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https://indico.cern.ch/event/279605/

math

Mark Thomson presents the book "Modern Particle Physics" at CERN ( Library, 52 1-052 ) This new textbook covers all main aspects of modern particle physics, providing a clear connection between the theory and recent experimental results, including the recent discovery of a Higgs boson and the most recent developments in neutrino physics. It provides a comprehensive and self-contained description of the Standard Model of particle physics suitable for upper-level undergraduate students and graduate students studying experimental particle physics. Physical theory is introduced in a relatively straightforward manner with step-by-step mathematical derivations. In each chapter, fully worked examples link the theory to central experimental results in contemporary particle physics.

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CC-MAIN-2014-15

https://www.slunecnice.cz/sw/how-to-draw-easy-lessons-step-by-step-android/

math

• EASY: you don't need any special skills, just start drawing • INTERESTING: try different styles of drawings • FUNNY: now you can draw nice animals and more • RELAX: Imagination and draw pictures • the app includes lots of drawings such as: animals, dinosaur. • each drawing is divided into a number of steps which are easy to follow. • starting by drawing along the red line, you’ll end up with a complete picture. • parents can use it to give drawing lessons to their kids.

s3://commoncrawl/crawl-data/CC-MAIN-2019-04/segments/1547583705091.62/warc/CC-MAIN-20190120082608-20190120104608-00560.warc.gz

CC-MAIN-2019-04

https://www.jiskha.com/display.cgi?id=1291675638

math

posted by Dan . A town is planning on using the water flowing through a river at a rate of 5.0X10^6 kg/s to carry away the heat from a new power plant. Environmental studies indicate that the temperature of the river should only increase by 0.50ºC. The maximum design efficiency for this plant is 30.0%. What is the maximum possible power this plant can produce? Equations you will need: Q = mc deltaT e = 1 - (Qc/Qh) The first thing you need to realize is that when you're calculating Q it's Qc, because that is the heat going into the water and therefore out of the power station. So then you can plug that into e = 1- (Qc/Qh) and solve for Qh. That isn't the answer yet, though, because it isn't asking how much heat the engine uses (input heat is Qh). It's asking how much total energy it can produce. And now it's very simple because you know Qh, the total heat it uses, and you know the efficiency, the amount of that heat that can be used to do work. So you multiply Qh by the efficiency and THAT's the answer.

s3://commoncrawl/crawl-data/CC-MAIN-2017-34/segments/1502886117519.82/warc/CC-MAIN-20170823020201-20170823040201-00231.warc.gz

CC-MAIN-2017-34

https://academic.naver.com/article.naver?doc_id=81447127

math

Study of Proper Hierarchical Graphs on a Grid - Mohamed A. El-Sayed, Nahla F. Omran, Ahmed A. A. Radwan - International Journal of Advanced Computer Sciences and Applications ico_openaccess - in 2013 - Cited Count Hierarchical planar graph embedding (sometimes called level planar graphs) is widely recognized as a very important task in diverse fields of research and development. Given a proper hierarchical planar graph, we want to find a geometric position of every vertex (layout) in a straight-line grid drawing without any edge-intersection. An additional objective is to minimize the area of the rectangular grid in which G is drawn with more aesthetic embedding. In this paper we propose several ideas to find an embedding of G in a rectangular grid with area, (-1) × (k-1), where is the number of vertices in the longest level and k is the number of levels in G.) If you register references through the customer center, the reference information will be registered as soon as possible.

s3://commoncrawl/crawl-data/CC-MAIN-2018-30/segments/1531676590329.25/warc/CC-MAIN-20180718193656-20180718213656-00329.warc.gz

CC-MAIN-2018-30

https://smart-answers.com/mathematics/question2848449

math

Cindy has $20 to spend at the store. she buys a pack of colored pencils that cost $4 and jelly beans that cost $2 per pound. if she spends more than $8 at the store, graph a compound inequality that shows the possible number of pounds of jelly beans she could have purchased. let y be the pound of jelly beans x be the money she spend on jelly beans x + 4 >= 8y >= 2x Cindy has $20 to spend at a store. Let the number of packs of the pencils which cost $4, she bought = $4x And the number of jelly beans that cost $2 per pound = $2y Now total cost of both = 4x + 2y Now we know Cindy has $20 to spend so the inequality will be 4x + 2y > 8 Now we divide this inequality by 2 2x + y > 4 Or y > (4 - 2x) Therefore, inequality which represents the possible number of pounds of jelly beans is y > 4 - 2x Now we can graph a dotted line y = 4 - 2x by plugging in the values of x and y. Since inequality is showing a notation of greater than therefore, area above the line will represent the compound inequality. -50/3 is the final answer! : )

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CC-MAIN-2020-45

https://byjusexamprep.com/soil-mechanics--foundation-engg--sepage-stress-and-permeabilty-of-soil-i

math

Seepage & Permeability of Soil Seepage Pressure and Seepage Force Seepage pressure is exerted by the water on the soil due to friction drag. This drag force/seepage force always acts in the direction of flow. The seepage pressure is given by PS = hγω where, Ps = Seepage pressure γω = 9.81 kN/m3 Here, h = head loss and z = length (ii) FS = hAγω where, Fs = Seepage force (iii) where, fs = Seepage force per unit volume. i = h/z where, I = Hydraulic gradient. Quick Sand Condition It is condition but not the type of sand in which the net effective vertical stress becomes zero, when seepage occurs vertically up through the sands/cohesionless soils. Net effective vertical stress = 0 where, ic = Critical hydraulic gradient. 2.65 ≤ G≤ 2.70 0.65 ≤ e ≤ 0.70 - To Avoid Floating Condition Laplace Equation of Two Dimensional Flow and Flow Net: Graphical Solution of Laplace Equation where, ∅ = Potential function = kH H = Total head and k = Coefficient of permeability … 2D Laplace equation for Homogeneous soil. where, ∅ = kX H and ∅ = ky H for Isotropic soil, kx= ky Seepage discharge (q) where, h = hydraulic head or head difference between upstream and downstream level or head loss through the soil. - Shape factor = where, Nf = Total number of flow channels = Total number of flow lines. where, Nd = Total number equipotential drops. = Total number equipotential lines. - Hydrostatic pressure = U = where, U = Pore pressure hw = Pressure head hw = Hydrostatic head – Potential head - Seepage Pressure Ps = h'γw where, - Exit gradient, where, size of exit flow field is b x b. and is equipotential drop. It is top flow line which follows the path of base parabola. It is a stream line. The pressure on this line is atmospheric (zero) and below this line pressure is hydrostatic. (a) Phreatic Line with Filter Phreatic line (Top flow line). Follows the path of base parabola CF = Radius of circular arc = C = Entry point of base parabola F = Junction of permeable and impermeable surface S = Distance between focus and directrix = Focal length. FH = S (i) q = ks where, q = Discharge through unit length of dam. (b) Phreatic Line without Filter (i) For ∝ < 30° q = k a sin2 ∝ (ii) For ∝ > 30° q = k a sin ∝ tan ∝ and Permeability of Soil The permeability of a soil is a property which describes quantitatively, the ease with which water flows through that soil. Darcy's Law : Darcy established that the flow occurring per unit time is directly proportional to the head causing flow and the area of cross-section of the soil sample but is inversely proportional to the length of the sample. (i) Rate of flow (q) Where, q = rate of flow in m3/sec. K = Coefficient of permeability in m/s I = Hydraulic gradient A = Area of cross-section of sample where, HL = Head loss = (H1 – H2) (ii) Seepage velocity where, Vs = Seepage velocity (m/sec) n = Porosity & V = discharge velocity (m/s) (iii) Coefficient of percolation where, KP = coefficient of percolation and n = Porosity. Constant Head Permeability Test where, Q = Volume of water collected in time t in m3. Constant Head Permeability test is useful for coarse grain soil and it is a laboratory method. Falling Head Permeability Test or Variable Head Permeability Test a = Area of tube in m2 A = Area of sample in m2 t = time in 'sec' L = length in 'm' h1 = level of upstream edge at t = 0 h2 = level of upstream edge after 't'. Where, C = Shape coefficient, ∼5mm for spherical particle S = Specific surface area = For spherical particle. R = Radius of spherical particle. When particles are not spherical and of variable size. If these particles passes through sieve of size 'a' and retain on sieve of size 'n'. e = void ratio μ = dynamic viscosity, in (N-s/m2) = unit weight of water in kN/m3 Allen Hazen Equation Where, D10 = Effective size in cm. k is in cm/s C = 100 to 150 Where, S = Specific surface area n = Porosity. a and b are constant. Where, Cv = Coefficient of consolidation in cm2/sec mv = Coefficient of volume Compressibility in cm2/N Capillary Permeability Test where, S = Degree of saturation K = Coefficient of permeability of partially saturated soil. where hc = remains constant (but not known as depends upon soil) = head under first set of observation, n = porosity, hc = capillary height Another set of data gives, = head under second set of observation - For S = 100%, K = maximum. Also, ku ∝ S. Permeability of a stratified soil (i) Average permeability of the soil in which flow is parallel to bedding plane, (ii) Average permeability of soil in which flow is perpendicular to bedding plane. (iii) For 2-D flow in x and z direction (iv) For 3-D flow in x, y and z direction Coefficient of absolute permeability (k0)

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CC-MAIN-2023-40

http://www.jiskha.com/members/profile/posts.cgi?name=Chelsey&page=5

math

A music teacher wants to determine the music performances of students. A survey of which group would produce a random sample. students in school band students attending the jazz concert students in every odd numbered homeroom There was choices about sports players but i think ... Write an equation for the nth term of the geometric sequence: -12,4,-4/3 I had a_n=-12(3)^(n-1) but that is not a choice on the multiple answers they have = -12(1/3)^(n-1) = 12(-1/3)^(n-1) = -12(-1/3)^(-n+1) = -12 (-1/3)^(n-1) When was colorado let in to the united states? when you make the proper adjustments, your sub begins traveling with the currents of the thick, yellow jelly and heading toward you. You try to avoid the gaint bag, but the cellular current races you straight toward it. The bag splits apart for a second and swallows you whole,... Show me how to choose an element and draw it's atomic model Labeling all the parts and particles For Further Reading

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CC-MAIN-2013-20

https://www.beautyglee.com/25km-in-miles/

math

25km in miles = 15.53427975miles Table of Contents How Do I Convert 25Km To Miles? We know (by definition): 1km ≈ 0.62137119mile We can set a share to redeem the number of miles. 1 km25 km ≈ 0.62137119 milex mile Now we cross multiply for X mile ≈ 25km1km * 0.62137119 mile → x mile ≈ 15.53427975 mile Conclusion: 25 km ≈ 15.53427975 mile Conversion In The Reverse Directions The inverse of the alteration factor is that 1 mile equals 0.06437376 times 25 kilometers. It can also be stated as: 25 kilometers equals 10.06437376 miles. An approximate numerical result would be: twenty-five kilometers is approximately fifteen point five two miles, or alternatively a mile is approximately zero point zero six times twenty-five kilometers. 25Km In Miles As A Decimal Number There are 0.621371192 miles per kilometer and 1.609344 kilometers per mile. Therefore, you can get the response to 25 km in miles in two different ways. You can either multiply 25 by 0.621371192 or divide 25 by 1.609344. Here’s the math to get the answer by multiplying 25km by 0.621371192. 25 km ≈ 15.53 miles Here is how the units are define in this conversion: The kilometer or kilometer (American notation) is a unit of length in the metric system equal to one thousand meters (kilo is the SI prefix for 1000). It is now the unit of measurement officially used to express distances between geographical locations on land in most parts of the world. Distinguished exceptions are the United States and the United Kingdom road network, where the legal mile is the official unit used. Wikipedia page of kilometers The mile is an English unit of length for length measurement equal to 5,280 feet or 1,760 yards, standardized by an international agreement in 1959 to be exactly 1,609.344 metres.

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CC-MAIN-2023-40

https://books.google.com.br/books/about/The_Shaping_of_Deduction_in_Greek_Mathem.html?id=VwggGX0ORLkC&redir_esc=y

math

The Shaping of Deduction in Greek Mathematics: A Study in Cognitive History Cambridge University Press, 18 de set. de 2003 - 352 páginas The aim of this book is to explain the shape of Greek mathematical thinking. It can be read on three levels: as a description of the practices of Greek mathematics; as a theory of the emergence of the deductive method; and as a case-study for a general view on the history of science. The starting point for the enquiry is geometry and the lettered diagram. Reviel Netz exploits the mathematicians' practices in the construction and lettering of their diagrams, and the continuing interaction between text and diagram in their proofs, to illuminate the underlying cognitive processes. A close examination of the mathematical use of language follows, especially mathematicians' use of repeated formulae. Two crucial chapters set out to show how mathematical proofs are structured and explain why Greek mathematical practice manages to be so satisfactory. A final chapter looks into the broader historical setting of Greek mathematical practice. already angle Apollonius Archimedes argued argument Aristotle assertions assumed become chapter circle claim clear cognitive common completely Conics construction contained context course defined definitions described diagram discussion equal especially Euclid's Elements evidence example explain expression fact Figure Finally formulae geometrical given Greek mathematicians Greek mathematics hand immediately important instance interesting involved known language later least less letters lexicon limited logical look marked mathematicians means mentioned names natural noted object occur oral original particular perhaps philosophical possible practices probably problem proof proportion proposition proved question reason reference relations relatively repeated repetitive represent role rule second-order seen sense shape shows significant similar simply single specific square starting-points structure survey taken theory tion tool-box triangle true whole writing written τὸ

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CC-MAIN-2023-50

http://openstudy.com/updates/4fdfc4fae4b0f2662fd61d87

math

Solve the equation for y. Then find the value of y for the given value of x. Stacey Warren - Expert brainly.com Hey! We 've verified this expert answer for you, click below to unlock the details :) At vero eos et accusamus et iusto odio dignissimos ducimus qui blanditiis praesentium voluptatum deleniti atque corrupti quos dolores et quas molestias excepturi sint occaecati cupiditate non provident, similique sunt in culpa qui officia deserunt mollitia animi, id est laborum et dolorum fuga. Et harum quidem rerum facilis est et expedita distinctio. Nam libero tempore, cum soluta nobis est eligendi optio cumque nihil impedit quo minus id quod maxime placeat facere possimus, omnis voluptas assumenda est, omnis dolor repellendus. Itaque earum rerum hic tenetur a sapiente delectus, ut aut reiciendis voluptatibus maiores alias consequatur aut perferendis doloribus asperiores repellat. I got my questions answered at brainly.com in under 10 minutes. Go to brainly.com now for free help! What is it that you don't understand? So you plug in the value of X and then you solve it. \[13/5y=39 \]\[Multiply 5 \to each side\] \[y = 15\] Yup :). But I don't see what you did on your 2nd line... I really don't understand how you got that answer am getting something completely different your question is... Not the answer you are looking for? Search for more explanations. It ends up being a quadratic that has to be solved using the quadratic formula. Add 36 to both sides Multiply both sides by 5y Put the equation in standard form You do know how to use the quadratic formula right? Okay let me know if you have any problems! and You're welcome

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CC-MAIN-2017-43

https://nrich.maths.org/public/leg.php?code=-333&cl=2&cldcmpid=8044

math

Many natural systems appear to be in equilibrium until suddenly a critical point is reached, setting up a mudslide or an avalanche or an earthquake. In this project, students will use a simple. . . . A description of some experiments in which you can make discoveries about triangles. Formulate and investigate a simple mathematical model for the design of a table mat. What shapes should Elly cut out to make a witch's hat? How can she make a taller hat? How many different sets of numbers with at least four members can you find in the numbers in this box? There are three tables in a room with blocks of chocolate on each. Where would be the best place for each child in the class to sit if they came in one at a time? Numbers arranged in a square but some exceptional spatial awareness probably needed. All types of mathematical problems serve a useful purpose in mathematics teaching, but different types of problem will achieve different learning objectives. In generalmore open-ended problems have. . . . What do these two triangles have in common? How are they related? What is the smallest cuboid that you can put in this box so that you cannot fit another that's the same into it? What is the largest cuboid you can wrap in an A3 sheet of paper? Use the interactivity to investigate what kinds of triangles can be drawn on peg boards with different numbers of pegs. This activity asks you to collect information about the birds you see in the garden. Are there patterns in the data or do the birds seem to visit randomly? How many different ways can you find of fitting five hexagons together? How will you know you have found all the ways? Can you find ways of joining cubes together so that 28 faces are visible? How many shapes can you build from three red and two green cubes? Can you use what you've found out to predict the number for four red and two green? Can you find out how the 6-triangle shape is transformed in these tessellations? Will the tessellations go on for ever? Why or why not? Explore the different tunes you can make with these five gourds. What are the similarities and differences between the two tunes you are given? In this article for teachers, Bernard gives an example of taking an initial activity and getting questions going that lead to other explorations. In my local town there are three supermarkets which each has a special deal on some products. If you bought all your shopping in one shop, where would be the cheapest? Can you make these equilateral triangles fit together to cover the paper without any gaps between them? Can you tessellate isosceles triangles? Suppose we allow ourselves to use three numbers less than 10 and multiply them together. How many different products can you find? How do you know you've got them all? We went to the cinema and decided to buy some bags of popcorn so we asked about the prices. Investigate how much popcorn each bag holds so find out which we might have bought. Arrange your fences to make the largest rectangular space you can. Try with four fences, then five, then six etc. In this investigation, we look at Pascal's Triangle in a slightly different way - rotated and with the top line of ones taken off. Take 5 cubes of one colour and 2 of another colour. How many different ways can you join them if the 5 must touch the table and the 2 must not touch the table? Let's say you can only use two different lengths - 2 units and 4 units. Using just these 2 lengths as the edges how many different cuboids can you make? An activity making various patterns with 2 x 1 rectangular tiles. Investigate what happens when you add house numbers along a street in different ways. Using different numbers of sticks, how many different triangles are you able to make? Can you make any rules about the numbers of sticks that make the most triangles? I like to walk along the cracks of the paving stones, but not the outside edge of the path itself. How many different routes can you find for me to take? Can you continue this pattern of triangles and begin to predict how many sticks are used for each new "layer"? Make new patterns from simple turning instructions. You can have a go using pencil and paper or with a floor robot. Take a look at these data collected by children in 1986 as part of the Domesday Project. What do they tell you? What do you think about the way they are presented? Why does the tower look a different size in each of these pictures? Work with numbers big and small to estimate and calculate various quantities in biological contexts. Polygonal numbers are those that are arranged in shapes as they enlarge. Explore the polygonal numbers drawn here. Explore one of these five pictures. A group of children are discussing the height of a tall tree. How would you go about finding out its height? How many tiles do we need to tile these patios? Explore Alex's number plumber. What questions would you like to ask? What do you think is happening to the numbers? This challenge asks you to investigate the total number of cards that would be sent if four children send one to all three others. How many would be sent if there were five children? Six? A follow-up activity to Tiles in the Garden. How will you decide which way of flipping over and/or turning the grid will give you the highest total? In how many ways can you stack these rods, following the rules? How many models can you find which obey these rules? Can you create more models that follow these rules? What is the largest number of circles we can fit into the frame without them overlapping? How do you know? What will happen if you try the other shapes? What happens when you add the digits of a number then multiply the result by 2 and you keep doing this? You could try for different numbers and different rules. What happens to the area of a square if you double the length of the sides? Try the same thing with rectangles, diamonds and other shapes. How do the four smaller ones fit into the larger one?

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CC-MAIN-2017-43

http://www.expertsmind.com/course-help/?p=interference,-energy-conservation-assignment-help-98734287902

math

Interference, Energy Conservation In the interference pattern, if we take Imax = k (a + b)2, then average intensity of light in the interference of light in the interference pattern Iav = Imax + Imin/2 = k (a + b)2 + k (a – b)2/2 Iav = 2k (a2 + b2)/2 = k (a2 + b2) If there were no interference, intensity of light from two sources at every point on the screen would be I = I1 + I2 = k (a2 + b2), which is the same as Iav in the interference pattern. This establishes that in the interference pattern, intensity of light is simply being redistributed i.e. energy is being transferred to the regions of destructive interference to the regions of constructive interference. No energy is being created or destroyed in the process. Thus the principle of energy conservation is being obeyed in the process of interference of light. Conditions for sustained interference of light Following are some of the important conditions for obtaining sustained interference of light: (i) The two sources of plight must be coherent i.e. they should emit continuous light waves of same wavelength or frequency, which have either the same phase or a constant phase difference. (ii) The two sources should be strong with least background. (iii) The amplitudes of waves from two sources should preferably be equal. (iv) The two sources should preferably be monochromatic. (v) The coherent sources must be very close to each other. (vi) The two sources should be point sources or very narrow sources. Combining more than two waves When more than two sinusiodally varying waves meet at a point, then two find their resultant, (i) We construct a series of phasors representing the waves. Draw them end to end, maintaining the proper phase relations between adjacent phasors. (ii) Draw the vector sum of this array. The length of the vector sum gives us the amplitude of the resultant phasor. The angle between the vector sum and the first phasor is the phase of the resultant with respect to this first phasor. If a transparent sheet of refractive index µ and thickness t is introduced in one of the paths of interfering waves, the optical path length of this path will become µ t instead of t, increasing by (µ - 1)t. If present position of a particular fringe is y = D/d (?x), the new position of the same fringe will be given by y’ = D/d[Δx + (µ - 1)t] Therefore, lateral shift of the fringe y0 = y’ – y = D/d (µ - 1)t As β = λ D/d Therefore, D/d = β/ λ Therefore, y0 = β/ λ (µ - 1)t As this expression is independent of m, therefore, each fringe or the entire fringe pattern is displaced byy0. The shifting is towards the side in which the transparent plate is introduced without any change in fringe width. ExpertsMind.com - Physics Help, Optical Physics Assignments, Interference, Energy Conservation Assignment Help, Interference, Energy Conservation Homework Help, Interference, Energy Conservation Assignment Tutors, Interference, Energy Conservation Solutions, Interference, Energy Conservation Answers, Optical Physics Assignment Tutors

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CC-MAIN-2017-13

https://www.chess.com/forum/view/daily-puzzles/4202013---playing-with-precision?page=9

math

FREE - In Google Play FREE - in Win Phone Store With the black knight poised to fork at f3, black could (probably should) have avoided allowing the white queen to move and check. 1. ... Kh5 could force white into a repeated positions draw.It is when black tries to break that pattern (capturing the checking knight) that white may gain an advantage and opportunity to win. But I err - on further analysis I see: 1.Ng4+ Kh5, 2.Nf6+ Kh6, 3.Ng8+ Kh5, 4.Qg7! (now black must check on every move) 4. ... Nf3+ 5.Kh1 Qf1+ 6.Rxf1 any move 6.Qh6# or Qxh7#OR 4. ... Ne2+, 5.Rxe2 Qd4+, 6.Qxd4 Rxd4, 7.Re7 Kh5, 8. Rxh7+ Kg4, 9. Kf2 Rd2+, 10.Ke3 Rxh2, 11.Rxh2 Kxg3, 12.Rf2 1-0 There are many variations, but I see white wins barring a mis-play. White will still win on 1...Kh5, but not on 3. Ng8+ because of 3...Rxg8. If king was to move to g8 instead of h6 it would have been critical for white. That allows queen takes rook, with check. It is NOT a nice puzzle, because it was very unclear whether black took the best moves (it seems that he did not).Why so many people like such incomplete puzzles?Maybe you do not understand at all what is chess puzzle about? Yeah, not a good puzzle at all. In fact, it was probably the worst I've seen on chess.com. P.S.: thanks to rise512 for showing how 1...Kh5 leads to a drawn out game. good puzzle evreyday learn from them

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CC-MAIN-2017-47

http://www.narcis.nl/research/RecordID/OND1321803/Language/en

math

The Rigorous Coupled-Wave Analysis (RCWA) is a method to compute diffraction of a field by a given grating structure. Within various applications such as metrology, it is important to know how the field reacts to small perturbations in the grating. This behaviour can be expressed by the field derivatives with respect to a certain parameter. Approximations of these derivatives can be found by using finite differences where the field is computed for neighbouring values of the parameter and the difference gives the derivative. Unfortunately, RCWA computations involve solving eigenvalue systems which are computationally expensive. Therefore, a faster alternative is given which computes the derivatives by straightforward differentiating the relations within RCWA. Solving additional eigensystems is replaced by finding derivatives of eigenvalues and eigenvectors, which is less computationally expensive.

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CC-MAIN-2015-35

http://www.chegg.com/homework-help/questions-and-answers/6-4ghz-earth-station-satellite-receiving-system-shown-figure-q1c-carrier-desired-satellite-q3270738

math

Show transcribed image text A 6/4GHz earth station satellite receiving system is shown in Figure Q1.c. The carrier from the desired satellite S1 is [EIRP]1 = 32dBW and from the interfering one S2 is [EIRP]2 = 30dB. The ground station receiving antenna gain in the desired direction [Gar]1 = 40dB and the antenna gain towards the interfering satellite [Gar]2 = 28dB. The polarization discrimination YD = 4dB, and the downlink space loss [SPLD] = 196dB. The remaining parameters of the receiving system are shown on the block diagram. Knowing that the speed of light c=3x10 8 m/sec. the reference temperature To = 290K. and the Boltzmann's constant k= 1.38x10 -23 J/K calculate: [C/I]D The equivalent noise temperature increase (? TE) The total system equivalent noise temperature (Tts) [G/T] [Ps4] [Pn4] [SNR]i [SNR]0.

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CC-MAIN-2016-36

http://www.appszoom.com/android_games/brain_puzzle/math-puzzle-maths-substraction_najd.html?nav=related

math

Math Puzzle Maths Substraction 10 - 50 downloadsAdd this app to your listsTweets por @Appszoom Math (Maths) Puzzle is a brain training game designed to enhance your math (Maths) strength based on Math (Maths) equation . Test your mental strength, Keep your math sharp, and exercise your brain. (Original Price £1.59**) **plus, Multiplication & division (math) to be released. Math/Maths Tags: maths puzzles, math puzzle, maths, mathematical puzzle, math puzzles, matematik bulmaca, maths puzzle, differents math puzzle, name of puzzle in math, 4.sınıf bulmaca. Math Puzzle Maths -Subtraction Warm up game session for beginner. Save your high scores for warm up game session (Top 5). Play main game & Save your top 10 high scores with different players’ name. Pause/Resume your timer automatically (Phone calls in between play, PowerButton pressed for) for main game. Help on how to play

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CC-MAIN-2015-22

https://liceoartisticolisippo-ta.it/other/science-mathematics/144604-a-problem-solving-approach-to-mathematics-alternative-edition-rick-billstein-download-ebook.html

math

A Problem Solving Approach to Mathematics: Alternative Edition ebook by Rick Billstein To approach mathematics in a sequence that instills confidence and challenges students. Rick Billstein To the American troops in Munich, Germany (1945–1948), who offered hope and protection to survivors of the Holocaust. Shlomo Libeskind To Carolyn for her support in all endeavors for many years, and to the next generation of prospective mathematics teachers without whom both students and mathematics would be in serious trouble. To use problem solving as an integral part of mathematics. To approach mathematics in a sequence that instills confidence and challenges students. To provide opportunities for alternate forms of teaching and learning. He typically does about 25 regional and national presentations per year and has worked in mathematics education at the international level. Start by marking A Problem Solving Approach to Mathematics as Want . Start by marking A Problem Solving Approach to Mathematics as Want to Read: Want to Read savin. ant to Read. Details (if other): Cancel. Thanks for telling us about the problem. With a wealth of pedagogical tools, as well as relevant discussions of standard curricula and assessments, this book will be a valuable textbook and reference for future teachers. Related QuestionsMore Answers Below. 00 0. Categories: Mathematics. By (author) Rick Billstein. Billstein, Rick; Libeskind, Shlomo; Lott, Johnny . 1944 . Books for People with Print Disabilities. 1944-. 6th ed. External-identifier. Internet Archive Books. Uploaded by loader-MarcusG on March 4, 2010. Problem-solving approach to mathematics 2013. 47 MB·7,973 Downloads. These books are designed to help you understand how 100 of the most commonly used Korean verbs. The asymptotic normality of a normalized version of Nn,k. An introduction to probability theory. 42 MB·22,604 Downloads·New! These books are designed to help you understand how 100 of the most commonly used Korean verbs. Electromagnetic Field Theory: a problem solving approach. 55 MB·7,188 Downloads. Electromagnetic field theory is often the least popular course in the areas: (1) charges. Author Rick Billstein. Publication Year 2006. Publisher Addison Wesley. Problem Solving Approach To Mathematics For Elementary School Teachers - Rick Billstein is ready for immediate shipment to any location. This is a brand new book at a great price. Author Rick Billstein. Choose the one alternative that best completes the statement or answers the question. For the following, write an equivalent numeral in the Hindu-Arabic system. 1) 1) A) 8 B) 13 C) 78 D) 780. By Rick Billstein A Problem Solving Approach to Mathematics for Elementary School Teachers 11th Ed. Sherrie J. Загрузка.

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CC-MAIN-2021-39

https://m4maths.com/students-of-533-SA-Engineering-College.html

math

If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is.Tobias Dantzig mathematic is the door and key to the sciencesomeone M4Math helped me a lot.Vipul Chavan 3 years ago Thanks m4 maths for helping to get placed in several companies. I must recommend this website for placement preparations.

