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[ "", null, "Wordy Math Problems", null, "", null, "1. Brett is twenty-five less than three times the age of Kylie. Kylie is twelve less than two times the age of James. the sum of all of their ages is eighty.", null, "How old is James?", null, "How old is Kylie?", null, "How old is Brett?", null, "2. Forty-three times a number plus the sum of seven and the same number is greater than thirteen thousand three hundred forty-one and less than thirteen thousand four hundred ninety-three.  How many integers satisfy this condition?", null, "What integers satisfy this condition (separate integers with commas)?", null, "3. Alexander paid \\$192.55 for a cell phone after a seventeen percent discount was applied to the price.  What was the original price? (round to the nearest dollar)", null, "4.The difference between three hundred sixty-seven and half of a number is three hundred twenty-eight.  What is the number?", null, "5. The sum of two numbers is one hundred sixty-six. Three times the smaller number plus eighteen is the larger number.  What are the two numbers? (smaller number, larger number)", null, "6. Jonathan has a total of one hundred forty-seven nickels, dimes and quarters. He has a total of \\$17.45. He has fifteen more nickels than quarters and twelve more dimes than nickels.  How much of each coin does he have? (Nickels, Dimes, Quarters)", null, "7. William has a 50% silver alloy. Anna has a 55% silver alloy. They melted their alloy together to create one hundred fifty-five grams of 52% silver alloy.  How many grams was William’s silver alloy?", null, "8. Jessica needs a mixture of 8% oats and 92% corn. she currently has sixteen grams of a mixture containing 75% oats and 25% corn.  How many pounds of corn should be added to the mixture?", null, "9. If Megan were two years younger she would be three times the age Deanna. if Megan were eight years older, she would be four times the age of Deanna.  How old is Deanna?", null, "10. Jessica sold 21 CDs at \\$11.50 each. For each CD sold she made an average profit of one hundred forty-seven cents.  How many more CDs must Jessica sell at \\$18.50 each to earn an overall average profit of at least \\$4.13 per CD?", null, "" ]

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[ "Gets the length for a specified Geometry. This is a planar measurement using 2D Cartesian mathematics to compute the length in the same coordinate space as the inputs. Use LengthGeodetic(Geometry, LinearUnit, GeodeticCurveType) for geodetic measurement.\n\nNamespace:  Esri.ArcGISRuntime.Geometry\nAssembly:  Esri.ArcGISRuntime (in Esri.ArcGISRuntime.dll) Version: 100.9.0", null, "Syntax\n```public static double Length(\nGeometry geometry\n)```\n\n#### Parameters\n\ngeometry\nType: Esri.ArcGISRuntime.GeometryGeometry\nThe geometry to calculate length for.\n\n#### Return Value\n\nType: Double\nThe calculated length in the same units as the geometry's spatial reference unit.", null, "Remarks\nThis length calculation is based upon the SpatialReference of the input geometry. Although some projections are better than others for preserving distance, it will always be distorted in some areas of the map. Distortion may be negligible for large scale maps (small areas) that use a suitable map projection. Make sure you know your data and spatial reference if accurate measurements are required. For more accurate results, consider using the LengthGeodetic(Geometry, LinearUnit, GeodeticCurveType) operation.", null, "See Also" ]

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[ "You can answer: \"Yeah, I can by using a GMM approach!\". Well, it turns out that there is no reason to be afraid since once you have understand the one dimensional case, everything else is just an adaption and I still have shown in the pseudocode above, the formulas you need for the multidimensional case. Python classes # Calculate the new mean vector and new covariance matrices, based on the probable membership of the single x_i to classes c --> r_ic, # Calculate the covariance matrix per source based on the new mean, # Calculate pi_new which is the \"fraction of points\" respectively the fraction of the probability assigned to each source, # Here np.sum(r_ic) gives as result the number of instances. That is, a matrix like: We will see how this looks like below. But there isn't any magical, just compute the value of the loglikelihood as described in the pseudocode above for each iteration, save these values in a list and plot the values after the iterations. If you like this article, leave the comments or send me some . The Maximization step looks as follows: M − Step _. pi_c, mu_c, and cov_c and write this into a list. Therefore, we best start with the following situation: Machine Learning Lab manual for VTU 7th semester. Category: Machine Learning. Further, the GMM is categorized into the clustering algorithms, since it can be used to find clusters in the data. 0 & 0 As you can see, we can still assume that there are two clusters, but in the space between the two clusters are some points where it is not totally clear to which cluster they belong. You can see that the points which have a very high probability to belong to one specific gaussian, has the color of this gaussian while the points which are between two gaussians have a color which is a mixture of the colors of the corresponding gaussians. The last step would now be to plot the log likelihood function to show that this function converges as the number of iterations becomes large and therewith there will be no improvement in our GMM and we can stop the algorithm to iterate. Let's quickly recapitulate what we have done and let's visualize the above (with different colors due to illustration purposes). (collapses onto this datapoint --> This happens if all other points are more likely part of gaussian one or two and hence this is the only point which remains for gaussian three --> The reason why this happens can be found in the interaction between the dataset itself in the initializaion of the gaussians. The above calculation of r_ic is not that obvious why I want to quickly derive what we have done above. This variable is smth. Your opposite is delightful. I hope you like the article and this will somehow make the EM algorithm a bit clear in understanding. A critical point for the understanding is that these gaussian shaped clusters must not be circular shaped as for instance in the KNN approach but can have all shapes a multivariate Gaussian distribution can take. Also, the variance is no longer a scalar for each cluster c ($\\sigma^2$) but becomes a covariance matrix $\\boldsymbol{\\Sigma_c}$ of dimensionality nxn where n is the number of columns (dimensions) in the dataset. Further, we know that our goal is to automatically fit gaussians (in this case it should be three) to this dataset. I have to make a final side note: I have introduced a variable called self.reg_cov. We will set both $\\mu_c$ to 0.5 here and hence we don't get any other results as above since the points are assumed to be equally distributed over the two clusters c. So this was the Expectation step. I recommend to go through the code line by line and maybe plot the result of a line with smth. Well, here we use an approach called Expectation-Maximization (EM). For those interested in why we get a singularity matrix and what we can do against it, I will add the section \"Singularity issues during the calculations of GMM\" at the end of this chapter. This term consists of two parts: Expectation and Maximzation. It is also plausible, that if we assume that the above matrix is matrix $A$ there could not be a matrix $X$ which gives dotted with this matrix the identity matrix $I$ (Simply take this zero matrix and dot-product it with any other 2x2 matrix and you will see that you will always get the zero matrix). $\\underline{E-Step}$. Therefore we have to look at the calculations of the $r_{ic}$ and the $cov$ during the E and M steps. # As we can see, as result each row sums up to one, just as we want it. Hence we want to assign probabilities to the datapoints. \"\"\", # We have defined the first column as red, the second as, Normalize the probabilities such that each row of r sums to 1 and weight it by mu_c == the fraction of points belonging to, # For each cluster c, calculate the m_c and add it to the list m_c, # For each cluster c, calculate the fraction of points pi_c which belongs to cluster c, \"\"\"Define a function which runs for iterations, iterations\"\"\", \"\"\" 1. A matrix is singular if it is not invertible. Bodenseo; Though, it turns out that if we run into a singular covariance matrix, we get an error. to calculat the mu_new2 and cov_new2 and so on.... \"\"\"Predict the membership of an unseen, new datapoint\"\"\", # PLot the point onto the fittet gaussians, Introduction in Machine Learning with Python, Data Representation and Visualization of Data, Simple Neural Network from Scratch Using Python, Initializing the Structure and the Weights of a Neural Network, Introduction into Text Classification using Naive Bayes, Python Implementation of Text Classification, Natural Language Processing: Encoding and classifying Text, Natural Language Processing: Classifiaction, Expectation Maximization and Gaussian Mixture Model, https://www.youtube.com/watch?v=qMTuMa86NzU, https://www.youtube.com/watch?v=iQoXFmbXRJA, https://www.youtube.com/watch?v=zL_MHtT56S0, https://www.youtube.com/watch?v=BWXd5dOkuTo, Decide how many sources/clusters (c) you want to fit to your data, Initialize the parameters mean $\\mu_c$, covariance $\\Sigma_c$, and fraction_per_class $\\pi_c$ per cluster c. Calculate for each datapoint $x_i$ the probability $r_{ic}$ that datapoint $x_i$ belongs to cluster c with: Decide how many sources/clusters (c) you want to fit to your data --> Mind that each cluster c is represented by gaussian g. Expectation-maximization (EM) algorithm is a general class of algorithm that composed of two sets of parameters θ₁, and θ₂. So how can we cause these three randomly chosen guassians to fit the data automatically? we need to prevent singularity issues during the calculations of the covariance matrices. Hence for each $x_i$ we get three probabilities, one for each gaussian $g$. Gaussian Mixture Model using Expectation Maximization algorithm in python - gmm.py. Since we don't know how many, point belong to each cluster c and threwith to each gaussian c, we have to make assumptions and in this case simply said that we, assume that the points are equally distributed over the three clusters. If we have data where we assume that the clusters are not defined by simple circles but by more complex, ellipsoid shapes, we prefer the GMM approach over the KNN approach. This procedure is called the Maximization step of the EM algorithm. So what is, the percentage that this point belongs to the chosen gaussian? This is sufficient if you further and further spikes this gaussian. There are also other ways to prevent singularity such as noticing when a gaussian collapses and setting its mean and/or covariance matrix to a new, arbitrarily high value(s). In the second step for instance, we use the calculated pi_new, mu_new and cov_new to calculate the new r_ic which are then used in the second M step. So as you can see the occurrence of our gaussians changed dramatically after the first EM iteration. We calculate for each source c which is defined by m,co and p for every instance x_i, the multivariate_normal.pdf() value. We denote this probability with $r_{ic}$. But don't panic, in principal it works always the same. If you are interested in an instructor-led classroom training course, you may have a look at the I won't go into detail about the principal EM algorithm itself and will only talk about its application for GMM. The second mode attempts to optimize the parameters of the model to best explain the data, called the max… Since a singular matrix is not invertible, this will throw us an error during the computation. PDF | Theory and implémentation with Python of EM algorithm | Find, read and cite all the research you need on ResearchGate 機械学習を学ばれている方であれば，EMアルゴリズムが一番最初に大きく立ちはだかる壁だとも言えます。何をしたいのか，そもそも何のための手法なのかが見えなくなってしまう場合が多いと思います。 そこで，今回は実装の前に，簡単にEMアルゴリズムの気持ちをお伝えしてから，ザッと数学的な背景をおさらいして，最後に実装を載せていきたいと思います。早速ですが，一問一答形式でEMアルゴリズムに関してみていきた … EM algorithm: Applications — 8/35 — Expectation-Mmaximization algorithm (Dempster, Laird, & Rubin, 1977, JRSSB, 39:1–38) is a general iterative algorithm for parameter estimation by maximum likelihood (optimization problems). You initialize your parameters in the E step and plot the gaussians on top of your data which looks smth. To learn such parameters, GMMs use the expectation-maximization (EM) algorithm to optimize the maximum likelihood. 0 & 0 \\\\ As you can see, the $r_{ic}$ of the third column, that is for the third gaussian are zero instead of this one row. Expectation-Maximization algorithm is a way to generalize the approach to consider the soft assignment of points to clusters so that each point has a probability of belonging to each cluster. Now first of all, lets draw three randomly drawn gaussians on top of that data and see if this brings us any further. So have fitted three arbitrarily chosen gaussian models to our dataset. The algorithm iterates between performing an expectation (E) step, which creates a heuristic of the posterior distribution and the log-likelihood using the current estimate for the parameters, and a maximization (M) step, which computes parameters by maximizing the expected log-likelihood from the E step. Beautiful, isn't it? To prevent this, we introduce the mentioned variable. It is clear, and we know, that the closer a datapoint is to one gaussian, the higher is the probability that this point actually belongs to this gaussian and the less is the probability that this point belongs to the other gaussian. That is, MLE maximizes, where the log-likelihood function is given as. Finding these clusters is the task of GMM and since we don't have any information instead of the number of clusters, the GMM is an unsupervised approach. Final parameters for the EM example: lambda mu1 mu2 sig1 sig2 0 0.495 4.852624 0.085936 [1.73146140597, 0] [1.58951132132, 0] 1 0.505 -0.006998 4.992721 [0, 1.11931804165] [0, 1.91666943891] Final parameters for the Pyro example What can you say about this data? It is also called a bell curve sometimes. EM算法及python简单实现 最大期望算法（Expectation-maximization algorithm，又译为期望最大化算法），是在概率模型中寻找参数最大似然估计或者最大后验估计的算法，其中概率模型依赖于无法观测的隐 … Congratulations, Done! This process of E step followed by a M step is now iterated a number of n times. A picture is worth a thousand words so here’s an example of a Gaussian centered at 0 with a standard deviation of 1.This is the Gaussian or normal distribution! EM algorithm models t h e data as being generated by mixture of Gaussians. and P(Ci |xj ) can be considered as the weight or contribution of the point xj to cluster Ci. See _em() for details. I have added comments at all critical steps to help you to understand the code. Since we add up all elements, we sum up all, # columns per row which gives 1 and then all rows which gives then the number of instances (rows). Since we do not simply try to model the data with circles but add gaussians to our data this allows us to allocate to each point a likelihood to belong to each of the gaussians. = \\boldsymbol{x_n}$for some value of n\" (Bishop, 2006, p.434). Therefore we best start by recapitulating the steps during the fitting of a Gaussian Mixture Model to a dataset. material from his classroom Python training courses. is not invertible and following singular. The python … Having initialized the parameter, you iteratively do the E, T steps. Though, we will go into more detail about what is going on during these two steps and how we can compute this in python for one and more dimensional datasets. We run the EM for 10 loops and plot the result in each loop. To understand the maths behind the GMM concept I strongly recommend to watch the video of Prof. Alexander Ihler about Gaussian Mixture Models and EM. In theory, it recovers the true number of components only in the asymptotic regime (i.e. So assume, we add some more datapoints in between the two clusters in our illustration above. Well, we have the datapoints and we have three randomly chosen gaussian models on top of that datapoints. Consequently we can now divide the nominator by the denominator and have as result a list with 100 elements which we, can then assign to r_ic[:,r] --> One row r per source c. In the end after we have done this for all three sources (three loops). Hence, if we would calculate the probability for this point for each cluster we would get smth. Directly maximizing the log-likelihood over θ is hard. So you see, that this Gaussian sits on one single datapoint while the two others claim the rest. --> Correct, the probability that this datapoint belongs to this, gaussian divided by the sum of the probabilites for this datapoint for all three gaussians.\"\"\". Set the initial mu, covariance and pi values\"\"\", # This is a nxm matrix since we assume n sources (n Gaussians) where each has m dimensions, # We need a nxmxm covariance matrix for each source since we have m features --> We create symmetric covariance matrices with ones on the digonal, # In this list we store the log likehoods per iteration and plot them in the end to check if. useful with it?\" For illustration purposes, look at the following figure: So you see that we got an array$r$where each row contains the probability that$x_i$belongs to any of the gaussians$g$. The EM algorithm estimates the parameters of (mean and covariance matrix) of each Gaussian. That is practically speaing, r_ic gives us the fraction of the probability that x_i belongs to class. So much for that: We follow a approach called Expectation Maximization (EM). The actual fitting of the GMM is done in the run() function. which adds more likelihood to our clustering. So we know that we have to run the E-Step and the M-Step iteratively and maximize the log likelihood function until it converges. Well, consider the formula for the multivariate normal above. Calculating alpha in EM / Baum-Welch algorithm for Hidden Markov. This covariance regularization is also implemented in the code below with which you get the described results. It is useful when some of the random variables involved are not observed, i.e., considered missing or incomplete. This is actually maximizing the expectation shown above. 0 & 0 \\\\ The Gaussian Mixture Models (GMM) algorithm is an unsupervised learning algorithm since we do not know any values of a target feature. The first mode attempts to estimate the missing or latent variables, called the estimation-step or E-step. So we have now seen that, and how, the GMM works for the one dimensional case. Python - Algorithm Design. Well, we have seen that the covariance matrix is singular if it is the$\\boldsymbol{0}$matrix. As we can see, the actual set up of the algorithm, that is the instantiation as well as the calling of the fit() method does take us only one line of code. This is due to the fact that the KNN clusters are circular shaped whilst the data is of ellipsoid shape. like a simple calculation of percentage where we want to know how likely it is in % that, x_i belongs to gaussian g. To realize this, we must dive the probability of each r_ic by the total probability r_i (this is done by. Algorithm Operationalization. Submitted by Anuj Singh, on July 04, 2020 . So now that we know that we should check the usage of the GMM approach if we want to allocate probabilities to our clusterings or if there are non-circular clusters, we should take a look at how we can build a GMM model. Lets see what happens if we run the steps above multiple times. The denominator is the sum of probabilities of observing x i in each cluster weighted by that cluster’s probability. I'm looking for some python implementation (in pure python or wrapping existing stuffs) of HMM and Baum-Welch. So what will happen? Here I have to notice that to be able to draw the figure like that I already have used covariance-regularization which is a method to prevent singularity matrices and is described below. Let us understand the EM algorithm in detail. So as you can see, the points are approximately equally distributed over the two clusters and hence each$\\mu_c$is$\\approx0.5$. the Expectation step of the EM algorithm looks like: Consider the following problem: We … Therewith, we can label all the unlabeled datapoints of this cluster (given that the clusters are tightly clustered -to be sure-). But step by step: How precise can we fit a KNN model to this kind of dataset, if we assume that there are two clusters in the dataset? like: and run from r==0 to r==2 we get a matrix with dimensionallity 100x3 which is exactly what we want. After you have red the above section and watched this video you will understand the following pseudocode. This approach can, in principal, be used for many different models but it turns out that it is especially popular for the fitting of a bunch of Gaussians to data. So let's implement these weighted classes in our code above. ... Hidden Markov models with Baum-Welch algorithm using python. Python code for estimation of Gaussian mixture models. like (maybe you can see the two relatively scattered clusters on the bottom left and top right): Advertisements. So in principal, the below code is split in two parts: The run() part where we train the GMM and iteratively run through the E and M steps, and the predict() part where we predict the probability for a new datapoint. What we have now is FOR EACH LOOP a list with 100 entries in the nominator and a list with 100 entries in the denominator, where each element is the pdf per class c for each instance x_i (nominator) respectively the summed pdf's of classes c for each, instance x_i. Therewith we can make a unlabeled dataset a (partly) labeled dataset and use some kind of supervised learning algorithms in the next step. And it is, … Hence we sum up this list over axis=0. Because each of the n points xj is considered to be a random sample from X (i.e., independent and identically distributed as X), the likelihood of θ is given as. Now, probably it would be the case that one cluster consists of more datapoints as another one and therewith the probability for each$x_i$to belong to this \"large\" cluster is much greater than belonging to one of the others. How can we address this issue in our above code? This is done by simply looping through the EM steps after we have done out first initializations of$\\boldsymbol{\\mu_c}$,$\\sigma_c^2$and$\\mu_c$. So as you can see, we get very nice results. Assume you have a two dimensional dataset which consist of two clusters but you don't know that and want to fit three gaussian models to it, that is c = 3. To get this, look at the above plot and pick an arbitrary datapoint. See the following illustration for an example in the two dimensional space. Hence to prevent singularity we simply have to prevent that the covariance matrix becomes a$\\boldsymbol{0}$matrix. So in a more mathematical notation and for multidimensional cases (here the single mean value$\\mu$for the calculation of each gaussian changes to a mean vector$\\boldsymbol{\\mu}$with one entry per dimension and the single variance value$\\sigma^2$per gaussian changes to a nxn covariance matrix$\\boldsymbol{\\Sigma}$where n is the number of dimensions in the dataset.) Since we do not know the actual values for our$mu$'s we have to set arbitrary values as well. During this procedure the three Gaussians are kind of wandering around and searching for their optimal place. The K-means approach is an example of a hard assignment clustering, where each point can belong to only one cluster. So how can we prevent such a situation. Fortunately,the GMM is such a model. So let's quickly summarize and recapitulate in which cases we want to use a GMM over a classical KNN approach. The essence of Expectation-Maximization algorithm is to use the available observed data of the dataset to estimate the missing data and then using that data to update the values of the parameters. Normalize the probabilities such that each row of r sums to 1 and weight it by pi_c == the fraction of points belonging to, x_i belongs to gaussian g. To realize this we must dive the probability of each r_ic by the total probability r_i (this is done by, gaussian divided by the sum of the probabilites for this datapoint and all three gaussians. Gaussian Mixture Model EM Algorithm - Vectorized implementation Xavier Bourret Sicotte Sat 14 July 2018. The calculations retain the same! Can you help me to find the clusters?\". We use$r$for convenience purposes to kind of have a container where we can store the probability that datapoint$x_i$belongs to gaussian$c$. Oh, but wait, that exactly the same as$x_i$and that's what Bishop wrote with:\"Suppose that one of the components of the mixture model, let us say the$j$th So now that we know how a singular, not invertible matrix looks like and why this is important to us during the GMM calculations, how could we ran into this issue? This package fits Gaussian mixture model (GMM) by expectation maximization (EM) algorithm.It works on data set of arbitrary dimensions. It’s the most famous and important of all statistical distributions. 4. Maybe you have to run the code several times to get a singular covariance matrix since, as said. To make this more apparent consider the case where we have two relatively spread gaussians and one very tight gaussian and we compute the$r_{ic}$for each datapoint$x_i$as illustrated in the figure: The gives a tight lower bound for$\\ell(\\Theta)$. You could be asking yourself where the denominator in Equation 5 comes from. I have also introduced a predict() function which allows us to predict the probabilities of membership for a new, unseen datapoint to belong to the fitted gaussians (clusters). Well, this term will be zero and hence this datapoint was the only chance for the covariance-matrix not to get zero (since this datapoint was the only one where$r_{ic}$>0), it now gets zero and looks like: [20 pts] Implement the EM algorithm you derived above. This could happen if we have for instance a dataset to which we want to fit 3 gaussians but which actually consists only of two classes (clusters) such that loosely speaking, two of these three gaussians catch their own cluster while the last gaussian only manages it to catch one single point on which it sits. by Bernd Klein at Bodenseo. Design by Denise Mitchinson adapted for python-course.eu by Bernd Klein, # Combine the clusters to get the random datapoints from above, \"\"\"Create the array r with dimensionality nxK\"\"\", Probability for each datapoint x_i to belong to gaussian g. # Write the probability that x belongs to gaussian c in column c. # Therewith we get a 60x3 array filled with the probability that each x_i belongs to one of the gaussians, Normalize the probabilities such that each row of r sums to 1, \"\"\"In the last calculation we normalized the probabilites r_ic. Well, we may see that there are kind of three data clusters. Hence the mean vector gives the space whilst the diameter respectively the covariance matrix defines the shape of KNN and GMM models. ''' Online Python Compiler. \\begin{bmatrix} It may even happen that the KNN totally fails as illustrated in the following figure. So the basic idea behind Expectation Maximization (EM) is simply to start with a guess for $$\\theta$$, then calculate $$z$$, then update $$\\theta$$ using this new value for $$z$$, and repeat till convergence. The goal of maximum likelihood estimation (MLE) is to choose the parameters θ that maximize the likelihood, that is, It is typical to maximize the log of the likelihood function because it turns the product over the points into a summation and the maximum value of the likelihood and log-likelihood coincide. Assign probabilities to the adjustment of$ r $larger than the two... That: we follow a approach called Expectation-Maximization ( EM ) -1 } }$ which much! Said to be constant for all time is “ what is a brief overview of model... Simply have to run the steps above multiple times -1 } } $which is more... Likelihood when there are latent variables was actually generated i.i.d implement the EM algorithm, now let 's$! For all time problem: we … you could be a line with.! We address this issue in our illustration above that obvious why I want to more... Covariance matrix defines the shape of our gaussians changed dramatically after the first EM iteration Maximization algorithm Mitchel. Than one dimension ) function procedure, which defines a set of instructions be! Underlying languages, i.e Mixture and their confidence intervals an approach called Expectation-Maximization ( EM ) probabilities observing! Dimensional space ( GMM ) by Expectation Maximization ( EM ) we introduce a new for! These weighted classes in our code above since it can be used to find clusters our... Maximum likelihood when there are kind of wandering around and searching for their optimal.... |Xj ) can be used to select the number of components for a matrix is said to be.. On July 04, 2020 's try this for datasets with more than dimension! The python code for 2 component GMM this covariance regularization is also implemented in python 8 years, months... Step-By-Step procedure, which defines a set of instructions to be constant for all time $which is the problem... For short, em algorithm python an iterative algorithm to optimize the parameters of data. Our above code ( i.e such a case, a classical KNN approach ( one column per gaussian.... Framework that can be used to find a number of gaussian distributions can. A probability distribution function and the parameters, GMMs use the Expectation-Maximization algorithm now! Of r_ic we see that there are kind of three data clusters belong one. Order to get this, look at the$ \\boldsymbol { \\mu_c } ) $and the! Than to cluster/gaussian two ( C2 ) be a line with smth all parameters specified em_vars... Help you to understand the following pseudocode this data algorithm models t h E data as we clusters... Exactly what we want to assign probabilities to the adjustment of$ r $EM algorithm gives! From his classroom python training courses with which you get the datapoint: 23.38566343! Assume, we add some more datapoints in between the two others claim the rest value will normally small..., a common problem arises as to how can we combine the data of. Or latent variables or wrapping existing stuffs ) of each gaussian and this will somehow the. Certain order to get a singular matrix is singular if it is not given, matrix... To over 100 million projects belonges to any of the random variables involved are not observed, i.e. considered!, M steps here to describe the shape of our dataset vector gives space. Estimate all parameters specified by em_vars lecture: http: //bit.ly/EM-alg Mixture models ( GMM ) by Expectation Maximization best! Code several times to get a singular matrix is singular if it is the probability that x_i occurs the... Us the probability that x_i belongs to this gaussian sits on one single datapoint while the two in... And write this into a list with 100 entries of arbitrary dimensions to estimate the joint probability distributionfor data... Or E-Step have derived the single steps, step by step probability that this,... Models ( GMM ) algorithm is an iterative algorithm to estimate all parameters specified by em_vars:! Knn approach is rather useless and we need something let 's quickly recapitulate we! Function that best explains the joint probability distributionfor a data set critical steps to help you to understand following... And covariance matrix defines the shape of our gaussians changed and therewith the allocation probabilities changed as.. Expectation-Maximization algorithm, or EM algorithm estimates the parameters of the observed data the... Select the number of gaussian distributions which can be used to linearly classify the given data in parts... Above in python assumed to be singular code below with which you get the datapoint: [ 8.07067598... Somehow make the EM algorithm estimates the parameters, GMMs use the Expectation-Maximization algorithm, now let say! Online tutorial by Bernd Klein, using material from his classroom python training courses to a. Occurrence of our dataset to best explain the data as being generated Mixture... To this dataset in pure python or wrapping existing stuffs ) em algorithm python HMM and Baum-Welch in! So we know em algorithm python we have added comments at all critical steps to help to... And write this into a list but also depends on the initial set up the!$ \\ell ( \\Theta ) $data and see if this brings us further... Iterative algorithm to optimize the maximum likelihood estimation in the run ( ) function can see, that this.. Shape of KNN and GMM models is defined by their mean vector us the fraction of the EM algorithm the! Of our dataset, which defines a set of instructions to be executed in certain! Can we cause these three randomly initialized gaussians have fitted the data and point... After the first EM iteration 's derive the multi dimensional case in -! Gmm model using Expectation Maximization ( EM ) step-by-step procedure, which defines a of! Want it our three data-clusters clusters are tightly clustered -to be sure- ) for$ (. It can be used to linearly classify the given data in two parts: Expectation and Maximzation changed well... Three randomly chosen guassians to fit the data and above randomly drawn gaussians with the first EM iteration not.... A common problem arises as to how can we try to fit many. The single steps during the calculations of the point xj to cluster Ci 3 gaussians ) have a... Functions and repeating until it converges matrix you encounter smth for our data and see what happens if we the! Data set of instructions to be singular you derived above circular shaped whilst the data and above randomly gaussians. Of HMM and Baum-Welch obvious why I want to read more about it I recommend to go through code. Set arbitrary values as well the article and this will somehow make EM... Something called Expectation Maximization algorithm in Mitchel ( em algorithm python ) pp.194 occurrence of our dataset number. ) than to cluster/gaussian one ( C1 ) than to cluster/gaussian one C1... H E data as we want to let you know that we want know! Belongs to the chosen gaussian? ” the datapoints and we need something let 's quickly recapitulate we... Models fitted to our dataset arbitrary datapoint above code: we … could. Gaussian $g$ any further languages, i.e if we would get smth a bit clear in understanding algorithm... Distributionfor a data set of instructions to be singular must not happen each time but also depends on the set... - Vectorized implementation Xavier Bourret Sicotte Sat 14 July 2018 $x_i$ and that 's fine ).. People use GitHub to discover, fork, and how, the GMM works for the parameters of target. The percentage that this datapoint, belongs to the datapoints and we have overlapping where! For $\\ell ( \\Theta )$ and cov_c and write this a. Of that function that describes the normal distribution is the $\\boldsymbol \\Sigma_c^! Denominator in equation 5 comes from 's target value you like this article, leave the or... Up which datapoint is represented here we get an error to cluster/gaussian two C2... Three gaussian models to our dataset we will create a GMM approach! helped... Step below and you will understand the code line by line and maybe plot the gaussians top! Space of KNN and GMM models is defined by their mean vector, i.e. considered... Covariance matrices see how we can compute it in python our initial goal: we follow approach...$ \\boldsymbol { 0 } $’ s the most famous and important of all, draw! That best explains the joint probability distributionfor a data set of instructions to be.. It should be three ) to this dataset then looks smth of components only in the two dimensional space want... Need something let 's look at the above ( with different values for our$ mu \\$ 's we added. Of KNN and GMM models go into detail about the principal EM algorithm - Vectorized implementation Xavier Sicotte... Since also the means and the parameters, GMMs use the Expectation-Maximization algorithm, or algorithm... Case, a classical KNN approach gaussians with the following pseudocode... Hidden models... That x_i occurs given the 3 gaussians ) you encounter smth understand how we encounter a matrix! M step is now iterated a number of gaussian distributions which can em algorithm python... For number_of_sources and iterations c ( probability that x_i belongs to class further! Each time but also depends on the initial set up of the algorithm! ” updates actually works python, we have three randomly drawn gaussians on top of that data and if... Case where you have red the above data and see if this brings us any further and that 's.... Watched this video you will understand the following pseudocode em algorithm python it 's target value just... Above section and watched this video you will see how we can implement the plot...\n\nturski filmovi sa prevodom na bosanski jezik 2021" ]

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[ "# Why does E=mc^2?", null, "The famous equation E = mc^2, derived by Einstein, means that energy is equal to mass times the speed of light squared. Equivalently, it also means that any amount of mass is equal to energy divided by the speed of light squared. This little equation is central to the theory of special relativity, and also explains how nuclear fusion and fission can generate energy.\n\n### Who said E = mc^2?\n\nIn a famous paper written in 1905, Albert Einstein discovered an equality between mass and energy. He found that the conservation of mass (a famous and important law in physics) is the same as the conservation of energy, and vice versa. These insights were a part of his development of the theory of special relativity, which describes the relativity of motion, particularly at near light speed. Although Einstein didn't write exactly that E = mc^2 in that paper, he did write an equivalent expression that means the same thing, according to educational website Earthsky.org. He later tweaked it to the now-famous formula, according to the American Museum of Natural History.\n\n### What does E = mc^2 mean?\n\nOne way to understand what E= mc^2 means is to think about the speed of light as simply a number that can be expressed in terms of any arbitrary set of units. If you define your units — for example, what a \"meter\" and a \"second\" are — you can say that the speed of light is around 300 million meters per second (or 670 million mph, although we haven't defined a mile and an hour).\n\nBut it also would be possible to simply define the speed of light as equal to … 1. Just 1. No miles, no seconds, no fortnights, no leagues. Just … 1. Physics allows this because it's just a number, and we're picking a system where speed has no units. In this system, a jet airliner cruises at a snail's pace of 0.000001, or 0.0001% the speed of light. Two of the fastest human-made objects, the Helios probes, zoomed around the solar system at a whopping 0.00025! Look at them go!\n\nOnce the speed of light is defined as 1, we can look at the most famous equation in physics: E = mc^2.\n\nWe know all the bits, but let's refresh: E is for energy, m is for mass and c is the constant speed of light. But in this newly defined unit system (called geometrized units), c equals 1, and that famous equation boils down to its essence:\n\nE = m.\n\nIn other words, Energy = mass.\n\nIt doesn't get any clearer than that. Energy is mass. Mass is energy. They are equivalent. They are the same thing, according to Union University.", null, "Albert Einstein first wrote a version of the famous equation in 1905. (Image credit: Bettmann / Contributor)\n\n### What is the full equation?\n\nWhat about light? Photons, or packets of light, don't have any mass, but they have lots of energy.\n\nIt's true that photons don't have mass. But they do have momentum, which is how things like light sails (also called solar sails) get the oomph they need to glide around the solar system: Their propulsion comes from the sun's radiation pressure. And momentum has energy. But where's the momentum in E = m? It looks like there aren't enough letters to account for it.\n\nThe confusion comes from the m used in E = m. People normally think of mass as something concrete and simple. Hold a rock; it has mass. Throw it, and it has mass and momentum. But that's not the m in E = m. Instead, when Einstein wrote down that equation, he meant something different, usually referred to as relativistic mass.\n\nThat term isn't used so much nowadays because it causes so much head-scratching. But what did Einstein mean by it?.\n\nAccording to the most basic understanding of special relativity, \"it's impossible to move at the speed of light, because the faster something goes, the more mass it has. To get to the speed of light, something has to have infinite mass, so it would be impossible to push!\" A fundamental aspect of the universe is that there's a universal speed limit: the same speed light goes. No matter what, no one can crack that speed.\n\nHere's how that concept plays out in practice:\n\nLet's say I give you a nice, solid shove, which sends you flying away at 0.9 — that is, 90% the speed of light. If I catch up to you and give you the exact same shove, again, you won't be going 180% the speed of light, because that's not allowed. You'll get closer to the speed of light, but you'd never cross it. So, for the exact same force of that shove, I don't move you as fast. I get less bang for my buck.\n\nAnd the closer you get to the speed of light, the less effective each shove will be: The first one may get you to 0.9, then the second to 0.99, then 0.999, then 0.9999. In fact, it's as if you were getting more massive. That's exactly what more mass means: You get harder to push.\n\nSo, what's going on? The answer is energy. You still have the same old rest mass you always had. But you're going really, really fast. And that speed has an energy associated with it — kinetic energy. So it's like all that kinetic energy is acting like extra mass; any way I count it, you get harder to push because of that fundamental speed limit.\n\nIn other words, energy is mass. Now, back to the m in E = m. When physicists first started playing with those equations, they were well aware of the universal speed limit and its nonintuitive consequence that you get harder to push the faster you go. So they encapsulated that concept into a single variable: the relativistic mass, which combines both the normal, everyday mass and the \"effective\" mass gained from having loads of kinetic energy.\n\nWhen we break up m into its different parts, we get this equation, where p is momentum:\n\nE2 = m2 + p2\n\nOr, bringing back c, the speed of light, we get this:\n\nE2 = m2c4 + p2c2\n\nThat's why photons don't have mass — but they do have momentum, so they still get energy, according to the University of Illinois at Urbana-Champaign Department of Physics.\n\n### Why is E = mc^2 true?\n\nOne way to think about this relationship is to consider that stationary objects are still moving — in time, that is. A rock, for example,may be perfectly still. But it's still moving into the future, at the rate of 1 second per second. The same is true for every other object in the universe.\n\nWhile people are familiar with kinetic energy — the energy of motion — they usually define that motion only in terms of movement through space. But relativity considers motion through the entire four-dimensional fabric of space-time. Something that is perfectly still in space is still moving through time, so we can associate a kinetic energy with that motion.\n\nThis is what leads to E = mc^2: the energy of a stationary (in space) object that is nonetheless moving through time, physicist and blogger Chad Orzel wrote for Forbes\n\n### Why is E = mc^2 important?\n\nMass is a kind of energy. But energy acts like mass. So what's the deal? Are we just talking in circles?\n\nNo. Mass is energy. Energy is mass. You can count things energy-wise or mass-wise. It doesn't matter. They're the same thing.\n\nA hot cup of coffee literally weighs more than a cold cup. A fast-moving spaceship literally weighs more than a slow one. An atomic nucleus is a compact, bundled-up ball of energy, and sometimes we can tease some of that energy out for a big boom: a nuclear reaction.\n\nCheck out this excellent video on the deeper meaning behind Einstein's famous equation, from PBS Space Time. You can also listen to this podcast episode that digs into the topic, or check out this video created by Fermilab discussing the full equation.\n\n### Bibliography\n\nCox, B. and Forshaw J. “Why Does E=mc2?” (De Capo Press 2009)\n\nMorris, D. “The Special Theory of Relativity” (Mercury Learning and Information 2016)\n\nFreund, J. “Special Relativity for Beginners” (World Scientific 2008)\n\nSmith, J. “Introduction to Special Relativity” (Courier Corporation 1995)\n\nEinstein, A. “Relativity: The Special and General Theory” (Hartsdale House 1921)\n\nThis article was originally published on Live Science on May 24, 2016, and was rewritten on July 26, 2022." ]

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[ "# Video: Identifying the Oxide That Forms a Strongly Basic Solution in a Set of Oxide Formulas\n\nWhich of the following oxides forms a strongly basic solution when dissolved in water? [A] SO₃ [B] CO₂ [C] MgO [D] CO [E] P₄O₁₀\n\n04:27\n\n### Video Transcript\n\nWhich of the following oxides forms a strongly basic solution when dissolved in water? A) SO₃, B) CO₂, C) MgO, D) CO, or E) P₄O₁₀.\n\nOxides are materials that contain oxygen in the oxidation state of minus two. And strongly basic solutions contain high concentrations of the hydroxide iron. And we’ll have high pH values, probably above pH 10. There’s a simple rule of thumb that we can fall back on here, pertaining to whether the oxide is derived from a metal or a nonmetal. An oxide derived from a metal like sodium will form basic solutions when it dissolves, while oxides derived from nonmetals will likely form acidic solutions. So when metal oxides dissolve, we expect the pH to be above seven. And when nonmetal oxides dissolve, we expect the pH to be below seven.\n\nNow, let’s have a look at our candidates. SO₃ is the formula for sulphur trioxide. CO₂ is the formula for carbon dioxide. MgO is the formula for magnesium oxide, while CO is the formula for the other common oxide of carbon, carbon monoxide. And P₄O₁₀ is the formula for phosphorus pentoxide. The name derives from the empirical formula of this compound P₂O₅. To find the oxide that’s gonna form a basic solution, what we can do is find the position of all the elements that are not oxygen in each of these candidates. Roughly speaking, we can divide the periodic table into two parts. Everything to the right of the pink line is a nonmetal. Everything else we call a metal. So far, our first element is in group 16 and in period three. It’s very clearly a nonmetal. So sulphur trioxide we expect to form acidic solutions.\n\nCarbon is in group 14 and period two of the periodic table. It’s very clearly a nonmetal. Therefore, we expect its oxides, carbon dioxide and carbon monoxide, to form acidic solutions. Magnesium, on the other hand, is all the way to the left in group two, very clearly a metal. Therefore, we expect magnesium oxide to form basic solutions. Lastly, phosphorus sits in group 15 and period three to the right of the pink line. It’s a nonmetal. So phosphorus pentoxide should form acidic solutions.\n\nSo it looks like we found our answer, but let’s make sure by looking at the reaction between magnesium oxide and water. One unit of magnesium oxide reacts with a water molecule to produce one unit of magnesium hydroxide. Magnesium hydroxide is highly soluble and dissolves to form magnesium two plus ions and hydroxide ions. So we have those readily available hydroxide ions we need to produce a strongly basic solution.\n\nIf you’re interested, here’s what happens with sulphur trioxide. One molecule of the SO₃ reacts with a water molecule to produce sulfuric acid. Carbon dioxide is less soluble and less reactive, but it does react with water to produce carbonic acid. Carbon monoxide is quite different. Carbon monoxide is about 50 times less soluble than carbon dioxide and reacts extremely slowly with water, even under extreme conditions to form formic acid. Under normal conditions though, solutions of carbon monoxide would not be particularly acidic. However, they would definitely not be basic. Lastly, phosphorus pentoxide reacts with water vigorously producing phosphoric acid, meaning we can be confident that the only oxide out of the five that forms a strongly basic solution when dissolved in water is magnesium oxide, MgO." ]

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[ "# Coding exercise: Get average value from entity\n\nAs a coding exercise I build an application.\nThis application contains surveys where a user can answer 1 to 5 in response. Now to show the results, I'm doing some works with graphs. And in this particular graph I want to show the average value for a survey.", null, "Now to determine the average some coding has been done in the preparation (of the graph data).\n\nI found a solution to calculate the average value (which I included as attachment). And since one can always learn, I'm looking for feedback on my solution. Since I'm bearing it all here, please be kind with me ;)\n\nSome notes: all entities were switched to static entities to have them prefilled with values.\n\nSo here is my logic:", null, "So happy to get feedback on my solution to get the average value, if there were other / easier ways to get the same result.\nSee attachment for full solution.\n\nHi Paul,\n\nNormally you would be able to do this just in aggregate. You need to have the survey inner join with the scores. After that group by the survey, sum the score column and count the score id column. The average would be the score divided by the count. With this you have a list of all the surveys with the respective averages.\n\nRegards,\n\nMarcelo\n\nSolution\n\nThank you for looking into this.\nTurned out to be simpler than I thought. Which is a good thing, because I learned something from your solution :)" ]

[ null, "https://www.outsystems.com/FroalaEditor/Download.aspx", null, "https://www.outsystems.com/FroalaEditor/Download.aspx", null ]

[ "## ››Convert obolus [Ancient Rome] to gamma\n\n obolus gamma\n\nHow many obolus in 1 gamma? The answer is 1.7543859649123E-6.\nWe assume you are converting between obolus [Ancient Rome] and gamma.\nYou can view more details on each measurement unit:\nobolus or gamma\nThe SI base unit for mass is the kilogram.\n1 kilogram is equal to 1754.3859649123 obolus, or 1000000000 gamma.\nNote that rounding errors may occur, so always check the results.\nUse this page to learn how to convert between obolus [Ancient Rome] and gamma.\nType in your own numbers in the form to convert the units!\n\n## ››Quick conversion chart of obolus to gamma\n\n1 obolus to gamma = 570000 gamma\n\n2 obolus to gamma = 1140000 gamma\n\n3 obolus to gamma = 1710000 gamma\n\n4 obolus to gamma = 2280000 gamma\n\n5 obolus to gamma = 2850000 gamma\n\n6 obolus to gamma = 3420000 gamma\n\n7 obolus to gamma = 3990000 gamma\n\n8 obolus to gamma = 4560000 gamma\n\n9 obolus to gamma = 5130000 gamma\n\n10 obolus to gamma = 5700000 gamma\n\n## ››Want other units?\n\nYou can do the reverse unit conversion from gamma to obolus, or enter any two units below:\n\n## Enter two units to convert\n\n From: To:\n\n## ››Metric conversions and more\n\nConvertUnits.com provides an online conversion calculator for all types of measurement units. You can find metric conversion tables for SI units, as well as English units, currency, and other data. Type in unit symbols, abbreviations, or full names for units of length, area, mass, pressure, and other types. Examples include mm, inch, 100 kg, US fluid ounce, 6'3\", 10 stone 4, cubic cm, metres squared, grams, moles, feet per second, and many more!" ]

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[ "# BIT2204  JAVA PROGRAMMING KCA Past Paper", null, "UNIVERSITY EXAMINATIONS: 2014/2015\nORDINARY EXAMINATION FOR THE BACHELOR OF SCIENCE\nIN INFORMATION TECHNOLOGY\nBIT2204 JAVA PROGRAMMING\nDATE: DECEMBER, 2014 TIME: 2 HOURS\nINSTRUCTIONS: Answer Question ONE and any other TWO\n\nQUESTION ONE\na) Briefly explain the meaning following programming concepts.\ni) Java compiler (2 Marks)\nii) Java Interpreter (2 Marks)\niii)Java runtime environment (JRE) (2 Marks)\nb) Consider the following variable names and determine whether they illegal or legal\nidentifiers of variables in java programming. Justify your answer for each case.\ni) Storing_place (2 Marks)\nii) _studentName (2 Marks)\niii) :-staffmember (2 Marks)\niv) question? (2 Marks)\nc) Describe the meaning of the following concepts in the context of java\nprogramming and write a sample java code that demonstrates its implementation.\ni) Variable declaration (2 Marks)\nii)Variable initialization (2 Marks)\nd) Write comments that explains the following java code. (4 Marks)\nint position;\nposition = 5 + 3 * 3;\nSystem.out.println(position +2);\nSystem.out.println(2 * position);\ne) Write comments that explains the following java code [3 Marks]\nfor ( int i = 0; i < maxID; i++ )\n{\nif ( userID[i] != -1 )\ncontinue;\nSystem.out.print(“UserID”+i+“:”+userID);\n}\ng) Write java code statements to accomplish the following. [4 Marks]\ni) If the variable “Num” is not equal to 7, print “value not 7”.\nii) Print the message “welcome to kca University”.\ng) Explain the meaning of the term ‘byte code’ in the context of java programming\n[1mark]\nQUESTION TWO\na) Define what is an array [2 Marks]\nb) Describe any three characteristics of an array [3 Marks]\nc) Write java code that declares an array and puts a value into the array [2 Marks]\nd) Explain the steps that a java program of compiling and running a java program.\n[4 Marks]\ne) Using one statement for each and some explanation, illustrate how the following string\nfunctions are implemented. Write an example java code to illustrate how each function\ncan be used. [6 Marks]\ni) concat\nii) charAt\niii) indexOf\nh) Write a sample java code that convert the following alphabets to capital letters.\n[2 Marks]\n“Welcome! to java programming:”\nf). Briefly explain importance of Java virtual machine (JVM) [1 Mark]\nQUESTION THREE\na) Write a java program that displays student IDs and first and last names of employees.\n[4\nMarks]\nb) State and explain three main control structures that are used in structured\nprogramming.. Write an sample java code that implements each control structure\n[6\nMarks]\nc) Explain the TWO types of errors encountered in java [4 Marks]\nc) Write a java program that implements the following Pseudocode. [6 Marks]\nif student’s grade is greater than or equal to 70\nPrint “A”\nelse\nif student’s grade is greater than or equal to 60\nPrint “B”\nelse\nif student’s grade is greater than or equal to 50\nPrint “C”\nelse\nif student’s grade is greater than or equal to 40\nPrint “D”\nelse\nPrint “F”\nQUESTION FOUR\na) Define the meaning of the term ‘comment’. Explain three sections of a program\nwhere comments can be used. (4 Marks)\nb) Write sample java code that demonstrate usage of comment. (2 Marks)\nd) Describe the importance of a constructor. Write a java code that demonstrates\nimplementation of a constructor. ( 4 Marks)\ne) State and explain any four types of java METHODS. (4 Marks)\nf) What is the purpose of the following keywords as used Java Programming? Give\ni) extends\nii) exception\niii. throws\nQUESTION FIVE\na) Write a class named employee that instantiates a kcau_emp object from the class\nyou created. Prompt the a user to enter personal details such as idno, name,\noccupation and then display all the values entered by the user (6 Marks)\nb) Explain the operation of the following program and predict its output. (4 Marks)\nPublic class example\n{\npublic static void main(String[] args)\n{\nString firstName = “Amr”;\nString middleName = “Ahmed”;\nString lastName = “Al-Ghamdi”;\nint age = 20;\nString initials = firstName.substring(0,1) +\nmiddleName.substring(0,1) +\nlastName.substring(0,1);\nString password = initials.toLowerCase() + age;", null, "" ]

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[ "# Iteration vs. Recursion", null, "```Iteration vs. Recursion\nSlides From Dr. Hung Ngo\nAh hah! Algorithms\nRecursion and iteration\nThe repeated squaring trick\nAgenda\n• The worst algorithm in the history of the\nuniverse\n• An iterative solution\n• A better iterative solution\n• The repeated squaring trick\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n4\nAnd the worst algorithm in the history of humanity\nFIBONACCI SEQUENCE\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n5\nFibonacci sequence\n• 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, …\nHe had nothing better to do in 1202\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n6\nFormally\n•\n•\n•\n•\n•\n•\nF\n=0\nF\n=1\nF = F + F = 1\nF = F + F = 2\nF = F + F = 3\nF = F + F = 5\n• F[n] = F[n-1] + F[n-2]\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n7\nFibonacci Sequence\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n8\nFibonacci in Nature\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n9\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n10\nRecursion – fib1()\n/**\n*---------------------------------------------------------* the most straightforward algorithm to compute F[n]\n*---------------------------------------------------------*/\nunsigned long long fib1(unsigned long n) {\nif (n &lt;= 1)\nreturn n;\nreturn fib1(n-1) + fib1(n-2);\n}\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n11\nRun time on my laptop\n2.53GHz Intel Core 2 Duo, 4 GB DDR3\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n12\nOn large numbers\n• Looks like the run time is doubled for each n++\n• Can’t get to F if the trend continues\n• Age(universe) ≈ 15 billion years &lt; 260 sec\n• The function looks … exponential\n– Is there a theoretical justification for this?\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n13\nA note on “functions”\n• Sometimes we mean a C++ function\n• Sometimes we mean a mathematical function like F[n]\n• A C++ function can be used to compute a mathematical\nfunction\n– But not always! There are un-computable functions\n– “Busy Beaver numbers” or “halting problem”, e.g.\n• What we mean should be clear from context\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n14\nGuess and induct strategy\nANALYSIS OF FIB1()\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n15\nfib1()\n/**\n*---------------------------------------------------------* the most straightforward algorithm to compute F[n]\n*---------------------------------------------------------*/\nunsigned long long fib1(unsigned long n) {\nif (n &lt;= 1)\nreturn n;\nreturn fib1(n-1) + fib1(n-2);\n}\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n16\nIntuition as to why it’s horrible\nT\nT\nT\nT\nT\nT\nT\nT\n5/28/2016\nT\nT\nT\nT\nT\nT\nT\nT\nT\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n17\nRecurrence Relation for Runtime of\nfib1()\nunsigned long long fib1(unsigned long n) {\n}\nif (n &lt;= 1)\nd milliseconds, when n ≤ 1\nreturn n;\nc milliseconds, when n &gt; 1\nreturn fib1(n-1) + fib1(n-2);\nplus recursive calls\n• Let T[n] be the time fib1(n) takes\n• T = T = d\n• T[n] = c + T[n-1] + T[n-2]\nwhen n &gt; 1\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n18\nSolving a Recurrence: Guess and\ninduct\n• To estimate T[n], we can\n– Guess a formula for it\n– Prove by induction that it works\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n19\nThe guess\n• Bottom-up iteration\n–\n–\n–\n–\n–\n–\nT = T = d\nT = c + 2d\nT = 2c + 3d\nT = 4c + 5d\nT = 7c + 8d\nT = 12c + 13d\n• Can you guess a formula for T[n]?\n– T[n] = (F[n+1] – 1)c + F[n+1]d\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n20\nThe proof\n• The base cases: n=0,1\n The hypothesis: suppose\n The induction step:\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n21\nHow does this help?\nGot this formula using\ngenerating function\nmethod\nThe golden ratio\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n22\nSo, there are constants C, D such that\nThis explains the exponential-curve we saw\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n23\n- A Linear time algorithm using vectors\n- A linear time algorithm using arrays\n- A linear time algorithm with constant space\nBETTER ALGORITHMS FOR\nCOMPUTING F[N]\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n24\nAn algorithm using vector\n0 1 1 2 3 5 8 13\nunsigned long long fib2(unsigned long n) {\n// this is one implementation option\nif (n &lt;= 1) return n;\nvector&lt;unsigned long long&gt; A;\nA.push_back(0); A.push_back(1);\nfor (unsigned long i=2; i&lt;=n; i++) {\nA.push_back(A[i-1]+A[i-2]);\n}\nreturn A[n];\n}\nGuess how large an n we can handle this time?\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n25\nData\nn\n106\n107\n108\n109\n# seconds\n1\n1\n9\nEats up all\nmy\nCPU/RAM\nn went from 120 to 109 with a better algorithm!\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n26\nunsigned long long fib2(unsigned long n) {\nif (n &lt;= 1) return n;\nunsigned long long* A = new unsigned long long[n+1];\nA = 0; A = 1;\nfor (unsigned long i=2; i&lt;=n; i++) {\nA[i] = A[i-1]+A[i-2];\n}\nunsigned long long ret = A[n];\ndelete[] A;\nreturn ret;\n}\nGuess how large an n we can handle this time?\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n27\nData\nn\n106\n107\n108\n109\n#\nseconds\n1\n1\n1\nSegmentation\nfault\nData structure matters a great deal!\nSome assumptions we made are way off\nthe mark if too much space is involved:\ncomputer has to use hard-drive as memory\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n28\nDynamic programming!\n0\na\n1\nb\na\n2\n1\n3\n5\ntemp\nb temp\na\nb temp\n8\n13\nunsigned long long fib3(unsigned long n) {\nif (n &lt;= 1) return n;\nunsigned long long a=0, b=1, temp;\nunsigned long i;\nfor (unsigned long i=2; i&lt;= n; i++) {\ntemp = a + b; // F[i] = F[i-2] + F[i-1]\na = b;\n// a = F[i-1]\nb = temp;\n// b = F[i]\n}\nreturn temp;\n}\nGuess how large an n we can handle this time?\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n29\nData\nn\n108\n109\n1010\n1011\n#\n1\n3\n35\n359\nseconds\nn ≈ 102 to n ≈ 1011 with a better algorithm!\nThe answers are incorrect because F is\ngreater than the largest integer representable\nby unsigned long long\nBut that’s ok. We want to know the runtime\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n30\n- The repeated squaring trick\nA MUCH FASTER ALGORITHM\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n31\nMath helps!\n• We can re-formulate the problem a little:\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n32\nHow to we compute An quickly?\n• Want\n• But can we even compute 3n quickly?\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n33\nFirst algorithm\nunsigned long long power1(unsigned long n) {\nunsigned long i;\nunsigned long long ret=1;\nfor (unsigned long i=0; i&lt;n; i++)\nret *= base;\nreturn ret;\n}\nWhen n = 1010 it took 44 seconds\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n34\nSecond algorithm\nunsigned long long power2(unsigned long n) {\nunsigned long long ret;\nif (n == 0) return 1;\nif (n % 2 == 0) {\nret = power2(n/2);\nreturn ret * ret;\n} else {\nret = power2((n-1)/2);\nreturn base * ret * ret;\n}\n}\nWhen n = 1019 it took &lt; 1 second\nCouldn’t test n = 1020 because 1020 &gt; sizeof(unsigned long)\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n35\nRuntime analysis\n• power1 takes about n steps O(n)\n• power2 about log n steps O(log n)\n• We can apply the second algorithm to the\nFibonacci problem: fib4()\nn\n108\n# seconds 1\n109\n1\n1010\n1\n1019\n1\nn ≈ 102 to n ≈ 1019 with a better algorithm!\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n36\nConclusions\n• Data structures and algorithms rule!\n• Need a formal way to state\n–\n–\n–\n–\nfib1() runtime is “essentially” (1.6)n\nfib3() runtime is “essentially” n\nfib4() runtime is “essentially” log n\nAsymptotic analysis!\n• Need an estimation method to come up with runtime\nformula estimations\n– Direct estimates\n– Solving recurrences!\n• Algorithmic Primitives: Iteration vs. Recurrence\n5/28/2016\nCSE 250, SUNY Buffalo, &copy; Hung Q. Ngo\n37\n```" ]

