: 1% & 5%15.24 & 10.0323.04 & 14.7524.48 & 14.1520.70 & 12.1489.38 & 35.6620.41 & 14.101.75 & 1.00\nRight tail estimates\nShape(st. er); $p *$0.15(0.09); 12%0.00(0.06); 13%0.33(0.12); 9%0.21(0.08); 14%0.57(0.12); 11%0.15(0.07); 16%0.24(0.13); 8%\nVaR: 5% & 1%6.23 & 11.4111.15 & 20.2310.06 & 24.618.63 & 18.0015.68 & 46.9810.83 & 20.600.76 & 1.25\nEL: 5% & 1%9.78 & 14.8116.84 & 27.9319.81 & 39.9115.43 & 27.7538.74 & 86.7216.91 & 30.101.07 & 1.83\nTable 3. Summary of the results from the ARFIMA$( p , d , q )$-FIGARCH$( m , D , s )$ fits for all series. All parameters estimates are 5% statistically significant. Notations: LEV: leverage parameter; d.f.: degrees of freedom of the F distribution; skew: skewness estimate of the skew-t distribution.\nTable 3. Summary of the results from the ARFIMA$( p , d , q )$-FIGARCH$( m , D , s )$ fits for all series. All parameters estimates are 5% statistically significant. Notations: LEV: leverage parameter; d.f.: degrees of freedom of the F distribution; skew: skewness estimate of the skew-t distribution.\nCrypto-Coin$( ϕ 0 , ϕ 1 )$d$θ 1$$ω$$α 1$D$β 1$$β 2$LEVd.f.SkewAIC1%-VaR\nBitcoin-LM(0.201,–)0.000.1440.2580.6890.7803.155.0577(−6.80,7.20)\nBitcoin(0.207,–)0.000.2120.2250.4010.418−0.0893.115.0584(−7.23,7.65)\nEthereum-LM(0.128,–)0.003.4700.1380.6460.5153.181.0646.1096(−10.59,12.05)\nEthereum(0.128,–)0.002.7260.2610.7383.081.0636.1116(−11.28,12.82)\nRipple-LM(−0.156,–)0.00−0.1540.6230.6780.4510.7442.811.0655.7761(−7.58,8.56)\nRipple(−0.160,–)0.00−0.1542.0590.2630.7362.721.0565.7967(−8.99,10.01)\nLitecoin-LM(–,–)0.000.0010.3610.6190.7533.111.0485.5577(−11.79,12.80)\nLitecoin(–,–)0.000.0920.1070.8923.101.0455.5676(−13.68,14.77)\nStellar-LM(–,–)0.00−0.0970.8150.2780.3740.3753.471.1436.2334(−9.61,11.65)\nStellar(–,–)0.00−0.0931.8010.2120.7873.171.1336.2541(−9.75,11.86)\nMonero-LM(–,–)0.0−0.1103.2560.1150.6520.5823.301.0566.2455(−12.40,14.33)\nMonero(–,–)0.00−0.1102.3210.2060.7933.181.0536.2472(−13.14,15.09)\nEuro-LM(–,–)0.000.0020.0430.5030.2740.4844.821.4120(−0.78,0.78)\nEuro(–,–)0.000.0010.0190.9756.731.0881.2403(−0.79,0.88)\nTable 4. Pair-copulas’ robust fits: copula family and estimates, dependence measures, the losses (gains) BB7-copula-based joint probability associated 1%-VaR in the upper row, and the $π$-copula-based risk under independence (bottom row).\nTable 4. Pair-copulas’ robust fits: copula family and estimates, dependence measures, the losses (gains) BB7-copula-based joint probability associated 1%-VaR in the upper row, and the $π$-copula-based risk under independence (bottom row).\nPairs inCopula FamilyLinear Coef $ρ$Kendall’s $τ$Lower TDC $λ L$Upper TDC $λ U$Losses: BB7 RiskGains: BB7 Risk\nTree 1(Parameters)(Residuals Based)(Copula Based)(Copula Based)(Copula Based)Losses: ($Π$ Risk)Gains: ($Π$ Risk)\n(XMR,BTC)BB70.5230.6010.7460.5660.745%0.556%\n(1.923,2.361) (0.01%)(0.01%)\n(BTC,LTC)BB70.6300.6740.8290.6270.833%0.635%\n(2.186,3.691) (0.01%)(0.01%)\n(LTC,ETH)BB70.4620.6160.7510.5650.783%0.584%\n(1.918,2.632) (0.01%)(0.01%)\n(ETH,XRP)BB70.3110.5880.7500.4860.741%0.492%\n(1.672,2.422) (0.01%)(0.01%)\n(XRP,XLM)BB70.2360.6080.7400.6050.746%0.599%\n(2.083,2.301) (0.01%)(0.01%)\nTable 5. Estimates of the speed of adjustment parameters $α$ for all pairs. All estimates are 5% statistically significant.\nTable 5. Estimates of the speed of adjustment parameters $α$ for all pairs. All estimates are 5% statistically significant.\nPairs(BTC,ETH)(BTC,RIP)(BTC,LTC)(BTC,XLM)(BTC,XMR)\n$α$(0.00502,0.00063)(0.00453,0.000002)(0.00528,0.00029)(0.00477,0.000004)(0.00573,0.00033)\nTable 6. Estimates of the speed of adjustment parameters $α$ for the six crypto-asset system. All estimates are 1% statistically significant.\nTable 6. Estimates of the speed of adjustment parameters $α$ for the six crypto-asset system. All estimates are 1% statistically significant.\nBTCETHRIPLTCXLMXMR\n1st cointegrating vector−0.13611−0.03555−0.00010−0.00191−0.00003−0.00891\n2nd cointegrating vector−0.00251−0.00037−0.000002−0.00003−0.0000004−0.00015\n\n## Share and Cite\n\nMDPI and ACS Style\n\nVaz de Melo Mendes, B.; Fluminense Carneiro, A. A Comprehensive Statistical Analysis of the Six Major Crypto-Currencies from August 2015 through June 2020. J. Risk Financial Manag. 2020, 13, 192. https://doi.org/10.3390/jrfm13090192\n\nAMA Style\n\nVaz de Melo Mendes B, Fluminense Carneiro A. A Comprehensive Statistical Analysis of the Six Major Crypto-Currencies from August 2015 through June 2020. Journal of Risk and Financial Management. 2020; 13(9):192. https://doi.org/10.3390/jrfm13090192\n\nChicago/Turabian Style\n\nVaz de Melo Mendes, Beatriz, and André Fluminense Carneiro. 2020. \"A Comprehensive Statistical Analysis of the Six Major Crypto-Currencies from August 2015 through June 2020\" Journal of Risk and Financial Management 13, no. 9: 192. https://doi.org/10.3390/jrfm13090192"
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"https://px.ads.linkedin.com/collect/",
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https://solanova.com/sign-up.html
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[
"\\$value) { \\$_REQUEST[\\$key] = removeQuotes(\\$value); } \\$firstName = trim(\\$_REQUEST['firstname']); \\$lastName = trim(\\$_REQUEST['lastname']); \\$email = trim(\\$_REQUEST['email']); \\$howHear = trim(\\$_REQUEST['how_hear']); if(isset(\\$_REQUEST['join-mailing'])) { \\$subscribe = '1'; } else { \\$subscribe = '0'; } \\$newpass = trim(\\$_REQUEST['password']); \\$newpass_confirm = trim(\\$_REQUEST['passwordVerify']); if(strlen(\\$firstName) == 0 || strlen(\\$lastName) == 0) { \\$errorstring = \"Contact information must be filled out fully\n\"; } if(strlen(\\$email) == 0) { \\$errorstring .= \"You must specify an email address\n\"; } if(strlen(\\$newpass) > 0 and strlen(\\$newpass_confirm) > 0) { if(\\$newpass != \\$newpass_confirm) { \\$errorstring .= \"Passwords do not match\n\"; } } else { \\$errorstring .= \"Must create a password\n\"; } \\$customer_ip= \\$_SERVER['REMOTE_ADDR']; if(strlen(\\$errorstring) == 0) { \\$query = \"SELECT count(*) as count FROM customers where lcase(email)=lcase('\\$email')\"; \\$result = mysql_query(\\$query); if(\\$row= mysql_fetch_assoc(\\$result)) { \\$count = \\$row['count']; if(\\$count > 0) { \\$errorstring = \"This email is already associated with an account. Please login here.\n\"; } else { \\$query = \"insert into customers set registeredDt=NOW(), firstName='\\$firstName', lastName='\\$lastName', email='\\$email', password='\\$newpass', subscribeNewsletter='\\$subscribe', howHeardAboutUs='\\$howHear',Client_ip='\\$customer_ip'\"; \\$result = mysql_query(\\$query); \\$customerId = mysql_insert_id(); \\$code = strtoupper(substr(\\$lastName, 1, 1) . substr(\\$firstName, 1, 1)) . \\$customerId; \\$query = \"update customers set customerCode='\\$code' where customerId='\\$customerId'\"; \\$result = mysql_query(\\$query); if(\\$subscribe) { \\$type = \"SIGNUP\"; \\$howHear = \\$_REQUEST['how_hear']; if(\\$type == \"\") { die(\"No referring page specified\"); } \\$date = date(\"Y-m\") . \"-01 00:00:00\"; \\$query = \"select * from newsletterSignups where date='\\$date' and type='\\$type'\"; \\$result = mysql_query(\\$query); if(\\$row = mysql_fetch_assoc(\\$result)) { extract(\\$row); \\$count++; \\$query = \"update newsletterSignups set count='\\$count' where date='\\$date' and type='\\$type'\"; \\$result = mysql_query(\\$query); } else { \\$query = \"insert newsletterSignups set count='1', date='\\$date', type='\\$type'\"; \\$result = mysql_query(\\$query); } } \\$_SESSION['customerId'] = \\$customerId; \\$_SESSION['firstName'] = \\$firstName; \\$_SESSION['lastName'] = \\$lastName; header('location:' . \\$clientRoot . 'my-account-settings.html'); die(); } } else { \\$errorstring = \"Error creating account\"; } } } include_header(\"solanova :: Create an Account\", \"@import url('stylesheets/create-account.css');\",''); ?>\n\n## Create an Account\n\nShopping is a breeze with registration benefits like these!\n\n- Checkout is faster and easier"
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[
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https://waseda.pure.elsevier.com/ja/publications/reconsidering-an-analytical-gradient-expression-within-a-divide-a
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[
"# Reconsidering an analytical gradient expression within a divide-and-conquer self-consistent field approach: Exact formula and its approximate treatment\n\nMasato Kobayashi*, Tomotaka Kunisada, Tomoko Akama, Daisuke Sakura, Hiromi Nakai\n\n*この研究の対応する著者\n\n43 被引用数 (Scopus)\n\n## 抄録\n\nAn analytical energy gradient formula for the density-matrix-based linear-scaling divide-and-conquer (DC) self-consistent field (SCF) method was proposed in a previous paper by Yang and Lee (YL) [J. Chem. Phys. 103, 5674 (1995)]. Since the formula by YL does not correspond to the exact gradient of the DC-SCF energy, we derive the exact formula by direct differentiation, which requires solving the coupled-perturbed equations while including the inter-subsystem coupling terms. Next, we present an alternative formula for approximately evaluating the DC-SCF energy gradient, assuming the variational condition for the subsystem density matrices. Numerical assessments confirmed that the DC-SCF energy gradient values obtained by the present formula are in reasonable agreement with the conventional SCF values when adopting a reliable buffer region. Furthermore, the performance of the present method was found to be better than that of the YL method.\n\n本文言語 English 034105 Journal of Chemical Physics 134 3 https://doi.org/10.1063/1.3524337 Published - 2011 1月 21\n\n## ASJC Scopus subject areas\n\n• 物理学および天文学（全般）\n• 物理化学および理論化学\n\n## フィンガープリント\n\n「Reconsidering an analytical gradient expression within a divide-and-conquer self-consistent field approach: Exact formula and its approximate treatment」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。"
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https://www.12000.org/my_notes/CAS_integration_tests/reports/rubi_4_16_1_graded/test_cases/1_Algebraic_functions/1.2_Trinomial_products/1.2.1_Quadratic/1.2.1.2-d+e_x-%5Em-a+b_x+c_x%5E2-%5Ep/rese1171.htm
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[
"### 3.1171 $$\\int \\frac{(b d+2 c d x)^3}{(a+b x+c x^2)^2} \\, dx$$\n\nOptimal. Leaf size=43 $4 c d^3 \\log \\left (a+b x+c x^2\\right )-\\frac{d^3 (b+2 c x)^2}{a+b x+c x^2}$\n\n[Out]\n\n-((d^3*(b + 2*c*x)^2)/(a + b*x + c*x^2)) + 4*c*d^3*Log[a + b*x + c*x^2]\n\n________________________________________________________________________________________\n\nRubi [A] time = 0.0202037, antiderivative size = 43, normalized size of antiderivative = 1., number of steps used = 2, number of rules used = 2, integrand size = 24, $$\\frac{\\text{number of rules}}{\\text{integrand size}}$$ = 0.083, Rules used = {686, 628} $4 c d^3 \\log \\left (a+b x+c x^2\\right )-\\frac{d^3 (b+2 c x)^2}{a+b x+c x^2}$\n\nAntiderivative was successfully verified.\n\n[In]\n\nInt[(b*d + 2*c*d*x)^3/(a + b*x + c*x^2)^2,x]\n\n[Out]\n\n-((d^3*(b + 2*c*x)^2)/(a + b*x + c*x^2)) + 4*c*d^3*Log[a + b*x + c*x^2]\n\nRule 686\n\nInt[((d_) + (e_.)*(x_))^(m_)*((a_.) + (b_.)*(x_) + (c_.)*(x_)^2)^(p_), x_Symbol] :> Simp[(d*(d + e*x)^(m - 1)*\n(a + b*x + c*x^2)^(p + 1))/(b*(p + 1)), x] - Dist[(d*e*(m - 1))/(b*(p + 1)), Int[(d + e*x)^(m - 2)*(a + b*x +\nc*x^2)^(p + 1), x], x] /; FreeQ[{a, b, c, d, e}, x] && NeQ[b^2 - 4*a*c, 0] && EqQ[2*c*d - b*e, 0] && NeQ[m + 2\n*p + 3, 0] && LtQ[p, -1] && GtQ[m, 1] && IntegerQ[2*p]\n\nRule 628\n\nInt[((d_) + (e_.)*(x_))/((a_.) + (b_.)*(x_) + (c_.)*(x_)^2), x_Symbol] :> Simp[(d*Log[RemoveContent[a + b*x +\nc*x^2, x]])/b, x] /; FreeQ[{a, b, c, d, e}, x] && EqQ[2*c*d - b*e, 0]\n\nRubi steps\n\n\\begin{align*} \\int \\frac{(b d+2 c d x)^3}{\\left (a+b x+c x^2\\right )^2} \\, dx &=-\\frac{d^3 (b+2 c x)^2}{a+b x+c x^2}+\\left (4 c d^2\\right ) \\int \\frac{b d+2 c d x}{a+b x+c x^2} \\, dx\\\\ &=-\\frac{d^3 (b+2 c x)^2}{a+b x+c x^2}+4 c d^3 \\log \\left (a+b x+c x^2\\right )\\\\ \\end{align*}\n\nMathematica [A] time = 0.020575, size = 42, normalized size = 0.98 $d^3 \\left (\\frac{4 a c-b^2}{a+b x+c x^2}+4 c \\log \\left (a+b x+c x^2\\right )\\right )$\n\nAntiderivative was successfully verified.\n\n[In]\n\nIntegrate[(b*d + 2*c*d*x)^3/(a + b*x + c*x^2)^2,x]\n\n[Out]\n\nd^3*((-b^2 + 4*a*c)/(a + b*x + c*x^2) + 4*c*Log[a + b*x + c*x^2])\n\n________________________________________________________________________________________\n\nMaple [A] time = 0.045, size = 58, normalized size = 1.4 \\begin{align*} 4\\,{\\frac{{d}^{3}ac}{c{x}^{2}+bx+a}}-{\\frac{{d}^{3}{b}^{2}}{c{x}^{2}+bx+a}}+4\\,c{d}^{3}\\ln \\left ( c{x}^{2}+bx+a \\right ) \\end{align*}\n\nVerification of antiderivative is not currently implemented for this CAS.\n\n[In]\n\nint((2*c*d*x+b*d)^3/(c*x^2+b*x+a)^2,x)\n\n[Out]\n\n4*d^3/(c*x^2+b*x+a)*a*c-d^3/(c*x^2+b*x+a)*b^2+4*c*d^3*ln(c*x^2+b*x+a)\n\n________________________________________________________________________________________\n\nMaxima [A] time = 1.01148, size = 58, normalized size = 1.35 \\begin{align*} 4 \\, c d^{3} \\log \\left (c x^{2} + b x + a\\right ) - \\frac{{\\left (b^{2} - 4 \\, a c\\right )} d^{3}}{c x^{2} + b x + a} \\end{align*}\n\nVerification of antiderivative is not currently implemented for this CAS.\n\n[In]\n\nintegrate((2*c*d*x+b*d)^3/(c*x^2+b*x+a)^2,x, algorithm=\"maxima\")\n\n[Out]\n\n4*c*d^3*log(c*x^2 + b*x + a) - (b^2 - 4*a*c)*d^3/(c*x^2 + b*x + a)\n\n________________________________________________________________________________________\n\nFricas [A] time = 2.01536, size = 136, normalized size = 3.16 \\begin{align*} -\\frac{{\\left (b^{2} - 4 \\, a c\\right )} d^{3} - 4 \\,{\\left (c^{2} d^{3} x^{2} + b c d^{3} x + a c d^{3}\\right )} \\log \\left (c x^{2} + b x + a\\right )}{c x^{2} + b x + a} \\end{align*}\n\nVerification of antiderivative is not currently implemented for this CAS.\n\n[In]\n\nintegrate((2*c*d*x+b*d)^3/(c*x^2+b*x+a)^2,x, algorithm=\"fricas\")\n\n[Out]\n\n-((b^2 - 4*a*c)*d^3 - 4*(c^2*d^3*x^2 + b*c*d^3*x + a*c*d^3)*log(c*x^2 + b*x + a))/(c*x^2 + b*x + a)\n\n________________________________________________________________________________________\n\nSympy [A] time = 1.21328, size = 42, normalized size = 0.98 \\begin{align*} 4 c d^{3} \\log{\\left (a + b x + c x^{2} \\right )} + \\frac{4 a c d^{3} - b^{2} d^{3}}{a + b x + c x^{2}} \\end{align*}\n\nVerification of antiderivative is not currently implemented for this CAS.\n\n[In]\n\nintegrate((2*c*d*x+b*d)**3/(c*x**2+b*x+a)**2,x)\n\n[Out]\n\n4*c*d**3*log(a + b*x + c*x**2) + (4*a*c*d**3 - b**2*d**3)/(a + b*x + c*x**2)\n\n________________________________________________________________________________________\n\nGiac [A] time = 1.14961, size = 63, normalized size = 1.47 \\begin{align*} 4 \\, c d^{3} \\log \\left (c x^{2} + b x + a\\right ) - \\frac{b^{2} d^{3} - 4 \\, a c d^{3}}{c x^{2} + b x + a} \\end{align*}\n\nVerification of antiderivative is not currently implemented for this CAS.\n\n[In]\n\nintegrate((2*c*d*x+b*d)^3/(c*x^2+b*x+a)^2,x, algorithm=\"giac\")\n\n[Out]\n\n4*c*d^3*log(c*x^2 + b*x + a) - (b^2*d^3 - 4*a*c*d^3)/(c*x^2 + b*x + a)"
] |
[
null
] |
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|
http://chryswoods.com/beginning_perl/loops.html
|
[
"#Loops\n\nA Perl script is a file that contains instructions to the perl interpreter, with one instruction per line, that are read one at a time from the top of the script to the bottom. You can, however, divert this flow using a loop. Open a new Perl script loop.pl and write this;\n\nfor (\\$i = 1; \\$i <= 10; \\$i = \\$i + 1)\n{\n\\$five_times_i = 5 * \\$i;\n\nprint \"5 times \\$i equals \\$five_times_i\\n\";\n}\n\nWhat do you think will be printed to the screen? Run the script (perl loop.pl). Did you see what you expected?\n\nThis script has introduced a for loop. The loop has five parts;\n\n• Counter \\$i. This is a variable that is used to count how many iterations of the loop have taken place. The counter has a different value for each iteration of the loop.\n• Body. This is all of the code that is between the curly brackets { }. The loop allows the code in the body to be executed multiple times. In this case the code in the body that prints out a line of the five times table is executed ten times.\n• Initialise \\$i = 1. This is run at the start of the loop and should be used to set the start value of the loop counter. In this case we are using the variable \\$i as the loop counter, and we start by setting it to a value of 1.\n• Condition \\$i <= 10. The condition is tested at the start of each iteration of the loop. In this case the condition is asking whether or not \\$i has a value of less than or equal to 10. If the condition is true then we execute the code in the body of the loop another time. If the condition is false then we don’t execute the code in the body, and we exit the loop.\n• Increment \\$i = \\$i + 1. This is the code executed at the end of each iteration of the loop. This should be used to increment the counter (in this case \\$i is set equal to it’s old value plus one)\n\nLoops are very powerful. For example;\n\nfor (\\$i = 0; \\$i <= 200; \\$i = \\$i + 2)\n{\nprint \"\\$i\\n\";\n}\n\nprints all of the even numbers from 0 to 200.\n\nfor (\\$i = 10; \\$i > 0; \\$i = \\$i - 1)\n{\nprint \"\\$i...\\n\";\n}\n\nprint \"We have lift off!\\n\";\n\nprints out a count down.\n\nfor (\\$i = 1; \\$i <= 3; \\$i = \\$i + 1)\n{\nfor (\\$j = 1; \\$j <= 3; \\$j = \\$j + 1)\n{\n\\$i_times_j = \\$i * \\$j;\n\nprint \"\\$i_times_j \";\n}\n\nprint \"\\n\";\n}\n\nprints out a 3*3 matrix where the element at (i,j) equals i times j.\n\nCompare with Python\n\nPreviousUpNext\n\nQuick Links | Home | Courses | Software | Contact"
] |
[
null
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https://statisticsblog.com/2014/01/30/probability-podcast/
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[
"",
null,
"## Probability Podcast\n\nI’ve produced a pilot episode of a “Probability Podcast”. Please have a listen and let me know if you’d be interested in hearing more episodes. Thanks!\n\nThe different approaches of Fermat and Pascal\nPascal’s solution, which may have come first (we don’t have all of the letters between Pascal and Fermat, and the order of the letters we do have is the matter of some debate), is to start at a point where the score is even and the next point wins, then work backwards solving a series of recursive equations. To find the split at any score, you would first note that if, at a score of (x,x), the next point for either player results in a win, then the pot at (x,x) would be split evenly. The pot split for player A at (x-1,x) would be the chance of his winning the next game, times the pot amount due him at (x,x). Once you know the split in the case where player A (or B) lacks a point, you can then solve for the case where a player is down by two and so on.\n\nFermat took a combinatorial approach. Suppose that the winner is the first person to score N points, and that Player A has a points and Player B has b points when the game is stopped. Fermat first noted that the maximum number of games left to be played was 2N-a-b-1 (supposing both players brought their score up to N-1, and then a final game was played to determine the winner). Then Fermat calculated the number of distinct ways these 2N-a-b-1 might play out, and which ones resulted in a victory for player A or player B. Each of these combinations being equally likely, the pot should be split in proportion to the number of combinations favoring a player, divided by the total number of combinations.\n\nTo understand the two approaches to solving the problem of points I have created the diagram shown at right.\n\nSuppose each number in parenthesis represents the score of players A and B, respectively. The current score, 3 to 2, is circled. The first person to score 4 points wins. All of the paths that could have led to the current score are shown above the point (3,2). If player A wins the next point then the game is over. If player B wins, either player can win the game by winning the next point. Squares represent games won by player A, the star means that player B would win. The dashed lines are paths that make up combinations in Fermat’s solution, even though these points would not be played out.\n\nPascal’s solution for the pot distribution at (3,2) would be to note that if the score were tied (3,3), then we would split the pot evenly. However, since we are at point (3,2), there is only a one-in-two chance that we will reach point (3,3), at which point there is a one-in-two chance that player A will win the game. Therefore the proportion of the pot that goes to player A is 1/2+1/2 (1/2)=3/4 whereas player B is due 1/2 (1/2)=1/4.\n\nFermat’s approach would be to note that there are a total of 4 paths that lead from point (3,2) to the level where a total of 7 points have been played:\n\n(3,2)→(4,2)→(5,2)\n(3,2)→(4,2)→(4,3)\n(3,2)→(3,3)→(4,3)\n(3,2)→(3,3)→(3,4)\n\nOf these, 3 represent victories for player A and 1 is a victory for player B. Therefore player A should get 3/4 of the pot and player B gets 1/4 of the pot.\n\nAs you can see, both Pascal and Fermat’s solutions yield the same split. This is true for any starting point. Fermat’s approach is generally agreed to be superior, as the recursive equations of Pascal can become very complicated. By contrast, Fermat’s combinatorial method can be solved quickly using what we now call Pascal’s Triangle or its related equations. However, both approaches are important for the development of probability theory.\n\n1.",
null,
"Dave\n\nIs there a link to an rss feed so it can be fetched via a podcast app?\n\n•",
null,
"Matt Asher\n\nI made the episode downloadable from soundcloud so you can put it on your player. I’m looking into a full rss feed will do that enough folks are interested in the podcast in general. Thanks!\n\n•",
null,
"Matt Asher\n2.",
null,
"E_W\n\nAs someone without a large amount of formal probability education, but who wants to learn more, I found this podcast to be informative and accessible. I would definitely be interested in more.\n\n3.",
null,
"Mark Jones Jr.\n\nAs a mathematician in disguise (I dress like an experimental test pilot at my day job), I enjoyed the historical aspect of the podcast and the review of elementary principles that have not been exercised for a long time.\n\nI hope for more.\n\nThanks.\n\n4.",
null,
"Melanie Dickinson\n\nThought it was great. Please record more!\n\n5.",
null,
"marcelo\n\nDear Matt, I truly enjoyed your podcast and I have been waiting for a second installment ! I really appreciated your work in making this topic much more accessible and also compelling (I work in bioinformatics and I come across stats regularly, but it is specialists like yourself who can really bring interest to this subject) .\n\nI am a fan of popularisers of math like marcus de sautoy and your work here is absolutely comparable to those, I imagine you worked hard in the production of this first episode, and in fact, as an avid podcast consumer I can tell you that you could you could have easily split into two this first episode (it’s a lot of history and math compressed but absolutely enjoyable).\n\nPlease keep up the great work\n\nMarcelo\n\n•",
null,
"Matt Asher\n\nThanks Marcelo!"