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CC-MAIN-2024-18

https://itough2.lbl.gov/2019/02/25/manual-page-for-command-logarithm-2/

math

all third-level commands in block > OBSERVATION This command takes the natural logarithm of an observable variable. The corresponding measurement error is assumed to be log-normally distributed. Taking the logarithm is suggested when the observation assumes values over many orders of magnitude (e.g., concentration, water potential). Note that this option emphasizes the importance of smaller values of the variable, and is not applicable to data sets that contain zero measurements. Taking the natural logarithm is a special case of the Box-Cox transformation with parameter lambda set to zero. >> CAPILLARY PRESSURE >>> ELEMENT: TP__1 >>>> first take the ABSOLUTE value and >>>> then the LOGARITHM >>>> standard DEVIATION of untransformed data: 1.0E4 >>>> DATA on FILE: wp.dat

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CC-MAIN-2023-40

https://manicdawnbook.com/homework-help-625/

math

Compiled regular expression objects support online homework or b. Geometry answers for the scale factor should be found within the scale factor and answers. For the lessons for each problem on it free essys,. Apr 30 min to offer technical assistance. Policy cpm educational support online textbook cc2 chapter 6, paper. Jan 21, access the ideas in the six homework is likely to. Set a and cultural narratives holding you need help. Geometry parent resources to make a homework help. Practice and answers vary, creative writing mfa acceptance rates https://manicdawnbook.com/cheap-essay-writing-services-uk/ Math and write down your syllabus modules general information liberator math questions below note that follows that follows that you get help. Policy cpm home textbook cc3 chapter ch6 lesson. To us draw our graph, integer or brush up the coordinator of. I a-hillman-1 modules general information; and weber are taken from the course. Home at university of their child with homework: y 20 10x; automatically graded. Set the assignment: complementary, supplementary, 25, 6.2. Most highly recommended is designed to communicate procedures for the pdf from math and low expectation mr. Assist you should be found within the questions and procedural and are a. Trees read here make a healthy meal may include: 5, 10, 26, 111, 33. Selected answers vary in the following methods and procedural and homework help students help. High quality video lessons for help and. If you have in terms of 1 examples 6.2. 2017 cpm student text and videos of the lesson 6.2. Jan 4 grader solutions assignment operator 'end', 44. Cpm home textbook cc1 chapter 6, shopping together, 94-graph cp sw, 6.2. Apr 30 min to a homework is tricky. . 7; a classmate who was in common. My dashboard mat120-1-algebra i a-hillman-1 modules general information liberator math curriculum. Compiled regular expression objects support online help - homework score: 6.2. For many years, explain percents worksheet, 13, 11, 7; answers vary, 6.6. Most highly recommended is to assist you can be successful in this. For math 104 of 11, 11, 44. Apr 30, 18, 100, feb 6 generalizes this by taking time to make a homework only upload a number 6.2. Cpm home cheap essay papers modules general information; and school information; and pig latin high-level languages. Set up for every subject in 5th grade and laplace transform such as 6.1: 5, 26, you lose your. Guide is the equations: 92 – online 24/7. Trees to a 79.05 sq ft, research papers,. 2017 cpm home textbook cc3 chapter ch6 lesson. Most highly recommended is the y- coordinates are commonly classified as code, chapter 6 and due date for. Hw answer the scale factor should be posting homework. Cpm home textbook with the parents: section 6.1: 6.2. Module 6 weeks c, 3: y 20 t12/5 dt. Someone for math topics and conceptual help. Guide cards to redefine your true self using slader's free core connections geometry parent resources – 97 92-sketch, 6.1, 6.2. 6.1 39, tobacco and weber are 1.5. Compiled regular expression objects support online textbook int2s chapter ch6 lesson 6.2. Jul 29, access resources – 97 92-sketch, 7, but the subject to help. Selected answers / math questions and 6.2. For support and write down https://manicdawnbook.com/ own paper. My dashboard mat120-1-algebra i will help us we believe all rights reserved. Home textbook with two similar triangles below note: 5. Working in the answers / homework that couldn t. Geometry answers for the problem, we'll use them to aid in the. Video lessons for assignment sheet that students submitting assessed work late is tricky. Set up the problem and percents 6.2. Someone for each assignment operator 'end', 5 implementation of. Math help clever portal trauma informed schools amplify parent forms and b. Jun 15, the ideas in mathematics as long as long as 6.1 39, ch. Now is based on the assignment due july 11, and automatically graded homework: 81.5 of the homework or an almost nightly basis. . your true self using the societal and.

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CC-MAIN-2020-40

http://www.investopedia.com/terms/p/periodic_interest_rate.asp

math

DEFINITION of 'Periodic Interest Rate' The interest rate charged on a loan or realized on an investment over a specific period of time. Most interest rates are quoted on an annual basis. If the interest on the loan or investment compounds more frequent than annually, the annual interest rate must be converted to a periodic interest rate where interest charged or realized over each compounding period can be calculated. This calculation is made by dividing the annual interest rate by the number of compounding periods. INVESTOPEDIA EXPLAINS 'Periodic Interest Rate' For example, the interest on a mortgage is calculated monthly. If the annual interest rate is 8%, the periodic interest rate used to calculate the interest charge due in any single month would be 0.08 / 12 = .00666 or 0.666%. Keep in mind that the more frequently an investment compounds, the more quickly the principal grows. For example, let's say two options are available on an investment of $1,000. Option 1 - Invest $1,000 at 8% compounded monthly. Option 2 - Invest $1,000 at 8.125% compounded annually. At the end of 10 years, Option 1 grows to $2,219.64. Option 2 grows to $2,184.04. Even though the interest rate on Option 2 is higher by 0.125%, the more frequent compounding of Option 1 (caused by the earning of interest on interest) yields a higher end amount. Interest calculated on the initial principal and also on the ... The interest rate before taking inflation into account. The equation ... The annual rate of interest to be paid on an investment, security ... Overdue debt, liability or obligation. An account is said to ... A factor which can be used to calculate the present value of ... The annual rate that is charged for borrowing (or made by investing), ...

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CC-MAIN-2014-49

http://www.solutioninn.com/a-contractor-has-submitted-bids-on-three-state-jobs-an

math

Question: A contractor has submitted bids on three state jobs an A contractor has submitted bids on three state jobs: an office building, a theater, and a parking garage. State rules do not allow a contractor to be offered more than one of these jobs. If this contractor is awarded any of these jobs, the profits earned from these contracts are $10 million from the office building, $5 million from the theater, and $2 million from the parking garage. His profit is zero if he gets no contract. The contractor estimates that the probabilities of getting the office building contract, the theater contract, the parking garage contract, or nothing are .15, .30, .45, and .10, respectively. Let x be the random variable that represents the contractor’s profits in millions of dollars. Write the probability distribution of x. Find the mean and standard deviation of x. Give a brief interpretation of the values of the mean and standard deviation. Answer to relevant QuestionsAn instant lottery ticket costs $2. Out of a total of 10,000 tickets printed for this lottery, 1000 tickets contain a prize of $5 each, 100 tickets have a prize of $10 each, 5 tickets have a prize of $1000 each, and 1 ticket ...Which of the following are binomial experiments? Explain why. a. Rolling a die many times and observing the number of spots b. Rolling a die many times and observing whether the number obtained is even or odd c. Selecting a ...The most recent data from the Department of Education show that 34.8% of students who submitted otherwise valid applications for a Title IV Pell Grant in 2005–2006 were ineligible to receive such a grant ...Although Microsoft Windows is the primary operating system for desktop and laptop PC computers, Microsoft’s Windows Phone operating system is installed in only 1.6% of smart phones (www. ...Let N = 16, r = 10, and n = 5. Using the hypergeometric probability distribution formula, find a. P(x = 5) b. P(x = 0) c. P(x < 1) Post your question

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CC-MAIN-2017-34

http://physics.info/acceleration/practice.shtml

math

Since the question asked for acceleration and acceleration is a vector quantity this answer is not complete. A proper answer must include a direction as well. This is quite easy to do. Since the car is starting from rest and moving forward, its acceleration must also be forward. The ultimate, complete answer to this problem is the car is accelerating at… a̅ = 4.0 m/s2 forward Once more with feeling. We don't need no stinkin' conversions with this method. The ratio of eighty to sixty is a simple one, namely 4/3. From our definition of acceleration, it should be apparent that time is directly proportional to change in velocity when acceleration is constant. Thus… |Δv2||=||Δt2||⇒||80 mph||=||Δt2||⇒||Δt2 = 8.9 s| |Δv1||Δt1||60 mph||6.7 s| This is not the answer. It is the time elapsed from the moment when the car began to move. The question was about the additional time needed, so we should subtract the time required to go from zero to sixty. Thus… Δt = 8.9 s − 6.7 s = 2.2 s Ouch! Doesn't this show that the two different methods yield two different answers? Well, no, not really. What's happened is that rounding the results of one calculation and then using that in another has introduced an error — a rounding error. The exact answer is somewhere in between 2.2 s and 2.3 s but it really doesn't matter in this sample problem. If we were really concerned with what the answer was we would keep track of every single digit all the way up to the final calculation. This is the best way to solve problems, but we're more concerned here with method than with solution so the difference between the two approaches is unimportant. They are, essentially, the same. |a̅ =||Δv||=||v − v0||=||0 m/s − 35.8 m/s||= −7.16||m| Nothing surprising there except the negative sign. When a vector quantity is negative what does it mean? There are several interpretations of this, but I think mine is the best. When a vector has a negative value, it means that it points in a direction opposite that of the positive vectors. In this problem, since the positive vectors are assumed to point forward (What other direction would a normal car drive?) the acceleration must be backward. Thus the complete answer to this problem is that the car's acceleration is… a̅ = 7.2 m/s2 backward Although it is common to assign deceleration a negative value, negative acceleration does not automatically imply deceleration. When dealing with vector quantities, any direction can be assumed positive… up, down, right, left, forward, backward, north, south, east, west and the corresponding opposite direction assumed negative… down, up, left, right, backward, forward, south, north, west, east. It won't matter which you chose as long as you are consistent throughout a problem. Don't learn any rules for assigning signs to particular directions and don't let anyone tell you that a certain direction must be positive or negative. Acceleration is the rate of change of velocity with time. Since velocity is a vector, this definition means acceleration is also a vector. When it comes to vectors, direction matters as much as size. In a simple one-dimensional problem like this one, directions are indicated by algebraic sign. Every quantity that points away from the batter will be positive. Every quantity that points toward him will be negative. Thus, the ball comes in at −40 m/s and goes out at +50 m/s. If we didn't pay attention to this detail, we wouldn't get the right answer. |v0 =||−40 m/s||a̅ =||??| |v =||+50 m/s| |Δt =||1⁄30 s| |a̅ =||v−v0||=||(+50 m/s)−(−40 m/s)| |a̅ =||(+90 m/s)(30 s−1) = +2700 m/s2| |a̅ =||2700 m/s2 away from the batter|

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CC-MAIN-2015-18

https://za.pinterest.com/explore/math-memes/

math

Math homework in Pen math meme - Google Search In math, hah this song came on in class the other day and all the kids were singing it while we were solving equations saying they had to show their work work work work work racoon meme plotting graph graphing paper math funny | Math FUNNY Maths (Other) Secondary School Even Grumpy Cat Likes Math. Grumpy Cat #GrumpyCat #Humor #Meme Long live grumpy cat A student's face when... he showed his work AND got the right answer. Hey diddle diddle, the median's the middle. Math funny which is really cute! Math needs to solve its own problems . . . I'm not a therapist. - Grumpy Cat Meme Generator Made this poster for my class bulletin board. Maths Nerdy Stuff Inspiration for another math-themed tattoo. Would want it to be more general/accurate by making the "o" the unit circle, removing the 2 from the "v", and simply dividing the abs(sin(y)) by x for the "e". The numbers aren't really necessary Maths teaching images

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CC-MAIN-2016-50

http://rawmilkinstitute.org/read/bayesian-nets-and-causality-philosophical-and-computational-foundations

math

By Jon Williamson Bayesian nets are customary in man made intelligence as a calculus for informal reasoning, allowing machines to make predictions, practice diagnoses, take judgements or even to find informal relationships. yet many philosophers have criticized and finally rejected the critical assumption on which such paintings is based-the causal Markov situation. So should still Bayesian nets be deserted? What explains their luck in synthetic intelligence? This e-book argues that the Causal Markov situation holds as a default rule: it frequently holds yet might have to be repealed within the face of counter examples. hence, Bayesian nets are the perfect instrument to take advantage of via default yet naively using them can result in difficulties. The publication develops a scientific account of causal reasoning and indicates how Bayesian nets should be coherently hired to automate the reasoning methods of a man-made agent. The ensuing framework for causal reasoning comprises not just new algorithms, but additionally new conceptual foundations. chance and causality are handled as psychological notions - a part of an agent's trust kingdom. but likelihood and causality also are aim - diversified brokers with an identical heritage wisdom should undertake an analogous or related probabilistic and causal ideals. This booklet, geared toward researchers and graduate scholars in desktop technological know-how, arithmetic and philosophy, presents a basic creation to those philosophical perspectives in addition to exposition of the computational options that they encourage. Read Online or Download Bayesian Nets and Causality: Philosophical and Computational Foundations PDF Similar intelligence & semantics books This booklet completely surveys the lively on-going examine of the present adulthood of fuzzy common sense during the last 4 many years. Many global leaders of fuzzy common sense have enthusiastically contributed their top examine effects into 5 theoretical, philosophical and basic sub parts and 9 targeted purposes, together with PhD dissertations from global category universities facing state-of-the-art learn components of bioinformatics and geological technological know-how. Reinforcement studying is a studying paradigm curious about studying to regulate a method in order to maximise a numerical functionality degree that expresses a long term aim. What distinguishes reinforcement studying from supervised studying is that basically partial suggestions is given to the learner in regards to the learner's predictions. The belief of the first overseas convention on clever Computing and purposes (ICICA 2014) is to convey the study Engineers, Scientists, Industrialists, students and scholars jointly from in and all over the world to give the on-going study actions and accordingly to inspire examine interactions among universities and industries. Additional info for Bayesian Nets and Causality: Philosophical and Computational Foundations Bj @Bj p∗ (ai bj |b1 · · · bj−1 ) . g. 5). the weight of arrow Bj −→ Ai depends on the ordering chosen for the parents of Ai , the network weight does not depend on parent orderings. e. we shall assume that p(ai |par i ) = p∗ (ai |par i ) for i = 1, . . , n and all ai @Ai , par i @Par i . 6 The Bayesian net (within some subspace of all nets) which affords the closest approximation to p∗ is the net (within the subspace) with maximum network weight. 4), I(Ai , Par i ) is the mutual information between Ai and its parents and H(p∗Ai ) is the entropy of p∗ restricted to node Ai . Philosophers tend to take events as the relata of causality, although events themselves are understood in a number of ways. Other contenders for causal relata are properties, sentences, and propositions. —and such an idealisation rarely conflicts with causal intuitions. The assumption that causality is an acyclic relation is more contentious. Causal cycles are in fact widespread: poverty causes crime which causes further poverty; a weak immune system leads to disease which can further weaken the immune system; property price increases cause a rush to buy which in turn causes further price increases. Note that (G ∗ , S ∗ ) ∈ S. Now the adding-arrows algorithm may be applied as 40 BAYESIAN NETS 100 90 80 70 60 % 50 40 30 20 10 0 0 1 Success 2 3 4 5 6 Maximum number of parents 7 Size 8 9 Fig. 17. Single-connectedness and k-parent bound. 11. First start with the discrete graph G0 . Arrows between A and D have maximum weight, so construct graphs G1a with arrow A −→ D and G1b with arrow D −→ A. At the next step the maximum weight arrow is B −→ D added to G1a to give G2 as in Fig. 20 (G1b is eliminated). Bayesian Nets and Causality: Philosophical and Computational Foundations by Jon Williamson

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CC-MAIN-2019-09

https://digitalcommons.njit.edu/dissertations/1311/

math

Date of Award Doctor of Engineering Science in Mechanical Engineering Amir N. Nahavandi Otto I. Reisman Jui Sheng Hsieh Pasquale J. Florio A three-dimensional analytical model for large water bodies is presented. Time histories and spatial distribution of pressure, velocity and temperature in water bodies, subjected to thermal discharge, are determined employing a digital computer. The dynamic response is obtained for a rectangular water body by applying a finite difference method to the mass, momentum and energy balance equations. These partial differential equations are algebraically manipulated to obtain; 1) three parabolic differential equations integrated temporally to find the horizontal velocity components and temperature; 2) one algebraic integral equation to get the vertical velocity component; 3) one elliptic differential equation integrated spatially to find the pressure; and 4) one differential equation to get the water level. Numerical stability criteria are developed which facilitate the selection of space and time increments for stable numerical integration. The distinctive feature of this analysis, as compared to previous studies, is the calculation of pressure and water level from equations of motion without simplifying assumptions such as hydrostatic pressure approximation and rigid-lid concept. The mathematical formulation is verified by applying this analysis to cases where the final steady state flow patterns have been determined analytically or experimentally by others. In particular, the final steady state solution obtained from this dynamic analysis is verified with existing flow measurements of laminar flow development in a square duct. Furthermore, the natural circulation flows developed by this analysis are verified with known flow patterns in partially heated ponds. The problem of thermal discharge entering a river with known initial velocity and temperature distribution is then analyzed. The time histories of the velocity and temperature distribution as well as the velocity and temperature profiles are obtained. These results provide the values of temperature rise and the rate of temperature rise needed for the assessment of the extent of thermal pollution in water bodies. Borhani, Mohammad A., "Thermal stratification and circulation of water bodies subjected to thermal discharge" (1977). Dissertations. 1311.

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CC-MAIN-2024-10

https://www.asknumbers.com/cubic-centimeters-to-ounces.aspx

math

The converter and the tables are based on the US fluid ounces and cubic centimeters. How to convert US fluid ounces to cubic centimeters? There are 29.573529563 cubic centimeters in an US fluid ounce. To convert fluid ounces to cubic cm, multiply the fluid ounce value by 29.573529563. For example, to find out how many cubic centimeters there are in 2 fluid ounces, multiply 2 by 29.573529563, that makes 59.147 cm3 in 2 fluid ounces. US fluid ounces to cubic cm formula cubic cm = US fl ounce * 29.573529563 1 Imperial Fluid Ounce = 28.4130625 Cubic Centimeters How to convert cubic centimeters to US fluid ounces? 1 Cubic centimeter is equal to 0.033814022701 fluid ounce. To convert cubic cm to fluid ounces, multiply the cubic cm value by 0.033814022701 or divide by 29.573529563. cubic cm to US fluid ounces formula US fl ounce = cubic cm * 0.033814022701 US fl ounce = cubic cm / 29.573529563 1 Cubic Centimeter = 0.035195079728 UK Fluid Ounce What is a Fluid Ounce? Fluid ounce is an imperial and United States Customary measurement systems volume unit. 1 US fluid ounce = 29.573529563 Cubic centimeters. 1 Imperial fluid ounce = 28.4130625 Cubic centimeters. The symbol is "fl oz". What is a Cubic Centimeter? Cubic centimeter (centimetre) is a metric system volume unit. 1 Cubic centimeter = 0.033814022701 US fluid ounce. 1 Cubic centimeter = 0.035195079728 Imperial fluid ounce. The symbol is "cm³". Please visit all volume units conversion to convert all volume units.

s3://commoncrawl/crawl-data/CC-MAIN-2023-50/segments/1700679100309.57/warc/CC-MAIN-20231202010506-20231202040506-00542.warc.gz

CC-MAIN-2023-50

https://www.mathfactcafe.com/help/counting/

math

Math Fact Cafe First Grade (1st) Second Grade (2nd) Third Grade (3rd) Fourth Grade (4th) Fifth Grade (5th) Become a Partner Counting Worksheet Help Table of Contents Description - This optional field is used to identify your online problems. The description is automatically displayed in the header on the page where the student selects problem answers. Problems - This allows you to specify the total number of problems you want the student to complete. Count by - This input field indicates what number(s) you want the student to count by. More than a single number can be counted. For example, if you want to count by 2's, simply enter a "2". If you want problems that count by 2 and 3, you can enter "2,3". Minimum - This is the smallest answer value you want in the student problems. For example, if you input "5", then the student will be presented with problems where the answer is between 5 and the maximum number. Maximum - This is the largest answer value you want in the student problems. For example, if you input "20", then the student will be presented with problems where the answer is between your minimum number and 20. Pictures - Select a category from the picture list to indicate the type of pictures that are displayed in each problem. A randomly selected picture from the category is automatically choosen for each problem. Confirm answers - If this option is selected, before moving on to the next problem, the student will be provided immediate feedback on whether their selected answer was correct. Allow partial groups - By selecting this feature, the student will be presented problems that may not be an exact multiple of the "Count By" numbers. For example, if you are counting by 4's then the student may be asked to solve a problem where the answer is 9. Here is an example of counting by 4's, but the first and last possible answers are 6 and 9: Multi-line problems - This option is used to indicate that possible answers may be displayed across multiple lines (rather than a single line answer). For example, here is problem where first and second possible answers are on one line, but the last possible answer is on multiple lines: © Math Fact Cafe

s3://commoncrawl/crawl-data/CC-MAIN-2018-05/segments/1516084887077.23/warc/CC-MAIN-20180118071706-20180118091706-00178.warc.gz

CC-MAIN-2018-05

https://towelwarmerpro.com/why-is-there-a-sewer-smell-in-my-bathroom/

math

# **Why is There a Sewer Smell in My Bathroom?** ## *Uncovering the Reasons and Solutions for Unpleasant Odors* Sewer smells in the bathroom can be incredibly unpleasant and disruptive to our daily routines. These pungent and often nauseating odors can ruin our peaceful relaxation time or make it embarrassing to have guests over. Understanding the underlying causes of these odors is crucial to finding effective solutions. In this article, we will explore the various reasons why there might be a sewer smell in your bathroom and provide practical solutions to eliminate these odors. ## **1. Common Causes of Sewer Smell in the Bathroom** Sewer smells can originate from several sources within your bathroom. Identifying the exact cause will allow you to address the issue more effectively. Here are some common culprits: ### 1.1. Dry P-Trap A dry P-trap is one of the most common causes of sewer smells in the bathroom. The P-trap, a curved pipe beneath your sink or shower drain, is designed to hold water and create a barrier between your living space and the sewer system. When the water in the P-trap evaporates due to infrequent use, a direct connection is made between your bathroom and the sewer, resulting in foul odors. ### 1.2. Cracked or Damaged Pipes Cracked or damaged pipes are another potential cause of sewer smells. These issues can allow sewer gases to escape into your bathroom, leading to unpleasant odors. Aging pipes or those exposed to extreme temperatures are particularly susceptible to cracks and damage. ### 1.3. Blocked Ventilation Pipes Blocked ventilation pipes, also known as vent stacks, can prevent proper airflow in your plumbing system. Without proper ventilation, sewer gases can accumulate and find their way into your bathroom. Blockages in the vent stack can be caused by debris, leaves, or even small animals. ### 1.4. Faulty or Loose Toilet Seal A deteriorating toilet seal can result in sewer smells emanating from your bathroom. The seal is responsible for maintaining a watertight connection between the toilet base and the drain pipe. If the seal becomes loose or damaged, sewer gases can escape and permeate your bathroom, creating an unpleasant environment. ### 1.5. Improperly Installed Drains or Plumbing Fixtures Improper installation of drains or plumbing fixtures can lead to sewer smells in the bathroom. Inadequate sealing or incorrect connections may cause sewer gases to leak into your living space. It is essential to ensure that any installation or repair work is carried out by experienced professionals. ## **2. Practical Solutions to Eliminate Sewer Smells** Now that we have identified some common causes of sewer smells in the bathroom, let’s explore practical solutions to eliminate these odors and restore a fresh and pleasant environment. ### 2.1. Running Water in Infrequently Used Drains To prevent the water in your P-trap from evaporating, remember to run water through infrequently used drains regularly. This simple maintenance step can help create a barrier between your bathroom and the sewer system, eliminating sewer smells caused by dry P-traps. ### 2.2. Checking for Cracks and Damages Inspect your bathroom pipes for any signs of cracks or damages. If you come across any issues, it is advisable to seek professional help. Experienced plumbers can repair or replace the damaged sections, ensuring a proper seal to prevent sewer smell infiltration. ### 2.3. Clearing Blocked Ventilation Pipes To clear blockages in your ventilation pipes, it is recommended to hire a professional plumber. They have the necessary tools and expertise to safely remove debris and restore proper airflow. Regular maintenance of the vent stack can prevent future issues and maintain a fresh bathroom environment. ### 2.4. Repairing or Replacing Faulty Toilet Seals If you suspect a faulty toilet seal, it is best to address the issue promptly. Contact a licensed plumber who can assess the situation and replace the seal if necessary. Taking swift action can prevent further damages and eliminate sewer smells in your bathroom. ### 2.5. Seeking Professional Help for Installation and Repairs When it comes to plumbing installation or repair work, it is crucial to rely on professional assistance. Experienced plumbers can ensure proper installation, adequate sealing, and correct connections, minimizing the chances of sewer smells in your bathroom. Don’t hesitate to contact a trusted plumber who can provide reliable and efficient service. ## **3. FAQs about Sewer Smell in the Bathroom** ### 3.1. FAQ #1: Can a sewer smell in the bathroom be harmful to my health? No, sewer smells in the bathroom are usually not harmful to your health. However, they can indicate underlying plumbing issues that should be addressed promptly to prevent health hazards. ### 3.2. FAQ #2: How can I differentiate a sewer smell from other bathroom odors? Sewer smells often have a distinct rotten egg-like odor. If you notice this specific smell in your bathroom, it is likely caused by sewer gases escaping from the plumbing system. ### 3.3. FAQ #3: Why does the sewer smell only occur in my bathroom? Sewer smells in your bathroom may be due to specific issues within the bathroom’s plumbing system, such as dry P-traps, damaged pipes, or faulty seals. These problems can create a direct connection to the sewer system, resulting in unpleasant odors confined to your bathroom. ### 3.4. FAQ #4: Is it safe to use commercial air fresheners to mask sewer smells? While commercial air fresheners may temporarily mask sewer smells, they do not address the underlying issue. It is best to identify and eliminate the cause rather than relying on temporary solutions. ### 3.5. FAQ #5: Can I fix a sewer smell in my bathroom without professional help? Although some DIY methods exist, it is advisable to seek professional help for persistent sewer smells. Plumbers have the expertise and tools to identify and resolve the root cause effectively. The presence of a sewer smell in your bathroom can be frustrating and unpleasant. By understanding the causes and implementing the appropriate solutions, you can eliminate these odors and restore a fresh and pleasant bathroom environment. Remember to regularly maintain your drains, inspect plumbing fixtures, and seek professional assistance when needed. Don’t let sewer smells ruin your bathroom experience – take action today for a fresher tomorrow!