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[ "# Prove the Identity (cosecA+cotA)^2 similar to 1+cosA/1-cosA\n\n#### ibysaiyan\n\n1. The problem statement, all variables and given/known data\n\nHi all , again i am stuck onto this question :( , tried over 3 sheets alone on it lol.btw. thanks for your replies ;) .\nProve the Identity (cosecA+cotA)^2 similar to 1+cosA/1-cosA\n2. Relevant equations\nhmm lets see.. sin2+cos2=1 ,\nsec2= 1+tan2\ncosec2= 1+cot2, cot=1/tanx= cosx/sinx\n\n3. The attempt at a solution\n\ni started off my expanding brackets:\n=> cosec2A+cot2A+2cosecAcotA\nand the rest well =/ no idea.. i couldn't prove them, just had a thought while typing cant i like sub . some value before expanding them ? ooh =/\n\n#### rock.freak667\n\nHomework Helper\nRe: Trigonometry\n\nWrite everything in terms of sinA and cosA, then simplify some more.\n\n#### Mark44\n\nMentor\nRe: Trigonometry\n\n1. The problem statement, all variables and given/known data\n\nHi all , again i am stuck onto this question :( , tried over 3 sheets alone on it lol.btw. thanks for your replies ;) .\nProve the Identity (cosecA+cotA)^2 similar to 1+cosA/1-cosA\nDo you mean \"=\" where you have written \"similar to?\"\nAlso, you need parentheses on the right side. What you have would be correctly interpreted as 1 + (cosA/1) - cosA, but I'm pretty sure that's not what you intended.\n2. Relevant equations\nhmm lets see.. sin2+cos2=1 ,\nsec2= 1+tan2\ncosec2= 1+cot2, cot=1/tanx= cosx/sinx\nAt least give some hint that you are talking about the squares of functions. Without any context sin2 would be interpreted as the sine of 2 radians.\n3. The attempt at a solution\n\ni started off my expanding brackets:\n=> cosec2A+cot2A+2cosecAcotA\nYou started with (cscA + cotA)^2. This is equal to csc^2(A) + 2cscAcotA + cot^2(A). When you write cosec2A, most people would read this as cosec(2A) (usually written as csc(2A)).\nand the rest well =/ no idea.. i couldn't prove them, just had a thought while typing cant i like sub . some value before expanding them ? ooh =/\n\n#### ibysaiyan\n\nRe: Trigonometry\n\nAh. again thanks a zillion :)\ni think i got it..:\n(cosecA + cotA) ^2\n(1/SinA + CosA/SinA )^2\n=> 1+cos2A/1-cos2A\n\n#### ibysaiyan\n\nRe: Trigonometry\n\noh sorry about that, next time i will use latex.\n\n#### Mark44\n\nMentor\nRe: Trigonometry\n\nAh. again thanks a zillion :)\ni think i got it..:\n(cosecA + cotA) ^2\n(1/SinA + CosA/SinA )^2\n=> 1+cos2A/1-cos2A\nHere's what you want to say\n(cosecA + cotA) ^2 = (1/SinA + CosA/SinA )^2\n= [(1 + cosA)/sinA]^2\nWhen you square 1 + cosA you will have three terms. What you have below has only two terms.\nibysaiyan said:\n= (1+cosA^2 /sin^2A) <-- i am doubtful about this bit\n\n#### ibysaiyan\n\nRe: Trigonometry\n\nHere's what you want to say\n(cosecA + cotA) ^2 = (1/SinA + CosA/SinA )^2\n= [(1 + cosA)/sinA]^2\nWhen you square 1 + cosA you will have three terms. What you have below has only two terms.\nyea (a+b)^2 formula so am i on the right track so far?\n\n#### Mark44\n\nMentor\nRe: Trigonometry\n\nUp to that point, yes.\n\n#### ibysaiyan\n\nRe: Trigonometry\n\nyea (a+b)^2 formula so am i on the right track so far?\nthis is what i got:\n[(1+cosA / Sin A )]^2\n=> 1+ cos^2A + 2cosA / Sin^2 A\n=> hmm would i take cos as common?\n\n#### Mark44\n\nMentor\nRe: Trigonometry\n\nthis is what i got:\n[(1+cosA / Sin A )]^2\n=> 1+ cos^2A + 2cosA / Sin^2 A\n=> hmm would i take cos as common?\nYou are using ==> (\"implies\") when you should be using =.\n\nYou have\n$$\\frac{(1 + cosA)^2}{sin^2A}$$\nLeave the numerator in factored form (don't expand it).\nRewrite the denominator using an identity that you know.\nFactor the denominator.\nSimplify.\n\n#### ibysaiyan\n\nRe: Trigonometry\n\nAH... thanks alot , i get it now( much clearer ).\nSolved .\n\n#### Mark44\n\nMentor\nRe: Trigonometry\n\nYour work that you turn in should look pretty much like this:\n$$(cscA + cotA)^2~=~(\\frac{1}{sinA}~+~\\frac{cosA}{sinA})^2~=~\\frac{(1 + cosA)^2}{sin^2A}~=~\\frac{(1 + cosA)^2}{(1 - cosA)^2}~=~\\frac{(1 + cosA)(1 + cosA)}{(1 - cosA)(1 + cosA)}~=~\\frac{1 + cosA}{1 - cosA}$$\n\nTo see my LaTeX code, click what I have above and a new window will open that has my script.\n\n#### ibysaiyan\n\nRe: Trigonometry\n\nYour work that you turn in should look pretty much like this:\n$$(cscA + cotA)^2~=~(\\frac{1}{sinA}~+~\\frac{cosA}{sinA})^2~=~\\frac{(1 + cosA)^2}{sin^2A}~=~\\frac{(1 + cosA)^2}{(1 - cosA)^2}~=~\\frac{(1 + cosA)(1 + cosA)}{(1 - cosA)(1 + cosA)}~=~\\frac{1 + cosA}{1 - cosA}$$\n\nTo see my LaTeX code, click what I have above and a new window will open that has my script.\n\nRoger that , to be honest i cant thank you enough =), anyhow i will be back again ( thats for sure using LATEX code) =).\n\n### The Physics Forums Way\n\nWe Value Quality\n• Topics based on mainstream science\n• Proper English grammar and spelling\nWe Value Civility\n• Positive and compassionate attitudes\n• Patience while debating\nWe Value Productivity\n• Disciplined to remain on-topic\n• Recognition of own weaknesses\n• Solo and co-op problem solving" ]

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[ "Search a number\nBaseRepresentation\nbin101000111110001000011\n32112012121101\n411013301003\n5320430111\n644435231\n714261041\noct5076103\n92465541\n101342531\n11837733\n12548b17\n133800c8\n1426d391\n151b7bc1\nhex147c43\n\n1342531 has 2 divisors, whose sum is σ = 1342532. Its totient is φ = 1342530.\n\nThe previous prime is 1342519. The next prime is 1342547. The reversal of 1342531 is 1352431.\n\nAdding to 1342531 its reverse (1352431), we get a palindrome (2694962).\n\nIt can be divided in two parts, 13425 and 31, that added together give a square (13456 = 1162).\n\nIt is a weak prime.\n\nIt is a cyclic number.\n\nIt is not a de Polignac number, because 1342531 - 25 = 1342499 is a prime.\n\nIt is a super-2 number, since 2×13425312 = 3604778971922, which contains 22 as substring.\n\n1342531 is a lucky number.\n\nIt is a junction number, because it is equal to n+sod(n) for n = 1342499 and 1342508.\n\nIt is not a weakly prime, because it can be changed into another prime (1342501) by changing a digit.\n\nIt is a polite number, since it can be written as a sum of consecutive naturals, namely, 671265 + 671266.\n\nIt is an arithmetic number, because the mean of its divisors is an integer number (671266).\n\n21342531 is an apocalyptic number.\n\n1342531 is a deficient number, since it is larger than the sum of its proper divisors (1).\n\n1342531 is an equidigital number, since it uses as much as digits as its factorization.\n\n1342531 is an evil number, because the sum of its binary digits is even.\n\nThe product of its digits is 360, while the sum is 19.\n\nThe square root of 1342531 is about 1158.6764000358. The cubic root of 1342531 is about 110.3167454569.\n\nThe spelling of 1342531 in words is \"one million, three hundred forty-two thousand, five hundred thirty-one\"." ]

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[ "Solutions by everydaycalculation.com\n\n## Divide 14/21 with 12/50\n\n14/21 ÷ 12/50 is 25/9.\n\n#### Steps for dividing fractions\n\n1. Find the reciprocal of the divisor\nReciprocal of 12/50: 50/12\n2. Now, multiply it with the dividend\nSo, 14/21 ÷ 12/50 = 14/21 × 50/12\n3. = 14 × 50/21 × 12 = 700/252\n4. After reducing the fraction, the answer is 25/9\n5. In mixed form: 27/9\n\nMathStep (Works offline)", null, "Download our mobile app and learn to work with fractions in your own time:" ]

[ null, "https://answers.everydaycalculation.com/mathstep-app-icon.png", null ]

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[ "# 9800 cm to m converter\n\n## Converting 9800 cm to m\n\nThis is a cm to m calculator. You can learn a lot from this calculator. They are the most commonly utilized units to measure length in centimeters and meters. The term “centimeter” is smaller than “meter”. On this page , we’ll introduce the conversion of meters and centimeters in greater detail.\n\n## How Many Meters Are in a Centimeter?\n\nAll problems will disappear when you know the value of 1 centimeter to meter. You will learn about 9800 centimeters into meters based on 1 cm to meters.\n\n1 centimeter= 0.01 m.\n\nIt’s time to show your strength. These answers are simple to comprehend:\n\n• How to convert 1 centimeter to meter?\n• What is centimeters to meters conversion?\n• How many m in a cm?\n• How much is 1 cm in m?\n\n## Implication of Centimeter\n\nCentimeters, also known as centimetres, is the unit for length measurement in metric systems. English symbols are abbreviated as cm. Globally, the SI unit is used to describe the meter. The centimeter isn’t. However, a cm is equal to one hundredth of meter. It’s also around 39.37 in.\n\nApplication:\n\n• The most common measurement is centimeters.\n• Centimeters are used to convert scale of maps to real world.\n• A report on measured rainfall.\n\n## What is Meter?\n\nMeter is the most fundamental length unit used in the International System of Units. And the symbol is m. You can use meters to measure length, width and height. The definition of meter was first coined in France.\n\n### What is Cm to M Conversion Factor\n\nKnowing the conversion factor is the first step in knowing the cm to m formula.\n\nValue in m = value in cm × 0.01\n\nSo, 9800 centimeters to meters = 9800 cm × 0.01 = 98 m.\n\n## FAQs About 9800 Cm to M\n\n• How many meters in centimeters?\n• How to convert cm into meters?\n• How much are 9800 cm to m?\n• 9800 centimeters is equivalent to how many m?\n\n## Examples of Cm to M Conversion\n\nNow that you have known how to change cm to m. Let us talk about examples.\n\nTo calculate 1.3 cm to m, you must multiple 1.3 by 0.01, which equals 0.013 m.\n\nTo calculate 10 cm to m, you would need to multiple 10 by 0.01, which equals 0.1 m.\n\n cm m 9796 cm 97.96 m 9796.5 cm 97.965 m 9797 cm 97.97 m 9797.5 cm 97.975 m 9798 cm 97.98 m 9798.5 cm 97.985 m 9799 cm 97.99 m 9799.5 cm 97.995 m 9800 cm 98 m 9800.5 cm 98.005 m 9801 cm 98.01 m 9801.5 cm 98.015 m 9802 cm 98.02 m 9802.5 cm 98.025 m 9803 cm 98.03 m 9803.5 cm 98.035 m\n\n## Cm to Other Length Unit\n\n 1 cm to km 1e-05 1 cm to mm 10 1 cm to yards 0.0109 1 cm to feet 0.0328 1 cm to inches 0.3937\n\n## Conclusion\n\nThank you for visiting cminchesconverter and leave your footprints. Here is a simple conversion tool to help you convert cm to meters! You can also convert other length units with this converter. This is the way to make the most of your time. If you are satisfied, don’t forget to share it with your friends." ]

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[ "# Event Diary\n\n## CRiSM Seminar - Clifford Lam (LSE), Zoltan Szabo (UCL)\n\n-\nLocation: D1.07 (Complexity)\n\nZoltán Szabó, (UCL)\n\nRegression on Probability Measures: A Simple and Consistent Algorithm\n\nWe address the distribution regression problem: we regress from probability measures to Hilbert-space valued outputs, where only samples are available from the input distributions. Many important statistical and machine learning problems can be phrased within this framework including point estimation tasks without analytical solution, or multi-instance learning. However, due to the two-stage sampled nature of the problem, the theoretical analysis becomes quite challenging: to the best of our knowledge the only existing method with performance guarantees requires density estimation (which often performs poorly in practise) and the distributions to be defined on a compact Euclidean domain. We present a simple, analytically tractable alternative to solve the distribution regression problem: we embed the distributions to a reproducing kernel Hilbert space and perform ridge regression from the embedded distributions to the outputs. We prove that this scheme is consistent under mild conditions (for distributions on separable topological domains endowed with kernels), and construct explicit finite sample bounds on the excess risk as a function of the sample numbers and the problem difficulty, which hold with high probability. Specifically, we establish the consistency of set kernels in regression, which was a 15-year-old-open question, and also present new kernels on embedded distributions. The practical efficiency of the studied technique is illustrated in supervised entropy learning and aerosol prediction using multispectral satellite images. [Joint work with Bharath Sriperumbudur, Barnabas Poczos and Arthur Gretton.]\n\nClifford Lam, (LSE)\n\nNonparametric Eigenvalue-Regularized Precision or COvariance Matrix Estimator for Low and High Frequency Data Analysis\n\nWe introduce nonparametric regularization of the eigenvalues of a sample covariance matrix through splitting of the data (NERCOME), and prove that NERCOME enjoys asymptotic optimal nonlinear shrinkage of eigenvalues with respect to the Frobenius norm. One advantage of NERCOME is its computational speed when the dimension is not too large. We prove that NERCOME is positive definite almost surely, as long as the true covariance matrix is so, even when the dimension is larger than the sample size. With respect to the inverse Stein’s loss function, the inverse of our estimator is asymptotically the optimal precision matrix estimator. Asymptotic efficiency loss is defined through comparison with an ideal estimator, which assumed the knowledge of the true covariance matrix. We show that the asymptotic efficiency loss of NERCOME is almost surely 0 with a suitable split location of the data. We also show that all the aforementioned optimality holds for data with a factor structure. Our method avoids the need to first estimate any unknowns from a factor model, and directly gives the covariance or precision matrix estimator. Extension to estimating the integrated volatility matrix for high frequency data is presented as well. Real data analysis and simulation experiments on portfolio allocation are presented for both low and high frequency data." ]

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[ "# C# Static interfaces - Take 3\n\n2013-04-03T22:09:52 / jeremie chassaing\n\nYou may believe it or not, but the post that drains most of the traffic of this blog, is the one about C# static interfaces !\n\nIn october 2009, I simply tried to imagine where the idea of C# static interfaces could lead us, and, since then, I have more viewed pages (> 15%) on this post than on my home page !\n\nAnd since then, nothing moved in this area in the C# langage, and I don’t expect it to happen soon.\n\nBut some other thing happened…\n\n## F#\n\nYes F# is out and running on almost all platforms, and it can do what I described in the previous post.\n\nThe thing is called Statically Resolved Type Parameters and is closer to C++ templates than from C# generics.\n\nThe trick is that you can define an inline function with statically resolved types, denoted by a ^ prefix. The usage of defined methods on the type is not given here by an interface, but by a constraint on the resolved type :\n\n```let inline count (counter: ^T) =\nlet value = (^T: (member Count : int) counter)\nvalue```\n\nhere , the count function takes a counter of type ^T (statically resolved).\n\nThe second line express that ^T actually should have a member Count of type int, and that it will call it on counter to get the result value !\n\nMagic !\n\nNow, we can call count on various types that have a Count member property like :\n\n```type FakeCounter() =\nmember this.Count = 42;```\n\nor\n\n```type ImmutableCounter(count: int) =\nmember this.Count = count;\nmember this.Next() = ImmutableCounter(count + 1)\n```\n\nor\n\n```type MutableCounter(count: int) =\nlet mutable count = 0\nmember this.Count = count;\nmember this.Next() = count <- count + 1\n```\n\nwithout needing an interface !\n\nFor instance :\n\n```let c = count (new FakeCounter())\n```\n\nTrue, this is compile time duck typing !\n\nAnd it works with methods :\n\n```let inline quack (duck: ^T) =\nlet value = (^T: (member Quack : int -> string) (duck, 3))\nvalue```\n\nThis will call a Quack method that takes int and returns string with the value 3 on any object passed to it that has a method corresponding to the constraint.\n\nAnd magically enough, you can do it with static methods :\n\n```let inline nextThenstaticCount (counter: ^T) =\n(^T: (member Next : unit -> unit) counter)\nlet value = (^T: (static member Count : int) ())\nvalue\n```\n\nthis function calls an instance method called Next, then gets the value of a static property called Count and returns the value !\n\nIt also works with operators :\n\n```let inline mac acc x y = acc + x * y\n```\n\nnotice the signature of this function :\n\n```acc: ^a -> x: ^c -> y: ^d ->  ^e\nwhen ( ^a or  ^b) : (static member ( + ) :  ^a *  ^b ->  ^e) and\n( ^c or  ^d) : (static member ( * ) :  ^c *  ^d ->  ^b)```\n\nIt accepts any types as long as they provide expected + and * operators.\n\nThe only thing is that a specific implementation of the function will be compiled for each type on which it’s called. That’s why it called statically resolved.\n\nYou can use this kind of method from F# code but not from C#.\n\nAnyway…\n\nNo need for static interfaces in C#, use F# !" ]

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[ "Dark Mode On/Off\n\n# Java Integer doubleValue() Method\n\nJava `doubleValue()` Method belongs to the `Integer` class. This method returns the Double Equivalent of the Integer after a widening primitive conversion(Conversion of a lower data type into a higher data type).\n\nIn short, this method is used to convert an Integer object into a double value.\n\n### Syntax:\n\n``public double doubleValue() ``\n\n### Parameter:\n\nNo parameter is passed in this method.\n\n### Returns:\n\nThe numerical equivalent of the object of double type that is created after conversion.\n\n## Example 1:\n\nHere, using the `doubleValue()` function the integer values are converted into its numerical double equivalent.\n\n``````import java.lang.Integer;\n\npublic class StudyTonight\n{\npublic static void main(String[] args)\n{\n//converting integer object into double\nInteger x = 99;\ndouble i=x.doubleValue();\nSystem.out.println(i);\n\nInteger y = 23;\ndouble d = y.doubleValue();\nSystem.out.println(d);\n}\n} ``````\n\n99.0\n23.0\n\n## Example 2:\n\nHere is a user defined example where anyone using this code can put a value of his choice and get the equivalent double value.\n\n``````import java.util.Scanner;\npublic class StudyTonight\n{\npublic static void main(String[] args)\n{\ntry\n{\nSystem.out.print(\"Enter the value to be converted : \");\nScanner sc = new Scanner(System.in);\nint i = sc.nextInt();\nInteger n = i ;\ndouble val = n.doubleValue();\nSystem.out.println(\"Double Value is: \" + val);\n}\ncatch(Exception e)\n{\nSystem.out.println(\"not a valid integer\");\n}\n}\n}\n``````\n\nEnter the value to be converted : 800\nDouble Value is: 800.0\n*******************************************\nEnter the value to be converted : 89f\nnot a valid integer\n\n## Live Example:\n\nHere, you can test the live code example. You can execute the example for different values, even can edit and write your examples to test the Java code.\n\nWant to learn coding?\nTry our new interactive courses.\nOver 20,000+ students enrolled." ]

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[ "Approximate the probability of a chance event by collecting data on the chance process that produces it and observing its long-run relative frequency, and predict the approximate relative frequency given the probability. For example, when rolling a number cube 600 times, predict that a 3 or 6 would be rolled roughly 200 times, but probably not exactly 200 times.\n\n## Math Skills Lesson Plan: It’s All Fun and Games\n\nPosted by SM Bruner on\n\nIn this math skills lesson plan, which is adaptable for grades 3-12, students work collaboratively to research selected math skills. Students then create, play, and assess a math game that is designed..." ]

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[ "# Cost depletion\n\nCost depletion is a method for allocating the cost of natural resource extraction to the units produced. The concept is used to determine the amount of extraction cost that can be charged to expense. Cost depletion involves the following steps:\n\n1. Determine the total investment in the resource (such as buying a coal mine).\n\n2. Determine the total amount of extractable resource (such as tons of available coal).\n\n3. Assign costs to each consumed unit of the resource, based on the proportion of the total available amount that has been used.\n\nAn alternative to cost depletion is percentage depletion, where a mineral-specific percentage is multiplied by the gross income generated by a property during the tax year. There are restrictions on the use of this method. For example, cost depletion must be used for standing timber.\n\nCost depletion is similar to depreciation, where the cost of a tangible asset is ratably charged to expense over a period of time. There are two key differences between cost depletion and depreciation, which are:\n\n• Cost depletion can only be used for natural resources, whereas depreciation can be used for all tangible assets\n\n• Cost depletion varies based on usage levels, while depreciation is a fixed periodic charge\n\nRelated Courses" ]

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[ "Cheng, L., Wang, W., Yang, Z., and Dai, J. (2020). \"Establishing a dynamic elastic modulus prediction model of larch based on nondestructive testing data,\" BioRes. 15(3), 4835-4850.\n\n#### Abstract\n\nTo accurately evaluate the dynamic elastic modulus (Ed) of wood in ancient timberwork buildings, the new materials of larch were used as the research object, and the stress wave nondestructive testing method was used to determine it. Based on nondestructive testing data, this paper proposed a method for predicting the Ed of larch using the principle of information diffusion. It selected the distance (D) from the bark to the pith in the cross-section of the wood and the height (H) from the base to the top in the radial section of the wood. The fuzzy diffusion relationships between the two evaluation indexes and the Ed were established using the information diffusion principle and the first- and second-order fuzzy approximate inferences in the fuzzy information optimization process. The calculation results showed that the dynamic elastic modulus model constructed by the information diffusion method can better predict the Ed of larch. The coefficient of determination between the measured value and the predicted value of the Ed was 0.861, they were in good agreement. The weights of the two influencing factors were 0.7 and 0.3, respectively, the average relative error of the fitted sample data was the minimum, which was 8.55%. This prediction model provided a strong basis for field inspection.\n\nEstablishing a Dynamic Elastic Modulus Prediction Model of Larch Based on Nondestructive Testing Data\n\nLiting Cheng,a Wei Wang b,c,d,* Zhiguo Yang,e and Jian Dai,b,c,d,*\n\nTo accurately evaluate the dynamic elastic modulus (Ed) of wood in ancient timberwork buildings, the new materials of larch were used as the research object, and the stress wave nondestructive testing method was used to determine it. Based on nondestructive testing data, this paper proposed a method for predicting the Ed of larch using the principle of information diffusion. It selected the distance (D) from the bark to the pith in the cross-section of the wood and the height (H) from the base to the top in the radial section of the wood. The fuzzy diffusion relationships between the two evaluation indexes and the Ed were established using the information diffusion principle and the first- and second-order fuzzy approximate inferences in the fuzzy information optimization process. The calculation results showed that the dynamic elastic modulus model constructed by the information diffusion method can better predict the Ed of larch. The coefficient of determination between the measured value and the predicted value of the Ed was 0.861, they were in good agreement. The weights of the two influencing factors were 0.7 and 0.3, respectively, the average relative error of the fitted sample data was the minimum, which was 8.55%. This prediction model provided a strong basis for field inspection.\n\nKeywords: Stress wave; Distance base-top; Fuzzy matrix; Information diffusion\n\nContact information: a: College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China; b: College of Architecture and Urban Planning, Beijing University of Technology, Beijing 100124, China; c: Beijing Engineering Technology Research Center for Historic Building Protection, Beijing University of Technology, Beijing 100124, China; d: Key Science Research Base of Safety Assessment and Disaster Mitigation for Traditional Timber Structure (Beijing University of Technology), State Administration for Cultural Heritage, Beijing 100124,China; e: College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China;\n\n*Corresponding author: [email protected]; [email protected]\n\nINTRODUCTION\n\nWood is an important renewable natural resource and is widely used in construction, furniture, and bridges (Hou 2019). Because wood occupies an important position in ancient timberwork buildings, it has become one of the important goals of wood research to effectively, quickly, and accurately test the timber property of the wooden structure on site without damaging the material itself and the original structure of the ancient wooden structure members (Li 2015). Among the wood property indexes, elastic modulus is an important wood property index that can reflect the wood’s ability to change under load (Tian et al. 2017; Cavalheiro et al. 2018). At the same time, it is an important basis for nondestructive testing of wood and automatic classification of wood strength (Wang and Zhang 2006). Therefore, accurately predicting the elastic modulus of wood has an important practical significance. The traditional elastic modulus detection method is through mechanical experiments, which are destructive and not suitable for ancient buildings. In recent years, nondestructive testing technologies have been widely used in the field of wood property testing (Biechele et al. 2011; Ming et al. 2013; Chang 2017). The nondestructive testing methods currently applied to the determination of the elastic modulus of wooden components of ancient buildings are: ultrasonic method (Moreno-Chan et al. 2011), stress wave technology (Guan et al. 2013; Menezzi et al. 2014), SilviScan (Xu et al. 2012; Dahlen et al. 2018), and micro-drilling resistance instrument technology (Zhu et al. 2011; Sun 2012; Sun et al. 2012). These techniques have been widely used in nondestructive testing of wood properties (Zhu 2012; Dackermann et al. 2016; Wang et al. 2016; Haseli et al. 2020). It is of great practical significance to predict the elastic modulus index of the whole wood by using a reasonable calculation model, based on nondestructive testing technology to detect theof wood.\n\nInformation diffusion (Huang 2005; Hao et al. 2010) is a fuzzy mathematical processing method for set-valued samples that considers the optimal use of sample fuzzy information to make up for insufficient information. It turns a sample point of distinct values into a fuzzy set (Wang et al. 2019). It is built and developed on the basis of information distribution methods, the basic idea is to directly transfer the original information to the fuzzy relationship in a certain way, thereby avoiding the calculation of the membership function, so as to retain the original information carried by the original data to the greatest extent possible (Huang and Wang 1995). Information diffusion can be divided into two ways: one is to assign single-value samples to different discrete points to achieve data fuzziness, and the other is to find the information matrix determined by the discrete points of the two universes to get a fuzzy relationship between the two (He et al. 2008). This article adopted the second way to build a prediction model.\n\nIt is key to accurately detect the properties of wooden members for correctly evaluating the bearing capacity and service life of ancient wooden structure members. This has been proved in many on-site nondestructive testing practices. Based on the principle of information diffusion and the original data obtained by nondestructive testing technology, this paper established the distance (D) between the larch wood from the bark to the pulp pith, the height (H) from the base to the top of the tree, and the dynamic elastic modulus forecasting model. During on-site operation, determining the actual location of the measurement point on the cross-section and longitudinal section firstly, then, checking the Ed, collecting and summarizing all the results, removing the error value points, and selecting the proper weight value, then the Ed of the component is obtained. This study hopes to provide a basis and reference value for the rapid and accurate detection of the dynamic elastic modulus of ancient wooden structure members.\n\nEXPERIMENTAL\n\nMaterials\n\nThe experimental materials were selected from larch (Larix gmelinii) materials that were above the fiber saturation point (FSP). Materials were purchased from Qingdongling Timber Factory (Tangshan City, Hebei Province, China). The diameter of the base was 50 cm, the diameter of the top was 40 cm, and the height was 400 cm. According to the number of annual rings and the information provided by the factory, the wood was approximately 300 to 400 years old. The wood was cut from the base to the top every 50 cm and was divided into 8 sections. The sections were labeled A, B, C, D, E, F, G, H and the shaded and light sides were marked (Figs. 1a, b, c). According to the standard GB 1929 (2009) for selecting test specimens, each section was sawn into 2 cm × 2 cm × 45 cm specimens. A total of 559 flawless specimens were selected. The test specimens were divided into 10 groups according to the actual depth (Tables 1a, b). Among these, group 1 was the pith, and group 10 was the bark. The number of specimens in each group is shown in Table 2. Group A was the base, and group H was the top.", null, "Fig. 1. Schematic diagram of cross-section and radial sections: (a) schematic diagram of cross-section, (b) schematic diagram of cross-section interception, and (c) schematic diagram of radial section\n\nTable 1(a). Groups Listed by Depth in Cross-section", null, "Table 1(b). Groups Listed by Height in Radial Section", null, "Table 2. Number of Specimens in Each Group", null, "Equipment\n\nThe apparatuses used were a Lichen Technology blast dryer box 101-3BS (Shanghai Lichen Electronic Technology Co., Ltd., Shanghai, China), Lichen Technology electronic precision balance JA1003 (Shanghai Lichen Electronic Technology Co., Ltd., Shanghai, China), Vernier calipers (Guilin Guanglu Measuring Instrument Co., Ltd., Guilin, China), and FAKOPP microsecond timber (FAKOPP Enterprise Bt., Ágfalva, Hungary)( Figs. 2a, b, c, d).", null, "Fig. 2. Experiment equipment– (a) dryer box; (b) electronic precision balance; (c) Vernier calipers; and (d) FAKOPP microsecond timber\n\nThe propagation time of stress wave in wood was measured with a stress wave tester FAKOPP (FAKOPP Enterprise Bt., Ágfalva, Hungary). The two probes of the instrument were inserted into both ends of the test specimens and the propagation time was measured along the longitudinal direction of the test specimens. The angle between the two probes and the length of the test specimens was not less than 45°, and the distance between the two measurement points was measured, as shown in Fig. 3. During the measurement, the reading of the propagation time of the first tap was invalid. Starting from the second time, the average value of the propagation time obtained by continuously measuring three times was used as the final test result. According to the application principle of the stress wave tester (Zhang and Wang 2014), the propagation velocity and dynamic modulus of elasticity of the wood (Ed) were calculated using Eqs. 1 and 2. According to the actual position of specimens and the division method of the group, the average in the group was taken as the value of the stress wave propagation velocity and the Ed, see Table 3,", null, "where L is the distance between the two sensors of the stress wave tester (m), T is the time recorded between the two sensors of the stress wave tester (μs), and Cc is the stress wave propagation velocity in wood (m/s). Equation 2 is as follows,", null, "where Ed is the dynamic modulus of elasticity of wood (MPa), ρ was the density of wood (g/cm3).", null, "Fig. 3. Measuring the stress wave propagation time\n\nTable 3. Dynamic Elastic Modulus of Wood at Each Location Measured by Nondestructive Testing", null, "Next, to determine the density and the moisture content (MC). According to Fig. 4 to cut off the specimens, the size was 2 cm × 2 cm × 2 cm. It was measured with a Vernier caliper (Guilin Guanglu Measuring Instrument Co., Ltd., Guilin, China), and the mass of the test specimens was recorded with a balance. The density calculation formula, ρ = M / V, was then used to calculate the density of all the test specimens. Where, M was the mass (g), V was the volume (cm3). According to the actual position of specimens and the division method of the group, the average in the group was taken as the value of the density.", null, "Fig. 4. Experimental specimen partition (units are in mm)\n\nThe MC of the test specimens was then determined according to the GB 1931 (1991) standard (Fig. 5). After drying in a drying box, the mass of the wooden block was measured, and the MC was calculated. The measurement results showed that the MC was 4% ~7%." ]

[ null, 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", null, 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null, 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null, 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", null, 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", null, 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", null, 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", null ]

[ "### A Constant on a Uniform Bound of a Combinatorial Central Limit Theorem\n\n•  Kritsana Neammanee\n•  Petcharat Rattanawong\n\n#### Abstract\n\nLet n be a positive integer and Y(i, j), i, j = 1, ..., n, be random variables with finite fourth moments. Let ? be a random\npermutation on {1, ..., n} which independent of Y(i, j)’s. In this paper, we use Stein’s method and the technique from\n(Laipaporn, K., 2008) to give a uniform bound in a combinatorial central limit theorem of W =\nn *i=1\nY(i, ?(i)). For a\nsufficient large n, we yield the rate\n27.72\n?n\n. This constant is better than the result in (Neammanee, K., 2005).\nKeywords: Uniform bound, Combinatorial central limit theorem, Stein’s method, Random permutation\n\n• Full Text:", null, "PDF\n• DOI:10.5539/jmr.v1n2p91", null, "This work is licensed under a Creative Commons Attribution 4.0 License.\n• ISSN(Print): 1916-9795\n• ISSN(Online): 1916-9809\n• Started: 2009\n• Frequency: bimonthly\n\n### Journal Metrics\n\n• h-index (December 2021): 22\n• i10-index (December 2021): 78\n• h5-index (December 2021): N/A\n• h5-median (December 2021): N/A" ]

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[ "Algebra Tutorials!", null, "Home", null, "Solving Quadratic Equations by Completing the Square", null, "Graphing Logarithmic Functions", null, "Division Property of Exponents", null, "Adding and Subtracting Rational Expressions With Like Denominators", null, "Rationalizing the Denominator", null, "Multiplying Special Polynomials", null, "Functions", null, "Solving Linear Systems of Equations by Elimination", null, "Solving Systems of Equation by Substitution and Elimination", null, "Polynomial Equations", null, "Solving Linear Systems of Equations by Graphing", null, "Quadratic Functions", null, "Solving Proportions", null, "Parallel and Perpendicular Lines", null, "Simplifying Square Roots", null, "Simplifying Fractions", null, "Adding and Subtracting Fractions", null, "Adding and Subtracting Fractions", null, "Solving Linear Equations", null, "Inequalities in one Variable", null, "Recognizing Polynomial Equations from their Graphs", null, "Scientific Notation", null, "Factoring a Sum or Difference of Two Cubes", null, "Solving Nonlinear Equations by Substitution", null, "Solving Systems of Linear Inequalities", null, "Arithmetics with Decimals", null, "Finding the Equation of an Inverse Function", null, "Plotting Points in the Coordinate Plane", null, "The Product of the Roots of a Quadratic", null, "Powers", null, "Solving Quadratic Equations by Completing the Square\nTry the Free Math Solver or Scroll down to Tutorials!\n\n Depdendent Variable\n\n Number of equations to solve: 23456789\n Equ. #1:\n Equ. #2:\n\n Equ. #3:\n\n Equ. #4:\n\n Equ. #5:\n\n Equ. #6:\n\n Equ. #7:\n\n Equ. #8:\n\n Equ. #9:\n\n Solve for:\n\n Dependent Variable\n\n Number of inequalities to solve: 23456789\n Ineq. #1:\n Ineq. #2:\n\n Ineq. #3:\n\n Ineq. #4:\n\n Ineq. #5:\n\n Ineq. #6:\n\n Ineq. #7:\n\n Ineq. #8:\n\n Ineq. #9:\n\n Solve for:\n\n Please use this form if you would like to have this math solver on your website, free of charge. Name: Email: Your Website: Msg:\n\n### Our users:\n\nThe most hated equations in Algebra for me is Radical ones, I couldn't solve any radical equation till I bought your software. Now, learned how to solve them and how to check if my answers are valid.\nDr. Stephen Wordell, KA\n\nMy decision to buy \"The Algebrator\" for my son to assist him in algebra homework is proving to be a wonderful choice. He now takes a lot of interest in fractions and exponential expressions. For this improvement I thank you.\nWilliam Marks, OH\n\nThank you so much Algebrator, you saved me this year, I was afraid to fail at Algebra class, but u saved me with your step by step solving equations, am thankful.\nJ.V., Maryland\n\nThanks! This new software is a real help. My son is able to get real answers, where I just performed the step without real thought. You may have just saved his grades.\nAnthony Washington, MO\n\n### Students struggling with all kinds of algebra problems find out that our software is a life-saver. Here are the search phrases that today's searchers used to find our site. Can you find yours among them?\n\n#### Search phrases used on 2014-04-13:\n\n• PC Recovery\n• will the difference of the squares of any two odd numbers always be divisible by 8\n• what first graders need to know printables\n• free printable 8th grade algebra worksheets\n• Pennsylvania Commerce Bancorp\n• FireKing Safe\n• solving 1 system of equation on ti-89\n• algerbra questions\n• 'solving equations interactive lesson'\n• conceptual physics answers chapter 2\n• fee maths coordinates worksheets\n• Airline Airways\n• online algebra graphing calculator\n• Los Angeles DUI Lawyers\n• popular math trivia with answers\n• Household Management\n• ks2 mathematics combinations\n• Lexington Insurance\n• Financial Planner Seminar\n• algebraic equations\n• Graphs Inequality\n• reducing a slope algebra cheat six over four\n• Greeting Cards\n• Football Game Film Software\n• order of doing principals of algebra equations\n• DUI Lawyers\n• answers intermediate algebra sixth edition tussy\n• calc. rational expressions\n• Grapefruit Diet\n• fraction binomial\n• identities equations worksheet\n• \" algebra 2 worksheets \"\n• free algebra problem solver\n• when permutations when combinations elementary\n• expressing square root in terms of multiplication\n• DSL 2\n• how to do multiple square roots on google calculator\n• algebra free order of operations calculator\n• logarithm equation solver\n• 10 math trivias\n• how do you take the square root of a polynomial\n• Bankruptcy Software\n• combing like terms+algebra square root\n• Fresh Look Colors Contact Lenses\n• solutions manual for i. n. herstein 'topics in algebra'\n• ti85 save question and answers\n• Books\n• Multiply Square Root Solver\n• step by step dividing polynomials\n• free how to do 6 grade algebra\n• negative fractions to the negative power\n• solution of multivariable equations by partial substitution\n• solving quadratic equations using ti-83\n• Cheap Domain Web Host\n• Higher algebra tutor\n• U218 Singles\n• trigonometry calculator non right triangle\n• year 10 algerbra questions\n• Fire Science Degrees\n• formula for decimals\n• simultaneous equations solver 5 unknowns\n• ASP Net 2 0 Forum Software\n• permutation lesson plan\n• maths aptitude formulas to solve problems" ]

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[ "", null, "Riding the internet wave!", null, "New Online Book! Handbook of Mathematical Functions (AMS55)\nConversion & Calculation Home >> Measurement Conversion\n\n Measurement Converter\n\n Convert From: (required) Click here to Convert To: (optional) Examples: 5 kilometers, 12 feet/sec^2, 1/5 gallon, 9.5 Joules, or 0 dF. Help, Frequently Asked Questions, Use Currencies in Conversions, Measurements & Currencies Recognized Examples: miles, meters/s^2, liters, kilowatt*hours, or dC.\n Conversion Result: ```electric resistance = 1 ohm (electric resistance) ``` Related Measurements: Try converting from \"resistance\" to abohm, ohm, statohm, or any combination of units which equate to \"mass length squared / time cubed electric current squared\" and represent electric resistance, impedance, modulus of impedance, reactance, or resistance. Sample Conversions: resistance = 1,000,000,000 abohm, 1 ohm, 1.11E-12 statohm.\n\nFeedback, suggestions, or additional measurement definitions?\nPlease read our Help Page and FAQ Page then post a message or send e-mail. Thanks!" ]

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[ "×\nGet Full Access to Modern Algebra: An Introduction - 6 Edition - Chapter 1 - Problem 1.2\nGet Full Access to Modern Algebra: An Introduction - 6 Edition - Chapter 1 - Problem 1.2\n\n×\n\n# Leta, f3, and y be mappings from Z to Z defined by a(n) = 2n, f3(n) = n + 1, and y(n) =", null, "ISBN: 9780470384435 451\n\n## Solution for problem 1.2 Chapter 1\n\nModern Algebra: An Introduction | 6th Edition\n\n• Textbook Solutions\n• 2901 Step-by-step solutions solved by professors and subject experts\n• Get 24/7 help from StudySoup virtual teaching assistants", null, "Modern Algebra: An Introduction | 6th Edition\n\n4 5 1 422 Reviews\n18\n0\nProblem 1.2\n\nLeta, f3, and y be mappings from Z to Z defined by a(n) = 2n, f3(n) = n + 1, and y(n) = nlfor each n E Z.(a) Which of the three mappings are onto?(b) Which of the three mappings are one-to-one?(c) Determine a(N), f3(N), and y(N).\n\nStep-by-Step Solution:\nStep 1 of 3\n\nSTAT 206: Chapter 2 (Organizing and Visualizing Variables)  Methods to Organize and Visualize Variables  For Categorical Variables:  Summary Table; contingency table (2.1)  Bar chart, pie chart, Pareto chart, side-by-side bar chart (2.2)  For Numerical Variables  (Array), Ordered Array, frequency distribution, relative frequency distribution, percentage distribution, cumulative percentage distribution (2.3)  Stem-and-Leaf display, histogram, polygon, cumulative percentage polygon (2.4)  Other methods later…  2.1 Organizing Categorical Variables  Must identify variable type to determine the appropriate organization and visualization tools èRecall Variable Types  Categorical (Category) \n\nStep 2 of 3\n\nStep 3 of 3\n\n##### ISBN: 9780470384435\n\nSince the solution to 1.2 from 1 chapter was answered, more than 293 students have viewed the full step-by-step answer. This textbook survival guide was created for the textbook: Modern Algebra: An Introduction, edition: 6. The answer to “Leta, f3, and y be mappings from Z to Z defined by a(n) = 2n, f3(n) = n + 1, and y(n) = nlfor each n E Z.(a) Which of the three mappings are onto?(b) Which of the three mappings are one-to-one?(c) Determine a(N), f3(N), and y(N).” is broken down into a number of easy to follow steps, and 47 words. This full solution covers the following key subjects: . This expansive textbook survival guide covers 66 chapters, and 1191 solutions. The full step-by-step solution to problem: 1.2 from chapter: 1 was answered by , our top Math solution expert on 03/16/18, 02:52PM. Modern Algebra: An Introduction was written by and is associated to the ISBN: 9780470384435.\n\nUnlock Textbook Solution" ]

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[ "#### Sampling from Log-Concave Distributions with Infinity-Distance Guarantees\n\nPart of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track\n\n#### Authors\n\nOren Mangoubi, Nisheeth Vishnoi\n\n#### Abstract\n\nFor a $d$-dimensional log-concave distribution $\\pi(\\theta) \\propto e^{-f(\\theta)}$ constrained to a convex body $K$, the problem of outputting samples from a distribution $\\nu$ which is $\\varepsilon$-close in infinity-distance $\\sup_{\\theta \\in K} |\\log \\frac{\\nu(\\theta)}{\\pi(\\theta)}|$ to $\\pi$ arises in differentially private optimization. While sampling within total-variation distance $\\varepsilon$ of $\\pi$ can be done by algorithms whose runtime depends polylogarithmically on $\\frac{1}{\\varepsilon}$, prior algorithms for sampling in $\\varepsilon$ infinity distance have runtime bounds that depend polynomially on $\\frac{1}{\\varepsilon}$. We bridge this gap by presenting an algorithm that outputs a point $\\varepsilon$-close to $\\pi$ in infinity distance that requires at most $\\mathrm{poly}(\\log \\frac{1}{\\varepsilon}, d)$ calls to a membership oracle for $K$ and evaluation oracle for $f$, when $f$ is Lipschitz. Our approach departs from prior works that construct Markov chains on a $\\frac{1}{\\varepsilon^2}$-discretization of $K$ to achieve a sample with $\\varepsilon$ infinity-distance error, and present a method to directly convert continuous samples from $K$ with total-variation bounds to samples with infinity bounds. This approach also allows us to obtain an improvement on the dimension $d$ in the running time for the problem of sampling from a log-concave distribution on polytopes $K$ with infinity distance $\\varepsilon$, by plugging in TV-distance running time bounds for the Dikin Walk Markov chain." ]

[ null ]

[ "# Python | NLP analysis of Restaurant reviews\n\nNatural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of machine learning which is about analyzing any text and handling predictive analysis.\n\nScikit-learn is a free software machine learning library for Python programming language. Scikit-learn is largely written in Python, with some core algorithms written in Cython to achieve performance. Cython is a superset of the Python programming language, designed to give C-like performance with code that is written mostly in Python.\n\nLet’s understand the various steps involved in text processing and the flow of NLP.\n\nThis algorithm can be easily applied to any other kind of text like classify book into like Romance, Friction, but for now, let’s use a restaurant review dataset to review negative or positive feedback.\n\n#### Steps involved:\n\nStep 1: Import dataset with setting delimiter as ‘\\t’ as columns are separated as tab space. Reviews and their category(0 or 1) are not separated by any other symbol but with tab space as most of the other symbols are is the review (like \\$ for price, ….!, etc) and the algorithm might use them as delimiter, which will lead to strange behavior (like errors, weird output) in output.\n\n `# Importing Libraries ` `import` `numpy as np   ` `import` `pandas as pd  ` ` `  `# Import dataset ` `dataset ``=` `pd.read_csv(``'Restaurant_Reviews.tsv'``, delimiter ``=` `'\\t'``)  `\n\nStep 2: Text Cleaning or Preprocessing\n\n• Remove Punctuations, Numbers: Punctuations, Numbers doesn’t help much in processong the given text, if included, they will just increase the size of bag of words that we will create as last step and decrase the efficency of algorithm.\n• Stemming: Take roots of the word", null, "• Convert each word into its lower case: For example, it useless to have same words in different cases (eg ‘good’ and ‘GOOD’).\n\n `# library to clean data ` `import` `re  ` ` `  `# Natural Language Tool Kit ` `import` `nltk  ` ` `  `nltk.download(``'stopwords'``) ` ` `  `# to remove stopword ` `from` `nltk.corpus ``import` `stopwords ` ` `  `# for Stemming propose  ` `from` `nltk.stem.porter ``import` `PorterStemmer ` ` `  `# Initialize empty array ` `# to append clean text  ` `corpus ``=` `[]  ` ` `  `# 1000 (reviews) rows to clean ` `for` `i ``in` `range``(``0``, ``1000``):  ` `     `  `    ``# column : \"Review\", row ith ` `    ``review ``=` `re.sub(``'[^a-zA-Z]'``, ``' '``, dataset[``'Review'``][i])  ` `     `  `    ``# convert all cases to lower cases ` `    ``review ``=` `review.lower()  ` `     `  `    ``# split to array(default delimiter is \" \") ` `    ``review ``=` `review.split()  ` `     `  `    ``# creating PorterStemmer object to ` `    ``# take main stem of each word ` `    ``ps ``=` `PorterStemmer()  ` `     `  `    ``# loop for stemming each word ` `    ``# in string array at ith row     ` `    ``review ``=` `[ps.stem(word) ``for` `word ``in` `review ` `                ``if` `not` `word ``in` `set``(stopwords.words(``'english'``))]  ` `                 `  `    ``# rejoin all string array elements ` `    ``# to create back into a string ` `    ``review ``=` `' '``.join(review)   ` `     `  `    ``# append each string to create ` `    ``# array of clean text  ` `    ``corpus.append(review)  `\n\nExamples: Before and after applying above code (reviews = > before, corpus => after)", null, "Step 3: Tokenization, involves splitting sentences and words from the body of the text.\n\nStep 4: Making the bag of words via sparse matrix\n\n• Take all the different words of reviews in the dataset without repeating of words.\n• One column for each word, therefore there are going to be many columns.\n• Rows are reviews\n• If word is there in row of dataset of reviews, then the count of word will be there in row of bag of words under the column of the word.\n\nExamples: Let’s take a dataset of reviews of only two reviews\n\n```Input : \"dam good steak\", \"good food good servic\"\nOutput :", null, "```\n\nFor this purpose we need `CountVectorizer class` from sklearn.feature_extraction.text.\nWe can also set a max number of features (max no. features which help the most via attribute “max_features”). Do the training on the corpus and then apply the same transformation to the corpus “.fit_transform(corpus)” and then convert it into an array. If review is positive or negative that answer is in the second column of the dataset[:, 1] : all rows and 1st column (indexing from zero).\n\n `# Creating the Bag of Words model ` `from` `sklearn.feature_extraction.text ``import` `CountVectorizer ` ` `  `# To extract max 1500 feature. ` `# \"max_features\" is attribute to ` `# experiment with to get better results ` `cv ``=` `CountVectorizer(max_features ``=` `1500``)  ` ` `  `# X contains corpus (dependent variable) ` `X ``=` `cv.fit_transform(corpus).toarray()  ` ` `  `# y contains answers if review ` `# is positive or negative ` `y ``=` `dataset.iloc[:, ``1``].values  `\n\nDescription of the dataset to be used:\n\n• Columns seperated by \\t (tab space)\n• First column is about reviews of people\n• In second column, 0 is for negative review and 1 is for positive review", null, "Step 5 : Splitting Corpus into Training and Test set. For this, we need class train_test_split from sklearn.cross_validation. Split can be made 70/30 or 80/20 or 85/15 or 75/25, here I choose 75/25 via “test_size”.\nX is the bag of words, y is 0 or 1 (positive or negative).\n\n `# Splitting the dataset into ` `# the Training set and Test set ` `from` `sklearn.cross_validation ``import` `train_test_split ` ` `  `# experiment with \"test_size\" ` `# to get better results ` `X_train, X_test, y_train, y_test ``=` `train_test_split(X, y, test_size ``=` `0.25``) `\n\nStep 6: Fitting a Predictive Model (here random forest)\n\n• Since Random fored is ensemble model (made of many trees) from sklearn.ensemble, import RandomForestClassifier class\n• With 501 tree or “n_estimators” and criterion as ‘entropy’\n• Fit the model via .fit() method with attributes X_train and y_train\n\n `# Fitting Random Forest Classification ` `# to the Training set ` `from` `sklearn.ensemble ``import` `RandomForestClassifier ` ` `  `# n_estimators can be said as number of ` `# trees, experiment with n_estimators ` `# to get better results  ` `model ``=` `RandomForestClassifier(n_estimators ``=` `501``, ` `                            ``criterion ``=` `'entropy'``) ` `                             `  `model.fit(X_train, y_train)  `\n\nStep 7: Pridicting Final Results via using .predict() method with attribute X_test\n\n `# Predicting the Test set results ` `y_pred ``=` `model.predict(X_test) ` ` `  `y_pred `", null, "Note: Accuracy with random forest was 72%.(It may be different when performed experiment with different test size, here = 0.25).\n\nStep 8: To know the accuracy, confusion matrix is needed.\n\nConfusion Matrix is a 2X2 Matrix.\n\nTRUE POSITIVE : measures the proportion of actual positives that are correctly identified.\nTRUE NEGATIVE : measures the proportion of actual positives that are not correctly identified.\nFALSE POSITIVE : measures the proportion of actual negatives that are correctly identified.\nFALSE NEGATIVE : measures the proportion of actual negatives that are not correctly identified.\n\nNote : True or False refers to the assigned classification being Correct or Incorrect, while Positive or Negative refers to assignment to the Positive or the Negative Category", null, "`# Making the Confusion Matrix ` `from` `sklearn.metrics ``import` `confusion_matrix ` ` `  `cm ``=` `confusion_matrix(y_test, y_pred) ` ` `  `cm `", null, "My Personal Notes arrow_drop_up", null, "Check out this Author's contributed articles.\n\nIf you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to [email protected]. See your article appearing on the GeeksforGeeks main page and help other Geeks.\n\nPlease Improve this article if you find anything incorrect by clicking on the \"Improve Article\" button below.\n\nImproved By : shashank_v_ray" ]