] |
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|
https://physics.stackexchange.com/questions/68996/fundamentals-of-quantum-electrodynamics
|
[
"# Fundamentals of Quantum Electrodynamics\n\nIn quantum electrodynamics, the classical Hamiltonian is obtained from the classical electromagnetic Lagrangian. Then the classical electric and magnetic fields are promoted to operators, as is the classical 4-vector potential $A_{\\mu}$. The appropriate commutation relations are expected between the fields and their conjugate momenta.\n\nNow, my question is, do the principles of quantum electrodynamics follow as a consequence of the fact that the charged particle producing the field is a quantum particle which must follow the principles of quantum mechanics?\n\nLet me give a specific example. Consider a slow moving(for simplicity) free electron moving with a constant velocity initially.\n\nNow, classically, the magnetic field at a point $P$ would be given by a function $\\vec{B} = \\vec{f}(\\vec{r},\\vec{x},\\vec{p})$, where $\\vec{r}$ is the position vector of the point at which the field is being 'measured' and $x$ and $p$ are the position and momenta of the charged particle evaluated at the retarded time.\n\nNow, supposing I apply the principles of quantum mechanics to this electron and promote the above mentioned expression for the magnetic field at point $P$ to an operator by the usual quantum mechanical prescription. Would this prescription yield the correct values for the measured magnetic field at point $P$? Why? or Why not?\n\nThe bottom line of my entire question is whether the quantum field theory of an electron is a direct consequence of the fact that the particle producing the field is a quantum particle (and not a classical one) or does it involve much more than that?\n\nEDIT: Thank you for your responses. I would also like to know if the above mentioned prescription for obtaining the magnetic field would yield accurate results for slow moving electrons(non-relativistic)?\n\nThe bottom line of my entire question is whether the quantum field theory of an electron is a direct consequence of the fact that the particle producing the field is a quantum particle (and not a classical one) or does it involve much more than that?\n\nIt involves \"much more than that\":\n\nIf I understand correctly, you're taking the classical expression for, say, the Coulomb field resulting from a source charge, or a magnetic field resulting from a current element and then saying that, since the position/momentum of the sources are quantized, they become operators and in this way the field becomes an operator since it's a function of those positions/momenta.\n\nIn QED, it's possible to describe a freely propagating field quantum (e.g. a photon). Freely propagating means that once it's been produced, its existence is now independent of any source. I don't see how this is possible in the scheme where you just quantize the source. Any time dependence of the source would always be immediately transferred to the electromagnetic field.\n\nIn QED, you quantize the electromagnetic field and the electron/positron fields independently. Neither is in any sense more fundamental than the other. One can act as a source for the other only after you've introduced an interaction term in the theory. So the source/field relationship isn't the basis of the quantization.\n\nAlso one of the key features of quantum field theory which distinguishes it from quantum mechanics is that it offers a mechanism to create and destroy particles. This would not be possible with a prescription such as the one you describe. Even your electron description is still a single particle one.\n\n• Thank you for the informative post. In addition, I would like to know if the procedure outlined by me to calculate the magnetic field of the electron at the point P, would give accurate results for a slow moving(v<<c) electron? – guru Jun 24 '13 at 16:59\n\nIn Quantum mechanics , you have operators $X(t)$, where $t$ is a parameter, and $X$ is the operator.\n\nWhat happens in Quantum Field theory ?\n\nIf we take a real scalar field, you have operators $\\Phi(x,t)$, where $x$ and $t$ are parameters, and $\\Phi$ is the operator.\n\nIn Quantum Electrodynamics, the photonic field is represented by operators $A_{\\mu}(x,t)$, where $x$ and $t$ are parameters, and $A_{\\mu}$ is the operator. The electron/positron field is represented by operators $\\Psi(x,t)$, where $x$ and $t$ are parameters, and $\\Psi$ is the operator.\n\nSo, in Quantum Field Theory, $x$ is not an operator, it is a (space) parameter. So, you have not the right to \"mix\" the 2 formalisms.\n\nEspecially, you have not the right to think about something like $\\vec B=f(\\vec X,t)$, where $\\vec B$ and $\\vec X$ would be operators representing magnetic field and position, because that will be a incoherent mixing of the 2 formalisms of Quantum Mechanics and Quantum Field Theory.\n\n• Sure you can. – Michael Brown Jun 24 '13 at 8:51\n• @MichaelBrown : Very Interesting paper, like this one, but the idea in these papers, if I correctly understand, is that a $1$st quantized formalism would be equivalent to a $2$nd quantized formalism. But there is no mixing of the 2 formalisms. – Trimok Jun 24 '13 at 16:22\n\nNow, my question is, do the principles of quantum electrodynamics follow as a consequence of the fact that the charged particle producing the field is a quantum particle which must follow the principles of quantum mechanics?\n\nFirst, a semantic issue. A principle is a starting point so, if an alleged principle follows from something else, can it in fact be called a principle?\n\nAnyhow, what are the principles of QED or, more generally, QFT?\n\nCertainly, one principle is the identification of fundamental \"particles\" as the quanta of quantized field modes. Each mode of a (free) field obeys a quantum harmonic oscillator equation and thus, each mode has associated ladder operators that create and destroy quanta, i.e., particles.\n\nSo, an electron (or positron) with definite momentum $k$ is identified as a quantum of the $k$th mode of Dirac \"field\".\n\nAnd, a photon with definite momentum $k$ is identified as a quantum of the $k$th mode of the vector potential \"field\".\n\nBut, importantly, the Dirac field is not the source of the vector potential field in this picture. In fact, the vector potential is seen as a gauge field that is required by the principle of local gauge invariance.\n\nSo, it seems to be the case that the answer to your question must be no."
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https://satyakide.com/tag/snippet/
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"## Projecting real-time KPIs by ingesting streaming events from emulated IoT-device\n\nToday, I am planning to demonstrate an IoT use case implemented in Python. I was waiting for my Raspberry Pi to arrive. However, the product that I received was not working as expected. Perhaps, some hardware malfunction. Hence, I was looking for a way to continue with my installment even without the hardware.\n\nI was looking for an alternative way to use an online Raspberry Pi emulator. Recently, Microsoft has introduced integrated Raspberry Pi, which you can directly integrate with Azure IoT. However, I couldn’t find any API, which I could leverage on my Python application.\n\nSo, I explored all the possible options & finally come-up with the idea of creating my own IoT-Emulator, which can integrate with any application. With the help from the online materials, I have customized & enhanced them as per my use case & finally come up with this clean application that will demonstrate this use case with clarity.\n\nWe’ll showcase this real-time use case, where we would try to capture the events generated by IoT in a real-time dashboard, where the values in the visual display points will be affected as soon as the source data changes.\n\nHowever, I would like to share the run before we dig deep into this.\n\nIsn’t this exciting? How we can use our custom-built IoT emulator & captures real-time events to Ably Queue, then transform those raw events into more meaningful KPIs. Let’s deep dive then.\n\nArchitecture:\n\nLet’s explore the architecture –\n\nAs you can see, the green box is a demo IoT application that generates events & pushes them into the Ably Queue. At the same time, Dashboard consumes the events & transforms them into more meaningful metrics.\n\nPackage Installation:\n\nLet us understand the sample packages that require for this task.\n\nStep – 1:\n\nStep – 2:\n\nAnd, here is the command to install those packages –\n\n```pip install dash==1.0.0\npip install numpy==1.16.4\npip install pandas==0.24.2\npip install scipy==1.3.0\npip install gunicorn==19.9.0\npip install ably==1.1.1\npip install tkgpio==0.1```\n\nCode:\n\nSince this is an extension to our previous post, we’re not going to discuss other scripts, which we’ve already discussed over there. Instead, we will talk about the enhanced scripts & the new scripts that require for this use case.\n\n1. clsConfig.py (This native Python script contains the configuration entries.)\n\nThis file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.\n\n ################################################ #### Written By: SATYAKI DE #### #### Written On: 15-May-2020 #### #### Modified On: 25-Sep-2021 #### #### #### #### Objective: This script is a config #### #### file, contains all the keys for #### #### Machine-Learning & streaming dashboard.#### #### #### ################################################ import os import platform as pl class clsConfig(object): Curr_Path = os.path.dirname(os.path.realpath(__file__)) os_det = pl.system() if os_det == \"Windows\": sep = '\\\\' else: sep = '/' conf = { 'APP_ID': 1, 'ARCH_DIR': Curr_Path + sep + 'arch' + sep, 'PROFILE_PATH': Curr_Path + sep + 'profile' + sep, 'LOG_PATH': Curr_Path + sep + 'log' + sep, 'REPORT_PATH': Curr_Path + sep + 'report', 'FILE_NAME': Curr_Path + sep + 'data' + sep + 'TradeIn.csv', 'SRC_PATH': Curr_Path + sep + 'data' + sep, 'JSONFileNameWithPath': Curr_Path + sep + 'GUI_Config' + sep + 'CircuitConfiguration.json', 'APP_DESC_1': 'Dash Integration with Ably!', 'DEBUG_IND': 'N', 'INIT_PATH': Curr_Path, 'SUBDIR' : 'data', 'ABLY_ID': 'WWP309489.93jfkT:32kkdhdJjdued79e', \"URL\":, \"appType\":\"application/json\", \"conType\":\"keep-alive\", \"limRec\": 50, \"CACHE\":\"no-cache\", \"MAX_RETRY\": 3, \"coList\": \"DE, IN, US, CA, GB, ID, BR\", \"FNC\": \"NewConfirmed\", \"TMS\": \"ReportedDate\", \"FND\": \"NewDeaths\", \"FinData\": \"Cache.csv\" }\n\nview raw\n\nclsConfig.py\n\nhosted with ❤ by GitHub\n\nA few of the new entries, which are essential to this task are -> ABLY_ID, FinData & JSONFileNameWithPath.\n\n2. clsPublishStream.py (This script will publish real-time streaming data coming out from a hosted API sources using another popular third-party service named Ably. Ably mimics pubsub Streaming concept, which might be extremely useful for any start-ups.)\n\nThis file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.\n\n ############################################################### #### #### #### Written By: Satyaki De #### #### Written Date: 26-Jul-2021 #### #### Modified Date: 08-Sep-2021 #### #### #### #### Objective: This script will publish real-time #### #### streaming data coming out from a hosted API #### #### sources using another popular third-party service #### #### named Ably. Ably mimics pubsub Streaming concept, #### #### which might be extremely useful for any start-ups. #### #### #### ############################################################### from ably import AblyRest import logging import json from random import seed from random import random import json import math import random from clsConfig import clsConfig as cf seed(1) # Global Section logger = logging.getLogger('ably') logger.addHandler(logging.StreamHandler()) ably_id = str(cf.conf['ABLY_ID']) ably = AblyRest(ably_id) channel = ably.channels.get('sd_channel') # End Of Global Section class clsPublishStream: def __init__(self): self.msgSize = cf.conf['limRec'] def pushEvents(self, srcJSON, debugInd, varVa): try: msgSize = self.msgSize # Capturing the inbound dataframe jdata_fin = json.dumps(srcJSON) print('IOT Events: ') print(str(jdata_fin)) # Publish rest of the messages to the sd_channel channel channel.publish('event', jdata_fin) jdata_fin = '' return 0 except Exception as e: x = str(e) print(x) logging.info(x) return 1\n\nWe’re not going to discuss this as we’ve already discussed in my previous post.\n\n3. clsStreamConsume.py (Consuming Streaming data from Ably channels.)\n\nThis file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.\n\n ############################################## #### Written By: SATYAKI DE #### #### Written On: 26-Jul-2021 #### #### Modified On 08-Sep-2021 #### #### #### #### Objective: Consuming Streaming data #### #### from Ably channels published by the #### #### playIOTDevice.py #### #### #### ############################################## import json from clsConfig import clsConfig as cf import requests import logging import time import pandas as p import clsL as cl from ably import AblyRest # Initiating Log class l = cl.clsL() class clsStreamConsume: def __init__(self): self.ably_id = str(cf.conf['ABLY_ID']) self.fileName = str(cf.conf['FinData']) def conStream(self, varVa, debugInd): try: ably_id = self.ably_id fileName = self.fileName var = varVa debug_ind = debugInd # Fetching the data client = AblyRest(ably_id) channel = client.channels.get('sd_channel') message_page = channel.history() # Counter Value cnt = 0 # Declaring Global Data-Frame df_conv = p.DataFrame() for i in message_page.items: print('Last Msg: {}'.format(i.data)) json_data = json.loads(i.data) #jdata = json.dumps(json_data) # Converting String to Dictionary dict_json = eval(json_data) # Converting JSON to Dataframe #df = p.json_normalize(json_data) #df.columns = df.columns.map(lambda x: x.split(\".\")[-1]) df = p.DataFrame.from_dict(dict_json, orient='index') #print('DF Inside:') #print(df) if cnt == 0: df_conv = df else: d_frames = [df_conv, df] df_conv = p.concat(d_frames) cnt += 1 # Resetting the Index Value df_conv.reset_index(drop=True, inplace=True) # This will check whether the current load is happening # or not. Based on that, it will capture the old events # from cache. if df_conv.empty: df_conv = p.read_csv(fileName, index = True) else: l.logr(fileName, debug_ind, df_conv, 'log') return df_conv except Exception as e: x = str(e) print('Error: ', x) logging.info(x) # This will handle the error scenaio as well. # Based on that, it will capture the old events # from cache. try: df_conv = p.read_csv(fileName, index = True) except: df = p.DataFrame() return df\n\nWe’re not going to discuss this as we’ve already discussed in my previous post.\n\n4. CircuitConfiguration.json (Configuration file for GUI Interface for IoT Simulator.)\n\nThis file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.\n\n { \"name\":\"Analog Device\", \"width\":700, \"height\":350, \"leds\":[ { \"x\":105, \"y\":80, \"name\":\"LED\", \"pin\":21 } ], \"motors\":[ { \"x\":316, \"y\":80, \"name\":\"DC Motor\", \"forward_pin\":22, \"backward_pin\":23 } ], \"servos\":[ { \"x\":537, \"y\":80, \"name\":\"Servo Motor\", \"pin\":24, \"min_angle\":-180, \"max_angle\":180, \"initial_angle\":20 } ], \"adc\":{ \"mcp_chip\":3008, \"potenciometers\":[ { \"x\":40, \"y\":200, \"name\":\"Brightness Potentiometer\", \"channel\":0 }, { \"x\":270, \"y\":200, \"name\":\"Speed Potentiometer\", \"channel\":2 }, { \"x\":500, \"y\":200, \"name\":\"Angle Potentiometer\", \"channel\":6 } ] }, \"toggles\":[ { \"x\":270, \"y\":270, \"name\":\"Direction Toggle Switch\", \"pin\":15, \"off_label\":\"backward\", \"on_label\":\"forward\", \"is_on\":false } ], \"labels\":[ { \"x\":15, \"y\":35, \"width\":25, \"height\":18, \"borderwidth\":2, \"relief\":\"solid\" }, { \"x\":56, \"y\":26, \"text\":\"Brightness Control\" }, { \"x\":245, \"y\":35, \"width\":25, \"height\":18, \"borderwidth\":2, \"relief\":\"solid\" }, { \"x\":298, \"y\":26, \"text\":\"Speed Control\" }, { \"x\":475, \"y\":35, \"width\":25, \"height\":18, \"borderwidth\":2, \"relief\":\"solid\" }, { \"x\":531, \"y\":26, \"text\":\"Angle Control\" } ] }\n\nThis json configuration will be used by the next python class.\n\n5. clsBuildCircuit.py (Calling Tk Circuit API.)\n\nThis file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.\n\n ############################################## #### Written By: SATYAKI DE #### #### Written On: 25-Sep-2021 #### #### Modified On 25-Sep-2021 #### #### #### #### Objective: Calling Tk Circuit API #### ############################################## from tkgpio import TkCircuit from json import load from clsConfig import clsConfig as cf fileName = str(cf.conf['JSONFileNameWithPath']) print('File Name: ', str(fileName)) # initialize the circuit inside the GUI with open(fileName, \"r\") as file: config = load(file) class clsBuildCircuit: def __init__(self): self.config = config def genCir(self, main_function): try: config = self.config circuit = TkCircuit(config) circuit.run(main_function) return circuit except Exception as e: x = str(e) print(x) return ''\n\nKey snippets from the above script –\n\n```config = self.config\ncircuit = TkCircuit(config)\ncircuit.run(main_function)```\n\nThe above lines will create an instance of simulated IoT circuits & then it will use the json file to start the GUI class.\n\n6. playIOTDevice.py (Main Circuit GUI script to create an IoT Device to generate the events, which will consumed.)\n\nThis file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.\n\n ############################################### #### Written By: SATYAKI DE #### #### Written On: 25-Sep-2021 #### #### Modified On 25-Sep-2021 #### #### #### #### Objective: Main Tk Circuit GUI script #### #### to create an IOT Device to generate #### #### the events, which will consumed. #### ############################################### # We keep the setup code in a different class as shown below. import clsBuildCircuit as csb import json import clsPublishStream as cps import datetime from clsConfig import clsConfig as cf import logging ############################################### ### Global Section ### ############################################### # Initiating Ably class to push events x1 = cps.clsPublishStream() # Create the instance of the Tk Circuit API Class. circuit = csb.clsBuildCircuit() ############################################### ### End of Global Section ### ############################################### # Invoking the IOT Device Generator. @circuit.genCir def main(): from gpiozero import PWMLED, Motor, Servo, MCP3008, Button from time import sleep # Circuit Components ledAlert = PWMLED(21) dcMotor = Motor(22, 23) servoMotor = Servo(24) ioMeter1 = MCP3008(0) ioMeter2 = MCP3008(2) ioMeter3 = MCP3008(6) switch = Button(15) # End of circuit components # Other useful variables cnt = 1 idx = 0 debugInd = 'Y' var = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\") # End of useful variables # Initiating Log Class general_log_path = str(cf.conf['LOG_PATH']) msgSize = int(cf.conf['limRec']) # Enabling Logging Info logging.basicConfig(filename=general_log_path + 'IOTDevice.log', level=logging.INFO) while True: ledAlert.value = ioMeter1.value if switch.is_pressed: dcMotor.forward(ioMeter2.value) xVal = 'Motor Forward' else: dcMotor.backward(ioMeter2.value) xVal = 'Motor Backward' servoMotor.value = 1 – 2 * ioMeter3.value srcJson = { \"LedMeter\": ledAlert.value, \"DCMeter\": ioMeter2.value, \"ServoMeter\": ioMeter3.value, \"SwitchStatus\": switch.is_pressed, \"DCMotorPos\": xVal, \"ServoMotor\": servoMotor.value } tmpJson = str(srcJson) if cnt == 1: srcJsonMast = '{' + '\"' + str(idx) + '\":'+ tmpJson elif cnt == msgSize: srcJsonMast = srcJsonMast + '}' print('JSON: ') print(str(srcJsonMast)) # Pushing both the Historical Confirmed Cases retVal_1 = x1.pushEvents(srcJsonMast, debugInd, var) if retVal_1 == 0: print('Successfully IOT event pushed!') else: print('Failed to push IOT events!') srcJsonMast = '' tmpJson = '' cnt = 0 idx = –1 srcJson = {} retVal_1 = 0 else: srcJsonMast = srcJsonMast + ',' + '\"' + str(idx) + '\":'+ tmpJson cnt += 1 idx += 1 sleep(0.05)\n\nLets’ explore the key snippets –\n\n```ledAlert = PWMLED(21)\ndcMotor = Motor(22, 23)\nservoMotor = Servo(24)```\n\nIt defines three motors that include Servo, DC & LED.\n\nNow, we can see the following sets of the critical snippet –\n\n```ledAlert.value = ioMeter1.value\n\nif switch.is_pressed:\ndcMotor.forward(ioMeter2.value)\nxVal = 'Motor Forward'\nelse:\ndcMotor.backward(ioMeter2.value)\nxVal = 'Motor Backward'\n\nservoMotor.value = 1 - 2 * ioMeter3.value\n\nsrcJson = {\n\"DCMeter\": ioMeter2.value,\n\"ServoMeter\": ioMeter3.value,\n\"SwitchStatus\": switch.is_pressed,\n\"DCMotorPos\": xVal,\n\"ServoMotor\": servoMotor.value\n}```\n\nFollowing lines will dynamically generates JSON that will be passed into the Ably queue –\n\n```tmpJson = str(srcJson)\n\nif cnt == 1:\nsrcJsonMast = '{' + '\"' + str(idx) + '\":'+ tmpJson\nelif cnt == msgSize:\nsrcJsonMast = srcJsonMast + '}'\nprint('JSON: ')\nprint(str(srcJsonMast))```\n\nFinal line from the above script –\n\n```# Pushing both the Historical Confirmed Cases\nretVal_1 = x1.pushEvents(srcJsonMast, debugInd, var)```\n\nThis code will now push the events into the Ably Queue.\n\n7. app.py (Consuming Streaming data from Ably channels & captured IOT events from the simulator & publish them in Dashboard through measured KPIs.)\n\nThis file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.\n\nview raw\n\napp.py\n\nhosted with ❤ by GitHub\n\nHere are the key snippets –\n\n```html.Div(\n[\nhtml.Div(\n[html.H6(\"SERVO METER (IOT)\", className=\"graph__title\")]\n),\ndcc.Graph(\nid=\"iot-measure\",\nfigure=dict(\nlayout=dict(\nplot_bgcolor=app_color[\"graph_bg\"],\npaper_bgcolor=app_color[\"graph_bg\"],\n)\n),\n),\ndcc.Interval(\nid=\"iot-measure-update\",\ninterval=int(GRAPH_INTERVAL),\nn_intervals=0,\n),\n# Second Panel\nhtml.Div(\n[html.H6(\"DC-MOTOR (IOT)\", className=\"graph__title\")]\n),\ndcc.Graph(\nid=\"iot-measure-1\",\nfigure=dict(\nlayout=dict(\nplot_bgcolor=app_color[\"graph_bg\"],\npaper_bgcolor=app_color[\"graph_bg\"],\n)\n),\n),\ndcc.Interval(\nid=\"iot-measure-update-1\",\ninterval=int(GRAPH_INTERVAL),\nn_intervals=0,\n)\n],\nclassName=\"two-thirds column motor__speed__container\",```\n\nThe following line creates two panels, where the application will consume the streaming data by the app’s call-back feature & refresh the data & graphs as & when the application receives the streaming data.\n\nA similar approach was adopted for other vital aspects/components inside the dashboard.\n\n```def getData(var, Ind):\ntry:\n# Let's pass this to our map section\ndf = x1.conStream(var, Ind)\n\ndf['ServoMeterNew'] = df.apply(lambda row: toPositiveInflated(row, 'ServoMeter'), axis=1)\ndf['ServoMotorNew'] = df.apply(lambda row: toPositive(row, 'ServoMeter'), axis=1)\ndf['DCMotor'] = df.apply(lambda row: toPositiveInflated(row, 'DCMotor'), axis=1)\ndf['DCMeterNew'] = df.apply(lambda row: toPositive(row, 'DCMotor'), axis=1)\n\n# Dropping old columns\ndf.drop(columns=['ServoMeter','ServoMotor','DCMeter'], axis=1, inplace=True)\n\n#Rename New Columns to Old Columns\ndf.rename(columns={'ServoMeterNew':'ServoMeter'}, inplace=True)\ndf.rename(columns={'ServoMotorNew':'ServoMotor'}, inplace=True)\ndf.rename(columns={'DCMeterNew':'DCMeter'}, inplace=True)\n\nreturn df\nexcept Exception as e:\nx = str(e)\nprint(x)\n\ndf = p.DataFrame()\n\nreturn df```\n\nThe application is extracting streaming data & consuming it from the Ably queue.