arflech
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^The organization is Wolfram Research.
p4wn3r wrote:
Now finish her.
In English, grammatical gender is almost always restricted to living things with distinct sexes (and even then, animals are often referred to with "it" rather than "he" or "she"); even though my best guess is that you referred to the feminine "integral" (same spelling in English, but slightly different pronunciation), in this case the correct translation of "lhe" would be "it" rather than "her" P.S.: I don't actually have a deep understanding of Portuguese, but I do know Spanish well and I used Google Translate and sometimes Wiktionary to test any similarities I thought were there, like I thought the word for "her" in the accusative case would be either "la" or "a" but it's not quite so analogous. Also here is a suitable Google link for the translation I gave you; I had just earlier used the very well-written definition you gave, without relying on a Portuguese-English dictionary, but I wanted to see what Google said.
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arflech wrote:
In English, grammatical gender is almost always restricted to living things with distinct sexes (and even then, animals are often referred to with "it" rather than "he" or "she")
In most English-speaking countries pets are usually referred to with gender pronouns. In nautical terminology a ship is always a "she".
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A pet is a living thing with a distinct sex!
Player (42)
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I already knew "it" was the correct pronoun to use. I used "her" mostly as a tiny joke, in a sense close to what Warp pointed out, like pets or ships. Portuguese translation should be "Agora termine-a". In Portuguese pronouns are different when used as direct or indirect objects. "lhe/lhes" are for indirect objects and "o/a/os/as" for direct objects.
arflech
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for some reason I thought "her" was being used as a direct object (accusative case) rather than indirect (dative case) and now that you told me what you told me, it does look a bit more analogous to Spanish (accusative third-person pronouns are "lo/la/los/las" and dative third-person pronouns are "le/les" except that if both are used, the latter is replaced by the reflexive third-person dative/accusative pronoun "se")
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Am I starting to get annoying with roughly one problem per weekend? I'll stop if someone tells me to. Anyway... The segment AB is the diameter of a semicircle. A smaller circle of center C is inscribed in this semicircle. Let D be the tangency point of the circle with the diameter AB and E the tangency point of the circle with the arc AB. Find the measure of the angle AED.
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This one seemsa bit...easy? By symmetry, AD = BD = DE. Also arc AE = BE. So AED is an isosceles right-angled triangle, and angle AED is 45 degrees. Alternatively, the angle between arc AE and line DE is 90 degrees. Not sure what exactly you were asking for so you get both.
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There's more than one way to inscribe the smaller circle, it's not required that AD=BD, you must prove that the angle AED is still 45º for all possible ways to inscribe the smaller circle. EDIT: A picture is worth over nine thousand words:
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I don't have time to work on this now, but it looks like a good approach would be to prove angle FCD is a right angle, where F is the other point where AE intersects the smaller circle.
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Here's a solution by inversion geometry (circle inversion). Take p4wn3r's image above, extend semicircle AB to a circle, extend diameter AB to a line, and erase C from all existence, just for fun. Now invert with center E (doesn't matter which radius) and you get something like the following: Note: - A maps to A', B to B', D to D'. We don't care what E maps to. E is just E. - The outer circle through A, B, E maps to the inner line through A'B'. The inner circle through D, E maps to the outer line through D'. Both lines are parallel. This is because, in the original, both circles are tangent at E. - The line through AB (through D) maps to the circle through E, A', D', B'. E is on the opposite side of A'B' from D', since the original line has A, D, B in that order. - Furthermore, this circle is special. It is tangent to the outer line at D', since the original line is tangent to the inner circle at D. As well, A'B' is its diameter, since AB is the diameter of the outer circle in the original, and inversion preserves angles (angles formed locally by intersecting curves are preserved). - Finally, the lines through A'E and through D'E are fixed under inversion, so angle A'ED'= angle AED. Now, since the lines are parallel, with one going through the diameter A'B' and the other tangent at D', the arc A'D' must be a 90° arc. Make a point G diametrically opposite D'. Then angle A'GD' must be 45°. So angle A'ED' = 45°. From what was said above, angle AED = angle A'ED' = 45°. I wonder what a solution without inversion geometry looks like. I suppose p4wn3r has one.