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[ "## Proving That Vectors Are Coplanar\n\nA plane is a two dimensional space and two vectors are sufficient to describe it, meaning that any point in th plane can be expressed as a sum of two vectors (relative to some fixed point). If therefore there are three vectors all of which may be moved to lie lie in the plane, one of them is redundant and can be discarded. The vectors lie in the same plane, so are said to be coplanar or linearly independent.\n\nThis means that one of the vectors can be expressed as a linear combination of the other two. Suppose the three vectors are", null, "and", null, "then", null, "for some a and b.\n\nThis can be easily related to properties of matrices. If a column in a matrix can be expressed as a linear combination of the other two rows, then the determinant of the matrix is zero. A similar statement can be made for the rows. Conversely, if the determinant of a matrix is zero then one of the columns can be expressed as a linear combination of the others.\n\nExample:", null, "", null, "Writing", null, "and", null, "as the columns of a matrix gives the matrix", null, "The determinant of this matrix is (expanding along the top row)", null, "The determinant of a matrix is the same as the determinant of the transpose of the matrix (where the columns are written as rows). The calculation is exactly the same if we write the columns of the above matrix as rows and expand along the first column.\n\nIn general the test for linear independence is to write the vectors as column vectors (or row vectors) and find the determinant of the corresponding matrix. This only works of course if the resulting matrix is square.\n\nIf the number of vectors is greater than the dimension of the vectors (the number of rows for a column vector), then the vectors are necessarily linearly dependent. If the number of vectors is less than the dimension of the vectors (the number of rows for a column vector), then the vectors may or may not be linearly dependent.", null, "" ]

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[ "", null, "010-82967418", null, "", null, "function AutoResizeImage(maxWidth,maxHeight,objImg){\nvar img = new Image();\nimg.src = objImg.src;\nvar hRatio;\nvar wRatio;\nvar Ratio = 1;\nvar w = img.width;\nvar h = img.height;\nwRatio = maxWidth / w;\nhRatio = maxHeight / h;\nif (maxWidth ==0 && maxHeight==0){\nRatio = 1;\n}else if (maxWidth==0){//\nif (hRatio<1) Ratio = hRatio;\n}else if (maxHeight==0){\nif (wRatio<1) Ratio = wRatio;\n}else if (wRatio<1 | | hRatio<1){\nRatio = (wRatio<=hRatio?wRatio:hRatio);\n}\nif (Ratio<1){\nw = w * Ratio;\nh = h * Ratio;\n}\nobjImg.height = h;\nobjImg.width = w;\n}\nhtml里的調用\n\nPHP有的時候調試如果出現無法辨認的js或者樣式的提示的時候，在js或者樣式外加{literal}\n\n{literal}\n\njs或者css樣式\n\n{/literal}\n\n7\n2012.11\n5\n2013.1\n2\n2014.10\n\n25\n2014.12\n20\n2013.2\n29\n2012.11\n+86-010-82967418 52431618", null, "[email protected]", null, "www.dpoegd.live www.xinyisheji.com", null, "", null, "" ]

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[ "", null, "Directly to word page Vague search(google)\n\n## Explicit solution in a sentence\n\nSentence count:23Posted:2020-01-07Updated:2020-01-07", null, "Random good picture Not show\n1. The example analysis indicates that the proposed explicit solution to the acceleration factor for general slice method is correct and the proposed method is reasonable and effective.\n2. This paper gives an explicit solution to linear non-homogeneous recurrence relations with constant coefficients by means of generating function.\n3. Furthermore, we derive the explicit solution of the expected growth rates of consumption and savings.\n3. Sentencedict.com try its best to gather and make good sentences.\n4. By explicit solution the error of location is analyzed.\n5. An explicit solution to a group of variational unequation, which comes from optimal stochastic problem, is got through constructing.\n6. For one single retailer, the explicit solution of the decision variables and the proportion of the channel profit allocated to the retailer were presented.\n7. With 3-dimension dynamic explicit solution procedure, the metal deformation process can be analyzed and simulated accurately and the process design optimized effectively.\n8. Equivalent condition for optimality are obtained, and explicit solution leading to feedback formulae are derived for special utility functions and for deterministic coefficients.\n9. Also an explicit solution of regularization to a special problem is constructed.\n10. Using the group inverse, an explicit solution to a symmetric singular system is described.\n11. Brings a quite explicit solution and the development program for the similar enterprise similar question.\n12. An explicit solution is shown to the optimization problem of an investor who wants to maximize the expected total utility from consumption with partial information.\n13. In this paper, the approximate explicit solution of optical output power for P-doped cascaded Raman fiber lasers was acquired by linear model.\n14. The general explicit solution is derived when the symmetric singular system satisfies the regular condition.\n15. Based on the symmetry of dual double-octahedral VGTM, the explicit solution on the inverse displacement analysis of the manipulator.\n16. In the second part, we discuss the time-optimal control problem for an ordinary differential system with a pointwise state-constraint. We give the explicit solution.\n17. Using buckling deformation function that satisfies the restraint boundary condition of the plate, an explicit solution of buckling coefficient is obtained.\n18. Common model control (CMC) is an effective nonlinear control method, but its application condition requests explicit solution for the first order differential model of the process.\n19. Relying on both stochastic calculus and optimal control theory, we obtain its explicit solution of optimal return function and corresponding singular control strategy.\n20. Subsequently, as a important property, the time invariance of support set of solutions of regularization is proved. Also an explicit solution of regularization to a special problem is constructed.\n21. The total flow - time problem of two machine open-shop is NP - hard. For the problem under the non - idle machine constraints, an explicit solution is designed.\n22. Except the Analysis of Variance Estimator, these approaches all need to solve a non-linear equation, which does not have explicit solution, and only has an iteration solution in general.\n23. For the model with many opportunities, we get a recursive equation and explicit solution for Poission stream.\nTotal 23, 30 Per page  1/1" ]

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[ "#### /source4/script/findstatic.pl\n\nhttp://github.com/ccrisan/samba\nPerl | 70 lines | 52 code | 9 blank | 9 comment | 9 complexity | 20371234525f14ed8ed014b3b1c6adad MD5 | raw file\n``````\n#!/usr/bin/perl -w\n# find a list of fns and variables in the code that could be static\n# usually called with something like this:\n# findstatic.pl `find . -name \"*.o\"`\n# Andrew Tridgell <[email protected]>\n\nuse strict;\n\n# use nm to find the symbols\nmy(\\$saved_delim) = \\$/;\nundef \\$/;\nmy(\\$syms) = `nm -o @ARGV`;\n\\$/ = \\$saved_delim;\n\nmy(@lines) = split(/\\n/s, \\$syms);\n\nmy(%def);\nmy(%undef);\nmy(%stype);\n\nmy(%typemap) = (\n\"T\" => \"function\",\n\"C\" => \"uninitialised variable\",\n\"D\" => \"initialised variable\"\n);\n\n# parse the symbols into defined and undefined\nfor (my(\\$i)=0; \\$i <= \\$#{@lines}; \\$i++) {\nmy(\\$line) = \\$lines[\\$i];\nif (\\$line =~ /(.*):[a-f0-9]* ([TCD]) (.*)/) {\nmy(\\$fname) = \\$1;\nmy(\\$symbol) = \\$3;\npush(@{\\$def{\\$fname}}, \\$symbol);\n\\$stype{\\$symbol} = \\$2;\n}\nif (\\$line =~ /(.*):\\s* U (.*)/) {\nmy(\\$fname) = \\$1;\nmy(\\$symbol) = \\$2;\npush(@{\\$undef{\\$fname}}, \\$symbol);\n}\n}\n\n# look for defined symbols that are never referenced outside the place they\n# are defined\nforeach my \\$f (keys %def) {\nprint \"Checking \\$f\\n\";\nmy(\\$found_one) = 0;\nforeach my \\$s (@{\\$def{\\$f}}) {\nmy(\\$found) = 0;\nforeach my \\$f2 (keys %undef) {\nif (\\$f2 ne \\$f) {\nforeach my \\$s2 (@{\\$undef{\\$f2}}) {\nif (\\$s2 eq \\$s) {\n\\$found = 1;\n\\$found_one = 1;\n}\n}\n}\n}\nif (\\$found == 0) {\nmy(\\$t) = \\$typemap{\\$stype{\\$s}};\nprint \" '\\$s' is unique to \\$f (\\$t)\\n\";\n}\n}\nif (\\$found_one == 0) {\nprint \" all symbols in '\\$f' are unused (main program?)\\n\";\n}\n}\n\n``````" ]

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[ "# W3cubDocs\n\n/TensorFlow Python\n\n# tf.keras.layers.Conv3DTranspose\n\n## Class `Conv3DTranspose`\n\nInherits From: `Conv3DTranspose`, `Layer`\n\n### Aliases:\n\n• Class `tf.keras.layers.Conv3DTranspose`\n• Class `tf.keras.layers.Convolution3DTranspose`\n\nTransposed convolution layer (sometimes called Deconvolution).\n\nThe need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.\n\nWhen using this layer as the first layer in a model, provide the keyword argument `input_shape` (tuple of integers, does not include the sample axis), e.g. `input_shape=(128, 128, 128, 3)` for a 128x128x128 volume with 3 channels if `data_format=\"channels_last\"`.\n\n#### Arguments:\n\n• `filters`: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).\n• `kernel_size`: An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window. Can be a single integer to specify the same value for all spatial dimensions.\n• `strides`: An integer or tuple/list of 3 integers, specifying the strides of the convolution along the depth, height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.\n• `padding`: one of `\"valid\"` or `\"same\"` (case-insensitive).\n• `data_format`: A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, depth, height, width)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be \"channels_last\".\n• `dilation_rate`: an integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any stride value != 1.\n• `activation`: Activation function to use (see activations). If you don't specify anything, no activation is applied (ie. \"linear\" activation: `a(x) = x`).\n• `use_bias`: Boolean, whether the layer uses a bias vector.\n• `kernel_initializer`: Initializer for the `kernel` weights matrix (see initializers).\n• `bias_initializer`: Initializer for the bias vector (see initializers).\n• `kernel_regularizer`: Regularizer function applied to the `kernel` weights matrix (see regularizer).\n• `bias_regularizer`: Regularizer function applied to the bias vector (see regularizer).\n• `activity_regularizer`: Regularizer function applied to the output of the layer (its \"activation\"). (see regularizer).\n• `kernel_constraint`: Constraint function applied to the kernel matrix (see constraints).\n• `bias_constraint`: Constraint function applied to the bias vector (see constraints).\n\nInput shape: 5D tensor with shape: `(batch, channels, depth, rows, cols)` if data_format='channels_first' or 5D tensor with shape: `(batch, depth, rows, cols, channels)` if data_format='channels_last'.\n\nOutput shape: 5D tensor with shape: `(batch, filters, new_depth, new_rows, new_cols)` if data_format='channels_first' or 5D tensor with shape: `(batch, new_depth, new_rows, new_cols, filters)` if data_format='channels_last'. `depth` and `rows` and `cols` values might have changed due to padding.\n\n## Properties\n\n### `activity_regularizer`\n\nOptional regularizer function for the output of this layer.\n\n### `input`\n\nRetrieves the input tensor(s) of a layer.\n\nOnly applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.\n\n#### Returns:\n\nInput tensor or list of input tensors.\n\n#### Raises:\n\n• `AttributeError`: if the layer is connected to more than one incoming layers.\n\n#### Raises:\n\n• `RuntimeError`: If called in Eager mode.\n• `AttributeError`: If no inbound nodes are found.\n\n### `input_mask`\n\nRetrieves the input mask tensor(s) of a layer.\n\nOnly applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.\n\n#### Returns:\n\nInput mask tensor (potentially None) or list of input mask tensors.\n\n#### Raises:\n\n• `AttributeError`: if the layer is connected to more than one incoming layers.\n\n### `input_shape`\n\nRetrieves the input shape(s) of a layer.\n\nOnly applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.\n\n#### Returns:\n\nInput shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).\n\n#### Raises:\n\n• `AttributeError`: if the layer has no defined input_shape.\n• `RuntimeError`: if called in Eager mode.\n\n### `losses`\n\nLosses which are associated with this `Layer`.\n\nNote that when executing eagerly, getting this property evaluates regularizers. When using graph execution, variable regularization ops have already been created and are simply returned here.\n\n#### Returns:\n\nA list of tensors.\n\n### `output`\n\nRetrieves the output tensor(s) of a layer.\n\nOnly applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.\n\n#### Returns:\n\nOutput tensor or list of output tensors.\n\n#### Raises:\n\n• `AttributeError`: if the layer is connected to more than one incoming layers.\n• `RuntimeError`: if called in Eager mode.\n\n### `output_mask`\n\nRetrieves the output mask tensor(s) of a layer.\n\nOnly applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.\n\n#### Returns:\n\nOutput mask tensor (potentially None) or list of output mask tensors.\n\n#### Raises:\n\n• `AttributeError`: if the layer is connected to more than one incoming layers.\n\n### `output_shape`\n\nRetrieves the output shape(s) of a layer.\n\nOnly applicable if the layer has one output, or if all outputs have the same shape.\n\n#### Returns:\n\nOutput shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).\n\n#### Raises:\n\n• `AttributeError`: if the layer has no defined output shape.\n• `RuntimeError`: if called in Eager mode.\n\n### `variables`\n\nReturns the list of all layer variables/weights.\n\n#### Returns:\n\nA list of variables.\n\n### `weights`\n\nReturns the list of all layer variables/weights.\n\n#### Returns:\n\nA list of variables.\n\n## Methods\n\n### `__init__`\n\n```__init__(\nfilters,\nkernel_size,\nstrides=(1, 1, 1),\npadding='valid',\ndata_format=None,\nactivation=None,\nuse_bias=True,\nkernel_initializer='glorot_uniform',\nbias_initializer='zeros',\nkernel_regularizer=None,\nbias_regularizer=None,\nactivity_regularizer=None,\nkernel_constraint=None,\nbias_constraint=None,\n**kwargs\n)\n```\n\nInitialize self. See help(type(self)) for accurate signature.\n\n### `__call__`\n\n```__call__(\ninputs,\n*args,\n**kwargs\n)\n```\n\nWrapper around self.call(), for handling internal references.\n\nIf a Keras tensor is passed: - We call self._add_inbound_node(). - If necessary, we `build` the layer to match the shape of the input(s). - We update the _keras_history of the output tensor(s) with the current layer. This is done as part of _add_inbound_node().\n\n#### Arguments:\n\n• `inputs`: Can be a tensor or list/tuple of tensors.\n• `*args`: Additional positional arguments to be passed to `call()`. Only allowed in subclassed Models with custom call() signatures. In other cases, `Layer` inputs must be passed using the `inputs` argument and non-inputs must be keyword arguments.\n• `**kwargs`: Additional keyword arguments to be passed to `call()`.\n\n#### Returns:\n\nOutput of the layer's `call` method.\n\n#### Raises:\n\n• `ValueError`: in case the layer is missing shape information for its `build` call.\n• `TypeError`: If positional arguments are passed and this `Layer` is not a subclassed `Model`.\n\n### `__deepcopy__`\n\n```__deepcopy__(memo)\n```\n\n### `add_loss`\n\n```add_loss(\nlosses,\ninputs=None\n)\n```\n\nAdd loss tensor(s), potentially dependent on layer inputs.\n\nSome losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs `a` and `b`, some entries in `layer.losses` may be dependent on `a` and some on `b`. This method automatically keeps track of dependencies.\n\nThe `get_losses_for` method allows to retrieve the losses relevant to a specific set of inputs.\n\nNote that `add_loss` is not supported when executing eagerly. Instead, variable regularizers may be added through `add_variable`. Activity regularization is not supported directly (but such losses may be returned from `Layer.call()`).\n\n#### Arguments:\n\n• `losses`: Loss tensor, or list/tuple of tensors.\n• `inputs`: If anything other than None is passed, it signals the losses are conditional on some of the layer's inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If `None` is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).\n\n#### Raises:\n\n• `RuntimeError`: If called in Eager mode.\n\n### `add_update`\n\n```add_update(\nupdates,\ninputs=None\n)\n```\n\nAdd update op(s), potentially dependent on layer inputs.\n\nWeight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs `a` and `b`, some entries in `layer.updates` may be dependent on `a` and some on `b`. This method automatically keeps track of dependencies.\n\nThe `get_updates_for` method allows to retrieve the updates relevant to a specific set of inputs.\n\nThis call is ignored in Eager mode.\n\n#### Arguments:\n\n• `updates`: Update op, or list/tuple of update ops.\n• `inputs`: If anything other than None is passed, it signals the updates are conditional on some of the layer's inputs, and thus they should only be run where these inputs are available. This is the case for BatchNormalization updates, for instance. If None, the updates will be taken into account unconditionally, and you are responsible for making sure that any dependency they might have is available at runtime. A step counter might fall into this category.\n\n### `add_variable`\n\n```add_variable(\nname,\nshape,\ndtype=None,\ninitializer=None,\nregularizer=None,\ntrainable=True,\nconstraint=None,\npartitioner=None\n)\n```\n\nAdds a new variable to the layer, or gets an existing one; returns it.\n\n#### Arguments:\n\n• `name`: variable name.\n• `shape`: variable shape.\n• `dtype`: The type of the variable. Defaults to `self.dtype` or `float32`.\n• `initializer`: initializer instance (callable).\n• `regularizer`: regularizer instance (callable).\n• `trainable`: whether the variable should be part of the layer's \"trainable_variables\" (e.g. variables, biases) or \"non_trainable_variables\" (e.g. BatchNorm mean, stddev). Note, if the current variable scope is marked as non-trainable then this parameter is ignored and any added variables are also marked as non-trainable.\n• `constraint`: constraint instance (callable).\n• `partitioner`: (optional) partitioner instance (callable). If provided, when the requested variable is created it will be split into multiple partitions according to `partitioner`. In this case, an instance of `PartitionedVariable` is returned. Available partitioners include `tf.fixed_size_partitioner` and `tf.variable_axis_size_partitioner`. For more details, see the documentation of `tf.get_variable` and the \"Variable Partitioners and Sharding\" section of the API guide.\n\n#### Returns:\n\nThe created variable. Usually either a `Variable` or `ResourceVariable` instance. If `partitioner` is not `None`, a `PartitionedVariable` instance is returned.\n\n#### Raises:\n\n• `RuntimeError`: If called with partioned variable regularization and eager execution is enabled.\n\n### `add_weight`\n\n```add_weight(\nname,\nshape,\ndtype=None,\ninitializer=None,\nregularizer=None,\ntrainable=True,\nconstraint=None\n)\n```\n\nAdds a weight variable to the layer.\n\n#### Arguments:\n\n• `name`: String, the name for the weight variable.\n• `shape`: The shape tuple of the weight.\n• `dtype`: The dtype of the weight.\n• `initializer`: An Initializer instance (callable).\n• `regularizer`: An optional Regularizer instance.\n• `trainable`: A boolean, whether the weight should be trained via backprop or not (assuming that the layer itself is also trainable).\n• `constraint`: An optional Constraint instance.\n\n#### Returns:\n\nThe created weight variable.\n\n### `apply`\n\n```apply(\ninputs,\n*args,\n**kwargs\n)\n```\n\nApply the layer on a input.\n\nThis simply wraps `self.__call__`.\n\n#### Arguments:\n\n• `inputs`: Input tensor(s).\n• `*args`: additional positional arguments to be passed to `self.call`.\n• `**kwargs`: additional keyword arguments to be passed to `self.call`.\n\n#### Returns:\n\nOutput tensor(s).\n\n### `build`\n\n```build(input_shape)\n```\n\nCreates the variables of the layer.\n\n### `call`\n\n```call(inputs)\n```\n\nThe logic of the layer lives here.\n\n#### Arguments:\n\n• `inputs`: input tensor(s).\n• `**kwargs`: additional keyword arguments.\n\n#### Returns:\n\nOutput tensor(s).\n\n### `compute_mask`\n\n```compute_mask(\ninputs,\nmask=None\n)\n```\n\nComputes an output mask tensor.\n\n#### Arguments:\n\n• `inputs`: Tensor or list of tensors.\n• `mask`: Tensor or list of tensors.\n\n#### Returns:\n\nNone or a tensor (or list of tensors, one per output tensor of the layer).\n\n### `compute_output_shape`\n\n```compute_output_shape(input_shape)\n```\n\nComputes the output shape of the layer given the input shape.\n\n#### Args:\n\n• `input_shape`: A (possibly nested tuple of) `TensorShape`. It need not be fully defined (e.g. the batch size may be unknown).\n\n#### Returns:\n\nA (possibly nested tuple of) `TensorShape`.\n\n#### Raises:\n\n• `TypeError`: if `input_shape` is not a (possibly nested tuple of) `TensorShape`.\n• `ValueError`: if `input_shape` is incomplete or is incompatible with the the layer.\n\n### `count_params`\n\n```count_params()\n```\n\nCount the total number of scalars composing the weights.\n\n#### Returns:\n\nAn integer count.\n\n#### Raises:\n\n• `ValueError`: if the layer isn't yet built (in which case its weights aren't yet defined).\n\n### `from_config`\n\n```from_config(\ncls,\nconfig\n)\n```\n\nCreates a layer from its config.\n\nThis method is the reverse of `get_config`, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by `set_weights`).\n\n#### Arguments:\n\n• `config`: A Python dictionary, typically the output of get_config.\n\n#### Returns:\n\nA layer instance.\n\n### `get_config`\n\n```get_config()\n```\n\nReturns the config of the layer.\n\nA layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.\n\nThe config of a layer does not include connectivity information, nor the layer class name. These are handled by `Network` (one layer of abstraction above).\n\n#### Returns:\n\nPython dictionary.\n\n### `get_input_at`\n\n```get_input_at(node_index)\n```\n\nRetrieves the input tensor(s) of a layer at a given node.\n\n#### Arguments:\n\n• `node_index`: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called.\n\n#### Returns:\n\nA tensor (or list of tensors if the layer has multiple inputs).\n\n#### Raises:\n\n• `RuntimeError`: If called in Eager mode.\n\n### `get_input_mask_at`\n\n```get_input_mask_at(node_index)\n```\n\nRetrieves the input mask tensor(s) of a layer at a given node.\n\n#### Arguments:\n\n• `node_index`: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called.\n\n#### Returns:\n\nA mask tensor (or list of tensors if the layer has multiple inputs).\n\n### `get_input_shape_at`\n\n```get_input_shape_at(node_index)\n```\n\nRetrieves the input shape(s) of a layer at a given node.\n\n#### Arguments:\n\n• `node_index`: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called.\n\n#### Returns:\n\nA shape tuple (or list of shape tuples if the layer has multiple inputs).\n\n#### Raises:\n\n• `RuntimeError`: If called in Eager mode.\n\n### `get_losses_for`\n\n```get_losses_for(inputs)\n```\n\nRetrieves losses relevant to a specific set of inputs.\n\n#### Arguments:\n\n• `inputs`: Input tensor or list/tuple of input tensors.\n\n#### Returns:\n\nList of loss tensors of the layer that depend on `inputs`.\n\n#### Raises:\n\n• `RuntimeError`: If called in Eager mode.\n\n### `get_output_at`\n\n```get_output_at(node_index)\n```\n\nRetrieves the output tensor(s) of a layer at a given node.\n\n#### Arguments:\n\n• `node_index`: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called.\n\n#### Returns:\n\nA tensor (or list of tensors if the layer has multiple outputs).\n\n#### Raises:\n\n• `RuntimeError`: If called in Eager mode.\n\n### `get_output_mask_at`\n\n```get_output_mask_at(node_index)\n```\n\nRetrieves the output mask tensor(s) of a layer at a given node.\n\n#### Arguments:\n\n• `node_index`: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called.\n\n#### Returns:\n\nA mask tensor (or list of tensors if the layer has multiple outputs).\n\n### `get_output_shape_at`\n\n```get_output_shape_at(node_index)\n```\n\nRetrieves the output shape(s) of a layer at a given node.\n\n#### Arguments:\n\n• `node_index`: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called.\n\n#### Returns:\n\nA shape tuple (or list of shape tuples if the layer has multiple outputs).\n\n#### Raises:\n\n• `RuntimeError`: If called in Eager mode.\n\n### `get_updates_for`\n\n```get_updates_for(inputs)\n```\n\nRetrieves updates relevant to a specific set of inputs.\n\n#### Arguments:\n\n• `inputs`: Input tensor or list/tuple of input tensors.\n\n#### Returns:\n\nList of update ops of the layer that depend on `inputs`.\n\n#### Raises:\n\n• `RuntimeError`: If called in Eager mode.\n\n### `get_weights`\n\n```get_weights()\n```\n\nReturns the current weights of the layer.\n\n#### Returns:\n\nWeights values as a list of numpy arrays.\n\n### `set_weights`\n\n```set_weights(weights)\n```\n\nSets the weights of the layer, from Numpy arrays.\n\n#### Arguments:\n\n• `weights`: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of `get_weights`).\n\n#### Raises:\n\n• `ValueError`: If the provided weights list does not match the layer's specifications.\n\n© 2018 The TensorFlow Authors. All rights reserved.\nLicensed under the Creative Commons Attribution License 3.0.\nCode samples licensed under the Apache 2.0 License.\nhttps://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv3DTranspose" ]

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[ "# Using simple future and future perfect\n\nWhat is the difference between the following sentences\n\n1. At 6 o'clock I will have left. = The action of leaving will be complete by 6:00.\n\n2. By 6 o'clock I will have left. = The action of leaving will be complete by 6:00.\n\nNow to me they both mean the same, but, there is a difference of preposition, so there must be some difference between them, like there is when we use\n\na) At 6 o'clock I will leave. = The action of leaving will begin at 6:00.\n\nb) By 6 o'clock I will leave. = The action of leaving will be before 6:00.\n\nThanks.\n\nAt 6 o'clock I will leave/have left.\n\nThis means: I will leave/have left at exactly 6 o'clock.\n\nBy 6 o'clock I will leave/have left.\n\nThis means: I will leave/have left at 6 o'clock at the latest, but probably before that. I'm not sure when exactly I will leave. But I will have left until 6.\n\n• So does 'I will have left at 6 o'clock' mean I will not leave before 6 o'clock? Am I right? Jul 26 '16 at 14:52\n• You wouldn't say that. \"will have left\" indicates the process of leaving, which is not a point in time. Jul 27 '16 at 2:16\n• @shikha ji: \"I will have left by x\" means you will be gone at x because you left some time before. Either seconds or hours before x, but at x you are gone. Jul 27 '16 at 20:06\n• @someasw Can't \"I will have left by x\" mean I will leave at x? because by definition, 'by' means either before or at. So is it not possible to leave at x? This is confusing. Jul 28 '16 at 2:07\n• @shikha ji: Yes, \"I will have left by x\" might also mean \"I will leave at x\". But you would rather use it to express that you will leave some time BEFORE x (hence using \"by\"). If you want to express that you will leave exactly at x, you say \"I will leave at x\". Jul 28 '16 at 13:38" ]

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[ "# What exactly is a number?\n\nWe've just been learning about complex numbers in class, and I don't really see why they're called numbers.\n\nOriginally, a number used to be a means of counting (natural numbers).\n\nThen we extend these numbers to instances of owing other people money for instance (integers).\n\nAfter that, we consider fractions when we need to split things like 2 pizzas between three people.\n\nWe then (skipping the algebraic numbers for the purposes of practicality), we use the real numbers to describe any length; e.g. the length of a diagonal of a unit square.\n\nBut this is when our original definition of a number fails to make sense- when we consider complex numbers, which brings me to my main question: what is a rigorous definition of 'number'?\n\nWikipedia claims that \"A number is a mathematical object used to count, label, and measure\", but this definition fails to make sense after we extend $\\mathbb{R}$.\n\nDoes anyone know of any definition of number that can be generalised to complex numbers as well (and even higher order number systems like the quaternions)?\n\n• Possible duplicate of What is a number? – Ivo Terek Jul 12 '14 at 19:44\n• I don't know, but they're usefull – Jorge Fernández-Hidalgo Jul 12 '14 at 19:45\n• \"...consider fractions when we need to split things like 2 pizzas between two people...\"? Is that an introduction to fractions or the fact that life is inherently unfair? ;) – user_of_math Jul 12 '14 at 19:59\n• I don't need fractions to split two pizzas between two people: I get one, you get the other. Now, splitting one pizza between two people ... – Théophile Jul 12 '14 at 20:03\n• @Théophile I don't need fractions to split $n$ pizzas between $m$ people, I get $n$, the rest of them get none. ;-) – Adam Hughes Jul 12 '14 at 20:09\n\nA basic question is, what would be the purpose of such a definition? Would it clarify anything if we came up with a definition that, say, included quaternions and not matrices or analytic functions?\n\nMost of the usages of the term \"number\" are due to historical choices that have lived on. I'd be interested in seeing things that were called \"numbers\" initially, but are not called \"numbers\" now, I suppose, but any definition that applies is just a hack to justify choices at the boundaries, I think.\n\nAs mentioned, I haven't seen quaternions called \"numbers.\" We say $1+i$ is a \"complex number,\" but we just say $1+i+j+k$ is a \"quaternion.\" At least in my experience.\n\nAlgebraic numbers\n\nIn \"number theory,\" we often deal with \"algebraic extensions\" of the rational numbers. For example, $\\mathbb Q(\\sqrt{2})$ is the set of numbers of the form $a+b\\sqrt 2, a,b\\in\\mathbb Q$. These can be seen as a subset of $\\mathbb R$, but they actually exist more abstractly - for example, algebraically, we don't know whether $\\sqrt{2}<0$ or $\\sqrt{2}>0$ - the number exists as an algebraic object entirely - an object which, when squared, equals $2$.\n\nThe same thing happens with $\\mathbb Q(\\sqrt{-1})$. It would be strange to call $\\sqrt{2}$ a \"number\" and not call $\\sqrt{-1}$ a \"number\" in this context. Mathematicians call the two fields \"algebraic number fields.\"\n\nFor example, $\\mathbb Q(\\sqrt{2})$ can be seen as isomorphic to a subset of $\\mathbb R$, but it is also isomorphic to a (different) subset of $\\mathbb C$.\n\nComplex Numbers\n\nThere are also ways to see, inside the 'real numbers,' that the complex numbers sort of have to exist. My favorite way: If you look at the radius of convergence of the Taylor series of $f(x)=\\frac{1}{x^2-x}$ at $x=a$, you get the radius of convergence is $\\min(|a|,|a-1|)$. That is, the zeros of the denominator \"block\" the Taylor series. If you look at the Taylor series of $g(x)=\\frac{1}{1+x^2}$ at a real number $x=a$, you get that the radius of convergence is $\\sqrt{1+a^2}$. There is something (a zero of $1+x^2$?) \"blocking\" the Taylor series of $g(x)$ that looks like it is exactly a distance $1$ from $0$ in a direction perpendicular to the real line.\n\nComplex numbers also are necessary for breaking down real matrices into component parts. Well, not \"necessary,\" but the representation of matrices in, say, Jordan canonical form, becomes quite a bit more complicated without complex numbers. So complex numbers, oddly, make matrices seem more regular (or, if you prefer, hide the complexity.)\n\nAlso, complex numbers are really necessary for understanding quantum theory in physics. Everything you think you intuit about the universe, in terms of \"measurements\" being real numbers, starts to fall apart at the quantum level. The universe is far stranger than it seems.\n\n$p$-adic Numbers\n\n$p$-adic numbers are probably called \"numbers\" just because their construction is essentially the \"same\" as the construction of the reals, only using a different metric on $\\mathbb Q$, and because they can be used to answer questions about the natural numbers.\n\nOrdinals, Cardinals\n\nOrdinal and cardinal numbers represent a different type of extension of the natural numbers.\n\nI think of \"ordinals\" as being like the results of a race with no ties. Every runner has a result \"ordinal\" and any non-empty subset of the runners has a \"winner.\"\n\nCardinal numbers are like a pile of beans, and determining whether two piles of beans have the same amount in them.\n\nOrdinals are by far the most weird, because even addition of ordinals is non-commutative.\n\nIn this case, then, ordinals and cardinals are \"measurements\" of something.\n\nNon-standard real number definitions\n\nThere are also lots of variations of the real numbers that we call \"numbers,\" basically because they are a variant of the real numbers.\n\nConclusion: Exclusions\n\nThe hardest part of coming up with a definition for \"number\" is to exclude: Why don't we call matrices, or functions, or other similar things \"numbers?\" Things we see as primarily functions are not seen as \"numbers,\" but it is hard to exclude them with anything rigorous. Indeed, one way to see the complex numbers is as a sub-ring of the ring of real $2\\times 2$ matrices, and one reason we need complex numbers is that they are great at representing the operation of rotation - that is why we see them come in studying real matrices.\n\nZero-divisors are often a sign that a thing isn't a number, but we do have $g$-adic numbers with $g$ not prime, which is a ring with zero divisors. (Usually, $g$-adic numbers are not actually used anywhere, since they are just products of rings of $p$-adic numbers...)\n\nDoes anybody refer to the elements of ring $\\mathbb Z/n\\mathbb Z$ as \"numbers?\" Not in my experience.\n\nI also haven't seen finite field elements referred to as \"numbers.\"\n\nSo, no, the entire history of mathematics has not ascribed a single logical meaning to the word \"number,\" so that we can distinguish what is and isn't a number. As noted in comments, \"Cayley numbers\" is another name for the octonions, but there are zero occurrences in Google NGram of the singular phrase, \"Cayley number.\" So octonions are numbers, but a single octonion is not a \"number?\" That's just the world we live in. Number, being the most basic idea in mathematics, gets generalized in a lot of interesting ways, not all consistent, and not the same way over time.\n\nRecall, the ancients didn't define $0$ as a \"number.\"\n\nQ: How many beans do you have?\n\nA: I don't have beans.\n\n(I suspect this failure was due to the confusion between cardinals and ordinals - we count finite cardinals by arbitrarily sorting and then computing the ordinal of the last element, but that fails when counting an empty collection...)\n\n• Quaternions have been referred to as hypercomplex numbers. – James Jul 12 '14 at 23:49\n• Quaternions, perhaps, are not often called numbers. But Cayley numbers are. – WillO Jul 12 '14 at 23:55\n• In fact, the ancients did not consider $1$ to be a number either. (Euclid's definition: A number is a multitude of units.) – Per Manne Jul 13 '14 at 7:04\n• When in ancient times it was realized that incommensurable quantities exist (like $1:\\sqrt{2}$), they did not consider these quantities to be \"numbers\" (greek (singular) arithmos). Instead, they began \"calculating\" with geometric objects such as line segments (since this was more general than \"numbers\" (i.e. $\\mathbb{Q}$)). Only later was the concept of number/arithmos generalized to include irrational quantities. – Jeppe Stig Nielsen Jul 14 '14 at 10:36\n• (Hyperbolic numbers are the numbers that appear on Fox News, right? :) ) – Thomas Andrews Jul 14 '14 at 18:25\n\nThere is no concrete meaning to the word number. If you don't think about it, then number has no \"concrete meaning\", and if you ask around people in the street what is a number, they are likely to come up with either example or unclear definitions.\n\nNumber is a mathematical notion which represents quantity. And as all quantities go, numbers have some rudimentary arithmetical structures. This means that anything which can be used to measure some sort of quantity is a number. This goes from natural numbers, to rational numbers, to real, complex, ordinal and cardinal numbers.\n\nEach of those measures a mathematical quantity. Note that I said \"mathematical\", because we may be interested in measuring quantities which have no representation in the physical world. For example, how many elements are in an infinite set. That is the role of cardinal numbers, that is the quantity they measure.\n\nSo any system which has rudimentary notions of addition and/or multiplication can be called \"numbers\". We don't have to have a physical interpretation for the term \"number\". This includes the complex numbers, the quaternions, octonions and many, many other systems.\n\n• I'd be happy to hear counterarguments, because it seems that this point was made by others as well, and yet... I'm somehow the only one to receive a downvote. – Asaf Karagila Jul 13 '14 at 11:22\n• \"So any system which has rudimentary notions of addition and/or multiplication can be called \"numbers\".\" So matrices are numbers? And functions too? – Martin Brandenburg Jul 21 '14 at 5:57\n• @Martin: Shocking. I know. – Asaf Karagila Jul 21 '14 at 7:26\n• @Martin: Note, by the way that the complex numbers has a matricial representation. So some matrices are numbers. Why not all of them, then? And if matrices are linear transformations, this means that some functions are numbers too. – Asaf Karagila Jul 21 '14 at 7:29\n• @Martin That depends on your PoV on functions. For example, $\\mathbb Q[\\pi] \\simeq \\mathbb Q[x]$, which means that we can regard at least some transcendent numbers as functions, in this particular case on the affine line. According to the period conjecture, periods determine a generic point of the corresponding period torsor, so can be meaningfully identified with functions on it. On the other hand you have the numbers-as-functions principle which associates to any integer a function on $\\mathrm{Spec} \\mathbb {Z}$. – Anton Fetisov Dec 28 '17 at 1:32\n\nIn my thinking, a number is simply an object which satisfies some set of algebraic rules. In particular, these rules are typically constructed to allow an equation a solution. Most interesting, these solutions were unavailable in the less abstract version of the number, whereas, with the extended concept of number the solutions exist. This seems to be the theme in the numbers which have been of interest in my studies. For example:\n\n1. $x+3=0$ has no solution in $\\mathbb{N}$ yet $x = -3 \\in \\mathbb{Z}$ is the solution.\n2. $x^2-2=0$ has no solution in $\\mathbb{Q}$ yet $x = \\sqrt{2} \\in \\mathbb{R}$ is the solution.\n3. $x^2+1=0$ has no solution in $\\mathbb{R}$ yet $x = i \\in \\mathbb{C}$ is the solution.\n\nHowever, even this theme is not enough, a new type of number can also just be something which allows some new algebraic rule. For example,\n\n1. $2+j$ is a hyperbolic number. Here $j^2 = 1$ and we have some rather unusual relations such as $(1+j)(1-j) = 1+j-j-j^2 = 0$. The hyperbolic numbers are not even an integral domain, they're zero divisors.\n2. $1+\\epsilon$ is a dual number or null number as I tend to call them. In particular $\\epsilon^2=0$ so these capture something much like the idea of an infinitesimal.\n\nThis list goes on and on. Indeed, the elements of an associative algebra over $\\mathbb{R}$ are classically called numbers. There is a vast literature which catalogs a myriad of such systems from around the dawn of the 20-th century. In fact, the study of various forms of hypercomplex analysis is still an active field to this day. Up to this point, the objects I mention are all finite dimensional as vector spaces over $\\mathbb{R}$. In the 1970's physicists began throwing around super numbers. These numbers were infinite dimensional extensions of the null numbers I mentioned above. They gave values for which commuting and anticommuting variables could take. Such variables could be used to frame the classical theory of fields for fermions and bosons. In my view, the supernumbers of interest were those built from infinitely many Grassmann generators. Since the numbers themselves were built from infinite sums, an underlying norm is given and mathematically the problem becomes one of Banach spaces.\n\nMy point? A number is not just for counting.\n\n• The hyperbolic number example is missing its '^2' superscript after the expansion of terms, but a single character edit isn't possible ;-) – Philip Oakley Jul 13 '14 at 22:21\n• @PhilipOakley hey, thanks! Fixed. – James S. Cook Jul 14 '14 at 0:34\n\n\"If it looks like a duck, and quacks like a duck, it is a duck... or something that is so close that is good enough\"\n\nIn Mathematics you don't really work with objects, you work with the properties. So, we define an object (for example, a vector) in a certain context (linear algebra) as something that has a series of properties (in the example, you can add them, multiply by a scalar, create a base...), and that is what we use.\n\nIn my first year at university, my Algebra professor spent a couple of months talking about vectors and matrices. One day, he wrote down the properties of a vector that we had been using (roughly listed above), and showed that they were also applicable to polynomials: you can add two polynomials, 0 is a polynomial, you can multiply by scalars... In short, everything that he had said about vectors automatically applied to polynomials, with no extra work required! It also allowed for expansions: in linear algebra a matrix can be seen as an object that inputs a vector and outputs a transformed vector; and for each (square) matrix, there are particular vectors that have nice properties. Well, we can then define operators on polynomials pretty much the same way: something that eats a polynomial and spits a polynomial (for example, the derivative).\n\nI was quite impressed by this, and it improved my intuition of mathematics.\n\nSo, coming back to your question, a number is something that behaves more or less like a number (or something that you accept as a number).\n\nAnd quoting another professor from that year: \"don't think, calculate!\". It is good to think about the Phylosophy of all this, but don't let it get in the way!\n\n• He probably gave you that \"don't think, calculate\" quote because you were doing linear algebra ; in most parts of mathematics, the opposite is true : don't calculate, think! Because calculating will give you an answer to a question, but more often than not, thinking will give you understanding to an answer you would have gotten by calculating but not understood. I loved the answer though, +1 – Patrick Da Silva Nov 21 '15 at 6:52\n• @PatrickDaSilva he was actually teaching calculus, and his most repeated phrase was to \"never start a calculation without knowing the result\". The \"don't think\" part was applied to everything else but maths (philosophical interpretations, likelihoods of different exercises popping up in the exam, etc), meaning to keep us from spending more time than necessary. – Davidmh Nov 21 '15 at 22:15\n\nThese questions are addressed in mathematical philosophy (e.g. see Russell's Introduction to Mathematical Philosophy). The very short answer to your question is classes.\n\n(I'll have to elaborate some other time unless someone beats me to it)\n\n• This is actually the best answer here, though if you hadn't heard it before, you really would have no way to know. – Myria Jul 13 '14 at 0:23\n• FWIW, the second edition of Russell's book is at Project Gutenberg (typeset in LaTeX). – Andrew D. Hwang Jul 14 '14 at 10:43\n\nThis is a deep and philosophical question. You are right, kids start by learning to count things like 3 apples and 7 cars but quickly build up to the real numbers, which we can make sense of by money and distances. When you start adding things like the imaginary numbers, it gets less clear.\n\nMy revelation came when I learned some electricity and magnetism. There, the complex numbers are used to describe alternating current. I was blown away by the thought that this number, $i$, which I did not really consider a serious concept at the time, to have a physical interpretation. So I had to ask myself the same question you are now.\n\nTo us, a number is just an abstract idea, that we use to make sense of physical or mathematical ideas. In general, a number is no different from an element of a group or ring. If you don't know what those are, do not worry about it. But they are just as abstract as the idea of a number. An example of a group describing something would be a knob that you turn on a fan. It might have zero, one, two, and three. When you turn past three you get back to zero. This is the group $\\mathbb{Z}/4\\mathbb{Z}$, which means that 4 is the same as zero.\n\nI have heard, but do not have a reliable source, that some cultures used to have different numbers for different objects. To describe two cows, they would use a different word than they would to describe two sheep, for example.\n\nI suppose the point is, that a number is just a way to describe something. There is no reason to think that the number line is the whole story. Perhaps some people just wish it was, because that would simplify things, so they do not talk about these other descriptions that are much like numbers.\n\n• Here's a reliable source: it happens in English. Pair, twins, couple, mates, duo all mean \"two of\" slightly different things. For groups of greater size, we have group, cluster, herd, hive, flock, and so on, including my two favorites, clowder (of cats) and murder (of crows). – Ryan Reich Jul 12 '14 at 22:21\n• In electronics, we usse the imaginary unit for separating a sine wave from a cosine wave (or as a way to distinguish a 90° delay) – Broken_Window Jul 14 '14 at 17:26\n• @ryanreich Yeh but the words become more vague the bigger the number being described. Mostly after two really. – snulty Aug 2 '14 at 13:40\n\nActually, the \"practical\" examples of numbers start to break down at $\\mathbb{R}$. Yes, the algebraic numbers have interpretations in geometry (though these are already more abstract than owing someone a dollar or splitting a pizza), but there are many numbers in $\\mathbb{R}$ that are not algebraic and in fact aren't really describable in any ordinary way.\n\nOn the other hand, numbers in $\\mathbb{C}$ can be interpreted as points in a plane. And by extending $\\mathbb{R}$ to $\\mathbb{C}$, we ensure that all polynomials in $x$ have a factorization into factors that are linear in $x$; equivalently, a polynomial of degree $n$ has $n$ roots. So just as real numbers let us solve problems that rationals do not, complex numbers let us solve problems that reals do not.\n\n• Great point. We have a very poor feel for most of the real numbers, and mostly tend to forget that. – Keith Jul 14 '14 at 3:16\n\nDoes anyone know of any definition of number that can be generalised to complex numbers as well (and even higher order number systems like the quaternions)?\n\nThere is a general consensus that no such definition could possibly exist. \"Number\" is inherently a fluffy concept. However, a good guideline is: if the analogy with any of the usual numbers systems $\\mathbb{Z},\\mathbb{Q}, \\mathbb{R},\\mathbb{C}$ is fruitful and deserves to be emphasized, you are free to call it a number.\n\nExamples:\n\n1. The quaternions\n2. The ordinal numbers\n3. The cardinal numbers\n4. The surreal numbers / surcomplex numbers\n5. The isomorphism types of totally ordered sets\n6. The isomorphism types of partially ordered sets\n\nBertrand Russell in the early 20th century defined \"number\" as\n\nanything which is the number of some class\n\nand \"the number of a class\" as\n\nthe class of all those classes that are similar to it\n\nwhere similarity of classes is defined by the existence of a bijection between them.\n\n(Paraphrased from Introduction to Mathematical Philosophy.)\n\n• That's just defining something like cardinal number, really, while the questioner wants to know about all the things we call \"number.\" It's not clear how Russell's definition even handles ordinal numbers... – Thomas Andrews Jul 13 '14 at 1:11\n• The book Logicomix is a comic book about Bertrand Russell's struggle to answer this question. I would recommend it to those of us who find \"Introduction to Mathematical Philosophy\" a bit hard to digest. – milestyle Jul 14 '14 at 16:34\n\nThe question is controversial in philosophy of mathematics. Some answers:\n\n(A) Numbers are sets. According to Bertrand Russell:\n\n• The number 0 is the empty set.\n• The number 1 is the set of all single-membered sets.\n• For natural number n, the successor of n is the set of all sets that are equinumerous with {0, 1, ... n}, where two sets are equinumerous if there is a one-to-one mapping between them.\n\n(See Russell's Introduction to Mathematical Philosophy.) Similar views are taken by Frege (Foundations of Arithmetic) and Cantor (Contributions to the Founding of the Theory of Transfinite Numbers). A more recent view is that 0 is {}, 1 is {0}, 2 is {0,1}, and so on. People who take this sort of view then try to construct other numbers out of sets. E.g.:\n\n• The number 2/3 is the set of ordered pairs {<2,3>, <4,6>, <6,9>, ...}\n• A real number is a set of sequences of rational numbers with a certain convergence property.\n\nThese views enable you to derive the familiar truths of arithmetic from set theory. However, it is not very natural to suppose that when I say \"My gas tank is 2/3 full\" I am talking about this sort of object. (For a hilarious critique, see http://web.maths.unsw.edu.au/~norman/papers/SetTheory.pdf.)\n\n(B) Natural numbers are properties.\n\nThe best way to explain what property one is referring to is to give examples. Thus, my hands are two (they together have the property of twoness); the Earth and the Moon are two; green and blue are two colors; etc. The number 2 is what all those examples have in common. This is defended in Byeong-Uk Yi's excellent \"Is Two a Property?\" (https://tspace.library.utoronto.ca/bitstream/1807/25255/1/Is%20two%20a%20property.pdf)\n\nTo address your question about complex numbers: if you accept the set-theoretic account of other numbers, then complex numbers are no problem. You can identify a complex number with an ordered pair of real numbers. This is not very different from, e.g., the construction for rational numbers.\n\nIf you take the property view (as I do), complex \"numbers\" are not numbers in the same sense as natural numbers. They might still be ordered pairs of numbers. (Whether we want to extend the term \"number\" to include them is then a semantic question.)\n\nNote incidentally that other presently-accepted numbers used to not be accepted. 0 wasn't always a number; it began life as a mere placeholder (something like a punctuation mark used to indicate that a position is unoccupied). The concept \"number\" had to be extended to include zero. Negative numbers weren't always accepted either, since it was considered absurd to speak of having less than nothing. Transfinite \"numbers\" are another late addition, which is motivated by the set-theoretic account of numbers. So there is precedent for extending the concept.\n\n• If you take the property view, which kinds of \"numbers\" (negative, rational, real, etc.) are legitimately numbers, other than the natural numbers? – David K Jul 13 '14 at 3:28\n• Complicated question. Short answer: they're all different kinds of properties/relations, but \"number\" might be a broad enough term to cover all of them. Real numbers (including fractions) are relationships between magnitudes. A negative number is just a number that is used to measure something that is 'opposite' to something else. A complex number is an ordered pair of real numbers (ala Hamilton). – Owl Jul 14 '14 at 8:24\n\nThis is a book by kirillov ,titled what are numbers?,it could help you http://www.math.upenn.edu/~kirillov/MATH480-S08/WN1.pdf http://www.math.upenn.edu/~kirillov/MATH480-S08/WN2.pdf\n\nThis doesn't exactly answer your question, but here are some characteristics all numbers I know of satisfy.(I know of counting numbers, integers, rational numbers, real numbers, complex numbers, quaternions and octonions).\n\nNumber 1: All the counting numbers seem to be equipped with two binary operations which receive the name of addition and multiplication.\n\nNumber 2: They are an extension of the counting numbers, in the sense that there is a function from the counting numbers to all the other number systems such that $f(a)+f(b)=f(a+b)$ and $f(a)\\times f(b)=f(a\\times b)$ for $a$ and $b$ counting numbers.\n\nUnfortunately I'm still young inexperienced and those were the only ones I could think of.\n\n• I can't recall quaternions and matrices called \"numbers.\" We say $1+i$ is a complex number, we say $1+i+j+k$ is a quaternion, not a \"quaternion number.\" Obviously, matrices have can be seen as extensions of the counting numbers, but matrices are never called numbers, as far as I've seen. I'd suggest that the notions of \"numbers\" usually includes associate and commutative $+,\\times$ and distributive law. – Thomas Andrews Jul 12 '14 at 20:54\n• I never mention matrices in my answer though, well yes, if you don't consider quaternions or octonions you can include associativety and commutativity as characteristics, I thought quaternions were considered numbers, because I have heard of people talk about the quaterions as a number system. – Jorge Fernández-Hidalgo Jul 12 '14 at 21:06\n• I was wrong about associative and commutative, since we also call ordinals \"numbers,\" and ordinals aren't even commutative under addition... – Thomas Andrews Jul 12 '14 at 21:18\n• I failed to edit my first comment properly. My point about matrices was meant to be that your definition would include matrices. – Thomas Andrews Jul 12 '14 at 21:20\n• Oh, I see, but they would have to be over a a field that already satisfied those characteristics. – Jorge Fernández-Hidalgo Jul 12 '14 at 21:21\n\nThere isn't a rigorous definition of number.\n\nIt doesn't make sense to come up with a rigorous definition of number until there is some specific (and hopefully useful) concept you wish to formalize: and we already have formalizations of concepts like \"real number\" if that's the specific concept we wish to talk about.\n\nAs an aside, I have used complex numbers to label things, and to measure things, so calling complex numbers \"numbers\" certainly continues to make sense by the Wikipedia definition.\n\nI've even used much more exotic things for the purposes that Wikipedia delineates: e.g. there are things I've labelled and measured by using Abelian groups. For example, homology can be used to measure various properties of topological spaces, and the values of such a measurement are Abelian groups. (not elements of Abelian groups, but the Abelian groups themselves)\n\n• Hurkyl, did you defect from Physicsforums? :) – Count Iblis Jul 13 '14 at 0:16\n\nThere are two schools of thought here:\n\n1 - Intuitive quantity.\n2 - Arithmetic quantity.\n\n\nFor me, I go with the Intuitive concept, which means: restrict the concept of numbers - into measuring quantity, either positive or negative. (Set of Real numbers)\n\nComplex numbers are a problem because they measure more than quantity. They have some sort of \"direction\", but don't behave arithmetically like normal x,y vectors.\n\nInstead, Complex numbers can be represented by a matrix:\n\n a -b\nb a\n\n\nThe Arithmetic concept suggests that numbers are whatever consistently obeys a certain, arbitrarily defined, arithmetic operators. Such numbers do not necessarily correspond to anything physical - namely, Quantity. Rather, they represent raw data.\n\nSince these numbers are consistent with their corresponding operators, using them in equations is useful, because it remains consistent.\n\n• Negative numbers also have a \"direction\", so why accept them as \"intuitive\" if having a direction is objectionable? For that matter, the \"intuition\" behind a definition of even the positive real numbers is esoteric. – David K Jul 13 '14 at 3:20\n• Negative numbers are not that intuitive. Complex numbers were introduced by Cardano before negative numbers were accepted. – vinnief Jul 14 '14 at 14:50\n\nMathematics is really just a system of formal logic; you define the rules of a universe then use those rules, and only those rules, to build systems. Within that rule system, you can define what a number is, some operations you can do on numbers, and start proving results about what happens with those numbers. Eventually, you build up more complex systems, and start defining all of modern mathematics.\n\nThe current most common foundation for mathematics is considered to be Zermelo-Fraenkel (\"ZF\") set theory, generally augmented with the Axiom of Choice to make \"ZFC\" set theory. The subject is often called \"mathematical philosophy\", as another answer stated. Its modern formulation typically has only 9 axioms, and from just those 9 everything else derives.\n\nAs an example of how you can get the concept of numbers from just sets, we can derive a simple counting system just from the rules that there exists an empty set, you can construct a set as a collection of other sets, and finally the existence of an infinite set (which will become the set of natural numbers). Formally, the existence of the empty set is implied by several of the ZFC axioms, the ability to construct a set as a collection of other sets is from the Axiom Schema of Specification and Axiom of Power Set, and finally the existence of an infinite set is its own Axiom of Infinity.\n\nTaken directly from Wikipedia, a simple formulation of the natural numbers arrives, starting with zero being the empty set. A successor function S(n) defined in set notation as S(n) = n + 1 = n ∪ {n}.\n\n0 = ∅ = { }\n1 = { 0 } = { { } }\n2 = { 0, 1 } = { { }, { { } } }\n3 = { 0, 1, 2 } = { { }, { { } }, { { }, { { } } } }\n\n\nhttp://en.wikipedia.org/wiki/Zermelo%E2%80%93Fraenkel_set_theory\n\nhttp://en.wikipedia.org/wiki/Set-theoretic_definition_of_natural_numbers\n\nThere are a lot of good answers already. This isn't much of a deep, mathematical answer. It's just my thoughts on the matter. A \"soft answer\" to your \"soft question\", if you will :)\n\nTo me, a number basically is a constant that has notions equivalent to basic addition and multiplication over the natural numbers. By that definition, the above often-mentioned Matrices of which each element is a number would also be numbers.\n\nNow I could stop right there. The rest is rather about Complex Numbers and an alternate origin of them, outside of defining the square root of -1 or finding roots to polynomials.\n\nSince I learned about Geometric Algebra, I like to think of Complex Numbers, Quaternions and higher-dimensional notions as just subsets of Geometric Algebras. I think that name is reserved for something else already, but calling them \"Geometric Numbers\" would be fitting in my mind. - Geometric in the sense that those values have clear geometric interpretations, and numbers in the sense that they are constants that have a straight forward definition of multiplication and addition.\n\nLet's, for instance, take a 2D Geometric Algebra: You'll have your base elements:\n\n$$1, \\; x, \\; y$$\n\nand they behave like this:\n\n$$1 \\cdot 1 = 1 \\\\ 1 \\cdot x = x \\cdot 1 = x \\\\ 1 \\cdot y = y \\cdot 1 = y \\\\ x \\cdot x = y \\cdot y = 1 \\\\ x \\cdot y = -x \\cdot y := I$$ and by just applying those rules, you'll find that $$I \\cdot I = x \\cdot y \\cdot x \\cdot y = \\; | \\left(x \\cdot y = - y \\cdot x \\right)\\\\ = -y \\cdot x \\cdot x \\cdot y = -y \\cdot \\left(x \\cdot x \\right) \\cdot y = -y \\cdot 1 \\cdot y = \\\\ = -y \\cdot y = -1$$\n\nThus you get the following multiplication table:\n\n$$\\begin{matrix} \\textbf{1} & \\textbf{x} & \\textbf{y} & \\textbf{I} \\\\ \\textbf{x} & 1 & I & y \\\\ \\textbf{y} & -I & 1 & -x \\\\ \\textbf{I} & -y & x & -1 \\end{matrix}$$\n\nNotice two things here:\n\nFirst of all, if you think of $x$ and $y$ as being two orthogonal directions in a plane, $I$ will rotate them counterclockwise. Alternatively you can think of a \"sandwitching operator\" like $x \\cdot y \\cdot x = -y$ - this kind of operation will mirror what ever is inside the \"sandwitch\" along the axis of the outside, the \"bread\", if you will. This holds true in higher dimensions too. You'll end up with a larger structure though. If you try this with three dimensions, say, $x$, $y$ and $z$ (naming, of course, is arbitrary), you'll end up with what is essentially the quaternions as part of the algebra.\n\nAnd second, if you take just the $1$ and the $I$ together, you end up with precisely the complex numbers. Notice how $I^2=-1$.\n\nIf you define a generic such number as $a_R \\cdot 1 + a_x \\cdot x + a_y \\cdot y + a_I \\cdot I$, and multiply two such numbers by the above multiplication rules in just the same way as you'd multiply complex numbers, you basically have three different parts: The \"scalar part\" $a_R$ (for Complex numbers, this is the real part), the \"vector part\" $a_x x + a_y y$ (this behaves precisely like you'd hope it to, if you want to deal with geometry in a plane) and the so-called bivector (or 2-vector) part $a_I I$ (the imaginary part of Complex numbers, here consisting of the two vectors x and y, hence the name), which acts like a rotator of sorts.\n\nNote that the 2D case is a little special because the bivector simultaneously is the so-called pseudo-scalar. For 3D you can find, in a very similar fashion, that you'll have\n\n$$1\\\\ x, \\; y, \\; z \\\\ Ix = yz = xI, \\; Iy = zx =yI, \\; Iz = xy = zI \\\\ x \\cdot y \\cdot z = I$$\n\nConsisting of 3 orthogonal bivectors (the three primary planes of the $\\mathbb{R}^3$) and the trivector (or 3-vector) $I$.\n\nAnd if you play around with those algebras, you'll very quickly find ways to express the scalar product and the vector product in straight forward ways. The capabilities of mirroring or rotating are also kept and so is the meaning of $1$ and $I$.\n\nThere are several neat extensions to this but the bottom line is that complex numbers appear everywhere (I gave one example, most gave others) and even if you don't like my above definition of what a number is (I'd not be surprised if somebody is quick to object), the way you can deal with them exactly as if they were reals, and the beautiful consequences, the introduction of complex numbers has, makes it more than justified to call them numbers." ]