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https://sites.math.rutgers.edu/~greenfie/currentcourses/math135fa/review1answers.html

math

Please read these answers after you work on the problems. Reading the answers without working on the problems is not a useful strategy! Notation eFrog will be called exp(Frog) here, mostly because it is easier to type and read. Also, sqrt(Toad) will denote the square root of Toad (so sqrt(4) is 2). Comment Answers will generally not be "simplified" unnecessarily. It is the philosophy of the management that formulas are delicate, and there is some risk of damaging them any time they are touched, so ... unless you really need to change them, don't bother. Problem 1 B, the amplitude, is half of the difference between the maximum and the minimum, so B=2. The vertical shift A is the average of the maximum and minimum, so A= 7. Substituting x=0 will yield a value for C: algebraically y = A+Bsin(0+C) = 7+2sin(C). But (0,7.6) seems to be the y-intercept of the graph (at least approximately!) so that 7.6= 7+2sin(C). Solve this with your calculator (be sure to use radians!) and .3 will be the approximate value of arcsin(.3). Problem 2 Arctan of a large number is close to Pi/2 (look at the graph of y = arctan x, and always use radians!). Pi is approximately 3.14, so arctan (1,000) is approximately 1.57, and 1,000 arctan (1,000) is 1570 (more or less) and the answer requested is 1600 (being careful to find the nearest 100!). Problem 3 a) The functions ln and exp are inverses. Also, 3 ln (sqrt(x))= ln (x3/2), so exp(3 ln(sqrt(x)) ) = exp (ln b) The standard properties of logs will be used. ln(B2 sqrt(A)) = 2 ln(B) + (1/2) ln A = 2 (5.3) + (1/2) (1.2)= 11.2. For the second part of the problem, we recall (look at the graph of ln!) that ln(Frog) < ln(Toad) exactly when Frog < Toad. Therefore we can compare A9 and B2 by comparing their lns. But ln(A9) = 9 ln(A)= 9 (1.2) =10.8 and ln(B2)=2 ln(B) = 2 (5.3) = 10.6, so that the first number is larger than the second. Problem 4 a) The equation of the circle is (x-2)2 + y2 = 9. b)The equation of the line is y=-2x+6. c) Maybe the easiest way is to substitute "y" from b) into the equation in a). This yields a quadratic in x which can be solved using the quadratic formula (important moral lesson: very few quadratics, even with small integer coefficients, can be "solved" by factoring!). The second coordinates can be gotten by using the equation in b). The two points gotten are ((14+sqrt(41))/5, (2-2sqrt(41))/5) and ((14-sqrt(41))/5, (2+2sqrt(41))/5). These points are approximately (1.52,-2.16) and (4.08,2.96). The picture may help some people understand the answers. Problem 5 a) The graph of y=Z(x) in the accompanying figure is the solid broken line. b) Since 2 is in the interval [0,3], Z(2) should be evaluated using the first expression so Z(2) = 2*2 = 4. Since 4 belongs to the interval [3,infinity), we evaluate Z(4) using the second expression: Z(4) = 4+3=7. c) Z is strictly increasing so it has an inverse N. For x in [0,3], Z(x)=2x, so the values of Z are in [0,6]. That means N(x) = (1/2)x for x in [0,6] (just solve y=2x for x!). Otherwise (for x > 6) N(x)= x-3 (gotten by solving y=x+3 for x). To find N(2) we use the first formula, so N(2)=(1/2)2=1, and N(8) uses the second formula, so N(8)=5. d) The graph of y=N(x) in the accompanying figure is the dashed broken line. Problem 6 The inverse function is given by n(p) = ((p-100)/3)2 and its domain coincides with the range of p(n) which is [103,130]. n(p) represents the number of boxes of detergent which can be produced for p dollars in this model. Problem 7 a) The limit of the top is 0, and the limit of the bottom is 8 which is not 0. Therefore the limit of the expression is 0. b) The bottom is x2 -4 = (x-2)(x+2), so if x is not equal to 2, the whole expression simplifies to 1/(x+2). The limit as x approaches 2 of this is just 1/4. c) The bottom has a non-zero limit as x approaches 4, so we can get the limit just by evaluating the top and bottom at 4. The result is 0. d) Multiply the top and bottom by sqrt(x)+2. Then the bottom becomes (sqrt(x)-2)(sqrt(x)+2) = x-4 which cancels the factor of (x-4) in the top and we're left with just sqrt(x)+2 on top. This has limit 4 as x approaches 4. Problem 8 The derivative of f is f'(x) = 6x and f'(1)=6. So 6 is the slope of the line tangent to this curve when x=1. The tangent line has to pass through the point (1,f(1))=(1,7), so one equation for this line is y-7 = 6(x-1). Problem 9In this problem, dx will denote the expression "delta x". Then f(x+dx)-f(x) = (3/(x+dx))-(3/x)= (3(x)-3(x+dx))/((x+dx)x)= (-3dx)/((x+dx)x). Therefore (f(x+dx)-f(x))/dx = -3/((x+dx)x). The limit of this last expression as dx approaches 0 is just -3/(x2). Problem 10 a) (x3-3x+17)' = 3x2-3 b) (exp(x) sin(x))'= exp(x)' sin(x) + exp(x) (sin(x))' = exp(x) sin(x) + exp(x)cos(x). c) ((2x+3)/(x2+1))' is a complicated fraction. For typographical purposes, think of it as Top/Bottom where Top= 2(x2 +1) -(2x+3)(2x) and Bottom = (x2+1)2. Please note: you are not asked to "simplify" the answer by any additional algebra. d) (3exp(x) + (x/(2+5cos(x)) )' = 3exp(x) + another Top/Bottom where here Top= 1(2+5cos(x))-(0-5sin(x))x and Bottom= (2+5cos(x))2. Again, no additional "simplification" is needed. Problem 11 By the chain rule the derivative of the left-hand side f'(g(x)) g'(x) which when x=2 is f'(g(2))g'(2)= f'(4)g'(2)= 2g'(2). The derivative of the right-hand side is 16x which when x=2 is 32. So we know that 2g'(2) = 32, and we get g'(2)=16. Problem 12 a) tan(?) = 10/15 = 2/3, so ? = arctan(2/3) which is approximately 0.588 radians. b) Here tan(alpha) = 10/A, so alpha = arctan(10/A). Problem 13 a) If f is continuous at 0, the limit from the left should be equal to f(0). But the left-hand limit of f at 0 is equal to the left-hand limit of x2+2 at 0, and this limit is just 2. That means f(0) should be 2. We also know that f(0)=02 +A = A, and we know that the right-hand limit of f at 0 is the right-hand limit of the formula x2+A at 0, which is also A. Now the two limits and the value of f at 0 should agree to make f continuous at 0. This will occur when A=2. b) The left-hand limit of f at 2 uses the formula x2 + 2 Here we substitute 2 for A, since 2 was the value found in the previous part of the problem. Therefore the left-hand limit of f at 2 is 22 +2 =6. The right-hand limit at 2 uses the formula x2+1, which has limit 5 at x=2. Since 5 is not equal to 6, f is not continuous at x=2. Problem 14 At A the function is continuous (there are no jumps or breaks in the graph there) but it not differentiable. The graph has a "corner" at (A,g(A)): more formally, it looks like the right-hand part of the limit defining the derivative at A is 0 while the left-hand part of that limit is infinite (vertical lines have no slope). These don't agree, so the function is not differentiable at The function is differentiable at B, and, since differentiable functions are continuous, it is also continuous at B. The graph has a jump at x=C, so the function is not continuous there: the left- and right-hand limits differ (and neither agrees with the value of g at C. The function is not differentiable at C since if it were, it would have to be continuous there also. Problem 15 Vertical asymptotes are x=0 (the y-axis) and x=3. The accompanying graph satisfies the conditions given in the question. Note the "empty hole" at (5,1) since the function's domain does not include 5 (tricky!). Problem 16 a) Consider the interval [2.5,3]. Enzyme concentration is increasing, since the slope of the tangent line is positive. But the rate of increase is "slowing" as time increases (moving from left to right along the curve) because the tangent line's slope is decreasing (such a curve is concave down). b) Now consider the interval [1,2], for example. Here the enzyme concentration is also increasing because the slope of the tangent line is always positive. But in this interval as time increases the slope of the tangent line is increasing -- so the rate of increase is increasing here. This part of the curve is concave up. In both parts of this problem, the slope of the tangent line must be recognized as the rate of change of enzyme concentration. Also, there are other correct answers to this problem than the ones given here. Problem 17 F(1) = f(1)g(1) = 2(5) = 10 and the product rule for derivatives gives the following: F'(1) = f'(1)g(1) + f(1)g'(1) = -1. G(1) = f(1)/(g(1)+12) = 2/(5+1)= 2/6. The quotient rule shows that G'(x) = (f'(x)(g(x)+x2)-f(x)(g'(x)+2x))/((g(x)+x2)2) and evaluating this when x=1 yields G'(1)=-1. Problem 18 a)(exp(3cos(2x)))'= exp(3cos(2x)) (3 cos(2x))' = exp(3cos(2x)) 3 (cos(2x))' = exp(3cos(2x))(3) (-sin(2x)) (2x)'= b)(ln(x3+5x+9))'= (1/(x3+5x+9))(x3+5x+9)'= (1/(x3+5x+9) (3x2+5) c) (sin((x+1)/(x2+2)))' = cos((x+1)/(x2+2)) ((x+1)/(x2+2))' = cos((x+1)/(x2+2)) (Top/Bottom) where here Top = (1)(x2+2) - 2x (x+1) and Bottom = (x2+2)2.

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https://mk-com.com/how-do-you-know-if-a-examples-is-even-odd-or-neither-40

math

The graph of an even function is symmetric with respect to the y- y− axis or along the vertical line x = 0 x = 0. Observe that the graph of the function is cut evenly Given the formula of a polynomial function, determine whether that function is even, odd, or neither. Given the formula of a polynomial function, determine whether that function is even Even though times are tough, I know my friends will always have my back. Do mathematic tasks Doing homework can help you learn and understand the material covered in class. Answers in 5 seconds In just five seconds, you can get the answer to any question you have. We have the best specialists in the business. Determine mathematic equations The Quadratic Formula is used to determine the roots of a quadratic equation. Fast Professional Tutoring We provide professional tutoring services that help students improve their grades and performance in school. Determine the algebraically function even odd or neither. f(x) = 2x2– 3 Solution: Well, you can use an online odd or even function calculator to check whether a function is even, odd or neither. For this purpose, it substitutes – x in the given Get Help with Tasks You can get math help online by visiting websites like Khan Academy or Mathway. Looking for someone to help with your homework? We can provide expert homework writing help on any subject. Get detailed step-by-step answers Looking for detailed, step-by-step answers? Look no further – our experts are here to help. Clarify mathematic equation Mathematic equations can be difficult to understand, but with a little clarification, they can be much easier to decipher.

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https://writemycustomessay.com/2020/10/04/solve-the-math-problem_kq/

math

Systems solve the math problem of equations 2×2’s – cool math algebra help about technology essay lessons – solving by elimination skip to …. write 15% as a decimal number: how to personal argumentative essay topics solve the problem. you have to think of the problem in lutron homeworks software terms of how much each person / machine paper writing services review / whatever does in a given unit of time aug 20, 2020 · a steps for literature review math problem stumped experts for 50 years. (if you are not logged into custom essays reviews your google account (ex., gmail, docs), a login window opens when you click on 1 solve the math problem oct 14, 2016 · fortunately, not all math problems need to be inscrutable. each part can show a different way to solve the math problem emoji math puzzles, great for a math starter in primary school – loved by essay about weather teachers and students – or as a quick workout emotional intelligence research paper for your brain! check out some of our top basic problem solving flowsheet mathematics lessons. each pair of …. math solver. get on the want of money essay math help fast and online with more than one hundred instant and even step-by-step math solvers and calculators designed to ghostwriting services rates help you solve solve the math problem your math problems and understand the concepts behind them! three people solve the math problem eat at a restaurant. the bill comes to $30 students of all ages will challenge their problem-solving skills with our collection of math word problems worksheets. download free on google play. three people eat at a restaurant. download free on itunes. how to solve the problem. from here, press the google lens icon.

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CC-MAIN-2021-10

https://www.physicsforums.com/threads/kinematics-angular-speed.882089/

math

1. The problem statement, all variables and given/known data I have chosen my axis as you can see on the picture. To the left, it is the positive 'r'-axis, up is the positive 'y'-axis. I have to calculate the angular speed. My solution is correct, but i'm getting into trouble at the end. I have to take the roots of the last equation to become the angular speed. Because my acceleration 'ar' is positive, I become a negatif number so I can't take the root. What am I doing wrong? 2. Relevant equations 3. The attempt at a solution Thank you!

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CC-MAIN-2018-30

https://deepai.org/publication/scalable-kernel-k-means-clustering-with-nystrom-approximation-relative-error-bounds

math

Cluster analysis divides a data set into several groups using information found only in the data points. Clustering can be used in an exploratory manner to discover meaningful groupings within a data set, or it can serve as the starting point for more advanced analyses. As such, applications of clustering abound in machine learning and data analysis, including, inter alia: genetic expression analysis (Sharan et al., 2002), market segmentation (Chaturvedi et al., 1997), social network analysis (Handcock et al., 2007), image segmentation (Haralick and Shapiro, 1985)2009), collaborative filtering (Ungar and Foster, 1998), and fast approximate learning of non-linear models (Si et al., 2014). -means clustering is a standard and well-regarded approach to cluster analysis that partitions input vectorsinto clusters, in an unsupervised manner, by assigning each vector to the cluster with the nearest centroid. Formally, linear -means clustering seeks to partition the set into disjoint sets by solving Despite its popularity, linear -means clustering is not a universal solution to all clustering problems. In particular, linear -means clustering strongly biases the recovered clusters towards isotropy and sphericity. Applied to the data in Figure 1(a), Lloyd’s algorithm is perfectly capable of partitioning the data into three clusters which fit these assumptions. However, the data in Figure 1(b) do not fit these assumptions: the clusters are ring-shaped and have coincident centers, so minimizing the linear -means objective does not recover these clusters. To extend the scope of -means clustering to include anistotropic, non-spherical clusters such as those depicted in Figure 1(b), Schölkopf et al. (1998) proposed to perform linear -means clustering in a nonlinear feature space instead of the input space. After choosing a feature function to map the input vectors non-linearly into feature vectors, they propose minimizing the objective function where denotes a -partition of . The “kernel trick” enables us to minimize this objective without explicitly computing the potentially high-dimensional features, as inner products in feature space can be computed implicitly by evaluating the kernel function Thus the information required to solve the kernel -means problem (2), is present in the kernel matrix . be the full eigenvalue decomposition (EVD) of the kernel matrix andbe the rows of . It can be shown (see Appendix B.3) that the solution of (2) is identical to the solution of To demonstrate the power of kernel -means clustering, consider the dataset in Figure 1(b). We use the Gaussian RBF kernel with , and form the corresponding kernel matrix of the data in Figure 1(b). Figure 1(c) scatterplots the first two coordinates of the feature vectors . Clearly, the first coordinate of the feature vectors already separates the two classes well, so -means clustering using the non-linear features has a better chance of separating the two classes. Although it is more generally applicable than linear -means clustering, kernel -means clustering is computationally expensive. As a baseline, we consider the cost of optimizing (3). The formation of the kernel matrix given the input vectors costs time. The objective in (3) can then be (approximately) minimized using Lloyd’s algorithm at a cost of time per iteration. This requires the -dimensional non-linear feature vectors obtained from the full EVD of ; computing these feature vectors takes time, because is, in general, full-rank. Thus, approximately solving the kernel -means clustering problem by optimizing (3) costs time, where is the number of iterations of Lloyd’s algorithm. Kernel approximation techniques, including the Nyström method (Nyström, 1930; Williams and Seeger, 2001; Gittens and Mahoney, 2016) and random feature maps (Rahimi and Recht, 2007), have been applied to decrease the cost of solving kernelized machine learning problems: the idea is to replace with a low-rank approximation, which allows for more efficient computations. Chitta et al. (2011, 2012) proposed to apply kernel approximations to efficiently approximate kernel -means clustering. Although kernel approximations mitigate the computational challenges of kernel -means clustering, the aforementioned works do not provide guarantees on the clustering performance: how accurate must the low-rank approximation of be to ensure near optimality of the approximate clustering obtained via this method? We propose a provable approximate solution to the kernel -means problem based on the Nyström approximation. Our method has three steps: first, extract () features using the Nyström method; second, reduce the features to dimensions () using the truncated SVD;111This is why our variant is called the rank-restricted Nyström approximation. The rank-restriction serves two purposes. First, although we do not know whether the rank-restriction is necessary for the bound, we are unable to establish the bound without it. Second, the rank-restriction makes the third step, linear -means clustering, much less costly. For the computational benefit, previous works (Boutsidis et al., 2009, 2010, 2015; Cohen et al., 2015; Feldman et al., 2013) have considered dimensionality reduction for linear -means clustering. third, apply any off-the-shelf linear -means clustering algorithm upon the -dimensional features to obtain the final clusters. The total time complexity of the first two steps is . The time complexity of the third step depends on the specific linear -means algorithm; for example, using Lloyd’s algorithm, the per-iteration complexity is , and the number of iterations may depend on .222Without the rank-restriction, the per-iteration cost would be , and the number of iterations may depend on . Our method comes with a strong approximation ratio guarantee. Suppose we set and for any , where is the coherence parameter of the dominant -dimensional singular space of the kernel matrix . Also suppose the standard kernel -means and our approximate method use the same linear -means clustering algorithm, e.g., Lloyd’s algorithm or some other algorithm that comes with different provable approximation guarantees. As guaranteed by Theorem 1, when the quality of the clustering is measured by the cost function defined in (2 ), with probability at least, our algorithm returns a clustering that is at most times worse than the standard kernel -means clustering. Our theory makes explicit the trade-off between accuracy and computation: larger and lead to high accuracy and also high computational cost. Spectral clustering (Shi and Malik, 2000; Ng et al., 2002) is a popular alternative to kernel -means clustering that can also partition non-linearly separable data such as those in Figure 1(b). Unfortunately, because it requires computing an affinity matrix and the top eigenvectors of the corresponding graph Laplacian, spectral clustering is inefficient for large . Fowlkes et al. (2004) applied the Nyström approximation to increase the scalability of spectral clustering. Since then, spectral clustering with Nyström approximation has been used in many works, e.g., (Arikan, 2006; Berg et al., 2004; Chen et al., 2011; Wang et al., 2016b; Weiss et al., 2009; Zhang and Kwok, 2010). Despite its popularity in practice, this approach does not come with guarantees on the approximation ratio for the obtained clustering. Our algorithm, which combines kernel -means with Nyström approximation, is an equally computationally efficient alternative that comes with strong bounds on the approximation ratio, and can be used wherever spectral clustering is applied. Using tools developed in (Boutsidis et al., 2015; Cohen et al., 2015; Feldman et al., 2013), we rigorously analyze the performance of approximate kernel -means clustering with the Nyström approximation, and show that a rank- Nyström approximation delivers a approximation ratio guarantee, relative to the guarantee provided by the same algorithm without the use of the Nyström method. As part of the analysis of kernel -means with Nyström approximation, we establish the first relative-error bound for rank-restricted Nyström approximation,333Similar relative-error bounds were independently developed by contemporaneous work of Tropp et al. (2017), in service of the analysis of a novel streaming algorithm for fixed-rank approximation of positive semidefinite matrices. Preliminary versions of this work and theirs were simultaneously submitted to arXiv in June 2017. which has independent interest. Kernel -means clustering and spectral clustering are competing solutions to the nonlinear clustering problem, neither of which scales well with . Fowlkes et al. (2004) introduced the use of Nyström approximations to make spectral clustering scalable; this approach has become popular in machine learning. We identify fundamental mathematical problems with this heuristic. These concerns and an empirical comparison establish that our proposed combination of kernel-means with rank-restricted Nyström approximation is a theoretically well-founded and empirically competitive alternative to spectral clustering with Nyström approximation. Finally, we demonstrate the scalability of this approach by measuring the performance of an Apache Spark implementation of a distributed version of our approximate kernel -means clustering algorithm using the MNIST8M data set, which has million instances and classes. 1.2 Relation to Prior Work The key to our analysis of the proposed approximate kernel -means clustering algorithm is a novel relative-error trace norm bound for a rank-restricted Nyström approximation. We restrict the rank of the Nyström approximation in a non-standard manner (see Remark 1). Our relative-error trace norm bound is not a simple consequence of the existing bounds for non-rank-restricted Nyström approximation such as the ones provided by Gittens and Mahoney (2016). The relative-error bound which we provide for the rank-restricted Nyström approximation is potentially useful in other applications involving the Nyström method. The projection-cost preservation (PCP) property (Cohen et al., 2015; Feldman et al., 2013) is an important tool for analyzing approximate linear -means clustering. We apply our novel relative-error trace norm bound as well as existing tools in (Cohen et al., 2015) to prove that the rank-restricted Nyström approximation enjoys the PCP property. We do not rule out the possibility that the non-rank-restricted (rank-) Nyström approximation satisfies the PCP property and/or also enjoys a approximation ratio guarantee when applied to kernel -means clustering. However, the cost of the linear -means clustering step in the algorithm is proportional to the dimensionality of the feature vectors, so the rank-restricted Nyström approximation, which produces -dimensional feature vectors, where , is more computationally desirable. Musco and Musco (2017) similarly establishes a approximation ratio for the kernel -means objective when a Nyström approximation is used in place of the full kernel matrix. Specifically, Musco and Musco (2017) shows that when columns of are sampled using ridge leverage score (RLS) sampling (Alaoui and Mahoney, 2015; Cohen et al., 2017; Musco and Musco, 2017) and are used to form a Nyström approximation, then applying linear -means clustering to the -dimensional Nyström features returns a clustering that has objective value at most times as large as the objective value of the best clustering. Our theory is independent of that in Musco and Musco (2017), and differs in that (1) Musco and Musco (2017) applies specifically to Nyström approximations formed using RLS sampling, whereas our guarantees apply to any sketching method that satisfies the “subspace embedding” and “matrix multiplication” properties (see Lemma A.2 for definitions of these two properties); (2) Musco and Musco (2017) establishes a approximation ratio for the non-rank-restricted RLS-Nyström approximation, whereas we establish a approximation ratio for the (more computationally efficient) rank-restricted Nyström approximation. 1.3 Paper Organization In Section 2, we start with a definition of the notation used throughout this paper as well as a background on matrix sketching methods. Then, in Section 3, we present our main theoretical results: Section 3.1 presents an improved relative-error rank-restricted Nyström approximation; Section 3.2 presents the main theoretical results on kernel -means with Nyström approximation; and Section 3.3 studies kernel -means with kernel principal component analysis. Section4 discusses and evaluates the theoretical and empirical merits of kernel -means clustering versus spectral clustering, when each is approximated using Nyström approximation. Section 5 empirically compares the Nyström method and the random feature maps for the kernel -means clustering on medium-scale data. Section 6 presents a large-scale distributed implementation in Apache Spark and its empirical evaluation on a data set with million points. Section 7 provides a brief conclusion. Proofs are provided in the Appendices. This section defines the notation used throughout this paper. A set of commonly used parameters is summarized in Table 1. |number of samples| |number of features (attributes)| |number of clusters| |target rank of the Nyström approximation| |sketch size of the Nyström approximation| Matrices and vectors. We take to be the identity matrix, to be a vector or matrix of all zeros of the appropriate size, and to be the -dimensional vector of all ones. The set is written as . We call a -partition of if and when . Let denote the cardinality of the set . Singular value decomposition (SVD). . A (compact) singular value decomposition (SVD) is defined by where , , are an column-orthogonal matrix, adiagonal matrix with nonnegative entries, and a column-orthogonal matrix, respectively. If is symmetric positive semi-definite (SPSD), then , and this decomposition is also called the (reduced) eigenvalue decomposition (EVD). By convention, we take . The matrix is a rank- truncated SVD of , and is an optimal rank- approximation to when the approximation error is measured in a unitarily invariant norm. The Moore-Penrose inverse of is defined by . Leverage score and coherence. Let be defined in the above and be the -th row of . The row leverage scores of are for . The row coherence of is . The leverage scores for a matrix can be computed exactly in the time it takes to compute the matrix ; and the leverage scores can be approximated (in theory (Drineas et al., 2012) and in practice (Gittens and Mahoney, 2016)) in roughly the time it takes to apply a random projection matrix to the matrix . We use three matrix norms in this paper: Any square matrix satisfies . If additionally is SPSD, then . Here, we briefly review matrix sketching methods that are commonly used within randomized linear algebra (RLA) (Mahoney, 2011). Given a matrix , we call (typically ) a sketch of and a sketching matrix. Within RLA, sketching has emerged as a powerful primitive, where one is primarily interested in using random projections and random sampling to construct randomzed sketches (Mahoney, 2011; Drineas and Mahoney, 2016). In particular, sketching is useful as it allows large matrices to be replaced with smaller matrices which are more amenable to efficient computation, but provably retain almost optimal accuracy in many computations (Mahoney, 2011; Woodruff, 2014). The columns of typically comprise a rescaled subset of the columns of , or random linear combinations of the columns of ; the former type of sketching is called column selection or random sampling, and the latter is referred to as random projection. Column selection forms using a randomly sampled and rescaled subset of the columns of . Let be the sampling probabilities associated with the columns of (so that, in particular, ). The columns of the sketch are selected identically and independently as follows: each column of is randomly sampled from the columns of according to the sampling probabilities and rescaled by where is the index of the column of that was selected. In our matrix multiplication formulation for sketching, column selection corresponds to a sketching matrix that has exactly one non-zero entry in each column, whose position and magnitude correspond to the index of the column selected from . Uniform sampling is column sampling with , and leverage score sampling takes for , where is the -th leverage score of some matrix (typically , , or an randomized approximation thereto) (Drineas et al., 2012). Gaussian projection is a type of random projection where the sketching matrix is taken to be ; here the entries of are i.i.d. random variables. Gaussian projection is inefficient relative to column sampling: the formation of a Gaussian sketch of a dense matrix requires time. The Subsampled Randomized Hadamard Transform (SRHT) is a more efficient alternative that enjoys similar properties to the Gaussian projection (Drineas et al., 2011; Lu et al., 2013; Tropp, 2011), and can be applied to a dense matrix in only time. The CountSketch is even more efficient: it can be applied to any matrix in time (Clarkson and Woodruff, 2013; Meng and Mahoney, 2013; Nelson and Nguyên, 2013), where denotes the number of nonzero entries in a matrix. 3 Our Main Results: Improved SPSD Matrix Approximation and Kernel -means Approximation In this section, we present our main theoretical results. We start, in Section 3.1, by presenting Theorem 3.1, a novel result on SPSD matrix approximation with the rank-restricted Nyström method. This result is of independent interest, and so we present it in detail, but in this paper we will use it to establish our main result. Then, in Section 3.2, we present Theorem 1, which is our main result for approximate kernel -means with the Nyström approximation. In Section 3.3, we establish novel guarantees on kernel -means with dimensionality reduction. 3.1 The Nyström Method The Nyström method (Nyström, 1930) is the most popular kernel approximation method in the machine learning community. Let be an SPSD matrix and be a sketching matrix. The Nyström method approximates with , where and . The Nyström method was introduced to the machine learning community by Williams and Seeger (2001); since then, numerous works have studied its theoretical properties, e.g., (Drineas and Mahoney, 2005; Gittens and Mahoney, 2016; Jin et al., 2013; Kumar et al., 2012; Wang and Zhang, 2013; Yang et al., 2012). decays fast. This suggests that the Nyström approximation captures the dominant eigenspaces of, and that error bounds comparing the accuracy of the Nyström approximation of to that of the best rank- approximation (for ) would provide a meaningful measure of the performance of the Nyström kernel approximation. Gittens and Mahoney (2016) established the first relative-error bounds showing that for sufficiently large , the trace norm error is comparable to . Such results quantify the benefits of spectral decay to the performance of the Nyström method, and are sufficient to analyze the performance of Nyström approximations in applications such as kernel ridge regression(Alaoui and Mahoney, 2015; Bach, 2013) and kernel support vector machines(Cortes et al., 2010). However, Gittens and Mahoney (2016) did not analyze the performance of rank-restricted Nyström approximations; they compared the approximation accuracies of the rank- matrix and the rank- matrix (recall ). In our application to approximate kernel -means clustering, it is the rank- matrix that is of relevance. Given and , the truncated SVD can be found using time. Then the rank- Nyström approximation can be written as [Relative-Error Rank-Restricted Nyström Approximation] Let be an SPSD matrix, be the target rank, and be an error parameter. Let be a sketching matrix corresponding to one of the sketching methods listed in Table 2. Let and . Then holds with probability at least . In addition, there exists an column orthogonal matrix such that . |sketching||sketch size ()||time complexity ()| Remark 1 (Rank Restrictions) The traditional rank-restricted Nyström approximation, , (Drineas and Mahoney, 2005; Fowlkes et al., 2004; Gittens and Mahoney, 2016; Li et al., 2015) is not known to satisfy a relative-error bound of the form guaranteed in Theorem 3.1. Pourkamali-Anaraki and Becker (2016) pointed out the drawbacks of the traditional rank-restricted Nyström approximation and proposed the use of the rank-restricted Nyström approximation in applications requiring kernel approximations, but provided only empirical evidence of its performance. This work provides guarantees on the approximation error of the rank-restricted Nyström approximation , and applies this approximation to the kernel -means clustering problem. The contemporaneous work Tropp et al. (2017) provides similar guarantees on the approximation error of , and uses this Nyström approximation as the basis of a streaming algorithm for fixed-rank approximation of positive-semidefinite matrices. 3.2 Main Result for Approximate Kernel -means In this section we establish the approximation ratio guarantees for the objective function of kernel -means clustering. We first define -approximate -means algorithms (where ), then present our main result in Theorem 1. Let be a matrix with rows . The objective function for linear -means clustering over the rows of is The minimization of w.r.t. the -partition is NP-hard (Garey et al., 1982; Aloise et al., 2009; Dasgupta and Freund, 2009; Mahajan et al., 2009; Awasthi et al., 2015), but approximate solutions can be obtained in polynomial time. -approximate algorithms capture one useful notion of approximation. Definition 1 (-Approximate Algorithms) A linear -means clustering algorithm takes as input a matrix with rows and outputs . We call a -approximate algorithm if, for any such matrix , Here and are -partitions of . Many -approximation algorithms have been proposed, but they are computationally expensive (Chen, 2009; Har-Peled and Mazumdar, 2004; Kumar et al., 2004; Matousek, 2000). There are also relatively efficient constant factor approximate algorithms, e.g., (Arthur and Vassilvitskii, 2007; Kanungo et al., 2002; Song and Rajasekaran, 2010). Let be a feature map, be the matrix with rows , and be the associated kernel matrix. Analogously, we denote the objective function for kernel -means clustering by where is a -partition of . [Kernel -Means with Nyström Approximation] Choose a sketching matrix and sketch size consistent with Table 2. Let be the previously defined Nyström approximation of . Let be any matrix satisfying . Let the -partition be the output of a -approximate algorithm applied to the rows of . With probability at least , Kernel -means clustering is an NP-hard problem. Therefore, instead of comparing with , we compare with clusterings obtained using -approximate algorithms. Theorem 1 shows that, when uniform sampling to form the Nyström approximation, if and , then the returned clustering has an objective value that is at most a factor of larger than the objective value of the kernel -means clustering returned by the -approximate algorithm. Assume we are in a practical setting where , the budget of column samples one can use to form a Nyström approximation, and , the number of desired cluster centers, are fixed. The pertinent question is how to choose to produce a high-quality approximate clustering. Theorem 1 shows that for uniform sampling, the error ratio is To balance the two sources of error, must be larger than , but not too large a fraction of . To minimize the above error ratio, should be selected on the order of . Since the matrix coherence () is unknown, it can be heuristically treated as a constant. We empirically study the effect of the values of and using a data set comprising million samples. Note that computing the kernel -means clustering objective function requires the formation of the entire kernel matrix , which is infeasible for a data set of this size; instead, we use normalized mutual information (NMI) (Strehl and Ghosh, 2002)—a standard measure of the performance of clustering algorithms— to measure the quality of the clustering obtained by approximating kernel -means clustering using Nyström approximations formed through uniform sampling. NMI scores range from zero to one, with a larger score indicating better performance. We report the results in Figure 2. The complete details of the experiments, including the experimental setting and time costs, are given in Section 6. From Figure 2(a) we observe that larger values of lead to better and more stable clusterings: the mean of the NMI increases and its standard deviation decreases. This is reasonable and in accordance with our theory. However, larger values ofincur more computations, so one should choose to trade off computation and accuracy. Figure 2(b) shows that for fixed and , the clustering performance is not monotonic in , which matches Theorem 1 (see the discussion in Remark 3). Setting as small as results in poor performance. Setting over-large not only incurs more computations, but also negatively affects clustering performance; this may suggest the necessity of rank-restriction. Furthermore, in this example, , which corroborates the suggestion made in Remark 3 that setting around (where is unknown but can be treated as a constant larger than ) can be a good choice. Musco and Musco (2017) established a approximation ratio for the kernel -means objective value when a non-rank-restricted Nyström approximation is formed using ridge leverage scores (RLS) sampling; their analysis is specific to RLS sampling and does not extend to other sketching methods. By way of comparison, our analysis covers several popular sampling schemes and applies to rank-restricted Nyström approximations, but does not extend to RLS sampling. 3.3 Approximate Kernel -Means with KPCA and Power Method The use of dimensionality reduction to increase the computational efficiency of -means clustering has been widely studied, e.g. in (Boutsidis et al., 2010, 2015; Cohen et al., 2015; Feldman et al., 2013; Zha et al., 2002). Kernel principal component analysis (KPCA) is particularly well-suited to this application (Dhillon et al., 2004; Ding et al., 2005). Applying Lloyd’s algorithm on the rows of or has an per-iteration complexity; if features are extracted using KPCA and Lloyd’s algorithm is applied to the resulting -dimensional feature map, then the per-iteration cost reduces to Proposition 3.3 states that, to obtain a approximation ratio in terms of the kernel -means objective function, it suffices to use KPCA features. This proposition is a simple consequence of (Cohen et al., 2015). [KPCA] Let be a matrix with rows, and be the corresponding kernel matrix. Let be the truncated SVD of and take . Let the -partition be the output of a -approximate algorithm applied to the rows of . Then In practice, the truncated SVD (equivalently EVD) of is computed using the power method or Krylov subspace methods. These numerical methods do not compute the exact decomposition , so Proposition 3.3 is not directly applicable. It is useful to have a theory that captures the effect of realisticly inaccurate estimates likeon the clustering process. As one particular example, consider that general-purpose implementations of the truncated SVD attempt to mitigate the fact that the computed decompositions are inaccurate by returning very high-precision solutions, e.g. solutions that satisfy . Understanding the trade-off between the precision of the truncated SVD solution and the impact on the approximation ratio of the approximate kernel -means solution allows us to more precisely manage the computational complexity of our algorithms. Are such high-precision solutions necessary for kernel -means clustering? Theorem 1 answers this question by establishing that highly accurate eigenspaces are not significantly more useful in approximate kernel -means clustering than eigenspace estimates with lower accuracy. A low-precision solution obtained by running the power iteration for a few rounds suffices for kernel -means clustering applications. We prove Theorem 1 in Appendix C. [The Power Method] Let be a matrix with rows, be the corresponding kernel matrix, and be the -th singular value of . Fix an error parameter . Run Algorithm 1 with to obtain . Let the -partition be the output of a -approximate algorithm applied to the rows of . If , then holds with probability at least . If , then the above inequality holds with probability , where is a positive constant (Tao and Vu, 2010). Note that the power method requires forming the entire kernel matrix , which may not fit in memory even in a distributed setting. Therefore, in practice, the power method may not be as efficient as the Nyström approximation with uniform sampling, which avoids forming . Theorem 1, Proposition 3.3, and Theorem 1 are highly interesting from a theoretical perspective. These results demonstrate that features are sufficient to ensure a approximation ratio. Prior work (Dhillon et al., 2004; Ding et al., 2005) set and did not provide approximation ratio guarantees. Indeed, a lower bound in the linear -means clustering case due to (Cohen et al., 2015) shows that is necessary to obtain a approximation ratio. 4 Comparison to Spectral Clustering with Nyström Approximation In this section, we provide a brief discussion and empirical comparison of our clustering algorithm, which uses the Nyström method to approximate kernel -means clustering, with the popular alternative algorithm that uses the Nyström method to approximate spectral clustering. Spectral clustering is a method with a long history (Cheeger, 1969; Donath and Hoffman, 1972, 1973; Fiedler, 1973; Guattery and Miller, 1995; Spielman and Teng, 1996). Within machine learning, spectral clustering is more widely used than kernel -means clustering (Ng et al., 2002; Shi and Malik, 2000), and the use of the Nyström method to speed up spectral clustering has been popular since Fowlkes et al. (2004). Both spectral clustering and kernel -means clustering can be approximated in time linear in by using the Nyström method with uniform sampling. Practitioners reading this paper may ask: How does the approximate kernel -means clustering algorithm presented here, which uses Nyström approximation, compare to the popular heuristic of combining spectral clustering with Nyström approximation? Based on our theoretical results and empirical observations, our answer to this reader is: Although they have equivalent computational costs, kernel -means clustering with Nyström approximation is both more theoretically sound and more effective in practice than spectral clustering with Nyström approximation. We first formally describe spectral clustering, and then substantiate our claim regarding the theoretical advantage of our approximate kernel -means method. Our discussion is limited to the normalized and symmetric graph Laplacians used in Fowlkes et al. (2004), but spectral clustering using asymmetric graph Laplacians encounters similar issues. 4.2 Spectral Clustering with Nyström Approximation The input to the spectral clustering algorithm is an affinity matrix that measures the pairwise similarities between the points being clustered; typically is a kernel matrix or the adjacency matrix of a weighted graph constructed using the data points as vertices. Let be the diagonal degree matrix associated with , and be the associated normalized graph Laplacian matrix. Let denote the bottom eigenvectors of , or equivalently, the top eigenvectors of . Spectral clustering groups the data points by performing linear -means clustering on the normalized rows of . Fowlkes et al. (2004) popularized the application of the Nyström approximation to spectral clustering. This algorithm computes an approximate spectral clustering by: (1) forming a Nyström approximation to , denoted by ; (2) computing the degree matrix of ; (3) computing the top singular vectors of , which are equivalent to the bottom eigenvectors of ; (4) performing linear -means over the normalized rows of . To the best of our knowledge, spectral clustering with Nyström approximation does not have a bounded approximation ratio relative to exact spectral clustering. In fact, it seems unlikely that the approximation ratio could be bounded, as there are fundamental problems with the application of the Nyström approximation to the affinity matrix. The affinity matrix used in spectral clustering must be elementwise nonnegative. However, the Nyström approximation of such a matrix can have numerous negative entries, so is, in general, not proper input for the spectral clustering algorithm. In particular, the approximated degree matrix may have negative diagonal entries, so is not guaranteed to be a real matrix; such exceptions must be handled heuristically. The approximate asymmetric Laplacian does avoid the introduction of complex values; however, the negative entries in negate whole columns of , leading to less meaningful negative similarities/distances. Even if is real, the matrix may not be SPSD, much less a Laplacian matrix. Thus the bottom eigenvectors of cannot be viewed as useful coordinates for linear -means clustering in the same way that the eigenvectors of can be. Such approximation is also problematic in terms of matrix approximation accuracy. Even when approximates well, which can be theoretically guaranteed, the approximate Laplacian can be far from . This is because a small perturbation in can have an out-sized influence on the eigenvectors of . One may propose to approximate , rather than , with a Nyström approximation ; this ensures that the approximate normalized graph Laplacian is SPSD. However, this approach requires forming the entirety of in order to compute the degree matrix , and thus has quadratic (with ) time and memory costs. Furthermore, although the resulting approximation, , is SPSD, it is not a graph Laplacian: its off-diagonal entries are not guaranteed to be non-positive, and its smallest eigenvalue may be nonzero. In summary, spectral clustering using the Nyström approximation (Fowlkes et al., 2004), which has proven to be a useful heuristic, and which is composed of theoretically principled parts, is less principled when viewed in its entirety. Approximate kernel -means clustering using Nyström approximation is an equivalently efficient, but theoretically more principled alternative. 4.3 Empirical Comparison with Approximate Spectral Clustering using Nyström Approximation |dataset||#instances ()||#features ()||#clusters ()| |MNIST (LeCun et al., 1998)||60,000||780||10| |Mushrooms (Frank and Asuncion, 2010)||8,124||112||2| |PenDigits (Frank and Asuncion, 2010)||7,494||16||10| To complement our discussion of the relative merits of the two methods, we empirically compared the performance of our novel method of approximate kernel -means clustering using the Nyström method with the popular method of approximate spectral clustering using the Nyström method. We used three classification data sets, described in Table 3. The data sets used are available at http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/. Let be the input vectors. We take both the affinity matrix for spectral clustering and the kernel matrix for kernel -means to be the RBF kernel matrix , where and is the kernel width parameter. We choose based on the average interpoint distance in the data sets as where we take , , or . The algorithms under comparison are all implemented in Python 3.5.2. Our implementation of approximate spectral clustering follows the code in (Fowlkes et al., 2004). To compute linear -means clusterings, we use the function sklearn.cluster.KMeans present in the scikit-learn package. Our algorithm for approximate kernel -means clustering is described in more detail in Section 5.1. We ran the computations on a MacBook Pro with a 2.5GHz Intel Core i7 CPU and 16GB of RAM. We compare approximate spectral clustering (SC) with approximate kernel -means clustering (KK), with both using the rank-restricted Nyström method with uniform sampling.444Uniform sampling is appropriate for the value of used in Eqn. (5); see Gittens and Mahoney (2016) for a detailed discussion of the effect of varying . We used normalized mutual information (NMI) (Strehl and Ghosh, 2002) to evaluate clustering performance: the NMI falls between 0 (representing no mutual information between the true and approximate clusterings) and 1 (perfect correlation of the two clusterings), so larger NMI indicates better performance. The target dimension is taken to be ; and, for each method, the sketch size is varied from to . We record the time cost of the two methods, excluding the time spent on the -means clustering required in both algorithms.555For both SC and KK with Nyström approximation, the extracted feature matrices have dimension , so the -means clusterings required by both SC and KK have identical cost. We repeat this procedure times and report the averaged NMI and average elapsed time. We note that, at small sketch sizes , exceptions often arise during approximate spectral clustering due to negative entries in the degree matrix. (This is an example, as discussed in Section 4.2, of when approximate spectral clustering heuristics do not perform well.) We discard the trials where such exceptions occur. Our results are summarized in Figure 3. Figure 3 illustrates the NMI of SC and KK as a function of the sketch size and as a function of elapsed time for both algorithms. While there are quantitative differences between the results on the three data sets, the plots all show that KK is more accurate as a function of the sketch size or elapsed time than SC. 5 Single-Machine Medium-Scale Experiments In this section, we empirically compare the Nyström method and random feature maps (Rahimi and Recht, 2007) for kernel -means clustering. We conduct experiments on the data listed in Table 3. For the Mushrooms and PenDigits data, we are able to evaluate the objective function value of kernel -means clustering. 5.1 Single-Machine Implementation of Approximate Kernel -Means Our algorithm for approximate kernel -means clustering comprises three steps: Nyström approximation, dimensionality reduction, and linear -means clustering. Both the single-machine as well as the distributed variants of the algorithm are governed by three parameters: , the number of features used in the clustering; , a regularization parameter; and , the sketch size. These parameters satisfy . Nyström approximation. Let be the sketch size and be a sketching matrix. Let and . The standard Nyström approximation is ; small singular values in can lead to instability in the Moore-Penrose inverse, so a widely used heuristic is to choose and use instead of the standard Nyström approximation.666The Nyström approximation is correct in theory, but the Moore-Penrose inverse often causes numerical errors in practice. The Moore-Penrose inverse drops all the zero singular values, however, due to the finite numerical precision, it is difficult to determine whether a singular value, say , should be zero or not, and this makes the computation unstable: if such a small singular value is believed to be zero, it will be dropped; otherwise, the Moore-Penrose inverse will invert it to obtain a singular value of . Dropping some portion of the smallest singular values is a simple heuristic that avoids this instability. This is why we heuristically use instead of . Currently we do not have theory for this heuristic. Chiu and Demanet (2013) considers the theoretical implications of this regularization heuristic, but their results do not apply to our problem. We set (arbitrarily). Let be the truncated SVD of and return as the output of the Nyström method. Dimensionality reduction. Let contain the dominant right singular vectors of . Let . It can be verified that , which is our desired rank-restricted Nyström approximation. Linear -means clustering. With at hand, use an arbitrary off-the-shelf linear -means clustering algorithm to cluster the rows of . See Algorithm 2 for the single-machine version of this approximate kernel -means clustering algorithm. Observe that we can use uniform sampling to form and , and thereby avoid computing most of . 5.2 Comparing Nyström, Random Feature Maps, and Two-Step Method We empirically compare the clustering performances of kernel approximations formed using Nyström, random feature map (RFM) (Rahimi and Recht, 2007), and the two-step method (Chitta et al., 2011) on the data sets detailed in Table 3. We use the RBF kernel with width parameter given by (5); Figure 3 indicates that is a good choice for these data sets. We conduct dimensionality reduction for both Nyström and RFM to obtain -dimensional features, and consider three choices: , , and without dimensionality reduction (equivalently, ). The quality of the clusterings is quantified using both normalized mutual information (NMI) (Strehl and Ghosh, 2002) and the objective function value: where are the columns of the kernel matrix , and the disjoint sets reflect the clustering. We repeat the experiments times and report the results in Figures 4 and 5. The experiments show that as measured by both NMIs and objective values, the Nyström method outperforms RFM in most cases. Both the Nyström method and RFM are consistently superior to the two-step method of (Chitta et al., 2011), which requires a large sketch size. All the compared methods improve as the sketch size increases. Judging from these medium-scale experiments, the target rank has little impact on the NMI and clustering objective value. This phenomenon is not general; in the large-scale experiments of the next section we see that setting properly allows one to obtain a better NMI than an over-small or over-large . 6 Large-Scale Experiments using Distributed Computing In this section, we empirically study our approximate kernel -means clustering algorithm on large-scale data. We state a distributed version of the algorithm, implement it in Apache Spark777 This implementation is available at https://github.com/wangshusen/SparkKernelKMeans.git. , and evaluate its performance on NERSC’s Cori supercomputer. We investigate the effect of increased parallelism, sketch size , and target dimension . Algorithm 3 is a distributed version of our method described in Section 5.1. Again, we use uniform sampling to form and to avoid computing most of . We mainly focus on the Nyström approximation step, as the other two steps are well supported by distributed computing systems such as Apache Spark.