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Nice solution, FractalFusion. Here's one using elementary geometry: Take points F and G, the intersection of lines AE and BE with the smaller circle, respectively. Since the angle FEG = 90º, the triangle FEG is right and is inscribed in a semicircle. Therefore, FG is the diameter of the smaller circle and the points F, C and G are collinear. Now, take O as the center of the big circle, and draw the line OE. Since the circles are tangent, this line passes through C. Since the triangle AOE is isosceles, angle OAE = angle OEA = angle CEF. But, since the triangle ECF is isosceles, angle CEF = angle EFC = angle EFG. Finally, the triangles ABE and FGE have two equal angles and are similar. Knowing this similarity, we finally bring D into the story. By power of a point, AD^2 = AF AE and BD^2 = BG BE . Dividing the equations: (AD / BD)^2 = (AE / BE) * (AF / BG) . Using the similarity, (AD / BD)^2 = (AE / BE)^2 , since the ratios are positive, AD/BD = AE/BE. The last conclusion allows us to use the angle bisector theorem. Therefore, ED bisects the right angle AEB, so AED = 45º Q.E.D
arflech
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Try this on for size: Using whatever means you can think of, find the smallest remainder in (15x4-y2)/(x2+y)
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Do you mean the remainder in polynomial long division of smallest degree? 15x4 - y2 = (x2+y) (15x2 - 15y) + 14y2
arflech
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That's what I got, but I was unnerved because its degree in y wasn't less than 2, the degree of the denominator; it looks even worse when you consider the similar problem (15x2-y2)/(x+y) because the smallest remainder I could find was still 14y2 and that has an even higher degree than the denominator!
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(IMO 1969) Prove that there are infinitely many positive integers a such that the sequence (zn)n>=1, zn=n4+a does not contain any prime number.
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(n^2+2cn-2c^2)(n^2-2cn-2c^2) = n^4 + 4c^4. If a = 4c^4 for some positive integer c, then n^4+a cannot be prime unless one of these factors is +/- 1. Each factor can be written as (n +/- c)^2 - 3c^2, which is +/- 1 for some integer n if and only if 3c^2 +/- 1 is a perfect square. If c=2 mod 4, then 3c^2 +/- 1 will equal 3 or 5 mod 8, and thus cannot be a square. Hence, if a=4(4k-2)^4 for some k, then n^4 + a is composite for all n.
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It suffices to have a = 4c^4 for c>=2.
Nitrodon wrote:
(n^2+2cn-2c^2)(n^2-2cn-2c^2) = n^4 + 4c^4
Wrong, Sophie Germain's identity says: n^4 + 4c^4 = (n^2 - 2nc + 2c^2)(n^2 + 2nc + 2c^2) Each factor is (n +/- c)^2 + c^2 >= c^2 >= 4.