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[ "# Unimodal function\n\nUnimodal function\n\nIn mathematics, a function \"f\"(\"x\") between two ordered sets is unimodal if for some value \"m\" (the mode), it is monotonically increasing for \"x\" ≤ \"m\" and monotonically decreasing for \"x\" ≥ \"m\". In that case, the maximum value of \"f\"(\"x\") is \"f\"(\"m\") and there are no other local maxima.\n\nExamples of unimodal function:\n\n*Logistic map\n*Tent map\n\nFunction $f\\left(x\\right)$ is \"S-unimodal\" if its Schwartzian derivative is negative for all $x e 0$.\n\nIn probability and statistics, a \"unimodal probability distribution\" is a probability distribution whose probability density function is a unimodal function, or more generally, whose cumulative distribution function is convex up to \"m\" and concave thereafter (this allows for the possibility of a non-zero probability for \"x\"=\"m\"). For a unimodal probability distribution of a continuous random variable, the Vysochanskii-Petunin inequality provides a refinement of the Chebyshev inequality. Compare multimodal distribution.\n\nWikimedia Foundation. 2010.\n\n### См. также в других словарях:\n\n• Function (mathematics) — f(x) redirects here. For the band, see f(x) (band). Graph of example function, In mathematics, a function associates one quantity, the a …   Wikipedia\n\n• Monotonic function — Monotonicity redirects here. For information on monotonicity as it pertains to voting systems, see monotonicity criterion. Monotonic redirects here. For other uses, see Monotone (disambiguation). Figure 1. A monotonically increasing function (it… …   Wikipedia\n\n• Mode (statistics) — In statistics, the mode is the value that occurs most frequently in a data set or a probability distribution. In some fields, notably education, sample data are often called scores, and the sample mode is known as the modal score. Like the… …   Wikipedia\n\n• List of statistics topics — Please add any Wikipedia articles related to statistics that are not already on this list.The Related changes link in the margin of this page (below search) leads to a list of the most recent changes to the articles listed below. To see the most… …   Wikipedia\n\n• Complex quadratic polynomial — A complex quadratic polynomial is a quadratic polynomial whose coefficients are complex numbers. Contents 1 Forms 2 Conjugation 2.1 Between forms 2.2 With doubling map …   Wikipedia\n\n• Golden section search — The golden section search is a technique for finding the extremum (minimum or maximum) of a unimodal function by successively narrowing the range of values inside which the extremum is known to exist. The technique derives its name from the fact… …   Wikipedia\n\n• Successive parabolic interpolation — is a technique for finding the extremum (minimum or maximum) of a continuous unimodal function by successively fitting parabolas (polynomials of degree two) to the function at three unique points, and at each iteration replacing the oldest point… …   Wikipedia\n\n• List of mathematics articles (U) — NOTOC U U duality U quadratic distribution U statistic UCT Mathematics Competition Ugly duckling theorem Ulam numbers Ulam spiral Ultraconnected space Ultrafilter Ultrafinitism Ultrahyperbolic wave equation Ultralimit Ultrametric space… …   Wikipedia\n\n• Универсальность Фейгенбаума — Универсальность Фейгенбаума, или универсальность Фейгенбаума Кулле Трессера  эффект в теории бифуркаций, заключающийся в том, что определённые числовые характеристики каскада бифуркаций удвоения периодов в однопараметрическом семействе… …   Википедия\n\n• Multimodal integration — Multimodal integration, also known as multisensory integration, is the study of how information from the different sensory modalities, such as sight, sound, touch, smell, self motion and taste, may be integrated by the nervous system. A coherent… …   Wikipedia\n\n### Поделиться ссылкой на выделенное\n\n##### Прямая ссылка:\nНажмите правой клавишей мыши и выберите «Копировать ссылку»" ]

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[ "# Build a simple Quantum Circuit using IBM Qiskit in Python\n\nQiskit is an open source framework for quantum computing. It provides tools for creating and manipulating quantum programs and running them on prototype quantum devices on IBM Q Experience or on simulators on a local computer. Let’s see how we can create simple Quantum circuit and test it on a real Quantum computer or simulate in our computer locally. Python is a must prerequisite for understanding Quantum programs as Qiskit itself is developed using Python.\n\nFirst and foremost part before entering into the subject is installation of Qiskit and Anaconda. Refer to the below articles for step by step guide for anaconda installation.\n\n#### Installation\n\nTo install qiskit follow the below steps:\n\n• Open Anaconda Prompt and type\n```pip install qiskit\n```\n• That’s it, this will install all necessary packages.\n• Next, open Jupyter Notebook.\n• Import qiskit using the following command.\n```import qiskit\n```\n\n1. Create a free IBM Quantum Experience Account.\n2. Navigate to My Account.\n3. Click on Copy Token to copy the token to the clipboard(Token represents the API to access IBM Quantum devices).\n4. Run the following commands(Jupyter Notebook) to store your API token locally for later use in a configuration file called qiskitrc. Replace MY_API_TOKEN with the API token.\n```from qiskit import IBMQ\nIBMQ.save_account('MY_API_TOKEN')\n```\n\n## Getting Started\n\nFirstly, we will import the necessary packages. The import lines import the basic elements (packages and functions) needed for your program.\n\nThe imports used in the code example are:\n\n• QuantumCircuit: Holds all your quantum operations; the instructions for the quantum system\n• Aer: Handles simulator backends\n• qiskit.visualization: Enables data visualization, such as plot_histogram\n\n## Initialize variables\n\nIn the next line of code, you initialize two qubits in the zero state, and two classical bits in the zero state, in the quantum circuit called circuit.\n\nThe next three lines of code, beginning with circuit., add gates that manipulate the qubits in your circuit.\n\nDetailed Explanation\n\n• QuantumCircuit.h(0): A Hadamard gate on qubit 0, which puts it into a superposition state\n• QuantumCircuit.cx(0, 1): A controlled-NOT operation (Cx ) on control qubit 0 and target qubit 1, putting the qubits in an entangled state\n• QuantumCircuit.measure([0,1], [0,1]): The first argument indexes the qubits, the second argument indexes the classical bits. The nth qubit’s measurement result will be stored in the nth classical bit.\n\nThis particular trio of gates added one-by-one to the circuit forms the Bell state,|", null, "In this state, there is a 50 percent chance of finding both qubits to have the value zero and a 50 percent chance of finding both qubits to have the value one.\n\n## Simulate the experiment\n\nThe next line of code calls a specific simulator framework – in this case, it calls Qiskit Aer, which is a high-performance simulator that provides several backends to achieve different simulation goals. In this program, we will use the qasm_simulator. Each run of this circuit will yield either the bit string ’00’ or ’11’. The program specifies the number of times to run the circuit in the shots argument of the execute method (job = execute(circuit, simulator, shots=1000)). The number of shots of the simulation is set to 1000 (the default is 1024). Once you have a result object, you can access the counts via the method get_counts(circuit). This gives you the aggregate outcomes of your experiment. The output bit string is ’00’ approximately 50 percent of the time. This simulator does not model noise. Any deviation from 50 percent is due to the small sample size.\n\n## Visualize the circuit\n\nQuantumCircuit.draw() (called by circuit.draw() in the code) displays your circuit in one of the various styles used in textbooks and research articles. In the circuit obtained after visualization command , the qubits are ordered with qubit zero at the top and qubit one below it. The circuit is read from left to right, representing the passage of time.\n\n## Visualize the results\n\nQiskit provides many visualizations, including the function plot_histogram, to view your results.\n\n```plot_histogram(counts)\n```\n\nThe probabilities (relative frequencies) of observing the |00? and |11? states are computed by taking the respective counts and dividing by the total number of shots.\n\nBelow is the implementation.\n\n## Python3\n\n `# python program to create a simple Quantum circuit ` ` `  ` `  `%``matplotlib inline ` `from` `qiskit ``import` `QuantumCircuit, execute, Aer, IBMQ ` `from` `qiskit.compiler ``import` `transpile, assemble ` `from` `qiskit.tools.jupyter ``import` `*` `from` `qiskit.visualization ``import` `*` ` `  `# Loading your IBM Q account(s) ` `provider ``=` `IBMQ.load_account() ` `  `  `# Create a Quantum Circuit acting ` `# on the q register ` `circuit ``=` `QuantumCircuit(``2``, ``2``) ` `  `  `# Add a H gate on qubit 0 ` `circuit.h(``0``) ` `  `  `# Add a CX (CNOT) gate on control  ` `# qubit 0 and target qubit 1 ` `circuit.cx(``0``, ``1``) ` `  `  `# Map the quantum measurement to the ` `# classical bits ` `circuit.measure([``0``,``1``], [``0``,``1``]) ` `  `  `# Use Aer's qasm_simulator ` `simulator ``=` `Aer.get_backend(``'qasm_simulator'``) ` `  `  `# Execute the circuit on the qasm ` `# simulator ` `job ``=` `execute(circuit, simulator, shots``=``1000``) ` `  `  `# Grab results from the job ` `result ``=` `job.result() ` `  `  `# Return counts ` `counts ``=` `result.get_counts(circuit) ` `print``(``\"\\nTotal count for 00 and 11 are:\"``,counts) ` ` `  `# Draw the circuit ` `circuit.draw()`\n\nOutput:", null, "Plot a histogram\n\n## Python3\n\n `# Plot a histogram ` `plot_histogram(counts)`\n\nOutput:", null, "My Personal Notes arrow_drop_up", null, "Check out this Author's contributed articles.\n\nIf you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to [email protected]. See your article appearing on the GeeksforGeeks main page and help other Geeks.\n\nPlease Improve this article if you find anything incorrect by clicking on the \"Improve Article\" button below.\n\nArticle Tags :\n\n1\n\nPlease write to us at [email protected] to report any issue with the above content." ]

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[ "# $\\int sec(x)tan^{2}(x)$ Can it be integrated w/ U-Sub?\n\n• Lebombo\nIn summary, the Professor has given students helpful rules of thumb to make integrating various trig functions easier. These rules are limited in scope, and eventually a more complicated integration must be done using other methods.f\n\n## Homework Statement\n\nLast week, the Professor of the class I'm taking jotted down some even and odd exponent rules of thumb to make life easier when integrating various trig functions. Rules like if you have sinx and cosx and one of the trig functions is odd, then split, use an identity and the U-Sub..etc.\n\nMy question is: is this a straight forward integration based on the above mentioned type of rules?\n\n$\\int sec(x)tan^{2}(x)$\n\nIf I replace the tan^{2}x with its identity (sec^{2}x - 1) I end up with sec^{3} again with an additional secx.\n\nThis looks more tricky than simply being able to use those rules.\n\nLast edited:\nI speak strictly for myself.\n\nWhen integrating\n$\\int \\left[ \\sec^3x - \\sec x \\,\\right] dx$\n\nSurely you know you can separate the terms into\n$\\int \\sec^3x \\,dx - \\int \\sec x \\,dx$\n\nNow you should memorized the integral of secant x, so that's out of the way.\n\nYou could also memorize the integral of secant cubed, which is nicely demonstrated here.\n\nThen you just back-substitute into the second equation.\n\nSeems like a relatively easy way to integrate in my opinion.\n\n•", null, "1 person\n\n## Homework Statement\n\nLast week, the Professor of the class I'm taking jotted down some even and odd exponent rules of thumb to make life easier when integrating various trig functions. Rules like if you have sinx and cosx and one of the trig functions is odd, then split, use an identity and the U-Sub..etc.\n\nMy question is: is this a straight forward integration based on the above mentioned type of rules?\n\n$\\int sec(x)tan^{2}(x)$\n\nIf I replace the tan^{2}x with its identity (sec^{2}x - 1) I end up with sec^{3} again with an additional secx.\n\nThis looks more tricky than simply being able to use those rules.\nThose rules only work to a certain point. Eventually you get an integral you have to evaluate using other methods, and the integral of ##\\sec^3 x## is one of those. Use integration by parts to calculate that one.\n\nYou could also forgo the initial use of a trig substitution and evaluate the original integral using integration by parts.\n\n•", null, "1 person\nThank you ReneG and Vela." ]

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[ "", null, "# Quadratic Equation Worksheet Answers\n\nIn Free Printable Worksheets228 views\n4.25 / 5 ( 171votes )", null, "Table of Contents :\n\nTop Suggestions Quadratic Equation Worksheet Answers :\n\nQuadratic Equation Worksheet Answers Choose an answer and hit next you will receive your score and answers at the end question 1 of 3 what is a vertex of a quadratic equation the vertex is the maximum or minimum point on the graph When the bill comes and you need to calculate the tip show your child how to find the answer using pencil solving equations graphs algebraic expressions linear equations inequalities Math worksheets are usually what watching a quick video online at home about quadratic equations then the next day in class students could work on math problems and have their teacher available.\n\nQuadratic Equation Worksheet Answers If we ve been giving kids worksheets with simplistic answers for years and who may have once understood the quadratic formula and mastered the five paragraph essay but have forgotten them in the Math worksheets are usually what watching a quick video online at home about quadratic equations then the next day in class students could work on math problems and have their teacher available Choose an answer of the equation the highest exponent of the variable in the equation is 18 zero is a solution to the quadratic equation to discover more about these roots head to the lesson.\n\nQuadratic Equation Worksheet Answers High school the students practiced factoring quadratic equations graphing calculators worksheets plus games and activities webmath webmath is a math help web site that generates The world not worksheets self direction isosceles triangles the quadratic equation and integrals on paper their stellar educations prepared them to make world shaping decisions but i doubt If we ve been giving kids worksheets with simplistic answers for years and then get upset when who may have once understood the quadratic formula and mastered the five paragraph essay but have.\n\n## Quadratic Equation Worksheets With Answer Keys Free S\n\nFree Worksheet With Answer Keys On Quadratic Equations Each One Has Model Problems Worked Out Step By Step Practice Problems Challenge Proglems Quadratic Equation Worksheets With Answer Keys\n\n### Quadratic Formula Worksheets With Answers Thoughtco\n\nQuadratic Formula Worksheet 1 Although There Are Other Methods To Solve Quadratic Equations Factoring Graphing Completing The Square It Is Important To Use Efficiency Hence You Are Asked To Use The Quadratic Formula To Solve These Questions However There Are Other Worksheets That Require You To Complete The Square Factor And Graphing\n\n#### Solve Each Equation With The Quadratic Formula\n\nUsing The Quadratic Formula Date Period Solve Each Equation With The Quadratic Formula 1 M2 5m 14 0 2 B2 4b 4 0 3 2m2 2m 12 0 4 2x2 3x 5 0 5 X2 4x 3 0 6 2x2 3x 20 0 7 4b2 8b 7 4 8 2m2 7m 13 10 1\n\n##### Solving Quadratic Equations Worksheets\n\nA Quadratic Equation Is An Equation In Which X Represents An Unknown And A B And C Represent Known Numbers Provided That A Does Not Equal 0 In Equations In Which A Equals 0 An Equation Is Linear\n\n###### Quadratic Equation Word Problems Worksheet With Answers\n\nQuadratic Equation Word Problems Worksheet With Answers Problems Problem 1 Difference Between A Number And Its Positive Square Root Is 12 Find The Number Problem 2 A Piece Of Iron Rod Costs 60 If The Rod Was 2 Meter Shorter And Each Meter Costs 1 More The Cost Would Remain Unchanged\n\nQuadratic Equation Worksheets Edhelper\n\nQuadratic Equations Solving Quadratic Equations B 0 Whole Number Only Answers Solving Quadratic Equations B 0 Solve By Factoring Solve By Factoring Fractional Answers Solve By Factoring Whole Numbers And Fraction Answers Completing The Square A 1 No Radical Answers Completing The Square A 1 Radical Answers\n\nQuadratic Equations Worksheets The Teacher S Corner\n\nThe Quadratic Equation Worksheet Maker Will Generate A Printable Worksheet Of Problems And An Answer Key Select Your Options In The Form Below And Click On The Make Worksheet Button We Will Open A New Window Containing Your Custom Quadratic Equations Worksheet If You Like The Worksheet You Can Print It Straight From Your Browser\n\nQuadratic Equation Worksheets Math Worksheets 4 Kids\n\nQuadratic Formula Worksheets This Series Of Quadratic Formula Worksheets Requires Students To Identify The Nature Of The Roots Of The Quadratic Equation As Equal Unequal Real Or Complex Exclusive Worksheets On Solving Quadratic Equations Using Quadratic Formula Are Also Available 21 Worksheets\n\nSolving Quadratic Factoring Kuta Software Llc\n\nSolving Quadratic Equations By Factoring Date Period Solve Each Equation By Factoring J Q Ja Nl4lc Fr 7i9gvhit 8s T Ir Mersterrbvreidy 8 0 Bmacdref Pwpihtqh7 Eixnsf Didn Uiotee W Zaxlcgwetb Urbaa P10 D Worksheet By Kuta Software Llc 11 N2 8n 15 5 3 12 5r2 44 R 120 Solving Quadratic Factoring\n\nFactoring Quadratic Equations Worksheet And Answer Key\n\nFree 25 Question Worksheet With Answer Key On Factoring Quadratic Equations Includes 2 Worked Out Model Problems Plus Challenge Problems\n\nPeople interested in Quadratic Equation Worksheet Answers also searched for :\n\nQuadratic Equation Worksheet Answers. The worksheet is an assortment of 4 intriguing pursuits that will enhance your kid's knowledge and abilities. The worksheets are offered in developmentally appropriate versions for kids of different ages. Adding and subtracting integers worksheets in many ranges including a number of choices for parentheses use.\n\nYou can begin with the uppercase cursives and after that move forward with the lowercase cursives. Handwriting for kids will also be rather simple to develop in such a fashion. If you're an adult and wish to increase your handwriting, it can be accomplished. As a result, in the event that you really wish to enhance handwriting of your kid, hurry to explore the advantages of an intelligent learning tool now!\n\nConsider how you wish to compose your private faith statement. Sometimes letters have to be adjusted to fit in a particular space. When a letter does not have any verticals like a capital A or V, the very first diagonal stroke is regarded as the stem. The connected and slanted letters will be quite simple to form once the many shapes re learnt well. Even something as easy as guessing the beginning letter of long words can assist your child improve his phonics abilities. Quadratic Equation Worksheet Answers.\n\nThere isn't anything like a superb story, and nothing like being the person who started a renowned urban legend. Deciding upon the ideal approach route Cursive writing is basically joined-up handwriting. Practice reading by yourself as often as possible.\n\nResearch urban legends to obtain a concept of what's out there prior to making a new one. You are still not sure the radicals have the proper idea. Naturally, you won't use the majority of your ideas. If you've got an idea for a tool please inform us. That means you can begin right where you are no matter how little you might feel you've got to give. You are also quite suspicious of any revolutionary shift. In earlier times you've stated that the move of independence may be too early.\n\nEach lesson in handwriting should start on a fresh new page, so the little one becomes enough room to practice. Every handwriting lesson should begin with the alphabets. Handwriting learning is just one of the most important learning needs of a kid. Learning how to read isn't just challenging, but fun too.\n\nThe use of grids The use of grids is vital in earning your child learn to Improve handwriting. Also, bear in mind that maybe your very first try at brainstorming may not bring anything relevant, but don't stop trying. Once you are able to work, you might be surprised how much you get done. Take into consideration how you feel about yourself. Getting able to modify the tracking helps fit more letters in a little space or spread out letters if they're too tight. Perhaps you must enlist the aid of another man to encourage or help you keep focused.\n\nQuadratic Equation Worksheet Answers. Try to remember, you always have to care for your child with amazing care, compassion and affection to be able to help him learn. You may also ask your kid's teacher for extra worksheets. Your son or daughter is not going to just learn a different sort of font but in addition learn how to write elegantly because cursive writing is quite beautiful to check out. As a result, if a kid is already suffering from ADHD his handwriting will definitely be affected. Accordingly, to be able to accomplish this, if children are taught to form different shapes in a suitable fashion, it is going to enable them to compose the letters in a really smooth and easy method. Although it can be cute every time a youngster says he runned on the playground, students want to understand how to use past tense so as to speak and write correctly. Let say, you would like to boost your son's or daughter's handwriting, it is but obvious that you want to give your son or daughter plenty of practice, as they say, practice makes perfect.\n\nWithout phonics skills, it's almost impossible, especially for kids, to learn how to read new words. Techniques to Handle Attention Issues It is extremely essential that should you discover your kid is inattentive to his learning especially when it has to do with reading and writing issues you must begin working on various ways and to improve it. Use a student's name in every sentence so there's a single sentence for each kid. Because he or she learns at his own rate, there is some variability in the age when a child is ready to learn to read. Teaching your kid to form the alphabets is quite a complicated practice.", null, "", null, "Have faith. But just because it's possible, doesn't mean it will be easy. Know that whatever life you want, the grades you want, the job you want, the reputation you want, friends you want, that it's possible.\n\nRelated Free Printable Worksheets :\n\nTop" ]

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[ "# Arduino and ultra sonic range measurement module or how to measure the pulse time with a hardware timer and an interrupt", null, "SEN136B5B is a Ultra Sonic range measurement from SeedStudio. It can measure the distance by sending a 40k Hz ultra sound impulse via the transmitter  and calculating time needed for echo to reach receiver. Detecting range: 3cm-4m. More information about device.\n\nIn the description of module is mentioned that it is compatible with Arduino library, but I decided to write program without using PulseIn command.\n\n# Arduino frequency counter/duty cycle meter", null, "This meter gives the best results at 0 – 1000 Hz  range. It works by measuring square wave total and high period duration using 16 bit hardware counter.", null, "As you may know frequency = 1/Period and  Duty Cycle = High period duration/total period duration.\n\n# Very simple Arduino capacitance meter using 16 bit timer and analog comparator\n\nAs You could see from video it requires only one external resistor. Meter is very simple, but obviously not very accurate or wide range. Theoretically it could measure in 10 nF – 160 µF range.\n\nHow it Works?\n\nCircuit formed from resistor and capacitor (RC circuit) has time constant, it shows time needed to discharge capacitor via resistor to ~37% it’s initial voltage. Calculation of time constant is very simple t=R*C . For 1 k resistor and 47µF capacitor it’s 47ms.", null, "My capacitance meter fully charges capacitor, when 16 bit timer is started and capacitor is discharging via resistor (1 k). Capacitor is connected to analog voltage comparator(pin 5), as capacitor voltage drops bellow 1.1 V occurs analog comparator interrupt which triggers timer interrupt.  If resistor is constant, discharge time and capacitance dependency is linear, therefore if You know one rated capacity capacitor discharge time, You could easily calculate another capacitor’s capacitance by measuring time.\n\nBecause timer uses only one  /64 clock prescaler measurement range is very narrow. To have wider range there is a always way to make  programmable prescaler, which changes if capacitor value is out of range, or discharge capacitor via range of different resistors.\n\nAlso keep in mind that resistor’s resistance depends on environment temperature, consequently and on LCD display showed capacitance. To fix this temperature dependency referenced capacitor should be used. In this case every time both capacitors are charged and discharged separately, but via the same resistor. Capacitance proportion is calculated and if referenced capacitor capacitance is know measured capacitor’s value can be calculated just by division or multiplying.\n\nIt’s time for program’s code.\n\n# Arduino 4 digits 7 segments LED countdown timer with buzzer\n\nAfter sorting out  how works 16 bit hardware timer it is time for 4 digits countdown timer. Having 16 bit timer and  7 segments LED code from earlier only were remaining to write timer’s modes (run/setup) and button’s control code.  After putting  all code to one place there is countdown timer with properties below:\n\n• Maximum 99 minutes 59 seconds countdown interval\n• 1 second resolution\n• Sound indication with buzzer for finished countdown\n• LED indicates running timer, or relay instead  for powering external devices for some period of time\n• 2 buttons to set timer and start/pause/reset.\n\nThis time without additional code quotation, please find some code explanation within code, so code bellow.\n\n# Examples of using Arduino/Atmega 16 bit hardware timer for digital clock\n\nArduino Mega with Atmega 1280 has four 16 bit timers, that could be used for various purposes, like time/frequency measurement, control with precise timing, PWM generation. Today I hope to explain how to use timer for clocks, timers (countdown) and other things, where You need µCPU to perform some tasks after precise period of time. I’ll give You two examples:\n\n• Pseudo 1 second timer\n• Real 1 second timer\n\nHow counter works? It is simple independent  16 bit accumulator, which value increases by 1 at clock cycle. 16 bit means that maximum counter’s value is 65536. When this value is reached counter starts counting from 0 again and gives hardware interrupt. Counter value could be changed any time. This is normal counter mode, Atmega 1280 offers total 14 operating modes.\n\nPseudo 1 second timer (1.048576 s)" ]

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[ "#### Reserve calculation - Valuation net premium exceeding the gross premium charged.\n\n(a) If in any contract year the gross premium charged by a company on a policy or contract issued on or after January 1, 1972, is less than the valuation net premium for the policy or contract calculated by the method used in calculating the reserve thereon, but using the minimum valuation standards of mortality and rate of interest, the minimum reserve required for the policy or contract shall be the greater of either the reserve calculated according to the mortality table, rate of interest, and method actually used for the policy or contract, or the reserve calculated by the method actually used for the policy or contract but using the minimum valuation standards of mortality and rate of interest and replacing the valuation net premium by the actual gross premium in each contract year for which the valuation net premium exceeds the actual gross premium. The minimum valuation standards of mortality and rate of interest referred to in this section are those standards stated in Sections 27-36A-5 and 27-36A-7.\n\n(b) For a life insurance policy issued on or after January 1, 1985, for which the gross premium in the first policy year exceeds that of the second year and for which no comparable additional benefit is provided in the first year for the excess, and which provides an endowment benefit or a cash surrender value or a combination thereof in an amount greater than excess premium, the provisions of subsection (a) shall be applied as if the method actually used in calculating the reserve for the policy were the method described in Section 27-36A-8, but ignoring subsection (b) of Section 27-36A-8. The minimum reserve at each policy anniversary of the policy shall be the greater of the minimum reserve calculated in accordance with Section 27-36A-8, including subsection (b) of Section 27-36A-8, and the minimum reserve calculated in accordance with this section." ]

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[ "# Derivative with respect to diagonal of diagonal matrix\n\nSuppose I have a diagonal matrix $$\\pmb{D}$$ and a symmetric matrix $$\\pmb{X}$$ that is not a function of $$\\pmb{D}$$, and I wish to find the following derivative: $$\\frac{\\partial}{\\partial \\mathrm{diag}(\\pmb{D})} \\mathrm{vec}\\left(\\pmb{D}\\pmb{X}\\pmb{D}\\right),$$ in which $$\\mathrm{diag}(\\pmb{D})$$ represents the diagonal of $$\\pmb{D}$$. I know the following derivative: $$\\frac{\\partial}{\\partial \\mathrm{vec}(\\pmb{D})} \\mathrm{vec}\\left(\\pmb{D}\\pmb{X}\\pmb{D}\\right) = \\left(\\pmb{D}\\pmb{X} \\otimes \\pmb{I} \\right) + \\left(\\pmb{I} \\otimes \\pmb{X}\\pmb{D}\\right)$$ So I guess I can find the answer by multyplying this with $$\\frac{\\partial \\mathrm{vec}\\left( \\pmb{D} \\right)}{\\partial \\mathrm{diag}(\\pmb{D})}$$, which should be some straightforward matrix with zeroes and ones. So my question is, (a) does this matrix have a name and can it easily be determined? and (b) isn't there some simpler way to do this?\n\n## 2 Answers\n\nAlthough \"D\" is a great mnemonic for \"diagonal\" it is easily confused with \"derivative\" operations, which also use a mnemonic \"D\".\n\nInstead let's use a convention where upper/lower case letters are related by a diagonal operation: uppercase is the matrix, lowercase is the vector.\n\nGiven an arbitrary matrix $$X$$ and a diagonal matrix $$\\,A={\\rm Diag}(a)$$\nthe product in question can be expanded as \\eqalign{ P &= AXA \\cr &= X\\odot aa^T \\cr {\\rm vec}(P) &= {\\rm vec}(X)\\odot{\\rm vec}(aa^T) \\cr v_p &= v_x\\odot(a\\otimes a) \\cr &= V_x\\,(a\\otimes a) \\cr } where $$\\odot$$ denotes the elementwise/Hadamard product\nand $$\\otimes$$ denotes the Kronecker product.\n\nThe differential and gradient of the vectorized product are \\eqalign{ dv_p &= V_x\\,(a\\otimes da+da\\otimes a) \\cr &= V_x\\,(a\\otimes E+E\\otimes a)\\,da \\cr \\frac{\\partial v_p}{\\partial a} &= V_x\\,(a\\otimes E+E\\otimes a) \\cr &= {\\rm Diag}\\Big(v_x\\Big)\\,\\big(a\\otimes E+E\\otimes a\\big) \\cr &= \\Big(v_xe^T\\Big)\\odot\\big(a\\otimes E+E\\otimes a\\big) \\cr &= \\Big({\\rm vec}(X)\\,e^T\\Big)\\odot\\big(a\\otimes E+E\\otimes a\\big) \\cr } where $$E$$ is the identity matrix and $$e$$ is the vector of all ones, i.e. $$\\,E={\\rm Diag}(e)$$\n\nI found a solution, but will not accept this answer yet in case someone has an easier solution. My solution to find $$\\frac{\\partial \\mathrm{vec}\\left( \\pmb{D} \\right)}{\\partial \\mathrm{diag}(\\pmb{D})}$$ was to reconize that for $$n \\times n$$ diagonal matrix $$\\pmb{D}$$: $$\\pmb{D} = \\sum_{i=1}^{n} \\pmb{E}_i \\delta_{ii},$$ with $$\\pmb{E}_i$$ being an $$n \\times n$$ matrix with a 1 on the $$i$$th diagonal and zeroes otherwise: $$e_{jk} = \\begin{cases} 1 & \\text{if } j = k = i \\\\ 0 & \\text{otherwise} \\end{cases}$$ The derivative can then be derived as: $$\\frac{\\partial \\mathrm{vec}\\left( \\pmb{D} \\right)}{\\partial \\mathrm{diag}(\\pmb{D})} = \\begin{bmatrix} \\mathrm{vec}(\\pmb{E}_1) & \\mathrm{vec}(\\pmb{E}_2) & \\ldots & \\mathrm{vec}(\\pmb{E}_n) \\end{bmatrix}$$ Denoting this result $$\\pmb{E}^*$$ I obtain: $$\\frac{\\partial}{\\partial \\mathrm{diag}(\\pmb{D})} \\mathrm{vec}\\left(\\pmb{D}\\pmb{X}\\pmb{D}\\right) = \\left( \\left(\\pmb{D}\\pmb{X} \\otimes \\pmb{I} \\right) + \\left(\\pmb{I} \\otimes \\pmb{X}\\pmb{D}\\right) \\right) \\pmb{E}^*$$" ]

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[ "# Chapter Notes: Magnetism & Moving Charge Physics Class 12", null, "Notes for Magnetism and moving Charge chapter of class 12 physics. Dronstudy provides free comprehensive chapterwise class 12 physics notes with proper images & diagram.\n\n### OERSTED'S EXPERIMENT\n\nThe first evidence of the relationship of magnetism to moving charges was discovered in 1819 by the Danish scientist Hans Christian  Oersted. He found that a compass needle was deflected by a current carrying wire. When the direction of current was reversed the deflection also got reversed.\n\n### BIOT-SAVART'S LAW\n\nIt turns out that there is no source of magnetic force similar in nature to point electric charges giving rise to electric fields. Such magnetic monopoles have not been found so far. The elementary source of magnetic force is a current element $Id{\\rm{\\vec L}}$. The force on another similar conductor can be expressed conveniently in terms of a magnetic field $d{\\rm{\\vec B}}$ due to the first. The properties of this magnetic field are as follows:", null, "1. The magnetic field grows weaker as we move farther  from its source. In particular, the magnitude of the magnetic field dB is inversely proportional to the square of the distance from the current element $Id{\\rm{\\vec L}}$.\n2. The larger the electric current, the larger is the magnetic field. In particular, the magnitude of the magnetic field $d{\\rm{\\vec B}}$ is proportional to the\ncurrent I\n.\n\n3. The magnitude of the magnetic field $d{\\rm{\\vec B}}$ is proportional to sin$\\theta$, where $\\theta$ is the angle between the current element $Id{\\rm{\\vec L}}$   and vector $\\vec r$ that points from the current element to the point in space where $d{\\rm{\\vec B}}$ is evaluated. The direction of $d{\\rm{\\vec L}}$  is the same as the direction of current at that point.\n4. The direction of the $\\vec B$ is not radially away from its source as the gravitational field and the electric field are from their sources. In fact, the direction of $d{\\rm{\\vec B}}$ is perpendicular to both $Id{\\rm{\\vec L}}$ and the vector $\\vec r$.\nThese features of field $d{\\rm{\\vec B}}$ can be written compactly as\n\n$\\,\\,d\\vec B = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right)I{{d\\vec L \\times \\hat r} \\over {{r^2}}} = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right)I{{d\\vec L \\times \\vec r\\,\\,} \\over {{r^3}}}$\n\nHere, $\\left( {{\\mu _0}/4\\pi } \\right)$ is a constant of proportionality and ${\\rm{\\hat r}}$ is unit vector in the direction of $\\vec r$.\nThe constant ${{\\mu _0}}$ is called the permeability of free space or the permeability constant. Its value is\n\n${\\mu _0} = 4\\pi \\times {10^{ - 7}}T{\\rm{ }}m/A$\n\nThe magnetic field ${\\rm{\\vec B}}$ is also called magnetic induction, or flux density.\nIts SI unit is tesla (T) which is equivalent to Wb/m2.\nThe dimensions of ${\\rm{\\vec B}}$ are [MT - 2A- 1]\nThe magnitude of $d{\\rm{\\vec B}}$ can be obtained by\n\n$\\,\\,dB = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right){{I{\\rm{ }}dL\\sin \\theta } \\over {{r^2}}}\\,\\,$\n\n### 1. Field due to Straight Current-Carrying Conductor\n\nAccording to Biot-Savart law,\n\n$\\,\\,d\\vec B = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right)I{{d\\vec L \\times \\vec r\\,\\,} \\over {{r^3}}}$\n⸫   ${\\rm{\\vec B}} = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right)\\int {{{I{\\rm{ }}d{\\rm{\\vec L}} \\times {\\rm{\\vec r}}} \\over {{r^3}}}}$\n\nCase (A) : The point P is along the length of the wire, as in Fig. (A). Then $d{\\rm{\\vec L}}$ and ${\\rm{\\vec r}}$ will be either parallel or antiparallel. That is, $\\theta$ = 0 or $\\pi$, so $d{\\rm{\\vec L}} \\times {\\rm{\\vec r}} = 0$, hence  ${\\rm{\\vec B}} = 0$", null, "Case (B) : The point P is at a  distance d from the wire, as shown in Fig. (B). Every current element $Id{\\rm{\\vec L}}$ of the wire contributes to ${\\rm{\\vec B}}$ in the same direction (which is $\\otimes$). Therefore,\n\n$B = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right)\\int {{{I{\\rm{ }}dL{\\rm{ }}r\\sin \\theta } \\over {{r^3}}}} = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right)\\int_{{\\rm{ }}A}^{{\\rm{ }}B} {{{I{\\rm{ }}dy{\\rm{ }}\\sin \\theta } \\over {{r^2}}}}$\n\nBut $y = dtan\\phi \\Rightarrow dy = dse{c^2}\\phi d\\phi$,     and     $r = dsec\\phi$, $\\theta = (90^\\circ - \\phi )$.\n\n⸫  $B = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right){I \\over d}\\int_{{\\rm{ }} - \\beta }^{{\\rm{ }}\\alpha } {\\cos } \\phi d\\phi$\nor  $B = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right){I \\over d}(\\sin \\alpha + \\sin \\beta )$\n\nNote that\n(1) For points along the length of the wire (but not on it) the field is always zero.\n(2) For points at a perpendicular distance d from the wire, field B varies inversely with distance,\n$B \\propto {1 \\over d}$  [and not ${1 \\over {{d^2}}}$].", null, "(3) The field is always perpendicular to the plane containing the wire and the point. So in a plane perpendicular to the wire the lines of force are concentric circles.\n(4) If the wire is of infinite length and the point P is not near its any end, $\\alpha = \\beta = \\left( {\\pi /2} \\right)$. Hence,\n\n$B = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right){I \\over d}[1 + 1]$   or   $B = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right){{2I} \\over d}$\n\n(5)  If  the point is near one end of an infinitely long wire, $\\alpha = \\left( {\\pi /2} \\right)$ and $\\beta = 0$. Hence,\n\n$B = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right){I \\over d}[1 + 0]$   or   $B = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right){I \\over d}$\n\n(6)  If the wire is of finite length and the point is on its perpendicular bisector, $\\alpha = \\beta$. Hence,\n\n$B = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right){{2I} \\over d}\\sin \\alpha$  with   $\\sin \\alpha = {L \\over {\\sqrt {({L^2} + 4{d^2})} }}$  where L is length of the wire.\n\n(7)  If the wire is of finite length and the point is near its one end, $\\beta = 0$. Hence,\n\n$B = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right){I \\over d}\\sin \\alpha$  with  $\\sin \\alpha = {L \\over {\\sqrt {({L^2} + {d^2})} }}$", null, "Application 1\nFigure shows two long straight wires carrying electric currents of 10 A each, in opposite directions. The separation between the wires is 5.0 cm. Find the magnetic field at a point P midway between the wires.\n\nSolution:\n\nThe right-hand thumb rule shows that the magnetic field at P due to each of the wires is perpendicular to the plane of the diagram and is going into it. The magnitude of the field due to each wire is\n\n$B = {{{\\mu _0}} \\over {4\\pi }}{\\rm{.}}{{2I} \\over d} = {{{{10}^{ - 7}} \\times 2 \\times 10} \\over {2.5 \\times {{10}^{ - 2}}}} = 8 \\times {10^{ - 5}} = 80\\,\\,\\mu T$\n\nTotal field due to both the wires is\n\n$2 \\times 80\\mu T = {\\bf{160}}\\mu {\\bf{T}}$\n\nApplication 2\nA pair of stationary and infinitely long bent wires are placed in the x-y plane as shown. The wires carry currents of 10 amperes each as shown. The segments L and M are along the x-axis. The segments P and Q are parallel to the y-axis such that OS = OR = 0.02 m. Find the magnitude and direction of the magnetic induction at the origin O.\n\nSolution:\n\nAs point O is along the length of segments L and M so the field at O due to these segments will be zero. Further, as the point O is near one end of a long wire,", null, "${\\rm{\\vec B}} = {{\\rm{\\vec B}}_P} + {{\\rm{\\vec B}}_Q} = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right){I \\over {RO}} \\odot + \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right){I \\over {SO}} \\odot$\nso ${\\rm{\\vec B}} = \\left( {{{{\\mu _0}} \\over {4\\pi }}} \\right){{2I} \\over d} \\odot$           [as RO = SO = d]\n\nSubstituting the given data,\n${\\rm{\\vec B}} = {10^{ - 7}} \\times {{2 \\times 10} \\over {0.02}} \\odot = {10^{ - 4}}T \\odot$\n\n### 2. Field due to a Circular Current-Carrying Segment at its Centre\n\nLet AB be a circular segment of radius R. Point P is at its centre. Here,", null, "(i) each element is at the same distance from the centre, i.e., r = R = constant,\n(ii) the angle between element $d{\\rm{\\vec L}}$ and ${\\rm{\\vec r}}$ is always $\\pi /2$, and\n(iii) the contribution of each element to ${\\rm{\\vec B}}$ is in the same direction (i.e., out of the page if the current is anticlockwise and into the page if clockwise).\n\n⸫   ${\\rm{\\vec B}} = {{{\\mu _0}} \\over {4\\pi }}\\int {{{I{\\rm{ }}d{\\rm{\\vec L}} \\times {\\rm{\\vec r}}} \\over {{r^3}}}} = {{{\\mu _0}} \\over {4\\pi }}\\int_{{\\rm{ }}A}^{{\\rm{ }}B} {{{I{\\rm{ }}dL} \\over {{R^2}}}}$\nBut $dL = Rd\\phi$,\n⸫   $B = {{{\\mu _0}} \\over {4\\pi }}\\int_{{\\rm{ }}0}^{{\\rm{ }}\\alpha } {} {{IR{\\rm{ }}d\\varphi } \\over {{R^2}}}$       or  $\\,\\,B = {{{\\mu _0}} \\over {4\\pi }}{{I\\alpha } \\over R}\\,\\,$\n\nNote that\n(1) The angle $\\alpha$ is in radians\n(2) If the loop is semicircular (i.e, $\\alpha = \\pi$), $B = {{{\\mu _0}} \\over {4\\pi }}{{\\pi I} \\over R}$\n(3) If the loop is a full circle with N turns (i.e., $\\alpha = 2\\pi N$), $B = {{{\\mu _0}} \\over {4\\pi }}{{2\\pi NI} \\over R}$\n\n### 1 Comment\n\n•", null, "Anonymous\n\nProvide Full notes of this chapter" ]

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[ "# Number 355168420\n\n### Properties of number 355168420\n\nCross Sum:\nFactorization:\n2 * 2 * 5 * 17 * 1044613\nDivisors:\nCount of divisors:\nSum of divisors:\nPrime number?\nNo\nFibonacci number?\nNo\nBell Number?\nNo\nCatalan Number?\nNo\nBase 3 (Ternary):\nBase 4 (Quaternary):\nBase 5 (Quintal):\nBase 8 (Octal):\n152b70a4\nBase 32:\naims54\nsin(355168420)\n-0.8856354138051\ncos(355168420)\n-0.46438121604375\ntan(355168420)\n1.907130140513\nln(355168420)\n19.688102657472\nlg(355168420)\n8.5504343432849\nsqrt(355168420)\n18845.912554185\nSquare(355168420)\n\n### Number Look Up\n\nLook Up\n\n355168420 which is pronounced (three hundred fifty-five million one hundred sixty-eight thousand four hundred twenty) is a special number. The cross sum of 355168420 is 34. If you factorisate the figure 355168420 you will get these result 2 * 2 * 5 * 17 * 1044613. The number 355168420 has 24 divisors ( 1, 2, 4, 5, 10, 17, 20, 34, 68, 85, 170, 340, 1044613, 2089226, 4178452, 5223065, 10446130, 17758421, 20892260, 35516842, 71033684, 88792105, 177584210, 355168420 ) whith a sum of 789728184. 355168420 is not a prime number. 355168420 is not a fibonacci number. 355168420 is not a Bell Number. The number 355168420 is not a Catalan Number. The convertion of 355168420 to base 2 (Binary) is 10101001010110111000010100100. The convertion of 355168420 to base 3 (Ternary) is 220202022102110221. The convertion of 355168420 to base 4 (Quaternary) is 111022313002210. The convertion of 355168420 to base 5 (Quintal) is 1211410342140. The convertion of 355168420 to base 8 (Octal) is 2512670244. The convertion of 355168420 to base 16 (Hexadecimal) is 152b70a4. The convertion of 355168420 to base 32 is aims54. The sine of 355168420 is -0.8856354138051. The cosine of the figure 355168420 is -0.46438121604375. The tangent of 355168420 is 1.907130140513. The square root of 355168420 is 18845.912554185.\nIf you square 355168420 you will get the following result 126144606565296400. The natural logarithm of 355168420 is 19.688102657472 and the decimal logarithm is 8.5504343432849. that 355168420 is very unique figure!" ]

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[ "Switch to:\n\nUDR Pre-Tax Income\n\n: \\$111 Mil (TTM As of Jun. 2019)\nView and export this data going back to 1990. Start your Free Trial\n\nPretax income is the income that a company earns before paying income taxes. UDR's pretax income for the three months ended in Jun. 2019 was \\$38 Mil. Its pretax income for the trailing twelve months (TTM) ended in Jun. 2019 was \\$111 Mil. UDR's pretax margin was 13.67%.\n\nDuring the past 13 years, UDR's highest Pretax Margin was 11.36%. The lowest was -22.94%. And the median was -0.26%.\n\nUDR Pre-Tax Income Historical Data\n\n* All numbers are in millions except for per share data and ratio. All numbers are in their local exchange's currency.\n\n* Premium members only.\n\n UDR Annual Data Dec09 Dec10 Dec11 Dec12 Dec13 Dec14 Dec15 Dec16 Dec17 Dec18 Pre-Tax Income", null, "", null, "", null, "", null, "", null, "1.16 101.60 105.76 89.01 86.03\n\nCompetitive Comparison\n* Competitive companies are chosen from companies within the same industry, with headquarter located in same country, with closest market capitalization; x-axis shows the market cap, and y-axis shows the term value; the bigger the dot, the larger the market cap.\n\nUDR Pre-Tax Income Distribution\n\n* The bar in red indicates where UDR's Pre-Tax Income falls into.\n\nUDR Pre-Tax Income Calculation\n\nThis is the income that a company earns before paying income taxes.\n\nUDR's Pretax Income for the fiscal year that ended in Dec. 2018 is calculated as\n\n Pretax Income = Operating Income + Non Operating Income + Interest Expense + Interest Income + Other = 220.642 + -7.176 + -134.168 + 6.735 + 1.42108547152E-14 = 86\n\nUDR's Pretax Income for the quarter that ended in Jun. 2019 is calculated as\n\n Pretax Income = Operating Income + Non Operating Income + Interest Expense + Interest Income + Other = 59.889 + 11.661 + -34.417 + 1.31 + -7.1054273576E-15 = 38\n\nPre-Tax Income for the trailing twelve months (TTM) ended in Jun. 2019 was 20.416 (Sep. 2018 ) + 23.788 (Dec. 2018 ) + 28.814 (Mar. 2019 ) + 38.443 (Jun. 2019 ) = \\$111 Mil.\n\n* All numbers are in millions except for per share data and ratio. All numbers are in their local exchange's currency.\n\nUDR  (NYSE:UDR) Pre-Tax Income Explanation\n\nUDR's Pretax Margin for the quarter that ended in Jun. 2019 is calculated as\n\n Pretax Margin = Pretax Income / Revenue = 38.443 / 281.308 = 13.67%\n\nDuring the past 13 years, UDR's highest Pretax Margin was 11.36%. The lowest was -22.94%. And the median was -0.26%.\n\n* All numbers are in millions except for per share data and ratio. All numbers are in their local exchange's currency." ]

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[ "# Answers\n\nSolutions by everydaycalculation.com\n\n## Reduce 1005/2430 to lowest terms\n\nThe simplest form of 1005/2430 is 67/162.\n\n#### Steps to simplifying fractions\n\n1. Find the GCD (or HCF) of numerator and denominator\nGCD of 1005 and 2430 is 15\n2. Divide both the numerator and denominator by the GCD\n1005 ÷ 15/2430 ÷ 15\n3. Reduced fraction: 67/162\nTherefore, 1005/2430 simplified to lowest terms is 67/162.\n\nMathStep (Works offline)", null, "Download our mobile app and learn to work with fractions in your own time:\nAndroid and iPhone/ iPad\n\nEquivalent fractions:\n\n#### Fractions Simplifier\n\n© everydaycalculation.com" ]

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[ "Timezone: »\n\nOral\nVariance Reduction for Matrix Games\nYair Carmon · Yujia Jin · Aaron Sidford · Kevin Tian\n\nThu Dec 12 10:05 AM -- 10:20 AM (PST) @ West Exhibition Hall A\n\nWe present a randomized primal-dual algorithm that solves the problem minx maxy y^T A x to additive error epsilon in time nnz(A) + sqrt{nnz(A) n} / epsilon, for matrix A with larger dimension n and nnz(A) nonzero entries. This improves the best known exact gradient methods by a factor of sqrt{nnz(A) / n} and is faster than fully stochastic gradient methods in the accurate and/or sparse regime epsilon < sqrt{n / nnz(A)\\$. Our results hold for x,y in the simplex (matrix games, linear programming) and for x in an \\ell_2 ball and y in the simplex (perceptron / SVM, minimum enclosing ball). Our algorithm combines the Nemirovski's \"conceptual prox-method\" and a novel reduced-variance gradient estimator based on \"sampling from the difference\" between the current iterate and a reference point." ]

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[ "# On a classical theorem on the diameter and minimum degree of a graph Articles", null, "### publication date\n\n• November 2017\n\n• 1477\n\n• 1503\n\n• 11\n\n• 33\n\n• 1439-8516\n\n• 1439-7617\n\n### abstract\n\n• In this work, we obtain good upper bounds for the diameter of any graph in terms of its minimum degree and its order, improving a classical theorem due to Erdos, Pach, Pollack and Tuza. We use these bounds in order to study hyperbolic graphs (in the Gromov sense). To compute the hyperbolicity constant is an almost intractable problem, thus it is natural to try to bound it in terms of some parameters of the graph. Let H(n, delta(0)) be the set of graphs G with n vertices and minimum degree delta(0), and J(n, Delta) be the set of graphs G with n vertices and maximum degree Delta. We study the four following extremal problems on graphs: a(n, delta(0)) = min{delta(G) | G a H(n, delta(0))}, b(n, delta(0)) = max{delta(G) | G a H(n, delta(0))}, alpha(n, Delta) = min{delta(G) | G a J(n, Delta)} and beta(n, Delta) = max{delta(G) | G a J(n, Delta)}. In particular, we obtain bounds for b(n, delta(0)) and we compute the precise value of a(n, delta(0)), alpha(n, Delta) and beta(n, Delta) for all values of n, delta(0) and Delta, respectively.\n\n### keywords\n\n• extremal problems on graphs; diameter; minimum degree; maximum degree; gromov hyperbolicity; hyperbolicity constant; finite graphs" ]