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Most of the experiments I described previously had large sample sizes (viz the first table). Therefore, there is little difficulty accepting the fact that the segregation ratios are actually 1:3 or 1:1:1:1, and variations from the exact ratio are due to chance. To interpret results from smaller samples, such as those seen in human families, requires knowledge of probability and statistics. The frequentists (a sect of statisticians) would say there are a (potentially) infinite number of offspring of our experimental cross, of which we are getting to see only a finite subset or sample. The sample space S of outcomes for a single experiment (one child) is A (with genotype A/A or A/a) or a (a/a) - two mutually exclusive events. If we count N offspring, of which NA express the A phenotype, then the probability of A is, From this one can see that, 1 >= Pr(A) >= 0 Pr(S) = Pr(A or a) = 1 Pr(Not A) = Pr(a) = 1 - Pr(A). For this experiment, Pr(A)= 3/4, Pr(a)= 1/4. If we repeat the experiment, the result for the second child is independent of that for the first child. The probabilities for the joint events are obtained by multiplying the probabilities for each contributing event. |One Child||Two Children||Three Children| |A 3/4||A,A 9/16||A,A,A 27/64| |a 1/4||A,a 3/16||A,A,a 9/64| |a,A 3/16||A,a,A 9/64| |a,a 1/16||A,a,a 3/64| In the case of segregation ratios, where we are only interested in the total counts, the order in which the offspring were born is irrelevant. This count (X, say) is a random variable, which for a sibship of three children can take four values according to a particular probability distribution function (Pr(NA=X)): X in this case is comes from the binomial distribution with parameters n=3 and p=3/4. If we observed a large number of samples of size 3, with Pr(A)=3/4, the average or expectation for X would approach, as our earlier definition of probability would lead us to expect. We can use this understanding in a number of ways. Say we are given only three progeny arising from an F2 cross (P1: A a), and observe zero out of three were A. Is it likely that A or a is the dominant phenotype? From the tabulation, we can see that if A is dominant over a, the probability of observing such an outcome is only 1/64. If the converse was true, the probability of such an outcome would be 27/64. We will define one possibility A > a as the null hypothesis (the base hypothesis or H0) and a > A the alternative hypothesis (H1). The likelihood ratio for testing these two alternatives (in this simple case) is 27/64 divided by 1/64, which is 27. This says that a being dominant over A is 27 times more likely than the null hypothesis. In linkage analysis, we normally take the decimal log of this ratio (the LOD score), which in this case is 1.43. It is necessary then to choose a size of likelihood ratio that we regard as "conclusive proof" that one hypothesis is correct, and the other incorrect. In linkage analysis, a ratio of 1000 (LOD=3) is the number that has been chosen, at least partly, arbitrarily. The alternative viewpoint is to perform a one-sided test (in the direction of H1) of the null alone. If the null hypothesis (A>a) was true, we would see the observed result only in 1/64 replications (on average). This is the so-called Type I error rate. Traditionally, we set a critical P-value (probability of a result at least as extreme as the one observed) of 1/20. Using this criterion (alpha=0.05), we would reject H0, and accept H1. If we did not have a specific hypothesis (such as dominance), we might be inclined just to estimate Pr(A), and perhaps give a measure of how accurate this estimate is. We can do the latter by replacing the p parameter in the binomial probability function with an estimate from the sample. The sample size in the last example is too small for this, so I'll return to the frizzling cross where 23/93 chickens in the F2 were frizzled. We set p=23/93. Then the probabilities of each possible count out of 93 can be calculated |Pr(X=x|n,p) = n!/[x! (n-x)!] . px . The central 95% confidence interval is constructed by systematically adding up the probabilities on either side of the point estimate for p to give upper and lower values of X that contain 0.95 of the probability (so 0.475 on either side). So the estimate for the segregation ratio is 24.7% with 95% confidence interval from 16.4% to 34.8%. We might say that we are fairly (95%) certain that the segregation ratio is between 4/25 and 1/3, and because of the bell shape of the distribution, most likely to be close to 1/4. What determines the width of the confidence interval? The larger the sample size, the narrower the interval. That is, the more precise you can believe your point estimate to be. Ways to extend the binomial to more than two categories are via the multinomial or Poisson distributions. For example, in the codominant case F2, we can discriminate three categories of outcome: A=A/A, B=A/a, C=a/a; Pr(A)=Pr(C)=1/4; Pr(B)=1/2. For a sample of size N, N=NA+NB+NC, we expect E(NA)=Pr(A).N; E(NB)=Pr(B).N; E(NC)=Pr(C).N. Rather than tabulating the exact probabilities (which is a big job), we usually use tests based on asymptotic or large sample theory. Pearson showed in 1900, if NA, NB, NC are large enough, that under the null hypothesis (Pr(A),Pr(B),Pr(C)), |Frizzled (FF)||23||23.25 (93/4)| |Sl. Frizzled (Ff)||50||46.50 (93/2)| |Normal (ff)||20||23.25 (93/4)| Q = (23-23.25)2/23.25 + (50-46.5)2/46.5 + (20-23.25)2/23.25 = 0.72. If the null hypothesis was a perfect fit for the observed data, then Q would be 0. If there is any deviation from the null, Q will be greater than zero. In fact, under the null hypothesis, on average Q will be v, E(Q)=2 in this example. The degrees of freedom v is two because of the linear constraint that the probabilities must add up to one. Therefore, if we specify Pr(A) and Pr(B) for our null hypothesis, Pr(C) is not free to be anything other than 1-Pr(A)-Pr(B). We can conclude that the data is consistent with the null hypothesis, Pr(Q<=0.72 | v=2)=0.70. We can also construct a likelihood ratio which compares the null hypothesis to the alternative hypothesis which is specified by the observed counts (Pr(A)=23/94; Pr(B)=50/93; Pr(C)=20/93). This is different from the earlier example where we specified the alternative hypothesis. It turns out that, asymptotically, negative two times the natural log of this likelihood ratio is also distributed as a X2 with two degrees of freedom. Under the assumption of a Poisson (or a multinomial) distribution giving rise to each count, this likelihood ratio X2 (perhaps due to Fisher 1950) is In the example, G2 = 2( 23.log(23.5/23) + 50.log(46.5/50) + 20.log(23.5/20)) = 0.31. The likelihood ratio comparing the observed counts to those expected under Mendelism is approximately 0.86 (close to 1). Finally, we could construct confidence intervals for all three observed probabilities, much in the way we did for one of the probabilities earlier. We have previously seen this table for a testcross. The goodness-of-fit tests discussed allow us to determine whether it is plausible that these two loci are in fact unlinked. The null hypothesis has all four cells equal (expected value under this hypothesis 157/4). The Pearson X2 test gives, (18-39.25)2/39.25+(63-39.25)2/39.25+(63-39.25)2/39.25+(13-39.25) 2/39.25 = 57.8. and the likelihood ratio X2= 62.5. These X2's have three degrees of freedom, again because the fourth probability is fixed once the first three are set. This hypothesis actually has three parts (one for each degree of freedom): White and Coloured segregate equally, Frizzled and Normal segregate equally, and White and Frizzled are unlinked. In the case of the likelihood X2, this is the sum of the three one degree of freedom X2 testing each hypothesis. Pearson X2's cannot be partitioned this way. |Pr(White & Frizzled)=1/4||62.13||1||3.2210-15| This confirms our original qualitative assessment of this table. The 95% confidence interval for the estimate of c=31/157=19.7% is 13.8% to 25.8%. Linkage leads to deviation from the 9:3:3:1 ratios in the dihybrid intercross (dominant traits), so a test for linkage can be easily constructed. Estimating c is more complex, because we have to determine whether each parent's genotype was in coupling or repulsion. For example, the proportion of double recessive offspring under no linkage this is 1/16. If the mating is Coupling Coupling (AB/ab AB/ab), then this proportion is (1-c)2/4; if it was Repulsion Repulsion (Ab/aB Ab/aB), c2/4. In the Frizzled test-cross data, there were two crosses, one in coupling, and the other in repulsion. These seemed to give similar estimates for the recombination fraction between the two loci, but it would be useful to have a test for equality of homogeneity of these estimates. Our null hypothesis is then c1=c2. The observed counts were: |Cross 1||31 (19.7%)||126| |Cross 2||6 (18.2%)||27| If the null hypothesis is true, then both strata have the same expectation, and the best estimate for c will be (31+6)/(157+33)=19.47%. We can use this pooled estimate as the expected values for a X2 test: |Cross 1||30.6 (19.5%)||126.4| |Cross 2||6.4 (19.5%)||26.6| This gives a one degree of freedom X2=0.04, because there are two original proportions being explained by one hypothetical proportion. If there were six crosses, then there would be five degrees of freedom. This test is also known as the 2xN Pearson contingency chi-square. I have been using the Poisson distribution without having described it. If we return to a series of experiments described by the binomial distribution where the (constant) probability of a success Pr(A) or p is very small, but the number of experiments N or n is large so that the expected value of NA (np) is "appreciable", then the probability distribution function will approach, where m is E(NA). This distribution is very attractive. Unlike the binomial, where X is limited to be between zero and N, the Poisson hasn't specified the number of experiments, just that is greater than zero. Given the derivation, one can see why the Poisson distribution can be used to estimate the binomial probabilities when p is small. Furthermore, for multinomial data, we can regard each cell of counts as coming from a separate Poisson distribution of a given mean (mi), given by NPr(i). Other uses for the Poisson arise when we obtain counts arising from a period of time or a subdivision of length or area. The number of offspring during the lifetime of a mating is often modelled as being Poisson. Similarly, it has been used to describe the number of recombination events along the length of a chromosome (it underlies the choice of the Haldane mapping function). We have examined one limiting distribution for the binomial, the Poisson. If we increase the n parameter (regardless of p), then the binomial probability distribution function approaches the Gaussian or Normal distribution. The same is true for the X2 distribution when v becomes large, and the Poisson distribution when m is large. The Gaussian has a continuous, symmetrical, bell shaped probability distribution. It has two defining parameters, mu and sigma, which conveniently are the mean and standard deviation. The approximations then to various distributions then given using: These and other equalities allow a number of asymptotic tests to be constructed. Fisher and Snell (1948) report results from mice testcrossed for two traits jerker and ruby. |Cambridge (female, coupling)||51||48||30||44| |Bar Harbour (female, coupling)||32||30||31||47| |Cambridge (male, coupling)||17||12||15||17| |Bar Harbour (male, coupling)||20||13||13||14| |Cambridge (female, repulsion)||4||4||6||4| |Cambridge (male, repulsion)||5||5||4||8| Are je and ru linked? What statistical test would you perform? What result does it give? Would any single one of these studies be enough to decide without the others?

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https://web2.0calc.com/questions/please-help_66462

math

Spiderman has $350 in his account and his earnings $25 per day. Batman has $200 in his account and earns $35 per day. After how many days, d, will Spiderman have more than Batman. 350+25x is less then 200+35x 150 is less then 10 15 is less then x So x is 16 days, our final solution.

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https://community.anaplan.com/discussion/152564/applying-different-formulas-to-list

math

Applying different formulas to list Hi, i'd like to ask about how to apply different formulas on each item in a list. As You can see, Module 1 shows actual data from the current month. Module 2 is a forecast module, where the baseline column is from the previous month actual data, while the Growth % and Growth column is an input column for expected growth for the following month. Could you please give the solution since as far i know, a list is only able to capture the same formula for each of its item. Thank you. Module 1 and Blueprint Views Module 2 and Blueprint Views

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https://sprogram.com.ua/en/order

math

+38 098 888 58 89 Alexander General > Order Calculate the cost of your site! Value pricing is fluctuating, so some firms are from the ceiling price, depending on client capabilities. We define as transparent as the price of our products. How is the price the site. Very simply the price development of the site depends on the time spent by the designer, programmer, copywriter on the solution of the problem. So for your convenience, we have developed a "price calculator. Fill out the form below and you will see the price of your site. |powered by Steel Programming|

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https://asqrrd.org/tech-spot-sample-cre-questions-part-9-2/

math

1. The effects of failure rates on increased temperatures is called the: a. Thermal model b. Full scale Model c. Arrhenius Model d. Derating effects 2. Recognizing the nature of process variability, the process capability target is usually: a. looser than product specifications b. the same as product specifications c. tighter than product specifications d. not related to product specifications 3. (blank) software failure are the result of design errors. a. All b. Most c. Many d. No 4. How can linear acceleration be verified? a. with a probability plot b. with a hazard plot c. either a. or b. None of the above 5. The purpose of derating parts is to: a. Increase parts life b. Enhance overall system reliability c. Improve circuit design d. All of these 6. A sequential test plan is graphically shown in the figure below. If the first 3 items tested fail, and the next 18 succeed, what is the status of the test? a. Reject the null hypothesis b. Accept the null hypothesis c. Continue testing d. None of these 7. Inherent of intrinsic reliability: a. is that reliability which can only be improved by design change b. can be improved only by an improvement in the state of the art c. is that reliability estimated over stated period of time by a stated measurement technique. d. is not an estimated reliability 8.An item has a constant failure rate of 50, and a constant repair rate of 22, what is the steady-state availability? a. 0.983 b. 0.924 c. 0.815 d. none of the these 9. The failure rate for a carpet manufacturer is 3.7 per 1000 square yards. What is the probability of finding no defects in a random sample of 100 square yards. a. 0.7145 b. 0.6907 c. 0.9581 d. none of these 10. Given a shape parameter of 1.7 for the Weibull distribution, and assuming no failures, how long will 20 items have to be tested to give a reliability of 0.95 at t=100,000 with 90% confidence? a. 119.329 b. 160,896 c. 189,744 d. 276,901 Picture © B. Poncelet https://bennyponcelet.wordpress.com

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CC-MAIN-2024-10

https://www.knowpia.com/knowpedia/Semiperfect_number

math

In number theory, a semiperfect number or pseudoperfect number is a natural number n that is equal to the sum of all or some of its proper divisors. A semiperfect number that is equal to the sum of all its proper divisors is a perfect number. |Total no. of terms||infinity| |First terms||6, 12, 18, 20, 24, 28, 30| A primitive semiperfect number (also called a primitive pseudoperfect number, irreducible semiperfect number or irreducible pseudoperfect number) is a semiperfect number that has no semiperfect proper divisor. There are infinitely many such numbers. All numbers of the form 2mp, with p a prime between 2m and 2m+1, are primitive semiperfect, but this is not the only form: for example, 770. There are infinitely many odd primitive semiperfect numbers, the smallest being 945, a result of Paul Erdős: there are also infinitely many primitive semiperfect numbers that are not harmonic divisor numbers. Every semiperfect number is a multiple of a primitive semiperfect number.

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https://slideum.com/doc/1874974/pythagoras-theorem---thomas-whitham-maths-site--licensed

math

Pythagoras Theorem - thomas whitham maths site [licensed Transcript Pythagoras Theorem - thomas whitham maths site [licensed For each of the following right angled triangles find the length of the lettered side, giving your answers to 2 decimal places. In triangle ABC, angle B = 90⁰ AB = 7cm and AC = 11cm. Work out the length of BC, giving your answer correct to 1 decimal place. Work out the length of NM in the triangle drawn below. AQA June 2003 GCSE Paper A support for a flagpole is attached at a height of 3m and is fixed to the ground at a distance of 1.2m from the base. Calculate the length of the support (marked x on the diagram).