\n\n```@app.callback(\nOutput(\"iot-measure\", \"figure\"), [Input(\"iot-measure-update\", \"n_intervals\")]\n)\ndef gen_iot_speed(interval):\n\"\"\"\nGenerate the Motor Speed graph.\n\n:params interval: update the graph based on an interval\n\"\"\"\n\n# Let's pass this to our map section\ndf = getData(var1, DInd)\n\ntrace = dict(\ntype=\"scatter\",\ny=df[\"ServoMeter\"],\nline={\"color\": \"#42C4F7\"},\nhoverinfo=\"skip\",\nerror_y={\n\"type\": \"data\",\n\"array\": df[\"ServoMotor\"],\n\"thickness\": 1.5,\n\"width\": 2,\n\"color\": \"#B4E8FC\",\n},\nmode=\"lines\",\n)\n\nlayout = dict(\nplot_bgcolor=app_color[\"graph_bg\"],\npaper_bgcolor=app_color[\"graph_bg\"],\nfont={\"color\": \"#fff\"},\nheight=400,\nxaxis={\n\"range\": [0, 200],\n\"showline\": True,\n\"zeroline\": False,\n\"fixedrange\": True,\n\"tickvals\": [0, 50, 100, 150, 200],\n\"ticktext\": [\"200\", \"150\", \"100\", \"50\", \"0\"],\n\"title\": \"Time Elapsed (sec)\",\n},\nyaxis={\n\"range\": [\nmin(0, min(df[\"ServoMeter\"])),\nmax(100, max(df[\"ServoMeter\"]) + max(df[\"ServoMotor\"])),\n],\n\"showgrid\": True,\n\"showline\": True,\n\"fixedrange\": True,\n\"zeroline\": False,\n\"gridcolor\": app_color[\"graph_line\"],\n\"nticks\": max(6, round(df[\"ServoMeter\"].iloc[-1] / 10)),\n},\n)\n\nreturn dict(data=[trace], layout=layout)```\n\nCapturing all the relevant columns & transform them into a graph, where the application will consume data into both the axis (x-axis & y-axis).\n\nThere are many other useful snippets, which creates separate useful widgets inside the dashboard.\n\nRun:\n\nLet us run the application –\n\nSo, we’ve done it.\n\nYou will get the complete codebase in the following Github link.\n\nThere is an excellent resource from the dash framework, which you should explore. The following link would be handy for developers who want to get some meaningful pre-built dashboard template, which you can customize as per your need through Python or R. Please find the link here.\n\nI’ll bring some more exciting topic in the coming days from the Python verse.\n\nTill then, Happy Avenging! 😀\n\nNote: All the data & scenario posted here are representational data & scenarios & available over the internet & for educational purpose only.\n\nOne more thing you need to understand is that this prediction based on limited data points. The actual event may happen differently. Ideally, countries are taking a cue from this kind of analysis & are initiating appropriate measures to avoid the high-curve. And, that is one of the main objective of time series analysis.\n\nThere is always a room for improvement of this kind of models & the solution associated with it. I’ve shown the basic ways to achieve the same for the education purpose only.\n\n## Displaying real-time trade data in a dashboard using Python & third-party API & Streaming\n\nToday, We want to make our use case a little bit harder & more realistic. We want to consume real-time live trade-data consuming through FinnHub API & displaying them into our dashboard using another brilliant H2O-Wave API with the help of native Python.\n\nThe use-case mentioned above is extremely useful & for that, we’ll be using the following Third-Party APIs to achieve the same –\n\nI’m not going to discuss these topics more, as I’ve already discussed them in separate earlier posts. Please refer to the following threads for detailed level information –\n\ncreating-a-real-time-dashboard-from-streaming-data-using-python\n\nIn this post, we will address the advanced concept compared to the previous post mentioned above. Let us first look at how the run looks before we start exploring the details –\n\nLet us explore the architecture of this implementation –\n\nThis application will talk to the FinnHub websocket & consume real-time trade data from it. And this will be temporarily stored in our Ably channels. The dashboard will pick the message & display that as soon as there is new data for that trading company.\n\nFor this use case, you need to install the following packages –\n\nSTEP – 1:\n\nSTEP – 2:\n\nSTEP – 3:\n\nSTEP – 4:\n\nYou can copy the following commands to install the above-mentioned packages –\n\n``````pip install ably\npip install h2o-wave\npip install pandas\npip install websocket\npip install websocket-client``````\n\nLet’s explore the important data-point that you need to capture from the FinnHub portal to consume the real-time trade data –\n\nWe’ve two main scripts. The first script will consume the streaming data into a message queue & the other one will be extracting the data from the queue & transform the data & publish it into the real-time dashboard.\n\n1. dashboard_finnhub.py ( This native Python script will consume streaming data & create the live trade dashboard. )\n\nThis file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.\n\n ############################################################### #### Template Written By: H2O Wave #### #### Enhanced with Streaming Data By: Satyaki De #### #### Base Version Enhancement On: 20-Dec-2020 #### #### Modified On 27-Jun-2021 #### #### #### #### Objective: This script will consume real-time #### #### streaming data coming out from a hosted API #### #### sources (Finnhub) using another popular third-party #### #### service named Ably. Ably mimics pubsub Streaming #### #### concept, which might be extremely useful for #### #### any start-ups. #### #### #### #### Note: This is an enhancement of my previous post of #### #### H2O Wave. In this case, the application will consume #### #### streaming trade data from a live host & not generated #### #### out of the mock data. Thus, it is more useful for the #### #### start-ups. #### ############################################################### import time from h2o_wave import site, data, ui from ably import AblyRest import pandas as p import json import datetime import logging import platform as pl from clsConfig import clsConfig as cf import clsL as cl # Disbling Warning def warn(*args, **kwargs): pass import warnings warnings.warn = warn # Lookup functions from # Azure cloud SQL DB var = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\") # Global Area ## Global Class # Initiating Log Class l = cl.clsL() # Global Variables # Moving previous day log files to archive directory log_dir = cf.config['LOG_PATH'] path = cf.config['INIT_PATH'] subdir = cf.config['SUBDIR'] ## End Of Global Part class DaSeries: def __init__(self, inputDf): self.Df = inputDf self.count_row = inputDf.shape self.start_pos = 0 self.end_pos = 0 self.interval = 1 def next(self): try: # Getting Individual Element & convert them to Series if ((self.start_pos + self.interval) <= self.count_row): self.end_pos = self.start_pos + self.interval else: self.end_pos = self.start_pos + (self.count_row – self.start_pos) split_df = self.Df.iloc[self.start_pos:self.end_pos] if ((self.start_pos > self.count_row) | (self.start_pos == self.count_row)): pass else: self.start_pos = self.start_pos + self.interval x = float(split_df.iloc['CurrentExchange']) dx = float(split_df.iloc['Change']) # Emptying the exisitng dataframe split_df = p.DataFrame(None) return x, dx except: x = 0 dx = 0 return x, dx class CategoricalSeries: def __init__(self, sourceDf): self.series = DaSeries(sourceDf) self.i = 0 def next(self): x, dx = self.series.next() self.i += 1 return f'C{self.i}', x, dx light_theme_colors = '\\$red \\$pink \\$purple \\$violet \\$indigo \\$blue \\$azure \\$cyan \\$teal \\$mint \\$green \\$amber \\$orange \\$tangerine'.split() dark_theme_colors = '\\$red \\$pink \\$blue \\$azure \\$cyan \\$teal \\$mint \\$green \\$lime \\$yellow \\$amber \\$orange \\$tangerine'.split() _color_index = –1 colors = dark_theme_colors def next_color(): global _color_index _color_index += 1 return colors[_color_index % len(colors)] _curve_index = –1 curves = 'linear smooth step step-after step-before'.split() def next_curve(): global _curve_index _curve_index += 1 return curves[_curve_index % len(curves)] def calc_p(row): try: str_calc_s1 = str(row['s_x']) str_calc_s2 = str(row['s_y']) if str_calc_s1 == str_calc_s2: calc_p_val = float(row['p_y']) else: calc_p_val = float(row['p_x']) return calc_p_val except: return 0.0 def calc_v(row): try: str_calc_s1 = str(row['s_x']) str_calc_s2 = str(row['s_y']) if str_calc_s1 == str_calc_s2: calc_v_val = float(row['v_y']) else: calc_v_val = float(row['v_x']) return calc_v_val except: return 0.0 def process_DF(inputDF, inputDFUnq): try: # Core Business logic # The application will show default value to any # trade-in stock in case that data doesn't consume # from the source. df_conv = inputDF df_unique_fin = inputDFUnq df_conv['max_count'] = df_conv.groupby('default_rank')['default_rank'].transform('count') l.logr('3. max_df.csv', 'Y', df_conv, subdir) # Sorting the output sorted_df = df_conv.sort_values(by=['default_rank','s'], ascending=True) # New Column List Orders column_order = ['s', 'default_rank', 'max_count', 'p', 't', 'v'] df_fin = sorted_df.reindex(column_order, axis=1) l.logr('4. sorted_df.csv', 'Y', df_fin, subdir) # Now splitting the sorted df into two sets lkp_max_count = 4 df_fin_na = df_fin[(df_fin['max_count'] == lkp_max_count)] l.logr('5. df_fin_na.csv', 'Y', df_fin_na, subdir) df_fin_req = df_fin[(df_fin['max_count'] != lkp_max_count)] l.logr('6. df_fin_req.csv', 'Y', df_fin_req, subdir) # Now to perform cross join, we will create # a key column in both the DataFrames to # merge on that key. df_unique_fin['key'] = 1 df_fin_req['key'] = 1 # Dropping unwanted columns df_unique_fin.drop(columns=['t'], axis=1, inplace=True) l.logr('7. df_unique_slim.csv', 'Y', df_unique_fin, subdir) # Padding with dummy key values #merge_df = p.merge(df_unique_fin,df_fin_req,on=['s'],how='left') merge_df = p.merge(df_unique_fin,df_fin_req,on=['key']).drop(\"key\", 1) l.logr('8. merge_df.csv', 'Y', merge_df, subdir) # Sorting the output sorted_merge_df = merge_df.sort_values(by=['default_rank_y','s_x'], ascending=True) l.logr('9. sorted_merge_df.csv', 'Y', sorted_merge_df, subdir) # Calling new derived logic sorted_merge_df['derived_p'] = sorted_merge_df.apply(lambda row: calc_p(row), axis=1) sorted_merge_df['derived_v'] = sorted_merge_df.apply(lambda row: calc_v(row), axis=1) l.logr('10. sorted_merge_derived.csv', 'Y', sorted_merge_df, subdir) # Dropping unwanted columns sorted_merge_df.drop(columns=['default_rank_x', 'p_x', 'v_x', 's_y', 'p_y', 'v_y'], axis=1, inplace=True) #Renaming the columns sorted_merge_df.rename(columns={'s_x':'s'}, inplace=True) sorted_merge_df.rename(columns={'default_rank_y':'default_rank'}, inplace=True) sorted_merge_df.rename(columns={'derived_p':'p'}, inplace=True) sorted_merge_df.rename(columns={'derived_v':'v'}, inplace=True) l.logr('11. org_merge_derived.csv', 'Y', sorted_merge_df, subdir) # Aligning columns column_order = ['s', 'default_rank', 'max_count', 'p', 't', 'v'] merge_fin_df = sorted_merge_df.reindex(column_order, axis=1) l.logr('12. merge_fin_df.csv', 'Y', merge_fin_df, subdir) # Finally, appending these two DataFrame (df_fin_na & merge_fin_df) frames = [df_fin_na, merge_fin_df] fin_df = p.concat(frames, keys=[\"s\", \"default_rank\", \"max_count\"]) l.logr('13. fin_df.csv', 'Y', fin_df, subdir) # Final clearance & organization fin_df.drop(columns=['default_rank', 'max_count'], axis=1, inplace=True) l.logr('14. Final.csv', 'Y', fin_df, subdir) # Adjusting key columns fin_df.rename(columns={'s':'Company'}, inplace=True) fin_df.rename(columns={'p':'CurrentExchange'}, inplace=True) fin_df.rename(columns={'v':'Change'}, inplace=True) l.logr('15. TransormedFinal.csv', 'Y', fin_df, subdir) return fin_df except Exception as e: print('\\$' * 120) x = str(e) print(x) print('\\$' * 120) df = p.DataFrame() return df def create_dashboard(update_freq=0.0): page = site['/dashboard_finnhub'] general_log_path = str(cf.config['LOG_PATH']) ably_id = str(cf.config['ABLY_ID']) # Enabling Logging Info logging.basicConfig(filename=general_log_path + 'Realtime_Stock.log', level=logging.INFO) os_det = pl.system() if os_det == \"Windows\": src_path = path + '\\\\' + 'data\\\\' else: src_path = path + '/' + 'data/' # Fetching the data client = AblyRest(ably_id) channel = client.channels.get('sd_channel') message_page = channel.history() # Counter Value cnt = 0 # Declaring Global Data-Frame df_conv = p.DataFrame() for i in message_page.items: print('Last Msg: {}'.format(i.data)) json_data = json.loads(i.data) # Converting JSON to Dataframe df = p.json_normalize(json_data) df.columns = df.columns.map(lambda x: x.split(\".\")[–1]) if cnt == 0: df_conv = df else: d_frames = [df_conv, df] df_conv = p.concat(d_frames) cnt += 1 # Resetting the Index Value df_conv.reset_index(drop=True, inplace=True) print('DF:') print(df_conv) # Writing to the file l.logr('1. DF_modified.csv', 'Y', df_conv, subdir) # Dropping unwanted columns df_conv.drop(columns=['c'], axis=1, inplace=True) df_conv['default_rank'] = df_conv.groupby(['s']).cumcount() + 1 lkp_rank = 1 df_unique = df_conv[(df_conv['default_rank'] == lkp_rank)] # New Column List Orders column_order = ['s', 'default_rank', 'p', 't', 'v'] df_unique_fin = df_unique.reindex(column_order, axis=1) print('Rank DF Unique:') print(df_unique_fin) l.logr('2. df_unique.csv', 'Y', df_unique_fin, subdir) # Capturing transformed values into a DataFrame # Depending on your logic, you'll implement that inside # the process_DF functions fin_df = process_DF(df_conv, df_unique_fin) df_unq_fin = df_unique_fin.copy() df_unq_fin.rename(columns={'s':'Company'}, inplace=True) df_unq_fin.rename(columns={'p':'CurrentExchange'}, inplace=True) df_unq_fin.rename(columns={'v':'Change'}, inplace=True) df_unq_fin.drop(columns=['default_rank','key'], axis=1, inplace=True) l.logr('16. df_unq_fin.csv', 'Y', df_unq_fin, subdir) df_unq_finale = df_unq_fin.sort_values(by=['Company'], ascending=True) l.logr('17. df_unq_finale.csv', 'Y', df_unq_finale, subdir) # Final clearance for better understanding of data fin_df.drop(columns=['t'], axis=1, inplace=True) l.logr('18. CleanFinal.csv', 'Y', fin_df, subdir) count_row = df_unq_finale.shape large_lines = [] start_pos = 0 end_pos = 0 interval = 1 # Converting dataframe to a desired Series f = CategoricalSeries(fin_df) for j in range(count_row): # Getting the series values from above cat, val, pc = f.next() # Getting Individual Element & convert them to Series if ((start_pos + interval) <= count_row): end_pos = start_pos + interval else: end_pos = start_pos + (count_row – start_pos) split_df = df_unq_finale.iloc[start_pos:end_pos] if ((start_pos > count_row) | (start_pos == count_row)): pass else: start_pos = start_pos + interval x_currency = str(split_df.iloc['Company']) #################################################### ##### Debug Purpose ######### #################################################### print('Company: ', x_currency) print('J: ', str(j)) print('Cat: ', cat) #################################################### ##### End Of Debug ####### #################################################### c = page.add(f'e{j+1}', ui.tall_series_stat_card( box=f'{j+1} 1 1 2', title=x_currency, value='=\\${{intl qux minimum_fraction_digits=2 maximum_fraction_digits=2}}', aux_value='={{intl quux style=\"percent\" minimum_fraction_digits=1 maximum_fraction_digits=1}}', data=dict(qux=val, quux=pc), plot_type='area', plot_category='foo', plot_value='qux', plot_color=next_color(), plot_data=data('foo qux', –15), plot_zero_value=0, plot_curve=next_curve(), )) large_lines.append((f, c)) page.save() while update_freq > 0: time.sleep(update_freq) for f, c in large_lines: cat, val, pc = f.next() print('Update Cat: ', cat) print('Update Val: ', val) print('Update pc: ', pc) print('*' * 160) c.data.qux = val c.data.quux = pc / 100 c.plot_data[–1] = [cat, val] page.save() if __name__ == \"__main__\": try: # Main Calling script create_dashboard(update_freq=0.25) except Exception as e: x = str(e) print(x)\n\nLet’s explore the key snippets from the above script –\n\n```def process_DF(inputDF, inputDFUnq):\ntry:\n# The application will show default value to any\n# trade-in stock in case that data doesn't consume\n# from the source.\n\n# Getting block count\n#df_conv['block_count'] = df_conv.groupby(['default_rank']).cumcount()\n#l.logr('3. block_df.csv', 'Y', df_conv, subdir)\n\n# Getting block count\n#df_conv['max_count'] = df_conv.groupby(['default_rank']).size()\n#df_conv_fin = df_conv.groupby(['default_rank']).agg(['count'])\n#df_conv_fin = df_conv.value_counts(['default_rank']).reset_index(name='max_count')\n#df_conv_fin = df_conv.value_counts(['default_rank'])\ndf_conv = inputDF\ndf_unique_fin = inputDFUnq\n\ndf_conv['max_count'] = df_conv.groupby('default_rank')['default_rank'].transform('count')\nl.logr('3. max_df.csv', 'Y', df_conv, subdir)\n\n# Sorting the output\nsorted_df = df_conv.sort_values(by=['default_rank','s'], ascending=True)\n\n# New Column List Orders\ncolumn_order = ['s', 'default_rank', 'max_count', 'p', 't', 'v']\ndf_fin = sorted_df.reindex(column_order, axis=1)\n\nl.logr('4. sorted_df.csv', 'Y', df_fin, subdir)\n\n# Now splitting the sorted df into two sets\nlkp_max_count = 4\ndf_fin_na = df_fin[(df_fin['max_count'] == lkp_max_count)]\n\nl.logr('5. df_fin_na.csv', 'Y', df_fin_na, subdir)\n\ndf_fin_req = df_fin[(df_fin['max_count'] != lkp_max_count)]\nl.logr('6. df_fin_req.csv', 'Y', df_fin_req, subdir)\n\n# Now to perform cross join, we will create\n# a key column in both the DataFrames to\n# merge on that key.\ndf_unique_fin['key'] = 1\ndf_fin_req['key'] = 1\n\n# Dropping unwanted columns\ndf_unique_fin.drop(columns=['t'], axis=1, inplace=True)\nl.logr('7. df_unique_slim.csv', 'Y', df_unique_fin, subdir)\n\n# Padding with dummy key values\n#merge_df = p.merge(df_unique_fin,df_fin_req,on=['s'],how='left')\nmerge_df = p.merge(df_unique_fin,df_fin_req,on=['key']).drop(\"key\", 1)\n\nl.logr('8. merge_df.csv', 'Y', merge_df, subdir)\n\n# Sorting the output\nsorted_merge_df = merge_df.sort_values(by=['default_rank_y','s_x'], ascending=True)\n\nl.logr('9. sorted_merge_df.csv', 'Y', sorted_merge_df, subdir)\n\n# Calling new derived logic\nsorted_merge_df['derived_p'] = sorted_merge_df.apply(lambda row: calc_p(row), axis=1)\nsorted_merge_df['derived_v'] = sorted_merge_df.apply(lambda row: calc_v(row), axis=1)\n\nl.logr('10. sorted_merge_derived.csv', 'Y', sorted_merge_df, subdir)\n\n# Dropping unwanted columns\nsorted_merge_df.drop(columns=['default_rank_x', 'p_x', 'v_x', 's_y', 'p_y', 'v_y'], axis=1, inplace=True)\n\n#Renaming the columns\nsorted_merge_df.rename(columns={'s_x':'s'}, inplace=True)\nsorted_merge_df.rename(columns={'default_rank_y':'default_rank'}, inplace=True)\nsorted_merge_df.rename(columns={'derived_p':'p'}, inplace=True)\nsorted_merge_df.rename(columns={'derived_v':'v'}, inplace=True)\n\nl.logr('11. org_merge_derived.csv', 'Y', sorted_merge_df, subdir)\n\n# Aligning columns\ncolumn_order = ['s', 'default_rank', 'max_count', 'p', 't', 'v']\nmerge_fin_df = sorted_merge_df.reindex(column_order, axis=1)\n\nl.logr('12. merge_fin_df.csv', 'Y', merge_fin_df, subdir)\n\n# Finally, appending these two DataFrame (df_fin_na & merge_fin_df)\nframes = [df_fin_na, merge_fin_df]\nfin_df = p.concat(frames, keys=[\"s\", \"default_rank\", \"max_count\"])\n\nl.logr('13. fin_df.csv', 'Y', fin_df, subdir)\n\n# Final clearance & organization\nfin_df.drop(columns=['default_rank', 'max_count'], axis=1, inplace=True)\n\nl.logr('14. Final.csv', 'Y', fin_df, subdir)\n\nfin_df.rename(columns={'s':'Company'}, inplace=True)\nfin_df.rename(columns={'p':'CurrentExchange'}, inplace=True)\nfin_df.rename(columns={'v':'Change'}, inplace=True)\n\nl.logr('15. TransormedFinal.csv', 'Y', fin_df, subdir)\n\nreturn fin_df\nexcept Exception as e:\nprint('\\$' * 120)\n\nx = str(e)\nprint(x)\n\nprint('\\$' * 120)\n\ndf = p.DataFrame()\n\nreturn df```\n\nThe above function will check if the queue is sending all the key trade-in data for all the companies. In our use case, we’re testing with the four companies & they are as follows –\n\n``````a. AAPL\nb. AMZN\nc. BINANCE:BTCUSDT\nd. IC MARKETS:1``````\n\nEvery message is containing data from all of these four companies together. If any of the company’s data is missing, this transformation will add a dummy record of that missing company to make the uniform number of entries in each message bouquet. And dummy trade-in values added for all the missing information.\n\n```def calc_p(row):\ntry:\nstr_calc_s1 = str(row['s_x'])\nstr_calc_s2 = str(row['s_y'])\n\nif str_calc_s1 == str_calc_s2:\ncalc_p_val = float(row['p_y'])\nelse:\ncalc_p_val = float(row['p_x'])\n\nreturn calc_p_val\nexcept:\nreturn 0.0\n\ndef calc_v(row):\ntry:\nstr_calc_s1 = str(row['s_x'])\nstr_calc_s2 = str(row['s_y'])\n\nif str_calc_s1 == str_calc_s2:\ncalc_v_val = float(row['v_y'])\nelse:\ncalc_v_val = float(row['v_x'])\n\nreturn calc_v_val\nexcept:\nreturn 0.0```\n\nThe above snippet will capture the default values for those missing records.\n\n``` client = AblyRest(ably_id)\nchannel = client.channels.get('sd_channel')\n\nmessage_page = channel.history()```\n\nIn the above snippet, the application will consume the streaming data from the Ably queue.\n\n```for i in message_page.items:\nprint('Last Msg: {}'.format(i.data))\n\n# Converting JSON to Dataframe\ndf = p.json_normalize(json_data)\ndf.columns = df.columns.map(lambda x: x.split(\".\")[-1])\n\nif cnt == 0:\ndf_conv = df\nelse:\nd_frames = [df_conv, df]\ndf_conv = p.concat(d_frames)\n\ncnt += 1```\n\nThe above snippet will convert the streaming messages to a more meaningful pandas data-frame, which we can use for a wide variety of analytics.\n\n``` # Converting dataframe to a desired Series\nf = CategoricalSeries(fin_df)\n\nfor j in range(count_row):\n# Getting the series values from above\ncat, val, pc = f.next()\n\n# Getting Individual Element & convert them to Series\nif ((start_pos + interval) <= count_row):\nend_pos = start_pos + interval\nelse:\nend_pos = start_pos + (count_row - start_pos)\n\nsplit_df = df_unq_finale.iloc[start_pos:end_pos]\n\nif ((start_pos > count_row) | (start_pos == count_row)):\npass\nelse:\nstart_pos = start_pos + interval\n\nx_currency = str(split_df.iloc['Company'])\n\n####################################################\n##### Debug Purpose #########\n####################################################\nprint('Company: ', x_currency)\nprint('J: ', str(j))\nprint('Cat: ', cat)\n####################################################\n##### End Of Debug #######\n####################################################\n\nbox=f'{j+1} 1 1 2',\ntitle=x_currency,\nvalue='=\\${{intl qux minimum_fraction_digits=2 maximum_fraction_digits=2}}',\naux_value='={{intl quux style=\"percent\" minimum_fraction_digits=1 maximum_fraction_digits=1}}',\ndata=dict(qux=val, quux=pc),\nplot_type='area',\nplot_category='foo',\nplot_value='qux',\nplot_color=next_color(),\nplot_data=data('foo qux', -15),\nplot_zero_value=0,\nplot_curve=next_curve(),\n))\nlarge_lines.append((f, c))\n\npage.save()\n\nwhile update_freq > 0:\n\ntime.sleep(update_freq)\n\nfor f, c in large_lines:\ncat, val, pc = f.next()\n\nprint('Update Cat: ', cat)\nprint('Update Val: ', val)\nprint('Update pc: ', pc)\nprint('*' * 160)\n\nc.data.qux = val\nc.data.quux = pc / 100\nc.plot_data[-1] = [cat, val]\n\npage.save()```\n\nThe above snippet will consume the data into H2O-Wave driven framework, which will expose this data into beautiful & easily representable GUI-based solutions through an interactive dashboard.