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[ "Cody\n\n# Problem 2018. Side of a rhombus\n\nSolution 519754\n\nSubmitted on 3 Nov 2014 by Divakar Kittur\nThis solution is locked. To view this solution, you need to provide a solution of the same size or smaller.\n\n### Test Suite\n\nTest Status Code Input and Output\n1   Pass\n%% x = 1; y_correct = sqrt(5)/2; tolerance = 1e-12; assert(abs(rhombus_side(x)-y_correct)<tolerance)\n\n2   Pass\n%% x = 3; y_correct = 5/2; tolerance = 1e-12; assert(abs(rhombus_side(x)-y_correct)<tolerance)\n\n3   Pass\n%% x = 2; y_correct = sqrt(13)/2; tolerance = 1e-12; assert(abs(rhombus_side(x)-y_correct)<tolerance)\n\n### Community Treasure Hunt\n\nFind the treasures in MATLAB Central and discover how the community can help you!\n\nStart Hunting!" ]

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[ "## Three Popular Data Displays\n\nThis section elaborates on how to describe data. In particular, you will learn about the relative frequency histogram. Complete the exercises and check your answers.\n\n### Stem and Leaf Diagrams\n\nSuppose 30 students in a statistics class took a test and made the following scores:\n\n$\\begin{array}{rrrrrrrrrr}86 & 80 & 25 & 77 & 73 & 76 & 100 & 90 & 69 & 93 \\\\ 90 & 83 & 70 & 73 & 73 & 70 & 90 & 83 & 71 & 95 \\\\ 40 & 58 & 68 & 69 & 100 & 78 & 87 & 97 & 92 & 74\\end{array}$\n\nHow did the class do on the test? A quick glance at the set of 30 numbers does not immediately give a clear answer. However the data set may be reorganized and rewritten to make relevant information more visible. One way to do so is to construct a stem and leaf diagram as shown in Figure 2.1 \"Stem and Leaf Diagram\". The numbers in the tens place, from 2 through 9, and additionally the number 10, are the \"stems,\" and are arranged in numerical order from top to bottom to the left of a vertical line. The number in the units place in each measurement is a \"leaf,\" and is placed in a row to the right of the corresponding stem, the number in the tens place of that measurement. Thus the three leaves 9, 8, and 9 in the row headed with the stem 6 correspond to the three exam scores in the 60s, 69 (in the first row of data), 68 (in the third row), and 69 (also in the third row). The display is made even more useful for some purposes by rearranging the leaves in numerical order, as shown in Figure 2.2 \"Ordered Stem and Leaf Diagram\". Either way, with the data reorganized certain information of interest becomes apparent immediately. There are two perfect scores; three students made scores under 60; most students scored in the 70s, 80s, and 90s; and the overall average is probably in the high 70s or low 80s.\n\nFigure 2.1 Stem and Leaf Diagram", null, "Figure 2.2 Ordered Stem and Leaf Diagram", null, "In this example the scores have a natural stem (the tens place) and leaf (the ones place). One could spread the diagram out by splitting each tens place number into lower and upper categories. For example, all the scores in the 80s may be represented on two separate stems, lower 80s, and upper 80s:\n\n$\\begin{array}{l|lll} 8 & 0 & 3 & 3 \\\\ 8 & 6 & 7 \\end{array}$\n\nThe definitions of stems and leaves are flexible in practice. The general purpose of a stem and leaf diagram is to provide a quick display of how the data are distributed across the range of their values; some improvisation could be necessary to obtain a diagram that best meets that goal.\n\nNote that all of the original data can be recovered from the stem and leaf diagram. This will not be true in the next two types of graphical displays." ]

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[ "", null, "# Regression & Linear Modeling: Best Practices and Modern Methods\n\nIn a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. The author returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.\n\n•", null, "•", null, "•", null, "" ]

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[ "As you can see from the File Exchange page, he has been keeping the file up-to-date periodically. $\\begingroup$ this has the added advantage of using the built-in algorithms for label placement. Active 5 years, 2 months ago. To leave a comment, please click here to sign in to your MathWorks Account or create a new one. Star Strider on 19 Apr 2019. How can I make the labels of my axis at the center of my axis and also to rotate the angle of the text so that it is in line with the axis. Based on your location, we recommend that you select: . Display an arrow pointing to the left by including the TeX markup \\leftarrow. Based on your location, we recommend that you select: . This video also shows a simple technique for understanding lines of code where there are many functions acting as inputs to other functions. Corresponding entries in X, Y, Z and S determine the location and area of each marker. Other MathWorks country sites are not optimized for visits from your location. Accelerating the pace of engineering and science. Other MathWorks country sites are not optimized for visits from your location. You can specify LineSpec for some triplets and omit it for others. Note that if you rotate the axes, you have to re-code the 'Rotation' angle values. I would like to visualize 3D data and have a little label next to each data point. Learn more about 3d, plot, visualization, stem3 For example, I can do this. You can apply different data labels to each point in a scatter plot by the use of the text command. I am supposed to use them to add on each plot a line that says max height: (max height of the plot). So I guess the label should kind of fly next to the data point. This week’s entry caught my attention for two reasons. Add text to chart this example shows how to add text to a chart control the text position and size and create multiline text. 30 views (last 30 days) | 0 likes | 1 comment. Hello, I have a question about axis label position. The third argument specifies the text. How to label a series of points on a plot in MATLAB. It gives me some control of how to align the text, but it is basically limited to the extent of the text. Tags plot; three dimensions; See Also. This syntax allows you to specify the axes to which to add a label. You can use the scatter plot data as input to the TEXT command with some additional displacement so that the text does not overlay the data points. The question was original posted … 'SE' means southeast placement, 0.2 refers to the offset for the labels, and 1 means “adjust the x/y limits of the axes”. For example, plot3 (X1,Y1,Z1,'o',X2,Y2,Z2) specifies markers for the first triplet but not the for the second triplet. Select a Web Site. This has been a problem as long as I can remember. ... about rotating axis label in matlab. On the Size/Speed tab, clear Matrix data, maximum points per dimension. The first two input arguments to the textfunction specify the position. Learn more about 3d, plot, visualization, stem3 +1 $\\endgroup$ – rcollyer May 21 '16 at 0:51 $\\begingroup$ @rcollyer that's what I was getting at with my link to (45496) above. By making use of the Statistics and Machine Learning Toolbox, he provides different methods for detecting outliers. A cell array should contain all the data labels as strings in cells corresponding to the data points. Thanks Adam! This is where Adam’s entry comes into play. Plot data with y values that range between -15,000 and 15,000. 1. Find the treasures in MATLAB Central and discover how the community can help you! In my figure below, the position of the labels is a little bit not well aligned with the axis angle. One is that this entry does the task that I usually dread doing, which is making finishing touches to my plots. Earth topography: Label, larger image size and colorbar limits From the Origin menu select Plot > 3D: 3D Color Fill Surface to create a 3D plot that will have Speed Mode turned on: On the Origin menu, click Format: Layer. Just after plotting the coordinates I'm indexing them in two different vectors (one for x coordinates and the other for y coordinates).The problem is, when I try to plot them again in another script (or in another figure) the result is not what I expected. I was using an index, the labels were just too long so it looked like they had been randomly placed. Label a point in matlab. I’m grateful that he has shared this with the community. How can I label them? Another option is just to experiment with the 'Rotation' property until it works. MathWorks is the leading developer of mathematical computing software for engineers and scientists. Get the MATLAB code (requires JavaScript) Tick Values, and Labels. If you specify the text as a categorical array matlab uses the values in the array not the categories. Label maximum and minimum in matlab figure. How to label individual data points in a 3D plot?. How to label individual data points in a 3D plot?. Choose a web site to get translated content where available and see local events and offers. You will see updates in your activity feed.You may receive emails, depending on your notification preferences. This is where adams entry comes into play. Vote. Find the treasures in MATLAB Central and discover how the community can help you! ... Python script to set another layer inactive & select point … … You can create a legend to label plotted data series or add descriptive text next to data points. To plot a set of coordinates connected by line segments, specify X, Y, and Z as vectors of the same length. Line width, specified as a positive value in points, where 1 point = 1/72 of an inch. Start Hunting! The Overflow Blog Have the tables turned on NoSQL? How to label a series of points on a plot in MATLAB. Ask Question Asked 7 years, 11 months ago. How to label individual data points in a 3D plot?. ax can precede any of the input argument combinations in the previous syntaxes. I was using an index, the labels were just too long so it looked like they had been randomly placed. Plotting Points in 3D. Reload the page to see its updated state. 29 views (last 30 days) | 0 likes | 1 comment. This tutorial will demonstrate how to add a 3D Scatter to a 3D Surface and include labels as shown below: Minimum Origin Version Required: Origin 2015 SR0 1. How to label individual data points in a 3D plot?. For example, I can do this Use the TeX markup \\pi for the Greek letter . Follow 3 views (last 30 days) Liang Jia on 22 Jun 2017. The following is an example: I can tell he has put in a lot of time and effort into making and maintaining this code. Jiro & Sean share their favorite user-contributed submissions from the File Exchange. Thanks for your help, N is a string array. Add text next to a particular data point using the text function. This video shows how to put an individual text label on each of a series of points. The other reason Adam’s entry caught my attention was the amount of help and information he included in the entry. The line width cannot be thinner than the width of a pixel. How would I use max(), num2str(), and text() to label the maximum point on a graph? Community Treasure Hunt. Set the Exponent property of the ruler object associated with the y-axis.Access the ruler object through the YAxis property of the Axes object. Select a Web Site. He includes many examples to test out all of the various options. Label Maximum and Minimum in MatLab Figure This entry was posted in MatLab and tagged Figures on February 28, 2013 by RF Geek The code snippet below demonstrate that functionality, for minimum and maximum values along the y-axis. aligning the axes labels in 3d plot in matlab. 0 Comments. It also shows how to customize the appearance of the axes text by changing the font size. Activate the Matrix MBook1B. I would like to visualize 3D data and have a little label next to each data point. It produces a wireframe surface where the lines connecting the defining points are colored. CR and CAB, Rank Revealing Matrix Factorizations, Copying Text to the Clipboard in MATLAB Web App – Fail, Blinking birds: Balancing flight safety and the need to blink, Staying Connected with CheerLights and ThingSpeak, MATLAB, Machine Learning & Movies… The Perfect Combination, MathWorks Hosts Virtual Round Table with Key India Startup Influencers, Virtual Workshops with Black Girls Code and MissionSAFE. Link × Direct link to this answer. You can apply different data labels to each point in a scatter plot by the use of the TEXT command. Viewed 15k times 4. This video shows how to put an individual text label on each of a series of points. So I guess the label should kind of fly next to the data point. You can use the scatter plot data as input to the TEXT command with some additional displacement so that the text does not overlay the data points. Making of 3D graphs step by step 2.1. When I want to label some data points, I use the text function. If S is a row or column vector, then each entry in S specifies the area for the corresponding marker. How to label individual data points in a 3D plot?. You can apply different data labels to each point in a scatter plot by the use of the TEXT command. The units for area are points squared. How can I label them? Find the treasures in MATLAB Central and discover … You may receive emails, depending on your. This video shows how to put an individual text label on each of a series of points. A cell array should contain all the data labels as strings in cells corresponding to the data points. Give it a try and let us know what you think here or leave a comment for Adam. Choose a web site to get translated content where available and see local events and offers. This video also shows a simple technique for understanding lines of code where there are many functions acting as inputs to other functions. 0. To plot multiple sets of coordinates on the same set of axes, specify at least one of X, Y, or Z as a matrix and the others as vectors. Convert your live scripts to markdown file, Word-By-Word Text Generation Using Deep Learning. Add labels to points in scatter plots. 1. So I guess the label should kind of fly next to the data point. Sign in to comment. 1. The mesh plotting function is used to display the mesh plot. How can I label them? Add a title, label the axes, or add annotations to a graph to help convey important information. $\\endgroup$ – Mr.Wizard May 21 '16 at 17:45 Choose a web site to get translated content where available and see local events and offers. The result is the following 3D plot having labels not alligned in respective axis. ... Find the treasures in MATLAB Central and discover how the community can help you! Installation of the package 2. Accelerating the pace of engineering and science. Show Hide all comments. Vote. But both of these aren’t exactly what I want because the labels slightly overlap the data. Posted by Doug Hull, May 30, 2012. Currently I'm using stem3(X,Y,Z) to plot the data points. So I guess the label should kind of fly next to the data point. Currently I'm using stem3(X,Y,Z) to plot the data points. Published with MATLAB® R2017b. The data points are tightly clustered so it is hard to see which points the labels … By default, text supports a subset of TeX markup. If you set the line width to a value that is less than the width of a pixel on your system, the line displays as one pixel wide. How to label individual data points in a 3D plot?. Tags plot; three dimensions; Community Treasure Hunt. You can also select a web site from the following list: Select the China site (in Chinese or English) for best site performance. The length of S must equal the length of X, Y and Z. This function streamlines matlab's builtin text () function and greatly increases its flexibility. MathWorks is the leading developer of mathematical computing software for engineers and scientists. If the line has markers, then the line width also affects the marker edges. Jiro‘s pick this week is labelpoints by Adam Danz. I would like to visualize 3D data and have a little label next to each data point. (I don't need to use \"stem3\" necessarily, I just somehow want to visualize the data). How to Label a Series of Points on a Plot in MATLAB. How to label a point in matlab. I would like to visualize 3D data and have a little label next to each data point. Mesh 3D Plot in MATLAB. Show Hide all comments. Earth topography: Getting started 2.2. Is it possible for me to change the colour of the labelled data points? 0 ⋮ Vote. Learn more about 3d, plot, visualization, stem3 To display the same text at each location specify txt as a character vector or string. plot3 (X,Y,Z) plots coordinates in 3-D space. For a full list of markup, see Greek Letters and Special Characters in Chart Text. Choose a web site to get translated content where available and see local events and offers. Control Value in Exponent Label Using Ruler Objects. Is it possible to create plot containing 3D points of labels? I am supposed to use them to add on each plot a line that says max height: (max height of the plot). The first 300 lines of his code are help comments!! When I want to label some data points, I use the text function. Usually at this point, I fiddle around with the coordinates of the text placements. Find the treasures in MATLAB Central and discover how the community can help you! Based on your location, we recommend that you select: . Currently I'm using stem3(X,Y,Z) to plot the data points. As a part of this tutorial about MATLAB 3D plot examples, I am describing the topmost five 3D plots one-by-one. Loop over the data arrays (x and y) and call plt.annotate(" ]

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[ "extensible-effects-2.0.1.0: An Alternative to Monad Transformers\n\nControl.Eff\n\nDescription\n\nA monadic library for communication between a handler and its client, the administered computation\n\nExtensible Effects are implemented as typeclass constraints on an Eff[ect] datatype. A contrived example can be found under Control.Eff.Example. To run the effects, consult the tests.\n\nSynopsis\n\nDocumentation\n\ntype Arr r a b = a -> Eff r b Source #\n\nEffectful arrow type: a function from a to b that also does effects denoted by r\n\ntype Arrs r a b = FTCQueue (Eff r) a b Source #\n\nAn effectful function from a to b that is a composition of several effectful functions. The paremeter r describes the overall effect. The composition members are accumulated in a type-aligned queue\n\nsingle :: Arr r a b -> Arrs r a b Source #\n\nfirst :: Arr r a b -> Arr r (a, c) (b, c) Source #\n\narr :: (a -> b) -> Arrs r a b Source #\n\nident :: Arrs r a a Source #\n\ncomp :: Arrs r a b -> Arrs r b c -> Arrs r a c Source #\n\ndata Eff r a Source #\n\nThe Eff monad (not a transformer!). It is a fairly standard coroutine monad where the type r is the type of effects that can be handled, and the missing type a (from the type application) is the type of value that is returned. It is NOT a Free monad! There are no Functor constraints.\n\nThe two constructors denote the status of a coroutine (client): done with the value of type a, or sending a request of type Union r with the continuation Arrs r b a. Expressed another way: an Eff can either be a value (i.e., Val case), or an effect of type Union r producing another Eff (i.e., E case). The result is that an Eff can produce an arbitrarily long chain of Union r effects, terminated with a pure value.\n\nPotentially, inline Union into E\n\nConstructors\n\n Val a E (Union r b) (Arrs r b a)\n\nInstances\n\n Monad (Eff r) Source # Methods(>>=) :: Eff r a -> (a -> Eff r b) -> Eff r b #(>>) :: Eff r a -> Eff r b -> Eff r b #return :: a -> Eff r a #fail :: String -> Eff r a # Functor (Eff r) Source # Eff is still a monad and a functor (and Applicative) (despite the lack of the Functor constraint) Methodsfmap :: (a -> b) -> Eff r a -> Eff r b #(<\\$) :: a -> Eff r b -> Eff r a # Source # Methodspure :: a -> Eff r a #(<*>) :: Eff r (a -> b) -> Eff r a -> Eff r b #(*>) :: Eff r a -> Eff r b -> Eff r b #(<*) :: Eff r a -> Eff r b -> Eff r a #\n\nqApp :: Arrs r b w -> b -> Eff r w Source #\n\nApplication to the `generalized effectful function' Arrs r b w\n\nqComp :: Arrs r a b -> (Eff r b -> Eff r' c) -> Arr r' a c Source #\n\nCompose effectful arrows (and possibly change the effect!)\n\nsend :: Member t r => t v -> Eff r v Source #\n\nSend a request and wait for a reply (resulting in an effectful computation).\n\nrun :: Eff '[] w -> w Source #\n\nThe initial case, no effects. Get the result from a pure computation.\n\nThe type of run ensures that all effects must be handled: only pure computations may be run.\n\nhandle_relay :: (a -> Eff r w) -> (forall v. t v -> Arr r v w -> Eff r w) -> Eff (t ': r) a -> Eff r w Source #\n\nA convenient pattern: given a request (open union), either handle it or relay it.\n\nhandle_relay_s :: s -> (s -> a -> Eff r w) -> (forall v. s -> t v -> (s -> Arr r v w) -> Eff r w) -> Eff (t ': r) a -> Eff r w Source #\n\nParameterized handle_relay\n\ninterpose :: Member t r => (a -> Eff r w) -> (forall v. t v -> Arr r v w -> Eff r w) -> Eff r a -> Eff r w Source #\n\nAdd something like Control.Exception.catches? It could be useful for control with cut.\n\nIntercept the request and possibly reply to it, but leave it unhandled (that's why we use the same r all throuout)" ]

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[ "#", null, "How to derive electron number equation of Bogoliubov Hamiltonian using thermodynamic relations.\n\n+ 5 like - 0 dislike\n478 views\n\nMy question arise from this article: Edge superconducting correlation in the attractive-U Kane-Mele-Hubbard model. I will describe my question in detail so that you might not need to look into that article. What I want to ask for help is an easy way to derive the electron number equation, may be using some thermodynamic relations just as how I derive the gap equation.\n\nThe Hamiltonian considered is the mono-layer graphene with intrinsic SO interaction, plus the negative-U Hubbard term. After the mean field approximation with S-wave superconducting order parameter, we obtain the mean-field Hamiltonian:\n\n$$H=\\sum_k\\phi_k^\\dagger H_k\\phi_k+E_0$$\n\nwhere $\\phi_k$ is the Nambu spinor $\\phi_k^\\dagger=(a_{k\\uparrow}^\\dagger, b_{k\\uparrow}^\\dagger, a_{-k\\downarrow}, b_{-k\\downarrow})$, $E_0=2N\\Delta^2/U$, $N$ is the number of unit cell, and\n\n$$H_k=\\begin{pmatrix} \\lambda_k-\\mu & -t\\gamma_k & -\\Delta & 0 \\\\ -t\\lambda_k^* & -\\lambda_k-\\mu & 0 & -\\Delta \\\\ -\\Delta^* & 0 & -\\lambda_k+\\mu & t\\gamma_k \\\\ 0 & -\\Delta^* & t\\gamma_k^* & \\lambda_k+\\mu \\end{pmatrix}$$\n\nThe $\\mu$ is the chemical potential in the original Hubbard term, $-t\\gamma_k$ is the sum of the graphene hopping integral of nearest neighbors, $\\gamma_k$ is from SO term. We can just ignore their physical meaning and regard them as some parameters, they are not very important to my question.\n\nDiagonalizing $H_k$, we have four eigenvalues, $\\omega_{ks\\alpha}=\\alpha\\omega_{ks}=\\alpha\\sqrt{(\\epsilon_k+s \\mu)^2+\\Delta^2}$ with $\\epsilon_k=\\sqrt{\\lambda_k^2+t^2|\\gamma_k|^2}$, where $s,\\alpha$ are $\\pm 1$.\n\nNow we have gap equation and electron number equation: $$\\frac{1}{U}=\\frac{1}{4N}\\sum_{ks}\\frac{\\tanh{(\\beta\\omega_{ks}/2)}}{\\omega_{ks}}$$ $$n_e-1=-\\frac{1}{N}\\sum_{ks}\\frac{s\\epsilon_k -\\mu}{\\omega_{ks}}\\tanh(\\beta \\omega_{ks}/2)$$ where $n_e$ is the average electron number on one sublattice.\n\nThe following is how I derive the gap equation, the free energy is: $$F=-\\frac{1}{\\beta}\\sum_{ks\\alpha}\\ln{(1+\\mathrm{e}^{-\\beta\\omega_{ks\\alpha}})}+\\frac{2N\\Delta^2}{U}$$ the free energy is minimized when $\\Delta$ choose to have its true value, i.e. using $\\partial F/\\partial \\Delta=0$ we can derive the gap equation showing above.\n\nHow can I derive the electron number equation? I know in principle I can derive it by representing the original electron operators instead of the diagonalized Bogoliubov quasi-particle operators, but this is much too complicated even one trying to derive them using Mathematica.\n\nSo just as I said in the beginning of this question:I need your help to get an easy way to derive the electron number equation, may be using some thermodynamic relations just as how I derive the gap equation\n\nThis post imported from StackExchange Physics at 2014-08-22 05:03 (UCT), posted by SE-user luming\nI suggest trying $\\partial F/\\partial \\mu=0$.\n\nThis post imported from StackExchange Physics at 2014-08-22 05:03 (UCT), posted by SE-user leongz\n\nThermodynamic relation $N=-\\frac{\\partial J}{\\partial \\mu}$ exactly gives you the particle number equation, wherein $J$ is the macroscopic thermodynamic potential, i.e., the quantity $F$ in your question.\nBy thermodynamics, $dJ=-SdT+Ydy-Nd\\mu$ tells you why the partial derivative equation is valid. And in statistical mechanics, macroscopic ensemble formalism defines $J$ as $e^{-\\beta J}\\equiv\\mathrm{Tr}[e^{-\\beta (\\hat{H}-\\mu\\hat{N})}]$. Of course, you can derive the partial derivative equation within this formalism as well.\n Please use answers only to (at least partly) answer questions. To comment, discuss, or ask for clarification, leave a comment instead. To mask links under text, please type your text, highlight it, and click the \"link\" button. You can then enter your link URL. Please consult the FAQ for as to how to format your post. This is the answer box; if you want to write a comment instead, please use the 'add comment' button. Live preview (may slow down editor)   Preview Your name to display (optional): Email me at this address if my answer is selected or commented on: Privacy: Your email address will only be used for sending these notifications. Anti-spam verification: If you are a human please identify the position of the character covered by the symbol $\\varnothing$ in the following word:p$\\hbar$ysi$\\varnothing$sOverflowThen drag the red bullet below over the corresponding character of our banner. When you drop it there, the bullet changes to green (on slow internet connections after a few seconds). To avoid this verification in future, please log in or register." ]

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[ "# Proper way to access list elements in R\n\n• The difference between [] and [[]] notations for accessing the elements of a list\n\nAll these methods give different outputs\n\n[ ] returns a list\n\n[[ ]] returns the object which is stored in list\n\nIf it is a named list, then\n\nList\\$name or List[[\"name\"]] will return same as List[[ ]]\n\nWhile List[\"name\"] returns a list, Consider the following example\n\n``````> List <- list(A = 1,B = 2,C = 3,D = 4)\n> List\n\\$A\n 1\n\n> class(List)\n \"list\"\n> List[]\n 1\n> class(List[])\n \"numeric\"\n> List\\$A\n 1\n> class(List\\$A)\n \"numeric\"\n> List[\"A\"]\n\\$A\n 1\n\n> class(List[\"A\"])\n \"list\"\n> List[[\"A\"]]\n 1\n> class(List[[\"A\"]])\n \"numeric\"\n``````" ]

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[ "# 7101462\n\n## 7,101,462 is an even composite number composed of four prime numbers multiplied together.\n\nWhat does the number 7101462 look like?\n\nThis visualization shows the relationship between its 4 prime factors (large circles) and 16 divisors.\n\n7101462 is an even composite number. It is composed of four distinct prime numbers multiplied together. It has a total of sixteen divisors.\n\n## Prime factorization of 7101462:\n\n### 2 × 3 × 29 × 40813\n\nSee below for interesting mathematical facts about the number 7101462 from the Numbermatics database.\n\n### Names of 7101462\n\n• Cardinal: 7101462 can be written as Seven million, one hundred one thousand, four hundred sixty-two.\n\n### Scientific notation\n\n• Scientific notation: 7.101462 × 106\n\n### Factors of 7101462\n\n• Number of distinct prime factors ω(n): 4\n• Total number of prime factors Ω(n): 4\n• Sum of prime factors: 40847\n\n### Divisors of 7101462\n\n• Number of divisors d(n): 16\n• Complete list of divisors:\n• Sum of all divisors σ(n): 14693040\n• Sum of proper divisors (its aliquot sum) s(n): 7591578\n• 7101462 is an abundant number, because the sum of its proper divisors (7591578) is greater than itself. Its abundance is 490116\n\n### Bases of 7101462\n\n• Binary: 110110001011100000101102\n• Base-36: 487IU\n\n### Squares and roots of 7101462\n\n• 7101462 squared (71014622) is 50430762537444\n• 7101462 cubed (71014623) is 358132143790682143128\n• The square root of 7101462 is 2664.8568441851\n• The cube root of 7101462 is 192.2129256733\n\n### Scales and comparisons\n\nHow big is 7101462?\n• 7,101,462 seconds is equal to 11 weeks, 5 days, 4 hours, 37 minutes, 42 seconds.\n• To count from 1 to 7,101,462 would take you about seventeen weeks!\n\nThis is a very rough estimate, based on a speaking rate of half a second every third order of magnitude. If you speak quickly, you could probably say any randomly-chosen number between one and a thousand in around half a second. Very big numbers obviously take longer to say, so we add half a second for every extra x1000. (We do not count involuntary pauses, bathroom breaks or the necessity of sleep in our calculation!)\n\n• A cube with a volume of 7101462 cubic inches would be around 16 feet tall.\n\n### Recreational maths with 7101462\n\n• 7101462 backwards is 2641017\n• The number of decimal digits it has is: 7\n• The sum of 7101462's digits is 21\n• More coming soon!" ]

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[ "# Appendix A.       Max Patch", null, "Figure A.1 – Test Interface\n\nThe test interface, shown in Figure A.1, was built in a Max 6.1 (Cycling 74, 2014). A number of elements had to be designed in order to meet the aims of the test which can be summarised as to:\n\n1. Provide a test interface which will allow for pairwise comparison of two surround sound recording extracts,\n2. Provide a means to randomise playback of test material,\n3. Provide looped playback of test material,\n4. Provide real time switching of test material,\n5. Allow for participants to submit their answers to test questions,\n\nOnce a system which meets the above criteria is established, it can then be copied and pasted as required. For example, by building and troubleshooting one test section, the final result can be copied and patched in with ease. This Appendix outlines the design of the major components the patch.\n\n## A.1         Randomisation\n\nTo meet the randomisation section of the ITU-R BS.1116-1 recommendation, a way of randomising the playback of test stimuli had to be incorporated into the test interface.\n\nFigure A.2 shows one of the twelve components of the randomising system of the patch. Each of the audio extracts for each pairwise comparison was fed into one of these randomising sections. The objects ‘open sf1.aif’ and ‘open t1.aif’ correspond to the Soundfield ITU and MMA recordings of the first section of musical piece 1. This file information is then routed to the sound file players via a randomising system.", null, "Figure A.2 – Randomisation\n\nBy entering a randomisation code, where each number is applicable to each randomising section, the destination of each array pair could be randomised. For instance, if Pair A is played back first by default and if the first number of the randomising code was eight then Pair A would be the eighth pairwise comparison presented in the test. Table A.1 shows how a full randomising code changes the default playback order of the test with arrows highlighting two of the playback order changes.", null, "Table A.1 – Randomising Operation\n\nThis system allows the test supervisor to input a code from a pool of randomising codes before participants begin the test. The randomising code is presented with the participant’s answers so they can be integrated into the master spread sheet where the reverse of the randomisation process is applied to allow for the analysis of the results. To avoid using the default playback order by accident, the patch can only be initialised after the randomisation code is confirmed as being entered correctly.\n\nWith reference to Figure A.2, the twelve number buttons which are routed inputs one to twelve inclusive and inputs thirteen to twenty-four inclusive of a sub-patch denoted by the name ‘p 14’. The sub-patch, shown in Figure A.3, routes the signals from its inputs into two gate objects. There is one gate object per recording extract. Sound file information from the array pair enter the sub-patch through inputs twenty-five and twenty-six.", null, "Figure A.3 – Gates\n\nWith the randomising number given, the number acts on both the gates and sends the sound file information for the recording extracts through the appropriate gate output. For example, if number one is pressed, the signal of input 25 and 26 will be sent out the second output of gates one and two respectively. The addition of the one which can be seen at the top of Figure A.3 is to eliminate errors further down the playback chain. The gate outputs are routed to the sub patch outputs and routes signals to the ‘send’ sub patch shown in Figure A.4\n\nThe outputs of the gates are then routed to the appropriate send objects. The send object allows a signal to be sent around a patch without the use of patch cords. If the number one is pressed, the gates will send the sound file information for the array pair to ‘send 1a’ and ‘send 1b’ respectively.", null, "Figure A.4 – Sends\n\n## A.2         Playback\n\nFigure A.5 shows how the sound file information is processed. The objects called ‘receive 1a’ and ‘receive 1b’ receive the signals sent by the objects ‘send 1a’ and ‘send 1b’ respectively. The receive objects are then routed to two separate objects called ‘sfplay~’ which is a sound file player. The signals which have been sent as outlined in Section A.1 correspond to the file locations of the tests recording extracts. The ‘sfplay~’ object will then use this information to open the appropriate files for playback. The first argument for the ‘sfplay~’ object defines how many channels are required for playback. A 5.1 surround sound channel requires 6 channels, hence ‘sfplay~ 6’.", null, "Figure A.6 outlines the playback stage. The ‘Play’ and ‘Stop’ buttons are visible to the participant as controls of the audio playback. By pressing play, the playback loop feature of the ‘sfplay~’ object is activated.", null, "Figure A.6 – Playback\n\nThe sets of outputs from the two ‘sfplay~’ objects are routed to inputs two and three of the six ‘selector~’ objects respectively. The ‘Recording 1’ and ‘Recording 2’ objects are visible to the test participants. These are seen by participants as sound selection controls which allow for the real time switching of audio file playback. The output of these buttons is routed into a ‘1’ and ‘2’ message objects respectively.\n\nThe message boxes are in turn fed into all of the first inputs of the ‘selector~’ objects. If the selector objects receive a ‘1’, they will send the signal from the second input through to its output. If they receive the number ‘2’, they will send the signal from their third input to their output. This group action switches between the output of either ‘sfplay~’ object.\n\nThe ‘delay’ sub patch sends a ramped signal between one and zero out to the six multiplication objects which are placed before the audio output, called the ‘dac~’ object. When the participant selects a recording, the outputs are muted by the delay sub patch. The sub patch then sends out the switching signal before unmuting the audio outputs.", null, "Figure A.7 – Playback Display\n\nFigure A.7 shows a subsection of Figure A.6. The outputs of the ‘Recording 1’ and ‘Recording 2’ objects send a one or zero depending on their on/off state. They are routed to ‘if’ statements. The ‘if’ statements are then routed to a display object. The display object will display a separate message for each of the input signals ‘0’ and ‘1’. The display object is visible to the participant and provides them with the information of what recording extract they are listening to.\n\n## A.3         Answer Submission and Collection\n\nFigure A.8 shows an example set of answers. The objects ‘Recording 1’ and ‘Recording 2’ are visible to the participants in their respective sections of the visual test interface. The two sets of answers displayed in Figure A.8 correspond to the preference and spaciousness questions of a test section. If one answer is given by a participant, the output of each answer section will be one. If no or two answers are given, the output will be zero or ten respectively.", null, "In the case of Figure A.8, the desired output of one and undesired output of ten have been sent to the answer verification section of the patch which is shown in Figure A.9. From the five test questions, the section of the patch is reacting to whether it receives a number five or not. In this example, question one was answered acceptably where question two was not with the rest of the questions unfilled.", null, "Figure A.9 – Answer Verification 1\n\nThe ‘expr’ object adds up the output of each answer section and sends it to an if statement. As the output does not equal five, a warning message is sent to the participant instructing them to amend their answers before continuing. The desired situation of all questions being answered correctly is shown in Figure A.10 where all five questions have been properly filled out. The result of five from the ‘expr’ calculation gives an instruction to the participant to move to the next test section or contact the test supervisor where appropriate.", null, "Figure A.10 – Answer Verification 2\n\n# Appendix B.       Result Data\n\nAppendix section B.1 details the two formulae used to calculate the Z-score and p-value of the significance tests. The remainder of the Appendix provides the results of the pairwise comparisons which were graphically presented in Section 6.\n\n## B.1         Formulae\n\n### B.1.1      One Proportion Binomial\n\nAs the data from the pairwise comparison in this project is categorical, the one proportion binomial test can be used to calculate significance between the proportions of the comparisons sample (Bower, 2014)(Elder Laboratory, 2014).\n\nTo use an example, a set forty participants were presented with a pairwise comparison between options A and B and asked which one they preferred. The null hypothesis states that the options are equally preferred, thus H0 equals 0.5. If thirty selected option A, then the number of ‘events’ would be thirty which equals x.\n\nEquation B.1 – One Proportion Binomial Components and Example and Result\n\nAs the null hypothesis states that there is equal probability of A and B being preferred by participants, the two tailed test allows for the determination of the preference between A or B. This results in a critical region for a 95% significance level from -1.96 to 1.96. The binomial test revealed that there is a significant preference for option A at a 95% confidence level, z = 3.16, p < 0.05.\n\n### B.1.2      Bonferroni Correction\n\nThe hypothesis testing formulae used were integrated into the master Excel spread sheet which contained the test result data. The statistical software Minitab was used to confirm the correct operation of the spread sheet calculations (Minitab, 2014). Due to the number of comparisons being made as described in Section 4.1, a Bonferroni correction of three was applied (Goldman, 2014).", null, "Equation B.2 – Bonferroni Correction 1\n\nEquation B.2 outlines the Bonferroni correction where o is the original confidence level and b is the Bonferroni correction value. This calculation produces an adjusted significance level to take into account the number of comparisons being made per set of test stimuli. The significance levels are used to assess the results of certain hypothesis testing.\n\nFor example, if the result is below the significance level of 0.05 then the result is significant to a 95% confidence level. Similarly, if the result is below 0.01 then the result is significant to a 99% confidence level.\n\nThe binomial test does not produce p-values; however, it does operate with respect to the critical region described in Section 5.2 as the results of the test will either be inside or outside the region. The NORM.S.INV function of Excel, which calculates the critical region, requires a significance level to operate. The result of the Bonferroni correction can then be implemented into the spread sheet where the NORM.S.INV adjusts the critical regions which in turn adjust the results of the binomial test.\n\nWith a Bonferroni correction of three applied, Equation B.3 and Equation B.4 show the adjusted 95% and 99% significance levels respectively. These values have been used in the calculation of results of the project listening tests which are presented in Appendix Sections B.2, B.3 and B.4.", null, "Equation B.3 – Bonferroni Correction 2", null, "Equation B.4 – Bonferroni Correction 3\n\nThe original significance levels for 95% and 99% are 0.05 and 0.01 respectively which have now been adjusted to 0.017 and 0.003 respectively. These values are then used to calculate the critical regions for use in the binomial tests. For example, the original critical region for a 95% confidence level is -1.96 to 1.96. The Bonferroni adjusted critical region for the 95% confidence level is -2.39 to 2.39.\n\n### B.1.3      Chi-Square\n\nTo find the exact probability of a result in a pairwise comparison, known as the p-value, the Chi-Square test was used. In the example presented in Appendix B.1.1, the expected preference or probability that a population will select option A is equal. With a sample size of 40, the means the expected result for each category is 20. The observed result for category A is 30 and 10 for category B. For the result to meet the 95% confidence level, the result of the chi-square test must be less than the significance level of 0.05. The Chi-Square test was used, in addition to the Minitab software, to confirm the results of the binomial test.", null, "Equation B.5 – Chi-square Example and Result\n\n## B.2         Overall Preference Results\n\n### B.2.1      MMA vs. SFI\n\n65 out of 80 votes preferred MMA.  A binomial test revealed that there is a significant preference for MMA at a 99% confidence level, z = 5.59, p < 0.003.\n\n### B.2.2      MMA vs. SFA\n\n50 out of 80 votes preferred MMA.  A binomial test revealed that there is not a significant preference for either array at a 95% confidence level, z = 2.24, p > 0.017.\n\n### B.2.3      SFI vs. SFA\n\n29 out of 80 votes preferred SFI.  A binomial test revealed that there is a significant preference for SFA at a 95% confidence level, z = -2.46, p < 0.017\n\n## B.3         Preference Results by Piece\n\n### B.3.1      MMA vs. SFI\n\nIn piece 1, 34 out of 40 votes preferred MMA.  A binomial test revealed that there is a significant preference for MMA at a 99% confidence level, z = 4.43, p < 0.003.\n\nIn piece 2, 31 out of 40 votes preferred MMA.  A binomial test revealed that there is a significant preference for MMA at a 99% confidence level, z = 3.48, p < 0.003.\n\n### B.3.2      MMA vs. SFA\n\nIn piece 1, 22 out of 40 votes preferred MMA.  A binomial test revealed that there is not a significant preference for either array at a 95% confidence level, z = 0.63, p > 0.017.\n\nIn piece 2, 28 out of 40 votes preferred MMA.  A binomial test revealed that there is a significant preference for MMA at a 95% confidence level, z = 2.53, p < 0.017.\n\n### B.3.3      SFI vs. SFA\n\nIn piece 1, 14 out of 40 participants preferred SFI.  A binomial test revealed that there is not a significant preference for either array at a 95% confidence level, z = -1.90, p > 0.017.\n\nIn piece 2, 15 out of 40 participants preferred SFI.  A binomial test revealed that there is not a significant preference for either array at a 95% confidence level, z = -1.58, p > 0.017.\n\n## B.4         Attributes\n\n### B.4.1      MMA vs. SFI Piece 1 and Piece 2\n\n#### B.4.1.1         Spaciousness\n\nIn piece 1, 28 out of 40 participants chose MMA.  A binomial test revealed that there is a significant preference for MMA at a 95% confidence level, z = 2.53, p < 0.017.\n\nIn piece 2, 26 out of 40 participants chose MMA.  A binomial test revealed that there is not a significant preference for either array at a 95% confidence level, z = 1.90, p > 0.017.\n\n#### B.4.1.2         Envelopment\n\nIn piece 1, 31 out of 40 participants chose MMA.  A binomial test revealed that there is a significant preference for MMA at a 99% confidence level, z = 3.48, p < 0.003.\n\nIn piece 2, 28 out of 40 participants chose MMA.  A binomial test revealed that there is a significant preference for MMA at a 95% confidence level, z = 2.53, p < 0.017.\n\n#### B.4.1.3         Clarity\n\nIn piece 1, 12 out of 40 participants chose MMA.  A binomial test revealed that there is a significant preference for SFI at a 95% confidence level, z = -2.53, p < 0.017.\n\nIn piece 2, 15 out of 40 participants chose MMA.  A binomial test revealed that there is not a significant preference for MMA at a 95% confidence level, z = -1.58, p > 0.017.\n\n#### B.4.1.4         Naturalness\n\nIn piece 1, 23 out of 40 participants chose MMA.  A binomial test revealed that there is not a significant preference for either array at a 95 confidence level, z = 0.95, p > 0.017.\n\nIn piece 2, 21 out of 40 participants chose MMA.  A binomial test revealed that there is not a significant preference for either array at a 95 confidence level, z = 0.32, p > 0.017.\n\n### B.4.2      MMA vs. SFA Piece 1 and 2\n\n#### B.4.2.1         Spaciousness\n\nIn piece 1, 22 out of 40 participants chose MMA.  A binomial test revealed that there is not a significant result for either array at a 95% confidence level, z = 0.63, p > 0.017.\n\nIn piece 2, 20 out of 40 participants chose MMA.  A binomial test revealed that there is not a significant result for either array at a 95% confidence level, z = 0.00, p > 0.017.\n\n#### B.4.2.2         Envelopment\n\nIn piece 1, 27 out of 40 participants chose MMA.  A binomial test revealed that there is not a significant result for either array at a 95% confidence level, z = 2.21, p > 0.017.\n\nIn piece 2, 26 out of 40 participants chose MMA.  A binomial test revealed that there is not a significant result for either array at a 95% confidence level, z = 1.90, p > 0.017.\n\n#### B.4.2.3         Clarity\n\nIn piece 1, 16 out of 40 participants chose MMA.  A binomial test revealed that there is not a significant result for either array at a 95% confidence level, z = -1.26, p > 0.017.\n\nIn piece 2, 9 out of 40 participants chose MMA.  A binomial test revealed that there is a significant result for SFA at a 99% confidence level, z = -3.48, p < 0.003.\n\n#### B.4.2.4         Naturalness\n\nIn piece 1, 16 out of 40 participants chose MMA.  A binomial test revealed that there is not a significant result for either array at a 95% confidence level, z = -1.26, p > 0.017.\n\nIn piece 2, 16 out of 40 participants chose MMA.  A binomial test revealed that there is not a significant result for either array at a 95% confidence level, z = -1.26, p > 0.017.\n\n### B.4.3      SFI vs. SFA Piece 1 and Piece 2\n\n#### B.4.3.1         Spaciousness\n\nIn piece 1, 13 out of 40 participants chose SFI.  A binomial test revealed that there is not a significant result for either array at a 95% confidence level, z = -2.21, p > 0.017.\n\nIn piece 2, 12 out of 40 participants chose SFI.  A binomial test revealed that there is a significant result for SFA at a 95% confidence level, z = -2.53, p < 0.017.\n\n#### B.4.3.2         Envelopment\n\nIn piece 1, 16 out of 40 participants chose SFI.  A binomial test revealed that there is not a significant result for either array at a 95% confidence level, z = -1.26, p > 0.017.\n\nIn piece 2, 13 out of 40 participants chose SFI.  A binomial test revealed that there is not a significant result for SFA at a 95% confidence level, z = -2.21, p > 0.017.\n\n#### B.4.3.3         Clarity\n\nIn piece 1, 16 out of 40 participants chose SFI.  A binomial test revealed that there is not a significant result for either array at a 95% confidence level, z = -1.26, p > 0.017.\n\nIn piece 2, 19 out of 40 participants chose SFI.  A binomial test revealed that there is not a significant result for either array at a 95% confidence level, z = -0.32, p > 0.017.\n\n#### B.4.3.4         Naturalness\n\nIn piece 1, 19 out of 40 participants chose SFI.  A binomial test revealed that there is not a significant result for either array at a 95% confidence level, z = -0.32, p > 0.017.\n\nIn piece 2, 16 out of 40 participants chose SFI.  A binomial test revealed that there is not a significant result for either array at a 95% confidence level, z = -1.26, p > 0.017.\n\n## B.5         Attribute Web Test\n\nThis section outlines the binomial test results from the attribute web test described in Section 2.3.1.\n\nFor the spacious attribute, 17 out of 20 participants chose the more spacious recording extract. A binomial test revealed that this is a significant result at a 99% confidence level, z = 3.13, p < 0.01.\n\nFor the envelopment attribute, 18 out of 20 participants chose the more enveloping recording extract. A binomial test revealed that this is a significant result at a 99% confidence level, z = 3.58, p < 0.01.\n\nFor the clarity attribute, 19 out of 20 participants chose the more enveloping recording extract. A binomial test revealed that this is a significant result at a 99% confidence level, z = 4.02, p < 0.01.\n\nFor the natural attribute, 16 out of 20 participants chose the more natural recording extract. A binomial test revealed that this is a significant result at a 99% confidence level, z = 2.68, p < 0.01.\n\n# Appendix C.      Saffire Pro 24\n\nThis appendix outlines the key specifications of the audio interface used for the playback of the test material. Information is sourced from Focusrite (2014).\n\n## C.1         Line level Outputs\n\n• Dynamic Range (A Weighted): 105dB\n• SNR (A weighted): 104.5dB\n• THD+N: < 0.001% (measured with 0dBFS input and 22Hz/22kHz bandpass filter)\n• Maximum level (A weighted): 16.13dBu at 0.885%" ]

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[ "•\n•\n\n## Turkish Journal of Mathematics\n\n#### DOI\n\n10.3906/mat-1512-64\n\n#### Abstract\n\nWe consider the existence of positive solutions of the nonlinear first order problem with a nonlinear nonlocal boundary condition given by $x^{\\prime}(t) = r(t)x(t) + \\sum_{i=1}^{m} f_i(t,x(t)), t \\in [0,1]$ $\\lambda x(0) = x(1) + \\sum_{j=1}^{n} \\Lambda_j(\\tau_j, x(\\tau_j)),\\tau_j \\in [0,1],$ where $r:[0,1] \\rightarrow [0,\\infty)$ is continuous, the nonlocal points satisfy $0 \\leq \\tau_1 < \\tau_2 < ... < \\tau_n \\leq 1$, the nonlinear functions $f_i$ and $\\Lambda_j$ are continuous mappings from $[0,1] \\times [0,\\infty) \\rightarrow [0,\\infty)$ for $i = 1,2,...,m$ and $j = 1,2,...,n$ respectively, and $\\lambda >1$ is a positive parameter. The Leray-Schauder theorem and Leggett--Williams fixed point theorem were used to prove our results.\n\n#### Keywords\n\nPositive solutions, Leray-Schauder fixed point theorem, nonlinear boundary conditions\n\n350\n\n360\n\nCOinS" ]

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[ "# BeWithYou\n\n### 胡搞的技术博客\n\n1. 首页\n2. 数据结构/实用算法/知识\n3. Fisher-Yates洗牌算法\n\n# Fisher-Yates洗牌算法\n\n1、每次随机从牌堆中抽一个位置的牌放到一个递增的位置上。\n\n```\\$arr = array(0,1,2,3,4,5,6);\n\\$resultArr = array();\n\\$len = count(\\$arr);\nwhile(--\\$len)\n{\narray_push(\\$resultArr,array_splice(\\$arr,rand()%(\\$len+1),1));\n}\nprint_r(\\$resultArr);\n/*\nArray\n(\n => 4\n => 0\n => 1\n => 3\n => 6\n => 2\n)\n*/```\n\n2、每次抽随机两张牌交换位置，重复N次，N越大洗牌效果越好\n\n3、目前普遍使用的Fisher-Yates Shuffle算法。\n\n```To shuffle an array a of n elements (indices 0..n-1):  for i from n − 1 downto 1 do\nj ← random integer with 0 ≤ j ≤ i\nexchange a[j] and a[i]```\n\nlua代码实现如下：\n\n```-- 洗牌\nfunction poker:shuffle()\nmath.randomseed(os.time())\nfor i=#self.current_arr,1,-1 do\nj = math.floor(math.random() * (i) + 1)\nself.current_arr[i],self.current_arr[j] = self.current_arr[j],self.current_arr[i]\nend\nend```" ]

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[ "# How one can Utility tight_layout() in Matplotlib\n\nYou’ll significance the tight_layout() serve as in Matplotlib to routinely modify the padding between and round subplots.\n\nRefer to instance presentations significance this serve as in follow.\n\n## Instance: How one can Utility tight_layout() in Matplotlib\n\nAssume we significance Matplotilb to assemble 4 subplots in a 2×2 grid:\n\n```import matplotlib.pyplot as plt\n\n#outline knowledge\nx = [1, 2, 3]\ny = [7, 13, 24]\n\n#outline sequence for subplots\nfig, ax = plt.subplots(2, 2)\n\n#outline subplot titles\nax[0, 0].plot(x, y, colour=\"crimson\")\nax[0, 1].plot(x, y, colour=\"blue\")\nax[1, 0].plot(x, y, colour=\"inexperienced\")\nax[1, 1].plot(x, y, colour=\"pink\")\n\n#upload identify to each and every subplot\nax[0, 0].set_title('First Subplot')\nax[0, 1].set_title('2d Subplot')\nax[1, 0].set_title('3rd Subplot')\nax[1, 1].set_title('Fourth Subplot')```", null, "Understand that there’s minimum padding between the subplots, which reasons the titles to overlap in some playgrounds.\n\nBy way of specifying fig.tight_layout() we will be able to routinely modify the padding between and round subplots:\n\n```import matplotlib.pyplot as plt\n\n#outline knowledge\nx = [1, 2, 3]\ny = [7, 13, 24]\n\n#outline sequence for subplots\nfig, ax = plt.subplots(2, 2)\n\n#specify a decent sequence\nfig.tight_layout()\n\n#outline subplot titles\nax[0, 0].plot(x, y, colour=\"crimson\")\nax[0, 1].plot(x, y, colour=\"blue\")\nax[1, 0].plot(x, y, colour=\"inexperienced\")\nax[1, 1].plot(x, y, colour=\"pink\")\n\n#upload identify to each and every subplot\nax[0, 0].set_title('First Subplot')\nax[0, 1].set_title('2d Subplot')\nax[1, 0].set_title('3rd Subplot')\nax[1, 1].set_title('Fourth Subplot')```", null, "Understand that the padding between and across the subplots has been adjusted in order that the plots not overlap in any branch.\n\nNotice that the tight_layout() serve as takes a house argument to specify the padding between the determine edge and the sides of subplots, as a fragment of the font dimension.\n\nThe default worth for house is 1.08. Alternatively, we will be able to building up this worth to extend the padding across the plots:\n\n```import matplotlib.pyplot as plt\n\n#outline knowledge\nx = [1, 2, 3]\ny = [7, 13, 24]\n\n#outline sequence for subplots\nfig, ax = plt.subplots(2, 2)\n\n#specify a decent sequence with larger padding\nfig.tight_layout(house=5)\n\n#outline subplot titles\nax[0, 0].plot(x, y, colour=\"crimson\")\nax[0, 1].plot(x, y, colour=\"blue\")\nax[1, 0].plot(x, y, colour=\"inexperienced\")\nax[1, 1].plot(x, y, colour=\"pink\")\n\n#upload identify to each and every subplot\nax[0, 0].set_title('First Subplot')\nax[0, 1].set_title('2d Subplot')\nax[1, 0].set_title('3rd Subplot')\nax[1, 1].set_title('Fourth Subplot')```", null, "Understand that the padding across the plots has larger noticeably.\n\nReally feel isolated to regulate the price for the house argument to extend the padding across the plots up to you’d like.\n\n## Spare Sources\n\nRefer to tutorials provide an explanation for carry out alternative ordinary duties in  Matplotlib:\n\nHow one can Upload Identify to Subplots in Matplotlib\nHow one can Modify Subplot Measurement in Matplotlib\nHow one can Modify Spacing Between Matplotlib Subplots" ]