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https://www.jiskha.com/display.cgi?id=1337828124

math

posted by Need HELP PLEASE . How is solving for a specified variable in a formula similar to finding a solution for an equation or inequality? How is it different? give some examples Both are solved the same way, except with in inequality, multiplying/dividing by a negative number changes the direction of the carat. 6 > -2x -12 < x

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https://dailydoseofdiscernment.blogspot.com/2016/09/profit-first-2016-09-19.html

math

Monday, September 19, 2016 #Profit #First: 2016-09-19 • Lizard logic is profit-first logic. • Profit first is beneath primitive. • Worst decision ever is the profit-first decision. • Profit first is the enemy of humanity and sanity. • Profit first is the enemy of life and sustainability. Posted by Kevin Evertt FitzMaurice, M.S. at 9:27 PM

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https://study.com/academy/lesson/using-probability-to-solve-complex-genetics-problems.html

math

Artem has a doctor of veterinary medicine degree. Coins and Segregation Let's say you have two coins. One is a penny and the other is a quarter. The flip of each coin can get you a pair of possible outcomes, heads or tails. If you flip the penny and get heads, does that result have any bearing on whether or not you'll get a heads or tails when you flip the quarter next? No! That's because these are two independent events. Instead of flipping coins, when it comes to genetics, when each allele pair segregates during gamete (sex cell) formation, they do so independently as well, according to the Mendel's second law, the law of independent assortment. This means that a multi-character cross is like two or more independent monohybrid crosses that occur at the same time. Consequently, we can figure out the probability that specific genotypes will occur in the F2 generation using the concepts we're going to go over in this lesson. Using the image on your screen (see video), you can see that we have constructed a dihybrid cross between two RrYy F1 heterozygotes. These were derived from a parental generation using a cross between an RRYY and an rryy. The male gametes of the F1 generation, thus turn out to be RrYy and the female gametes of the F1 generation are also RrYy. R refers to the dominant allele for seed shape, round. Little r refers to the recessive allele for seed shape, wrinkled. Y refers to the dominant allele for seed color, yellow, and little y refers to the recessive allele for seed color, green. The image has constructed a square to help us figure out the probability of any genotype that may result in the F2 generation. For instance, we can see that, in total, we can have 16 different possible outcomes, ranging from RRYY to rryy. If I were to ask you, what is the probability that the F2 offspring will be RRYY? You'd look at the square and tell me that it would be 1/16, because only one such possibility exists. If I were to ask you, what is the probability that the offspring are RRYy? You'd tell me it is 1/8, because there are two such possible outcomes, and 2/16 = 1/8. Now, how can we use math to more quickly figure all of this out without having to construct a complex image of 16 possible outcomes or look through the square carefully to ensure we didn't miss the genotype in question? In this case, let's simplify everything to a Punnett square showing us a monohybrid cross of Yy plants and another Punnett square showing us a monohybrid cross of Rr plants (see video). We can see that for the monohybrid cross of Yy plants, the probabilities of the different offspring genotypes are ¼ for YY, ½ for Yy, and ¼ for yy. Similarly, using the monohybrid cross for Rr plants, the probabilities of the different offspring genotypes are ¼ for dominant (RR), ½ for heterozygous (Rr'), and ¼ for recessive (rr). That's pretty easy, since we only had to construct a square with four possible outcomes to get our probabilities. Actually, you didn't even have to construct the square to figure all of this out if you simply used the multiplication rule with respect to Mendelian inheritance, as outlined in another lesson. The multiplication rule is the multiplication of the probability of one event by the probability of the other event. Actually, we're going to use this same rule to solve these even more complex genetic problems. Let's say I ask you to tell me what the probability of RRYY is, without constructing a complex square as we did in the last section. How would you figure this out? Well, we now know that the probability of RR is ¼ and the probability of YY is also ¼. Using the multiplication rule, we multiply these two probabilities together to find the probability of RRYY. ¼ * ¼ = 1/16, the same answer as in the last section! Good. Now tell me what is the probability of getting RRYy in the F2 offspring? The probability of RR is 1/4 while the probability of Yy is 1/2. 1/4 * 1/2 = 1/8. Again, the math confirms our visual image from the prior section. Okay, one more example. What is the probability of getting an rrYY? It's simply ¼ * ¼ = 1/16. Now you know how to solve more complex genetic problems, either using visual images or math, thanks to Mendel's second law (the law of independent assortment) and the multiplication rule. The multiplication rule is the multiplication of the probability of one event by the probability of the other event. By using the math we went over in this lesson, you can easily solve similar problems on your own! To unlock this lesson you must be a Study.com Member. Create your account Register to view this lesson Unlock Your Education See for yourself why 30 million people use Study.com Become a Study.com member and start learning now.Become a Member Already a member? Log InBack Resources created by teachers for teachers I would definitely recommend Study.com to my colleagues. It’s like a teacher waved a magic wand and did the work for me. I feel like it’s a lifeline.

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https://codeforces.com/problemset/problem/47/D

math

|Codeforces Beta Round #44 (Div. 2)| Vasya tries to break in a safe. He knows that a code consists of n numbers, and every number is a 0 or a 1. Vasya has made m attempts to enter the code. After each attempt the system told him in how many position stand the right numbers. It is not said in which positions the wrong numbers stand. Vasya has been so unlucky that he hasn’t entered the code where would be more than 5 correct numbers. Now Vasya is completely bewildered: he thinks there’s a mistake in the system and it is self-contradictory. Help Vasya — calculate how many possible code variants are left that do not contradict the previous system responses. The first input line contains two integers n and m (6 ≤ n ≤ 35, 1 ≤ m ≤ 10) which represent the number of numbers in the code and the number of attempts made by Vasya. Then follow m lines, each containing space-separated si and ci which correspondingly indicate Vasya’s attempt (a line containing n numbers which are 0 or 1) and the system’s response (an integer from 0 to 5 inclusively). Print the single number which indicates how many possible code variants that do not contradict the m system responses are left.

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CC-MAIN-2021-39

http://www.solutioninn.com/calculate-roe-if-roi-12-rd-10-b

math

Question: Calculate ROE if ROI 12 RD 10 B Calculate ROE if ROI = 12%, RD = 10%, B = $200,000, SE = $300,000, and T = 0.35. Identify the business risk and financial risk. Relevant QuestionsWhat is the intercept and slope of the financial leverage (ROE-ROI) line in Practice Problem 13? Explain the meaning of the slope. What is the pecking order according to Myers’ argument?Explain the elements of Altman’s Z score as used in Practice Problem18.Susan and Celia are twins but have very different attitudes toward debt. Susan believes that firms should have a D/E ratio of 0.2 while Celia believes that the D/E ratio should be 1.1. Both sisters have agreed that Okanagan ...Summarize the main factors you need to consider if the CFO of your firm asks you to evaluate your firm’s capital structure.CGC Company is considering its dividend policy. Currently CGC pays no dividends, has cash flows from operations of $10 million per year (perpetual), and needs $8 million for capital expenditures. The firm has no debt and ... Post your question

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CC-MAIN-2017-39

https://senshido.info/relationship-between-and/relationship-between-light-intensity-and-voltage.php

math

Measuring and Calculating Lux Values The resistance of a LDR depends on light intensity. At low light levels, the LDR has a high resistance. As the light intensity increases, the resistance decreases. It only means that the relationship between voltage and current changes with the We might be tempted to say that the intensity I of the light is. Linearity: Often a linear relationship exists between the actual value of the quantity w and the output signal of the sensor, e.g. the voltage U. In this case: 0. Hence we have the coordinates of one point as well as the ascent of the line and for calculating any other point, we only need one coordinate. A-level Physics (Advancing Physics)/Sensors/Worked Solutions Meaning, if sensors' resistance RB is measured, it is possible to calculate from the equation of line, the intensity of light EB that falls on the sensor. Finding EB from the equation of line: The resistance can not be measured directly with microcontroller. For this the photoresistor is in the voltage divider. The output voltage of this voltage divider is converted to a specific variable by the analogue-digital converter ADC. To find the resistance, the output voltage U2 of the voltage divider must be calculated first, using the ADC value, also comparison voltage Uref of the converter must be taken into account: The formula is following: If circuit is used equipped with different components, respective variables need to be changed. Photoresistor [Robotic & Microcontroller Educational Knowledgepage - Network of Excellence] So somehow we have to compensate for the fact that two separate photosensitive elements—the human eye and the photosensor—are more or less sensitive to the countless different wavelengths of electromagnetic radiation concealed within the whitish light illuminating our environment. This seems hopeless, but the first thing to understand is that illuminance simply cannot be measured with the sort of precision we expect from thermometers or voltmeters or digital calipers. For that we would need either 1 a photosensitive device with spectral response identical to that of the human eye or 2 separate narrowband photodetectors, each with known sensitivity, fine-tuned to numerous wavelengths within the visible spectrum. Furthermore, such precision is by no means necessary or even possible. Who really cares whether their office is illuminated at lux or lux? Indeed, a high-precision lux measurement is almost a contradiction in terms, because illuminance is supposed to represent a human response to lighting conditions—and how often do humans agree on something so subjective? Bring two people into a room and ask them to rate the ambient brightness on a scale of 0 to So calculating lux is an exercise in approximation, and in this case, an approximate value is just fine. The following image shows the spectral response of the OSRAM photodiode mentioned above superimposed on the luminosity function: Trying to compensate for such extreme discrepancy is simply not a good use of time. The Easier Way Instead, select a device with a human-vision-based spectral response and with output-to-lux conversion information provided in the datasheet. Such devices are not hard to find; one example is the Fairchild phototransistor mentioned above. We should not need to adjust illuminance measurements based on the nature of the light source, because the process of calculating illuminance automatically accounts for variations in spectral composition. These are fairly consistent, but far from perfectly matched, so the light intensity detected by the phototransistor cannot be directly converted to lux. Light Intensity and Voltage Blog This graph shows how the photocurrent increases when the light intensity increases but the wavelength is held constant. The stopping potential is the same however, suggesting that the kinetic energy of the ejected electrons is the same and hence independent of light intensity. This is the opposite of what would be expected classically. - What is the relationship between light intensity and voltage? - 2.2: Photoelectric Effect - Relationship between INTENSITY of light vs. VOLTAGE (output) ? If light was of the classical wave-like nature, we would expect a time lag which would increase as the intensity of the light decreased. The total energy would be spread across a wavefront striking the entire cathode and nothing could happen until sufficient energy were absorbed. How an LDR (Light Dependent Resistor) Works - Kitronik Also, why would the frequency of the light make a difference to the stopping potential? The amount of energy in a wave is carried by its amplitude. Here is a plot of the measured stopping potentials obtained for several light frequencies for two different metals. The slope of this plot is the same in both cases and is equal to Planck's constant h.

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https://alchetron.com/Walter-Rudin

math

John Jay Gergen | May 2, 1921 Vienna, Austria (1921-05-02) | Professor Emeritus, University of Wisconsin-Madison Duke University (B.A. 1947, Ph.D. 1949) Mathematics textbooks; contributions to harmonic analysis and complex analysis American Mathematical Society Leroy P. Steele Prize for Mathematical Exposition (1993) May 20, 2010, Madison, Wisconsin, United States Principles of mathemat, Real and complex analysis, Fourier Analysis on Groups, The way I remember it, Function Theory in the Unit B Charles F. Dunkl Walter Rudin Wikipedia Walter Rudin (May 2, 1921 – May 20, 2010) was an Austrian-American mathematician and professor of Mathematics at the University of Wisconsin–Madison. In addition to his contributions to complex and harmonic analysis, Rudin was known for his mathematical analysis textbooks: Principles of Mathematical Analysis, Real and Complex Analysis, and Functional Analysis (informally referred to by students as "Baby Rudin", "Papa Rudin", and "Grandpa Rudin", respectively). Principles of Mathematical Analysis was written when Rudin was C. L. E. Moore Instructor at MIT, only two years after obtaining his Ph.D. from Duke University. Principles, acclaimed for its elegance and clarity, has since become a standard textbook for introductory real analysis courses in the United States. Rudin's analysis textbooks have also been influential in mathematical education worldwide, having been translated into 13 languages, including Russian, Chinese, and Spanish. Rudin was born into a Jewish family in Austria in 1921. They fled to France after the Anschluss in 1938. When France surrendered to Germany in 1940, Rudin fled to England and served in the Royal Navy for the rest of World War II. After the war he left for the United States, and earned his B.A. from Duke University in North Carolina in 1947, and two years later earned a Ph.D. from the same institution. After that he was a C.L.E. Moore instructor at MIT, briefly taught in the University of Rochester, before becoming a professor at the University of Wisconsin–Madison. He remained at the University for 32 years. His research interests ranged from harmonic analysis to complex analysis. In 1970 Rudin was an Invited Speaker at the International Congress of Mathematicians in Nice. He was awarded the Leroy P. Steele Prize for Mathematical Exposition in 1993 for authorship of the now classic analysis texts, Principles of Mathematical Analysis and Real and Complex Analysis. He received an honorary degree from the University of Vienna in 2006. In 1953, he married fellow mathematician Mary Ellen Estill, known for her work in set-theoretic topology. The two resided in Madison, Wisconsin, in the eponymous Walter Rudin House, a home designed by architect Frank Lloyd Wright. They had four children. Rudin died on May 20, 2010 after suffering from Parkinson's disease.Ph.D. thesis Rudin, Walter (1950). Uniqueness Theory for Laplace Series (Thesis). Duke University. "Uniqueness theory for Laplace series". Trans. Amer. Math. Soc. 68 (2): 287–303. 1950. MR 0033368. doi:10.1090/s0002-9947-1950-0033368-1. "Factorization in the group algebra of the real line". Proc Natl Acad Sci U S A. 43 (4): 339–340. 1957. PMC 528447 . PMID 16578475. doi:10.1073/pnas.43.4.339. "Zero-sets in polydiscs". Bull. Amer. Math. Soc. 73 (4): 580–583. 1967. MR 210934. doi:10.1090/s0002-9904-1967-11758-0. "Holomorphic maps that extend to automorphisms of a ball" (PDF). Proc. Amer. Math. Soc. 81 (3): 429–432. 1981. MR 597656. doi:10.1090/s0002-9939-1981-0597656-8. "Totally real Klein bottles in "(PDF)". Proc. Amer. Math. Soc. 82 (4): 653–654. 1981. MR 614897. doi:10.1090/s0002-9939-1981-0614897-1. Textbooks:Principles of Mathematical Analysis. (1953; 3rd ed., 1976, 342 pp.) Real and Complex Analysis. (1966; 3rd ed., 1987, 416 pp.) Functional Analysis. (1973; 2nd ed., 1991, 424 pp.) Monographs:Fourier Analysis on Groups. (1962) Function Theory in Polydiscs. (1969) Function Theory in the Unit Ball of ℂn. (1980) Autobiography:The Way I Remember It. (1991) Steele Prize for Mathematical Exposition (1993)

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http://forums.wolfram.com/mathgroup/archive/2004/Oct/msg00510.html

math

[Date Index] [Thread Index] [Author Index] Formatting numbers for output I'm trying to construct a GridBox with entries that have 2 digits after the decimal. So far, the only way I've seen to get rid of the NumberMarks is to use SetPrecision[x, :inf:], but that makes all the numbers into fractions. Is there a better alternative? Thanks, Alex -- The heart of mathematics consists of concrete examples and concrete problems.

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https://worldcoingallery.com/countries/Mauritania.html

math

Click on each type to view images. Click the green dollar signs for Coin Values Printable version of this page $ km1 1/5 Ouguiya (1973) $ km2 1 Ouguiya (1973) Image from Vladimir Startsev $ km6 1 Ouguiya (1974-2003) $ km3 5 Ouguiya (1973-2004) $ km3a 5 Ouguiya (2004--) copper plated steel $ km4 10 Ouguiya (1973-2004) $ km4a 10 Ouguiya (2004--) copper plated steel $ km5 20 Ouguiya (1973-2004) $ km5a 20 Ouguiya (2004-2009) copper plated steel $ km8 20 Ouguiya (2009,2010) Bi-metallic Image from Joost Geise $ km9 50 Ouguiya (2010) Bi-metallic Image from Joost Geise $ x1.1 500 Ouguiya (1984) runners Click here to view all. <--Return to Country Index This is my collection of world coins. Here you will find foreign coin photos, coin values and other information useful for coin collecting. Some coins came from the coin mint, some came from coin dealers, some came from coin auction. Some are stored in coin folders, others are stored using other coin supplies. Many of these coins are silver coins and a few are gold coins. This information is useful for coin collecting software and cataloging coin types and coin values in U.S. dollars.

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https://community.tableau.com/thread/212044

math

1 of 1 people found this helpful So firstly, you shouldn't need to create a new post for this. It makes sense to continue on in your existing discussion, especially given that others have already started contributing to it. It helps newcomers to the thread understand what you've already tried, and we don't end up with several threads for essentially what will be the same root cause. With regards to your problem, it's going to be really tough for us to provide insight here without seeing a Tableau Packaged Workbook including sample data. I'm sure you're hesitant to do that, so I'd suggest you mock up some sample data in excel and re-create the problem with sample data so we can take a look at that. You're right, I'm attaching a small sample from my data - the tables has the main Dimension and attributes that I use in the views... There are three sheets in the workbook: - One called "Master Report" - in which the calculation works. - The second called "Case" , It has the cross-tab for only "Case" calculation ---> "Total" shows zero - The third called "If", it has the cross-tab for the "If" calculation as showen up ---> "Total" isn't calculated correctly, it shows the "Total"* # of rows in the "Source" dimension" Hopefully this would help, Thank you! Total_Sample.twbx 53.8 KB On your if sheet, you could change the ClicksIf formula to; IF ATTR([Source])=ATTR([Source]) THEN WHEN "A" THEN ZN(SUM([WithoutCampaign (Sample)].[A Clicks])) WHEN "B" THEN ZN(SUM([WithoutCampaign (Sample)].[B Clicks])) WHEN "C" THEN ZN(SUM([WithoutCampaign (Sample)].[C Clicks])) ELSE (ZN(SUM([WithoutCampaign (Sample)].[A Clicks])) +ZN(SUM([WithoutCampaign (Sample)].[B Clicks])) +ZN(SUM([WithoutCampaign (Sample)].[C Clicks]))) Because you're joining, two sources, you are effectively duplicating the metrics and thus you need to divide by something. This will give you the correct totals, but your grand total is going to be off. Here's a rather long thread, but one which you should read - Re: Grand Total doesn't work!

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https://www.jiskha.com/display.cgi?id=1224463048

math

posted by CM An adult takes about 15 breaths per minute, with each breath having a volume of 500mL. if the air that is inhaled is "dry", but he exhaled air is 1 atm pressure is saturated with water vapor at 37 degree C(body temp), what mass of water is lost from the body in 1hr? The vapor pressure of water at 37degree C is 47.1 mmHg. I tried doing this with partial pressures but its not coming out right.Please Help!! The volume of air inhaled in 1 hour is (60 min/hr)(15 br./min) = 900 breaths (900 br.)(0.500L/breath) = 450 L To find moles of H2O, use the Ideal Gas Law: PV = nRT 1 atm = 760 mm Hg 47.1mmHg/760 mmHg/atm = 0.061974 atm P = 0.061974 atm for water vapor R = 0.08206 L.atm/K.mol T = 273 + 37 = 310K Find moles of H2O: n = PV / RT Mass of H2O in grams is: (moles H2O)(18.015 g/mol.H2O) but the air that came out has a pressure of 1atm. doesnt that mean it has the partial pressure of air and water in it?? the question was ....but the exhaled air at 1atm pressure is saturated with water vapor.....

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http://www.solutioninn.com/define-each-of-the-following-retail-terms-initial-markup-additional

math

Question: Define each of the following retail terms initial markup addit Define each of the following retail terms: initial markup, additional markup, markup cancellation, markdown, markdown cancellation. Relevant QuestionsExplain how to estimate the average cost of inventory when using the retail inventory method.When a company changes its inventory method to LIFO, an exception is made for the way accounting changes usually are reported. Explain the difference in the accounting treatment of a change to the LIFO inventory method from ...SLR Corporation has 1,000 units of each of its two products in its year-end inventory. Per unit data for each of the products are as follows:Determine the balance sheet carrying value of SLR's inventory assuming that the ...On January 1, 2011, Sanderson Variety Store adopted the dollar-value LIFO retail inventory method. Accounting records provided the following information:Calculate the inventory value at the end of the year using the ...Refer to the situation described in Exercise 9-3. Assume that Tatum Company prepares its financial statements according to IFRS.Required:1. Determine the balance sheet inventory carrying value at December 31, 2011, assuming ... Post your question

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http://mathhelpforum.com/math-challenge-problems/101502-triangle-inside-rectangle.html

math

Rectangle A(0,a), B(b,a), C(b,0), D(0,0). E is on diagonal AC, F on diagonal BD; u = AE = BF. G is situated below EF, forming isosceles triangle EFG; rectangle ABCD's center is inside triangle EFG. AreaABCD / areaEFG = d. What is the length of EG (or FG) in terms of a,b,d,u ? a=84, b=112, d=49, u=55 results in isosceles triangle equal sides=20 and base = 24.

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https://www.leadingedgeonly.com/innovation/view/supercalc

math

Automatically corrects errors in mathematical syntax. Calculators are used to get the right answer when you don’t know what that answer should be. Unfortunately if you make any slip in a calculation, you simply get the wrong answer. While this may be merely inconvenient for many, in a safety critical environment (such as a hospital — in radiotherapy or drug dosing) numerical errors can be fatal. This invention is a next generation general-purpose calculator that automatically corrects errors in mathematical syntax (something like a spell or grammar checker in a document) so that the conclusion is always consistent with the input. Numbers and equations are written freehand using everyday notation and are automatically recognised and formatted in a mathematically correct manner. Although input errors can still occur, the format makes them instantly recognisable and the invention makes them extremely easy to correct. In trials, a 60% pass rate using conventional calculators (for maths exams) was increased to 100% A patent has been granted; other patent applications are pending

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https://www.physicsforums.com/threads/strange-fluid-mechanics-formula-made-by-crazy-textbook-author.707392/

math

Please look at the picture I uploaded. Can somebody explain to me how the author of my book can claim that the "vertical component of the ring surface-tension force at the interface in the tube balances the weight of the column of fluid of height h"? This stuff is weird. How can TENSION FORCES which are on the TOP of a column of fluid support the weight of the column? I mean, it's obviously the pressure-difference between the air inside the tube and outside which lifts the water, assuming that the tube is closed on the top.

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http://www.solutioninn.com/a-market-researcher-selects-a-simple-random-sample-of-n-

math

Question: A market researcher selects a simple random sample of n A market researcher selects a simple random sample of n = 100 Twitter users from a population of over 100 million Twitter registered users. After analyzing the sample, she states that she has 95% confidence that the mean time spent on the site per day is between 15 and 57 minutes. Explain the meaning of this statement. Answer to relevant QuestionsWhy can you never really have 100% confidence of correctly estimating the population characteristic of interest? The human resource (HR) director of a large corporation wishes to study absenteeism among its mid- level managers at its central office during the year. A random sample of 25 mid- level managers reveals the following: • ...Claims fraud (illegitimate claims) and buildup (exaggerated loss amounts) continue to be major issues of concern among auto-mobile insurance companies. Fraud is defined as specific material misrepresentation of the facts of ...Many consumer groups feel that the U. S. Food and Drug Administration (FDA) drug approval process is too easy and, as a result, too many drugs are approved that are later found to be unsafe. On the other hand, a number of ...In Problems 9.18 and 9.19, what are the critical values of t if the level of significance, a, is 0.05 and the alternative hypothesis, H1, is μ ≠ 50? Post your question

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https://gradeboosts.com/explain-what-type-of-experiment-you-would-run-to-test-your-hypothesis/

math

Explain what type of experiment you would run to test your hypothesis.. For this discussion you need to: Start with a question or observation Use your question or observation to create a hypothesis Explain what type of experiment you would run to test your hypothesis. You need to include enough detail that I could run your experiment. You also need to identify your dependent and independent variables. Remember to get full credit you must respond to all prompts, respond in complete, coherent sentences, and your discussion must be a minimum of 5 sentences. Here is my example: The question I will be investigating is, “Are double stuff Oreos truly double stuffed?”. My hypothesis is that double stuff Oreos should have double the stuffing of regular Oreos. To test this hypothesis I will get bags of both regular Oreos and double stuff Oreos. I will run two types of experiments: one will look at weight, the other will look at volume. For the weight experiment I will scrape the filling off 10 regular Oreos and 10 double stuff Oreos. I will weigh the filling from each cookie and then find an average for my 10 cookies. If the data support my hypothesis, then the double stuff Oreo filling should be about twice the weight of the regular Oreo filling. The other experiment I will run is to compare volume. Volume is a three dimensional measurement so I will measure the radius and the height of the filling for 10 regular Oreos and 10 double stuff Oreos. Then I will use the equation for volume of a sphere to determine the volume of filling for each cookie. I will average the 10 measurements for each type of cookie. For the data to support my hypothesis, the volume of filling in a double stuff Oreo should be twice the volume of filling in a regular Oreo. The independent variable in my experiment is the type of cookie, the dependent variables are the weight of filling in the first experiment and the volume of filling in the second experiment.

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http://klamath.stanford.edu/~molinero/calendus/Grads/read/event_8545_Grads_read.html

math

by Jorge Cham Subject:   Film on Organ & Tissue Donation: The Kindness of Strangers-- Sponsor:   Women’s Center and the American Red Cross Date:   Wednesday, April 12, 2000 Time:   8pm - 9pm Location:   Castano Lounge [look for it in a campus map] Produced by Robert Redford’s son, James Redford, after he received a lifesaving liver transplant. The riveting documentary follows the real lives of people on the waiting list and the donors who make transplants possible. Event history: Submitted by ashsagar on 05-Apr-2000; Disclaimer | Email us!

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https://www.cas.uab.edu/mathrr/Export/details/12616.html

math

The Radon transform is an important topic in integral geometry which deals with the problem of expressing a function on a manifold in terms of its integrals over certain submanifolds. Solutions to such problems have a wide range of applications, namely to partial differential equations, group representations, X-ray technology, nuclear magnetic resonance scanning, and tomography. This second edition, significantly expanded and updated, presents new material taking into account some of the progress made in the field since 1980. The first chapter introduces the Radon transform and presents new material on the d-plane transform and applications to the wave equation. Chapter 2 places the Radon transform in a general framework of integral geometry known as a double fibration of a homogeneous space. Several significant examples are developed in detail. Two subsequent chapters treat some specific examples of generalized Radon transforms, for examples, antipodal manifold in compact 2-points homogeneous spaces, and orbital integrals in isotropic Lorentzian manifolds. A final chapter deals with Fourier transforms and distributions, developing all the tools needed in the work. Aimed at beginning graduate students, this monograph will be useful in the classroom or as a resource for self-study. Readers will find here an accessible introduction to Radon transform theory, an elegant topic in integral geometry.

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http://www.jiskha.com/display.cgi?id=1357251262

math

Posted by Anonymous on Thursday, January 3, 2013 at 5:14pm. Please help!!!!!!!!!!! Find all solutions to the equation in the interval [0,2π). Solve algebraically for exact solutions in the interval [0,2π). Use your grapher only to support your algebraic work. - Precalculus - bobpursley, Thursday, January 3, 2013 at 5:40pm Have you tried the double angle formulas? - Precalculus - Anonymous , Thursday, January 3, 2013 at 5:58pm Yes. It is just too hard and it would be nice if you would just explain it to me or show me an example. Please. - Precalculus - bobpursley, Thursday, January 3, 2013 at 6:07pm Hard? Why would anyone want to do something easy? 2cos^2x+cosx=cosx= cos(x)cos(x) - sin(x)sin(x) = cos²(x) - sin²(x) 2 cos^2 x+cosx=cos^2x-(1-cos^2x) 2 cos^2 x+cosx=2cos^2x-1 - Precalculus - Steve, Thursday, January 3, 2013 at 6:44pm the only way to make these easy is to do dozens of them, not just the assignments. The double angle formulas are critical to know. cos 2x + sin x=0 1 - 2sin^2 x + sin x = 0 rearrange things a bit to the usual order: 2sin^2 x - sin x - 1 = 0 (2sin x + 1)(sin x - 1) = 0 so, either sin x = -1/2 or sin x = 1 sin x = -1/2: x = 7pi/6 or 11pi/6 sin x = 1: x = pi/2 - Precalculus - Anonymous , Thursday, January 3, 2013 at 9:33pm Thanks for your help you guys. - Precalculus - yeah , Tuesday, November 29, 2016 at 4:26pm Answer This Question More Related Questions - Math, please help - Which of the following are trigonometric identities? (Can be... - Pre-Calculus - Find all solutions to the equation in the interval [0, 2pi) cos4x... - calculus - Find all solutions to the equation in the interval [0,2pi) Cosx-cos2x - identities trig? - find all solutions to the equation in the interval [0,2pie] ... - Trig--check answer - Solve the equation of the interval (0, 2pi) cosx=sinx I ... - Math - We can find the solutions of sin x = 0.6 algebraically. (Round your ... - Advanced Functions/Precalculus - Trigonometry Questions 1.) Find the exaqct ... - math 108 trig - 1.Solve, finding all solutions in [0, 2π). cosx sin2x + ... - Pre-Cal:(Cont.) - [Note: I've tried this problem 4 times already and still have ... - Trigonometry - Find all solutions of the equation in the interval [0,2pi] ...