arflech
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Let's say you're trying to check, by visual inspection, the presence of property P in a population, which is widely known to be randomly dispersed in a portion p with 0<p<1/2 of the population; property P is itself not readily visible, but property Q is, it is known to be randomly dispersed in a portion q with 0<p<q<1/2 of the population, and those with property q have property P at a rate r with p<r<p/q that is not widely known. What is the difference between labeling (1) everyone by default as ~P, (2) p of the population randomly as P, (3) those (and only those) with property Q as P, or (4) r of those with Q and (p-rq)/(1-q) of those with ~Q randomly as P (obviously only works if r is known)? I'll go ahead and try to answer this, because it has implications for social issues like "gaydar" and racial profiling. (1) false-positive (FP): 0, false-negative (FN): p, total error (ER): p (2) FP: (1-p)p, FN: (1-p)p, ER: 2(1-p)p (3) FP: (1-r)q, FN: p-rq, ER: p+q (4) FP: (1-r)rq+(1-q-p+rq)(p-rq)/(1-q), FN: (1-r)rq+(1-q-p+rq)(p-rq)/(1-q), ER: 2(1-r)rq+2(1-q-p+rq)(p-rq)/(1-q) The expressions for (1) are obvious, and (3) is fairly simple too (portion of (Q and ~P), and portion of P minus portion of (Q and P)); (2) arises from incorrectly classifying p of the ~P potion as P and incorrectly classifying (1-p) of the P portion as ~P, and (4) is similar, applied to both the Q and ~Q portions (and using the readily-verified fact that if r of the Q portion is P, then (p-rq)/(1-q) of the ~Q portion is P, because p-rq is the (P and ~Q) portion and 1-q is the ~Q portion). Surprisingly, (3), equivalent to brazen racial profiling or assuming that gay=swishy, has a higher total error rate than (1), equivalent to giving the benefit of doubt or using the presumption of heteronormativity, even though it does have a slightly smaller rate of false negatives. Also, going at random (2) is more error-prone (difference between (1) and (2) is 2p^2-p=(2p-1)p<0 because 0<p<1/2), although once again it has a lower false-negative rate. Meanwhile, as expected, (4), equivalent to more nuanced attempts at racial profiling or "gaydar," is less error-prone than (1), as can be proven by taking the derivative of the difference expression with respect to r and testing the limit as r->p. Of course, the question must be asked: Is it fair? To determine this, let's compare the portions of the Q and ~Q groups who are also P, that are detected as such. (1) Q: 0 ~Q: 0 (2) Q: p ~Q: p (3) Q: 1 ~Q: 0 (4) Q: r ~Q: (p-rq)/(1-q) The fairest one is actually (2), even though (4) may seem better at first glance; as r increases over p, more of the Q who are P get caught, while more of the ~Q who are P get away with it. However, it is not hard to see that those are also the portions of the Q and ~Q groups who are actually ~P, but still detected as P; by that measure, (1) is the fairest and (4) is even more appalling. The most surprising thing of all is that (4) is both more effective and less fair as r increases, so the arguments of both conservatives in favor of, and liberals in opposition to, racial profiling have merit.
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Suggestion: Use a change of variables that rotates the axes by an angle of 45º Bonus points if you can see the mathematical importance of the result of this integral.
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I solved it by a different method from the suggestion: I just transformed it into an infinite series. For xy<1 (satisfied by the bounds of the integral), 1/(1-xy) = 1 + xy + (xy)^2 + (xy)^3 + ... From there, the integrals are straightforward and ultimately give 1 + 1/4 + 1/9 + 1/16 + 1/25 + ... which is the famous Basel problem (I'd post a link to Wikipedia, but it would stick out on top of the spoiler tag). Euler found the solution so I don't have to-- the answer is pi^2/6. The mathematical importance? Maybe you're hinting at the Basel problem itself, or perhaps this kind of problem can be generalized to relate to the Riemann zeta function. Assuming I didn't make any boneheaded mistakes, I'm rather proud to have figured out one of these problems as they're usually pretty hard.
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You basically figured out the bonus points before the question. Try to use the suggestion to actually prove zeta(2)=pi^2/6
Tub
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Less of a challenge, more of a "help me with this". Let's say I have a discrete random variable with a known, finite set of possible outcomes. My dice could yield {1,2,3,4,5,6}, my pizza service could deliver {pizza with onions, pizza without onions} when I ordered without, the monster I've slain could drop one of {nothing, +5 Sword of Penis Envy, +3 Boots of Asskicking}. (Samples are of course independent from each other; we do not assume my pizza service to learn from past mistakes.) I want to determine the probability distribution by aquiring random samples. I can guess a distribution by dividing each outcome by the number of samples, but how likely is my distribution to be anywhere close to correct? Flipping a coin once would yield 100% chance heads, 0% chance tails. That may be my best guess given the data, but it's still useless. The quality of my guess is going to improve with the number of samples, but is there a way to quantify the quality of my guess? I'd like to make a statement like: "Given these samples, it's 95% likely that the chance to roll a 6 is between a and b." That would reduce the one-coinflip measurement to the statement: "The chance to flip heads is probably between ~5% and 100%." which is a lot less misleading. It seems like confidence intervals fit that definition, but I fail to apply the math listed there to my problem. Further googling didn't turn up anything useful, either. Can someone more immersed in statistics help?