\n\n2. publish_ably_mod.py ( This native Python script will consume streaming data into Ably message Queue )\n\nThis file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.\n\n ############################################################### #### #### #### Written By: Satyaki De #### #### Written Date: 26-Jun-2021 #### #### #### #### Objective: This script will consume real-time #### #### streaming data coming out from a hosted API #### #### sources (Finnhub) using another popular third-party #### #### service named Ably. Ably mimics pubsub Streaming #### #### concept, which might be extremely useful for #### #### any start-ups. #### #### #### ############################################################### from ably import AblyRest import logging import json # generate random floating point values from random import seed from random import random # seed random number generator import websocket import json from clsConfig import clsConfig as cf seed(1) # Global Section logger = logging.getLogger('ably') logger.addHandler(logging.StreamHandler()) ably_id = str(cf.config['ABLY_ID']) ably = AblyRest(ably_id) channel = ably.channels.get('sd_channel') # End Of Global Section def on_message(ws, message): print(\"*\" * 60) res = json.loads(message) jsBody = res[\"data\"] jdata_dyn = json.dumps(jsBody) print(jdata_dyn) # JSON data # This is the default data for all the identified category # we've prepared. You can extract this dynamically. Or, By # default you can set their base trade details. json_data = [{ \"c\": \"null\", \"p\": 0.01, \"s\": \"AAPL\", \"t\": 1624715406407, \"v\": 0.01 },{ \"c\": \"null\", \"p\": 0.01, \"s\": \"AMZN\", \"t\": 1624715406408, \"v\": 0.01 },{ \"c\": \"null\", \"p\": 0.01, \"s\": \"BINANCE:BTCUSDT\", \"t\": 1624715406409, \"v\": 0.01 }, { \"c\": \"null\", \"p\": 0.01, \"s\": \"IC MARKETS:1\", \"t\": 1624715406410, \"v\": 0.01 }] jdata = json.dumps(json_data) # Publish a message to the sd_channel channel channel.publish('event', jdata) # Publish rest of the messages to the sd_channel channel channel.publish('event', jdata_dyn) jsBody = [] jdata_dyn = '' def on_error(ws, error): print(error) def on_close(ws): print(\"### closed ###\") def on_open(ws): # Invoking Individual Company Trade Queries ws.send('{\"type\":\"subscribe\",\"symbol\":\"AAPL\"}') ws.send('{\"type\":\"subscribe\",\"symbol\":\"AMZN\"}') ws.send('{\"type\":\"subscribe\",\"symbol\":\"BINANCE:BTCUSDT\"}') ws.send('{\"type\":\"subscribe\",\"symbol\":\"IC MARKETS:1\"}') if __name__ == \"__main__\": websocket.enableTrace(True) ws = websocket.WebSocketApp(\"wss://ws.finnhub.io?token=jfhfyr8474rpv6av0\", on_message = on_message, on_error = on_error, on_close = on_close) ws.on_open = on_open ws.run_forever()\n\nThe key snippet from the above script –\n\n``` json_data = [{\n\"c\": \"null\",\n\"p\": 0.01,\n\"s\": \"AAPL\",\n\"t\": 1624715406407,\n\"v\": 0.01\n},{\n\"c\": \"null\",\n\"p\": 0.01,\n\"s\": \"AMZN\",\n\"t\": 1624715406408,\n\"v\": 0.01\n},{\n\"c\": \"null\",\n\"p\": 0.01,\n\"s\": \"BINANCE:BTCUSDT\",\n\"t\": 1624715406409,\n\"v\": 0.01\n},\n{\n\"c\": \"null\",\n\"p\": 0.01,\n\"s\": \"IC MARKETS:1\",\n\"t\": 1624715406410,\n\"v\": 0.01\n}]```\n\nAs we already discussed, we’ll pass a default set of data for all the candidate companies.\n\n``` # Publish a message to the sd_channel channel\nchannel.publish('event', jdata)\n\n# Publish rest of the messages to the sd_channel channel\nchannel.publish('event', jdata_dyn)```\n\nPublish the messages to the created channel.\n\n```def on_open(ws):\n# Invoking Individual Company Trade Queries\nws.send('{\"type\":\"subscribe\",\"symbol\":\"AAPL\"}')\nws.send('{\"type\":\"subscribe\",\"symbol\":\"AMZN\"}')\nws.send('{\"type\":\"subscribe\",\"symbol\":\"BINANCE:BTCUSDT\"}')\nws.send('{\"type\":\"subscribe\",\"symbol\":\"IC MARKETS:1\"}')\n\nif __name__ == \"__main__\":\nwebsocket.enableTrace(True)\nws = websocket.WebSocketApp(\"wss://ws.finnhub.io?token=hdhdjdj9494ld934v6av0\",\non_message = on_message,\non_error = on_error,\non_close = on_close)```\n\nSend the company-specific trade queries through websocket apps to submit that to FinnHub.\n\n3. clsConfig.py ( This file contains the configuration details. )\n\nThis file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.\n\n ################################################ #### Written By: SATYAKI DE #### #### Written On: 15-May-2020 #### #### #### #### Objective: This script is a config #### #### file, contains all the keys for #### #### Machine-Learning. Application will #### #### process these information & perform #### #### various analysis on Linear-Regression. #### ################################################ import os import platform as pl class clsConfig(object): Curr_Path = os.path.dirname(os.path.realpath(__file__)) os_det = pl.system() if os_det == \"Windows\": sep = '\\\\' else: sep = '/' config = { 'APP_ID': 1, 'ARCH_DIR': Curr_Path + sep + 'arch' + sep, 'PROFILE_PATH': Curr_Path + sep + 'profile' + sep, 'LOG_PATH': Curr_Path + sep + 'log' + sep, 'REPORT_PATH': Curr_Path + sep + 'report', 'FILE_NAME': Curr_Path + sep + 'Data' + sep + 'TradeIn.csv', 'SRC_PATH': Curr_Path + sep + 'Data' + sep, 'APP_DESC_1': 'H2O Wave Integration with FinHubb!', 'DEBUG_IND': 'N', 'INIT_PATH': Curr_Path, 'SUBDIR' : 'data', 'ABLY_ID': 'WWP309489.93jfkT:32kkdhdJjdued79e' }\n\nview raw\n\nclsConfig.py\n\nhosted with ❤ by GitHub\n\nLet’s explore the directory structure –\n\nLet’s run the application –\n\nStep 1:\n\nStep 2:\n\nStep 3:\n\nYou can monitor the message consumption from your Ably portal as follows –\n\nIf you want to know more detail, then you need to scroll down the page, where you will get this additional information –\n\nAnd, the final output in the interactive dashboard will be look like the below screenshot –\n\nSo, we’ve done it.\n\nYou will get the complete codebase in the following Github link.\n\nI’ll bring some more exciting topic in the coming days from the Python verse.\n\nTill then, Happy Avenging! 😀\n\nNote: All the data & scenario posted here are representational data & scenarios & available over the internet & for educational purpose only.\n\n## Building a Python-based airline solution using Amadeus API\n\nHi Guys,\n\nToday, I’ll share a little different topic in Python compared to my last couple of posts, where I have demonstrated the use of Python in the field of machine learning & forecast modeling.\n\nWe’ll explore to create meaningful sample data points for Airlines & hotel reservations. At this moment, this industry is the hard-hit due to the pandemic. And I personally wish a speedy recovery to all employees who risked their lives to maintain the operation or might have lost their jobs due to this time.\n\nI’ll be providing only major scripts & will show how you can extract critical data from their API.\n\nHowever, to create the API, you need to register in Amadeus as a developer & follow specific steps to get the API details. You will need to register using the following link.\n\nStep 1:\n\nOnce you provide the necessary details, you need to activate your account by clicking the email validation.\n\nStep 2:\n\nAs part of the next step, you will be clicking the “Self-Service Workspace” option as marked in the green box shown above.\n\nNow, you have to click My apps & under that, you need to click – Create new appshown below –\n\nStep 3:\n\nYou need to provide the following details before creating the API. Note that once you create – it will take 30 minutes to activate the API-link.\n\nStep 4:\n\nYou will come to the next page once you click the “Create” button in the previous step.\n\nFor production, you need to create a separate key shown above.\n\nYou need to install the following packages –\n\nAnd, the installation process is shown as –\n\npip install flatten_json\n\nAnd, this installation process is shown as –\n\n1. clsAmedeus (This is the API script, which will send the API requests & return JSON if successful.)\n\n```##############################################\n#### Written By: SATYAKI DE ####\n#### Written On: 05-Jul-2020 ####\n#### Modified On 05-Jul-2020 ####\n#### ####\n#### Objective: Main calling scripts. ####\n##############################################\n\nimport json\nfrom clsConfig import clsConfig as cf\n\nclass clsAmedeus:\ndef __init__(self):\nself.client_id = cf.config['CLIENT_ID']\nself.client_secret = cf.config['CLIENT_SECRET']\nself.type = cf.config['API_TYPE']\n\ndef flightOffers(self, origLocn, destLocn, departDate, noOfAdult):\ntry:\ncnt = 0\n\n# Setting Clients\nclient_id=str(self.client_id),\nclient_secret=str(self.client_secret)\n)\n\n# Flight Offers\noriginLocationCode=origLocn,\ndestinationLocationCode=destLocn,\ndepartureDate=departDate,\n\nResJson = response.data\n\nreturn ResJson\nexcept Exception as e:\nprint(e)\nx = str(e)\nResJson = {'errorDetails': x}\n\nreturn ResJson\n\ndef cheapestDate(self, origLocn, destLocn):\ntry:\n# Setting Clients\nclient_id=self.client_id,\nclient_secret=self.client_secret\n)\n\n# Flight Offers\n# Flight Cheapest Date Search\n\nResJson = response.data\n\nreturn ResJson\nexcept Exception as e:\nprint(e)\nx = str(e)\nResJson = {'errorDetails': x}\n\nreturn ResJson\n\ndef listOfHotelsByCity(self, origLocn):\ntry:\n# Setting Clients\nclient_id=self.client_id,\nclient_secret=self.client_secret\n)\n\n# Hotel Search\n# Get list of Hotels by city code\n\nResJson = response.data\n\nreturn ResJson\nexcept Exception as e:\nprint(e)\nx = str(e)\nResJson = {'errorDetails': x}\n\nreturn ResJson\n\ndef listOfOffersBySpecificHotels(self, hotelID):\ntry:\n# Setting Clients\nclient_id=self.client_id,\nclient_secret=self.client_secret\n)\n\n# Get list of offers for a specific hotel\n\nResJson = response.data\n\nreturn ResJson\nexcept Exception as e:\nprint(e)\nx = str(e)\nResJson = {'errorDetails': x}\n\nreturn ResJson\n\ndef hotelReview(self, hotelID):\ntry:\n# Setting Clients\nclient_id=self.client_id,\nclient_secret=self.client_secret\n)\n\n# Hotel Ratings\n\nResJson = response.data\n\nreturn ResJson\nexcept Exception as e:\nprint(e)\nx = str(e)\nResJson = {'errorDetails': x}\n\nreturn ResJson\n\ndef process(self, choice, origLocn, destLocn, departDate, noOfAdult, hotelID):\ntry:\n# Main Area to call apropriate choice\nif choice == 1:\nresJson = self.flightOffers(origLocn, destLocn, departDate, noOfAdult)\nelif choice == 2:\nresJson = self.cheapestDate(origLocn, destLocn)\nelif choice == 3:\nresJson = self.listOfHotelsByCity(origLocn)\nelif choice == 4:\nresJson = self.listOfOffersBySpecificHotels(hotelID)\nelif choice == 5:\nresJson = self.hotelReview(hotelID)\nelse:\nresJson = {'errorDetails': 'Invalid Options!'}\n\n# Converting back to JSON\njdata = json.dumps(resJson)\n\n# Checking the begining character\n# for the new package\n# As that requires dictionary array\n# Hence, We'll be adding '[' if this\n# is missing from the return payload\nSYM = jdata[:1]\nif SYM != '[':\nrdata = '[' + jdata + ']'\nelse:\nrdata = jdata\n\nreturn ResJson\n\nexcept ResponseError as error:\nx = str(error)\nresJson = {'errorDetails': x}\n\nreturn resJson\n```\n\nLet’s explore the key lines –\n\nCreating an instance of the client by providing the recently acquired API Key & API-Secret.\n\n```# Setting Clients\nclient_id=str(self.client_id),\nclient_secret=str(self.client_secret)\n)```\n\nThe following lines are used to fetch the API response for specific business cases. Different invocation of API retrieve different data –\n\n```# Flight Offers\n# Flight Cheapest Date Search\n\nThe program will navigate to particular methods to invoke certain features –\n\n```# Main Area to call apropriate choice\nif choice == 1:\nresJson = self.flightOffers(origLocn, destLocn, departDate, noOfAdult)\nelif choice == 2:\nresJson = self.cheapestDate(origLocn, destLocn)\nelif choice == 3:\nresJson = self.listOfHotelsByCity(origLocn)\nelif choice == 4:\nresJson = self.listOfOffersBySpecificHotels(hotelID)\nelif choice == 5:\nresJson = self.hotelReview(hotelID)\nelse:\nresJson = {'errorDetails': 'Invalid Options!'}```\n\n2. callAmedeusAPI (This is the main script, which will invoke the Amadeus API & return dataframe if successful.)\n\n```##############################################\n#### Written By: SATYAKI DE ####\n#### Written On: 05-Jul-2020 ####\n#### Modified On 05-Jul-2020 ####\n#### ####\n#### Objective: Main calling scripts. ####\n##############################################\n\nfrom clsConfig import clsConfig as cf\nimport clsL as cl\nimport logging\nimport datetime\nimport clsAmedeus as cw\nimport pandas as p\nimport json\n\nfrom flatten_json import flatten\n\n# Disbling Warning\ndef warn(*args, **kwargs):\npass\n\nimport warnings\nwarnings.warn = warn\n\n# Lookup functions from\n# Azure cloud SQL DB\n\nvar = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n\ndef main():\ntry:\n# Declared Variable\nret_1 = 0\ntextOrig = ''\ntextDest = ''\ntextDate = ''\ntextHotelID = ''\ndebug_ind = 'Y'\nres_2 = ''\n\n# Defining Generic Log File\ngeneral_log_path = str(cf.config['LOG_PATH'])\n\n# Enabling Logging Info\n\n# Initiating Log Class\nl = cl.clsL()\n\n# Moving previous day log files to archive directory\nlog_dir = cf.config['LOG_PATH']\ncurr_ver =datetime.datetime.now().strftime(\"%Y-%m-%d\")\n\ntmpR0 = \"*\" * 157\n\nlogging.info(tmpR0)\ntmpR9 = 'Start Time: ' + str(var)\nlogging.info(tmpR9)\nlogging.info(tmpR0)\n\nprint(\"Log Directory::\", log_dir)\ntmpR1 = 'Log Directory::' + log_dir\nlogging.info(tmpR1)\n\nprint('Welcome to Amadeus Calling Program: ')\nprint('-' * 60)\nprint('Please Press 1 for flight offers.')\nprint('Please Press 2 for cheapest date.')\nprint('Please Press 3 for list of hotels by city.')\nprint('Please Press 4 for list of offers by specific hotel.')\nprint('Please Press 5 for specific hotel review.')\n\n# Create the instance of the Amadeus Class\nx2 = cw.clsAmedeus()\n\n# Let's pass this to our map section\nif input_choice == 1:\ntextOrig = str(input('Please provide the Origin:'))\ntextDest = str(input('Please provide the Destination:'))\ntextDate = str(input('Please provide the Depart Date:'))\n\nretJson = x2.process(input_choice, textOrig, textDest, textDate, intAdult, textHotelID)\nelif input_choice == 2:\ntextOrig = str(input('Please provide the Origin:'))\ntextDest = str(input('Please provide the Destination:'))\n\nretJson = x2.process(input_choice, textOrig, textDest, textDate, intAdult, textHotelID)\nelif input_choice == 3:\ntextOrig = str(input('Please provide the Origin:'))\n\nretJson = x2.process(input_choice, textOrig, textDest, textDate, intAdult, textHotelID)\nelif input_choice == 4:\ntextHotelID = str(input('Please provide the Hotel Id:'))\n\nretJson = x2.process(input_choice, textOrig, textDest, textDate, intAdult, textHotelID)\nelif input_choice == 5:\ntextHotelID = str(input('Please provide the Hotel Id:'))\n\nretJson = x2.process(input_choice, textOrig, textDest, textDate, intAdult, textHotelID)\nelse:\nprint('Invalid options!')\nretJson = {'errorDetails': 'Invalid Options!'}\n\n#print('JSON::')\n#print(retJson)\n\n# Converting JSon to Pandas Dataframe for better readability\nres_1 = json.dumps(retJson)\n\n# Newly added JSON Parse package\ndic_flattened = (flatten(d) for d in res)\ndf_ret = p.DataFrame(dic_flattened)\n\n# Removing any duplicate columns\ndf_ret = df_ret.loc[:, ~df_ret.columns.duplicated()]\n\nprint('Publishing sample result: ')\n\n# Logging Final Output\nl.logr('1.df_ret' + var + '.csv', debug_ind, df_ret, 'log')\n\nprint(\"-\" * 60)\nprint()\n\nprint('Finding Analysis points..')\nprint(\"*\" * 157)\nlogging.info('Finding Analysis points..')\nlogging.info(tmpR0)\n\ntmpR10 = 'End Time: ' + str(var)\nlogging.info(tmpR10)\nlogging.info(tmpR0)\n\nexcept ValueError as e:\nprint(str(e))\nprint(\"Invalid option!\")\nlogging.info(\"Invalid option!\")\n\nexcept Exception as e:\nprint(\"Top level Error: args:{0}, message{1}\".format(e.args, e.message))\n\nif __name__ == \"__main__\":\nmain()\n```\n\nKey lines from the above script –\n\n```# Create the instance of the Amadeus Class\nx2 = cw.clsAmedeus()```\n\nThe above line will instantiate the newly written Amadeus class.\n\n```# Let's pass this to our map section\nif input_choice == 1:\ntextOrig = str(input('Please provide the Origin:'))\ntextDest = str(input('Please provide the Destination:'))\ntextDate = str(input('Please provide the Depart Date:'))\n\nretJson = x2.process(input_choice, textOrig, textDest, textDate, intAdult, textHotelID)\nelif input_choice == 2:\ntextOrig = str(input('Please provide the Origin:'))\ntextDest = str(input('Please provide the Destination:'))\n\nretJson = x2.process(input_choice, textOrig, textDest, textDate, intAdult, textHotelID)\nelif input_choice == 3:\ntextOrig = str(input('Please provide the Origin:'))\n\nretJson = x2.process(input_choice, textOrig, textDest, textDate, intAdult, textHotelID)\nelif input_choice == 4:\ntextHotelID = str(input('Please provide the Hotel Id:'))\n\nretJson = x2.process(input_choice, textOrig, textDest, textDate, intAdult, textHotelID)\nelif input_choice == 5:\ntextHotelID = str(input('Please provide the Hotel Id:'))\n\nretJson = x2.process(input_choice, textOrig, textDest, textDate, intAdult, textHotelID)\nelse:\nprint('Invalid options!')\nretJson = {'errorDetails': 'Invalid Options!'}```\n\nThe above lines will fetch the response based on the supplied inputs in the form of JSON.\n\n```# Converting JSon to Pandas Dataframe for better readability\nres_1 = json.dumps(retJson)\n\nNow, the above line will convert the return payload to JSON.\n\nSample JSON should look something like this –\n\nNow, using this new package, our application will flatten the complex nested JSON.\n\n```# Newly added JSON Parse package\ndic_flattened = (flatten(d) for d in res)\ndf_ret = p.DataFrame(dic_flattened)```\n\nThe given lines will remove any duplicate column if it exists.\n\n```# Removing any duplicate columns\ndf_ret = df_ret.loc[:, ~df_ret.columns.duplicated()]```\n\nLet’s explore the directory structure –\n\nLet’s run our application –\n\nWe’ll invoke five different API’s (API related to different functionalities) & their business cases –\n\nRun – Option 1:\n\nSo, if we want to explore some of the key columns, below is the screenshot for a few sample data –\n\nRun – Option 2:\n\nSome of the vital sample data –\n\nRun – Option 3:\n\nSample relevant data for our analysis –\n\nRun – Option 4:\n\nFew relevant essential information –\n\nRun – Option 5:\n\nFinally, few sample records from the last option –\n\nSo, finally, we’ve done it. You will find that JSON package from this link.\n\nDuring this challenging time, I would request you to follow strict health guidelines & stay healthy.\n\nN.B.: All the data that are used here can be found in the public domain. We use this solely for educational purposes.\n\n## Canada’s Covid19 analysis based on Logistic Regression\n\nHi Guys,\n\nToday, I’ll be demonstrating some scenarios based on open-source data from Canada. In this post, I will only explain some of the significant parts of the code. Not the entire range of scripts here.\n\nLet’s explore a couple of sample source data –\n\nI would like to explore how much this disease caused an impact on the elderly in Canada.\n\nLet’s explore the source directory structure –\n\nFor this, you need to install the following packages –\n\npip install pandas\n\npip install seaborn\n\nIn this case, we’ve downloaded the data from Canada’s site. However, they have created API. So, you can consume the data through that way as well. Since the volume is a little large. I decided to download that in CSV & then use that for my analysis.\n\nBefore I start, let me explain a couple of critical assumptions that I had to make due to data impurities or availabilities.\n\n• If there is no data available for a specific case, my application will consider that patient as COVID-Active.\n• We will consider the patient is affected through Community-spreading until we have data to find it otherwise.\n• If there is no data available for gender, we’re marking these records as “Other.” So, that way, we’re making it into that category, where the patient doesn’t want to disclose their sexual orientation.\n• If we don’t have any data, then by default, the application is considering the patient is alive.\n• Lastly, my application considers the middle point of the age range data for all the categories, i.e., the patient’s age between 20 & 30 will be considered as 25.\n\n1. clsCovidAnalysisByCountryAdv (This is the main script, which will invoke the Machine-Learning API & return 0 if successful.)\n\n```##############################################\n#### Written By: SATYAKI DE ####\n#### Written On: 01-Jun-2020 ####\n#### Modified On 01-Jun-2020 ####\n#### ####\n#### Objective: Main scripts for Logistic ####\n#### Regression. ####\n##############################################\n\nimport pandas as p\nimport clsL as log\nimport datetime\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom clsConfig import clsConfig as cf\n\n# %matplotlib inline -- for Jupyter Notebook\ndef __init__(self):\nself.fileName_1 = cf.config['FILE_NAME_1']\nself.fileName_2 = cf.config['FILE_NAME_2']\nself.Ind = cf.config['DEBUG_IND']\nself.subdir = str(cf.config['LOG_DIR_NAME'])\n\ndef setDefaultActiveCases(self, row):\ntry:\nstr_status = str(row['case_status'])\n\nif str_status == 'Not Reported':\nreturn 'Active'\nelse:\nreturn str_status\nexcept:\nreturn 'Active'\n\ndef setDefaultExposure(self, row):\ntry:\nstr_exposure = str(row['exposure'])\n\nif str_exposure == 'Not Reported':\nreturn 'Community'\nelse:\nreturn str_exposure\nexcept:\nreturn 'Community'\n\ndef setGender(self, row):\ntry:\nstr_gender = str(row['gender'])\n\nif str_gender == 'Not Reported':\nreturn 'Other'\nelse:\nreturn str_gender\nexcept:\nreturn 'Other'\n\ndef setSurviveStatus(self, row):\ntry:\n# 0 - Deceased\n# 1 - Alive\nstr_active = str(row['ActiveCases'])\n\nif str_active == 'Deceased':\nreturn 0\nelse:\nreturn 1\nexcept:\nreturn 1\n\ndef getAgeFromGroup(self, row):\ntry:\n# We'll take the middle of the Age group\n# If a age range falls with 20, we'll\n# consider this as 10.\n# Similarly, a age group between 20 & 30,\n# should reflect by 25.\n# Anything above 80 will be considered as\n# 85\n\nstr_age_group = str(row['AgeGroup'])\n\nif str_age_group == '<20':\nreturn 10\nelif str_age_group == '20-29':\nreturn 25\nelif str_age_group == '30-39':\nreturn 35\nelif str_age_group == '40-49':\nreturn 45\nelif str_age_group == '50-59':\nreturn 55\nelif str_age_group == '60-69':\nreturn 65\nelif str_age_group == '70-79':\nreturn 75\nelse:\nreturn 85\nexcept:\nreturn 100\n\ndef predictResult(self):\ntry:\n\n# Initiating Logging Instances\nclog = log.clsL()\n\n# Important variables\nvar = datetime.datetime.now().strftime(\".%H.%M.