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https://pasyanthi.net/forums/reply/29817/

math

Challenge Question 5 – Two Smudged Digits The product of two numbers is seen but two of the digits are smudged. So you can only see: 3 a b x 6 1 = 1 9 9 4 7 where a and b are the smudged digits. Can you find the smudged digits (please explain your answer)?

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https://pastirskicentar.com/88470-math-honors-thesis-ucsd/

math

Prerequisites: AP Calculus AB score of 4 or more, or AP Calculus BC score of 3 or more, or math 20A. Further Topics in Combinatorial Mathematics (4) Continued development of a topic in combinatorial mathematics. Participation in the Freshman Honors Program is by invitation, and based on a combination of high school GPA and SAT or ACT scores. Basic Topics in Algebra I (4) Recommended for all students specializing in algebra. And those associated with driving while speaking on behalf of your proposed research sufficiently challenging, what of those who have led many astray. A maximum of fourteen percent of graduating seniors may be so honored, and ranking is based on the GPA for at math honors thesis ucsd least 72 letter-grade units of course work at the University of California. . It is likely to suggest and yet total commitment and attachment to attach or fix the prefix per- means through. Topics in Computational and Applied Mathematics (4) Introduction to varied topics in computational and applied mathematics. Students who have not completed math 267A may enroll with consent of instructor. This multimodality course will focus on several topics of study designed to develop conceptual understanding and mathematical relevance: linear relationships; exponents and polynomials; rational expressions and equations; models of quadratic and polynomial functions and radical equations; exponential and logarithmic functions; and geometry and trigonometry. In recent years, topics have included Morse theory and general relativity. Survey of finite difference, finite element, and other numerical methods for the solution of elliptic, parabolic, and hyperbolic partial differential equations. Convex Analysis and Optimization III (4) Convex optimization problems, linear matrix inequalities, second-order cone programming, semidefinite programming, sum of squares of polynomials, positive polynomials, distance geometry. Vector fields, gradient fields, divergence, curl. Numerical differentiation: divided differences, degree of precision. 2019 All content within this entry is strictly the property of Nathan J Barbara, and is not for public use without permission. Graduate students will do an extra paper, project, or presentation per instructor. Interestingly, levy and nathan sznaider explore the implications math honors thesis ucsd of the septuagint and if they had uncovered the rolls and unrolled the parchments, the king as a rampart which one is happy. Seminar in Computational and Applied Mathematics (1) Various topics in computational and applied mathematics. Your work with the comic or humorous. Prior enrollment in math 109 is highly recommended. Mathematics Graduate Research Internship (24) An enrichment program that provides work experience with public/private sector employers and researchers. Plagiarism still occurs if a verb is past tense. As did the assignment title approaching your lecturer consequences of information-driven reexivity are that culture shapes the ends justify the apparent univore, innovations often also undergo reframing. Calculus and Analytic math honors thesis ucsd Geometry for Science and Engineering (4) Vector geometry, vector functions and their derivatives. So if you leave the distinct religious identity would, in principle, modest and unrenowned works of art genesis and subsequent collapse of a kind of project to be fundamentally oriented to a very useful for conceptualizing the status claims. Prerequisites: math 20D and either math 18 or math honors thesis ucsd math 20F or math 31AH, and math 109 or math 31CH, and math 180A. Newtons methods for nonlinear equations in one and many variables. Most of these packages are built on the Python programming language, but no prior experience with mathematical software or computer programming is expected. Theorem proving, Model theory, soundness, completeness, and compactness, Herbrands theorem, Skolem-Lowenheim theorems, Craig interpolation. Students may not receive credit for both math 18 and 31AH. Iterative methods for large sparse systems of linear equations. Are there too many outliers. The Freshman Honors Program consists of a year-long Freshman Honors Seminar and occasional enrichment opportunities. Regardless of topic or format, students in ERC 92 gain valuable knowledge and skills. . Antiderivatives, definite integrals, the Fundamental Theorem of Calculus, methods of integration, areas and volumes, separable differential equations. Students may not receive credit for both math 174 and phys 105, ames 153 or 154. Page writing the creative, dynamic nature of commemoration, however, daniel levy and sznaider similarly highlight the ways in which the media are adapted to most archetypically embody their cultures and subcultures gary alan fine is john cowles professor of sociology. (Students may not receive credit for both math 140B and math 142B.) Prerequisites: math 142A or math 140A, or consent of instructor. Students who have not completed math 221A may enroll with consent of instructor. Prerequisites: math 100A-B-C and math 140A-B-C. The listings of quarters in which courses will be offered are only tentative. UC San Diego offers many opportunities for students to develop research skills and to explore career options. . Keep up the presumed signatory or signatories or the intellectual bourgeoisie turned to non-us movements, however, they mischaracterize constitutivists merely as resource wars or imperialist adventuresthey certainly are thosebut more fundamentally as wars of systemic self-equilibration, and potentially global and local identities. Woman have you been able to listen. Parameter estimation, method of moments, maximum likelihood. Prerequisites: math 18 or math 20F or math 31AH and math 20C (or math 21C) or math 31BH with a grade of C or better. For course descriptions not found in the. Like, along with my advisor, Professor David Lake, I completed a thesis as part of the ucsd political science honors program. In recent years topics have included: generalized cohomology theory, spectral sequences, K-theory, homotophy theory. Calculus for Science and Engineering (4) Integral calculus of one variable and its applications, with exponential, logarithmic, hyperbolic, and trigonometric functions. Study of tests based on Hotellings. Enumeration, formal power series and formal languages, generating functions, partitions. Mathematical Methods in Physics and Engineering (4) Complex variables with applications. Seminar in Number Theory (1) Various topics in number theory. Prerequisites: math 140B or consent of instructor. Space-time finite element methods.

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https://fa.wikipedia.org/wiki/%D8%AC%D8%A7%D9%85%D8%B9%D9%87_%D8%A2%D9%85%D8%A7%D8%B1%DB%8C?match=en

math

جامعه آماری (به انگلیسی: Statistical population) ، عبارت است از مجموعه کامل اندازههای ممکن یا اطلاعات ثبت شده از یک صفت کیفی، در مورد گردآورده کامل واحدها، که میخواهیم استنباطهایی راجع به آن انجام دهیم. منظور از عمل گردآوردن دادهها، استخراج نتایج درباره جامعه میباشد. یا به بیان سادهتر، در هر بررسی آماری، مجموعه عناصر مورد نظر را جامعه مینامند در نتیجه جامعه، مجموعه تمام مشاهدات ممکن است که میتوانند با تکرار یک آزمایش حاصل شوند. In statistics, a population is a set of similar items or events which is of interest for some question or experiment. A statistical population can be a group of existing objects (e.g. the set of all stars within the Milky Way galaxy) or a hypothetical and potentially infinite group of objects conceived as a generalization from experience (e.g. the set of all possible hands in a game of poker). A common aim of statistical analysis is to produce information about some chosen population. In statistical inference, a subset of the population (a statistical sample) is chosen to represent the population in a statistical analysis. The ratio of the size of this statistical sample to the size of the population is called a sampling fraction. It is then possible to estimate the population parameters using the appropriate sample statistics. A subset of a population that shares one or more additional properties is called a subpopulation. For example, if the population is all Egyptian people, a subpopulation is all Egyptian males; if the population is all pharmacies in the world, a subpopulation is all pharmacies in Egypt. By contrast, a sample is a subset of a population that is not chosen to share any additional property. Descriptive statistics may yield different results for different subpopulations. For instance, a particular medicine may have different effects on different subpopulations, and these effects may be obscured or dismissed if such special subpopulations are not identified and examined in isolation. Similarly, one can often estimate parameters more accurately if one separates out subpopulations: the distribution of heights among people is better modeled by considering men and women as separate subpopulations, for instance. Populations consisting of subpopulations can be modeled by mixture models, which combine the distributions within subpopulations into an overall population distribution. Even if subpopulations are well-modeled by given simple models, the overall population may be poorly fit by a given simple model – poor fit may be evidence for the existence of subpopulations. For example, given two equal subpopulations, both normally distributed, if they have the same standard deviation but different means, the overall distribution will exhibit low kurtosis relative to a single normal distribution – the means of the subpopulations fall on the shoulders of the overall distribution. If sufficiently separated, these form a bimodal distribution; otherwise, it simply has a wide peak. Further, it will exhibit overdispersion relative to a single normal distribution with the given variation. Alternatively, given two subpopulations with the same mean but different standard deviations, the overall population will exhibit high kurtosis, with a sharper peak and heavier tails (and correspondingly shallower shoulders) than a single distribution.

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CC-MAIN-2020-05

https://edmonkey.com/category/excel/

math

HINTS: #2. =SUM(Hourly Wage*Hours)+(Hourly Wage*Overtime*$Overtime$Rate) #6. Highlight A3-A17 before applying a Sort Filter #7. -Page Layout Tab -Scale To Fit Menu – Margins Tab -Center on Page -CHECK “Horizontally” -CHECK “Vertically” Don’t Forget to add the appropriate footer with: Name, Page #, and Assignment #. Look at your assignment in Print Preview, ensure that it looks […] HINT: Don’t forget to divide the interest rate by 12. Why? If you were paying an annual interest rate of 10% you would need to divide that interest rate by 12 in order to find out the monthly interest rate. In this case: (.10/12) Fit the assignment to one page, apply the appropriate footer, print and submit assignment. In Part 2 on Page 146 You are asked to: Insert in cell C6 a formula that calcualtes the future value of an investment. TIP: Here is that formula for cell C6: =FV($C$5,B6,-$D$3) You will printing two documents for this assignment. 1. With a contribution of $2000 2. With a contribution of $3000 PRINT: Scale […] Case Study Part 1 asks to “In column G, insert a formula using the PMT function (enter the Pv as a negative).” TIP 1: Don’t forget to enter the Pv as a negative. Why a negative? Because that is the amount to BORROW. TIP 2: When entering the interest rate (E7), make sure and divide […] Microsoft Excel 2007 Level 1 & 2 Rutkosky, Seguin, Rutkosky 2008 by Paradigm Publishing, Inc Chapter 1 Pages 7-36 • Identify the various elements of an Excel Workbook • Create, Save, and print a workbook • Enter data in a workbook • Edit data in a workbook • Insert a formula using the Sum button […]

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CC-MAIN-2023-40

https://campuscrosswalk.org/net-present-value-and-b-internal-rate/

math

In net present value E. ayback and average In proper capital budgeting analysis we evaluate incremental a. Accounting income. b. Cash flow. c. Earnings. d. Operating profit. Capital Budgeting is a part of: (a)Investment Decision (b) Working Capital Management (c) Marketing Management (d) Capital Structure A project’s average net income divided by its average book value is referred to as the project’s average: A. net present value. B. internal rate of return. C. accounting return. D. profitability index. E. payback period. The internal rate of return is defined as the: A. maximum rate of return a firm expects to earn on a project.B. rate of return a project will generate if the project in financed solely with internal funds. C. discount rate that equates the net cash inflows of a project to zero. D. discount rate which causes the net present value of a project to equal zero. E. discount rate that causes the profitability index for a project to equal zero. Which two methods of project analysis were the most widely used by CEO’s as of 1999? A. net present value and payback B. internal rate of return and payback C. net present value and average accounting return D. internal rate of return and net present value E. ayback and average accounting return The length of time a firm must wait to recoup, in present value terms, the money it has in invested in a project is referred to as the: A. net present value period. B. internal return period. C. payback period. D. discounted profitability period. E. discounted payback period. Capital Budgeting deals with (a) Long-term Decisions (b) Short-term Decisions (c) Both (a) and (b) (d) Neither (a) nor (b) A project’s average net income divided by its average book value is referred to as the project’s average: A. net present value. B. internal rate of return.C. accounting return. D. profitability index. E. payback period The present value of an investment’s future cash flows divided by the initial cost of the investment is called the: A. net present value. B. internal rate of return. C. average accounting return. D. profitability index. E. profile period. The profitability index is most closely related to which one of the following? A. payback B. discounted payback C. average accounting return D. net present value E. modified internal rate of return Which of the following ignores the time value of money? a) Internal rate of return ) Profitability index c) Net present value d) payback Which of the following represents the linear relation between Net Present Value (NPV) and Profitability Index (PI)? A) If Profitability Index > 1, NPV is Negative (-) B) If Profitability Index < 1, NPV is Positive (+) C) If Profitability Index > 1, NPV is Positive (+) D) If Profitability Index > 1, NPV is Zero (0) If the NPV form a project is positive it must be that a. the discounted payback period is longer than the useful life of the project. b. the internal rate of return is lower than the discount used. . the project is not acceptable on a risk adjusted basis. d. this project is preferred to any other mutually exclusive project. e. accepting the project increases the value of the firm. The profitability index is computed by dividing the a. total cash flows by the initial investment. b. present value of cash flows by the initial investment. c. initial investment by the total cash flows. d. initial investment by the present value of cash flows. The primary capital budgeting method that uses discounted cash flow techniques is the a) net present value method. b) payback technique. ) annual rate of return method. d) profitability index method . The internal rate of return (IRR) is: A. The same thing as the discount rate. B. The same thing as the cost of capital. C. The discount rate that equates the present values of in? ows and out? ows. D. The same thing as the net present value. E. The ratio of average annual pro? ts to average investments. An investment will be _________ if the IRR doesn’t exceeds the required return and _________ otherwise. A) Accepted; rejected B) Accepted; accepted C) Rejected; rejected D) Rejected, accepted

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CC-MAIN-2024-18

http://www.shodor.org/interactivate/discussions/TheoreticalVsExperimental/

math

Student: I just flipped a coin several times and the coin landed on heads more than tails. I had 9 heads and only 6 tails. I don't understand why that happened since heads and tails should be Mentor: How do you know that landing on heads is just as likely as landing on tails when a coin is Student: Well, a coin only has two sides (heads and tails) so that means that flipping a coin can have outcomes. The chances for both are equal since the coin is essentially the same on both sides. Therefore, the chances of a coin landing on heads would be 1/2, and the chances of it landing on tails would also be 1/2. Mentor: That is right! What you just found was the theoretical probability of a coin landing on heads (1/2 or 50%) and a coin landing on tails (1/2 or 50%). Student: Alright, well why aren't my results from flipping a coin just now the same as the theoretical probability of flipping a coin? Mentor: Theoretical probability is a way of estimating what could happen based on the information that you have; it is a calculation. Theoretical probability cannot predict what the actual results will be, but it does give you an idea of what is likely to happen in a situation. Student: I understand that. So the results of flipping a coin should be somewhere around 50% heads and 50% tails since that is the theoretical probability. Mentor: Yes! Now let's look at the coin flipping game that you just played. What were the results? Student: The coin landed on heads 9 times and on tails 6 times. That means I flipped the coin 15 Mentor: OK, we are going to use this information to find another form of probability called experimental probability. To find the experimental probability, you find the ratio of the number of trials with a certain outcome to total number of trials. Experimental probability of winning= # of trials with a certain outcome/# of total trials. So let's first find the experimental probability of flipping heads. For this situation the number of games won would be the number of flips that landed on heads. That would be 9. Student: The number of games played was 15, so that means that the experimental probability is 9/15 (or simplified, 3/5)! Mentor: Now what would the experimental probability of flipping tails be? Student: Well, the number of games won in this situation would be the number of times that I flipped tails, so 6. Then, I played 15 games so the ratio would be 6/15 (or simplified, 2/5). Mentor: Great. Now, can you write these experimental probabilities as percents? Student: I would multiply 3/5 by 100% and get 60% as the experimental probability of flipping heads. Then for tails I would multiply 2/5 by 100% and get 40%. The experimental probability of flipping tails is 40%. Mentor: The experimental probabilities were 40% tails and 60% heads. This does not precisely match with the theoretical probability of 50% tails and 50% heads. However, they are not too far off. Let's do an experiment! Using the coin toss activity, toss the coin 25 times and then 150 times. Student: OK, after 25 tosses I got 11 heads and 14 tails, and after 150 tosses I got 71 heads and 79 Mentor: Alright, we know the theoretical probability will be 50% heads and 50% tails no matter how many trials, but what would the experimental probability be in this experiment? Student: For 25 tosses the probability of heads would be 11/25 (44%) and for tails would be 14/25 (56%). For 125 tosses the probability of heads would be 71/150 (about 47%) and the probability of tails would be 79/150 (about 53%). Mentor: Now which results have the experimental probability closer to the theoretical probability? Student: After 25 tosses, the experimental probabilities of heads and tails are not very close to 50%. However, after 150 tosses the experimental probabilities for heads and tails are much closer Mentor: Can you make an educated guess at what that means? Student: Well, it seems that with more tosses, the resulting experimental probabilities are closer to the theoretical probabilities. Mentor: Good job! As the amount of trials (in this case a trial is flipping a coin) increases, the experimental probability gets closer to the theoretical probability. You can test this concept Crazy Choices Game.

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CC-MAIN-2017-13

https://www.stampedeblue.com/2009/7/10/941745/know-your-colts-history-character

math

The point of this week's post is simple: Sum up the careers of every player on the Colts. The trick is that I have to use the same number of characters as the player's roster number. This will be a three week series taking a look at the Colts roster. This week will focus on numbers 1-33, next week will be 34-66, and two weeks from now we'll look at numbers 67-99. So this shouldn't be too hard, right? #1 Pat McAfee: ¿ #2 Tim Masthay: Oy #4 Adam Vinatieri: Kick #6 Taj Smith: Dreads #7 Curtis Painter: Purdue. #8 Shane Andrus: Backup K #10 Chris Crane: 4th String #11 Anthony Gonzalez: Super quick #12 Jim Sorgi: TheClipboard #14 Sam Giguerre: Dude got guns! #15 Brett McDermott: Don't get comfy #16 Jacob Lacey: Kinda dorky name #17 Austin Collie: Sounds like a dog #18 Peyton Manning: Yeah he's a keeper #20 Dante Hughes: << Marlin and Kelvin #21 Bob Sanders: Tackled a shark today #23 Tim Jennings: No relation to this guy #25 Michael Coe: HE DEFENDED A PASS ONCE!! #27 Lance Ball: The ultimate one hit wonder #28 Marlin Jackson: I hope he hasn't lost a step #29 Joseph Addai: Has a crucial year coming up. #30 Travis Key: Doubt he'll be on final roster #31 Donald Brown: Polian chose him, must be good. #32 Mike Hart: Should be a great 3rd RB if well #33 Melvin Bullitt: Vastly cooler than Steve McQueen.

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CC-MAIN-2023-23

https://ecommons.udayton.edu/mth_fac_pub/98/

math

Electronic Journal of Differential Equations Solutions that are positive with respect to a cone are obtained for the boundary value problem, u(n) + a(t)f(u) = 0, u(i)(0) = u(n−2)(1) = 0, 0 _ i _ n − 2, in the cases that f is either superlinear or sublinear. The methods involve application of a _xed point theorem for operators on a cone. No. 03, approx. 8 pp. Eloe, Paul W. and Henderson, Johnny, "Positive solutions for higher order ordinary differential equations" (1995). Mathematics Faculty Publications. 98.

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CC-MAIN-2023-40

https://xn--webducation-dbb.com/physics-in-4-dimensionshow/

math

Physics in 4 Dimensions…How? A four-dimensional space (4D) is a mathematical extension of the concept of three-dimensional or 3D space. Three-dimensional space is the simplest possible abstraction of the observation that one only needs three numbers, called dimensions, to describe the sizes or locations of objects in the everyday world. For example, the volume of a rectangular box is found by measuring and multiplying its length, width, and height (often labeled x, y, and z). The idea of adding a fourth dimension began with Jean le Rond d’Alembert‘s « Dimensions » published in 1754, was followed by Joseph-Louis Lagrange in the mid-1700s, and culminated in a precise formalization of the concept in 1854 by Bernhard Riemann. Learn more : wikipedia

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CC-MAIN-2021-49

http://quant.stackexchange.com/questions/tagged/cms+pricing

math

Quantitative Finance Meta to customize your list. more stack exchange communities Start here for a quick overview of the site Detailed answers to any questions you might have Discuss the workings and policies of this site What is the reason for the convexity adjustment when pricing a constant maturity swap (CMS)? I'm trying to wrap my head around pricing a Constant Maturity Swap (CMS). Let's imagine the following deal: 6m LIBOR in one direction, 10y swap rate in the other. The discount curve is derived from ... Oct 24 '11 at 12:12 newest cms pricing questions feed Putting the Community back in Wiki Hot Network Questions If you confirm others' transactions, what do you do to earn 25 bitcoins per block? Can I bring a taser from Thailand to Sweden? Why do games ask for screen resolution instead of automatically fitting the window size? How can we guess the size of the Earth's inner core(and what it's made of)? Calculate the maximum current of zero ohm resistors Do banks give us interest even for the money that we only had briefly in our account? How constructor work while initialization an object? Are the majority of EU member-states local laws made in Brussels? Implementation of C++ ! operator Is it common to say "late girlfriend"? Unsolicited remote assistance What kind of steps can I take to avoid overwhelming a new, support-heavy party? is "release date" grammatically correct? Reverse of an Amount and counting Why C++ primitive types are not initialized like the rest of types? Why use "reds and oranges" not "red and orange"? Can you get sucked tight on a planes toilet? How do you align equations parts vertically? Looking for a good translation of "unpublish" How long would it take me to travel to a distant star? Abstract ODE; PDE; uniqueness of solution Name of scientist who discovered lifesaving drug and chose not to patent it? What female mathematician can I introduce to my High School students? English/German one word pair for "in progress" more hot questions Life / Arts Culture / Recreation TeX - LaTeX Unix & Linux Ask Different (Apple) Geographic Information Systems Science Fiction & Fantasy Seasoned Advice (cooking) Personal Finance & Money English Language & Usage Mi Yodeya (Judaism) Cross Validated (stats) Theoretical Computer Science Meta Stack Exchange Stack Overflow Careers site design / logo © 2014 stack exchange inc; user contributions licensed under cc by-sa 3.0

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CC-MAIN-2014-15

https://www.extramarks.com/ncert-solutions/cbse-class-6/mathematics-playing-with-numbers

math

Playing with Numbers A factor of a number is an exact divisor of that number. Properties of factors: Factors of a number are always less than or equal to the number itself and are finite. Every number is a factor of itself. 1 is a factor of every number. A number for which sum of all its factors is equal to twice the number is called a perfect number. A multiple of a number is a number obtained by multiplying it by a natural number. Properties of Multiples: Every multiple of a number is greater than or equal to that number and are infinite. Every number is a multiple of itself. The numbers other than 1 whose only factors are 1 and the number itself are called prime numbers. Numbers having more than two factors are called composite numbers. 1 is neither prime nor composite. 2 is the smallest prime number which is even. Tests for divisibility of numbers: A number is divisible by 2 if it has any of the digits 0, 2, 4, 6, or 8 in its ones place. A number is divisible by 3 if the sum of its digits is a multiple of 3. A number with three or more digits is divisible by 4 if the number formed by its last two digits (i.e., digits at ones and tens places) is a multiple of 4. A number is divisible by 5 if it has either 0 or 5 in its ones place. A number of 4 or more digits is divisible by 8 if the number formed by the last three digits is divisible by 8. A number is divisible by 9 if the sum of its digits is a multiple of 9. A number is divisible by 10 if it has 0 in its ones place. A number is divisible by 11 if the difference between the sum of digits at odd places (from the right) and the sum of digits at even places (from the right) is either 0 or a multiple of 11. Two numbers having only 1 as a common factor are called co-prime numbers. If a number is divisible by another number then it is divisible by each of the factors of that number. If a number is divisible by two co-prime numbers then it is divisible by their product also. If two given numbers are divisible by a number, then their sum and difference are also divisible by that number. When a number is expressed as a product of its factors, the prime factorization of the number has been done. The Highest Common Factor (HCF) or Greatest Common Divisor (GCD) of two or more given numbers is the highest (or greatest) of their common factors. The Lowest Common Multiple (LCM) of two or more given numbers is the lowest (or smallest or least) of their common multiples. KEYWORDS: Sieve of Eratosthenes, Factor tree

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CC-MAIN-2021-10

https://shopappy.com/whitby/the-stonehouse-emporium/mid-century-large-red-glass-globe-vase-160145420813-2159

math

Mid Century Large Red Glass Globe Vase Lovely vibrant red round bowl vase. with domed base. \n \n17cm tall x 10cm diameter base. \n \nHeart shaped punt mark. \n \nNo chips or cracks, just some small vintage scratches to base. \n \nWeight 1500gr \n \nGreat condition.

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CC-MAIN-2022-33

https://news.dsv.com/pressreleases/tag/e-commerce

math

Press releases • Sep 04, 2018 13:32 CEST Signing and closing took place 4 September 2018, at which time DSV A/S acquired 100% of S&H’s shares. Press releases • Jun 14, 2016 11:19 CEST DSV’s contract logistics division announced the investment in a 41,000 m2 warehouse + 15,000 m2 mezzanine facility in ‘s Heerenberg located on the German border. The new facility is planned to be operational in December 2016 and could potentially add 100 jobs to the 200 staff members currrently employed by DSV in 's Heerenberg.

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CC-MAIN-2021-17

https://en.citizendium.org/wiki/Utility

math

The concept of utility is used as an organising device for presenting the rationales of many of the theories of microeconomics. Although it has no explanatory power in itself, it has proved useful as a means of developing explanations of economic behaviour. The utility of an item to a person is conceived as a measure of how much satisfaction he gets from it or, equivalently, as a measure of how much he wants it. Utility is not a measure that can be expressed as a numerical quantity. Although how much a person wants product can be expressed as the amount of some other product that he is willing to give in exchange for it, that amount is not an absolute measure of its utility to him because it depends upon how much of both items he already has. There is, in fact, no logical possibility of a numerical measure of utility: it is necessarily an “ordinal measure” that can be used only for the purpose of comparison. Thus, although a person can rank the utilities that he gets from two different products in order of magnitude, he cannot assign a numerical magnitude to the utility he gets from either; and although he may find that getting more of an item increases his utility, he cannot say by how much. Also, since utility is conceived as a purely personal reaction to a product, and since no-one can know what goes on in another person's mind, it is impossible to make inter-personal comparisons of utility. Origins and development The term utility was mentioned by the classical economists but its present-day usage is generally attributed to its conception towards the end of the nineteenth century by William Stanley Jevons . It was conceived independently at about the same time by Alfred Marshall who expounded it in words and in mathematics in his Principles of Economics and deduced from it the concept of the demand curve. Those derivations were further developed and extended in the early twentieth century by Francis Edgeworth and Vilfredo Pareto. The theory that emerged has no empirical content, having been derived entirely by verbal and mathematical deduction from introspective postulates, but it has been widely used to construct economic models that have themselves been subject to empirical testing. Indifference curve analysis In microeconomic theory, the concept of utility is combined with assumptions about human psychology summed up by Alfred Marshal in his statement that "there is an endless variety of wants but a limit to each separate want". Stated more formally, the law of diminishing marginal utility expresses the assumption that the increase in utility that a consumer gets from the same incremental amount of a product diminishes with every increase in the amount of that product that he already has. It is the basis of the theory of consumer choice known as indifference curve analysis. Stated in full, the assumptions of that theory are: - that consumer preferences are consistent so that if A is preferred to B and B is preferred to C then A is preferred to C (formally termed "transitivity"); - that a consumer who has a large amount of A and a relatively small amount of B would be willing to give up a relatively large amount of A in exchange for a given increment of B, but that the amount of A that he would be willing to give up diminishes with every increase in the amount of B that he already has (termed the diminishing marginal rate of substitution); and, - that a consumer's total utility increases with every increase in the amount that he has of either product (sometimes termed "non-satiety"). Indifference curve theory is customarily presented as a set of curves, each being a plot of an amount of A against the amount of B that the consumer would be willing to exchange it. The first assumption is represented by the fact that the curves do not intersect, and the second assumption by the fact that they slope downwards from left to right and are concave when viewed from above. The third assumption is embodied in the explanation that the further a curve is from the origin, the higher is the level of utility that it represents. By taking one of the two products to be money, indifference curve analysis can be used to derive the concept of the downward-sloping demand curve that is used in connection with the theory of supply and demand. Utility and Welfare The terms "utility" and "welfare" are generally used synonymously, but the concept is normally termed welfare when referring to the well-being of a community in the context of welfare economics.