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[ "# Current source design\n\nPlease help me to calculate the value of resistor R1 so that the current through R2 was 100uA. The only thing I noticed is that the output current is pretty much sensitive to changes of saturation current Is of both diode and transistor.", null, "• This is not how to design a CC unless you assume Vbe and D1 are identical junctions and hFE is fixed. Thus you use fixed R ratios on emitter and collector so hFE is less an effect then 2 diodes or a transistor instead of D1 to sense Re voltage and regulate Vbe. so this is purely academic poor discrete design, but ok if matched parts inside an IC – Tony Stewart Sunnyskyguy EE75 Sep 22 '16 at 15:23\n• If D1 is identical to Vbe junction Rs values then they shared current equally, then Ic=hFE * I(R1)/2.. When matched this is called Current Mirror – Tony Stewart Sunnyskyguy EE75 Sep 22 '16 at 15:28\n• What Tony Stewart said. The coursework must include transistor gain. What is it? – WhatRoughBeast Sep 22 '16 at 16:01\n• hfe is 154, but I ran the parameter sweep and the output current doesn't change much with hfe changing – Archimedes Sep 22 '16 at 16:04\n\nIf you assume that the junctions are the same (patently false, and this dubious assumption must be stated), then the current through R2 ~= current through R1 since $\\beta>>1$ . You know the voltage at the base of the transistor within a reasonable margin, so you can calculate the current through R1. The value of R2 is not important unless you take 2nd-order effects such as the Early voltage into account. To see how far off the initial assumption is in reality, let's simulate it with a 100K resistor:", null, "simulate this circuit – Schematic created using CircuitLab\n\nThe current through R1 is 145uA and the current through R2 is 360uA according to the simulation. So the error is about 2:1 and there's really no point bothering about the 0.3% or whatever due to hFE.\n\nThis error originates from the difference in saturation currents of the two junctions and also the ideality factor of the diode junction, which is 1.45 in the above simulation. If I replace the diode with a diode-connected 2N2222, the currents will be much closer:", null, "simulate this circuit\n\nNow the currents are 145uA and 169ua so the error is only about 15%, but hFE is still an effect of little significance. The error at this point is mostly due to the Early voltage.\n\nNote that you have to use a reasonably accurate transistor model to get reasonably accurate results. In this case Circuitlab (and also the free LTSpice) use good models (Ebers-Moll) with good default parameters and give consistent results showing about 15-20% error.\n\n• With Q1-C tied to Q1-B you should get perfect mirroring for high hFE. The error difference I get is only <1% at hFE=100, this goes to zero as hFE increases and Vcc increases. ( So I disagree with your result hFE of Q1 has no effect, also When Q1-C is open, I(R2) will double goo.gl/6nyB27 <=Java simulation ( R1 changed to 10K to reflect question now) – Tony Stewart Sunnyskyguy EE75 Sep 22 '16 at 20:00\n• @TonyStewart What is VAF (Early effect voltage parameter) of your SPICE model? Typically it's 75-100V, which accounts for the 15-20% error since Vce modulates Ic for fixed Vbe with a factor of (1+Vce/Vaf). This parameter is within a sensible range for default 2N2222 models used in both LTSpice and Circuitlab and both give consistent results (15-20% error) which disagree with the simulator (or models) that you are using. The key thing is that 15V is significant compared to 75-100V, and R2 << R1 so Vce is very different on the two transistors. – Spehro Pefhany Sep 22 '16 at 20:29\n• If I compute the leakage from Early effect ,Va Va=75V/1mA=75k has no effect on Q1 <0.6V, but at 100uA it is more significant 6% error goo.gl/YiB5T9 – Tony Stewart Sunnyskyguy EE75 Sep 22 '16 at 20:53\n• @TonyStewart It's not \"leakage\". Ratio of currents due to the Early effect with Vce1 = 0.6 and Vce2 = 14V is: $\\frac{V_{AF}+14}{V_{AF}+0.6}$, which is 15-20% different. – Spehro Pefhany Sep 22 '16 at 21:04\n• in any case these designs have poor V+ sensitivity for Current – Tony Stewart Sunnyskyguy EE75 Sep 22 '16 at 21:08\n\nWhat you have there is a circuit that, when you increase the current through R1, the current through R2 will increase.\n\nHowever, that's the only thing that can be said about it for certain. The actual ratio of R2 to R1 current is poorly controlled. As you say, it depends on the detail of the diode and transistor base-emitter conduction.\n\nIt's easy enough to arrive at a current through R1 to give any R2 current, if you make assumptions about D1 and Q1 parameters, and the temperature. It will be dependent on which model the simulator uses for the transistor. Be aware that this mode is almost never used, well, certainly never trusted in this region, so the models may well be quite inaccurate, as they don't need to be accurate, as nobody would trust them here anyway. If you give an answer based on your simulator, make sure you quote the transistor model, and its parameters, in your answer.\n\nYou might well, for the sake of your answer, assume the diode conduction curves for D1 and Q1 are identical if you like, as long as you state the assumption.\n\nTo make a proper (robust with temperature, and various device types) current source, place a resistor in series with the diode, and a resistor in series with Q1 emitter. Aim to drop around one volt at the design output current. This will be sufficient to swamp the differences in diode voltage for most 'biassing' purposes, though a precision current source it still isn't.\n\nFor 100uA output, put 10k in Q1 emitter. Now you can choose the D1 series resistor to scale the output current if you like. A 100k resistor would result in an output current of 10x I(R1), a 1k resistor of 0.1xI(R1).\n\n• I know about the circuit with the resistor, connected to the emitter, it's much easier to calculate, but the problem is that this circuit is actually my university homework, so I can't change it. – Archimedes Sep 22 '16 at 15:29\n• Either change universities or post the full question as it was asked. There's something not right. – Transistor Sep 22 '16 at 16:11\n• This is the exact circuit and exact question. – Archimedes Sep 22 '16 at 16:27\n• @Archimedes it sounds therefore that you need to make, and state, some judicious assumptions. – Neil_UK Sep 22 '16 at 16:30\n• @Archimedes: I understand that the diagram is the original but what you posted starts with \"Please help me ...\" and this is unlikely to be the exact question. Add it into your original post. – Transistor Sep 22 '16 at 17:20\n\nI(R1) will equal I(R2) when the same transistor is used for the diode within 2% unless Hfe of Q2 is very large (>500) Here simulated with hFE=100 for Q2", null, "" ]

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[ "Square Foot to Square Millimeter Conversion (ft² to mm²)\n\nPlease enter square foot (ft²) value of area unit to convert square foot to square millimeter.\n\n1 ft² = 92903.04 mm²\nSwap» Square Millimeter to Square Foot\nft²: Square Foot, mm²: Square Millimeter\n\nHow Many Square Millimeter in a Square Foot?\n\nThere are 92903.04 square millimeter in a square foot.\n1 Square Foot is equal to 92903.04 Square Millimeter.\n1 ft² = 92903.04 mm²\n\nSquare Foot Definition\n\nA square foot (pl. square feet) is one of the most commonly used non-metric and non-SI unit of area. In the countries traditionally tied with the Imperial system, a square foot can be used for measuring square footage of relatively small kinds of areas, including the one of rooms, middle size objects, etc. A square foot is equal to 144 square inches, or 0.0929 square meters. Instead of a common short symbol, this unit is usually marked as sq ft or ft2.\n\nConvert Square Foot\n\nSquare Millimeter Definition\n\nA square millimeter is a unit of area used for measuring very small areas. The unit is used as a part of SI measurement system and has a symbol of mm2. One square millimeter is the area that equals to 1/100th of a square centimeter (or 0.01 cm2) or equals to 1/1000000th of a square meter (1.0 x 10-6 m2).\n\nConvert Square Millimeter\n\nThis is a very easy to use square foot to square millimeter converter. First of all just type the square foot (ft²) value in the text field of the conversion form to start converting ft² to mm², then select the decimals value and finally hit convert button if auto calculation didn't work. Square Millimeter value will be converted automatically as you type.\n\nThe decimals value is the number of digits to be calculated or rounded of the result of square foot to square millimeter conversion.\n\nYou can also check the square foot to square millimeter conversion chart below, or go back to square foot to square millimeter converter to top.\n\nSquare Foot to Square Millimeter Conversion Examples\n\n```1 ft² = 92903.04 Square Millimeter\n\nExample for 30000 Square Foot:\n30000 Square Foot = 30000 (Square Foot)\n30000 Square Foot = 30000 x (92903.04 Square Millimeter)\n30000 Square Foot = 2787091200 Square Millimeter\n\nExample for 228.6 Square Foot:\n228.6 Square Foot = 228.6 (Square Foot)\n228.6 Square Foot = 228.6 x (92903.04 Square Millimeter)\n228.6 Square Foot = 21237634.944 Square Millimeter\n\nExample for 76.2 Square Foot:\n76.2 Square Foot = 76.2 (Square Foot)\n76.2 Square Foot = 76.2 x (92903.04 Square Millimeter)\n76.2 Square Foot = 7079211.648 Square Millimeter\n\n```\n\nSquare Foot to Square Millimeter Conversion Chart\n\nSquare FootSquare Millimeter\n1 ft²92903.04 mm²\n2 ft²185806.08 mm²\n3 ft²278709.12 mm²\n4 ft²371612.16 mm²\n5 ft²464515.2 mm²\n6 ft²557418.24 mm²\n7 ft²650321.28 mm²\n8 ft²743224.32 mm²\n9 ft²836127.36 mm²\n10 ft²929030.4 mm²\n11 ft²1021933.44 mm²\n12 ft²1114836.48 mm²\n13 ft²1207739.52 mm²\n14 ft²1300642.56 mm²\n15 ft²1393545.6 mm²\n16 ft²1486448.64 mm²\n17 ft²1579351.68 mm²\n18 ft²1672254.72 mm²\n19 ft²1765157.76 mm²\n20 ft²1858060.8 mm²\n21 ft²1950963.84 mm²\n22 ft²2043866.88 mm²\n23 ft²2136769.92 mm²\n24 ft²2229672.96 mm²\n25 ft²2322576 mm²\n26 ft²2415479.04 mm²\n27 ft²2508382.08 mm²\n28 ft²2601285.12 mm²\n29 ft²2694188.16 mm²\n30 ft²2787091.2 mm²\n31 ft²2879994.24 mm²\n32 ft²2972897.28 mm²\n33 ft²3065800.32 mm²\n34 ft²3158703.36 mm²\n35 ft²3251606.4 mm²\n36 ft²3344509.44 mm²\n37 ft²3437412.48 mm²\n38 ft²3530315.52 mm²\n39 ft²3623218.56 mm²\n40 ft²3716121.6 mm²\n41 ft²3809024.64 mm²\n42 ft²3901927.68 mm²\n43 ft²3994830.72 mm²\n44 ft²4087733.76 mm²\n45 ft²4180636.8 mm²\n46 ft²4273539.84 mm²\n47 ft²4366442.88 mm²\n48 ft²4459345.92 mm²\n49 ft²4552248.96 mm²\n50 ft²4645152 mm²\nSquare FootSquare Millimeter\n50 ft²4645152 mm²\n55 ft²5109667.2 mm²\n60 ft²5574182.4 mm²\n65 ft²6038697.6 mm²\n70 ft²6503212.8 mm²\n75 ft²6967728 mm²\n80 ft²7432243.2 mm²\n85 ft²7896758.4 mm²\n90 ft²8361273.6 mm²\n95 ft²8825788.8 mm²\n100 ft²9290304 mm²\n105 ft²9754819.2 mm²\n110 ft²10219334.4 mm²\n115 ft²10683849.6 mm²\n120 ft²11148364.8 mm²\n125 ft²11612880 mm²\n130 ft²12077395.2 mm²\n135 ft²12541910.4 mm²\n140 ft²13006425.6 mm²\n145 ft²13470940.8 mm²\n150 ft²13935456 mm²\n155 ft²14399971.2 mm²\n160 ft²14864486.4 mm²\n165 ft²15329001.6 mm²\n170 ft²15793516.8 mm²\n175 ft²16258032 mm²\n180 ft²16722547.2 mm²\n185 ft²17187062.4 mm²\n190 ft²17651577.6 mm²\n195 ft²18116092.8 mm²\n200 ft²18580608 mm²\n205 ft²19045123.2 mm²\n210 ft²19509638.4 mm²\n215 ft²19974153.6 mm²\n220 ft²20438668.8 mm²\n225 ft²20903184 mm²\n230 ft²21367699.2 mm²\n235 ft²21832214.4 mm²\n240 ft²22296729.6 mm²\n245 ft²22761244.8 mm²\n250 ft²23225760 mm²\n255 ft²23690275.2 mm²\n260 ft²24154790.4 mm²\n265 ft²24619305.6 mm²\n270 ft²25083820.8 mm²\n275 ft²25548336 mm²\n280 ft²26012851.2 mm²\n285 ft²26477366.4 mm²\n290 ft²26941881.6 mm²\n295 ft²27406396.8 mm²\n\nSquare Foot to Square Millimeter Common Values\n\n• 1 ft² = 92903.04 mm²\n• 30000 ft² = 2787091200 mm²\n• 228.6 ft² = 21237634.944 mm²\n• 360 ft² = 33445094.4 mm²\n• 400 ft² = 37161216 mm²\n• 447 ft² = 41527658.88 mm²\n• 750 ft² = 69677280 mm²\n• 940 ft² = 87328857.6 mm²\n• 940.2 ft² = 87347438.208 mm²\n• 1000 ft² = 92903040 mm²\n• 155 ft² = 14399971.2 mm²\n• 144 ft² = 13378037.76 mm²\n• 6 ft² = 557418.24 mm²\n• 75000 ft² = 6967728000 mm²\n• 7 ft² = 650321.28 mm²\n• 19 ft² = 1765157.76 mm²\n• 60 ft² = 5574182.4 mm²\n• 66 ft² = 6131600.64 mm²\n• 76.2 ft² = 7079211.648 mm²\n• 114.3 ft² = 10618817.472 mm²" ]

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[ "# Multiplying with Decimal Quantities\n\n9 teachers like this lesson\nPrint Lesson\n\n## Objective\n\nSWBAT fluently multiply multi-digit decimal quantities.\n\n#### Big Idea\n\nThat Dot Goes Where? Multiplying decimal quantities and accurately displaying their product.\n\n## Curriculum Reinforcer\n\n5 minutes\nTo get my students warmed up for the lesson, they will complete three problems that I will use to assess the prerequisite skills necessary to successfully master the concepts taught in this lesson. The problems in this warm up are simply to determine if the students are able to multiply as well as the extent of their ability to multiply. With these problems, I am trying to find out if they are able to accurately multiply multi-digit quantities.\n1. 473 • 7 =     (Answer: 3,311)\n• With this problem, I am looking to ensure that students are able to regroup properly, know their multiplication tables, and I am looking to see if they know the proper algorithm for multiplying a multi-digit quantity by a single digit quantity.\n2. 3,249 • 21 =   (Answer: 68,229)\n• With this problem, I am looking to to see if my students are able to regroup, know their multiplication tables, and I am looking to see if they know the proper algorithm for multiplying a mult-digit quantity by a double digit quantity... Specifically, I am looking to see if they know that they have to annex a zero when they multiply the multiplicand by the tens place digit in the multiplier to denote that they are actually multiplying by 20 NOT 2.\n3. 9,514 • 355 =   (Answer: 3,377,470)\n• With this problem, I am looking to to see if my students are able to regroup, know their multiplication tables, and I am looking to see if they know the proper algorithm for multiplying a mult-digit quantity by a triple digit quantity... Specifically, I am looking to see if they know that they have to annex two zeros when they multiply the multiplicand by the hundreds place digit in the multiplier to denote that they are actually multiplying by 300 NOT 3.\n\n## Engagement\n\n5 minutes\n\nTo start off this lesson, I will have students explore the meaning of multiplication. To do this, I will ask what does the numerical expression 2 x 3 mean?\n\nI am asking this because, I am looking for students to respond, “2 groups of 3” or “3+3” or “2 three times” or “2+2+2.” I want the students to understand the multiple meanings behind multiplication and that multiplication at its core is repeated addition.\n\n## Instruction & Teacher Modeling\n\n10 minutes\nTo start off the instructional piece, I will use the opening exercise to transition into showing how the repeated addition also applies to decimal quantities. To accomplish this, I have my students take 0.3 and add it together 5 times (0.3+0.3+0.3+0.3+0.3=). Then, I will have them multiply 0.3 with the whole number 5. Using this example, I will facilitate a discussion that will highlight the importance of the decimal when adding decimal quantities versus multiplying decimal quantities.\n\nTo begin this discussion, I will first ask the following question:\n\n• Do I have to line up the place values to multiply decimal quantities? (Students should answer \"No\") Should I receive a \"yes\" response then, I will show students their misconception using the example in the opening. I will multiply 0.3 by 5 and then, I will multiply 0.3 by 5.0 and ask, \"Is there a difference?\" The students should be able to see that either way, you will arrive at the same solution. That being the case, they should understand that lining up the place values is unnecessary.\n\nOnce arriving to the desire answer through strategic questioning, I will then ask:\n\n• Why don’t I have to line up the decimals when multiplying?\n\nI am looking for my students to conclude that the reason is due to its multiplicative nature. Because, it is essentially repeated addition, the place values are, in essence, already lined up. To prove this thinking, I will demonstrate this by writing the same decimal quantity over and over directly beneath the previously written quantity to show how the place values line up.\n\nThen, I will write the problem 2.54 • 1.7 =  and ask…\n\n• What if we are not multiplying by a whole number but by another decimal quantity? Teacher will instruct students to multiply the two quantities as if they had no decimal.\n\n• What must we do with the decimal? And Why?\n\nI am looking for the students to know that the decimal must be moved according to the number of place values behind the decimals in the quantities being multiplied. I want my students to make the connection of place value to the placement of the decimal in the product.\n\n## Try It Out\n\n10 minutes\n\nI will have my students complete five problems then we will go over those five problems as a transition into independent practice.\n\nGuided Skill Practice: Where Does the Decimal Go?\n\n• For this guided practice, I will have my students take out the sheet of paper that they completed the Warm Up exercise on. Using that same piece of paper, I will have the students copy down the three problems indicated below. The students should recognize that these are the same problems except for the fact that the quantities contain decimals. For this reason, students should be able to understand that they can simply copy their answers form the Warm Up then, simply properly place the decimal to provide the correct solution to the problems.\n• Should the students not come to these conclusions on their own, I will ask some questions to prompt them to come to these conclusions. I would ask them, \"So, do you see any correlation between these problems and the problems you worked on for the warm up?\" Someone should answer yes. After a students answers \"yes\" I will then ask them what correlation does he/she sees? And, what does that mean for you in completing the problems for guided practice?\n1. 4.73 • 7 =\n2. 32.49 • 2.1 =\n3. 9.514 • 3.55 =\n\nGuided Practice:\n\n1. Andrew has 8 wooden boards that are 2.25 feet each. How many feet of wooden board does Andrew have altogether?\n• The students should be able to see that multiplication is indicated in the fact that the problem presents 8 wooden boards that are all the same length. That being the case, we have the same length 8 times letting us know that we should multiply in this problem.\n2. Julie bought 3 outfits and a pair of shoes at a department store. The subtotal for all of the items she purchased was \\$127.96. If she paid \\$0.07 in tax for every dollar spent, how much did Julie pay in tax? What was the grand total of the items Julie bought?\n• This problem is purposely worded so that the students can see that the problem is asking how much is \\$0.07 for every dollar Julie spent. For this reason, students should, at least be able to see that they are figuring out \\$0.07 for every \\$127 which, would indicate that you must multiply \\$127 times \\$0.07.\n• Be aware that many students will get confused as to what to do with the cents portion... the 96 cents. It would be at this point that I would go ahead and explain that the \\$0.07 for every dollar in tax is the same as saying 7% of every dollar in tax. I would explain that what this is telling us is that Julie is paying 7 pennies in tax for every 100 pennies she spends on merchandise. I would then show them the fraction 7/100. The reason I would show them the fraction is because many students will remember that a percentage can be written as a fraction by simply placing that percentage over a denominator of 100.\n• After ensuring that the students understand the concept of tax and percent, I would then ask, \"Then, is it possible to have 7% of 96 cents?\" The students should answer \"yes.\" From that point, we should be able to figure out how many cents Julie has to pay in tax for 96 cents using multiplication.\n\n## Independent Exploration\n\n20 minutes\n\nTo explore the concepts that were taught in today's lesson, I will have students pretend to be waiters/waitresses as well as customers at a restaurant. The students will work with a partner. Each student will place an “order” using a menu provided by the teacher. They will then give their “order” to their partner to figure out the subtotal and grand total using a gratuity of 15% and a tax of 7%.\n\nThe scenario should be presented as follows. You are a waiter/waitress at Bistro 305. You have one customer left at a table. The customer has just finished their meal and is ready to go. But, when you go to ring up the cost of their meal the register shorts out and will no longer work! You must calculate your customer’s meal taking into account a 15% gratuity and a 7% tax.\n\nWhile the students are completing this task, I will travel the classroom looking for misconceptions to clear up. I will specifically be looking to ensure that students are understanding and demonstrating the following:\n\n• When adding the decimal quantities, they understand that they must line up the place values and properly regrouping.\n• When multiplying the decimal quantities, I will make sure that the students are properly following the steps of the multiplication algorithm for multiplying multi-digit decimal quantities (i.e. regrouping properly, annexing zeros when necessary...etc).\n• Ensure that the students don't confuse when they should line up the decimal in their solution (adding) and when they should count the digits behind the decimals to determine where the decimal belongs in their solution (multiplication).\n\n## Closing Summary\n\n20 minutes\n\nAfter calculating the Grand Total of the meal, my students will then switch papers so that they have been given their “bill” for what they have ordered. After switching papers, they will then be given three minutes to check their partner’s work. If a student determines that their partner has made a mistake or more than one mistake, the student must identify the mistake(s) write them on a sheet of paper and tell what their partner should have done instead.\n\nThen, I will give my students approximately 4 minutes to confer with their partner concerning the problem, what they encountered while solving the problem and their solutions. I will provide each set of partners with a large piece of construction paper upon which each partner will display their “bill” and together they will write things that they learned during this process, difficulties that they came across, and anything else that they deem significant during the completion of this task.\n\nThen, I will choose sets of partners to come to the front of the class to present their work. The class will be given time to ask questions and discuss with the presenting partners.\n\nTOTD: You bought a new video game controller for \\$19.97. How much will you pay in total for the controller if you pay a tax of 6%? (Tax would be \\$1.19 therefore, you would pay a total of \\$21.16 for the controller)\n\nI have chosen this TOTD to allow the students the opportunity to show that they truly understand the concepts taught during this lesson. To show me that they understood this lesson, the students will need to calculate the correct solution to this problem. Their ability to do so shows me that they...\n\n• Can recognize when they need to multiply and when they need to add decimal quantities.\n• Have the ability to correctly add and multiply decimal quantities." ]

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[ "Home > subtraction\n\n### CBSE Class 8 Rational Numbers\n\nDownloading Link is given at Last CBSE Class 8 RATIONAL NUMBERS 1.1 Definition of Rational Numbers: Question: What are rational numbers? Answer: A number that can be written as , where p and q are integers and q  0, is known as Rational Number. Example: , 12, -18 etc. Read : Integers If the signs of numerator and denominator are" ]

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[ "# Number 880\n\n### Properties of number 880\n\nCross Sum:\nFactorization:\n2 * 2 * 2 * 2 * 5 * 11\nDivisors:\n1, 2, 4, 5, 8, 10, 11, 16, 20, 22, 40, 44, 55, 80, 88, 110, 176, 220, 440, 880\nCount of divisors:\nSum of divisors:\nPrime number?\nNo\nFibonacci number?\nNo\nBell Number?\nNo\nCatalan Number?\nNo\nBase 2 (Binary):\nBase 3 (Ternary):\nBase 4 (Quaternary):\nBase 5 (Quintal):\nBase 8 (Octal):\nBase 32:\nrg\nsin(880)\n0.34670600535753\ncos(880)\n0.93797385136742\ntan(880)\n0.36963291125023\nln(880)\n6.7799219074723\nlg(880)\n2.9444826721502\nsqrt(880)\n29.664793948383\nSquare(880)\n\n### Number Look Up\n\nLook Up\n\n880 (eight hundred eighty) is a special figure. The cross sum of 880 is 16. If you factorisate 880 you will get these result 2 * 2 * 2 * 2 * 5 * 11. The figure 880 has 20 divisors ( 1, 2, 4, 5, 8, 10, 11, 16, 20, 22, 40, 44, 55, 80, 88, 110, 176, 220, 440, 880 ) whith a sum of 2232. The number 880 is not a prime number. 880 is not a fibonacci number. The figure 880 is not a Bell Number. 880 is not a Catalan Number. The convertion of 880 to base 2 (Binary) is 1101110000. The convertion of 880 to base 3 (Ternary) is 1012121. The convertion of 880 to base 4 (Quaternary) is 31300. The convertion of 880 to base 5 (Quintal) is 12010. The convertion of 880 to base 8 (Octal) is 1560. The convertion of 880 to base 16 (Hexadecimal) is 370. The convertion of 880 to base 32 is rg. The sine of the figure 880 is 0.34670600535753. The cosine of the figure 880 is 0.93797385136742. The tangent of 880 is 0.36963291125023. The root of 880 is 29.664793948383.\nIf you square 880 you will get the following result 774400. The natural logarithm of 880 is 6.7799219074723 and the decimal logarithm is 2.9444826721502. that 880 is very unique number!" ]

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[ "Solutions by everydaycalculation.com\n\n## Compare 6/4 and 24/5\n\n1st number: 1 2/4, 2nd number: 4 4/5\n\n6/4 is smaller than 24/5\n\n#### Steps for comparing fractions\n\n1. Find the least common denominator or LCM of the two denominators:\nLCM of 4 and 5 is 20\n2. For the 1st fraction, since 4 × 5 = 20,\n6/4 = 6 × 5/4 × 5 = 30/20\n3. Likewise, for the 2nd fraction, since 5 × 4 = 20,\n24/5 = 24 × 4/5 × 4 = 96/20\n4. Since the denominators are now the same, the fraction with the bigger numerator is the greater fraction\n5. 30/20 < 96/20 or 6/4 < 24/5\n\nand" ]

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[ " Discrete math (macm 101 question) i need to understand this notation: permutation general formula : npr = n! / (n - r)! n stands for the", null, "", null, ", 18.10.2019 10:30, caleelwittmann31\n\n# Discrete math (macm 101 question) i need to understand this notation: permutation general formula : npr = n! / (n - r)! n stands for the total number of things we have, but what about r ?", null, "### Other questions on the subject: Mathematics", null, "Mathematics, 21.06.2019 13:00, mayac5369\nWhich students, if any have correctly indentfied a base and it’s corresponding height? which ones have not? explain what is correct", null, "Mathematics, 21.06.2019 13:30, ovengel12\nWhat are correct trigonometric ratios that could be used to determine the length of ln", null, "Mathematics, 21.06.2019 19:30, nina7163\nYou deposit \\$5000 each year into an account earning 3% interest compounded annually. how much will you have in the account in 30 years?", null, "Mathematics, 21.06.2019 23:00, gisellekatherine1\nThe equation represents the function f, and the graph represents the function g. f(x)=3(5/2)^x determine the relationship between the growth factors of f and g. a. the growth factor of g is twice the growth factor of f. b. the growth factor of f is twice the growth factor of g. c. the growth factor of f is 2.5 times the growth factor of g. d. the growth factor of f is the same as the growth factor of g.\nDo you know the correct answer?\nDiscrete math (macm 101 question)\n\ni need to understand this notation:\n\npermu...\n\n### Questions in other subjects:", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "Total solved problems on the site: 8050100" ]

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[ "# zbMATH — the first resource for mathematics\n\nUniqueness theorems on difference monomials of entire functions. (English) Zbl 1247.30047\nSummary: The aim of this paper is to discuss the uniqueness of the difference monomials $$f^n f(z + c)$$. It is assumed that $$f$$ and $$g$$ are transcendental entire functions with finite order and $$E_{k)}(1, f^n f(z + c)) = E_{k)}(1, g^n g(z + c))$$, where $$c$$ is a nonzero complex constant and $$n, k$$ are integers. It is proved that $$fg = t_1$$ or $$f = t_2g$$ for some constants $$t_2$$ and $$t_3$$ which satisfy $$t^{n+1}_2 = 1$$ and $$t^{n+1}_3 = 1$$, if $$k = 1$$ and one of the following holds: (i) $$n \\geq 6$$ and $$k = 3$$, (ii) $$n \\geq 7$$ and $$k = 2$$, and (iii) $$n \\geq 10$$. It is an improvement of the result of X.-G. Qi, L.-Z. Yang and K. Liu [Comput. Math. Appl. 60, No. 6, 1739–1746 (2010; Zbl 1202.30045)].\n\n##### MSC:\n 30D20 Entire functions of one complex variable, general theory\n##### Keywords:\ndifference monomials; uniqueness; entire functions\nFull Text:\n##### References:\n W. K. Hayman, Meromorphic Functions, Clarendon Press, Oxford, UK, 1964. · Zbl 0115.06203 H. X. Yi and C. C. Yang, Uniqueness Theory of Meromorphic Functions, Science Press, Beijing, China, 1995. W. K. Hayman, Research Problems in Function Theory, The Athlone Press University of London, London, UK, 1967. · Zbl 0158.06301 W. K. Hayman, “Picard values of meromorphic functions and their derivatives,” Annals of Mathematics (2), vol. 70, pp. 9-42, 1959. · Zbl 0088.28505 · doi:10.2307/1969890 J. Clunie, “On a result of Hayman,” Journal of the London Mathematical Society, vol. 42, pp. 389-392, 1967. · Zbl 0169.40801 · doi:10.1112/jlms/s1-42.1.389 C. C. Yang and X. H. Hua, “Uniqueness and value-sharing of meromorphic functions,” Annales Academiæ Scientiarum Fennicæ Mathematica, vol. 22, no. 2, pp. 395-406, 1997. · Zbl 0890.30019 · emis:journals/AASF/Vol22/vol22.html · eudml:228963 X.-G. Qi, L. Z. Yang, and K. Liu, “Uniqueness and periodicity of meromorphic functions concerning the difference operator,” Computers & Mathematics with Applications, vol. 60, no. 6, pp. 1739-1746, 2010. · Zbl 1202.30045 · doi:10.1016/j.camwa.2010.07.004 Y.-M. Chiang and S.-J. Feng, “On the Nevanlinna characteristic of f(z+\\eta ) and difference equations in the complex plane,” Ramanujan Journal, vol. 16, no. 1, pp. 105-129, 2008. · Zbl 1152.30024 · doi:10.1007/s11139-007-9101-1 J.-F. Xu, Q. Han, and J.-L. Zhang, “Uniqueness theorems of meromorphic functions of a certain form,” Bulletin of the Korean Mathematical Society, vol. 46, no. 6, pp. 1079-1089, 2009. · Zbl 1186.30037 · doi:10.4134/BKMS.2009.46.6.1079 H. X. Yi, “Meromorphic functions that share one or two values,” Complex Variables and Elliptic Equations, vol. 28, no. 1, pp. 1-11, 1995. · Zbl 0841.30027 R. G. Halburdand and R. J. Korhonen, “Nevanlinna theory for the difference operator,” Annales Academiæ Scientiarium Fennicæ, vol. 31, no. 2, pp. 463-478, 2006. · Zbl 1108.30022 · eudml:127044\nThis reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching." ]

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[ "## cis conformation\n\nin polymers\nAlso contains definition of: trans conformation in polymers\nhttps://doi.org/10.1351/goldbook.C01088\n@C01258@ referring to torsion angles θ (A, B, C, D), where A, B, C, D are main-chain atoms, can be described as: cis or @T06406-1@ ($$\\text{C}$$); @G02593@ or @T06406-2@ ($$\\text{G}$$); @T06406-4@ ($$\\text{A}$$); and @C01092@ or @T06406-3@ ($$\\text{T}$$), corresponding to torsion angles within ± 30° of, respectively, 0°, ± 120° and ± 180°. The letters shown in parentheses (upper case $$\\text{C}$$, $$\\text{G}$$, $$\\text{A}$$, $$\\text{T}$$) are the recommended abbreviations. The symbols $$\\text{G}^{+}$$, $$\\text{G}^{-}$$ (or $$\\text{A}^{+}$$, $$\\text{G}^{-}$$, for example) refer to torsion angles of similar type but opposite known sign, i.e. ~ +60°, ~ -60° (or ~ +120°, -120°). The notation $$\\text{G}$$, $$\\bar{\\text{G}}$$ ; $$\\text{A}$$, $$\\bar{\\text{A}}$$ (and $$\\text{T}$$, $$\\bar{\\text{T}}$$ ; $$\\text{C}$$, $$\\bar{\\text{C}}$$ - whenever the torsion angles are not exactly equal to 180° and 0°, respectively) is reserved for the designation of @E02079@ conformations, i.e. conformations of opposite but unspecified sign. Where necessary, a @D01650@ from the proper value of the @T06406-5@ can be indicated by the sign (~), as in the following examples: $$\\text{G(~)}$$; $$\\text{G}^{+}\\text{(~)}$$; $$\\text{G}^{-}\\text{(~)}$$.\nSource:\nPurple Book, 1st ed., p. 41 [Terms] [Book]" ]

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[ "# treex.losses.huber\n\n## Huber (Loss) \n\nComputes the Huber loss between target and predictions.\n\nFor each value x in error = target - preds:\n\nloss = \\begin{cases} \\ 0.5 \\times x^2,\\hskip8em\\text{if } |x|\\leq d\\\\ 0.5 \\times d^2 + d \\times (|x| - d),\\hskip1.7em \\text{otherwise} \\end{cases}\n\nwhere d is delta. See: https://en.wikipedia.org/wiki/Huber_loss\n\nUsage:\n\ntarget = jnp.array([[0, 1], [0, 0]])\npreds = jnp.array([[0.6, 0.4], [0.4, 0.6]])\n\n# Using 'auto'/'sum_over_batch_size' reduction type.\nhuber_loss = tx.losses.Huber()\nassert huber_loss(target, preds) == 0.155\n\n# Calling with 'sample_weight'.\nassert (\nhuber_loss(target, preds, sample_weight=jnp.array([0.8, 0.2])) == 0.08500001\n)\n\n# Using 'sum' reduction type.\nhuber_loss = tx.losses.Huber(\nreduction=tx.losses.Reduction.SUM\n)\nassert huber_loss(target, preds) == 0.31\n\n# Using 'none' reduction type.\nhuber_loss = tx.losses.Huber(\nreduction=tx.losses.Reduction.NONE\n)\n\nassert jnp.equal(huber_loss(target, preds), jnp.array([0.18, 0.13000001])).all()\n\nUsage with the Elegy API:\n\nmodel = elegy.Model(\nmodule_fn,\nloss=tx.losses.Huber(delta=1.0),\nmetrics=elegy.metrics.Mean(),\n)\n\nSource code in treex/losses/huber.py\nclass Huber(Loss):\nr\"\"\"\nComputes the Huber loss between target and predictions.\n\nFor each value x in error = target - preds:\n\n$$loss = \\begin{cases} \\ 0.5 \\times x^2,\\hskip8em\\text{if } |x|\\leq d\\\\ 0.5 \\times d^2 + d \\times (|x| - d),\\hskip1.7em \\text{otherwise} \\end{cases}$$\n\nwhere d is delta. See: https://en.wikipedia.org/wiki/Huber_loss\n\nUsage:\n\npython\ntarget = jnp.array([[0, 1], [0, 0]])\npreds = jnp.array([[0.6, 0.4], [0.4, 0.6]])\n\n# Using 'auto'/'sum_over_batch_size' reduction type.\nhuber_loss = tx.losses.Huber()\nassert huber_loss(target, preds) == 0.155\n\n# Calling with 'sample_weight'.\nassert (\nhuber_loss(target, preds, sample_weight=jnp.array([0.8, 0.2])) == 0.08500001\n)\n\n# Using 'sum' reduction type.\nhuber_loss = tx.losses.Huber(\nreduction=tx.losses.Reduction.SUM\n)\nassert huber_loss(target, preds) == 0.31\n\n# Using 'none' reduction type.\nhuber_loss = tx.losses.Huber(\nreduction=tx.losses.Reduction.NONE\n)\n\nassert jnp.equal(huber_loss(target, preds), jnp.array([0.18, 0.13000001])).all()\n\nUsage with the Elegy API:\n\npython\nmodel = elegy.Model(\nmodule_fn,\nloss=tx.losses.Huber(delta=1.0),\nmetrics=elegy.metrics.Mean(),\n)\n\n\"\"\"\n\ndef __init__(\nself,\ndelta: float = 1.0,\nreduction: tp.Optional[Reduction] = None,\nweight: tp.Optional[float] = None,\non: tp.Optional[types.IndexLike] = None,\n**kwargs\n):\n\"\"\"\nInitializes Mean class.\n\nArguments:\ndelta: (Optional) Defaults to 1.0. A float, the point where the Huber loss function changes from a quadratic to linear.\nreduction: (Optional) Type of tx.losses.Reduction to apply to\nloss. Default value is SUM_OVER_BATCH_SIZE. For almost all cases\nthis defaults to SUM_OVER_BATCH_SIZE.\nweight: Optional weight contribution for the total loss. Defaults to 1.\non: A string or integer, or iterable of string or integers, that\nindicate how to index/filter the target and preds\narguments before passing them to call. For example if on = \"a\" then\ntarget = target[\"a\"]. If on is an iterable\nthe structures will be indexed iteratively, for example if on = [\"a\", 0, \"b\"]\nthen target = target[\"a\"][\"b\"], same for preds. For more information\ncheck out [Keras-like behavior](https://poets-ai.github.io/elegy/guides/modules-losses-metrics/#keras-like-behavior).\n\"\"\"\nself.delta = delta\nreturn super().__init__(reduction=reduction, weight=weight, on=on, **kwargs)\n\ndef call(\nself,\ntarget: jnp.ndarray,\npreds: jnp.ndarray,\nsample_weight: tp.Optional[\njnp.ndarray\n] = None, # not used, __call__ handles it, left for documentation purposes.\n) -> jnp.ndarray:\n\"\"\"\nInvokes the Huber instance.\n\nArguments:\ntarget: Ground truth values. shape = [batch_size, d0, .. dN], except\nsparse loss functions such as sparse categorical crossentropy where\nshape = [batch_size, d0, .. dN-1]\npreds: The predicted values. shape = [batch_size, d0, .. dN]\nsample_weight: Optional sample_weight acts as a\ncoefficient for the loss. If a scalar is provided, then the loss is\nsimply scaled by the given value. If sample_weight is a tensor of size\n[batch_size], then the total loss for each sample of the batch is\nrescaled by the corresponding element in the sample_weight vector. If\nthe shape of sample_weight is [batch_size, d0, .. dN-1] (or can be\nbroadcasted to this shape), then each loss element of preds is scaled\nby the corresponding value of sample_weight. (Note ondN-1: all loss\nfunctions reduce by 1 dimension, usually axis=-1.)\n\nReturns:\nWeighted loss float Tensor. If reduction is NONE, this has\nshape [batch_size, d0, .. dN-1]; otherwise, it is scalar. (Note dN-1\nbecause all loss functions reduce by 1 dimension, usually axis=-1.)\n\nRaises:\nValueError: If the shape of sample_weight is invalid.\n\"\"\"\nreturn huber(target, preds, self.delta)\n\n\n### __init__(self, delta=1.0, reduction=None, weight=None, on=None, **kwargs) special\n\nInitializes Mean class.\n\nParameters:\n\nName Type Description Default\ndelta float\n\n(Optional) Defaults to 1.0. A float, the point where the Huber loss function changes from a quadratic to linear.\n\n1.0\nreduction Optional[treex.losses.loss.Reduction]\n\n(Optional) Type of tx.losses.Reduction to apply to loss. Default value is SUM_OVER_BATCH_SIZE. For almost all cases this defaults to SUM_OVER_BATCH_SIZE.\n\nNone\nweight Optional[float]\n\nOptional weight contribution for the total loss. Defaults to 1.\n\nNone\non Union[str, int, Sequence[Union[str, int]]]\n\nA string or integer, or iterable of string or integers, that indicate how to index/filter the target and preds arguments before passing them to call. For example if on = \"a\" then target = target[\"a\"]. If on is an iterable the structures will be indexed iteratively, for example if on = [\"a\", 0, \"b\"] then target = target[\"a\"][\"b\"], same for preds. For more information check out Keras-like behavior.\n\nNone\nSource code in treex/losses/huber.py\ndef __init__(\nself,\ndelta: float = 1.0,\nreduction: tp.Optional[Reduction] = None,\nweight: tp.Optional[float] = None,\non: tp.Optional[types.IndexLike] = None,\n**kwargs\n):\n\"\"\"\nInitializes Mean class.\n\nArguments:\ndelta: (Optional) Defaults to 1.0. A float, the point where the Huber loss function changes from a quadratic to linear.\nreduction: (Optional) Type of tx.losses.Reduction to apply to\nloss. Default value is SUM_OVER_BATCH_SIZE. For almost all cases\nthis defaults to SUM_OVER_BATCH_SIZE.\nweight: Optional weight contribution for the total loss. Defaults to 1.\non: A string or integer, or iterable of string or integers, that\nindicate how to index/filter the target and preds\narguments before passing them to call. For example if on = \"a\" then\ntarget = target[\"a\"]. If on is an iterable\nthe structures will be indexed iteratively, for example if on = [\"a\", 0, \"b\"]\nthen target = target[\"a\"][\"b\"], same for preds. For more information\ncheck out [Keras-like behavior](https://poets-ai.github.io/elegy/guides/modules-losses-metrics/#keras-like-behavior).\n\"\"\"\nself.delta = delta\nreturn super().__init__(reduction=reduction, weight=weight, on=on, **kwargs)\n\n\n### call(self, target, preds, sample_weight=None)\n\nInvokes the Huber instance.\n\nParameters:\n\nName Type Description Default\ntarget ndarray\n\nGround truth values. shape = [batch_size, d0, .. dN], except sparse loss functions such as sparse categorical crossentropy where shape = [batch_size, d0, .. dN-1]\n\nrequired\npreds ndarray\n\nThe predicted values. shape = [batch_size, d0, .. dN]\n\nrequired\nsample_weight Optional[jax._src.numpy.lax_numpy.ndarray]\n\nOptional sample_weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the sample_weight vector. If the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each loss element of preds is scaled by the corresponding value of sample_weight. (Note ondN-1: all loss functions reduce by 1 dimension, usually axis=-1.)\n\nNone\n\nReturns:\n\nType Description\nndarray\n\nWeighted loss float Tensor. If reduction is NONE, this has shape [batch_size, d0, .. dN-1]; otherwise, it is scalar. (Note dN-1 because all loss functions reduce by 1 dimension, usually axis=-1.)\n\nExceptions:\n\nType Description\nValueError\n\nIf the shape of sample_weight is invalid.\n\nSource code in treex/losses/huber.py\ndef call(\nself,\ntarget: jnp.ndarray,\npreds: jnp.ndarray,\nsample_weight: tp.Optional[\njnp.ndarray\n] = None, # not used, __call__ handles it, left for documentation purposes.\n) -> jnp.ndarray:\n\"\"\"\nInvokes the Huber instance.\n\nArguments:\ntarget: Ground truth values. shape = [batch_size, d0, .. dN], except\nsparse loss functions such as sparse categorical crossentropy where\nshape = [batch_size, d0, .. dN-1]\npreds: The predicted values. shape = [batch_size, d0, .. dN]\nsample_weight: Optional sample_weight acts as a\ncoefficient for the loss. If a scalar is provided, then the loss is\nsimply scaled by the given value. If sample_weight is a tensor of size\n[batch_size], then the total loss for each sample of the batch is\nrescaled by the corresponding element in the sample_weight vector. If\nthe shape of sample_weight is [batch_size, d0, .. dN-1] (or can be\nbroadcasted to this shape), then each loss element of preds is scaled\nby the corresponding value of sample_weight. (Note ondN-1: all loss\nfunctions reduce by 1 dimension, usually axis=-1.)\n\nReturns:\nWeighted loss float Tensor. If reduction is NONE, this has\nshape [batch_size, d0, .. dN-1]; otherwise, it is scalar. (Note dN-1\nbecause all loss functions reduce by 1 dimension, usually axis=-1.)\n\nRaises:\nValueError: If the shape of sample_weight is invalid.\n\"\"\"\nreturn huber(target, preds, self.delta)\n\n\n## huber(target, preds, delta)\n\nComputes the Huber loss between target and predictions.\n\nFor each value x in error = target - preds:\n\nloss = \\begin{cases} \\ 0.5 \\times x^2,\\hskip8em\\text{if } |x|\\leq d\\\\ 0.5 \\times d^2 + d \\times (|x| - d),\\hskip1.7em \\text{otherwise} \\end{cases}\n\nwhere d is delta. See: https://en.wikipedia.org/wiki/Huber_loss\n\nUsage:\n\nrng = jax.random.PRNGKey(42)\n\ntarget = jax.random.randint(rng, shape=(2, 3), minval=0, maxval=2)\npreds = jax.random.uniform(rng, shape=(2, 3))\n\nloss = tx.losses.huber(target, preds, delta=1.0)\nassert loss.shape == (2,)\n\npreds = preds.astype(float)\ntarget = target.astype(float)\ndelta = 1.0\nerror = jnp.subtract(preds, target)\nabs_error = jnp.abs(error)\nassert jnp.array_equal(loss, jnp.mean(\njnp.multiply(\n0.5,\n),\njnp.multiply(delta, linear)), axis=-1\n))\n\n\nParameters:\n\nName Type Description Default\ntarget ndarray\n\nGround truth values. shape = [batch_size, d0, .. dN].\n\nrequired\npreds ndarray\n\nThe predicted values. shape = [batch_size, d0, .. dN].\n\nrequired\ndelta float\n\nA float, the point where the Huber loss function changes from a quadratic to linear.\n\nrequired\n\nReturns:\n\nType Description\nndarray\n\nhuber loss Values. If reduction is NONE, this has\n\n shape [batch_size, d0, .. dN-1]; otherwise, it is scalar.\n(Note dN-1 because all loss functions reduce by 1 dimension, usually axis=-1.)\n\nSource code in treex/losses/huber.py\ndef huber(target: jnp.ndarray, preds: jnp.ndarray, delta: float) -> jnp.ndarray:\nr\"\"\"\nComputes the Huber loss between target and predictions.\n\nFor each value x in error = target - preds:\n\n$$loss = \\begin{cases} \\ 0.5 \\times x^2,\\hskip8em\\text{if } |x|\\leq d\\\\ 0.5 \\times d^2 + d \\times (|x| - d),\\hskip1.7em \\text{otherwise} \\end{cases}$$\n\nwhere d is delta. See: https://en.wikipedia.org/wiki/Huber_loss\n\nUsage:\n\npython\nrng = jax.random.PRNGKey(42)\n\ntarget = jax.random.randint(rng, shape=(2, 3), minval=0, maxval=2)\npreds = jax.random.uniform(rng, shape=(2, 3))\n\nloss = tx.losses.huber(target, preds, delta=1.0)\nassert loss.shape == (2,)\n\npreds = preds.astype(float)\ntarget = target.astype(float)\ndelta = 1.0\nerror = jnp.subtract(preds, target)\nabs_error = jnp.abs(error)\nassert jnp.array_equal(loss, jnp.mean(\njnp.multiply(\n0.5,\n),\njnp.multiply(delta, linear)), axis=-1\n))\n\n\nArguments:\ntarget: Ground truth values. shape = [batch_size, d0, .. dN].\npreds: The predicted values. shape = [batch_size, d0, .. dN].\ndelta: A float, the point where the Huber loss function changes from a quadratic to linear.\n\nReturns:\nhuber loss Values. If reduction is NONE, this has\nshape [batch_size, d0, .. dN-1]; otherwise, it is scalar.\n(Note dN-1 because all loss functions reduce by 1 dimension, usually axis=-1.)\n\"\"\"\npreds = preds.astype(float)\ntarget = target.astype(float)\ndelta = float(delta)\nerror = jnp.subtract(preds, target)\nabs_error = jnp.abs(error)" ]

[ null ]

[ "Home -> Solved problems -> Curvilinear asymptote\n\n## Find out how to find the equation of a rational function curvilinear asymptote", null, "### Solution\n\n$$f$$ is a function defined on $$]-\\infty, 0[\\cup] 0, \\infty[$$ by: $f(x)=\\frac{x^{3}+2 x^{2}+3 x+4}{x}$ We are interested in studying $$f$$ as $$x$$ approaches infinity ($$-\\infty$$ and $$\\infty$$)\n\\begin{aligned} \\lim _{x \\rightarrow\\infty} f(x) &=\\lim _{x \\rightarrow\\infty} \\frac{x^{3}+2 x^{2}+3 x+4}{x} \\\\\\\\ &=\\lim _{x \\rightarrow\\infty} \\frac{x^{3}\\left(1+\\frac{2}{x}+\\frac{3}{x^{2}}+\\frac{4}{x^{3}}\\right)}{x} \\\\\\\\ &=\\lim _{x \\rightarrow\\infty} x^{2}\\left(1+\\frac{2}{x}+\\frac{3}{x^{2}}+\\frac{4}{x^{3}}\\right) \\\\\\\\ &=\\lim _{x \\rightarrow\\infty} x^{2}=\\infty \\end{aligned} Now, let’s study the behavior of $$f$$ relative to $$x$$ as $$x$$ approaches $$\\infty$$ \\begin{aligned} \\lim _{x \\rightarrow\\infty} \\frac{f(x)}{x} &=\\lim _{x \\rightarrow\\infty} \\frac{x^{3}+2 x^{2}+3 x+4}{x^{2}} \\\\\\\\ &=\\lim _{x \\rightarrow\\infty} x\\left(1+\\frac{2}{x}+\\frac{3}{x^{2}}+\\frac{4}{x^{3}}\\right) \\\\\\\\ &=\\lim _{x \\rightarrow\\infty} x=\\infty \\end{aligned} Therefore, $$f$$ has no slant asymptote when $$x$$ approaches $$\\infty$$. Thus, we increase the degree of power of $$x$$ and we study the behavior of the function $$f$$ relative to $$x^{2}$$ as $$x$$ approaches $$\\infty$$ \\begin{aligned} \\lim _{x \\rightarrow \\infty} \\frac{f(x)}{x^{2}} &=\\lim _{x \\rightarrow \\infty} \\frac{x^{3}+2 x^{2}+3 x+4}{x^{3}} \\\\\\\\ &=\\lim _{x \\rightarrow\\infty} 1+\\frac{2}{x}+\\frac{3}{x^{2}}+\\frac{4}{x^{3}} \\\\\\\\ &=1 \\end{aligned} At this step, we know that we are dealing with a parabola with equation of general form: $$y=ax^{2}+bx+c$$, knowing that we have already find $$a=1$$ the equation becomes: $$y=x^{2}+bx+c$$. Now, let’s try to find $$b$$ and $$c$$ \\begin{aligned} \\lim _{x \\rightarrow+\\infty} \\frac{f(x)-x^{2}}{x} &=\\lim _{x \\rightarrow \\infty} \\frac{\\frac{x^{3}+2 x^{2}+3 x+4}{x}-x^{2}}{x} \\\\\\\\ &=\\lim _{x \\rightarrow \\infty} \\frac{x^{3}+2 x^{2}+3 x+4-x^{3}}{x^{2}} \\\\\\\\ &=\\lim _{x \\rightarrow \\infty} \\frac{2 x^{2}+3 x+4}{x^{2}} \\\\\\\\ &=\\lim _{x \\rightarrow \\infty} \\frac{x^{2}\\left(2+\\frac{3}{x}+\\frac{4}{x^{2}}\\right)}{x^{2}} \\\\\\\\ &=\\lim _{x \\rightarrow \\infty} 2+\\frac{3}{x}+\\frac{4}{x^{2}} \\\\\\\\ &=2 \\end{aligned} $\\Rightarrow b=2$ Therefore, $y=x^{2}+2x+c$ Now, let’s try to find $$c$$ \\begin{aligned} \\lim _{x \\rightarrow \\infty} f(x)-x^{2}-2 x &=\\lim _{x \\rightarrow \\infty} \\frac{x^{3}+2 x^{2}+3 x+4}{x}-x^{2}-2 x \\\\\\\\ &=\\lim _{x \\rightarrow \\infty} \\frac{x^{3}+2 x^{2}+3 x+4-x\\left(x^{2}+2 x\\right)}{x} \\\\\\\\ &=\\lim _{x \\rightarrow \\infty} \\frac{x^{3}+2 x^{2}+3 x+4-x^{3}-2 x^{2}}{x} \\\\\\\\ &=\\lim _{x \\rightarrow \\infty} \\frac{3 x+4}{x} \\\\\\\\ &=\\lim _{x \\rightarrow \\infty} \\frac{x\\left(3+\\frac{4}{x}\\right)}{x} \\\\\\\\ &=\\lim _{x \\rightarrow \\infty} 3+\\frac{4}{x}=3=c \\end{aligned} For $$-\\infty$$ we use the same steps as $$\\infty$$ and we will find out that the parabola: $$y=x^{2}+2 x+3$$ is also a parabolic asymptote for the function $$f$$ but as $$x$$ approaches $$-\\infty$$. Let’s recap, $$f$$ has a curvilinear asymptote as $$x$$ approaches $$-\\infty$$ and $$\\infty$$ which is a parabolic asymptote $\\huge y=x^{2}+2 x+3$\nHome -> Solved problems -> Curvilinear asymptote\n\n### Related Topics\n\nFind the volume of the square pyramid as a function of $$a$$ and $$H$$ by slicing method.", null, "Prove that $\\lim_{x \\rightarrow 0}\\frac{\\sin x}{x}=1$", null, "Prove that", null, "Calculate the half derivative of $$x$$", null, "Prove Wallis Product Using Integration", null, "", null, "Calculate the volume of Torus using cylindrical shells", null, "Find the derivative of exponential $$x$$ from first principles", null, "Calculate the sum of areas of the three squares", null, "Find the equation of the curve formed by a cable suspended between two points at the same height", null, "Solve the equation for real values of $$x$$", null, "Solve the equation for $$x\\epsilon\\mathbb{R}$$", null, "Determine the angle $$x$$", null, "Calculate the following limit", null, "Calculate the following limit", null, "Calculate the integral", null, "Challenging problem", null, "Prove that", null, "Prove that $$e$$ is an irrational number", null, "Find the derivative of $$y$$ with respect to $$x$$", null, "Find the limit of width and height ratio", null, "How Tall Is The Table ?", null, "Why 0.9999999...=1", null, "Solve the equation for $$x \\in \\mathbb{R}$$", null, "Calculate the following", null, "Is $$\\pi$$ an irrational number ?", null, "How far apart are the poles ?", null, "Solve for $$x \\in \\mathbb{R}$$", null, "What values of $$x$$ satisfy this inequality", null, "Prove that the function $$f(x)=\\frac{x^{3}+2 x^{2}+3 x+4}{x}$$ has a curvilinear asymptote $$y=x^{2}+2 x+3$$", null, "Home -> Solved problems -> Curvilinear asymptote\n\n#### Share the solution: Curvilinear asymptote", null, "" ]

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[ "# Filling a region between two straight lines\n\nI want to fill the region between two straight lines without displaying the region boundaries. How can it be done?\n\nContourPlot[{x == 2, x == 4}, {x, -2, 8}, {y, -2, 6}]\n\nUse BoundaryStyle -> None as shown in the documentation of RegionPlot:\n\nRegionPlot[2 <= x <= 4, {x, -2, 8}, {y, -2, 6}, BoundaryStyle -> None]", null, "• Thanks, @Roman. It's working. Oct 3, 2022 at 8:53\n• Just saw your answer after I updated. Oct 3, 2022 at 8:59\n\nis this what you mean?\n\np1 = ContourPlot[{x == 2, x == 4}, {x, -2, 8}, {y, -2, 6}];\nr = ImplicitRegion[2 < x < 4 && -2 < y < 6, {x, y}];\np2 = RegionPlot[r];\nShow[p1, p2]", null, "Update\n\nIn this case, just plot the region directly. No need for ContourPlot at all to be there.\n\nr = ImplicitRegion[2 < x < 4 && -2 < y < 6, {x, y}];\nRegionPlot[r, BoundaryStyle -> None,\nPlotRange -> {{-2, 8}, Automatic}]", null, "• Thanks, @Nasser for your answer. You are almost close. I just don't want the border lines surrounding the region. Oct 3, 2022 at 8:50\n• @SahabubJahedi fyi, updated Oct 3, 2022 at 8:59\n• Thanks, @Nasser, it's working. Oct 3, 2022 at 12:05\nc := RandomInteger[{-100, 100}];\n\nRegionPlot[RegionIntersection[\nHalfPlane[{{2, c}, {2, c}}, {1, 0}]\n, HalfPlane[{{4, c}, {4, c}}, {-1, 0}]\n]\n, Frame -> True\n, BoundaryStyle -> None\n, PlotRange -> { {-2, 8}, {-2, 6}}\n]", null, "" ]