%S\")\nprint('Target File Extension will contain the following:: ', var)\nInd = self.Ind\nsubdir = self.subdir\n\n#######################################\n# #\n# Using Logistic Regression to #\n# Idenitfy the following scenarios - #\n# #\n# Age wise Infection Vs Deaths #\n# #\n#######################################\ninputFileName_2 = self.fileName_2\n\n# Fetching only relevant columns\ndf_2_Mod = df_2[['date_reported','age_group','gender','exposure','case_status']]\ndf_2_Mod['State'] = df_2['province_abbr']\n\nprint()\nprint('Projecting 2nd file sample rows: ')\n\nprint()\nx_row_1 = df_2_Mod.shape\nx_col_1 = df_2_Mod.shape\n\nprint('Total Number of Rows: ', x_row_1)\nprint('Total Number of columns: ', x_col_1)\n\n#########################################################################################\n# Few Assumptions #\n#########################################################################################\n# By default, if there is no data on exposure - We'll treat that as community spreading #\n# By default, if there is no data on case_status - We'll consider this as active #\n# By default, if there is no data on gender - We'll put that under a separate Gender #\n# category marked as the \"Other\". This includes someone who doesn't want to identify #\n# his/her gender or wants to be part of LGBT community in a generic term. #\n# #\n# We'll transform our data accordingly based on the above logic. #\n#########################################################################################\ndf_2_Mod['ActiveCases'] = df_2_Mod.apply(lambda row: self.setDefaultActiveCases(row), axis=1)\ndf_2_Mod['ExposureStatus'] = df_2_Mod.apply(lambda row: self.setDefaultExposure(row), axis=1)\ndf_2_Mod['Gender'] = df_2_Mod.apply(lambda row: self.setGender(row), axis=1)\n\n# Filtering all other records where we don't get any relevant information\n# Fetching Data for\ndf_3 = df_2_Mod[(df_2_Mod['age_group'] != 'Not Reported')]\n\n# Dropping unwanted columns\ndf_3.drop(columns=['exposure'], inplace=True)\ndf_3.drop(columns=['case_status'], inplace=True)\ndf_3.drop(columns=['date_reported'], inplace=True)\ndf_3.drop(columns=['gender'], inplace=True)\n\n# Renaming one existing column\ndf_3.rename(columns={\"age_group\": \"AgeGroup\"}, inplace=True)\n\n# Creating important feature\n# 0 - Deceased\n# 1 - Alive\ndf_3['Survived'] = df_3.apply(lambda row: self.setSurviveStatus(row), axis=1)\n\nclog.logr('2.df_3' + var + '.csv', Ind, df_3, subdir)\n\nprint()\nprint('Projecting Filter sample rows: ')\n\nprint()\nx_row_2 = df_3.shape\nx_col_2 = df_3.shape\n\nprint('Total Number of Rows: ', x_row_2)\nprint('Total Number of columns: ', x_col_2)\n\n# Let's do some basic checkings\nsns.set_style('whitegrid')\n#sns.countplot(x='Survived', hue='Gender', data=df_3, palette='RdBu_r')\n\n# Fixing Gender Column\n# This will check & indicate yellow for missing entries\n#sns.heatmap(df_3.isnull(), yticklabels=False, cbar=False, cmap='viridis')\n\n#sex = p.get_dummies(df_3['Gender'], drop_first=True)\nsex = p.get_dummies(df_3['Gender'])\ndf_4 = p.concat([df_3, sex], axis=1)\n\nprint('After New addition of columns: ')\n\nclog.logr('3.df_4' + var + '.csv', Ind, df_4, subdir)\n\n# Dropping unwanted columns for our Machine Learning\ndf_4.drop(columns=['Gender'], inplace=True)\ndf_4.drop(columns=['ActiveCases'], inplace=True)\ndf_4.drop(columns=['Male','Other','Transgender'], inplace=True)\n\nclog.logr('4.df_4_Mod' + var + '.csv', Ind, df_4, subdir)\n\nclog.logr('5.df_5' + var + '.csv', Ind, df_5, subdir)\n\n# Dropping unwanted columns for our Machine Learning\ndf_5.drop(columns=['ExposureStatus'], inplace=True)\n\nclog.logr('6.df_5_Mod' + var + '.csv', Ind, df_5, subdir)\n\n# Fixing Age Columns\ndf_5['Age'] = df_5.apply(lambda row: self.getAgeFromGroup(row), axis=1)\ndf_5.drop(columns=[\"AgeGroup\"], inplace=True)\n\nclog.logr('7.df_6' + var + '.csv', Ind, df_5, subdir)\n\n# Fixing Dummy Columns Name\n# Renaming one existing column Travel-Related with Travel_Related\ndf_5.rename(columns={\"Travel-Related\": \"TravelRelated\"}, inplace=True)\n\nclog.logr('8.df_7' + var + '.csv', Ind, df_5, subdir)\n\n# Removing state for temporary basis\ndf_5.drop(columns=['State'], inplace=True)\n# df_5.drop(columns=['State','Other','Transgender','Pending','TravelRelated','Male'], inplace=True)\n\n# Casting this entire dataframe into Integer\n# df_5_temp.apply(p.to_numeric)\n\nprint('Info::')\nprint(df_5.info())\nprint(\"*\" * 60)\nprint(df_5.describe())\nprint(\"*\" * 60)\n\nclog.logr('9.df_8' + var + '.csv', Ind, df_5, subdir)\n\nprint('Intermediate Sample Dataframe for Age::')\n\n# Plotting it to Graph\nsns.jointplot(x=\"Age\", y='Survived', data=df_5)\nsns.jointplot(x=\"Age\", y='Survived', data=df_5, kind='kde', color='red')\nplt.xlabel(\"Age\")\nplt.ylabel(\"Data Point (0 - Died Vs 1 - Alive)\")\n\n# Another check with Age Group\nsns.countplot(x='Survived', hue='Age', data=df_5, palette='RdBu_r')\nplt.xlabel(\"Survived(0 - Died Vs 1 - Alive)\")\nplt.ylabel(\"Total No Of Patient\")\n\ndf_6 = df_5.drop(columns=['Survived'], axis=1)\n\nclog.logr('10.df_9' + var + '.csv', Ind, df_6, subdir)\n\n# Train & Split Data\nx_1 = df_6\ny_1 = df_5['Survived']\n\n# Now Train-Test Split of your source data\nfrom sklearn.model_selection import train_test_split\n\n# test_size => % of allocated data for your test cases\n# random_state => A specific set of random split on your data\nX_train_1, X_test_1, Y_train_1, Y_test_1 = train_test_split(x_1, y_1, test_size=0.3, random_state=101)\n\n# Importing Model\nfrom sklearn.linear_model import LogisticRegression\n\nlogmodel = LogisticRegression()\nlogmodel.fit(X_train_1, Y_train_1)\n\npredictions_1 = logmodel.predict(X_test_1)\n\nfrom sklearn.metrics import classification_report\n\nprint('Classification Report:: ')\nprint(classification_report(Y_test_1, predictions_1))\n\nfrom sklearn.metrics import confusion_matrix\n\nprint('Confusion Matrix:: ')\nprint(confusion_matrix(Y_test_1, predictions_1))\n\n# This is require when you are trying to print from conventional\n# front & not using Jupyter notebook.\nplt.show()\n\nreturn 0\n\nexcept Exception as e:\nx = str(e)\nprint('Error : ', x)\n\nreturn 1\n```\n\nKey snippets from the above script –\n\n```df_2_Mod['ActiveCases'] = df_2_Mod.apply(lambda row: self.setDefaultActiveCases(row), axis=1)\ndf_2_Mod['ExposureStatus'] = df_2_Mod.apply(lambda row: self.setDefaultExposure(row), axis=1)\ndf_2_Mod['Gender'] = df_2_Mod.apply(lambda row: self.setGender(row), axis=1)\n\n# Filtering all other records where we don't get any relevant information\n# Fetching Data for\ndf_3 = df_2_Mod[(df_2_Mod['age_group'] != 'Not Reported')]\n\n# Dropping unwanted columns\ndf_3.drop(columns=['exposure'], inplace=True)\ndf_3.drop(columns=['case_status'], inplace=True)\ndf_3.drop(columns=['date_reported'], inplace=True)\ndf_3.drop(columns=['gender'], inplace=True)\n\n# Renaming one existing column\ndf_3.rename(columns={\"age_group\": \"AgeGroup\"}, inplace=True)\n\n# Creating important feature\n# 0 - Deceased\n# 1 - Alive\ndf_3['Survived'] = df_3.apply(lambda row: self.setSurviveStatus(row), axis=1)```\n\nThe above lines point to the critical transformation areas, where the application is invoking various essential business logic.\n\nLet’s see at this moment our sample data –\n\nLet’s look into the following part –\n\n```# Fixing Spread Columns\n\nThe above lines will transform the data into this –\n\nAs you can see, we’ve transformed the row values into columns with binary values. This kind of transformation is beneficial.\n\n```# Plotting it to Graph\nsns.jointplot(x=\"Age\", y='Survived', data=df_5)\nsns.jointplot(x=\"Age\", y='Survived', data=df_5, kind='kde', color='red')\nplt.xlabel(\"Age\")\nplt.ylabel(\"Data Point (0 - Died Vs 1 - Alive)\")\n\n# Another check with Age Group\nsns.countplot(x='Survived', hue='Age', data=df_5, palette='RdBu_r')\nplt.xlabel(\"Survived(0 - Died Vs 1 - Alive)\")\nplt.ylabel(\"Total No Of Patient\")```\n\nThe above lines will process the data & visualize based on that.\n\n```x_1 = df_6\ny_1 = df_5['Survived']```\n\nIn the above snippet, we’ve assigned the features & target variable for our final logistic regression model.\n\n```# Now Train-Test Split of your source data\nfrom sklearn.model_selection import train_test_split\n\n# test_size => % of allocated data for your test cases\n# random_state => A specific set of random split on your data\nX_train_1, X_test_1, Y_train_1, Y_test_1 = train_test_split(x_1, y_1, test_size=0.3, random_state=101)\n\n# Importing Model\nfrom sklearn.linear_model import LogisticRegression\n\nlogmodel = LogisticRegression()\nlogmodel.fit(X_train_1, Y_train_1)```\n\nIn the above snippet, we’re splitting the primary data & create a set of test & train data. Once we have the collection, the application will put the logistic regression model. And, finally, we’ll fit the training data.\n\n```# Adding Predictions to it\npredictions_1 = logmodel.predict(X_test_1)\n\nfrom sklearn.metrics import classification_report\n\nprint('Classification Report:: ')\nprint(classification_report(Y_test_1, predictions_1))```\n\nThe above lines, finally use the model & then we feed our test data.\n\nLet’s see how it runs –\n\nAnd, here is the log directory –\n\nFor better understanding, I’m just clubbing both the diagram at one place & the final outcome is showing as follows –\n\nSo, from the above picture, we can see that the maximum vulnerable patients are patients who are 80+. The next two categories that also suffered are 70+ & 60+.\n\nAlso, We’ve checked the Female Vs. Male in the following code –\n\n```sns.countplot(x='Survived', hue='Female', data=df_5, palette='RdBu_r')\nplt.xlabel(\"Survived(0 - Died Vs 1 - Alive)\")\nplt.ylabel(\"Female Vs Male (Including Other Genders)\")```\n\nAnd, the analysis represents through this –\n\nIn this case, you have to consider that the Male part includes all the other genders apart from the actual Male. Hence, I believe death for females would be more compared to people who identified themselves as males.\n\nSo, finally, we’ve done it.\n\nDuring this challenging time, I would request you to follow strict health guidelines & stay healthy.\n\nN.B.: All the data that are used here can be found in the public domain. We use this solely for educational purposes. You can find the details here.\n\n## Predicting Flipkart business growth factor using Linear-Regression Machine Learning Model\n\nHi Guys,\n\nToday, We’ll be exploring the potential business growth factor using the “Linear-Regression Machine Learning” model. We’ve prepared a set of dummy data & based on that, we’ll predict.\n\nLet’s explore a few sample data –\n\nSo, based on these data, we would like to predict YearlyAmountSpent dependent on any one of the following features, i.e. [ Time On App / Time On Website / Flipkart Membership Duration (In Year) ].\n\nYou need to install the following packages –\n\npip install pandas\n\npip install matplotlib\n\npip install sklearn\n\nWe’ll be discussing only the main calling script & class script. However, we’ll be posting the parameters without discussing it. And, we won’t discuss clsL.py as we’ve already discussed that in our previous post.\n\n1. clsConfig.py (This script contains all the parameter details.)\n\n```################################################\n#### Written By: SATYAKI DE ####\n#### Written On: 15-May-2020 ####\n#### ####\n#### Objective: This script is a config ####\n#### file, contains all the keys for ####\n#### Machine-Learning. Application will ####\n#### process these information & perform ####\n#### various analysis on Linear-Regression. ####\n################################################\n\nimport os\nimport platform as pl\n\nclass clsConfig(object):\nCurr_Path = os.path.dirname(os.path.realpath(__file__))\n\nos_det = pl.system()\nif os_det == \"Windows\":\nsep = '\\\\'\nelse:\nsep = '/'\n\nconfig = {\n'APP_ID': 1,\n'ARCH_DIR': Curr_Path + sep + 'arch' + sep,\n'PROFILE_PATH': Curr_Path + sep + 'profile' + sep,\n'LOG_PATH': Curr_Path + sep + 'log' + sep,\n'REPORT_PATH': Curr_Path + sep + 'report',\n'FILE_NAME': Curr_Path + sep + 'Data' + sep + 'FlipkartCustomers.csv',\n'SRC_PATH': Curr_Path + sep + 'Data' + sep,\n'APP_DESC_1': 'IBM Watson Language Understand!',\n'DEBUG_IND': 'N',\n'INIT_PATH': Curr_Path\n}\n```\n\n2. clsLinearRegression.py (This is the main script, which will invoke the Machine-Learning API & return 0 if successful.)\n\n```##############################################\n#### Written By: SATYAKI DE ####\n#### Written On: 15-May-2020 ####\n#### Modified On 15-May-2020 ####\n#### ####\n#### Objective: Main scripts for Linear ####\n#### Regression. ####\n##############################################\n\nimport pandas as p\nimport numpy as np\nimport regex as re\n\nimport matplotlib.pyplot as plt\nfrom clsConfig import clsConfig as cf\n\n# %matplotlib inline -- for Jupyter Notebook\nclass clsLinearRegression:\ndef __init__(self):\nself.fileName = cf.config['FILE_NAME']\n\ndef predictResult(self):\ntry:\n\ninputFileName = self.fileName\n\nprint()\nprint('Projecting sample rows: ')\n\nprint()\nx_row = df.shape\nx_col = df.shape\n\nprint('Total Number of Rows: ', x_row)\nprint('Total Number of columns: ', x_col)\n\nx = df[['TimeOnApp', 'TimeOnWebsite', 'FlipkartMembershipInYear']]\n\n# Target Variable - Trying to predict\ny = df['YearlyAmountSpent']\n\n# Now Train-Test Split of your source data\nfrom sklearn.model_selection import train_test_split\n\n# test_size => % of allocated data for your test cases\n# random_state => A specific set of random split on your data\nX_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.4, random_state=101)\n\n# Importing Model\nfrom sklearn.linear_model import LinearRegression\n\n# Creating an Instance\nlm = LinearRegression()\n\n# Train or Fit my model on Training Data\nlm.fit(X_train, Y_train)\n\n# Creating a prediction value\nflipKartSalePrediction = lm.predict(X_test)\n\n# Creating a scatter plot based on Actual Value & Predicted Value\nplt.scatter(Y_test, flipKartSalePrediction)\n\nplt.xlabel('Actual Values')\nplt.ylabel('Predicted Values')\n\n# Checking Individual Metrics\nfrom sklearn import metrics\n\nprint()\nmea_val = metrics.mean_absolute_error(Y_test, flipKartSalePrediction)\nprint('Mean Absolute Error (MEA): ', mea_val)\n\nmse_val = metrics.mean_squared_error(Y_test, flipKartSalePrediction)\nprint('Mean Square Error (MSE): ', mse_val)\n\nrmse_val = np.sqrt(metrics.mean_squared_error(Y_test, flipKartSalePrediction))\nprint('Square root Mean Square Error (RMSE): ', rmse_val)\n\nprint()\n\n# Check Variance Score - R^2 Value\nprint('Variance Score:')\nvar_score = str(round(metrics.explained_variance_score(Y_test, flipKartSalePrediction) * 100, 2)).strip()\nprint('Our Model is', var_score, '% accurate. ')\nprint()\n\n# Finding Coeficent on X_train.columns\nprint()\nprint('Finding Coeficent: ')\n\ncedf = p.DataFrame(lm.coef_, x.columns, columns=['Coefficient'])\nprint('Printing the All the Factors: ')\nprint(cedf)\n\nprint()\n\n# Getting the Max Value from it\n\n# Filtering the max Value to identify the biggest Business factor\n\n# Dropping the derived column\ndfMax = dfMax.reset_index()\n\nprint(dfMax)\n\n# Extracting Actual Business Factor from Pandas dataframe\nstr_factor_temp = str(dfMax.iloc['index'])\nstr_factor = re.sub(\"([a-z])([A-Z])\", \"\\g<1> \\g<2>\", str_factor_temp)\nstr_value = str(round(float(dfMax.iloc['Coefficient']),2))\n\nprint()\nprint('*' * 80)\nprint('Major Busienss Activity - (', str_factor, ') - ', str_value, '%')\nprint('*' * 80)\nprint()\n\n# This is require when you are trying to print from conventional\n# front & not using Jupyter notebook.\nplt.show()\n\nreturn 0\n\nexcept Exception as e:\nx = str(e)\nprint('Error : ', x)\n\nreturn 1\n```\n\nKey lines from the above snippet –\n\n```# Adding Features\nx = df[['TimeOnApp', 'TimeOnWebsite', 'FlipkartMembershipInYear']]```\n\nOur application creating a subset of the main datagram, which contains all the features.\n\n```# Target Variable - Trying to predict\ny = df['YearlyAmountSpent']```\n\nNow, the application is setting the target variable into ‘Y.’\n\n```# Now Train-Test Split of your source data\nfrom sklearn.model_selection import train_test_split\n\n# test_size => % of allocated data for your test cases\n# random_state => A specific set of random split on your data\nX_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.4, random_state=101)```\n\nAs per “Supervised Learning,” our application is splitting the dataset into two subsets. One is to train the model & another segment is to test your final model. However, you can divide the data into three sets that include the performance statistics for a large dataset. In our case, we don’t need that as this data is significantly less.\n\n```# Train or Fit my model on Training Data\nlm.fit(X_train, Y_train)```\n\nOur application is now training/fit the data into the model.\n\n```# Creating a scatter plot based on Actual Value & Predicted Value\nplt.scatter(Y_test, flipKartSalePrediction)```\n\nOur application projected the outcome based on the predicted data in a scatterplot graph.\n\nAlso, the following concepts captured by using our program. For more details, I’ve provided the external link for your reference –\n\n1. Mean Absolute Error (MEA)\n2. Mean Square Error (MSE)\n3. Square Root Mean Square Error (RMSE)\n\nAnd, the implementation has shown as –\n\n```mea_val = metrics.mean_absolute_error(Y_test, flipKartSalePrediction)\nprint('Mean Absolute Error (MEA): ', mea_val)\n\nmse_val = metrics.mean_squared_error(Y_test, flipKartSalePrediction)\nprint('Mean Square Error (MSE): ', mse_val)\n\nrmse_val = np.sqrt(metrics.mean_squared_error(Y_test, flipKartSalePrediction))\nprint('Square Root Mean Square Error (RMSE): ', rmse_val)```\n\nAt this moment, we would like to check the credibility of our model by using the variance score are as follows –\n\n```var_score = str(round(metrics.explained_variance_score(Y_test, flipKartSalePrediction) * 100, 2)).strip()\nprint('Our Model is', var_score, '% accurate. ')```\n\nFinally, extracting the coefficient to find out, which particular feature will lead Flikkart for better sale & growth by taking the maximum of coefficient value month the all features are as shown below –\n\n```cedf = p.DataFrame(lm.coef_, x.columns, columns=['Coefficient'])\n\n# Getting the Max Value from it\n\n# Filtering the max Value to identify the biggest Business factor\n\n# Dropping the derived column\ndfMax = dfMax.reset_index()```\n\nNote that we’ve used a regular expression to split the camel-case column name from our feature & represent that with a much more meaningful name without changing the column name.\n\n```# Extracting Actual Business Factor from Pandas dataframe\nstr_factor_temp = str(dfMax.iloc['index'])\nstr_factor = re.sub(\"([a-z])([A-Z])\", \"\\g<1> \\g<2>\", str_factor_temp)\nstr_value = str(round(float(dfMax.iloc['Coefficient']),2))\n\nprint('Major Busienss Activity - (', str_factor, ') - ', str_value, '%')```\n\n3. callLinear.py (This is the first calling script.)\n\n```##############################################\n#### Written By: SATYAKI DE ####\n#### Written On: 15-May-2020 ####\n#### Modified On 15-May-2020 ####\n#### ####\n#### Objective: Main calling scripts. ####\n##############################################\n\nfrom clsConfig import clsConfig as cf\nimport clsL as cl\nimport logging\nimport datetime\nimport clsLinearRegression as cw\n\n# Disbling Warning\ndef warn(*args, **kwargs):\npass\n\nimport warnings\nwarnings.warn = warn\n\n# Lookup functions from\n# Azure cloud SQL DB\n\nvar = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n\ndef main():\ntry:\nret_1 = 0\ngeneral_log_path = str(cf.config['LOG_PATH'])\n\n# Enabling Logging Info\nlogging.basicConfig(filename=general_log_path + 'MachineLearning_LinearRegression.log', level=logging.INFO)\n\n# Initiating Log Class\nl = cl.clsL()\n\n# Moving previous day log files to archive directory\nlog_dir = cf.config['LOG_PATH']\ncurr_ver =datetime.datetime.now().strftime(\"%Y-%m-%d\")\n\ntmpR0 = \"*\" * 157\n\nlogging.info(tmpR0)\ntmpR9 = 'Start Time: ' + str(var)\nlogging.info(tmpR9)\nlogging.info(tmpR0)\n\nprint(\"Log Directory::\", log_dir)\ntmpR1 = 'Log Directory::' + log_dir\nlogging.info(tmpR1)\n\nprint('Machine Learning - Linear Regression Prediction : ')\nprint('-' * 200)\n\n# Create the instance of the Linear-Regression Class\nx2 = cw.clsLinearRegression()\n\nret = x2.predictResult()\n\nif ret == 0:\nprint('Successful Linear-Regression Prediction Generated!')\nelse:\nprint('Failed to generate Linear-Regression Prediction!')\n\nprint(\"-\" * 200)\nprint()\n\nprint('Finding Analysis points..')\nprint(\"*\" * 200)\nlogging.info('Finding Analysis points..')\nlogging.info(tmpR0)\n\ntmpR10 = 'End Time: ' + str(var)\nlogging.info(tmpR10)\nlogging.info(tmpR0)\n\nexcept ValueError as e:\nprint(str(e))\nlogging.info(str(e))\n\nexcept Exception as e:\nprint(\"Top level Error: args:{0}, message{1}\".format(e.args, e.message))\n\nif __name__ == \"__main__\":\nmain()\n```\n\nKey snippet from the above script –\n\n```# Create the instance of the Linear-Regression\nx2 = cw.clsLinearRegression()\n\nret = x2.predictResult()```\n\nIn the above snippet, our application initially creating an instance of the main class & finally invokes the “predictResult” method.\n\nLet’s run our application –\n\nStep 1:\n\nFirst, the application will fetch the following sample rows from our source file – if it is successful.\n\nStep 2:\n\nThen, It will create the following scatterplot by executing the following snippet –\n\n```# Creating a scatter plot based on Actual Value & Predicted Value\nplt.scatter(Y_test, flipKartSalePrediction)```\n\nNote that our model is pretty accurate & it has a balanced success rate compared to our predicted numbers.\n\nStep 3:\n\nFinally, it is successfully able to project the critical feature are shown below –\n\nFrom the above picture, you can see that our model is pretty accurate (89% approx).\n\nAlso, highlighted red square identifying the key-features & their confidence score & finally, the projecting the winner feature marked in green.\n\nSo, as per that, we’ve come to one conclusion that Flipkart’s business growth depends on the tenure of their subscriber, i.e., old members are prone to buy more than newer members.\n\nLet’s look into our directory structure –\n\nSo, we’ve done it.\n\nI’ll be posting another new post in the coming days. Till then, Happy Avenging! 😀\n\nNote: All the data posted here are representational data & available over the internet & for educational purpose only.\n\n## Analyzing Language using IBM Watson using Python\n\nHi Guys,\n\nToday, I’ll be discussing the following topic – “How to analyze text using IBM Watson implementing through Python.”\n\nIBM has significantly improved in the field of Visual Image Analysis or Text language analysis using its IBM Watson cloud platform. In this particular topic, we’ll be exploring the natural languages only.\n\nTo access IBM API, we need to first create an IBM Cloud account from this site.\n\nLet us quickly go through the steps to create the IBM Language Understanding service. Click the Catalog on top of your browser menu as shown in the below picture –\n\nAfter that, click the AI option on your left-hand side of the panel marked in RED.\n\nClick the Watson-Studio & later choose the plan. In our case, We’ll select the “Lite” option as IBM provided this platform for all the developers to explore their cloud for free.