m00
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The math behind confidence intervals is not as ugly as it seems. Supposing that your sample is equivalent to throwing a lot of dice into the air, taking notes of their values and dividing the desired event over the total amount thrown, you basically have a perfect measuring instrument for the sample, thus systematic errors are zero (actually in real life there are rounding errors, you may decide to include them, but most of the time they can be considered negligible in comparison to statistical errors). So, you only need to consider statistical errors. I'll consider that the values are in Gaussian distribution (I think English literature most commonly refers to it as normal distribution). Suppose you have the results of n samples taken the way I described before. Then take the mean of all samples xm. Define the standard deviation of this data collection as sigma = sqrt(sum((xi - xm)2)/(n-1)) The standard deviation measures how spread the data collected from all samples is, the larger it is, the higher is your uncertainty that their mean can correctly describe the value measured. Anyway, you basically want to take the mean of all those samples and take it as the probability of the event. To measure how good this approximation is, you take the standard deviation of the mean, defined by: sigmam = sigma/sqrt(n) = sqrt(sum((xi - xm)2)/n(n-1)) It's intuitive to use this value because if two data have the same standard deviation, but data A has more samples than data B, the mean value of A is more reliable than the one we got from B. Now, probability under Gaussian distribution is modeled through a function like: a exp(-bx2) and the probability that a value measured next will be within an interval (-a,a) is the area of the integral of this function divided by the integral of it in (-inf,inf), which is something proportional to sqrt(pi). The integral, however, cannot be expressed analytically in finite intervals, so numerical methods are used to evaluate the function and its inverse. What's necessary to know is that if you take the interval as (-sigmam,sigmam), and consider the amount of measures so big that it can be considered infinite, you'll get around 68% of certainty in your value in that interval. You can raise this certainty by making the interval larger by multiplying sigmam by a constant. Normally, 90% and 95% certainty are used. Since the values need to be evaluated numerically, they often come in a table: 50% 0,674 68,27% 1,000 80% 1,282 90% 1,645 95% 1,960 95,45% 2,000 99% 2,576 99,73% 3,000 These values, like I said before, consider an infinite amount of measures. However, if this value is not very big, you need to use a table for finite amounts, which I don't have access atm. In short, to evaluate the confidence interval, find sigma_m as defined by the formula, multiply it by one of the constants given that give the certainty you need and take this result as the uncertainty.
Tub
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Ok, say I flip a coin twice and get "heads" both times. With a fair coin, there's a 50% chance of getting the same side twice, so it's nothing unusual or conclusive. Let's encode "tails" as 0 and "heads" as 1, so we have x_1=1, x_2=1, n=2 I'm interested in P(X=1), because I don't trust the coin. x_m = 1, so P(X=1) = 1 is my initial guess. What's the likely range for the actual probability? sigma = sqrt((0+0)/1) = 0 sigma_m = 0/sqrt(2) = 0 In this case, the formula would suggest a 100% chance that my guess is correct, which is obviously wrong. Did I apply the formula wrong? If it breaks down with n=2, why would I trust it on larger samples?
m00
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Having a zero standard deviation doesn't mean your guess is 100% correct, it lets the formula undefined. The error function tends to zero everywhere when the standard deviation is zero (except at x=0, but the discontinuity has no effect on integration). If you were to divide the finite integral over the infinite one, you'd get 0/0, which is undefined. For bigger amounts, the chance of the standard deviation being zero is very rare, so the formula is generally more conclusive.