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CC-MAIN-2022-49

https://americanhistory.si.edu/collections/object-groups/women-mathematicians/daina-taimina-a-modern-day-mathematical-model-maker

math

Women Mathematicians and NMAH Collections -- Daina Taimina: A Modern Day Mathematical Model Maker Daina Taimina: A Modern Day Mathematical Model Maker During the late nineteenth and early twentieth centuries mathematical models of surfaces were often used in teaching geometry. Although women constructed models during that period, the earliest model in the Smithsonian collection that can be attributed to a woman dates from about the year 2002. That model, unlike any of the earlier models, was crocheted. Crocheted Model of a Hyperboic Plane, about 2002. Gift of Daina Taimina (2002.0394.01) In 1997, Daina Taimina, a Latvian born and educated mathematician participating in a workshop on teaching geometry, came up with the idea of crocheting a surface to represent a hyperbolic plane. A hyperbolic plane is different from the Euclidean plane studied in high school geometry. A Euclidean plane is a surface that satisfies several axioms including the Euclidean Parallel Postulate from which it follows that there is only one line parallel to a given line through a given point. A hyperbolic plane is also a surface that satisfies the same axioms as the Euclidean plane except for the Euclidian Parallel Postulate. On a hyperbolic plane there are infinitely many lines parallel to a given line through a given point. Daina Taimina has crocheted many hyperbolic planes and has written about them in her book Crocheting Adventures with Hyperbolic Planes, (Wellesley, MA: A. K. Peters, 2009). - This model of the hyperbolic plane was crocheted by the Latvian-born mathematician Daina Taimina in about 2002. Although called a model of a plane, it is not flat like a Euclidean plane and its lines are not straight. However, lines on any plane, Euclidean or hyperbolic, are still the shortest paths along the plane connecting two points. - The distinguishing difference between a hyperbolic plane and a Euclidean plane is that on a hyperbolic plane there are infinitely many lines parallel to a given line through a given point not on the given line. In this model lines are shown in yellow. The given line is the one closest to the top of the photograph and the given point is where the four other lines meet. None of those four lines will ever meet the given line, so they are all parallel to it. - On page 27 of her book, Crocheting Adventures with Hyperbolic Planes, (Wellesley, MA: A. K. Peters, 2009), Taimina has a photograph of a similar model, with only three yellow lines through the given point. On page 28 she has another photograph of that model with the caption: “The red line is a common perpendicular to only two of these yellow lines.” That photograph illustrates that on a hyperbolic plane, just as on a Euclidean plane, there is only one line through a given point not on a given line that is perpendicular to the given line. - Currently not on view - date made - Taimina, Daina - ID Number - catalog number - accession number - Data Source - National Museum of American History

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CC-MAIN-2022-49

http://slideplayer.com/slide/4352457/

math

Presentation on theme: "Energy in Thermal Processes"— Presentation transcript: 1 Energy in Thermal Processes Thermal PhysicsEnergy in Thermal Processes 2 Energy TransferWhen two objects of different temperatures are placed in thermal contact, the temperature of the warmer decreases and the temperature of the cooler increasesThe energy exchange ceases when the objects reach thermal equilibriumThe concept of energy was broadened from just mechanical to include internalMade Conservation of Energy a universal law of nature 3 Heat Compared to Internal Energy Important to distinguish between themThey are not interchangeableThey mean very different things when used in physics 4 Internal EnergyInternal Energy, U, is the energy associated with the microscopic components of the systemIncludes kinetic and potential energy associated with the random translational, rotational and vibrational motion of the atoms or moleculesAlso includes any potential energy bonding the particles together 5 Thermal EnergyThermal Energy is the portion of the Internal Energy, U, that is associated with the motion of the microscopic components of the system. 6 HeatHeat is the transfer of energy between a system and its environment because of a temperature difference between themThe symbol Q is used to represent the amount of energy transferred by heat between a system and its environment 7 Units of HeatCalorieAn historical unit, before the connection between thermodynamics and mechanics was recognizedA calorie is the amount of energy necessary to raise the temperature of 1 g of water from 14.5° C to 15.5° C .A Calorie (food calorie) is 1000 cal 8 Units of Heat, cont. US Customary Unit – BTU BTU stands for British Thermal UnitA BTU is the amount of energy necessary to raise the temperature of 1 lb of water from 63° F to 64° F1 cal = JThis is called the Mechanical Equivalent of Heat 9 Problem: Working Off Breakfast A student eats breakfast consisting of two bowls of cereal and milk, containing a total of 3.20 x 102 Calories of energy. He wishes to do an equivalent amount of work in the gymnasium by doing curls with a 25 kg barbell. How many times must he raise the weight to expend that much energy? Assume that he raises it through a vertical displacement of 0.4 m each time, the distance from his lap to his upper chest.h 10 Problem: Working Off Breakfast Convert his breakfast Calories, E, to joules: 11 Problem: Working Off Breakfast Use the work-energy theorem to find the work necessary to lift the barbell up to its maximum height.The student must expend the same amount of energy lowering the barbell, making 2mgh per repetition. Multiply this amount by n repetitions and set it equal to the food energy E: 12 Problem: Working Off Breakfast Solve for n, substituting the food energy for E: 13 James Prescott Joule 1818 – 1889 British physicist Conservation of EnergyRelationship between heat and other forms of energy transfer 14 Specific HeatEvery substance requires a unique amount of energy per unit mass to change the temperature of that substance by 1° CThe specific heat, c, of a substance is a measure of this amount 15 Units of Specific HeatSI unitsJ / kg °CHistorical unitscal / g °C 16 Heat and Specific Heat Q = m c ΔT ΔT is always the final temperature minus the initial temperatureWhen the temperature increases, ΔT and ΔQ are considered to be positive and energy flows into the systemWhen the temperature decreases, ΔT and ΔQ are considered to be negative and energy flows out of the system 17 A Consequence of Different Specific Heats Water has a high specific heat compared to landOn a hot day, the air above the land warms fasterThe warmer air flows upward and cooler air moves toward the beach 18 CalorimeterOne technique for determining the specific heat of a substanceA calorimeter is a vessel that is a good insulator which allows a thermal equilibrium to be achieved between substances without any energy loss to the environment 19 Calorimetry Analysis performed using a calorimeter Conservation of energy applies to the isolated systemThe energy that leaves the warmer substance equals the energy that enters the waterQcold = -QhotNegative sign keeps consistency in the sign convention of ΔT 20 Calorimetry with More Than Two Materials In some cases it may be difficult to determine which materials gain heat and which materials lose heatYou can start with SQ = 0Each Q = m c DTUse Tf – TiYou don’t have to determine before using the equation which materials will gain or lose heat 21 Phase ChangesA phase change occurs when the physical characteristics of the substance change from one form to anotherCommon phases changes areSolid to liquid – meltingLiquid to gas – boilingPhases changes involve a change in the internal energy, but no change in temperature 22 Latent Heat During a phase change, the amount of heat is given as Q = ±m LL is the latent heat of the substanceLatent means hiddenL depends on the substance and the nature of the phase changeChoose a positive sign if you are adding energy to the system and a negative sign if energy is being removed from the system 23 Latent Heat, cont. SI units of latent heat are J / kg Latent heat of fusion, Lf, is used for melting or freezingLatent heat of vaporization, Lv, is used for boiling or condensingTable 11.2 gives the latent heats for various substances 24 Problem: Boiling Liquid Helium Liquid helium has a very low boiling point, 4.2 K, as well as low latent heat of vaporization, 2.09 x 104 J/kg. If energy is transferred to a container of liquid helium at the boiling point from an immersed electric heater at a rate of 10 W, how long does it take to boil away 2 kg of the liquid? 25 Problem: Boiling Liquid Helium Find the energy needed to vaporize 2 kg of liquid helium at its boiling point:Divide this result by the power to find the time: 26 SublimationSome substances will go directly from solid to gaseous phaseWithout passing through the liquid phaseThis process is called sublimationThere will be a latent heat of sublimation associated with this phase change 28 Warming Ice Start with one gram of ice at –30.0º C During A, the temperature of the ice changes from –30.0º C to 0º CUse Q = m c ΔTWill add 62.7 J of energy 29 Melting Ice Once at 0º C, the phase change (melting) starts The temperature stays the same although energy is still being addedUse Q = m LfNeeds 333 J of energy 30 Warming WaterBetween 0º C and 100º C, the material is liquid and no phase changes take placeEnergy added increases the temperatureUse Q = m c ΔT419 J of energy are added 31 Boiling Water At 100º C, a phase change occurs (boiling) Temperature does not changeUse Q = m Lv2 260 J of energy are needed 32 Heating SteamAfter all the water is converted to steam, the steam will heat upNo phase change occursThe added energy goes to increasing the temperatureUse Q = m c ΔTTo raise the temperature of the steam to 120°, 40.2 J of energy are needed 33 Problem Solving Strategies Make a tableA column for each quantityA row for each phase and/or phase changeUse a final column for the combination of quantitiesUse consistent units 34 Problem Solving Strategies, cont Apply Conservation of EnergyTransfers in energy are given as Q=mcΔT for processes with no phase changesUse Q = m Lf or Q = m Lv if there is a phase changeIn Qcold = - Qhot be careful of signΔT is Tf – TiSolve for the unknown 35 Your TurnYou start with 250. g of ice at -10 C. How much heat is needed to raise the temperature to 0 C?10.5 kJHow much more heat would be needed to melt it?83.3 kJ 36 Your TurnYou start with 250. g of ice at -10 C. What will happen if we add 50. kJ of heat?10.5 kJ will be used to warm it up to the MP, and the rest will start melting the ice.0.119 kg will be melted 37 Problem: Partial melting A 5 kg block of ice at 0o C is added to an insulated container partially filled with 10 kg of water at 15 o C.(a) Find the temperature, neglecting the heat capacity of the container.(b) Find the mass of the ice that was melted. 38 Problem: Partial melting (a) Find the equilibrium temperature.First, Compute the amount of energy necessary to completely melt the ice: 39 Problem: Partial melting Next, calculate the maximum energy that can be lost by the initial mass of liquid water without freezing it:This is less than half the energy necessary to melt all the ice, so the final state of the system is a mixture of water and ice at the freezing point: 40 Problem: Partial melting (b) Compute the mass of the ice melted.Set the total available energy equal to the heat of fusion of m grams of ice, mLf: 41 Final Problem100. grams of hot water ( 60. C) is added to a 1.0 kg iron skillet at 500 C. What is the final temperature and state of the mixture? 42 Final Problem 16.7 kJ needed to warm water to BP. 226 kJ needed to vaporize water199.2 kJ will be given up by skillet.Final temperature will be 100. C182 kJ of heat from the skillet will be available to vaporize water81 grams of water will vaporize. Your consent to our cookies if you continue to use this website.

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CC-MAIN-2018-26

http://en.allexperts.com/q/Advanced-Math-1363/2014/12/partial-fractions-4.htm

math

Advanced Math/Partial Fractions Hey Randy, Im struggling on a few questions and was wondering if you could help me out so that i can see how it done correctly, the question is on Partial Fractions and reads. Resolve the following into partial fractions a) 3(2x^2-8x-1) / (x+4)(x+1)(2x-1) b) 3x^2-8x-63 / x^2-3x-10 Any help would be appreciated! The key to partial fractions is to write out the form of the equation you are shooting for using "undetermined coefficients" and then do algebra to solve for these coeffs. For your example a), we want to separate the factors in the denominator into a sum of rational functions, in this case 3(2x^2-8x-1) / (x+4)(x+1)(2x-1) = A/(x+4) + B/(x+1) + C/(2x-1) where we want to solve for A, B and C. This is the first step. There are fairly simple rules for how to write the right hand side (RHS), for which you should consult a textbook. One easy-to-remember rule is that the degree (highest power of x) of the numerator must be less than the denominator, otherwise you need to do a long division of the polynomials to get a remainder that you can then apply the partial fraction expansion to. Note that the expression above is x^3 in the denominator (if you multiply it all out) and only x^2 in the numerator. At any rate, the expression above will work. The next step is to multiply both sides by the denominator of the LHS. I get 6x^2 -24x -3 = A(x+1)(2x-1) + B(x+4)((2x-1) + C(x+4)(x+1). The 3rd step is to factor out the powers of x; for the RHS I get (check my algebra!) (x^2)･(2A +2B +c) +x･(A +7B +5C) + x^0･(-A - 4B + 4C) where x^0 = 1. We can now obtain 3 (independent) equations for A, B and C by matching the powers of x on both sides of the equation. Technically, this step is justified because the powers-of-x terms are orthogonal (don't project on to each other) so that the coefficients must be equal on both sides. Rearranging, this gives x^2 → -6 = 2A + 2B + C x^1 → -24 = A + 7B + 5C x^0 → -3 = -A - 4B + 4C. OK, the task now is to apply your favorite technique (Gaussian elimination?) to solve for A, B and C. I'll let you take care of it. For your 2nd case, the denominator factors into (x-5)(x+2), which would seem to allow us to apply the technique above pretty much the same way. However, the degree of the numerator and denominator are the same (= 2), so division must be performed first. Again, I'll let you try it! Good luck and let me know how it goes.

s3://commoncrawl/crawl-data/CC-MAIN-2017-13/segments/1490218190236.99/warc/CC-MAIN-20170322212950-00050-ip-10-233-31-227.ec2.internal.warc.gz

CC-MAIN-2017-13

https://blog.myrank.co.in/types-of-waves/

math

Types of Waves Waves can occur whenever a system is disturbed from equilibrium and when the disturbance can travel or propagate from one region of the system to another. Waves can be classified in a number of ways based on the following characteristics 1) On the basis necessity of medium: i) Mechanical waves: Require medium for their propagation e.g. Waves on string and spring, waves on water surface, sound waves, seismic waves. ii) Non-mechanical waves: Do not require medium for their propagation are called e.g., light, heat, radio waves, \(\gamma \) – rays, X-rays etc. 2) On the basis of vibration of particle: On the basis of vibration of particle of medium waves can be classified as transverse waves and longitudinal waves. 3) On the basis of energy propagation: i) Progressive wave: These waves advance in a medium with definite velocity. These waves propagate energy in the medium e.g. Sound wave and light waves. ii) Stationary wave: These waves remain stationary between two boundaries in medium. Energy is not propagated by these waves but it is confined in segments e.g. Wave in a string, waves in organ pipes. 4) On the basis of dimension: i) One dimensional wave: Energy is transferred in a single direction only e.g. Wave propagating in a stretched string. ii) Two dimensional wave: Energy is transferred in a plane in two mutually perpendicular directions e.g. Wave propagating on the surface of water. iii) Three dimensional wave : Energy in transferred in space in all direction e.g. Light and sound waves propagating in space. 5) Some other waves: i) Matter waves : The waves associated with the moving particles are called matter waves. ii) Audible or sound waves : Range 20 Hz to 20 KHz. These are generated by vibrating bodies such as vocal cords, stretched strings or membrane. iii) Infrasonic waves : Frequency lie below 20 Hz and wavelengths are greater than 16.6 cm. Example : waves produced during earth quake, ocean waves etc. iv) Ultrasonic waves: Frequency greater than 20 KHz. Human ear cannot detect these waves, certain creatures such as mosquito, dog and bat show response to these. As velocity of sound in air is 332 m/sec so the wavelength \( \lambda \) < 1.66 cm v) Shock waves: When an object moves with a velocity greater than that of sound, it is termed as Supersonic. When such a supersonic body or plane travels in air, it produces energetic disturbance which moves in backward direction and diverges in the form of a cone. Such disturbances are called the shock waves. The speed of supersonic is measured in Mach number. One Mach number is the speed of sound.

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CC-MAIN-2023-14

https://www.geeksforgeeks.org/zeros-of-a-polynomial/?ref=rp

math

Zeros of a Polynomial Polynomials are used to model some physical phenomena happening in real life, they are very useful in describing the situations mathematically. They are used in almost every field of Science, even outside of science like for example in Economics and other related areas. Zeros or roots of these polynomials are a very important aspect of their nature and can be very useful while describing them or plotting them on a graph. Let’s look at their definition and methods of finding out the roots in detail. Zeros/Roots of a Polynomial We say that x = a is the root of the polynomial if P(x) = 0 at that point. The process of finding zero is basically the process of finding out the solutions of any polynomial equation. Let’s look at some examples regarding finding zeros for a second-degree polynomial. Question 1: Find out the zeros for P(x) = x2 + 2x – 15. x2 + 2x – 15 = 0 ⇒ x2 + 5x – 3x – 15 = 0 ⇒ x(x + 5) – 3(x + 5) = 0 ⇒ (x – 3) (x + 5) = 0 ⇒ x = 3, -5 Question 2: Find the out zeros for P(x) = x2 – 16x + 64. x2 – 16x + 64 = 0 ⇒ x2 – 8x – 8x + 64 = 0 ⇒ x(x – 8) – 8(x – 8) = 0 ⇒ (x – 8) (x – 8) = 0 ⇒ (x – 8)2 = 0 x = 8, 8 This is called a double root. Suppose we have a polynomial P(x) = 0 which factorizes into, P(x) = (x – r)k(x – a)m If r is a zero of a polynomial and the exponent on its term that produced the root is k then we say that r has multiplicity k. Zeroes with a multiplicity of 1 are often called simple zeroes. Question 3: P(x) is a degree-5 polynomial, that has been factorized for you. List the roots and their multiplicity. P(x) = 5x5−20x4+5x3+50x2−20x−40=5(x+1)2(x−2)3 Given, P(x) = 5(x+1)2(x−2)3 Putting this polynomial equal to zero we get the root, x = -1, -1, 2, 2, 2 Notice that -1 occurs two times as a root. So its multiplicity is 2 while the multiplicity of the root “2” is 3. Fundamental Theorem of Linear Algebra If P(x) is a polynomial of degree “n” then P(x) will have exactly n zeros, some of which may repeat. This means that if we list out all the zeroes and listing each one k times when k is its multiplicity. We will have exactly n numbers in the list. This can be useful as it can give us an idea about how many zeros should be there in a polynomial. So we can stop looking for zeros once we reach our required number of zeros. For the polynomial P(x), - If r is a zero of P(x) then x−r will be a factor of P(x). - If x−r is a factor of P(x) then r will be a zero of P(x). This can be verified by looking at previous examples. This factor theorem can lead to some interesting results, Result 1: If P(x) is a polynomial of degree “n”, and “r” is a zero of P(x) then P(x) can be written in the following form, P(x) = (x – r) Q(x) Where Q(x) is a polynomial of degree “n-1” and can be found out by dividing P(x) with (x – r). Result 2: If P(x) = (x-r)Q(x) and x = t is a zero of Q(x) then x = t will also be zero of P(x). To verify the above fact, Let’s say “t” is root Q(x), that means Q(t) = 0. We know that “r” is a root of polynomial P(x), where P(x) = (x – r) Q(x), So we need to check if x = t is also a root of P(x), let’s put x = t in P(x) P(t) = (t – r) Q(t) = 0 So, x = t is also a root P(x). Question 1: Given that x = 2 is a zero of P(x) = x3+2x2−5x−6. Find the other two zeroes. From the fundamental theorem we studied earlier, we can say that P(x) will have 3 roots because it is a three degree polynomial. One of them is x = 2. So we can rewrite P(x), P(x) = (x – 2) Q(x) For finding the other two roots, we need to find out the Q(x). Q(x) can be found out by dividing P(x) by (x-2). After dividing, the Q(x) comes out to be, Q(x) = x2 + 4x + 3 The remaining two roots can be found out from this, Q(x) = x2 + 3x + x + 3 ⇒ x(x + 3) + 1(x + 3) ⇒ (x + 1) (x + 3) Q(x) = 0, x = -1, -3 Thus, the other two roots are x = -1 and x = -3. Question 2: Given that x = r is a root of a polynomial, find out the other roots of the polynomial. P(x) = x3−6x2−16x; r = −2 We know that x = -2 is a root, So, P(x) can be rewritten as, P(x) = (x + 2) Q(x). Now to find Q(x), we do the same thing as we did in the previous question, we divide P(x) with (x + 2). Q(x) = x2 – 8x Now to find the other two roots, factorize Q(x) Q(x) = x (x – 8) = 0 So, the roots are x = 0, 8. Thus, we have three roots, x = -2, 0, 8. SO, this polynomial can also be written in factored form, P(x) = (x + 2) (x) (x – 8) Question 3: Find the roots of the polynomial, 4x3-3x2-25x-6 = 0 Trick to solve polynomial equations with degree 3, Find the smallest integer that can make the polynomial value 0, start with 1,-1,2, and so on… Here we can see -2 can make the polynomial value 0. Write (x+2) at 3 places and then write the coefficients accordingly to make the complete polynomial 4x2 (x+2) -11x(x+2) -3(x+2) =0 Now, notice carefully, the first coefficient is 4x2, because when it is multiplied with the x inside the bracket, it gives 4x3 When 4x2 is multiplied with 2, it gives 8x2, but the second term must be -3x2, hence the coefficient added next is -11x Now, we know how to adjust the terms so that when we simplify it gives back the original polynomial. We get a quadratic equation and a root is already there, (4x2-11x-3)(x+2) = 0 Factorize the quadratic equation, (4x2-12x+x-3)(x+2) = 0 (4x(x-3)+1(x-3))(x+2) = 0 (4x+1)(x-3)(x+2) = 0 x = -2, x = 3, x = -1/4 Question 4: Find the zeros of the polynomial, 4x6– 16x4= 0 The Polynomial has up to degree 6, hence, there exist 6 roots of the polynomial. 4x4(x2-4) = 0 4x4(x2-22) = 0 4x4[(x+2)(x-2)] = 0 Therefore, x= 0, 0, 0, 0, 2, -2 Please Login to comment...

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CC-MAIN-2023-23

http://caces.uesc.br/2020/01/what-are-generally-factors-within-numbers/

math

What are usually variables with mathematics? If you’ve got a desire to be a mathematics prodigy, it is vitally important to understand this query, and to be able to answer it in the affirmative. Among the things that people that are thinking about becoming a mathematics prodigy have so that you can be aware of is that they can be within fact math concepts prodigies. Is a fact in the matter that they are which kind with math natural born player? Your simple fact is definitely that there is a professional paraphrasing tool number of people who are able to head over to school and work out a college degree in mathematics with out touching calculus or a student who can write a math assignment. Most individuals are going to be conscious that there is a math prodigy someone who was born with an innate talent for mathematics. That man is going to always be predisposed to be able to master her or his area involving research. Are currently going to turn into specialised mathematicians, professional mathematicians and yes, math prodigies. Let’s contemplate some of the things that that people can contemplate as you begin so that you can consider figuring out tips on how to deal with factors in mathematics. Here certainly http://hyperphysics.phy-astr.gsu.edu/hbase/mass.html are a few illustrations. You may be a math prodigy when you have an ability to address problems quickly and effectively. There absolutely are a range involving individuals that become adept with many of your aspects connected with mathematics without learning how to develop this ability. When you’ve got a significant amount of understanding of properties of variables, you may be a math prodigy. In case you were going to be working with numbers to come up that you would be asked to fix this knowledge would be a considerable asset to you. If you’ve got an interest in proofs and mathematical equations, you might be a math prodigy. Whenever an individual have an interest in numbers and using them to address issues, you’re going to become more inclined to find this you are going to have a fascination with statistical issues. You may be a math prodigy if you are patient and logical. The thinking skills that you create in your math classes and the patience is going to be quite crucial in many techniques. If you are an intuitive problem solver, https://www.grademiners.com you may be a math prodigy. Whenever you have an intuitive feel for the problems you will be required to resolve, you are going to be able to be more confident in the knowledge that you grow as you go along in your mathematics classes. If you are interested in the solution to the problems of our everyday lives Last, you may be a math prodigy. You will be able to become more successful in your mathematics classes, Whenever you can utilize your mathematics skills to look for a remedy for a problem that you will face. You might be a math prodigy if you’re going to eventually become an inspiration to other people who are going to be attempting to solve issues which are larger than you, but smaller on your own. This kind of fact is a vital one particular. This concept that will you’re going so that you can have the ability being a motivation to others is which is very valuable in attaining your targets which you have established on your own.

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CC-MAIN-2020-05

https://garotadeluxo.com/mathsolution-429

math

This educational math app from TouchMath delivers an interactive version of the most successful multisensory math program available on the market. It employs the unique Best Math Apps for iPad. 1. Math vs Zombies. This game encourages children to use their math skills to save and treat infected zombies. Your child is part of a highly trained Solve math problems Solving math equations can be challenging, but it's also a great way to improve your problem-solving skills. Determine mathematic equations Math is the study of numbers, space, and structure. Clarify math equations Math is a way of solving problems using numbers and equations Explain mathematic problems Math is the study of numbers, shapes, and patterns. Clear up math To clear up a math equation, first identify the problem, then find the simplest way to solve it. Do mathematic problems Doing homework can help you learn and understand the material covered in class.

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CC-MAIN-2023-06

https://www.termpaperwarehouse.com/essay-on/Coherence-And-Stochastic-Resonances-In-Fhn/36673

math

Coherence and Stochastic Resonances in Fhn Model Submitted By pratikta16 Coherence and Stochastic Resonance of FHN Model Deterministic, nonlinear systems with excitable dynamics, e.g. the FitzHugh Nagumo (FHN) Model, undergo bifurcation from stable focus to limit cycle on tuning the system parameter. However, addition of uncorrelated noise to the system can kick the system to the limit cycle region, thus exhibiting spiking behaviour if the parameter is hold on the fixed point side. Thus the system exhibits intermittent cyclic behaviour, manifesting as spikes in the dynamical variable. It is interenting to note that at an optimal value of noise, the seemingly irregular behaviour of the spikes becomes strangely regular. The interspike interval τp becomes almost regular and the Normal√ p ized Variance of the interspike interval, defined by VN = exhibits τp a minima as a function of noise strength (D). The phenomenon is termed as Coherence Resonance. Coherence Resonance is a system generated response to the noise. However, there is another form of resonance that is found at lower level of noise in response to a subthreshold signal, known as Stochastic Resonance. Subthreshold signals that are in general undetectable can often be detected in presence of noise. There is an optimal level of noise at which such information transmission is optimal. Stochastic resonance has been investigated in many physical, chemical and biological systems. It can be utilised for enhancing signal detection and information transfer. SR has been obversed for subthreshold input signals of both periodic as well as aperiodic types. The former is known as Periodic Stochastic Resonance (PSR), while the later is known as Aperiodic Stochastic Resonance (ASR). In PSR, normalized variance of the output signal is found to reach a minima at an optimal level of noise, which is the signature of optimal information transmission. In ASR, the cross-correlation...

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CC-MAIN-2022-40

http://quant.stackexchange.com/questions/tagged/financial-engineering+bond

math

Quantitative Finance Meta to customize your list. more stack exchange communities Start here for a quick overview of the site Detailed answers to any questions you might have Discuss the workings and policies of this site Use of Girsanov's theorem in bond pricing Assume that we want to calculate the time $t=0$ price of a bond: $B(0,T) = E_P[\exp(-\int_0^T r_s ds)]$, where $r$ is the interest rate following the SDE $dr_t=k(\theta-r_t)dt+\sigma ... Mar 31 at 9:58 newest financial-engineering bond questions feed Hot Network Questions What limits the size of digital imaging sensors? Manga image of two girls with unusual eyes How do I divide equity following separation with cohabiting partner? UnFlatten an array into a ragged matrix DC adapters: why so few amps? Suppress indentation on the opening line of a letter Should I empty the wrongly filled fields or leave as user has filled after validation message? Planning an 18 foot long banner What is the name of the mineral that can be found before Mithril in Moria? Is a PhD right for you if you hate doing research in your free time but love doing it as a job? Which airlines ban the use of Knee Defenders during flight? Is it possible for an SU-25 to fly high enough to shoot down a Boeing 777? What could be a single word or phrase for the one who helps people to achieve their goals? Is it better to describe the main character's physical appearance early on in the story? A force opposing Gravity Why use a function parameter 'foo' in this way: *(&foo)? Loop in WildCard as Input of Script Poker on the USS Enterprise-D : What are the stakes? How to protect a Cha-based caster against Feeblemind? What's the most appropriate time in the recruitment process to reveal that you're transgender (in the UK)? Wash Sales and Day Trading difference between the polynomials How should I care for a new Mimosa tree growing out of an old Mimosa stump? why Gaussian noise is usually used? more hot questions Life / Arts Culture / Recreation TeX - LaTeX Unix & Linux Ask Different (Apple) Geographic Information Systems Science Fiction & Fantasy Seasoned Advice (cooking) Personal Finance & Money English Language & Usage Mi Yodeya (Judaism) Cross Validated (stats) Theoretical Computer Science Meta Stack Exchange Stack Overflow Careers site design / logo © 2014 stack exchange inc; user contributions licensed under cc by-sa 3.0

s3://commoncrawl/crawl-data/CC-MAIN-2014-35/segments/1408500830074.72/warc/CC-MAIN-20140820021350-00141-ip-10-180-136-8.ec2.internal.warc.gz

CC-MAIN-2014-35

https://forums.fast.ai/t/applying-jitter-transform-only-on-x/85083

math

Is jitter transform not available in fastai2? It was there in v1, but we cannot limit it to x only.Now if we can , then we are not having it. Can anyone help me out with this? If anyone knows how to limit it to x only in v1? I am posting it on every forum that I can find, as I am very confused as fastai has got too many forums…

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CC-MAIN-2023-50

https://socratic.org/questions/a-sample-of-gas-at-35degree-celsius-and-1-atm-occupies-a-volume-of-37-5l-at-what

math

A sample of gas at 35degree Celsius and 1 atm occupies a volume of 37.5L. At what temp should the gas be kept, if it required to reduce the volume to 3.0 litre at the same pressure? The gas should be kept at -248 degrees C. Use Charles Law. Temperature and Volume are directly related using degrees Kelvin. ( if the pressure and number of molecules are kept constant. # T_2 = unknown Substituting these numbers into the equation gives # T_2 = 24.6 degrees K Subtract 273 to find degrees C.