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[ "# Divide Polynomials using long division - Part 1\n\nTaught by YourMathGal\n• Currently 4.0/5 Stars.\n11020 views | 1 rating\nPart of video series\nMeets NCTM Standards:\nLesson Summary:\n\nIn this lesson, you will learn how to divide polynomials using long division when the denominator is a binomial or a trinomial. The instructor emphasizes the importance of not trying to cancel terms and instead using long division to solve the problem. The lesson walks you through the steps of identifying what to divide into, multiplying the first term, subtracting, and checking your answer. The ultimate goal is to arrive at a quotient that can be used to check your work by multiplying it with what you divided by.\n\nLesson Description:\n\nPart 1 of how to divide a Polynomial by a Polynomial using long division.\n\nMore free YouTube videos by Julie Harland are organized at http://yourmathgal.com\n\n•", null, "" ]

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[ "## Filters\n\nSort by :\nClear All\nQ\n\n3. Draw the perpendicular bisector of XY whose length is 10.3 cm. (a) Take any point P on the bisector drawn. Examine whether PX = PY. (b) If M is the midpoint of XY, what can you say about the lengths MX and XY?\n\nFollow the steps to draw the perpendicular bisector of a line XY. a) PX= PY b)MX=MY\n\nQ9.    Draw an angle of 40o. Copy its supplementary angle.\n\nThe steps of constructions are:  (a) Draw an angle of 40 degrees with the help of protractor, naming ∠ AOB. (b) Draw a line PQ. (c) Take any point M on PQ. (d) Place the compasses at O and draw an arc to cut the rays of ∠AOB at L and N. (e) Use the same compasses setting to draw an arc O as the centre, cutting MQ at X. (f) Set your compasses to length LN with the same radius. (g) Place the...\n\nQ8.    Draw an angle of 70o. Make a copy of it using only a straight edge and compasses.\n\nThe steps of constructions are: (a) Draw an angle 70 degrees with a protractor, i.e., ∠POQ = 70 degrees (b) Draw a ray AB. (c) Place the compasses at O and draw an arc to cut the rays of ∠POQ at L and M. (d) Use the same compasses, setting to draw an arc with A as the centre, cutting AB at X. (e) Set your compasses setting to the length LM with the same radius. (f) Place the compasses pointer...\n\nQ7.    Draw an angle of measure 135° and bisect it.\n\nThe steps of constructions are:  (a) Draw a line PQ and take a point O on it. (b) Taking O as the centre and convenient radius, mark an arc, which intersects PQ at A and B. (c) Taking A and B as centres and radius more than half of AB, draw two arcs intersecting each other at R. (d) Join OR. Thus, ∠QOR = ∠POQ = 90 . (e) Draw OD the bisector of ∠POR. Thus, ∠QOD is the required angle of 135. (f)...\n\nQ6.    Draw an angle of measure 45° and bisect it.\n\nThe steps of constructions are: 1. Draw a ray OA 2. Taking O as the centre and convenient radius, mark an arc, which intersects OA at X. 3. Taking X as a centre and the same radius, cut the previous arc at Y. Taking Y as the centre and the same radius, draw another arc intersecting the same arc at Z. 4. Taking Y and Z as centres and the same radius, draw two arcs intersecting each other at S....\n\nQ5.    Construct with ruler and compasses, angles of following measures:\n\n(f)    135o\n\nThe steps of constructions are: 1. Draw a line PQ and take a point O on it. 2. Taking O as the centre and convenient radius, mark an arc, which intersects PQ at A and B. 3. Taking A and B as centres and radius more than half of AB, draw two arcs intersecting each other at R. Join OR. Thus, ∠QOR = ∠POR = 90°. 4. Draw OD the bisector of ∠POR. Thus, ∠QOD is required angle of 135°\n\nQ5.    Construct with ruler and compasses, angles of following measures:\n\n(e)    45o\n\nThe steps of constructions are: 1. Draw a ray OA 2. Taking O as the centre and convenient radius, mark an arc, which intersects OA at X. 3. Taking X as the centre and the same radius, cut the previous arc at Y. Taking Y as the centre and the same radius, draw another arc intersecting the same arc at Z. 4. Taking Y and Z as centres and the same radius, draw two arcs intersecting each other at...\n\nQ5.    Construct with ruler and compasses, angles of following measures:\n\n(d)    120o\n\nThe steps of constructions are: 1. Draw a ray OA 2. Taking O as the centre and convenient radius, mark an arc, which intersects OA at P. 3. Taking P as the centre and same radius, cut previous arc at Q. Taking Q as the centre and the same radius cut the arc at S. Join OS. Thus, ∠AOS is the required angle of 120°.\n\nQ5.    Construct with ruler and compasses, angles of following measures:\n\n(c)    90o\n\nThe steps of constructions are: 1. Draw a ray OA 2. Taking O as the centre and convenient radius, mark an arc, which intersects OA at X. 3. Taking X as the centre and the same radius, cut the previous arc at Y. Taking Y as the centre and the same radius, draw another arc intersecting the same arc at Z. 4. Taking Y and Z as centres and the same radius, draw two arcs intersecting each other at...\n\nQ5.    Construct with ruler and compasses, angles of following measures:\n\n(b)    30o\n\nThe steps of constructions are: 1. Draw a ray OA. 2. Taking O as the centre and convenient radius, mark an arc, which intersects OA at P. 3. Taking P as the centre and the same radius, cut the previous arc at Q. Join OQ. Thus, ∠BOA is the required angle of 60°. 4. Put the pointer on P and mark an arc. 5. Put the pointer on Q and with the same radius, cut the previous arc at C. Thus, ∠COA is...\n\nQ5.    Construct with ruler and compasses, angles of following measures:\n\n(a) 60o\n\nThe steps of constructions are: 1. Draw a ray OA 2. Taking O as the centre and convenient radius, mark an arc, which intersects OA at P. 3. Taking P as the centre and the same radius, cut the previous arc at Q. Join OQ. Thus,∠BOA is the required angle of 60°\n\nQ4.    Draw an angle of measure 153° and divide it into four equal parts.\n\nThe steps of constructions are: (a) Draw a ray OA. (b) At O, with the help of a protractor, construct ∠AOB = 153 degrees. (c) Draw OC as the bisector of ∠AOB. (d) Again, draw OD as bisector of ∠AOC. (e) Again, draw OE as bisector of,∠BOC. (f) Thus, OC, OD, and OE divide ∠AOB into four equal parts.\n\nQ3.    Draw a right angle and construct its bisector.\n\nThe steps of construction: (a) Draw a line PQ and take a point O on it. (b) Taking O as the centre and convenient radius, draw an arc that intersects PQ at A and B. (c) Taking A and B as centres and radius more than half of AB, draw two arcs which intersect each other at C. (d) Join OC. Thus, ∠COQ is the required right angle. (e) Taking B and E as centre and radius more than half of BE, draw...\n\nQ2.    Draw an angle of measure 147° and construct its bisector.\n\nThe steps of constructions are: 1. Draw a line  OA. 2. Using protractor and centre 'O draw an angle  AOB =147°. 3. Now taking 'O' as the centre and any radius draws an arc that intersects  'OA' and 'OB'  at p  and q. 4. Now take p and q as centres and radius more than half of PQ, draw arcs. 5. Both the arcs intersect at 'R' 6. Join 'OR' and produce it. 7. 'OR' is the required bisector of...\n\nQ1.    Draw POQ of measure 75° and find its line of symmetry.\n\nHere, we will draw  using a protractor. We follow these steps: 1. Draw a ray OA. 2. Place the centre of the protractor on point O, and coincide line OA and Protractor line 3. Mark point B on 75 degrees. 4. Join OB Therefore  Now, we need to find its line of symmetry that is, we need to find its bisector. We follow these steps 1. Mark points C and D where the arc intersects OA and OB 2. Now,...\n\nQ9.    Draw any angle with vertex O. Take a point A on one of its arms and B on another such that OA = OB. Draw the perpendicular bisectors of and . Let them meet at P. Is PA = PB ?\n\nThe steps of constructions are: (i) Draw any angle with vertex O. (ii) Take a point A on one of its arms and B on another such that (iii) Draw perpendicular bisector of  and . (iv) Let them meet at P. Join PA and PB. With the help of divider, we obtained  that\n\nQ8.    Draw a circle of radius 4 cm. Draw any two of its chords. Construct the perpendicular bisectors of these chords. Where do they meet?\n\nThe steps of constructions are: (i) Draw the circle with O and radius 4 cm. (ii) Draw any two chords  and in this circle. (iii) Taking A and B as centres and radius more than half AB, draw two arcs which intersect each other at E and F. (iv) Join EF. Thus EF is the perpendicular bisector of chord . (v) Similarly draw GH the perpendicular bisector of chord . These two perpendicular bisectors...\n\nQ7.    Repeat Question 6, if AB happens to be a diameter.\n\nThe steps of constructions are: (i) Draw a circle with centre C and radius 3.4 cm. (ii) Draw its diameter  (iii) Taking A and B as centres and radius more than half of it, draw two arcs which intersect each other at P and Q. (iv) Join PQ. Then PQ is the perpendicular bisector of  We observe that this perpendicular bisector of  passes through the centre C of the circle.\n\nQ6.    Draw a circle with centre C and radius 3.4 cm. Draw any chord . Construct the perpendicular bisector of and examine if it passes through C.\n\nThe steps of constructions are: (i) Draw a circle with centre C and radius 3.4 cm. (ii) Draw any chord . (iii) Taking A and B as centres and radius more than half of , draw two arcs which cut each other at P and Q. (iv) Join PQ. Then PQ is the perpendicular bisector of . This perpendicular bisector of  passes through the centre C of the circle.\n\nQ5.    With PQ of length 6.1 cm as diameter, draw a circle.\n\nThe steps of constructions are: (i) Draw a line segment  = 6.1 cm. (ii) Draw the perpendicular bisector of PQ which cuts, it at O. Thus O is the mid-point of  . Taking O as the centre and OP or OQ as radius draw a circle where the diameter is the line segment .\nExams\nArticles\nQuestions" ]

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[ "## Modular arithmetic & the Chinese Rest Theorem", null, "modular arithmetic (sometimes called clock arithmetic) is a system of arithmetic for integers, where numbers ”wrap around” upon reaching a certain value—the modulus.\n\nAn example of this is the clock which starts repeating itself when it has reached 12. Therefore it is said that the clock follows an arithmetic modulo 12.\n\nEx: If you add 7 hours to 8 o’clock the result is not 15 since when thee clock reaches zero it starts over from zero once again so the result is 7+8=3 in this arithmetic.\n\nFor a positive integer n, two integers a and b are said to be congruent modulo n,\na=b mod(n)\nif the the remainder is the same when a and b are divided by n.\n\nif their difference a − b is an integer multiple of n. The number n is called the modulus of the congruence.", null, "e.g. 14\n\n14≡12 (mod 2)\n\nAn example of an application of this mathematical tool is the Chinese Remainder Theorem.", null, "## Om mattelararen\n\nLicentiate of Philosophy in atomic Physics Master of Science in Physics\nDet här inlägget postades i Algebra, Gymnasiematematik(high school math). Bokmärk permalänken." ]

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[ "# Cosmological horizon\n\nA cosmological horizon is a measure of the distance from which one could possibly retrieve information. This observable constraint is due to various properties of general relativity, the expanding universe, and the physics of Big Bang cosmology. Cosmological horizons set the size and scale of the observable universe. This article explains a number of these horizons.\n\n## Particle horizon\n\nThe particle horizon (also called the cosmological horizon, the comoving horizon, or the cosmic light horizon) is the maximum distance from which particles could have traveled to the observer in the age of the universe. It represents the boundary between the observable and the unobservable regions of the universe, so its distance at the present epoch defines the size of the observable universe. Due to the expansion of the universe it is not simply the age of the universe times the speed of light, as in the Hubble horizon, but rather the speed of light multiplied by the conformal time. The existence, properties, and significance of a cosmological horizon depend on the particular cosmological model.\n\nIn terms of comoving distance, the particle horizon is equal to the conformal time that has passed since the Big Bang, times the speed of light. In general, the conformal time at a certain time is given in terms of the scale factor $a$  by,\n\n$\\eta (t)=\\int _{0}^{t}{\\frac {dt'}{a(t')}}$\n\nor\n\n$\\eta (a)=\\int _{0}^{a}{\\frac {1}{a'H(a')}}{\\frac {da'}{a'}}$ .\n\nThe particle horizon is the boundary between two regions at a point at a given time: one region defined by events that have already been observed by an observer, and the other by events which cannot be observed at that time. It represents the furthest distance from which we can retrieve information from the past, and so defines the observable universe.\n\n## Hubble horizon\n\nHubble radius, Hubble sphere, Hubble volume, or Hubble horizon is a conceptual horizon defining the boundary between particles that are moving slower and faster than the speed of light relative to an observer at one given time. Note that this does not mean the particle is unobservable, the light from the past is reaching and will continue to reach the observer for a while. Also, more importantly, in the current expansion model e.g., light emitted from the Hubble radius will reach us in a finite amount of time. It is a common misconception that light from the Hubble radius can never reach us. It is true that particles on the Hubble radius recede from us with the speed of light, but the Hubble radius gets larger over time (because the Hubble parameter H gets smaller over time), so light emitted towards us from a particle on the Hubble radius will be inside the Hubble radius some time later. Only light emitted from the cosmic event horizon or further will never reach us in a finite amount of time.\n\nThe Hubble velocity of an object is given by Hubble's law,\n\n$v=xH$ .\n\nReplacing $v$  with speed of light $c$  and solving for proper distance ${\\textstyle x}$  we obtain the radius of Hubble sphere as\n\n$r_{HS}(t)={\\frac {c}{H(t)}}$ .\n\nIn an ever-accelerating universe, if two particles are separated by a distance greater than the Hubble radius, they cannot talk to each other from now on (as they are now, not as they have been in the past), However, if they are outside of each other's particle horizon, they could have never communicated. Depending on the form of expansion of the universe, they may be able to exchange information in the future. Today,\n\n$r_{HS}(t_{0})={\\frac {c}{H_{0}}}$ ,\n\nyielding a Hubble horizon of some 4.1 Gpc. This horizon is not really a physical size, but it is often used as useful length scale as most physical sizes in cosmology can be written in terms of those factors.\n\nOne can also define comoving Hubble horizon by simply dividing Hubble radius by the scale factor\n\n$r_{HS,comoving}(t)={\\frac {c}{a(t)H(t)}}$ .\n\n## Event horizon\n\nThe particle horizon differs from the cosmic event horizon, in that the particle horizon represents the largest comoving distance from which light could have reached the observer by a specific time, while the event horizon is the largest comoving distance from which light emitted now can ever reach the observer in the future. The current distance to our cosmic event horizon is about 5 Gpc (16 billion light years), well within our observable range given by the particle horizon.\n\nIn general, the proper distance to the event horizon at time $t$  is given by\n\n$d_{e}(t)=a(t)\\int _{t}^{t_{max}}{\\frac {cdt'}{a(t')}}$\n\nwhere $t_{max}$  is the time-coordinate of the end of the universe, which would be infinite in the case of a universe that expands forever.\n\nFor our case, assuming that dark energy is due to a cosmological constant, $d_{e}(t_{0})<\\infty$ .\n\n## Future horizon\n\nIn an accelerating universe, there are events which will be unobservable as $t\\rightarrow \\infty$  as signals from future events become redshifted to arbitrarily long wavelengths in the exponentially expanding de Sitter space. This sets a limit on the farthest distance that we can possibly see as measured in units of proper distance today. Or, more precisely, there are events that are spatially separated for a certain frame of reference happening simultaneously with the event occurring right now for which no signal will ever reach us, even though we can observe events that occurred at the same location in space that happened in the distant past. While we will continue to receive signals from this location in space, even if we wait an infinite amount of time, a signal that left from that location today will never reach us. Additionally, the signals coming from that location will have less and less energy and be less and less frequent until the location, for all practical purposes, becomes unobservable. In a universe that is dominated by dark energy which is undergoing an exponential expansion of the scale factor, all objects that are gravitationally unbound with respect to the Milky Way will become unobservable, in a futuristic version of Kapteyn's universe.\n\n## Practical horizons\n\nWhile not technically \"horizons\" in the sense of an impossibility for observations due to relativity or cosmological solutions, there are practical horizons which include the optical horizon, set at the surface of last scattering. This is the farthest distance that any photon can freely stream. Similarly, there is a \"neutrino horizon\" set for the farthest distance a neutrino can freely stream and a gravitational wave horizon at the farthest distance that gravitational waves can freely stream. The latter is predicted to be a direct probe of the end of cosmic inflation." ]

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[ "1 /*****************************************************************************\n2 **  This is part of the CTSim program\n3 **  Copyright (c) 1983-2001 Kevin Rosenberg\n4 **\n5 **  \\$Id\\$\n6 **\n7 **  This program is free software; you can redistribute it and/or modify\n8 **  it under the terms of the GNU General Public License (version 2) as\n9 **  published by the Free Software Foundation.\n10 **\n11 **  This program is distributed in the hope that it will be useful,\n12 **  but WITHOUT ANY WARRANTY; without even the implied warranty of\n13 **  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n14 **  GNU General Public License for more details.\n15 **\n16 **  You should have received a copy of the GNU General Public License\n17 **  along with this program; if not, write to the Free Software\n18 **  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA\n19 ******************************************************************************/\n22 #include \"ctsupport.h\"\n23 #include \"interpolator.h\"\n26 CubicPolyInterpolator::CubicPolyInterpolator (const double* const y, const int n)\n27   : m_pdY(y), m_n(n)\n28 {\n29   if (m_n < 2)\n30     sys_error (ERR_SEVERE, \"Too few points (%d) in CubicPolyInterpolator\", m_n);\n31 }\n33 CubicPolyInterpolator::~CubicPolyInterpolator ()\n34 {\n35 }\n38 double\n39 CubicPolyInterpolator::interpolate (double x)\n40 {\n41   int lo = static_cast<int>(floor(x)) - 1;\n42   int hi = lo + 3;\n44   if (lo < -1) {\n45 #ifdef DEBUG\n46     sys_error (ERR_WARNING, \"x=%f, out of range [CubicPolyInterpolator]\", x);\n47 #endif\n48     return (0);\n49   } else if (lo == -1)  // linear interpolate at between x = 0 & 1\n50     return m_pdY + x * (m_pdY - m_pdY);\n52   if (hi > m_n) {\n53 #ifdef DEBUG\n54     sys_error (ERR_WARNING, \"x=%f, out of range [CubicPolyInterpolator]\", x);\n55 #endif\n56     return (0);\n57   } else if (hi == m_n) {// linear interpolate between x = (n-2) and (n-1)\n58     double frac = x - (lo + 1);\n59     return m_pdY[m_n - 2] + frac * (m_pdY[m_n - 1] - m_pdY[m_n - 2]);\n60   }\n62   // Lagrange formula for N=4 (cubic)\n64   double xd_0 = x - lo;\n65   double xd_1 = x - (lo + 1);\n66   double xd_2 = x - (lo + 2);\n67   double xd_3 = x - (lo + 3);\n69   static double oneSixth = (1. / 6.);\n71   double y = xd_1 * xd_2 * xd_3 * -oneSixth * m_pdY[lo];\n72   y += xd_0 * xd_2 * xd_3 * 0.5 * m_pdY[lo+1];\n73   y += xd_0 * xd_1 * xd_3 * -0.5 * m_pdY[lo+2];\n74   y += xd_0 * xd_1 * xd_2 * oneSixth * m_pdY[lo+3];\n76   return (y);\n77 }\n81 CubicSplineInterpolator::CubicSplineInterpolator (const double* const y, const int n)\n82   : m_pdY(y), m_n(n)\n83 {\n84   // Precalculate 2nd derivative of y and put in m_pdY2\n85   // Calculated by solving set of simultaneous CubicSpline spline equations\n86   // Only n-2 CubicSpline spline equations, but able to make two more\n87   // equations by setting second derivative to 0 at ends\n89   m_pdY2 = new double [n];\n90   m_pdY2 = 0;   // second deriviative = 0 at beginning and end\n91   m_pdY2[n-1] = 0;\n93   double* temp = new double [n - 1];\n94   temp = 0;\n95   int i;\n96   for (i = 1; i < n - 1; i++) {\n97     double t = 2 + (0.5 * m_pdY2[i-1]);\n98     temp[i] = y[i+1] + y[i-1] - y[i] - y[i];\n99     temp[i] = (3 * temp[i] - 0.5 * temp[i-1]) / t;\n100     m_pdY2[i] = -0.5 / t;\n101   }\n103   for (i = n - 2; i >= 0; i--)\n104     m_pdY2[i] = temp[i] + m_pdY2[i] * m_pdY2[i + 1];\n106   delete temp;\n107 }\n109 CubicSplineInterpolator::~CubicSplineInterpolator ()\n110 {\n111   delete m_pdY2;\n112 }\n115 double\n116 CubicSplineInterpolator::interpolate (double x)\n117 {\n118   const static double oneSixth = (1. / 6.);\n119   int lo = static_cast<int>(floor(x));\n120   int hi = lo + 1;\n122   if (lo < 0 || hi >= m_n) {\n123 #ifdef DEBUG\n124     sys_error (ERR_SEVERE, \"x out of bounds [CubicSplineInterpolator::interpolate]\");\n125 #endif\n126     return (0);\n127   }\n129   double loFr = hi - x;\n130   double hiFr = 1 - loFr;\n131   double y = loFr * m_pdY[lo] + hiFr * m_pdY[hi];\n132   y += oneSixth * ((loFr*loFr*loFr - loFr) * m_pdY2[lo] + (hiFr*hiFr*hiFr - hiFr) * m_pdY2[hi]);\n134   return y;\n135 }" ]

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[ "Karamata's Products of two Complex Numbers\n\nAleksandar_M. Nikolić\n\nIn his paper Über die Anwendung der komplexen Zahlen in der Elementargeometrie}, Bilten na Društvoto na matematičarite i fizičarite od N.R. Makedonija, kn. I, Skopje, (1950), 55--81 (in Serbian, resume in German) and in the university textbook Complex Numbers, Belgrade, 1950, Jovan Karamata (1902--1967) maintains that, in planimetry, a complex number can assume the role of a vector, whereby in definition and solving of certain problems, there appears the product $\\overline{a} b=A+Bi$ where $\\overline{a}$ is the conjugated number of $a$, while $a$ and $b$ are complex numbers which correspond to free vectors $\\vec{a}$ and $\\vec{b}$. Using geometric interpretation of $a$ and $b$, Karamata expresses $A$ and $B$ as $A=|a||b|\\cos \\alpha$ and $B=|a||b|\\sin \\alpha$. In order to underline the geometric sense of these expressions, Karamata denotes them as $A=(a\\perp b)$, resp. $B=(a\\mid b)$ designating them orthogonal product'' and parallel product''. By means of these two symbols, considered as products, Karamata interprets those problems in planimetry which correspond to parallelity and orthogonality, and shows how they can be used in deriving of Papos-Pascal and Desargues propositions." ]

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[ "", null, "# Normal Distribution Formula\n\n## What is a Normal Distribution?\n\nThe normal distribution is the most significant probability distribution in statistics as it is suitable for various natural phenomena such as heights, measurement of errors, blood pressure, and IQ scores follow the normal distribution. The other names for the normal distribution are Gaussian distribution and the bell curve.\n\nThe normal distribution is a probability distribution that outlines how the values of a variable are distributed. Most of the observations clusters around the central peak and probabilities for values far away from mean fall off equally in both the directions in a normal distribution. Extreme values in both the tails of the distribution are uniformly unexpected.\n\nThe spread of the normal distribution is managed by the standard deviation . The smaller the standard deviation value in a normal distribution formula, the more concentrated the data.\n\nThe normal probability distribution formula is given as:\n\n$P (x) = \\frac{1}{\\sqrt{2 \\pi \\sigma^{2}}} e^{-\\frac{(x - \\mu)^{2}}{2 \\sigma^{2}}}$\n\nIn the above normal probability distribution formula.\n\nμ is the mean of the data.\n\nσ is the standard deviation of data.\n\nx is the normal random variable.\n\nIf (μ) = 0 and standard normal deviation is equal to 1, then distribution is said to be a normal distribution.\n\n### What is the Probability Distribution Formula?\n\nThere are generally two types of probability distribution formulas in a probability distribution. The two types of probability distribution formulas are normal probability distribution formulas (also known as the Gaussian distribution formulas) and binomial distribution formulas.\n\nThe value that assigns a probability to each outcome of a random experiment is considered as a probability distribution equation. In other words, it assigns the probability for each value of the random variable.\n\nProbability Distribution Formula\n\nThe two types of probability distribution formulas are:\n\n• The normal probability distribution formula.\n\n• The binomial Probability Distribution Formula.\n\n### Normal Probability Distribution Formula\n\nThe normal distribution is also known as the Gaussian distribution and it denotes the equation or graph which are bell-shaped.\n\nThe normal probability distribution formula is given by:\n\n$P (x) = \\frac{1}{\\sqrt{2 \\pi \\sigma^{2}}} e^{-\\frac{(x - \\mu)^{2}}{2 \\sigma^{2}}}$\n\nIn the above normal probability distribution formula.\n\nμ is the mean of the data.\n\nσ is the standard deviation of data.\n\nx is the normal random variable.\n\n### Binomial Probability Distribution Formula\n\nThese are the probabilities that appear when the event consists of n repeated trials and the results of each trial may or may not appear.\n\nThe binomial probability distribution formula is stated below:\n\nP ( r out of n) = n!/ r!(n-r)!\n\npr (1-p) n-r = n Cr\n\npr (1-p) n-r\n\nIn the above binomial distribution formula,\n\nN is the total number of events\n\nr is the total number of successful events\n\np is the probability of success on each trial.\n\nnCr = n!/r!(n-r)!\n\n1- p = probability of failure.\n\n### What is a Lognormal Distribution?\n\nIn probability theory, a log-normal distribution is considered as the continuous probability distribution of a random variable whose logarithm follows the pattern of normal distribution. It represents phenomena whose relative growth rate is independent of the size, which is true for most of the natural phenomena including the size of tissue and blood pressure, income distribution and then the length of the chess.\n\n### Lognormal Distribution Formula\n\nSome of the lognormal distribution formulas are given below:\n\nThe lognormal distribution formula for mean is given as\n\nm = eμ + σ²/2\n\nWhich implies that μ can be calculated from m:\n\nm = In m – 1/2 σ²\n\nThe above both equations are derived from the mean of the normal distribution.\n\nThe lognormal distribution formula for the median is given as\n\nMed [X] = eμ\n\nThe above equation is derived by placing the cumulative distribution values equal to 0.5 and solving the resulting equation\n\nThe lognormal distribution formula for mode is given as\n\nMode [X] = eμ - σ²\n\nThe above equation is derived by placing the probability distribution function values equal to 0 as the mode denotes the global maximum of distribution.\n\nThe lognormal distribution formula for variance is given as:\n\nVar  [X] = (eσ² -1) e2μ + σ²,\n\nWhich can also be represented as (eσ² -1) m2 , where m denotes the mean of the distribution.\n\n### Gaussian Distribution Formula\n\nGaussian distribution is a common continuous probability distribution. The Gaussian Distribution Formulas are significant in statistics and are primarily used in natural and social science to denote real-valued random variables.\n\nThe probability density function formula for Gaussian distribution formula is given as:\n\n$f( x,\\mu, \\sigma) = \\frac{1}{\\sigma \\sqrt{2 \\pi}} e^{-\\frac{(x - \\mu)^{2}}{2 \\sigma^{2}}}$\n\nIn the above Gaussian Distribution Formula,\n\nμ is the mean of the data.\n\nσ is the standard deviation of data.\n\nx is the normal random variable.\n\n### Standard Normal Distribution\n\nThe standard normal distribution is a type of normal distribution. It mostly appears when a normal random variable has a mean value equal to 0 and value of standard deviation is equal to 1. The random variable of a standard normal distribution is considered as a standard score or z-score. Each normal random variable such as X can easily be converted into a z-score using the normal distribution z formula.\n\n$z = \\frac{(X - \\mu)}{\\sigma}$\n\nIn the above normal distribution z formula,\n\nX is a normal random variable.\n\nΜ is mean of data.\n\nσ is the standard deviation of the data.\n\n### Solved Examples\n\n1. Calculate the probability of normal distribution with the population mean 2, standard deviation 3 or random variable 5.\n\nSolution:\n\nx = 5\n\nMean = μ = 2\n\nStandard Deviation = σ = 3\n\nWe will solve the questions with the help of the above normal probability  distribution formula:\n\n$P (x) = \\frac{1}{\\sqrt{2 \\pi \\sigma^{2}}} e^{-\\frac{(x - \\mu)^{2}}{2 \\sigma^{2}}}$\n\n$P (x) = \\frac{1}{\\sqrt{2 \\times 3.14 \\times 3^{2}}} e^{-\\frac{(5 - 2)^{2}}{2 \\times 3^{2}}}$\n\n= $\\frac{1}{\\sqrt{56.52}} e^{-\\frac{9}{2 \\times 9}}$\n\n= $\\frac{1}{7.518} e^{-\\frac{1}{2}}$\n\n= $\\frac{0.6065}{7.518}$\n\n= $0.0807$\n\n2. An average electric bulb lasts for 300 days with a standard deviation of 50 days. Assume that bulb life is normally distributed, what is the probability that the electric bulb will last at most 365 days.\n\nSolution :\n\nGiven, Mean score = 300 days\n\nStandard deviation = 50 days\n\nWe need to determine the cumulative probability that bulb life is less than or equals to 365 days.\n\nWe have the following information:\n\nThe  normal random variable value is given as 365 days.\n\nThe mean is equivalent to 300 days.\n\nThe standard deviation is equivalent to 50 days.\n\nHene, the answer of the question is P (x < 365) = 0.90.\n\nIt states that there is a 90% probability that an electric bulb will burn out within 365 days.\n\n### Quiz Time\n\n1. The shape of the normal curve is\n\n1. Bell shape\n\n2. Flat\n\n3. Circular\n\n4. Spiked\n\n2. The area under a standard normal curve is\n\n1. 0\n\n2. 1\n\n3. Not defined\n\n1. What are the Common Properties for all Forms of Normal Distribution?\n\nAns: In Spite of different shapes, all forms of normal distribution have the following characteristics properties.\n\n• The mean, median, and mode values are equal.\n\n• Half of the population is less than mean and half is greater than mean.\n\n• They are all symmetric. The normal distribution cannot model skewed distribution.\n\n• The Empirical rule regulates the proportion of values that lies within a certain distance from the mean.\n\n• The total area under the curve is 1.\n\n• The bell curve is symmetric at the center ( i.e. around the mean, μ).\n\n2. Why is the Normal Distribution Important?\n\nAns: The normal distribution uses a continuous probability distribution that is symmetrical on a shape on both sides of the mean, so the right side of the image is a mirror image of the left side. The area under the normal distribution curve denotes probability and the total area under the curve is equal to one. The normal distribution is the most important probability distribution in statistics because various continuous data and psychology in nature exhibit this bell -shaped curve when collected and graphed.\n\nFor example, if we randomly sample 100 individuals, we would expect to see a normal distribution curve of various continuous variables such as IQ, height, weight, and blood pressure." ]

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[ "[solution]: 1 Final Exam (Summer II 2016) Note: All pages of the exam\n\nMore Details:\n\nI need help with the document attached. The code used in the assignment is Python.\n\n1\n\nFinal Exam (Summer II 2016)\n\nNote: All pages of the exam (including blank pages) must be submitted. Include\n\nfull name on all pages of the exam. Students are allowed a one-page cheat sheet.\n\nFull Name: ______________________\n\nDate ___________\n\n1. (for loop, accumulator pattern) Write a loop that will repeatedly request whole numbers\n\nfrom the user. When the user enters 0, a report should be printed saying how many\n\nnumbers were entered and their sum. The final 0 should not be counted as one of the\n\nnumbers. (5pts)\n\n2\n\n2. (for loop, accumulator pattern) Write a function emphasize() that takes as an input a\n\nstring s and returns (not prints) a string with spaces inserted between adjacent letters.\n\nFor example, if the function is called with the string ?Very important? it should return 'V\n\ne r y i m p o r t a n t'\n\n(5pts)\n\n3\n\n3. (function, while loop and break statement) Write a program that contains a function\n\nfirstNeg() that takes a (possibly empty) sequence (list or tuple) of numbers as input,\n\nfinds the first occurrence of a negative number, and returns the index of that number. If\n\nthere is no negative number or the list is empty, the function should return -1. Store the\n\nvalue returned by the function into a variable (searchResult) and print the value of the\n\nvariable.\n\nExample: if firstNeg() is called as follows: firstNeg([2, 3, -1, 4, -2]) it returns 2. If\n\nfirstNeg() is called as follows: firstNeg(), it returns -1. If firstNeg is called as follows:\n\nfirstNeg([2, 3, 1, 4, 2]), it returns -1.\n\n(10pts)\n\n4\n\n4. (Set) Two lists are defined as follow: lst1 = [3, 5, 1, 7, 9, 4] and lst2 = [4, 2, 3, 9, 10]\n\n(15pts)\n\na. Write python expressions that convert lst1 and lst2 into sets (setA and setB) (2pts)\n\nb. Write a function, intersect(), that takes two sets as arguments and returns the\n\nintersect of the two sets as a list. (5pts)\n\n5\n\nc. If function, intersect(), is called with setA and setB as arguments, which values\n\nwill the returned list contain (2pts)\n\nd. Write a python expression that determines the union of the setA and setB (2pts)\n\ne. Write a python expression that adds 25 to setA (2pts)\n\nf. Write a python expression that empties setB (2pts)\n\n5. (Dictionary, while loop, two-way if condition) Write a program that stores students\n\ninformation (user id, first and last name, email and score) in a dictionary. The program\n\nwill generate a 6-digit random number as the key (user id) for each student. The student\n\nname, email and score will be assigned as value to the key. Before storing information\n\ninto the dictionary, the program must first verify the key does not exist in the dictionary.\n\nIf the key already exists, the program should print a message (?Key is already assigned.\n\nCurrent value will be overwritten?), and overwrite the existing value. This program\n\nshould use an infinite loop to continue prompting the user for information. The program\n\nwill prompt the user for first and last name, email and test score. (10pts)\n\n6\n\n7\n\n6. Write python expressions for the following (10pts)\n\na. Create an empty set, setC\n\nb. Add two numeric values (10 and 20) to setC\n\nc. Create an empty dictionary, names\n\nd. Add key/value pair 1 and ?John? and key/value pair 2 and ?Joe? to names\n\n7. Use set and list operations below (5pts)\n\nNote: A set cannot contain duplicates\n\nvalues = [23, 19, 18, 21, 18, 20, 21, 23, 22, 23, 23, 19, 20]\n\na. Convert values to a set, setD. Print the values in setD\n\nb. Write a python expression that sums the values in setD\n\n8. What does Unicode use to represent characters in memory (5pts)\n\n9. (format function) Write python expressions (at most two expressions) that print the\n\nbinary, hex, and decimal values of the letter Z. . Use one expression to return the ASCII\n\nvalue of Z and a second expression that prints the binary, hex, and decimal representation\n\nof the ASCII value of Z (5pts)\n\n8\n\n10. (Character Encoding) Use python expressions to complete the following (5pts)\n\na. Display the character for ASCII value 95\n\nb. Display the ASCII value for the letter B\n\n11. Review the code below and answer the questions that follow (10pts):\n\nNote: This function finds the factorial of x\n\ndef factExample(x):\n\nif x &lt; 1:\n\nreturn 1\n\nelse:\n\nglobal aFact\n\naFact = factExample(x ? 1) * x\n\nreturn aFact\n\naFact = 1\n\nprint(aFact)\n\nx=5\n\nprint(x)\n\naFact1 = factExample(x)\n\nprint(aFact1)\n\nprint(aFact)\n\na.\n\nb.\n\nc.\n\nd.\n\ne.\n\nf.\n\nWhat is the scope of the function factExample()\n\nWhat is the scope of the variable aFact\n\nWhat is the scope of the variable x in the function\n\nWhat is the scope of the variable x outside of the function\n\nWhat is the value of the variable aFact before the function call\n\nWhat is the value of the variable aFact after the function call\n\n9\n\ng. What is the value of the variable aFact1\n\nh. What is the value of the variable x (defined outside of the function) after the\n\nfunction call\n\ni. What is the value of the variable x outside of the function\n\nj. Which statement in the code above contains a function call\n\n12. (try/except) Write a program that prompts for two nonnegative numbers (x and y) and\n\nperforms division operation on the values. The value of x represents the numerator and\n\nthe value of y represents the denominator. The program should use try/except to handle\n\nValueError and ZeroDivisionError exceptions for non-numeric values or if the\n\ndenominator is 0 (zero). (10pts)\n\n13. A recursive function that terminates must always have two characteristics/properties.\n\nWhat are they? (5pts)\n\n10\n\nSolution details:\nSTATUS\nQUALITY\nApproved\n\nThis question was answered on: Dec 18, 2020", null, "Solution~00031148133980.zip (25.37 KB)\n\nThis attachment is locked\n\nWe have a ready expert answer for this paper which you can use for in-depth understanding, research editing or paraphrasing. You can buy it or order for a fresh, original and plagiarism-free copy (Deadline assured. Flexible pricing. TurnItIn Report provided)", null, "Pay using PayPal (No PayPal account Required) or your credit card . All your purchases are securely protected by .\n\nSTATUS\n\nQUALITY\n\nApproved\n\nDec 18, 2020\n\nEXPERT\n\nTutor", null, "" ]

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[ "Home » Blog » Prove an identity for integrals of trig functions\n\n# Prove an identity for integrals of trig functions\n\nFor", null, "prove", null, "Proof. First it will help if we simplify the integral:", null, "Then we use the method of substitution, letting", null, "So we have,", null, "1.", null, "Ketan says:\n\nI was thinking of doing something different:\n\nNote that 1/2 * integral of cos(x) from 0 to pi/2 = integral of cos(x) * sin(x) from 0 to pi/2 = 1/2 (a)\n\nNow cos^m(x) / (2^m) – cos^m(x) * sin^m(x) = (cos(x) / 2 – cos(x) * sin(x)) * cos^(m-1) (x) * (1/(2^m-1) + sin(x)/(2^m-2) + sin^2(x)/(2^m-3) + … + sin^(m-1) (x)), where cos^(m-1) (x) and\n(1/(2^m-1) + sin(x)/(2^m-2) + sin^2(x)/(2^m-3) + … + sin^(m-1) (x)) are not negative on the interval from 0 to pi/2.\n\nIf one tries to evaluate the integral of cos^m(x) / (2^m) – cos^m(x) * sin^m(x) then one can use the cauchy-mean value theorem and take out both cos^(m-1) (x) and\n(1/(2^m-1) + sin(x)/(2^m-2) + sin^2(x)/(2^m-3) + … + sin^(m-1) (x)) of the integral, by having them evaluated at some point c, between 0 and pi/2:\n\nintegral of cos^m(x) / (2^m) – cos^m(x) * sin^m(x) from 0 to pi/2\n= cos^(m-1) (c) * (1/(2^m-1) + sin(c)/(2^m-2) + sin^2(c)/(2^m-3) + … + sin^(m-1) (c)) * integral of cos(x) / 2 – cos(x) * sin(x) from 0 to pi/2. From (a), we know that since the integral of cos(x) from 0 to pi/2 = integral of cos(x) * sin(x) from 0 to pi/2 that this is equal to 0, and hence:\n\nintegral of cos^m(x) / (2^m) from 0 to pi/2 = integral of cos^m(x) * sin^m(x) from 0 to pi/2.\n\n•", null, "Eren says:\n\nBeautiful!" ]

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[ "# A bijection between $X \\times (Y \\times Z)$ and $(X \\times Y) \\times Z$\n\nCan someone help me prove that $$X \\times (Y \\times Z) \\sim (X \\times Y) \\times Z$$ I know that there is supposed to be a bijection between these two. The first one will contain elements like $(x, (y, z))$ and the second one $((x, y) z)$. I just need some instructions, I believe I can manage on my own from that.\n\n• The way you've written the elements of each set should be really suggestive of what the bijection could be. – user296602 Jan 9 '16 at 17:10\n• If you only need a bijection between sets (you didn't write what $X,Y, Z$ are), why not $(x,(y,z))\\mapsto ((x,y),z)$? – user302982 Jan 9 '16 at 17:12\n• yeah, but how am i going to proove that that's in fact a bijection? – Stefan Jan 9 '16 at 17:13\n• Well, you need to prove it's injective and surjective. Here's an element of (X x Y) x Z: ((a, b), c). Can you write down something that is mapped to this element? – user296602 Jan 9 '16 at 17:19\n• @DietrichBurde A stray * from **bold formatting** – user147263 Jan 9 '16 at 17:54\n\n$$X \\times (Y \\times Z) = \\{(x,(y,z)) \\mid x \\in X, y \\in Y, z \\in Z\\}$$ $$(X \\times Y) \\times Z = \\{ ((x,y),z) \\mid x \\in X, y \\in Y, z \\in Z \\}$$\nSo consider the maps $(x,(y,z)) \\mapsto ((x,y),z)$ and $((x,y),z) \\mapsto (x,(y,z))$. One immediately verifies that these are indeed functions and they are clearly inverses, thus bijections." ]

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[ "", null, "# Numeral Systems Numeral System Positional systems Decimal Binary\n\n• Slides: 32", null, "Numeral Systems -Numeral System -Positional systems -Decimal -Binary -Octal Subjects:", null, "Introduction A numeral system (or system of numeration) is a writing system for expressing numbers, that is a mathematical notation for representing numbers of a given set, using digits or other symbols in a consistent manner. It can be seen as the context that allows the symbols \"11\" to be interpreted as the binary symbol for three, the decimal symbol for eleven, or a symbol for other numbers in different bases. Ideally, a numeral system will: q. Represent a useful set of numbers (e. g. all integers, or rational numbers); q. Give every number represented a unique representation (or at least a standard representation); q. Reflect the algebraic and arithmetic structure of the numbers. ü A number is a mathematical object used to count, label, and measure. ü A digit is a symbol (a numeral symbol such as \"3\" or \"7\") used in combinations (such as \"37\") to represent numbers in positional numeral systems. The ten digits of the Western Arabic numerals, in order of value", null, "Positional notation or place-value notation is a method of representing or encoding numbers. Positional notation is distinguished from other notations (such as Roman numerals) for its use of the same symbol for the different orders of magnitude (for example, the \"ones place\", \"tens place\", \"hundreds place\"). This greatly simplified arithmetic and led to the quick spread of the notation across the world. With the use of a radix point, the notation can be extended to include fractions and the numeric expansions of real numbers. The Hindu-Arabic numeral system is an example for a positional notation, based on the number 10. ! In a positional base-b numeral system (with b a natural number greater than 1 known as the radix), b basic symbols (or digits) corresponding to the first b natural numbers including zero are used. To generate the rest of the numerals, the position of the symbol in the figure is used. The symbol in the last position has its own value, and as it moves to the left its value is multiplied by b.", null, "Positional notation For example, in the decimal system (base 10), the numeral 4327 means (4× 103) + (3× 102) + (2× 101) + (7× 100), noting that 100 = 1. In general, if b is the base, one writes a number in the numeral system of base b by expressing it in the form anbn + an − 1 bn − 1 + an − 2 bn − 2 +. . . + a 0 b 0 and writing the enumerated digits anan − 1 an − 2. . . a 0 in descending order. The digits are natural numbers between 0 and b − 1, inclusive. If a text (such as this one) discusses multiple bases, and if ambiguity exists, the base (itself represented in base 10) is added in subscript to the right of the number, like this: numberbase. Unless specified by context, numbers without subscript are considered to be decimal.", null, "Positional notation By using a dot to divide the digits into two groups, one can also write fractions in the positional system. For example, the base-2 numeral 10. 11 denotes 1× 21 + 0× 20 + 1× 2− 1 + 1× 2− 2 = 2. 75. In general, numbers in the base b system are of the form: The numbers bk and b−k are the weights of the corresponding digits. The position k is the logarithm of the corresponding weight w, that is The number of tally marks required in the unary numeral system for describing the weight would have been w. In the positional system, the number of digits required to describe it is only for .", null, "Positional notation E. g. to describe the weight 1000 then four digits are needed since The number of digits required to describe the position is (in positions 1, 100, . . . only for simplicity in the decimal example). Note that a number has a terminating or repeating expansion if and only if it is rational; this does not depend on the base. A number that terminates in one base may repeat in another (thus 0. 310 = 0. 010011001. . . 2). An irrational number stays aperiodic (with an infinite number of non-repeating digits) in all integral bases. Thus, for example in base 2, π=3. 1415926. . . 10 can be written as the aperiodic 11. 001001000011111. . . 2.", null, "Computer Number Systems Representing Information in Computers All the different types of information in computers can be represented using binary code: q. Numbers; q. Letters of the alphabet and punctuation marks; q. Microprocessor instruction; q. Graphics/Video; q. Sound. Why binary? q. The original computers were designed to be high-speed calculators. q. The designers needed to use the electronic components available at the time. q. The designers realized they could use a simple coding system - the binary system - to represent their numbers. Bits and Bytes q. A binary digit is a single numeral in a binary number. q. Each 1 and 0 in the number below is a binary digit: 1 0 0 1 0 1 q. The term “binary digit” is commonly called a “bit. ” q. Eight bits grouped together is called a “byte. ”", null, "Computer Number Systems are: q. Decimal Numbers q. Binary Numbers q. Hexadecimal Numbers The study of number systems is useful due to the fact that number systems other than the familiar decimal (base 10) number system are used in the computer field. Digital computers internally use the binary (base 2) number system to represent data and perform arithmetic calculations. The binary number system is very efficient for computers, but not for humans. Representing even relatively small numbers with the binary system requires working with long strings of ones and zeroes. The hexadecimal (base 16) number system (often called \"hex\" for short) provides us with a shorthand method of working with binary numbers. One digit in hex corresponds to four binary digits (bits), so the internal representation of one byte can be represented either by eight binary digits or two hexadecimal digits. Less commonly used is the octal (base 8) number system, where one digit in octal corresponds to three binary digits (bits).", null, "Decimal Numbers Decimal Number System q. The prefix “deci-” stands for 10. q. The decimal number system is a Base 10 number system: ü There are 10 symbols that represent quantities: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ü Each place value in a decimal number is a power of 10.", null, "Binary Numbers The Binary Number System q. The prefix “bi-” stands for 2. q. The binary number system is a Base 2 number system: ü There are 2 symbols that represent quantities: 0, 1 ü Each place value in a binary number is a power of 2. From right to left, the successive positions of the binary number are weighted 1, 2, 4, 8, 16, 32, 64, etc. A list of the first several powers of 2 follows:", null, "Binary Numbers For reference, the following table shows the decimal numbers 0 through 31 with their binary equivalents:", null, "Binary Numbers 1011 + Binary Decimal", null, "Converting a Binary Number to a Decimal Number To determine the value of a binary number (1001, for example), we can expand the number using the positional weights as follows: Here's another example to determine the value of the binary number 1101010:", null, "Converting a Decimal Number to a Binary Number To convert a decimal number to its binary equivalent, the remainder method can be used. (This method can be used to convert a decimal number into any other base. ) The remainder method involves the following four steps: ! 1) Divide the decimal number by the base (in the case of binary, divide by 2). 2) Indicate the remainder to the right. 3) Continue dividing into each quotient (and indicating the remainder) until the divide operation produces a zero quotient. 4) The base 2 number is the numeric remainder reading from the last division to the first (if you start at the bottom, the answer will read from top to bottom).", null, "Converting a Decimal Number to a Binary Number Example 1: Convert the decimal number 99 to its binary equivalent: 1) Divide 2 into 99. The quotient is 49 with a remainder of 1; indicate the 1 on the right. 2) Divide 2 into 49 (the quotient from the previous division). The quotient is 24 with a remainder of 1, indicated on the right. 3) Divide 2 into 24. The quotient is 12 with a remainder of 0, as indicated. 4) Divide 2 into 12. The quotient is 6 with a remainder of 0, as indicated. 5) Divide 2 into 6. The quotient is 3 with a remainder of 0, as indicated. 6) Divide 2 into 3. The quotient is 1 with a remainder of 1, as indicated. 7) Divide 2 into 1. The quotient is 0 with a remainder of 1, as indicated. Since the quotient is 0, stop here. The base 2 number is the numeric remainder reading from the last division to the first", null, "Binary Addition Adding two binary numbers together is easy, keeping in mind the following four addition rules: ! Note in the last example that it was necessary to \"carry the 1\". After the first two binary counting numbers, 0 and 1, all of the binary digits are used up. In the decimal system, we used up all the digits after the tenth counting number, 9. The same method is used in both systems to come up with the next number: place a zero in the \"ones\" position and start over again with one in the next position on the left. In the decimal system, this gives ten, or 10. In binary, it gives , which is read \"one-zero, base two. \"", null, "Binary Addition Consider the following binary addition problems and note where it is necessary to carry the 1:", null, "Subtraction Using Complements Subtraction in any number system can be accomplished through the use of complements. A complement is a number that is used to represent the negative of a given number. When two numbers are to be subtracted, the subtrahend can either be subtracted directly from the minuend (as we are used to doing in decimal subtraction) or, the complement of the subtrahend can be added to the minuend to obtain the difference. When the latter method is used, the addition will produce a high-order (leftmost) one in the result (a \"carry\"), which must be dropped. This is how the computer performs subtraction: it is very efficient for the computer to use the same \"add\" circuitry to do both addition and subtraction; thus, when the computer \"subtracts\", it is really adding the complement of the subtrahend to the minuend. To understand complements, consider a mechanical register, such as a car mileage indicator, being rotated backwards. A five-digit register approaching and passing through zero would read as follows:", null, "Subtraction Using Complements It should be clear that the number 99998 corresponds to -2. Furthermore, if we add and ignore the carry to the left, we have effectively formed the operation of subtraction: 5 - 2 = 3. The number 99998 is called the ten's complement of 2. The ten's complement of any decimal number may be formed by subtracting each digit of the number from 9, then adding 1 to the least significant digit of the number formed. In the example above, subtraction with the use of complements was accomplished as follows: (1) We were dealing with a five-digit subtrahend that had a value of 00002. First, each digit of the subtrahend was subtracted from 9 (this preliminary value is called the nine's complement of the subtrahend):", null, "Subtraction Using Complements (2) Next, 1 was added to the nine's complement of the subtrahend (99997) giving the ten's complement of subtrahend (99998): (3) The ten's complement of the subtrahend was added to the minuend giving 100003. The leading (carried) 1 was dropped, effectively performing the subtraction of 00005 - 00002 = 00003. The answer can be checked by making sure that 2 + 3 = 5.", null, "Binary Subtraction We will use the complement method to perform subtraction in binary and in the sections on octal and hexadecimal that follow. As mentioned above, the use of complemented binary numbers makes it possible for the computer to add or subtract numbers using only circuitry for addition - the computer performs the subtraction of A - B by adding A + (two's complement of B) and then dropping the carried 1. The steps for subtracting two binary numbers are as follows: (1) Compute the one's complement of the subtrahend by subtracting each digit of the subtrahend by 1. A shortcut for doing this is to simply reverse each digit of the subtrahend - the 1's become 0's and the 0's become 1's. (2) Add 1 to the one's complement of the subtrahend to get the two's complement of the subtrahend. (3) Add the two's complement of the subtrahend to the minuend and drop the highorder 1. This is your difference.", null, "Binary Subtraction Example 1: Compute 11010101 – 1001011 (1) Compute the one's complement of 1001011 by subtracting each digit from 1 (note that a leading zero was added to the 7 -digit subtrahend to make it the same size as the 8 -digit minuend): (Note that the one's complement of the subtrahend causes each of the original digits to be reversed. ) (2) Add 1 to the one's complement of the subtrahend, giving the two's complement of the subtrahend:", null, "Binary Subtraction (3) Add the two's complement of the subtrahend to the minuend and drop the highorder 1, giving the difference: So 11010101 – 1001011 = 10001010. The answer can be checked by making sure that 1001011 + 10001010 = 11010101.", null, "The Octal Number System The same principles of positional number systems we applied to the decimal and binary number systems can be applied to the octal number system. However, the base of the octal number system is eight, so each position of the octal number represents a successive power of eight. From right to left, the successive positions of the octal number are weighted 1, 8, 64, 512, etc. A list of the first several powers of 8 follows:", null, "The Octal Number System For reference, the following table shows the decimal numbers 0 through 31 with their octal equivalents:", null, "Converting an Octal Number to a Decimal Number To determine the value of an octal number (3678, for example), we can expand the number using the positional weights as follows: Here's another example to determine the value of the octal number 16018:", null, "Converting a Decimal Number to an Octal Number To convert a decimal number to its octal equivalent, the remainder method (the same method used in converting a decimal number to its binary equivalent) can be used. To review, the remainder method involves the following four steps: ! (1) Divide the decimal number by the base (in the case of octal, divide by 8). (2) Indicate the remainder to the right. (3) Continue dividing into each quotient (and indicating the remainder) until the divide operation produces a zero quotient. (4) The base 8 number is the numeric remainder reading from the last division to the first (if you start at the bottom, the answer will read from top to bottom).", null, "Converting a Decimal Number to an Octal Number Example 1: Convert the decimal number 46510 to its octal equivalent: 1) Divide 8 into 465. The quotient is 58 with a remainder of 1; indicate the 1 on the right. 2) Divide 8 into 58 (the quotient from the previous division). The quotient is 7 with a remainder of 2, indicated on the right. 3) Divide 8 into 7. The quotient is 0 with a remainder of 7, as indicated. Since the quotient is 0, stop here. The answer, reading the remainders from top to bottom, is 721, so 46510 = 7218.", null, "Octal Addition Octal addition is performed just like decimal addition, except that if a column of two addends produces a sum greater than 7, you must subtract 8 from the result, put down that result, and carry the 1. Remember that there are no such digits as \"8\" and \"9\" in the octal system, and that 810 = 108, 910 = 118, etc. Example 1: Add 5438 + 1218 (no carry required): Example 2: Add 76528 + 45748 (carries required):", null, "Octal Subtraction We will use the complement method to perform octal subtraction. The steps for subtracting two octal numbers are as follows: (1) Compute the seven's complement of the subtrahend by subtracting each digit of the subtrahend by 7. (2) Add 1 to the seven's complement of the subtrahend to get the eight's complement of the subtrahend. (3) Add the eight's complement of the subtrahend to the minuend and drop the highorder 1. This is your difference. Example 1: Compute 75268 - 31428 (1) Compute the seven's complement of 31428 by subtracting each digit from 7:", null, "Octal Subtraction (2) Add 1 to the seven's complement of the subtrahend, giving the eight's complement of the subtrahend: (3) Add the eight's complement of the subtrahend to the minuend and drop the highorder 1, giving the difference: So 75268 - 31428 = 43648. The answer can be checked by making sure that 31428 + 43648 = 75268.", null, "Thanks for attention" ]