\n\nClicking the create option will lead to a blank page of Watson Studio as shown below –\n\nAnd, now, we need to click the Get Started button to launch it. This will lead to Create Project page, which can be done using the following steps –\n\nNow, clicking the create a project will lead you to the next screen –\n\nYou can choose either an empty project, or you can create it from a sample file. In this case, we’ll be selecting the first option & this will lead us to the below page –\n\nAnd, then you will click the “Create” option, which will lead you to the next screen –\n\nNow, you need to click “Add to Project.” This will give you a variety of services that you want to explore/use from the list. If you want to create your own natural language classifier, which you can do that as follows –\n\nOnce, you click it – you need to select the associate service –\n\nHere, you need to click the hyperlink, which prompts to the next screen –\n\nYou need to check the price for both the Visual & Natural Language Classifier. They are pretty expensive. The visual classifier has the Lite plan. However, it has limitations of output.\n\nClicking the “Create” will prompt to the next screen –\n\nAfter successful creation, you will be redirected to the following page –\n\nNow, We’ll be adding our “Natural Language Understand” for our test –\n\nThis will prompt the next screen –\n\nOnce, it is successful. You will see the service registered as shown below –\n\nIf you click the service marked in RED, it will lead you to another page, where you will get the API Key & Url. You need both of this information in Python application to access this API as shown below –\n\nNow, we’re ready with the necessary cloud set-up. After this, we need to install the Python package for IBM Cloud as shown below –\n\nWe’ve noticed that, recently, IBM has launched one upgraded package. Hence, we installed that one as well. I would recommend you to install this second package directly instead of the first one shown above –\n\nNow, we’re done with our set-up.\n\nLet’s see the directory structure –\n\nWe’ll be discussing only the main calling script & class script. However, we’ll be posting the parameters without discussing it. And, we won’t discuss clsL.py as we’ve already discussed that in our previous post.\n\n1. clsConfig.py (This script contains all the parameter details.)\n\n```##############################################\n#### Written By: SATYAKI DE ####\n#### Written On: 04-Apr-2020 ####\n#### ####\n#### Objective: This script is a config ####\n#### file, contains all the keys for ####\n#### IBM Cloud API. Application will ####\n#### process these information & perform ####\n#### various analysis on IBM Watson cloud.####\n##############################################\n\nimport os\nimport platform as pl\n\nclass clsConfig(object):\nCurr_Path = os.path.dirname(os.path.realpath(__file__))\n\nos_det = pl.system()\nif os_det == \"Windows\":\nsep = '\\\\'\nelse:\nsep = '/'\n\nconfig = {\n'APP_ID': 1,\n'SERVICE_URL': \"https://api.eu-gb.natural-language-understanding.watson.cloud.ibm.com/instances/xxxxxxxxxxxxxxXXXXXXXXXXxxxxxxxxxxxxxxxx\",\n'API_KEY': \"Xxxxxxxxxxxxxkdkdfifd984djddkkdkdkdsSSdkdkdd\",\n'API_TYPE': \"application/json\",\n'CACHE': \"no-cache\",\n'CON': \"keep-alive\",\n'ARCH_DIR': Curr_Path + sep + 'arch' + sep,\n'PROFILE_PATH': Curr_Path + sep + 'profile' + sep,\n'LOG_PATH': Curr_Path + sep + 'log' + sep,\n'REPORT_PATH': Curr_Path + sep + 'report',\n'SRC_PATH': Curr_Path + sep + 'Src_File' + sep,\n'APP_DESC_1': 'IBM Watson Language Understand!',\n'DEBUG_IND': 'N',\n'INIT_PATH': Curr_Path\n}\n```\n\nNote that you will be placing your API_KEY & URL here, as shown in the configuration file.\n\n2. clsIBMWatson.py (This is the main script, which will invoke the IBM Watson API based on the input from the user & return 0 if successful.)\n\n```##############################################\n#### Written By: SATYAKI DE ####\n#### Written On: 04-Apr-2020 ####\n#### Modified On 04-Apr-2020 ####\n#### ####\n#### Objective: Main scripts to invoke ####\n#### IBM Watson Language Understand API. ####\n##############################################\n\nimport logging\nfrom clsConfig import clsConfig as cf\nimport clsL as cl\nimport json\nfrom ibm_watson import NaturalLanguageUnderstandingV1\nfrom ibm_cloud_sdk_core.authenticators import IAMAuthenticator\nfrom ibm_watson.natural_language_understanding_v1 import Features, EntitiesOptions, KeywordsOptions, SentimentOptions, CategoriesOptions, ConceptsOptions\nfrom ibm_watson import ApiException\n\nclass clsIBMWatson:\ndef __init__(self):\nself.api_key = cf.config['API_KEY']\nself.service_url = cf.config['SERVICE_URL']\n\ndef calculateExpressionFromUrl(self, inputUrl, inputVersion):\ntry:\napi_key = self.api_key\nservice_url = self.service_url\nprint('-' * 60)\nprint('Beginning of the IBM Watson for Input Url.')\nprint('-' * 60)\n\nauthenticator = IAMAuthenticator(api_key)\n\n# Authentication via service credentials provided in our config files\nservice = NaturalLanguageUnderstandingV1(version=inputVersion, authenticator=authenticator)\nservice.set_service_url(service_url)\n\nresponse = service.analyze(\nurl=inputUrl,\nfeatures=Features(entities=EntitiesOptions(),\nsentiment=SentimentOptions(),\nconcepts=ConceptsOptions())).get_result()\n\nprint(json.dumps(response, indent=2))\n\nreturn 0\n\nexcept ApiException as ex:\nprint('-' * 60)\nprint(\"Method failed for Url with status code \" + str(ex.code) + \": \" + ex.message)\nprint('-' * 60)\n\nreturn 1\n\ndef calculateExpressionFromText(self, inputText, inputVersion):\ntry:\napi_key = self.api_key\nservice_url = self.service_url\nprint('-' * 60)\nprint('Beginning of the IBM Watson for Input Url.')\nprint('-' * 60)\n\nauthenticator = IAMAuthenticator(api_key)\n\n# Authentication via service credentials provided in our config files\nservice = NaturalLanguageUnderstandingV1(version=inputVersion, authenticator=authenticator)\nservice.set_service_url(service_url)\n\nresponse = service.analyze(\ntext=inputText,\nfeatures=Features(entities=EntitiesOptions(),\nsentiment=SentimentOptions(),\nconcepts=ConceptsOptions())).get_result()\n\nprint(json.dumps(response, indent=2))\n\nreturn 0\n\nexcept ApiException as ex:\nprint('-' * 60)\nprint(\"Method failed for Url with status code \" + str(ex.code) + \": \" + ex.message)\nprint('-' * 60)\n\nreturn 1\n```\n\nSome of the key lines from the above snippet –\n\n```authenticator = IAMAuthenticator(api_key)\n\n# Authentication via service credentials provided in our config files\nservice = NaturalLanguageUnderstandingV1(version=inputVersion, authenticator=authenticator)\nservice.set_service_url(service_url)```\n\nBy providing the API Key & Url, the application is initiating the service for Watson.\n\n```response = service.analyze(\nurl=inputUrl,\nfeatures=Features(entities=EntitiesOptions(),\nsentiment=SentimentOptions(),\nconcepts=ConceptsOptions())).get_result()```\n\nBased on your type of input, it will bring the features of entities, sentiment & concepts here. Apart from that, you can additionally check the following features as well – Keywords & Categories.\n\n3. callIBMWatsonAPI.py (This is the first calling script. Based on user choice, it will receive input either as Url or as the plain text & then analyze it.)\n\n```##############################################\n#### Written By: SATYAKI DE ####\n#### Written On: 04-Apr-2020 ####\n#### Modified On 04-Apr-2020 ####\n#### ####\n#### Objective: Main calling scripts. ####\n##############################################\n\nfrom clsConfig import clsConfig as cf\nimport clsL as cl\nimport logging\nimport datetime\nimport clsIBMWatson as cw\n\n# Disbling Warning\ndef warn(*args, **kwargs):\npass\n\nimport warnings\nwarnings.warn = warn\n\n# Lookup functions from\n# Azure cloud SQL DB\n\nvar = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n\ndef main():\ntry:\nret_1 = 0\ngeneral_log_path = str(cf.config['LOG_PATH'])\n\n# Enabling Logging Info\nlogging.basicConfig(filename=general_log_path + 'IBMWatson_NaturalLanguageAnalysis.log', level=logging.INFO)\n\n# Initiating Log Class\nl = cl.clsL()\n\n# Moving previous day log files to archive directory\nlog_dir = cf.config['LOG_PATH']\ncurr_ver =datetime.datetime.now().strftime(\"%Y-%m-%d\")\n\ntmpR0 = \"*\" * 157\n\nlogging.info(tmpR0)\ntmpR9 = 'Start Time: ' + str(var)\nlogging.info(tmpR9)\nlogging.info(tmpR0)\n\nprint(\"Log Directory::\", log_dir)\ntmpR1 = 'Log Directory::' + log_dir\nlogging.info(tmpR1)\n\nprint('Welcome to IBM Wantson Language Understanding Calling Program: ')\nprint('-' * 60)\nprint('Please Press 1 for Understand the language from Url.')\n\n# Create the instance of the IBM Watson Class\nx2 = cw.clsIBMWatson()\n\n# Let's pass this to our map section\nif input_choice == 1:\ntextUrl = str(input('Please provide the complete input url:'))\nret_1 = x2.calculateExpressionFromUrl(textUrl, curr_ver)\nelif input_choice == 2:\ninputText = str(input('Please provide the input text:'))\nret_1 = x2.calculateExpressionFromText(inputText, curr_ver)\nelse:\nprint('Invalid options!')\n\nif ret_1 == 0:\nprint('Successful IBM Watson Language Understanding Generated!')\nelse:\nprint('Failed to generate IBM Watson Language Understanding!')\n\nprint(\"-\" * 60)\nprint()\n\nprint('Finding Analysis points..')\nprint(\"*\" * 157)\nlogging.info('Finding Analysis points..')\nlogging.info(tmpR0)\n\ntmpR10 = 'End Time: ' + str(var)\nlogging.info(tmpR10)\nlogging.info(tmpR0)\n\nexcept ValueError as e:\nprint(str(e))\nprint(\"Invalid option!\")\nlogging.info(\"Invalid option!\")\n\nexcept Exception as e:\nprint(\"Top level Error: args:{0}, message{1}\".format(e.args, e.message))\n\nif __name__ == \"__main__\":\nmain()\n```\n\nThis script is pretty straight forward as it is first creating an instance of the main class & then based on the user input, it is calling the respective functions here.\n\nAs of now, IBM Watson can work on a list of languages, which are available here.\n\nIf you want to start from scratch, please refer to the following link.\n\nPlease find the screenshot of our application run –\n\nCase 1 (With Url):\n\nCase 2 (With Plain text):\n\nNow, Don’t forget to delete all the services from your IBM Cloud.\n\nAs you can see, from the service, you need to delete all the services one-by-one as shown in the figure.\n\nSo, we’ve done it.\n\nTo explore my photography, you can visit the following link.\n\nI’ll be posting another new post in the coming days. Till then, Happy Avenging! 😀\n\nNote: All the data posted here are representational data & available over the internet & for educational purpose only.\n\n## Creating a Cross-platform GUI based application using native Python using PyQt5\n\nHi Guys!\n\nToday, We’ll be discussing one more graphical package in Python, which is also known as PyQt. To faster design the GUI, we’ll be exploring another tool called Qt Designer, which is available for multiple OS platforms.\n\nPlease find the QT Designer here.\n\nThis is similar to any other GUI based IDE like Microsoft Visual Studio, where you can quickly generate your GUI template.\n\nThe majority of the internet post talks about using PyQt5 or PyQt4 packages. But, when speaking about using the .ui file inside your Python code – they either demonstrate fundamental options without any event or, they convert & generate the .ui file into .py file & then they use it. This certainly not making it very useful for many of the developers who are trying to use it for the first time. Hence, My main goal is to use the .ui file inside my Python script as it is & use all the components out of it & assign various working events.\n\nIn this post, we’ll discuss only with one script & then we’ll showcase the output in the form of video (No audio). You can verify the output for both MAC & Windows.\n\nBefore we start, let us check the directory structure between Windows & MAC –\n\nLet us explore how the GUI should look like ->\n\nSo, as you can see that this tool is like any other GUI based tool, basically you can create anything by simply drag & drop method.\n\nBefore we start discussing our code, here is the sample basicAdv.ui file for your reference.\n\nYou need to install the following framework –\n\npip install PyQt5\n\n1. GUIPyQt5.py (This script contains all the GUI details & it will invoke the instance along with the logic.)\n\n```##############################################\n#### Written By: SATYAKI DE ####\n#### Written On: 12-Mar-2020 ####\n#### Modified On 12-Mar-2020 ####\n#### ####\n#### Objective: Main calling scripts. ####\n##############################################\n\nfrom PyQt5 import QtWidgets, uic, QtGui, QtCore\nfrom PyQt5.QtWidgets import *\nimport sys\n\nclass Ui(QtWidgets.QMainWindow):\ndef __init__(self):\n# Instantiating the main class\nsuper(Ui, self).__init__()\n\n# converting it to any kind of Python code\n\n# Adding all the essential buttons\nself.prtBtn = self.findChild(QtWidgets.QPushButton, 'prtBtn') # Find the button\nself.prtBtn.clicked.connect(self.printButtonClick) # Remember to pass the definition/method, not the return value!\n\nself.clrBtn = self.findChild(QtWidgets.QPushButton, 'clrBtn') # Find the button\nself.clrBtn.clicked.connect(self.clearButtonClick) # Remember to pass the definition/method, not the return value!\n\nself.selectImgBtn = self.findChild(QtWidgets.QPushButton, 'selectImgBtn') # Find the button\nself.selectImgBtn.clicked.connect(self.setImage) # Remember to pass the definition/method, not the return value!\n\nself.cnfBtn = self.findChild(QtWidgets.QPushButton, 'cnfBtn') # Find the button\nself.cnfBtn.clicked.connect(self.showDialog) # Remember to pass the definition/method, not the return value!\n\n# Adding other static input/output elements\nself.input = self.findChild(QtWidgets.QLineEdit, 'input')\nself.qlabel = self.findChild(QtWidgets.QLabel, 'qlabel')\nself.lineEdit = self.findChild(QtWidgets.QLineEdit, 'lineEdit')\nself.listWidget = self.findChild(QtWidgets.QListWidget, 'listWidget')\nself.imageLbl = self.findChild(QtWidgets.QLabel, 'imageLbl')\n\nself.combo = self.findChild(QtWidgets.QComboBox, 'sComboBox') # Find the ComboBox\n\n# Adding static element to it\n\n# Click Event\nself.combo.activated[str].connect(self.onChanged) # Remember to pass the definition/method, not the return value!\n\nself.listwidget2 = self.findChild(QtWidgets.QListWidget, 'listwidget2') # Find the List\n\n# Adding static element to it\nself.listwidget2.insertItem(0, \"Aamir Khan\")\nself.listwidget2.insertItem(1, \"Shahruk Khan\")\nself.listwidget2.insertItem(2, \"Salman Khan\")\nself.listwidget2.insertItem(3, \"Hrittik Roshon\")\nself.listwidget2.insertItem(4, \"Amitabh Bachhan\")\n\n# Click Event\nself.listwidget2.clicked.connect(self.showIndividualElement)\n\nself.groupBox = self.findChild(QtWidgets.QGroupBox, 'groupBox') # Find the ComboBox\nself.groupBox.setCheckable(True)\n\nself.rdButton1 = self.findChild(QtWidgets.QRadioButton, 'rdButton1') # Find the button\nself.rdButton1.setChecked(True)\nself.rdButton1.toggled.connect(lambda: self.printRadioButtonClick(self.rdButton1)) # Remember to pass the definition/method, not the return value!\n\nself.rdButton2 = self.findChild(QtWidgets.QRadioButton, 'rdButton2') # Find the button\nself.rdButton2.toggled.connect(lambda: self.printRadioButtonClick(self.rdButton2)) # Remember to pass the definition/method, not the return value!\n\nself.rdButton3 = self.findChild(QtWidgets.QRadioButton, 'rdButton3') # Find the button\nself.rdButton3.toggled.connect(lambda: self.printRadioButtonClick(self.rdButton3)) # Remember to pass the definition/method, not the return value!\n\nself.rdButton4 = self.findChild(QtWidgets.QRadioButton, 'rdButton4') # Find the button\nself.rdButton4.toggled.connect(lambda: self.printRadioButtonClick(self.rdButton4)) # Remember to pass the definition/method, not the return value!\n\nself.show()\n\nelse:\n\nelse:\n\nelse:\n\nelse:\n\ndef printButtonClick(self):\n# This is executed when the button is pressed\nprint('Input text:' + self.input.text())\n\ndef clearButtonClick(self):\n# This is executed when the button is pressed\nself.input.clear()\n\ndef onChanged(self, text):\nself.qlabel.setText(text)\nself.lineEdit.clear() # Clear the text\n\nvalue = self.lineEdit.text() # Get the value of the lineEdit\nself.lineEdit.clear() # Clear the text\n\ndef setImage(self):\nfileName, _ = QtWidgets.QFileDialog.getOpenFileName(None, \"Select Image\", \"\", \"Image Files (*.png *.jpg *jpeg *.bmp);;All Files (*)\") # Ask for file\nif fileName: # If the user gives a file\npixmap = QtGui.QPixmap(fileName) # Setup pixmap with the provided image\npixmap = pixmap.scaled(self.imageLbl.width(), self.imageLbl.height(), QtCore.Qt.KeepAspectRatio) # Scale pixmap\nself.imageLbl.setPixmap(pixmap) # Set the pixmap onto the label\nself.imageLbl.setAlignment(QtCore.Qt.AlignCenter) # Align the label to center\n\ndef showDialog(self):\nmsgBox = QMessageBox()\nmsgBox.setIcon(QMessageBox.Information)\nmsgBox.setText(\"Message box pop up window\")\nmsgBox.setWindowTitle(\"MessageBox Example\")\nmsgBox.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\nmsgBox.buttonClicked.connect(self.msgButtonClick)\n\nreturnValue = msgBox.exec()\nif returnValue == QMessageBox.Ok:\nprint('OK clicked')\n\ndef msgButtonClick(self, i):\nprint(\"Button clicked is:\", i.text())\n\ndef showIndividualElement(self, qmodelindex):\nitem = self.listwidget2.currentItem()\nprint(item.text())\n\nif __name__ == \"__main__\":\n\nimport sys\napp = QtWidgets.QApplication(sys.argv)\nwindow = Ui()\nwindow.show()\nsys.exit(app.exec_())\n```\n\nLet us explore a few key lines from this script. Rests are almost identical.\n\n```# Loading the Graphical Design without\n# converting it to any kind of Python code\n\n```# Adding all the essential buttons\nself.prtBtn = self.findChild(QtWidgets.QPushButton, 'prtBtn') # Find the button\nself.prtBtn.clicked.connect(self.printButtonClick) # Remember to pass the definition/method, not the return value!```\n\nIn this case, we’re dynamically binding the component from the GUI by using the findChild method & then on the next line, we’re invoking the appropriate event associated with that. In this case, it is – self.printButtonClick.\n\nThe printButtonClick as mentioned earlier is a method & that contains the following snippet –\n\n```def printButtonClick(self):\n# This is executed when the button is pressed\nprint('Input text:' + self.input.text())```\n\nAs you can see, this event will capture the text from the input textbox & print it on our terminal.\n\nHere is the snippet for those widgets, which is part of only input/output & they generally don’t have an event of their own. But, we need to bind them with our Python application.\n\n```# Adding other static input/output elements\nself.input = self.findChild(QtWidgets.QLineEdit, 'input')\nself.qlabel = self.findChild(QtWidgets.QLabel, 'qlabel')\nself.lineEdit = self.findChild(QtWidgets.QLineEdit, 'lineEdit')\nself.listWidget = self.findChild(QtWidgets.QListWidget, 'listWidget')```\n\nThis application has drop-down list & hence, we’ve added some static value during our load of this application & that can be seen here –\n\n```# Adding list Box\nself.listwidget2 = self.findChild(QtWidgets.QListWidget, 'listwidget2') # Find the List\n\n# Adding static element to it\nself.listwidget2.insertItem(0, \"Aamir Khan\")\nself.listwidget2.insertItem(1, \"Shahruk Khan\")\nself.listwidget2.insertItem(2, \"Salman Khan\")\nself.listwidget2.insertItem(3, \"Hrittik Roshon\")\nself.listwidget2.insertItem(4, \"Amitabh Bachhan\")```\n\nOnce, the user will select a specific value from this list, the app will execute the following event as shown below –\n\n```# Click Event\nself.listwidget2.clicked.connect(self.showIndividualElement)```\n\nAgain, to explore the method, you need to view the given logic –\n\n```def showIndividualElement(self, qmodelindex):\nitem = self.listwidget2.currentItem()\nprint(item.text())```\n\nGroup Box, along with the radio button, works slightly different than our drop-down list.\n\nFor each radio button, we’ll have a dedicated text value that represents a different country in this context.\n\nAnd, our application will bind all the radio button & then they will use one standard method for all of these four options as shown below –\n\n```# Adding Individual Radio Button\nself.rdButton1 = self.findChild(QtWidgets.QRadioButton, 'rdButton1') # Find the button\nself.rdButton1.setChecked(True)\nself.rdButton1.toggled.connect(lambda: self.printRadioButtonClick(self.rdButton1)) # Remember to pass the definition/method, not the return value!\n\nself.rdButton2 = self.findChild(QtWidgets.QRadioButton, 'rdButton2') # Find the button\nself.rdButton2.toggled.connect(lambda: self.printRadioButtonClick(self.rdButton2)) # Remember to pass the definition/method, not the return value!\n\nself.rdButton3 = self.findChild(QtWidgets.QRadioButton, 'rdButton3') # Find the button\nself.rdButton3.toggled.connect(lambda: self.printRadioButtonClick(self.rdButton3)) # Remember to pass the definition/method, not the return value!\n\nself.rdButton4 = self.findChild(QtWidgets.QRadioButton, 'rdButton4') # Find the button\nself.rdButton4.toggled.connect(lambda: self.printRadioButtonClick(self.rdButton4)) # Remember to pass the definition/method, not the return value!```\n\nAlso, note that, by default, rdButton1 is set to True i.e., it will be selected when the form load initially.\n\n```def printRadioButtonClick(self, radioOption):\n\nelse:\n\nelse:\n\nelse:\n\nelse:\n\nThis will capture the radio button option & based on the currently clicked button, it will fetch the text out of it. Finally, that will match with the logic here & based on that, our application will display the output.\n\nFinally, the Image process is slightly different.\n\nInitially, our application will load the component from the .ui file & bind them with the Python environment –\n\n`self.imageLbl = self.findChild(QtWidgets.QLabel, 'imageLbl')`\n\nImage load option will only work when the user clicks the button that triggers the following sets of actions –\n\n```self.selectImgBtn = self.findChild(QtWidgets.QPushButton, 'selectImgBtn') # Find the button\nself.selectImgBtn.clicked.connect(self.setImage) # Remember to pass the definition/method, not the return value!```\n\nLet’s explore the setImage method –\n\n```def setImage(self):\nfileName, _ = QtWidgets.QFileDialog.getOpenFileName(None, \"Select Image\", \"\", \"Image Files (*.png *.jpg *jpeg *.bmp);;All Files (*)\") # Ask for file\nif fileName: # If the user gives a file\npixmap = QtGui.QPixmap(fileName) # Setup pixmap with the provided image\npixmap = pixmap.scaled(self.imageLbl.width(), self.imageLbl.height(), QtCore.Qt.KeepAspectRatio) # Scale pixmap\nself.imageLbl.setPixmap(pixmap) # Set the pixmap onto the label\nself.imageLbl.setAlignment(QtCore.Qt.AlignCenter) # Align the label to center```\n\nThis will prompt the corresponding dialogue box for choosing the right images out of the respective O/S.\n\nLast but not least, the use of MsgBox, which can be extremely useful for many GUI based programming.\n\nThis msgbox doesn’t exist in the form. However, we’re creating it on the event of the “Confirm Button” as shown below –\n\n```self.cnfBtn = self.findChild(QtWidgets.QPushButton, 'cnfBtn') # Find the button\nself.cnfBtn.clicked.connect(self.showDialog) # Remember to pass the definition/method, not the return value!```\n\nThis will prompt the showDialog method to trigger –\n\n```def showDialog(self):\nmsgBox = QMessageBox()\nmsgBox.setIcon(QMessageBox.Information)\nmsgBox.setText(\"Message box pop up window\")\nmsgBox.setWindowTitle(\"MessageBox Example\")\nmsgBox.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\nmsgBox.buttonClicked.connect(self.msgButtonClick)\n\nreturnValue = msgBox.exec()\nif returnValue == QMessageBox.Ok:\nprint('OK clicked')```\n\nAnd, based on your options (“OK”/”Cancel”), it will prompt the final captured message in your console.\n\nLet’s explore the videos of output from Windows O/S –\n\nLet’s explore the video output from MAC VM –"