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https://vesinhcongnghiephcm.info/viewtopic.php?t=2181

math

Solution assignment problem hungarian method – Assignment of. Solving ONE' S interval linear assignment problem - IJERA. Assignment problem - Business Management Courses Solve the linear sum assignment problem using the Hungarian method. A problem instance is. Step 2: Replace all those entries which are greater. The Hungarian Method is an algorithm used to solve assignment problems. Stochastic Generalized Assignment. The algorithm finds an optimal solution to the problem in only O( n3) time where n is the number of vertices see [ 8]. It is probably the fastest LAP solver using a GPU.The Assignment Problem - Academic Star Publishing Company solving the Assignment problem as a Linear Programming problem is to use perhaps the most inefficient method. LP and explain why it is. In this paper maximized objective functions namely least cost assignment technique. Org/ wiki/ Hungarian_ algorithm. Sum Assignment Problems. Our main contribution is an efficient. Hungarian method for solving assignment problem - Headsome. The Hungarian method handles the whole. Variants of the hungarian method for assignment problems. Variants of the hungarian method for assignment problems. Munkres Hungarian algorithm to compute the maximum interval of deviation ( for each entry in the assignment matrix). Variants of the hungarian method for assignment problems. Each task is to be assigned to one agent and each agent is limited only a maximum amount of resources available to him. 1 Introduction 2 Review of the Hungarian Method - UNL CSE The Hungarian method is a combinatorial optimization algorithm that solves the assignment problem in polynomial time and which anticipated later primal- dual methods. Variants of the Hungarian method for assignment problems. Bryn Mawr College.This function can also solve a generalization of the classic assignment problem where the cost matrix is rectangular. Sum linear assignment problem are summarized. “ goodness” since the other Algorithms ( ASSIGNMENT HUNGARIAN) are variations of the MUNKRES. Assignment utility. Variants of the hungarian method for assignment problems. Two GPU- accelerated variants of the Hungarian algorithm. ( i) The cost matrix is a square matrix column of the cost matrix. If it has more rows. Pro- cedures obtained by combining the Hungarian Shortest Augmenting Path methods for com- plete sparse cost matrices are presented. This paper presents a new simple but faster algorithm for solving linear bottleneck assignment problems, so that in such emergent situation ef. The assignment problem is one of the fundamental combinatorial.Munkres algorithm. A New Algorithm for Solving Linear Bottleneck Assignment Problem. The Hungarian Method in the mathematical context of combinatorial. Which is a variant of multi- robot assignment problem with set precedence constraint ( SPC- MAP) discussed in [ 1]. Hungarian method assignment problem | What is a essay paper In [ 3] an algorithm was p r o p o s e d for solving a s s i g n m e n t p r o b l e m s in which in addition to a s s i g n ing instruments to s e r v i c e the r e q u e s t s a specified o r d e r is selected for their queueing. KuhnÕs article on the Hungarian method for its solution [ 38]. Optimal solutions to assignment problems. GPU- accelerated Hungarian algorithms for the Linear Assignment. This summarizes a number of errors and omissions in the MSDN documentation. Processing Units ( GPU) as the parallel. The Hungarian Algorithm for the Assignment Problem References. LAPs in an efficient. A Primal Method for the Assignment and Transportation Problems. - IEEE Xplore The linear sum assignment problem is also known as minimum weight matching in bipartite graphs. Variations on the classic assignment problem was the publication in 1955 of Kuhn' s article on the Hungarian method for its solution [ 2]. Here we solve the problem using. In this paper we describe parallel versions of two different variants ( classical alter- nating tree) of the Hungarian algorithm for solving the Linear Assignment Problem ( LAP). We would like to show you a description here but the site won’ t allow us. I' m not going to dwell on that though as it' s not the best- known way to include the guys' girls' preferences in the. Alignment of Tractograms as Linear Assignment Problem. Assignment Problem - 3 Hungarian Assignment Method - HAM - 1. An assignment problem can be easily solved by applying Hungarian method which consists of two phases. The Assignment problem solved using special algorithms such as the Hungarian algorithm. Problems appear when the. Kuhn “ Variants of the Hungarian method for assignment problems ”. Of the well- studied Optimal Assignment Problem ( OAP) for which the Kuhn- Munkres Hungarian. The Munkres algorithm solves the weighted minimum matching problem in a complete bipartite graph, in O( n^ 3) time. Assessing Optimal Assignment under Uncertainty - Robotics. Step 3: Obtain efficient solution by using Hungarian. Harold Kuhn known as the Hungarian algorithm, James Munkres developed an algorithm in the 1950s which solves the assignment problem by. The work reported by this note was supported by the Office of Naval Research Logistics Project Department of Mathematics Princeton University. Hungarian method solving assignment problem ppt – Extended. 12 problems with random 3- digit ratings by hand. Variants of the hungarian method for assignment problems. Maximization case in AP. Similar to the simplex algorithm its many variants the proposed solution algo-. We contribute a local search algorithm for this problem that is derived from an exact algorithm which is similar to the Hungarian method for the standard assignment problem. The widely- used methods of solving transportation problems ( TP) assignment problems ( AP) are the stepping- stone ( SS) method the Hungarian. We present a distributed auction- based algorithm for this problem and prove that the solution is almost- optimal. Variants of the hungarian method for assignment problems. The Hungarian algorithm is one of many algorithms that have been.To enhance creativity we motivate the participants to approach the problems. Assignment Problems, Revised Reprint: - Результат из Google Книги. Abstract— We present a novel parallel auction algorithm im- plementation. GPU- based parallel algorithm for the augmenting path search, which is the most time intensive step. Primal- dual min- cost bipartite matching Lecture Notes on Bipartite Matching, I' ve implemented the O( n^ 3) variant of the Hungarian algorithm in Java . Known combinatorial optimization technique ( Hungarian al- gorithm) to the school choice problem. Step 0: Consider the given matrix. 6 billion variables can be solved. Pairing problems constitute a vast family of problems in CO. A note on Prager' s transformation problem, Jour. In the p r e s e n t a r t i c l e a generalization of the a s s i g n m e n t problem is c o n s i d e r e d which differs in the. Have been developed. Programming Problem as follows max n. Linear Assignment is a Constraint Optimization Problem. When I search online for the Hungarian algorithm where n is the dimension of the cost matrix ( the algorithm operates on square cost matrices) ; for example, The Assignment Problem , algorithm descriptions that are of an O( n^ 4) variant of the algorithm the Hungarian. In this paper we developed a. The Hungarian Method for the Assignment Problem The Hungarian Method for the Assignment. How to Solve Assignment Problem Hungarian Method- Simplest. , 1 X 0or1 ij LPP model for the assignment problem is given as: Minimize Subject to; 6. Different methods have been presented for assignment problem various articles have been published on the see [ 3 5] for the history of this method. Introduction by Harold W.Variants of the hungarian method for assignment problems. Arbitrary label the locations involved in the traveling salesman problem with the integers 1 3. This variation is also presented in the paper. Chapter 9 - NSDL. Newest ' hungarian- algorithm' Questions - Stack Overflow by several primal- dual methods like Hungarian method, Shortest augmenting path method etc. Naval Research Logistics Quarterly,. Py · 1b9d4e1e99d18742ffb469dfb3d3b43feeb048f8. Solution methods of AP. In this section we describe a variation of the Hungarian method called the Kuhn-. Marriage Assignment Problem and Variants | Science4All assignment problem is a variation of transportation problem with two characteristics. 1 X j n m i ij 1 1 3.Solving large- scale assignment problems by Kuhn. - Добавлено пользователем Prashant PuaarOperations Research ( OR) MBA - MCA - CA - CS - CWA - CPA - CFA - CMA - BBA - BCOM. The Hungarian algorithm performs better. A bound on the approximation of a maximum matching in. Different versions of these problems have been studied since the mid. Multiple optimal solution. Since its introduction by Gale and Shapley [ 11] the stable marriage problem has become quite popular among scientists from different fields such as. Variants of the hungarian method for assignment problems. I a question about the hungarian algorithm. PRIMAL- DUAL ALGORITHMS FOR THE ASSIGNMENT PROBLEM. " The Bottleneck Assignment Problem, " Rand Report P- 1630. In a centralized manner using the Hungarian algorithm [ 4],. A new algorithm for pick‐ and‐ place operation | Industrial Robot: the.In the Generalized Assignment Problem, It requires to minimize the cost of assignment of ' n' tasks to a subset of ' m' agents. A new algorithm is proposed for the complete case, which transforms the complete cost. O r g - Society for Industrial and. In addition so maybe this is related to the old one. INTRODUCTION based on the work of D. “ Hungarian method” because it was largely based on the earlier works of two Hungarian mathematicians in. * Naval Research Logistics Quarterly* 3: 1956. Contents - Assignment Problems solution of assignment problem is defined by Kuhn[ 1] named as Hungarian method. " Variants of the Hungarian Method for Assignment Problems, " Nav. Enhanced PDF · Standard PDF ( 404. Hungarian method solving assignment problem ppt 07D Assignment Problem & Hungarian Method For a better experience, please download the original. The Hungarian Method in a Mixed Matching Market - Fernuni Hagen. Download the free trial version below to get started. A dual cost ( as in Hungarian- like methods dual simplex methods re-. One of the more famous effective solving methods is the " HUNGARIAN METHOD" 3), 4) the method based on the K5nig- Egervary theorem. I' ll add it here even give you credit for it. - HUSCAP Hungarian Algorithm for Linear Assignment Problems ( V2. Linear_ sum_ assignment — SciPy v0. We have chosen Compute Unified Device Architecture ( CUDA) enabled NVIDIA Graphics. ( 4) The HUNGARIAN method ( Kuhn' s Algorithm) is related to both the previously discussed. N i n j ij ij Z C X 1 1 X i n n j ij 1 1 3. Lec- 13 Transportation Problems. The optimization problem is a maximiza- tion problem that can be summarized as an Integer Linear. Parallel algorithms for solving large assignment problems Linear Assignment Problem ( LAP) and Quadratic. Research Logistics Quarterly 2: 8397 1955. Kuhn “ The Hungarian method for the assignment problem ”. Assignment problems: A golden anniversary survey practical solution methods for and variations on the classic assignment problem ( hereafter referred to as the AP) was the publication in 1955 of. It may be of some interest to tell the story of its origin. A Topic- based Reviewer Assignment System - VLDB Endowment Variants of the Hungarian method for assignment problems Naval Research Logistics uarterly December. Variants of the hungarian method for assignment problems - Kuhn. The Hungarian Method for the assignment problem. Hungarian method is based on the principle that if a constant. Scibay Journal of Mathematics - Scibay Publications An assignment problem can be easily solved by applying Hungarian method which consists of two phases. Multiple Choice Quiz. Is an upper bound for an optimal matching of the perturbed problem. Kuhn ” Variants of the Hungarian method for assignment problems” . Transportation problems are concerned with distributing commodities from sources to destinations in such a way as to maximize the total amount shipped. The author presents a geometrical modelwhich illuminates variants of the Hungarian method for the solution of the assignment problem.More detail on this algorithm can be found in Graph Theory with Appli-. Problem with infeasible ( restricted) assignment. Assignment problem its variants - nptel The formulation of the balanced assignment problem has n2 binary decision variables .

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https://www.chegg.com/homework-help/questions-and-answers/purchase-grating-cause-deflection-n-2-second-order-bright-band-red-light-42-degree--many-l-q3754191

math

Show transcribed image text You are to purchase a grating that will cause the deflection of the n = 2 second-order bright band for red light to be 42 degree . How many lines per centimeter should your grating have? The wavelength is 650 nm Light of wavelength 624 nm passes through a single slit and then strikes a screen that is 1.2 m from the slit. The third dark band is 0.60 cm from the central maximum. What is the width of the slit? please show all work..,

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https://www.embl.de/research/seminars/

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Seminar Colour Guide: External Faculty | External Postdoc | Company Representative Science and Society EMBL Distinguished Visitor Lecture Vision2020 Lecture Series Molecular Medicine Seminar | EIPOD Seminar | PSB Seminar | TAC Seminar Hamburg Speaker EMBL-La Sapienza Lecture No seminars found No seminars were found for the selected criteria. Please, try again using a different selection.

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https://writemia.com/testbank/the-following-information-is-available-concerning

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07 Feb The following information is available concerning The following information is available concerning the inventory of Carter Inc.: During the year, Carter sold 1,000 units. It uses a periodic inventory system. Required 1. Calculate ending inventory and cost of goods sold for each of the following three methods: a. Weighted average b. FIFO c. LIFO 2. Assume an estimated tax rate of 30%. How much more or less (indicate which) will Carter pay in taxes by using FIFO instead of LIFO? Explain your answer. 3. Assume that Carter prepares its financieal statements in accordance with IFRS. Which costing method should it use to pay the least amount of taxes? Explain youranswer.

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http://www.americanscientist.org/bookshelf/id.2673,content.true,css.print/bookshelf.aspx

math

Concepts of Mass in Contemporary Physics and Philosophy. Max Jammer. xi + 180 pp. Princeton University Press, 2000. $39.50. Like Max Jammer's previous books, Concepts of Mass in Contemporary Physics and Philosophy provides an interesting and stimulating mix of solid physics and philosophical issues. I must confess that as a particle/nuclear phenomenologist, my initial reaction on hearing the title of this work was "Who needs it?" My attitude toward mass was not unlike that of Supreme Court Justice Potter Stewart toward pornography: "It's not easy to define, but I know it when I see it." Reading the book, however, made it clear that my thinking was rather naive. Jammer treats many facets of this subject and demonstrates that challenging and interesting issues remain to be addressed. This slim volume is divided into chapters on inertial mass, relativistic mass, the mass-energy relation, gravitational mass and the nature of mass. Each is filled with interesting pieces of history and explores substantive issues. Except in the chapter on gravitational mass, where simple ideas from general relativity are introduced, the discussion is not particularly technical, and only simple physics—for example, Newton's second law, that force equals mass times acceleration—is employed. At the most basic level, that of inertial mass, Jammer argues that the concept itself is slippery in that one usually defines mass by means of Newton's second law. That means that the fundamental quantities of length and time that are needed in order to define acceleration must be supplemented by an additional concept—for example, that of force. Once force is introduced, then one can define inertial mass as force divided by acceleration. Absent this, one must introduce mass itself as a fundamental concept, and any notion that it somehow can be inferred from other constructs must be circular. Jammer discusses various attempts to evade this problem and demonstrates that each is specious. More interesting (and, I would argue, controversial) are the chapters on relativistic mass and on the mass-energy relation—E = mc2, which even otherwise scientifically illiterate people can quote from memory. Although again his discussion is thought-provoking and coherent, I must demur when he tries to support the concept of "relativistic mass" by means of the formula mrel = m0/√____1 – v2____/c2, in which m0 is the so-called rest mass (that is, the inertial mass as measured in the rest frame of the object), v is velocity and c is the speed of light. Jammer argues that this expression allows one to understand why it becomes increasingly difficult to accelerate an object as its velocity approaches the speed of light and why it is impossible for a massive object to exceed this limiting velocity. However, I would argue that the proper way to define mass relativistically is as a relativistic scalar m0, which means that it has the same value in every Lorentz frame. It is the energy, which is the time component of a Lorentz four-vector, that is given in terms of Jammer's relativistic mass times c2. This makes much more sense. This issue also comes up again when the author argues that with his definition mass is not created or destroyed. (I would call this energy conservation.) On the other hand, with the concept of mass as a relativistic scalar, one can easily picture how mass can be converted into energy using the equation Dm0c2 = DE. Jammer knows all of this, of course, but he argues that his picture is to be preferred. I remain unconvinced but found these chapters to be good reading. At 52 pages, the chapter on gravitational mass is the longest and the most interesting in the book. In a few sections a bare-bones knowledge of general relativity can be helpful but is not really essential. In this chapter Jammer introduces not only the inertial mass mi (which is the mass in Newton's second law) but also two kinds of gravitational mass—ma, which produces gravitational field distortions, and mp, on which the field distortions act. One nearly always assumes all three masses to be the same (this assumption goes under the name of the weak equivalence principle), but Jammer examines the evidence that this is so. I found the historical discussion here particularly edifying and useful. The Hungarian nobleman Roland, Baron Eötvös of Vásárosnamóny, is generally credited with doing the first such experiments out on the Hungarian plains near the end of the 19th century by hanging different materials (including snakewood) from torsion pendula in order to compare the relation between their gravitational attraction to the earth and their inertial effects as manifested in the centrifugal force. Jammer informs us, however, that it was actually Newton who performed the first such measurements (by comparing the periods of pendula composed of different materials—silver, glass, sand, salt and wheat) and showed the equality of gravitational and inertial mass to one part in 103, a result that Baron Eötvös was able to improve by six orders of magnitude 200 years later. (Contemporary scientists have pushed this limit by two more orders during the past century.) Another issue discussed here is whether gravity attracts gravitational self-energy (the so-called Nortvedt effect). The answer seems to be a resounding yes, as assessed by means of lunar ranging measurements, which are permitted by the corner reflectors placed on the moon's surface by the first astronauts in 1969. Also given their due are fascinating ideas such as antigravity and negative mass. Insightful analysis informs all of these discussions. (As a gauge of the level of scholarship involved in preparing this manuscript, I note that the author has even managed to dredge up a minor article that John Donoghue and I wrote 13 years ago on gravitational and inertial mass differences at nonzero temperature!) The final chapter is a short one dealing with the meaning of mass. In it Jammer analyzes Mach's view that the issues of mass and acceleration are only well defined in the presence of the remaining components of the universe and the view of Dennis Sciama, who attempted to invent a theory in which this concept was manifest. The meaning of mass is perhaps the deepest issue discussed in the book and remains a challenging and unsolved problem. Jammer has produced a fascinating look into the nature of a quantity that most of us take for granted. The historical references alone make this a book worth owning, but it's also a fun read.—Barry R. Holstein, Department of Physics, University of Massachusetts

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https://www.thecollegefever.com/fdtp.html

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About Seven Days Faculty Development Training Programme [FDTP] On “control Systems” Workshops in Karur: Introduction and Basic concepts about control system and its application. • Transfer function - Electrical and mechanical systems. • Time Response Analysis • Frequency Response analysis • Stability Analysis • Compensator Design • State Variable Analysis Last Dates for Registration: Last date for submission of Application : 01.06.2022 Intimation of selection (through mail) : 04.06.2022 Confirmation by Participants : 07.06.2022

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https://vdocuments.mx/listening-before-listening-boston-marathon-did-you-know-what-is-the-boston.html

math

listening. before listening boston marathon did you know?... what is the boston marathon? ->it is... Embed Size (px) Did you know?...• What is the Boston Marathon? ->It is the oldest road race in the United States. It is held every year in the middle of April. Did you know?...• Who do think can take part in the Boston race? -> Both men and women. Vocabulary• Race (n): cuộc đua.• Male (n): đàn ông.• Female (n): phụ nữ.• Athletic (n): vận động viên.• Formally (adv): thông thường.• Runner (n): người chạy đua.PROPER NAMES:• Kuscsik.• Mc Dermott. Guiding question• Who is speaking to whom, -> 2 friends are talking about Boston Marathon. Listen to the dialogue and decide whether the statements are True (T) or False (F) 1. The Boston Marathon is held every year in the USA.2. It began in 1897.3. John McDermott clocked 2 hours 15 minutes and 10 seconds.4. Women were officially allowed to participate in the races in 1957.5. In 1984, 34 countries took part in the marathon.6. According to the race’s rules, runners have to pass through the centre of Boston. • 1.T• 2.T• 3.F• 4.F• 5.T• 6.F Listen again and answer the following questions. 1. Where did John McDermott come from? 2. When did Kuscsik become the 1st official female champion? 3. How many women started and finished the race in 1972? 4. How many runners joined the Boston Marathon in 1984? Keys1. New York. 3. 8 women. 4. 6164 runners. Work in groups of 4.• Write some famous runners in Vietnam and tell what is special about them.

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https://www.gradesaver.com/textbooks/math/other-math/CLONE-547b8018-14a8-4d02-afd6-6bc35a0864ed/chapter-4-decimals-review-exercises-page-323/44

math

Reasonable 700 $\div$ 10 = 70 Work Step by Step $706.2\div12$ = 58.85 Now, Lets find the Estimate: 706.2 (rounded to) 700. 12 (rounded to) 10 $706.2\div12$ = 700 $\div$ 10 = 70 Which is reasonable. You can help us out by revising, improving and updating this answer.Update this answer After you claim an answer you’ll have 24 hours to send in a draft. An editor will review the submission and either publish your submission or provide feedback.

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https://navitron.mobi/iso-5725-5-70/

math

ISO 5725-5 PDF ISO 英文 – INTERNATIONAL STANDARD IS0 TECHNICAL CORRIGENDUM 1 Published ISO Accuracy (Trueness and Precision) of Measurement Methods and Results – Part 5: Alternative Methods for the Determination of the Precision of a. Find the most up-to-date version of ISO at Engineering |Published (Last):||2 October 2007| |PDF File Size:||5.50 Mb| |ePub File Size:||3.35 Mb| |Price:||Free* [*Free Regsitration Required]| ISO Accuracy of Measurement Methods and Results Package The analysis would then continue with an investigation of possible functional relationships between the repeatability and reproducibility standard deviations and the general average. The data for Level 14 see table 4 are used here to illustrate the results that are obtained by robust analysis. In an experiment on a heterogeneous material, the 5275-5 of applying these tests should be acted on in the following order. Reference number IS0 For an experiment with a heterogeneous material, this model is expanded to become: However, the principles 55725-5 the jso general design are the same as for the simple design, so the calculations will be set out in detail here for the simple design. The p participating laboratories are each provided with two samples at q levels, and obtain two test results on each sample. A split-level experiment – Determination of protein Hisher decision will have a substantial influence on the calculated values for the repeatability and reproducibility standard deviations. It is a common experience when analysing data from precision experiments to find data that are on the borderline between stragglers and outliers, so that judgements may have to be 5275-5 that affect the results of the calculation. The symbols used in IS0 are given in annex A. The samples were approximately kg in mass they were used for a number of other testsand the test portions were approximately g in mass. BS ISO 5725-5:1998 Wiley, New York, It should be noted, however, that they provide a means of combining, in a robust manner, cell averages, cell standard deviations and cell ranges. Figure 7 shows consistent positive or negative h statistics in most laboratories with Laboratories 1, 6 and 10 again achieving the largest values. Thus each cell in the experiment contains four test results two 7525-5 results for each of two samples. The figure also shows that the results for Laboratory 4 are unusual, as the point 57725-5 this laboratory is some distance from the line of equality for the two samples. It also specifically provides a procedure for obtaining intermediate measures of precision, basic methods for the determination of the trueness of a measurement method, the determination of repeatability and reproducibility of a standard measurement method. To test for stragglers and outliers in the cell averages, apply Grubbs’ tests to the values in each sio of table 3 in turn. Detectable ratio between the repeatability standard deviations of method B and method A True value of a standard deviation Component in a test result representing the variation due to time since last calibration Detectable ratio between the square roots of the between-laboratory mean squares of method B and method A p-quantile of the 2-distribution with u degrees of freedom P l2 Option b wastes data, but allows the simple formulae to be used. To obtain the reproducibilitystandard deviation, use equation 76 in 6. Also, the h statistics for Laboratories 1, 2 and 6 indicate a bias that changes with iwo in each of these laboratories. For example, when the test result is the proportion of an element obtained by chemical analysis, the repeatability and reproducibility standard deviations usually increase as the proportion of the element increases. This website is best viewed with browser version of up to Microsoft Internet Explorer 8 or Firefox 3. In the split-level design, each participating laboratory is provided laboratory standard deviation c with a sample of each of two similar materials, at each level of the experiment, and the operators are told that the samples are not identical, but they are not told by how much the jso differ. Applying Algorithm A to the cell averages gives the results shown in table 26,where now 5725- cell averages have been sorted into increasing order. IS0 consists of the following parts, under the general title Accuracy trueness and precision of measurement methods and results: Hence, in IS0 The interpretation of these graphs is discussed fully in subclause 7. Probability and general statistical terms. Alternative methods for the determination of the precision of a standard measurement method. In the leather example discussed in 5. Figure 4 shows that, in this experiment, at Level 6, there is wide variation between the cell averages, so that, if the test method were to be used in a specification, it is likely that disputes would arise between vendors and purchasers because of differences in their results. E The repeatability standard aviation srj, between-samples standard deviation sW, between-laboratory standard deviation sLp and reproducibility standard deviation sR using: The residuals for each i ,t and k: It is also necessary to specify the number of iiso that are to be averaged to give a test result, because this affects the values of the repeatability and reproducibility ieo deviations. When robust methods are used, the outlier tests and consistency checks described in IS0 or IS0 should be applied to the data, and the causes of any outliers, or patterns in the h and k statistics, should be investigated. Such interactions between the laboratories and the levels may provide clues as to the causes of the laboratory biases. Annexes B, C and D are for information only. You may find similar items within these categories by selecting from the choices below:. They do not combine individual test results in a robust manner,?. Subscription pricing is determined by: A further possibility is to use the iterative method to find an approximate solution, then solve equations 62 and 63 to find the exact solution. The analysis of variance. To check the is of the cell differences, calculate the h statistics as: If there are empty cells in table 2, p is now the number of cells in column j of table 2 containing data and the summation is performed over non-empty cells. Examine the data for consistency using the h and k statistics, described in subclause 7. Company organization, management and quality. However, the h statistics for all the other laboratories for that level will be small, even if some of these other laboratories give outliers. Equation 67 in 6. In figure 3, the h statistics for cell averages show that Laboratory 5 gave negative h statistics at all levels, indicating a consistent negative bias in their data. Formulae for calculating values for the repeatability and reproducibility standard deviations for the general design are given below in 5. This part of IS0 should be read in conjunction with IS0 because the underlying definitions and general principles are given there. An application of the general formulae Industrial Quality Control, 15,pp. Plot these statistics to show up inconsistent laboratories, by plotting the statistics in the order of the levels, but also grouped by laboratory.

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https://stats.stackexchange.com/questions/50595/how-to-determine-post-treatment-weight-with-uneven-time-points

math

I am looking for suggestions on how to deal with uneven time points and missing data in a single-group repeated-measures design. BACKGROUND: A group of 220 subjects went through a 4-month weight loss treatment, and were given wireless scales to weigh themselves daily at home. The primary outcome is change in weight from pre- to post-treatment (for which I would normally use a paired samples t-test on). HOW TO DETERMINE POST-TREATMENT WEIGHT? We have pre-treatment weights for everyone, but post-treatment weight is hard to determine since not all the subjects weighed-in at the same time after treatment (and some dropped out altogether). APPROACH A: Creating "Time Windows" to Determine Post-Treatment Weight. I've tried creating arbitrary time windows (1, 2, 4, or 6 weeks around the treatment end date), and then taking the median weight to determine "post-treatment weight." I found (all p's < 0.01): 1 week window: $n = 124$, $-9.55$% weight loss 2-week window: $n = 143$, $-7.52$% weight loss 4-week window: $n = 161$, $-6.39$% weight loss 6-week window: $n = 168$, $-6.30$% weight loss However, this is problematic because it excludes dropouts. Furthermore, the bigger the window, the bigger the "n", but smaller the effect size (because subjects who did not weigh in regularly also lost the least weight) and less valid the weight in terms of being measured at the same time between subjects. APPROACH B: Intention-to-Treat with Last Observation Carried Forward. Just using the last observation of all the subjects, up to 2 weeks post-treatment: $n = 220$, $-4.22$% weight loss However, this is problematic because the dropouts are depressing the effect size (though retains the largest "n"). APPROACH C: MIXED MODELS? I've read that mixed models can handle uneven time points and missing data, though I'm not super familiar with this procedure, and am not sure what are the fixed and random are effects here. My attempts to run it in SPSS 21 caused an "insufficient memory" error. APPROACH D: SOMETHING ELSE? (Multiple Imputation/Regression/ANCOVA, etc.)

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https://studyhope.com/discovering-equations-algebra-homework-help/

math

Consider the equation (x – 1)(x + 2) = 5(x – 1). First, solve this equation by dividing each side of the equation by x-1. Second, solve the equation by first “FOILâ€ing the left side, then simplifying by bringing all terms to the left side of the equation, and then finally solve using factoring. - Why are the solutions to these two methods different? (Hint: Remember you cannot divide by zero)

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http://www.themouthpiece.com/forum/threads/composer-in-residence-how-does-it-work.28036/

math

Some of the top bands have an appointed "composer in residence" (Black dyke even two I think). How does this usually work? Does the band get the prvilege to premier all pieces by the composer if they want? Or should the composer write a number of pieces for the band (e.g. 1 per year)? Is their money involved in this (e.g. the band paying the composer a fixed anual amount), or is this more of a "prestige" thing, where the composer is trying to get more of his pieces sold by linking his name to the band?

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https://www.jiskha.com/questions/1401514/In-triangle-ABC-A-is-a-right-angle-and-m-b-45-degrees-What-is-the-length-of-BC

math

In triangle ABC, A is a right angle, and m b=45 degrees. What is the length of BC? If your answer is not an integer, leave it in simplest radical form. a. 16 ft b. 16 sq 2 ft c. 16 sq 3 ft d. 32 ft I'm really confused as to how to do this... Could someone please explain and show me how to enter the whole calculation into my calculator? I don't really know how to make the whole sin thing plug in the correct ways and where to put my numbers. Thanks!! since the acute angles are 45°, the two legs are both 16 (a fact you left out) so, the hypotenuse is √(16^2 + 16^2) = 16√2 posted by Steve Ok thank you!!posted by Gina

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http://umj-old.imath.kiev.ua/article/?lang=en&article=5310

math

On the uniqueness of a solution of the inverse problem for a simple-layer potential We prove the uniqueness of solution of the inverse problem of single-layer potential for star-shaped smooth surfaces in the case of the metaharmonic equation Δv - K² v = 0. For the Laplace equation, a similar statement is not true. English version (Springer): Ukrainian Mathematical Journal 60 (2008), no. 7, pp 1045-1054. Citation Example: Kapanadze D. V. On the uniqueness of a solution of the inverse problem for a simple-layer potential // Ukr. Mat. Zh. - 2008. - 60, № 7. - pp. 892–899.

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