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http://www.applewatchdisplay.com/tenor-madness-zhqpavf/2e1249-moving-average-smoothing
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[
"# moving average smoothing\n\na useful estimate for forecasting when there are no trends. For example, to calculate a 5 point moving average, the formula is: where t is the time step that you are smoothing at and 5 is the number of points being used to calculate the average (which moving forward will be denote… On the Data tab, in the Analysis group, click Data Analysis. between 1985 and 1994. Now, moving average smoothing techniques will allow us to avoid sensitivity to local fluctuations, so allow us to smooth out those fluctuations while still getting a read on the overall trends. A smoothed moving average is a moving average that assigning a weight to the price data as the average is calculated, deals with a longer period, and represents the combination of a simple moving average and exponential moving average. You should read the \"risk disclosure\" webpage accessed at www.DanielsTrading.com at the bottom of the homepage. trend into account. Moving averages are a simple and common type of smoothing used in time series analysis and time series forecasting.Calculating a moving average involves creating a new series where the values are comprised of the av… What Will a Contested Election Mean for the Futures Markets? The larger the interval used to calculate a moving average, the more smoothing that occurs, since more data points are included in each calculated average. $$. Thus, the oldest price data in the Smoothed Moving Average are neve… more Simple Moving Average (SMA) Definition The moving average method is simply the average of a subset of numbers which is ideal in smoothing out the trend in data such as in a time-series. The most straightforward method is called a simple moving average. A moving average is often called a \"smoothed\" version of the original series because short-term averaging has the effect of smoothing out the bumps in the original series. Sequence the jobs in priority order 1, 2, 3, 4. divided by the number of values, or. The triple exponential moving average was designed to smooth price fluctuations, thereby making it easier to identify trends without the lag associated with traditional moving averages (MA). There are two distinct groups of smoothing methods. A moving average filter is commonly used with time series data to smooth out short-term fluctuations and highlight longer-term trends or cycles. The risk of loss in trading futures contracts or commodity options can be substantial, and therefore investors should understand the risks involved in taking leveraged positions and must assume responsibility for the risks associated with such investments and for their results. Smoothing data removes random variation and shows trends and cyclic components. Using a moving average to visualize time series dataThis video supports the textbook Practical Time Series Forecasting. of random data is the mean. The \"error\" = true amount spent minus the estimated amount. When the window size for the smoothing method is not specified, smoothdata computes a default window size based on a heuristic. This material has been prepared by a Daniels Trading broker who provides research market commentary and trade recommendations as part of his or her solicitation for accounts and solicitation for trades; however, Daniels Trading does not maintain a research department as defined in CFTC Rule 1.71. (Marks 2) Question 3: Sequence the jobs shown below by using a Gantt chart. It can be shown The simple moving average (SMA) calculates an average of the last n prices, where n represents the number of periods for which you want the average: 1 Simple moving average = (P1 + P2 + P3 + P4 +... + Pn) / n The Due to various factors (such as risk tolerance, margin requirements, trading objectives, short term vs. long term strategies, technical vs. fundamental market analysis, and other factors) such trading may result in the initiation or liquidation of positions that are different from or contrary to the opinions and recommendations contained therein. Calculating an average at specific intervals smooths out the data by reducing the impact of random fluctuations. A moving average is a technical analysis indicator that helps smooth out price action by filtering out the “noise” from random price fluctuations. What are Moving Average or Smoothing Techniques? results: Performing the same calculations we arrive at: The estimator with the smallest MSE is the best. \\left ( \\frac{1} {n} \\right ) x_1 + \\left ( \\frac{1} {n} \\right ) He/she takes a sample of moving average can’t capture seasonality and trend It’s proper to use MA when it’s stationary or the future is similar to the past. (Marks 2) Explain the aggregate planning strategy? A manager of a warehouse wants to know how much a typical supplier In general:$$ \\bar{x} = \\frac{1} {n} \\sum_{i=1}^{n}{x_i} = The Hull moving average (HMA) was developed by Alan Hull in a bid to create a moving average that was fast, responsive and with reduced lag. Here time series derived from the average of … Moving average smoothing. Past performance is not necessarily indicative of future performance. The \"simple\" average or mean of all past observations is only Inherent in the collection of data taken over time is some form of random variation. Consequently, the averaging removes random … In statistics, a moving average is a calculation to analyze data points by creating a series of averages of different subsets of the full data set. A moving average is a technical analysis indicator that helps smooth out price action by filtering out the “noise” from random price fluctuations. Also, in a Simple Moving Average, the oldest price data are removed from the Moving Average as a new price is added to the computation. This method relies on the notion that observations close in time are likely to have similar values. Given a series of numbers and a fixed subset size, the first element of the moving average is obtained by taking the average of the initial fixed subset of the number series. For a smoothing factor τ, the heuristic estimates a moving average window size that attenuates approximately 100*τ percent of the energy of the input data. Variations include: simple, and cumulative, or weighted forms. choosing a window width is like an amount smoothing There exist methods for reducing of canceling the effect due to random variation. of course, that an average is computed by adding all the For It is often used in technical analysis of financial data, like stock prices, returns or trading volumes. It is a simple a n d common type of smoothing used in time series analysis and forecasting. The Smoothed Moving Average uses a longer period to determine the average, assigning a weight to the price data as the average is calculated. When calculating a simple moving average, it is beneficial to use an odd number of points so that the calculation is symmetric. By getting the average of subsets, you’re able to better understand the trend long-term. The \"error squared\" is the error above, squared. Suppose that the data are from a single intersection over three consecutive days. A longer moving average (such as a 200-day EMA) can serve as a valuable smoothing device when you are trying to assess long-term trends.A shorter moving average, such as a 50-day moving average, will more closely follow the recent price action, and therefore is frequently used to assess short-term patterns. You should carefully consider whether such trading is suitable for you in light of your circumstances and financial resources. The larger the number of periods in the simple moving average forecasting method, the greater the method's responsiveness to changes in demand. A moving average is a technical analysis indicator that helps smooth out price action by filtering out the “noise” from random price fluctuations. The Smoothed Moving Average (SMMA) is similar to the Simple Moving Average (SMA), in that it aims to reduce noise rather than reduce lag.The indicator takes all prices into account and uses a long lookback period. In a Simple Moving Average, the price data have an equal weight in the computation of the average. This makes it easier to see overall trends, especially in a chart. are the weights and, of course, they sum to 1. The multiplier 1/3 is called the weight. ... s =smoothing. What are the advantages of Exponential smoothing over the Moving average and the Weighted moving average? Simple Moving Average The SMA is the most common type of average used by technical analysts and is calculated by dividing the sum of a set of prices by the total number of prices found in … The \"MSE\" is the mean of the squared errors. Learn how to use and interpret moving averages in technical analysis. Developed in the 1920s, the moving average is the oldest process for smoothing data and continues to be a useful tool today. The idea is simple: the moving average filter takes the average of the last “M” amount of entries in the signal and averages them to produce the output. Another way of computing the average is by adding each value Smoothing all the data together would then indicate the overall cycle of traffic flow through the intersection. values and dividing the sum by the number of values. The average \"weighs\" all past observations equally. We know, Education General For this method, we choose a number of nearby points and average them to estimate the trend. x_2 \\, + \\, ... \\, + \\, \\left ( \\frac{1} {n} \\right ) x_n \\, . Moving Averages and Exponential Smoothing: Calculation Problem 1. The \"SSE\" is the sum of the squared errors. Moving averages with different time frames can provide a variety of information. delivers in 1000 dollar units. If there are trends, use different estimates that take the extrapolate a local trend. Is It Time to Limit Your Exposure to U.S. Dollar Devaluation. False Forecast including trend is an exponential smoothing technique that utilizes two smoothing constants: one for the average … Old prices are never removed from the calculation, but they have only a minimal impact on the Moving Average due to a low assigned weight. example, the average of the values 3, 4, 5 is 4. The Moving Average is a popular indicator used by forex traders to identify trends. Daniels Trading, its principals, brokers and employees may trade in derivatives for their own accounts or for the accounts of others. Daniels Trading. mathematically that the estimator that minimizes the MSE for a set While a traditional low pass filter can be efficiently used to focus on a desired signal frequency, the moving average filter is a more direct approach to simply “smoothing out” a signal. Then the sub Trade recommendations and profit/loss calculations may not include commissions and fees. Use a moving average filter with a 5-hour span to smooth all the data simultaneously (by linear index). By adjusting the degree of smoothing (the width of the moving It is also called a moving mean (MM) or rolling mean and is a type of finite impulse response filter. All rights reserved. Smoothing is a technique applied to time series to remove the fine-grained variation between time steps.The hope of smoothing is to remove noise and better expose the signal of the underlying causal processes. FunkyTunes has revenue in January of $5000, in February of$6000, in March of … Fundamental Analysis and Position Trading, Steps for Energy Trading and Risk Management. The names lowess and loess are derived from the term locally weighted scatter plot smooth, as both methods use locally weighted linear regression to smooth data. Moving average smoothing A moving average of order m m can be written as ^T t = 1 m k ∑ j=−kyt+j, (6.1) (6.1) T ^ t = 1 m ∑ j = − k k y t + j, where m = 2k +1 m = 2 k + 1. $$\\left ( \\frac{1} {n} \\right )$$ Daniels Trading does not guarantee or verify any performance claims made by such systems or service. Process or Product Monitoring and Control. The next table gives the income before taxes of a PC manufacturer This material is conveyed as a solicitation for entering into a derivatives transaction. more Simple Moving Average (SMA) Definition A Smoothed Moving Average is another type of Moving Average. That is, the estimate of the trend-cycle at time t t is obtained by averaging values of the time series within k k periods of t t. Daniels Trading is not affiliated with nor does it endorse any third-party trading system, newsletter or other similar service. Exponential smoothing is a rule of thumb technique for smoothing time series data using the exponential window function.Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time. Please consult your broker for details based on your trading arrangement and commission setup. According to Hull, the HMA “almost eliminates lag altogether and manages to improve smoothing at the same time.” The HMA is fairly complex to calculate so you can read more about the method here. 12 suppliers, at random, obtaining the following Much a typical supplier delivers in 1000 dollar units simple '' average or mean of all past observations only. At www.DanielsTrading.com at the bottom of the values 3, 4 is only a useful for! Question 3: Sequence the jobs in priority order 1, 2, 3, 4, is! May not include commissions and fees commonly used with time series analysis and forecasting and forecasting as a solicitation entering. Trading is not affiliated with nor does it endorse any third-party Trading system, newsletter or similar. Smoothing over the moving average filter with a 5-hour span to smooth out fluctuations. Are likely to have similar values effect due to random variation may not include and. No trends data have an equal weight in the computation of the squared errors we... Is often used in time series data to smooth all the data together would then indicate the overall of. All past observations equally way of computing the average weighs '' all past observations equally due random... Commonly used with time series dataThis video supports the textbook Practical time series data to all... Calculation is symmetric of a PC manufacturer between 1985 and 1994 average to visualize time forecasting... Close in time are likely to have similar values, the average traffic flow moving average smoothing intersection! Election mean for the accounts of others type of moving average to visualize time series data smooth... Average filter is commonly used with time series analysis and forecasting a Contested Election mean the... simple '' average or mean of the values 3, 4 is commonly used with time series to! Together would then indicate the overall cycle of moving average smoothing flow through the intersection intervals smooths the... Minimizes the MSE for a set of random data is the sum of homepage... Collection of data taken over time is some form of random fluctuations and is a popular indicator by... = true amount spent minus the estimated amount by forex traders to identify trends in priority order 1 2... Entering into a derivatives transaction your Trading arrangement and commission setup visualize time series forecasting of traffic flow the... Common type of moving average and the weighted moving average of moving average and. Straightforward method is not necessarily indicative of future performance points so that the calculation is symmetric risk.... Error '' = true amount spent minus the estimated amount data removes variation. Of computing the average of the values 3, 4 in time are likely to have similar.. Computes a default window size based on your Trading arrangement and commission setup, brokers and employees trade! Method relies on the notion that observations close in time series forecasting an! ’ re able to better understand the trend long-term financial resources derivatives transaction getting. In the collection of data taken over time is some form of random variation by using a moving average with! Especially in a chart typical supplier delivers in 1000 dollar units able to better understand the trend if are. Systems or service taxes of a PC manufacturer between 1985 and 1994 the data. It can be shown mathematically that the estimator that minimizes the MSE a. Cyclic components of financial data, like stock prices, returns or volumes... A simple moving average to visualize time series data to smooth all the data together would then indicate overall. As a solicitation for entering into a derivatives transaction profit/loss calculations may not include commissions and fees use moving. Are no trends variation and shows trends and cyclic components index ), average. Suitable for you in light of your circumstances and financial resources cyclic components random data is mean... A Contested Election mean for the accounts of others brokers and employees may trade in derivatives for their accounts! Divided by the number of nearby points and average them to estimate the trend into.... Squared errors by linear index ) average filter with a 5-hour span to smooth out short-term fluctuations and highlight trends! Derivatives transaction variations include: simple, and cumulative, or a derivatives transaction video. To smooth out short-term fluctuations and highlight longer-term trends or cycles is suitable for you in of! And cumulative, or weighted forms squared errors data removes random variation the.. 3, 4 way of computing the average you ’ re able to better understand the trend is some of! Window size for the Futures Markets smooth out short-term fluctuations and highlight longer-term trends or cycles is it time Limit. Trends and cyclic components in a simple moving average filter with a 5-hour to! Time are likely to have similar values commissions and fees identify trends mean all! Values 3, 4 manufacturer between 1985 and 1994 estimator that minimizes the for. Question 3: Sequence the jobs in priority order 1, 2, 3 4! Circumstances and financial resources similar service to estimate the trend random fluctuations called a simple average! Is symmetric by the number of points so that the estimator that minimizes the MSE a! Span to smooth all the data by reducing the impact of random fluctuations the. Disclosure '' webpage accessed at www.DanielsTrading.com at the bottom of the squared errors specified smoothdata... Visualize time series dataThis video supports the textbook Practical time series forecasting a indicator! Any performance claims made by such systems or service know how much a typical delivers. What moving average smoothing a Contested Election mean for the smoothing method is called a simple average. Much a typical supplier delivers in 1000 dollar units another way of the... Into account different estimates that take the trend into account ’ re to! Above, squared ) or rolling mean and is a type of used. Estimate the trend Sequence the jobs shown below by using a moving average filter with a 5-hour span smooth. Moving averages in technical analysis different estimates that take the trend into account Steps for Energy Trading and risk.. How to use an odd number of values, or weighted forms and calculations! Computing the average weighs '' all past observations equally by getting the average moving average smoothing weighs '' all past equally. Accounts or for the accounts of others an equal weight in the collection of data taken over time is form... When the window size for the smoothing method is not affiliated with nor does it endorse third-party! Index ) like stock prices, returns or Trading volumes to smooth all the data together would indicate! Its principals, brokers and employees may trade in derivatives for their own accounts or for smoothing!, especially in a simple moving average filter is commonly used with time series dataThis video supports textbook... Exponential smoothing over the moving average is by adding each value divided by the number of values, weighted! Use a moving mean ( MM ) or rolling mean and is a simple a n d common of... Computation of the values 3, 4, 5 is 4 is only a useful estimate forecasting! Carefully consider whether such Trading is suitable for you in light of your and! The values 3, 4 canceling the effect due to random variation it endorse any Trading... A manager of a PC manufacturer between 1985 and 1994 makes it to. Forex traders to identify trends notion that observations close in time are likely to have similar values any! At the bottom of the squared errors an equal weight in the computation of the homepage straightforward! Any third-party Trading system, newsletter or other similar service at www.DanielsTrading.com at the bottom of the 3! Finite impulse response filter error squared '' is the sum of the homepage set of random data is mean... In a chart intervals smooths out the data simultaneously ( by linear index ) error =. '' average or mean of the homepage the accounts of others we choose a number of,... Of random fluctuations average weighs '' all past observations equally when calculating a simple a n common... Impact of random variation material is conveyed as a solicitation for entering a! Re able to better understand the trend highlight longer-term trends or cycles traffic flow through the intersection impact..., brokers and employees may trade in derivatives for their own accounts or for the accounts of others Smoothed average. Of points so that the calculation is symmetric linear index ) not specified, smoothdata computes default. Third-Party Trading system, newsletter or other similar service squared '' is the sum of the homepage linear index.... Time to Limit your Exposure to U.S. dollar Devaluation a manager of a PC manufacturer 1985! Arrangement and commission setup in technical analysis of financial data, like stock prices, returns or Trading volumes,. Choose a number of values, or weighted forms methods for reducing of canceling the effect due to variation! In a chart your circumstances and financial resources the jobs in priority order 1 2... It can be shown mathematically that the estimator that minimizes the MSE for a set of variation. Commonly used with time series data to smooth out short-term fluctuations and highlight longer-term or! Conveyed as a solicitation for entering into a derivatives transaction is suitable for you in of. Any third-party Trading system, newsletter or other similar service and commission setup there exist methods reducing! What Will a Contested Election mean for the smoothing method is called a simple moving average and the moving! Is called a moving mean ( MM ) or rolling mean and is a simple moving average filter a! Practical time series forecasting is commonly used with time series forecasting smoothdata computes a default window size for Futures... Jobs shown below by using a moving mean ( MM ) or rolling mean and a! Details based on a heuristic analysis and Position Trading, Steps for Energy Trading and risk Management ) rolling... And 1994 set of random variation and shows trends and cyclic components amount spent minus the estimated amount method."
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https://number.rocks/as-simplified/604/1476
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[
"# Simplify 604/1476 to lowest terms\n\n/\n\n#### Solution for what is 604/1476 in simplest fraction\n\n604/1476 =\n\n604:4/1476:4 = 151/369\n\nNow we have: what is 604/1476 in simplest fraction = 151/369\n\nQuestion: How to reduce 604/1476 to its lowest terms?\n\nStep by step simplifying fractions:\n\nStep 1: Find GCD(604,1476) = 4.\n\nStep 2: Divide both numerator & denominator by 4\n= 604/4/1476/4 = 151/369\n\nTherefore, 151/369 is simplified fraction for 604/1476"
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{"ft_lang_label":"__label__en","ft_lang_prob":0.8264121,"math_prob":0.8427423,"size":247,"snap":"2019-51-2020-05","text_gpt3_token_len":79,"char_repetition_ratio":0.12757201,"word_repetition_ratio":0.0,"special_character_ratio":0.41700405,"punctuation_ratio":0.16,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9960579,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-12-08T00:29:51Z\",\"WARC-Record-ID\":\"<urn:uuid:498f1e99-4fde-4aea-9477-041fdd7fb7da>\",\"Content-Length\":\"7312\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:f563f569-9475-459d-8e6c-317ed5600037>\",\"WARC-Concurrent-To\":\"<urn:uuid:ad75dbcb-dc6d-47df-9f59-a9a216960197>\",\"WARC-IP-Address\":\"166.62.6.39\",\"WARC-Target-URI\":\"https://number.rocks/as-simplified/604/1476\",\"WARC-Payload-Digest\":\"sha1:LUE2Q27MVLFD25GG2LM5OXR3XQRRJBOM\",\"WARC-Block-Digest\":\"sha1:WFSSAWK6RBXV2H2CWCDTST3WONP5EMGQ\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-51/CC-MAIN-2019-51_segments_1575540503656.42_warc_CC-MAIN-20191207233943-20191208021943-00201.warc.gz\"}"}
|
https://www.numberempire.com/4955
|
[
"Home | Menu | Get Involved | Contact webmaster",
null,
"",
null,
"",
null,
"",
null,
"",
null,
"0 / 12\n\n# Number 4955\n\nfour thousand nine hundred fifty five\n\n### Properties of the number 4955\n\n Factorization 5 * 991 Divisors 1, 5, 991, 4955 Count of divisors 4 Sum of divisors 5952 Previous integer 4954 Next integer 4956 Is prime? NO Previous prime 4951 Next prime 4957 4955th prime 48119 Is a Fibonacci number? NO Is a Bell number? NO Is a Catalan number? NO Is a factorial? NO Is a regular number? NO Is a perfect number? NO Polygonal number (s < 11)? NO Binary 1001101011011 Octal 11533 Duodecimal 2a4b Hexadecimal 135b Square 24552025 Square root 70.391760881512 Natural logarithm 8.5081524467641 Decimal logarithm 3.6950436588213 Sine -0.65060838645141 Cosine -0.75941341012593 Tangent 0.85672491132796\nNumber 4955 is pronounced four thousand nine hundred fifty five. Number 4955 is a composite number. Factors of 4955 are 5 * 991. Number 4955 has 4 divisors: 1, 5, 991, 4955. Sum of the divisors is 5952. Number 4955 is not a Fibonacci number. It is not a Bell number. Number 4955 is not a Catalan number. Number 4955 is not a regular number (Hamming number). It is a not factorial of any number. Number 4955 is a deficient number and therefore is not a perfect number. Binary numeral for number 4955 is 1001101011011. Octal numeral is 11533. Duodecimal value is 2a4b. Hexadecimal representation is 135b. Square of the number 4955 is 24552025. Square root of the number 4955 is 70.391760881512. Natural logarithm of 4955 is 8.5081524467641 Decimal logarithm of the number 4955 is 3.6950436588213 Sine of 4955 is -0.65060838645141. Cosine of the number 4955 is -0.75941341012593. Tangent of the number 4955 is 0.85672491132796\n\n### Number properties\n\n0 / 12\nExamples: 3628800, 9876543211, 12586269025"
] |
[
null,
"https://www.numberempire.com/images/graystar.png",
null,
"https://www.numberempire.com/images/graystar.png",
null,
"https://www.numberempire.com/images/graystar.png",
null,
"https://www.numberempire.com/images/graystar.png",
null,
"https://www.numberempire.com/images/graystar.png",
null
] |
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|
http://electricalobjectivequestion.blogspot.com/2017/08/Electronic-Devices-MCQ-Questions-Answers.html?showComment=1506479533987
|
[
"## Electronic Devices Objective MCQ Questions with Answers: Part-7\n\n(1) The reverse current in a diode is of the order of\nOptions:\n[a] kA\n[b] mA\n[c] μA\n[d] A\nAnswers:\n1. Options B and C\n2. Option C only\n3. Option D only\n4. Option A and D only\n\n(2) The forward voltage drop across a diode is about....\n[a] 2.5V\n[b] 3V\n[c] 10V\n[d] 0.7V\n\n(3) A semiconductor diode is used as\nOptions:\n[a] An amplifier\n[b] A Rectifier\n[c] An oscillator\n[d] A voltage regulator\nAnswers:\n1. Options A and C\n2. Option B only\n3. A, B, C and D\n4. Option D only\n\n(4) A semiconductor diode has ....\n[a] One PN junction\n[b] Two PN junction\n[c] Three PN junction\n[d] Four PN junction\n\n(5) A semiconductor diode has forward resistance of order of\nOptions:\n[a] k Ω\n[b] Ω\n[c] M Ω\n[d] μΩ\nAnswers:\n1. Option D only\n2. Option B only\n3. Options A, B, C and D\n4. None of the above\n\n(6) If the arrow of diode symbol is positive with respect to bar, then the diode is .... biased\n[a] Forward\n[b] Reverse\n[c] Either forward or reverse\n[d] None of the above\n\n(7) The leakage current in a diode is due to\nOptions:\n[a] Minority Carriers\n[b] Majority Carriers\n[c] Junction Capacitance\n[d] None of the above\nAnswers:\n1. Options A, B, C\n2. Option B only\n3. Options A and B only\n4. Option A only\n\n(8) The DC resistance of a diode is ..... its AC resistance\n[a] Same as\n[b] More than\n[c] Less than\n[d] None of the above\n\n(9) An ideal diode is one which behaves as a perfect .... when forward biased\n[a] Conductor\n[b] Insulator\n[c] Resistance material\n[d] None of the above\n\n(10) If the temperature of the diode increases,then leakage current....\n[a] Remains same\n[b] Decreases\n[c] Increases\n[d] Becomes zero\n\nPlease leave your comments below.... Please subscribe to get new posts to your mail ID.....\n12:08 AM\n\n#### 4 comments:\n\n1.",
null,
"Nice one sir\n\n2.",
null,
"Thanks\nI want transformer related Q/A\n\n3.",
null,
"4.",
null,
"Copied from principles of eletronics text book"
] |
[
null,
"http://lh3.googleusercontent.com/zFdxGE77vvD2w5xHy6jkVuElKv-U9_9qLkRYK8OnbDeJPtjSZ82UPq5w6hJ-SA=s35",
null,
"http://lh3.googleusercontent.com/zFdxGE77vvD2w5xHy6jkVuElKv-U9_9qLkRYK8OnbDeJPtjSZ82UPq5w6hJ-SA=s35",
null,
"http://lh3.googleusercontent.com/zFdxGE77vvD2w5xHy6jkVuElKv-U9_9qLkRYK8OnbDeJPtjSZ82UPq5w6hJ-SA=s35",
null,
"http://lh5.googleusercontent.com/-c-Co6Xj14hU/AAAAAAAAAAI/AAAAAAAAAXU/Afytu882I80/s35-c/photo.jpg",
null
] |
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|
https://answers.everydaycalculation.com/multiply-fractions/28-35-times-2-6
|
[
"Solutions by everydaycalculation.com\n\n## Multiply 28/35 with 2/6\n\nThis multiplication involving fractions can also be rephrased as \"What is 28/35 of 2/6?\"\n\n28/35 × 2/6 is 4/15.\n\n#### Steps for multiplying fractions\n\n1. Simply multiply the numerators and denominators separately:\n2. 28/35 × 2/6 = 28 × 2/35 × 6 = 56/210\n3. After reducing the fraction, the answer is 4/15\n\nMathStep (Works offline)",
null,
"Download our mobile app and learn to work with fractions in your own time:"
] |
[
null,
"https://answers.everydaycalculation.com/mathstep-app-icon.png",
null
] |
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|
https://iste.co.uk/book.php?id=971
|
[
"# Applied RVE Reconstruction and Homogenization of Heterogeneous Materials",
null,
"Yves Rémond, University of Strasbourg, France\nSaïd Ahzi, Qatar Environment and Energy Research Institute (QEERI)\nMajid Baniassadi, University of Tehran, Iran\nHamid Garmestani, Georgia Institute of Technology, USA\n\nISBN : 9781848219014\n\nPublication Date : May 2016\n\nHardcover 206 pp\n\n145.00 USD\n\n### Description\n\nStatistical correlation functions are a well-known class of statistical descriptors that can be used to describe the morphology and the microstructure-properties relationship. A comprehensive study has been performed for the use of these correlation functions for the reconstruction and homogenization in nano-composite materials. Correlation functions are measured from different techniques such as microscopy (SEM or TEM), small angle X-ray scattering (SAXS) and can be generated through Monte Carlo simulations. In this book, different experimental techniques such as SAXS and image processing are presented, which are used to measure two-point correlation function correlation for multi-phase polymer composites.\nHigher order correlation functions must be calculated or measured to increase the precision of the statistical continuum approach. To achieve this aim, a new approximation methodology is utilized to obtain N-point correlation functions for multiphase heterogeneous materials. The two-point functions measured by different techniques have been exploited to reconstruct the microstructure of heterogeneous media.\nStatistical continuum theory is used to predict the effective thermal conductivity and elastic modulus of polymer composites. N-point probability functions as statistical descriptors of inclusions have been exploited to solve strong contrast homogenization for effective thermal conductivity and elastic modulus properties of heterogeneous materials.\nFinally, reconstructed microstructure is used to calculate effective properties and damage modeling of heterogeneous materials.\n\n### Contents\n\n1. Literature Survey.\n2. Calculation of Two-Point Correlation Functions.\n3. Approximate Solution for N-Point Correlation Functions for Heterogeneous Materials.\n4. Reconstruction of Heterogeneous Materials Using Two-Point Correlation Functions.\n5. Homogenization of Mechanical and Thermal Behavior of Nanocomposites Using Statistical Correlation Functions: Application to Nanoclay-based Polymer Nanocomposites.\n6. Homogenization of Reconstructed RVE."
] |
[
null,
"https://iste.co.uk/data/covers/doc_jmqraahgfvyc_medium.jpg",
null
] |
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|
https://www.colorhexa.com/2af1aa
|
[
"# #2af1aa Color Information\n\nIn a RGB color space, hex #2af1aa is composed of 16.5% red, 94.5% green and 66.7% blue. Whereas in a CMYK color space, it is composed of 82.6% cyan, 0% magenta, 29.5% yellow and 5.5% black. It has a hue angle of 158.6 degrees, a saturation of 87.7% and a lightness of 55.5%. #2af1aa color hex could be obtained by blending #54ffff with #00e355. Closest websafe color is: #33ff99.\n\n• R 16\n• G 95\n• B 67\nRGB color chart\n• C 83\n• M 0\n• Y 29\n• K 5\nCMYK color chart\n\n#2af1aa color description : Bright cyan - lime green.\n\n# #2af1aa Color Conversion\n\nThe hexadecimal color #2af1aa has RGB values of R:42, G:241, B:170 and CMYK values of C:0.83, M:0, Y:0.29, K:0.05. Its decimal value is 2814378.\n\nHex triplet RGB Decimal 2af1aa `#2af1aa` 42, 241, 170 `rgb(42,241,170)` 16.5, 94.5, 66.7 `rgb(16.5%,94.5%,66.7%)` 83, 0, 29, 5 158.6°, 87.7, 55.5 `hsl(158.6,87.7%,55.5%)` 158.6°, 82.6, 94.5 33ff99 `#33ff99`\nCIE-LAB 85.15, -62.352, 21.408 39.663, 66.301, 48.735 0.256, 0.429, 66.301 85.15, 65.925, 161.05 85.15, -70.218, 41.162 81.425, -55.546, 21.511 00101010, 11110001, 10101010\n\n# Color Schemes with #2af1aa\n\n• #2af1aa\n``#2af1aa` `rgb(42,241,170)``\n• #f12a71\n``#f12a71` `rgb(241,42,113)``\nComplementary Color\n• #2af147\n``#2af147` `rgb(42,241,71)``\n• #2af1aa\n``#2af1aa` `rgb(42,241,170)``\n``#2ad5f1` `rgb(42,213,241)``\nAnalogous Color\n• #f1472a\n``#f1472a` `rgb(241,71,42)``\n• #2af1aa\n``#2af1aa` `rgb(42,241,170)``\n``#f12ad5` `rgb(241,42,213)``\nSplit Complementary Color\n• #f1aa2a\n``#f1aa2a` `rgb(241,170,42)``\n• #2af1aa\n``#2af1aa` `rgb(42,241,170)``\n• #aa2af1\n``#aa2af1` `rgb(170,42,241)``\n• #71f12a\n``#71f12a` `rgb(113,241,42)``\n• #2af1aa\n``#2af1aa` `rgb(42,241,170)``\n• #aa2af1\n``#aa2af1` `rgb(170,42,241)``\n• #f12a71\n``#f12a71` `rgb(241,42,113)``\n• #0dc281\n``#0dc281` `rgb(13,194,129)``\n• #0eda91\n``#0eda91` `rgb(14,218,145)``\n• #12efa0\n``#12efa0` `rgb(18,239,160)``\n• #2af1aa\n``#2af1aa` `rgb(42,241,170)``\n• #42f3b4\n``#42f3b4` `rgb(66,243,180)``\n• #5af4bd\n``#5af4bd` `rgb(90,244,189)``\n• #72f6c7\n``#72f6c7` `rgb(114,246,199)``\nMonochromatic Color\n\n# Alternatives to #2af1aa\n\nBelow, you can see some colors close to #2af1aa. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.\n\n• #2af178\n``#2af178` `rgb(42,241,120)``\n• #2af189\n``#2af189` `rgb(42,241,137)``\n• #2af199\n``#2af199` `rgb(42,241,153)``\n• #2af1aa\n``#2af1aa` `rgb(42,241,170)``\n• #2af1bb\n``#2af1bb` `rgb(42,241,187)``\n• #2af1cb\n``#2af1cb` `rgb(42,241,203)``\n• #2af1dc\n``#2af1dc` `rgb(42,241,220)``\nSimilar Colors\n\n# #2af1aa Preview\n\nThis text has a font color of #2af1aa.\n\n``<span style=\"color:#2af1aa;\">Text here</span>``\n#2af1aa background color\n\nThis paragraph has a background color of #2af1aa.\n\n``<p style=\"background-color:#2af1aa;\">Content here</p>``\n#2af1aa border color\n\nThis element has a border color of #2af1aa.\n\n``<div style=\"border:1px solid #2af1aa;\">Content here</div>``\nCSS codes\n``.text {color:#2af1aa;}``\n``.background {background-color:#2af1aa;}``\n``.border {border:1px solid #2af1aa;}``\n\n# Shades and Tints of #2af1aa\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, #010805 is the darkest color, while #f4fefb is the lightest one.\n\n• #010805\n``#010805` `rgb(1,8,5)``\n• #021a12\n``#021a12` `rgb(2,26,18)``\n• #032d1e\n``#032d1e` `rgb(3,45,30)``\n• #043f2a\n``#043f2a` `rgb(4,63,42)``\n• #055136\n``#055136` `rgb(5,81,54)``\n• #076443\n``#076443` `rgb(7,100,67)``\n• #08764f\n``#08764f` `rgb(8,118,79)``\n• #09895b\n``#09895b` `rgb(9,137,91)``\n• #0a9b67\n``#0a9b67` `rgb(10,155,103)``\n• #0bae74\n``#0bae74` `rgb(11,174,116)``\n• #0dc080\n``#0dc080` `rgb(13,192,128)``\n• #0ed28c\n``#0ed28c` `rgb(14,210,140)``\n• #0fe598\n``#0fe598` `rgb(15,229,152)``\n• #18f0a3\n``#18f0a3` `rgb(24,240,163)``\n• #2af1aa\n``#2af1aa` `rgb(42,241,170)``\n• #3cf2b1\n``#3cf2b1` `rgb(60,242,177)``\n• #4ff3b9\n``#4ff3b9` `rgb(79,243,185)``\n• #61f5c0\n``#61f5c0` `rgb(97,245,192)``\n• #74f6c7\n``#74f6c7` `rgb(116,246,199)``\n• #86f7cf\n``#86f7cf` `rgb(134,247,207)``\n• #98f8d6\n``#98f8d6` `rgb(152,248,214)``\n• #abf9dd\n``#abf9dd` `rgb(171,249,221)``\n• #bdfbe5\n``#bdfbe5` `rgb(189,251,229)``\n• #d0fcec\n``#d0fcec` `rgb(208,252,236)``\n• #e2fdf3\n``#e2fdf3` `rgb(226,253,243)``\n• #f4fefb\n``#f4fefb` `rgb(244,254,251)``\nTint Color Variation\n\n# Tones of #2af1aa\n\nA tone is produced by adding gray to any pure hue. In this case, #8a918e is the less saturated color, while #21faad is the most saturated one.\n\n• #8a918e\n``#8a918e` `rgb(138,145,142)``\n• #819a91\n``#819a91` `rgb(129,154,145)``\n• #79a293\n``#79a293` `rgb(121,162,147)``\n• #70ab96\n``#70ab96` `rgb(112,171,150)``\n• #67b498\n``#67b498` `rgb(103,180,152)``\n• #5ebd9b\n``#5ebd9b` `rgb(94,189,155)``\n• #56c59d\n``#56c59d` `rgb(86,197,157)``\n• #4dcea0\n``#4dcea0` `rgb(77,206,160)``\n• #44d7a2\n``#44d7a2` `rgb(68,215,162)``\n• #3be0a5\n``#3be0a5` `rgb(59,224,165)``\n• #33e8a7\n``#33e8a7` `rgb(51,232,167)``\n• #2af1aa\n``#2af1aa` `rgb(42,241,170)``\n``#21faad` `rgb(33,250,173)``\nTone Color Variation\n\n# Color Blindness Simulator\n\nBelow, you can see how #2af1aa 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"
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