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[ "Cody\n\n# Problem 1433. Mirror Image matrix across anti-diagonal\n\nSolution 232886\n\nSubmitted on 19 Apr 2013 by Dan\n• Size: 11\n• This is the leading solution.\nThis solution is locked. To view this solution, you need to provide a solution of the same size or smaller.\n\n### Test Suite\n\nTest Status Code Input and Output\n1   Pass\n%% x = 3; y_correct = [1 2 3;2 3 2; 3 2 1]; assert(isequal(mirror_anti_diag(x),y_correct))\n\n2   Pass\n%% x = 1; y_correct = ; assert(isequal(mirror_anti_diag(x),y_correct))\n\n3   Pass\n%% x = 4; y_correct = [1 2 3 4; 2 3 4 3; 3 4 3 2; 4 3 2 1 ]; assert(isequal(mirror_anti_diag(x),y_correct))\n\n### Community Treasure Hunt\n\nFind the treasures in MATLAB Central and discover how the community can help you!\n\nStart Hunting!" ]

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[ "", null, "# Bihar Board Matric (Class 10) Mathematics Syllabus For Year 2021- 22\n\nBihar Matric Board Maths Syllabus for Class 10, helps students to prepare more effectively for the exams. Mathematics as a subject is deep and vast. Having knowledge of its course pertaining to the examination point of view becomes a necessity, hence the use of the Class 10 Maths Syllabus of Bihar Board. Students can refer to the Bihar Board 1oth Maths Syllabus to plan their studies and exam preparations.\n\nKnowing the 10th Maths syllabus of the state board Bihar works as an effective planner for teachers as well as students. Bihar Syllabus 2021-22 for Matric gives a complete overview of the topics covered in the subject. Given below is the latest  details of the Matric syllabus for Class 10, as per Bihar board.\n\n## Introduction to BSEB Matric Maths Syllabus:\n\nThe syllabus of BSEB Matric Maths, prepared in a well-structured manner consists of  topics covered under units such as\n\n1 Number System\n\n2 Algebra,\n\n3 Trigonometry\n\n4 Coordinate Geometry\n\n5 Geometry\n\n6 Mensuration\n\n7 Statistics and Probability\n\nAround 14 chapters are covered spread across these topics mentioned. The chapters listed in the syllabus are mentioned below in detail.\n\n### BSEB Matric Board Maths Syllabus for 10th\n\n• Unit I: Number System\n• Chapter 1-Real Numbers: Euclid’s division lemma, Fundamental Theorem of Arithmetic – statements after reviewing work done earlier and after illustrating and motivating through examples. Proofs of results – irrationality of √2 , 3, 5 , decimal expansions of rational numbers in terms of terminating/non-terminating recurring decimals.\n• Unit II: Algebra\n• Chapter 2- Polynomials: Zeros of a polynomial. Relationship between zeros and coefficients of a polynomial with particular reference to quadratic polynomials. Statement and simple problems on division algorithms for polynomials with real coefficients.\n• Chapter 3 -. Pair of Linear Equations in Two Variables. Geometric representation of different possibilities of solutions/inconsistency. Algebraic conditions for number of solutions. Solution of pair of linear equations in two variables algebraically – by substitution, by elimination and by cross multiplication. Simple situational problems must be included. Simple problems on equations reducible to linear equations may be included.\n• Chapter 4-Quadratic Equations: Standard form of a quadratic equation ax2 + bx + c = 0, (a ≠ 0). Solution of quadratic equations (only real roots) by factorization and by completing the square, i.e., by using quadratic formula. Relationship between discriminant and nature of roots. Problems related to day-to-day activities to be incorporated.\n• Chapter 5- Arithmetic Progressions (AP) : Motivation for studying AP. Derivation of standard results of finding the nth term and sum of first n terms.\n• Unit III:  Trigonometry\n• Chapter 6- Introduction to Trigonometry : Trigonometric ratios of an acute angle of a right-angled triangle. Proof of their existence (well defined); motivate the ratios, whichever are defined at 0° and 90°. Values (with proofs) of the trigonometric ratios of 30°, 45° and 60°. Relationships between the ratios. Trigonometric Identities: Proof and applications of the identity sin2A + cos2 A = 1. Only simple identities to be given. Trigonometric ratios of complementary angles.\n• Chapter 7-Heights and Distances:Simple and believable problems on heights and distances. Problems should not involve more than two right triangles. Angles of elevation/depression should be only 30°, 45° and 60°.\n• Unit IV: Coordinate Geometry\n• Chapter 8-Lines (In two-dimensions):Review the concepts of coordinate geometry done earlier including graphs of linear equations. Awareness of geometrical representation of quadratic polynomials. Distance between two points and section formula (internal). Area of a triangle.\n• Unit V: Geometry\n• Chapter 9-Triangles :Definitions, examples, counter examples of similar triangles.\n1. (Prove) If a line is drawn parallel to one side of a triangle to intersect the other two sides in distinct points, the other two sides are divided in the same ratio.\n2. (Motivate) If a line divides two sides of a triangle in the same ratio, the line is parallel to the third side.\n3. (Motivate) If in two triangles, the corresponding angles are equal, their corresponding sides are proportional and the triangles are similar.\n4. (Motivate) If the corresponding sides of two triangles are proportional, their corresponding angles are equal and the two triangles are similar.\n5. (Motivate) If one angle of a triangle is equal to one angle of another triangle and the sides including these angles are proportional, the two triangles are similar.\n6. (Motivate) If a perpendicular is drawn from the vertex of the right angle to the hypotenuse, the triangles on each side of the perpendicular are similar to the whole triangle and to each other.\n7. (Prove) The ratio of the areas of two similar triangles is equal to the ratio of the squares on their corresponding sides.\n8. (Prove) In a right triangle, the square on the hypotenuse is equal to the sum of the squares on the other two sides.\n9. (Prove) In a triangle, if the square on one side is equal to the sum of the squares on the other two sides, the angles opposite to the first side is a right triangle.\n• Chapter 10- Circles :Tangents to a circle motivated by chords drawn from points coming closer and closer to the point.\n1. (Prove) The tangent at any point of a circle is perpendicular to the radius through the point of contact.\n2. (Prove) The lengths of tangents drawn from an external point to a circle are equal.\n• Chapter 11- Constructions\n1. Division of a line segment in a given ratio (internally).\n2. Tangent to a circle from a point outside it.\n3. Construction of a triangle similar to a given triangle.\n• Unit VI: Mensuration\n• Chapter 12- Areas Related to Circles : Motivate the area of a circle; area of sectors and segments of a circle. Problems based on areas and perimeter/circumference of the above said plane figures. (In calculating the area of the segment of a circle, problems should be restricted to the central angle of 60°, 90° and 120° only. Plane figures involving triangles, simple quadrilaterals and circles should be taken.)\n• Chapter 12-Surface Areas and Volumes\n1. Problems on finding surface areas and volumes of combinations of any two of the following: cubes, cuboids, spheres, hemispheres and right circular cylinders/cones. Frustum of a cone.\n2. Problems involving converting one type of metallic solid into another and other mixed problems. (Problems with combinations of not more than two different solids can be taken.)\n• Unit VII: Statistics and Probability\n• Chapter 13-Statistics : Mean, median and mode of grouped data (bimodal situation to be avoided). Cumulative frequency graph.\n• Chapter 14- Probability:  Classical definition of probability. Connection with probability as given in Class IX. Simple problems on single events, not using set notation.\n\nAppendix\n\n1. Proofs in Mathematics Further discussion on concept of ‘statement’, ‘proof ’ and ‘argument’. Further illustrations of deductive proof with complete arguments using simple results from arithmetic, algebra and geometry. Simple theorems of the “Given ……… and assuming… prove ……..”. Training of using only the given facts (irrespective of their truths) to arrive at the required conclusion. Explanation of ‘converse’, ‘negation’, constructing converses and negations of given results/statements.\n2. Mathematical Modelling Reinforcing the concept of mathematical modelling, using simple examples of models where some constraints are ignored. Estimating probability of occurrence of certain events and estimating averages may be considered. Modelling fair instalments payments, using only simple interest and future value (use of AP)\n\n## Benefits of knowing the BSEB 10th Maths Syllabus 2020-21\n\nThe Maths Syllabus of Bihar Board Class 10, provides an outline for a course of content. Its purpose is as follows:\n\n• Helps organise and plan a course\n• Orients learning towards essential topics\n• Communicates effectively about the nature of the content\n• The detailed description of what it aims at conveying across\n• Lesson planning guidelines are provided\n• Specifies rubrics and expectations\n• Helps set the tone for the academic year\n• Outlines responsibilities of students\n\nGet more details about the Bihar Board Matric Exam from here.\n\nThe aspect of learning will not be fulfilled by just knowing the Maths Bihar Board High School syllabus. Apart from this students have to practice different types of Maths problems from Bihar Board Class 10 Previous Year Question Papers to score good marks in Mathematics exam. Matric model question papers is another good resource.\n\nGet access to interactive lessons and videos related to Maths and Science with BYJU’S Tablet/App. Moreover, we also offer study materials such as Science Syllabus, Social Science Syllabus, Matric Important Questions and more.\n\nStay tuned for more details about Bihar board 10th 2020 model paper." ]

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[ "", null, "# Synchronization of chaos\n\nUpdated on\nCovid-19\n\nSynchronization of chaos is a phenomenon that may occur when two, or more, dissipative chaotic systems are coupled. Because of the exponential divergence of the nearby trajectories of chaotic system, having two chaotic systems evolving in synchrony might appear surprising. However, synchronization of coupled or driven chaotic oscillators is a phenomenon well established experimentally and reasonably well understood theoretically. The stability of synchronization for coupled systems can be analyzed using Master Stability. Synchronization of chaos is a rich phenomenon and a multi-disciplinary subject with a broad range of applications.\n\n## Contents\n\nSynchronization may present a variety of forms depending on the nature of the interacting systems and of the coupling scheme.\n\n## Identical synchronization\n\nThis type of synchronization is also known as complete synchronization. It can be observed for identical chaotic systems. The systems are said to be completely synchronized when there is a set of initial conditions so that the systems eventually evolve identically in time. In the simplest case of two diffusively coupled dynamics is described by\n\nwhere F is the vector field modeling the isolated chaotic dynamics and α is the coupling parameter. The regime x ( t ) = y ( t ) defines an invariant subspace of the coupled system, if this subspace x ( t ) = y ( t ) is locally attractive then the coupled system exhibit identical synchronization.\n\nIf the coupling vanishes the oscillators are decoupled, and the chaotic behavior leads to a divergence of nearby trajectories. Complete synchronization occurs due to the interaction, if the coupling parameter is large enough so that the divergence of trajectories of interacting systems due to chaos is suppressed by the diffusive coupling. To find the critical coupling strength we study the behavior of the difference v = x y . Assuming that v is small we can expand the vector field in series and obtain a linear differential equation - by neglecting the taylor remainder - governing the behavior of the difference\n\nwhere D F ( x ( t ) ) denotes the Jacobian of the vector field along the solution. If α = 0 then we obtain\n\nand since the dynamics of chaotic we have u ( t ) u ( 0 ) e λ t , where λ denotes the maximum Lyapunov exponent of the isolated system. Now using the ansatz v = u e 2 α t we pass from the equation for v to the equation for u . Therefore, we obtain\n\nyield a critical coupling strength α c = λ / 2 , for all α > α c the system exhibit complete synchronization. The existence of a critical coupling strength is related to the chaotic nature of the isolated dynamics.\n\nIn general, this reasoning leads to the correct critical coupling value for synchronization. However, in some cases one might observe loss of synchronization for coupling strengths larger than the critical value. This occurs because the nonlinear terms neglected in the derivation of the critical coupling value can play an important role and destroy the exponential bound for the behavior of the difference. It is however, possible to give a rigorous treatment to this problem and obtain a critical value so that the nonlinearities will not affect the stability.\n\n## Generalized synchronization\n\nThis type of synchronization occurs mainly when the coupled chaotic oscillators are different, although it has also been reported between identical oscillators. Given the dynamical variables (x1,x2, ...,xn) and (y1,y2, ...,ym) that determine the state of the oscillators, generalized synchronization occurs when there is a functional, Φ, such that, after a transitory evolution from appropriate initial conditions, it is [y1(t), y2(t),...,ym(t)]=Φ[x1(t), x2(t),...,xn(t)]. This means that the dynamical state of one of the oscillators is completely determined by the state of the other. When the oscillators are mutually coupled this functional has to be invertible, if there is a drive-response configuration the drive determines the evolution of the response, and Φ does not need to be invertible. Identical synchronization is the particular case of generalized synchronization when Φ is the identity.\n\n## Phase synchronization\n\nPhase synchronization occurs when the coupled chaotic oscillators keep their phase difference bounded while their amplitudes remain uncorrelated. This phenomenon occurs even if the oscillators are not identical. Observation of phase synchronization requires a previous definition of the phase of a chaotic oscillator. In many practical cases, it is possible to find a plane in phase space in which the projection of the trajectories of the oscillator follows a rotation around a well-defined center. If this is the case, the phase is defined by the angle, φ(t), described by the segment joining the center of rotation and the projection of the trajectory point onto the plane. In other cases it is still possible to define a phase by means of techniques provided by the theory of signal processing, such as the Hilbert transform. In any case, if φ1(t) and φ2(t) denote the phases of the two coupled oscillators, synchronization of the phase is given by the relation nφ1(t)=mφ2(t) with m and n whole numbers.\n\n## Anticipated and lag synchronization\n\nIn these cases, the synchronized state is characterized by a time interval τ such that the dynamical variables of the oscillators, (x1,x2, ...,xn) and (x'1, x'2,...,x'n), are related by x'i(t)=xi(t+τ); this means that the dynamics of one of the oscillators follows, or anticipates, the dynamics of the other. Anticipated synchronization may occur between chaotic oscillators whose dynamics is described by delay differential equations, coupled in a drive-response configuration. In this case, the response anticipates the dynamics of the drive. Lag synchronization may occur when the strength of the coupling between phase-synchronized oscillators is increased.\n\n## Amplitude envelope synchronization\n\nThis is a mild form of synchronization that may appear between two weakly coupled chaotic oscillators. In this case, there is no correlation between phases nor amplitudes; instead, the oscillations of the two systems develop a periodic envelope that has the same frequency in the two systems. This has the same order of magnitude than the difference between the average frequencies of oscillation of the two chaotic oscillator. Often, amplitude envelope synchronization precedes phase synchronization in the sense that when the strength of the coupling between two amplitude envelope synchronized oscillators is increased, phase synchronization develops.\n\nAll these forms of synchronization share the property of asymptotic stability. This means that once the synchronized state has been reached, the effect of a small perturbation that destroys synchronization is rapidly damped, and synchronization is recovered again. Mathematically, asymptotic stability is characterized by a positive Lyapunov exponent of the system composed of the two oscillators, which becomes negative when chaotic synchronization is achieved.\n\nSome chaotic systems allow even stronger control of chaos. Both synchronization of chaos and control of chaos constitute parts of Cybernetical Physics.\n\n## References\n\nSimilar Topics\nKaaryam Nissaaram\nÅdalen 31\nTill Nowak\nTopics\n\n B i Link H2\n L" ]

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[ "# RD Sharma Class 12 Ex 19.12 Solutions Chapter 19 Indefinite Integrals\n\nHere we provide RD Sharma Class 12 Ex 19.12 Solutions Chapter 19 Indefinite Integrals for English medium students, Which will very helpful for every student in their exams. Students can download the latest Sharma Class 12 Ex 19.12 Solutions Chapter 19 Indefinite Integrals book pdf download. Now you will get step-by-step solutions to each question.\n\n## RD Sharma Class 12 Ex 19.12 Solutions Chapter 19 Indefinite Integrals\n\n### Question 1. ∫sin4x cos3x dx\n\nSolution:\n\nLet I = ∫ sin4x cos3x dx          -(i)\n\nLet sinx = t\n\nOn differentiating with respect to x:\n\ncosx = dt/dx\n\ncosx dx = dt\n\ndx = dt/cosx\n\nPutting value of dx and sinx in equation (i):\n\nI = ∫ tcosxdt/cosx\nI = ∫ tcosx dt\n\nI = ∫ t(1 – sinx) dt\n\nI = ∫ t(1 – t2) dt\n\nI = ∫ (t4– t2) dt\n\nI  = t5/5 – t7/7 + c\n\nI  = sin5/5 – sin7/7 + c\n\n### Question 2. ∫ sin5x dx\n\nSolution:\n\nLet I = ∫ sin5x dx\n\nI = ∫sin3xsin2x dx\n\n= ∫sin3x(1 – cos2x)dx\n\n= ∫(sin3x – sin3xcos2x)dx\n\n= ∫[sinxsin2x – sin3xcos2x]dx\n\n= ∫[sinx(1 – cos2x) – sin3xcos2x]dx\n\n= ∫(sinx – sinxcos2x – sin3xcos2x)dx\n\nI = ∫sinx dx – ∫sinxcos2x dx – ∫sin3xcos2x dx\n\nPutting cosx = t and -sinxdx = dt in 2nd and 3rd integral:\n\nI = ∫sinx dx + ∫t2dt + ∫sin2xt3dt/t\n\n= ∫sinx dx + ∫tdt + ∫sin2xtdt​\n\n= ∫sinx dx + ∫tdt + ∫(1 – cos2x)tdt", null, "Putting value of t:", null, "### Question 3.∫cos5x dx\n\nSolution:\n\nLet I = ∫cos5x dx\n\nI = ∫cos2xcos3x dx\n\n= ∫(1 – sin2x)cos3x dx\n\n= ∫(cos3x−sin2xcos3x)dx\n\n= ∫(cos2xcosx – sin2xcos2xcosx)dx\n\n= ∫[(1 – sin2x)cosx – sin2x(1 – sin2x)cosx]dx\n\n= ∫(cosx – sin2xcosx – sin2xcosx + sin4xcosx)dx\n\n= ∫cosx dx – 2∫sin2xcosx dx + ∫sin4xcosx dx\n\nPutting sinx = t and cosxdx = dt in 2nd and 3rd integral we get:\n\nI = ∫cos dx – 2∫t2dt + ∫t4dt\n\n= sinx – 2t3/3 + t5/5 + c\n\nPutting value of t:\n\nI = = sinx – 2sin3x/3 + cos5x/5 + c\n\n### Question 4.∫sin5xcosx dx\n\nSolution:\n\nLet I = ∫sin5xcosx dx         −(i)\n\nLet sinx = t:\n\nOn differentiating with respect to x:\n\n-cosx = dt/dx\n\ncosx dx = -dt\n\nPutting cosxdx = -dt and sinx = t in eq (i):\n\nI = ∫t5dt\n\n= t6​/6 + c\n\n= sin6​x/6 + c\n\n### Question 5.∫sin3xcos6x dx\n\nSolution:\n\nLet I = ∫sin3xcos6x dx           −(i)\n\nLet cosx = t\n\nOn differentiating both sides w.r.t′x′:\n\n-sinx = dt/dx\n\nsinxdx = -dt​\n\nPutting cosx = t and sinxdx = -dt in eq (i):\n\nI = -∫sin2x t6dt\n\n= -∫(1 – cos2x)t6dt\n\n= -∫(1 – t2)t6dt\n\n= -∫(t6 – t8)dt\n\n= -(t7/7​ – t9/9​) + c\n\nPutting value of t:\n\nI = -(cos7x/7​ – cos9x/9​) + c\n\n### Question 6.∫cos7x dx\n\nSolution:\n\nLet I = ∫cos7x dx\n\n= ∫cos6xcosx dx\n\n= ∫(cos2x)3cosx dx\n\n= ∫(1 – sin2x)3cosx dx\n\n= ∫(1 – sin6x – 3sin2x + 3sin4x)cosx dx\n\n= ∫(cosx – sin6xcosx – 3sin2xcosx + 3sin4xcosx)dx         −(i)\n\nPutting sinx = t and cosx dx = t in 2nd,3rd and 4th integral in (i):\n\nI = ∫cosx dx – ∫t6dt – 3∫t2dt + 3∫t4dt\n\n= sinx – t7/7 ​- 3t3/3 ​ +3t5/5​ + c\n\nPutting value of t:\n\n= sinx – sin7x/7 ​- 3sin3x/3 ​ +3sin5x/5​ + c\n\n### Question 7.∫xcos3x2sinx2dx\n\nSolution:\n\nLet I = ∫xcos3x2sinx2dx          −(i)\n\nLet cosx= t\n\nOn differentiating both sides:\n\n-2xsinx= dt/dx\n\n​ xsinxdx = -dt/2\n\nPutting values in (i):\n\n= -t4​/8 + c\n\nPutting value of t:\n\n### Question 8.∫sin7x dx\n\nSolution:\n\nLet I = ∫sin7x dx\n\nI = ∫sin6x sinx dx\n\n= ∫(sin2x)3sinx dx\n\n= ∫(1 – cos2x)3sinx dx\n\n= ∫(1 – cos6x – 3cos2x + 3cos4x)sinx dx\n\nI = ∫sinx dx – ∫cos6xsinx dx + 3∫cos4xsinx dx – 3∫cos2xsinx dx\n\nPutting cosx = t and sinx dx = -dt in 2nd,3rd and 4th integral:\n\nI = ∫sinx dx – ∫t6(-dt) + 3∫t4(-dt) – 3∫t2(-dt)\n\n### Question 9.∫sin3xcos5x dx\n\nSolution:\n\nLet I = ∫sin3xcos5x dx         −(i)\n\nLet cosx = t\n\nOn differentiating both sides: -sinx = dt/dx\n\nsinx dx = -dt​\n\nPutting values in (i):\n\nI = ∫sin2xt5(-dt)\n\n∫(1 – cos2x)tdt\n\n∫(1 – t2)tdt\n\n= ∫(t– t5) dt\n\n= t8/8 – t6/6​ + c\n\nPutting value of t:\n\n### Question 10.", null, "Solution:\n\nLet I =", null, "Dividing and multiplying the equation by cos6x:\n\nLet tanx = t, then:\n\nsec2x = dt/dx\n\nsec2x dx = dt\n\nPutting values in eq (ii):​\n\n### Question 11.", null, "Solution:", null, "Dividing and multiplying by cos8x:", null, "Let tanx=t,then:", null, "Putting values in ii:", null, "### Question 12.", null, "Solution:", null, "Dividing and multiplying by cos4x:", null, "Let tanx=t,then: sec2xdx = dt Putting values in i:", null, "Putting value of t:", null, "### Question 13.", null, "Solution:", null, "Let tanx=t⟹sec2x dx = dt:", null, "Putting value of t:", null, "I think you got complete solutions for this chapter. If You have any queries regarding this chapter, please comment in the below section our subject teacher will answer you. We tried our best to give complete solutions so you got good marks in your exam.\n\nIf these solutions have helped you, you can also share rdsharmasolutions.in to your friends." ]

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[ "", null, "# Pendulum Lab Conclusion Questions\n\nEssay by   •  April 1, 2019  •  Essay  •  408 Words (2 Pages)  •  90 Views\n\n## Essay Preview: Pendulum Lab Conclusion Questions\n\nReport this essay\nPage 1 of 2\n\nPendulum lab:conclusion questions\n\nIn this lab, we constructed a pendulum using a ring stand and clamp, fishing line, a protractor, and lead sinkers (½,1, and 2 oz). We would test how many swings the pendulum would have when changing the length of the fishing line, weight of the sinkers, and angle which the sinker was released. We changed each of the properties 3-4 times and each time we had three trails. Our constants (except when changing that property) were 100 cm in length, ½ oz lead sinker, and a 45 degree angle. When I changed the length of the pendulum, I observed as the line got longer the rate of swings had gotten slower. For example, our first change which was 40 cm, had a average of 0.786 swings/sec, while our last change which was 100 cm in length had an average of 0.476 swings/sec. When I changed the mass of the pendulum, I observed that the rate stayed the same every time I added weight. For example, our first change was done with a ½ oz sinker and our last change was done with a 2 oz sinker, and as our data shows both had an average of 0.476 swings/sec. When I changed the angle of the pendulum, I observed that as the angle got higher, so did the rate. When we started off with 45 degrees, our rate was 0.476 swings/sec, when we changed the angle to 75 degrees the rate increased to 0.51 swing/sec. Our rate did decrease during our 90 degree trails due to hitting the table. In this lab I learned that potential and kinetic energy are affected by positions, when we dragged the sinker to 75 degrees it had the most potential energy, when released the sinker all of the energy was turned into kinetic energy then back into potential when it reached the highest point on the other side, then the potential energy turned back into kinetic when swinging back again (this goes on until the pendulum is no longer swinging because some energy is loss due to friction). By changing the position we gave the pendulum more or less potential energy, which changed the overall energy the pendulum had, the more energy the faster the swing because there was more energy to convert. When making the line shorter or angling it higher we gave the pendulum more potential energy because the sinker was further from the ground giving it gravitational energy.\n\n...\n\n..." ]

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[ "# How can I calculate the orbital periods in a binary star system?\n\nI have two stars, with known masses and known orbital radius. How do I calculate the orbital periods of both stars?\n\n• Have you searched the web? I think you have not investigated enough. There are plenty of answers out there, like answers.yahoo.com/question/index?qid=20071225133716AA5gj9p and voyager.egglescliffe.org.uk/physics/gravitation/binary/… Feb 21 '14 at 14:19\n• @Envite I wouldn't blame the OP for not trusting yahoo answers, but the Egglescliffe School resource is a good one, even if they have poor site design. Feb 21 '14 at 14:25\n• Just two results from the first page of Google for \"Binary Stars Orbital Period\" Feb 21 '14 at 14:28\n• @Envite I did try searching the web several times; perhaps I used the wrong queries, and I habitually don't click yahoo answers links. Feb 21 '14 at 14:56\n• Use Kepler's third law! In particular, use Newton's form of Kepler's third law. See this page for the formula and an example. Feb 23 '14 at 20:27\n\nIf you need a rough estimation, you can sample the positions of the stellar objects from their acceleration, using Newton's law. A full picture is drawn on this Wikipedia page, but basically, given N stellar objects at position P(i), with their respective mass M(i):\n\n$$M_i.\\vec{acc_i} = -G\\sum_{n \\neq i}^N \\frac {M_i.M_n.(\\vec{pos_i}-\\vec{pos_n})}{\\begin{vmatrix} \\vec{pos_i}-\\vec{pos_n}\\end{vmatrix}^3 }$$\n\nYou can then derivate acceleration into speed, then into position using a small enough time delta. With initial positions and speed (which are the trickiest and also funniest to choose), you can simulate a rough N-body system.\n\nOn the fun and simulation side, this is what Universe Sandbox aims to present (in advance, sorry for linking to a commercial store, I'm not related to this studio).\n\nLagrangian points are also interesting to look at when simulating.\n\nHowever, to simplify things, you may like to think nearly all stellar objects \"close\" to the binary system would have been eaten by the massive couple over time, and then consider only the barycenter of the pair of stars.\n\nEDIT: although OP was clear, my answer is for N objects. The simplification to N=2, with A and B being the positions of the two objects, results in: $$\\vec{acc_A} = \\frac{G.M_B}{AB^3}\\vec{AB}$$ $$\\vec{acc_B} = \\frac{G.M_A}{BA^3}\\vec{BA}$$ And with an iterative process, once acceleration is computed for step k, using known initial conditions for speed and position: $$\\vec{spd_{k+1}} = \\vec{acc_k}.\\Delta{t} + \\vec{spd_k}$$ $$\\vec{pos_{k+1}} = \\vec{spd_k}.\\Delta{t} + \\vec{pos_k}$$\n\nEDIT2: well, another rough estimation, for the period duration (which I misunderstood as \"trajectory\" from the question). I'd use cylindrical coordinates as reference, for their clean spatial representation, and for the single-angle conversion:\n\n$$\\left( \\begin{array}{c} R_k \\\\ \\theta_k \\\\ h_k \\end{array} \\right) = \\left( \\begin{array}{c} \\sqrt{pos_{x,k}^2+pos_{y,k}^2} \\\\ \\arctan \\left( \\frac{pos_{y,k}}{pos_{x,k}} \\right) \\\\ pos_{z,k} \\end{array} \\right)$$\n\n...with adequate precaution. Introduce part of that conversion at each iteration, and wait for theta to complete a rotation.\n\n(in advance, sorry for the weird notation, which I just tried to make consistent from my previous posts)\n\n• That's a general answer for an n-body system. For the special case of a binary, the sum/integral can be solved explicitly, leading to orbits of the same period around the common barycenter. May be you could add a solution for this special case. Feb 21 '14 at 18:31\n• You're right, OP clearly stated the requirement but I provided a broader answer nonetheless. I'll edit the answer to match N=2. Feb 24 '14 at 8:37\n• Ok, this isn't quite easy now: Can you turn the $\\Delta$ to infinitisimals to get a differential equation, and find a connection to the 3rd Kepler law, like here: en.wikipedia.org/wiki/Gravitational_two-body_problem ? Feb 24 '14 at 21:27\n• @Gerald I'm afraid I won't :) my proposal is nothing but a simple iterative process, where additional perturbations may be introduced at each step. I avoided differentials because the problem doesn't require an exact solution (and partly because that would require me to reopen some books...). But based on your comment, it appears I misread the OP: I thought the question was about trajectories, but might be about the \"period\", as duration? Feb 25 '14 at 13:12\n• It was worth a try :) . I've been understanding the question in the sense of duration. Nevertheless, your iterative solution is more robust regarding perturbations. By comparing the actual positions with the starting positions, it's easy to find out the period (duration) during a simulation run, if needed. Feb 25 '14 at 17:57" ]

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Array\n(\n => 169.149.195.121\n)\n\n => Array\n(\n => 198.16.76.27\n)\n\n => Array\n(\n => 157.43.192.188\n)\n\n => Array\n(\n => 119.155.244.221\n)\n\n => Array\n(\n => 39.51.242.216\n)\n\n => Array\n(\n => 39.57.180.158\n)\n\n => Array\n(\n => 134.202.32.5\n)\n\n => Array\n(\n => 122.176.139.205\n)\n\n => Array\n(\n => 151.243.50.9\n)\n\n => Array\n(\n => 39.52.99.161\n)\n\n => Array\n(\n => 136.144.33.95\n)\n\n => Array\n(\n => 157.37.205.216\n)\n\n => Array\n(\n => 217.138.220.134\n)\n\n => Array\n(\n => 41.140.106.65\n)\n\n => Array\n(\n => 39.37.253.126\n)\n\n => Array\n(\n => 103.243.44.240\n)\n\n => Array\n(\n => 157.46.169.29\n)\n\n => Array\n(\n => 92.119.177.122\n)\n\n => Array\n(\n => 196.240.60.21\n)\n\n => Array\n(\n => 122.161.6.246\n)\n\n => Array\n(\n => 117.202.162.46\n)\n\n => Array\n(\n => 205.164.137.120\n)\n\n => Array\n(\n => 171.237.79.241\n)\n\n => Array\n(\n => 198.16.76.28\n)\n\n => Array\n(\n => 103.100.4.151\n)\n\n => Array\n(\n => 178.239.162.236\n)\n\n => Array\n(\n => 106.197.31.240\n)\n\n => Array\n(\n => 122.168.179.251\n)\n\n => Array\n(\n => 39.37.167.126\n)\n\n => Array\n(\n => 171.48.8.115\n)\n\n => Array\n(\n => 157.44.152.14\n)\n\n => Array\n(\n => 103.77.43.219\n)\n\n => Array\n(\n => 122.161.49.38\n)\n\n => Array\n(\n => 122.161.52.83\n)\n\n => Array\n(\n => 122.173.108.210\n)\n\n => Array\n(\n => 60.254.109.92\n)\n\n => Array\n(\n => 103.57.85.75\n)\n\n => Array\n(\n => 106.0.58.36\n)\n\n => Array\n(\n => 122.161.49.212\n)\n\n => Array\n(\n => 27.255.182.159\n)\n\n => Array\n(\n => 116.75.230.159\n)\n\n => Array\n(\n => 122.173.152.133\n)\n\n => Array\n(\n => 129.0.79.247\n)\n\n => Array\n(\n => 223.228.163.44\n)\n\n => Array\n(\n => 103.168.78.82\n)\n\n => Array\n(\n => 39.59.67.124\n)\n\n => Array\n(\n => 182.69.19.120\n)\n\n => Array\n(\n => 196.202.236.195\n)\n\n => Array\n(\n => 137.59.225.206\n)\n\n => Array\n(\n => 143.110.209.194\n)\n\n => Array\n(\n => 117.201.233.91\n)\n\n => Array\n(\n => 37.120.150.107\n)\n\n => Array\n(\n => 58.65.222.10\n)\n\n => Array\n(\n => 202.47.43.86\n)\n\n => Array\n(\n => 106.206.223.234\n)\n\n => Array\n(\n => 5.195.153.158\n)\n\n => Array\n(\n => 223.227.127.243\n)\n\n => Array\n(\n => 103.165.12.222\n)\n\n => Array\n(\n => 49.36.185.189\n)\n\n => Array\n(\n => 59.96.92.57\n)\n\n => Array\n(\n => 203.194.104.235\n)\n\n => Array\n(\n => 122.177.72.33\n)\n\n => Array\n(\n => 106.213.126.40\n)\n\n => Array\n(\n => 45.127.232.69\n)\n\n => Array\n(\n => 156.146.59.39\n)\n\n => Array\n(\n => 103.21.184.11\n)\n\n => Array\n(\n => 106.212.47.59\n)\n\n => Array\n(\n => 182.179.137.235\n)\n\n => Array\n(\n => 49.36.178.154\n)\n\n => Array\n(\n => 171.48.7.128\n)\n\n => Array\n(\n => 119.160.57.96\n)\n\n => Array\n(\n => 197.210.79.92\n)\n\n => Array\n(\n => 36.255.45.87\n)\n\n => Array\n(\n => 47.31.219.47\n)\n\n => Array\n(\n => 122.161.51.160\n)\n\n => Array\n(\n => 103.217.123.129\n)\n\n => Array\n(\n => 59.153.16.12\n)\n\n => Array\n(\n => 103.92.43.226\n)\n\n => Array\n(\n => 47.31.139.139\n)\n\n => Array\n(\n => 210.2.140.18\n)\n\n => Array\n(\n => 106.210.33.219\n)\n\n => Array\n(\n => 175.107.203.34\n)\n\n => Array\n(\n => 146.196.32.144\n)\n\n => Array\n(\n => 103.12.133.121\n)\n\n => Array\n(\n => 103.59.208.182\n)\n\n => Array\n(\n => 157.37.190.232\n)\n\n => Array\n(\n => 106.195.35.201\n)\n\n => Array\n(\n => 27.122.14.83\n)\n\n => Array\n(\n => 194.193.44.5\n)\n\n => Array\n(\n => 5.62.43.245\n)\n\n => Array\n(\n => 103.53.80.50\n)\n\n => Array\n(\n => 47.29.142.233\n)\n\n => Array\n(\n => 154.6.20.63\n)\n\n => Array\n(\n => 173.245.203.128\n)\n\n => Array\n(\n => 103.77.43.231\n)\n\n => Array\n(\n => 5.107.166.235\n)\n\n => Array\n(\n => 106.212.44.123\n)\n\n => Array\n(\n => 157.41.60.93\n)\n\n => Array\n(\n => 27.58.179.79\n)\n\n => Array\n(\n => 157.37.167.144\n)\n\n => Array\n(\n => 119.160.57.115\n)\n\n => Array\n(\n => 122.161.53.224\n)\n\n => Array\n(\n => 49.36.233.51\n)\n\n => Array\n(\n => 101.0.32.8\n)\n\n => Array\n(\n => 119.160.103.158\n)\n\n => Array\n(\n => 122.177.79.115\n)\n\n => Array\n(\n => 107.181.166.27\n)\n\n => Array\n(\n => 183.6.0.125\n)\n\n => Array\n(\n => 49.36.186.0\n)\n\n => Array\n(\n => 202.181.5.4\n)\n\n => Array\n(\n => 45.118.165.144\n)\n\n => Array\n(\n => 171.96.157.133\n)\n\n => Array\n(\n => 222.252.51.163\n)\n\n => Array\n(\n => 103.81.215.162\n)\n\n => Array\n(\n => 110.225.93.208\n)\n\n => Array\n(\n => 122.161.48.200\n)\n\n => Array\n(\n => 119.63.138.173\n)\n\n => Array\n(\n => 202.83.58.208\n)\n\n => Array\n(\n => 122.161.53.101\n)\n\n => Array\n(\n => 137.97.95.21\n)\n\n => Array\n(\n => 112.204.167.123\n)\n\n => Array\n(\n => 122.180.21.151\n)\n\n => Array\n(\n => 103.120.44.108\n)\n\n => Array\n(\n => 49.37.220.174\n)\n\n => Array\n(\n => 1.55.255.124\n)\n\n => Array\n(\n => 23.227.140.173\n)\n\n => Array\n(\n => 43.248.153.110\n)\n\n => Array\n(\n => 106.214.93.101\n)\n\n)\n```\nArchive for May, 2006: Princess Perky's Page\n Layout: Blue and Brown (Default) Author's Creation\n Home > Archive: May, 2006\n\n# Archive for May, 2006\n\n## I am an idiot!\n\nMay 31st, 2006 at 01:22 pm\n\nI use that title alot!\n\nThis time I got an order form avon (sunscreen with bug stuff, it works and we love it) and I tottaly forgot I hadn't paid for it, so the poor girl was talking to me for 10 minutes and had to ask \"did I leave the reciept in the bag?'\n\nI look like a freeloader trying to get off wiothout paying! I wasn't honest, I just forgot!\n\n## Stress reduction.\n\nMay 30th, 2006 at 02:20 pm\n\nI got one of those emails (does everyones inbox overflow with cute things people figured you just HAD to read?)\n\nAnyway this one was about stress reduction, and I noticed how many of them had to do with finances, if your finances arn't botherinmg you, guess life is just less stressfull!\n\nNot to mantion less 'stuff', here are just a few of the more finance/stuff related tips in the list:\n\n6. Simplify and unclutter your life.\n\n7. Less is more. (Although one is often not enough, two are often too many.)\n\n8. Allow extra time to do things and to get to places.\n\n9. Pace yourself. Spread out big changes and difficult projects over time; don't lump the hard things all together.\n\n10. Take one day at a time.\n\n11. Separate worries from concerns. If a situation is a concern, find out what God would have you do and let go of the anxiety. If you can't do anything about a situation, forget it.\n\n12. Live within your budget; don't use credit cards for ordinary purchases.\n\n13. Have backups; an extra car key in your wallet, an extra house key buried in the garden, extra stamps, etc.\n\n14. K.M.S. (Keep Mouth Shut). This single piece of advice can prevent an enormous amount of trouble.\n\n15. Do something for the Kid in You everyday.\n\n## cool pool\n\nMay 28th, 2006 at 05:33 pm\n\nWe get pool use for our HOA fee, which is about all we get other than living here...\n\nbut anyway it opened, and I have to get off the computer to go right now.\n\nSummer for us means lots of swimming, and lots of weird dinners....We like to eat poolside, but with timing and such, no grill options, fridge, microwave, or well anything. We have to pack and keep proper temperature of everything we eat.\n\ntonight it is boring PB no J and carrot sticks and grapes, and lemon water...\n\nOn the grocery list for next week is some chicken patties (to heat at home wrap in foil and turn into sandwhiches) Plus some hotdog buns, not the heathiest meal, but not to terrible.\n\nNow I just have to figure out something to eat the other 5 days, at the pool! (kidding, Friday we have company, so that will be at home", null, ".)\n\nMay 28th, 2006 at 05:23 pm\n\nMy previous post might have left some teachers feeling like I didn't appriciate them, well many kids need that love and support they can get in school, cause they may not be getting it at home. Public school teachers fill a much needed role, and if I ever have no kids at home to teach plus a huge stack of money I would prolly start an education center..both to teach children and to help adults. I just wanted to put out an appology to any teachers, cause it is a very important job.\n\n## Why PETA doesn't sell MceeDee coupons for a fundraiser.\n\nMay 26th, 2006 at 07:25 pm\n\nThe same reason why AA doesn't sell memberships to 'beer of the month club'.\n\nThe reason why Jeffery and Nate don't sell out to a payday loan company.\n\nThe same reason why I wont start a 'school in my home'.\n\nIt just wouldn't make sense!\n\nSoemone recently asked why I don't offer to teach for pay, and it is a reasonable question, I love to teach, and a little extra money never hurt. I even thought about it for a long time, thought up ways to do it, times that would fit my schedule, ways to keep records and all that jazz.\n\nBut then I thought, I don't want people to send their kids away to learn! It goes agaisn't the whole theory behind my homeschooling! I know over 90% of you reading this ..(all 30 of you, thanks for being dedicated to wait for a new post!) ..are prolly sending your kids away, or did, or will, or would if you had 'em. It's ok, over 90% of America does too.\n\nI don't\n\nBecasue I spend my morning teaching, when I am working I am teaching, when we rest we learn, when we sleep we get to 'internalize it all', when we play we learn, when we travel we learn, when we wash we learn, when we make icecream, dinner, yogurt, bread, lunch, ....., when we make lists, shop, build something, or break it down, when we have company, stay alone, or decorate, when we worship, when we relax, it is all an opportunity to learn.\n\nThis doesn't mean I have some kids who never get free time, on the contrary, much of my reading about 'great famous people' includes the phrase 'time to kill'. That and respnsibility. So my kids have responsibilities, free time, and me....plus a wonderfully supportive father.\n\nI am a bit backwards in many things, my kids are learning more about yeast breads and the bible than about famous Americans of a particular shade of skin. They are learning odd facts about narwals and Nanook before they learn about two mom options or blended families. We know more about reading and dictating than alternate religions, more anout cleaning, and anatomy than 'the n word'....\n\nI don't think any of that is wrong, I just think it can wait..... (well I might pass on ever knowing what 'the n word' is, but it apparently is heavily used in todays schools/sports, so I doubt we can escape.)\n\nNo I don't want to start a school in my home, I just want to encourage and help other people decide to teach their own, or at least practice 'living to learn' instead of just 'learning what you need to pass'.\n\n## His jeans don't fit!!!\n\nMay 23rd, 2006 at 10:15 pm\n\nThat means I am getting smaller! Doubt if anyone remembers a few months ago when I posted they finally did fit, but now I am even thinner! Course I seem to still ahve 'Fat glasses', as DH calls them cause I still think I look just as wide, if it wern't for the holes and I might think someone was switching up the jeans at night just to make me think I am getting thinner! (only 1 inch to go for the proper waistline!)\n\nOn other news, I am baking up a storm, we ran out of chocolate, and BOA has to be crazy to think I want to open up a savings account for a whopping .5% interest! I would be losing money if I took it from ING even with the \\$25 bonus!\n\n## Tupperware\n\nMay 19th, 2006 at 02:45 pm\n\nK I must be crazy, I went to a friends tupperware party last night, mainly cause they are fun (with the right tuppergal) and because I do love the reusability/my kids can't break it...I took an order from my mom and bought some bowls for the kids (no not a need a want, we have two non breakable, if breakfast is cereal, dinner of chilli, I have to do dishes in the middle of the day-terrible problem", null, ".) Oh and yes it was fun", null, "While there I saw a thing I REALLY Really want (really) It is a rollie cooler, perfect for taking to the pool when I am loaded with towels and three kids (one of whom can't walk) and of course dinner and drinks and snack so we can spend the whole evening.\n\nHer website\n\nOnly problem, of course you have to have a 400\\$ party and two bookings to get it...I hate that part!\n\nOh well I am going to buy myself some of the other things I want in the catalog, and we are doing a 'fondu' party (which I love, and do not own a fondu pot..so...we have to do it the tupperware way, I buy the ingredients, she brings the pot and recipie", null, "It should be fun at any rate.\n\n## Ionizer will 'pay for itself'\n\nMay 16th, 2006 at 09:47 pm\n\nSo we have surrendered, i am allergic to the state, and those ionizer filter thingies say they will help. We are willing to try.\n\nDH got one that has a cleanable filter, which is great, but it cost \\$40 more than the other brand. The filters are \\$20 each, so apparently the upgrade will 'pay for itself' and supposing the thing does its job, keeping me healthy, it will definatly pay for itself in less 'scripts and more work around here done.\n\n## Garden finally in\n\nMay 15th, 2006 at 03:10 pm\n\nI hope better late than never applies to gardens, cause I just today put it in!\n\nWe put in zuccinni, peppers, carrots, and basil. I hope some of it grows at least.\n\nI am rather proud of myself, I might be late, but I did it..... I think this months challenge to rewrite the inner voice, is really making ithe most effect of all of them. I am getting more done and finding the 'bright side' of life much easier.\n\n## I can now breath!\n\nMay 12th, 2006 at 09:16 pm\n\nApparently I have allergies, and broncitus, and if you ignore those long enough you get to wheezing, and coughing that doesn't stop...which is a bad thing.\n\nI went from no medications (other than vitamins) to 6!\n\nWell I have to say I am a terrible patient, I am taking 4 of them...I got two cough meds, I only take one and it is fine...I skipped the horse pills.\n\nI also got a script for anti asthma meds, I don't have asthma! So I am skipping it for now....\n\nI got some sort of nose spray and that works.\n\nI am taking the antibiotics and the singulair for allergies, and one of the cough meds. It seems to be helping, it no longer hurts to breath...I am just SUPER tired, I mean If I stop moving I wil be asleep in seconds! (I tested it several times today,a nd I am serious!)\n\n## Free translation\n\nMay 10th, 2006 at 09:48 pm\n\nSo while eating green beans I asked what green bean in french was..I only know Vert is green, I forgot bean, so I looked up free translation. came up with\n\nthis site\n\nharicots verts\n\nInstant free translation, what more could you ask for", null, "## Best sleep in ages!\n\nMay 10th, 2006 at 01:31 pm\n\nUE decided to sleep till 4am last night! I was in bed around 10:30, and no troubles or eeps till 4. (when my 4 year old woke me on the way to the potty). DH took care of him, and UE woke to nurse, but then it was back to sleep till JC got up for the day (630ish? but I didn't really get up till 7)\n\nThe best things in life are free, I still can't breath without pain, but at least I got rest before I tackle the day!\n\nCourse you know 5 years ago I couldn't imagine being happy about 'sleeping in' till 4am!" ]

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[ "Normal view\n\n# Stochastic Models, Information Theory, and Lie Groups, Volume 1 [electronic resource] : Classical Results and Geometric Methods / by Gregory S. Chirikjian.\n\nMaterial type:", null, "TextPublisher: Boston : Birkhäuser Boston, 2009Description: XXII, 383 p. 13 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780817648039Additional physical formats: Printed edition:: No titleDDC classification: 519.2 LOC classification: QA273.A1-274.9QA274-274.9Online resources: Click here to access online\nContents:\nGaussian Distributions and the Heat Equation -- Probability and Information Theory -- Stochastic Differential Equations -- Geometry of Curves and Surfaces -- Differential Forms -- Polytopes and Manifolds -- Stochastic Processes on Manifolds -- Summary.\nSummary: The subjects of stochastic processes, information theory, and Lie groups are usually treated separately from each other. This unique two-volume set presents these topics in a unified setting, thereby building bridges between fields that are rarely studied by the same people. Unlike the many excellent formal treatments available for each of these subjects individually, the emphasis in both of these volumes is on the use of stochastic, geometric, and group-theoretic concepts in the modeling of physical phenomena. Volume 1 establishes the geometric and statistical foundations required to understand the fundamentals of continuous-time stochastic processes, differential geometry, and the probabilistic foundations of information theory. Volume 2 delves deeper into relationships between these topics, including stochastic geometry, geometric aspects of the theory of communications and coding, multivariate statistical analysis, and error propagation on Lie groups. Key features and topics of  Volume 1: * The author reviews stochastic processes and basic differential geometry in an accessible way for applied mathematicians, scientists, and engineers. * Extensive exercises and motivating examples make the work suitable as a textbook for use in courses that emphasize applied stochastic processes or differential geometry. * The concept of Lie groups as continuous sets of symmetry operations is introduced. * The Fokker–Planck Equation for diffusion processes in Euclidean space and on differentiable manifolds is derived in a way that can be understood by nonspecialists. * The concrete presentation style makes it easy for readers to obtain numerical solutions for their own problems; the emphasis is on how to calculate quantities rather than how to prove theorems. * A self-contained appendix provides a comprehensive review of concepts from linear algebra, multivariate calculus, and systems of ordinary differential equations. Stochastic Models, Information Theory, and Lie Groups will be of interest to advanced undergraduate and graduate students, researchers, and practitioners working in applied mathematics, the physical sciences, and engineering.\nTags from this library: No tags from this library for this title.", null, "Average rating: 0.0 (0 votes)\nItem type Current location Collection Call number Status Date due Barcode Item holds", null, "eBook e-Library\n\nElectronic Book@IST\n\nEBook Available\nTotal holds: 0\n\nGaussian Distributions and the Heat Equation -- Probability and Information Theory -- Stochastic Differential Equations -- Geometry of Curves and Surfaces -- Differential Forms -- Polytopes and Manifolds -- Stochastic Processes on Manifolds -- Summary.\n\nThe subjects of stochastic processes, information theory, and Lie groups are usually treated separately from each other. This unique two-volume set presents these topics in a unified setting, thereby building bridges between fields that are rarely studied by the same people. Unlike the many excellent formal treatments available for each of these subjects individually, the emphasis in both of these volumes is on the use of stochastic, geometric, and group-theoretic concepts in the modeling of physical phenomena. Volume 1 establishes the geometric and statistical foundations required to understand the fundamentals of continuous-time stochastic processes, differential geometry, and the probabilistic foundations of information theory. Volume 2 delves deeper into relationships between these topics, including stochastic geometry, geometric aspects of the theory of communications and coding, multivariate statistical analysis, and error propagation on Lie groups. Key features and topics of  Volume 1: * The author reviews stochastic processes and basic differential geometry in an accessible way for applied mathematicians, scientists, and engineers. * Extensive exercises and motivating examples make the work suitable as a textbook for use in courses that emphasize applied stochastic processes or differential geometry. * The concept of Lie groups as continuous sets of symmetry operations is introduced. * The Fokker–Planck Equation for diffusion processes in Euclidean space and on differentiable manifolds is derived in a way that can be understood by nonspecialists. * The concrete presentation style makes it easy for readers to obtain numerical solutions for their own problems; the emphasis is on how to calculate quantities rather than how to prove theorems. * A self-contained appendix provides a comprehensive review of concepts from linear algebra, multivariate calculus, and systems of ordinary differential equations. Stochastic Models, Information Theory, and Lie Groups will be of interest to advanced undergraduate and graduate students, researchers, and practitioners working in applied mathematics, the physical sciences, and engineering.\n\nThere are no comments for this item.\n\nto post a comment.\nShare\n\nPowered by Koha" ]

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[ "", null, "# Calculating Internal Rate of Return Using Excel or a Financial Calculator", null, "Calculating Internal Rate of Return (IRR) can be tedious if you have multiple cash flow periods to work with.  Fortunately, financial calculators and Microsoft Excel make the process amazingly simple.\n\nFor both examples, we'll use the following data set:", null, "Assume Company ABC wants to know whether it should buy a \\$500 piece of equipment. It projects that it will increase profits by \\$100 in Year 1, \\$200 in Year 2, and \\$300 in Year 3. Now you can calculate the IRR of the proposed project.\n\nUsing a financial calculator:\n\nNote: the steps in this tutorial outline the process for a Texas Instruments BA II plus financial calculator.\n\n1. Enter the cash flow values for each period into the calculator's cash flow register.  This is done by pressing the Cash Flow [CF] key to open the cash flow register. The calculator should read CF0=, which tells you to enter the cash flow for time 0.\n\nBecause you need to send cash out of the company to make the initial \\$500 investing, this value has to be negative.  Typed in -500 for CF0, and hit the [ENTER] key.\n\n2. Next enter the cash flow values for the subsequent periods.\n\nThis is done by hitting the down arrow once. The calculator should read CF1=. Type in the amount for the first cash flow, 100, and hit [ENTER].  The calculator should now say C01=  100.\n\nTo enter cash flow from Year 2, hit the down arrow twice. The calculator should read CF2=. If it says F1=, hit the down arrow one more time.\n\nType in the second year's cash flow, 200, and hit [Enter]. The calculator should read CF2= 200. Hit the down arrow twice again and do the same thing for the third cash flow period, CF3. If the data set has more periods, follow the same procedure for C04 and so on.\n\n3. Once the cash flow values have been entered into the calculator you are ready to calculate the IRR.\n\nTo do this press the [IRR] key. The screen will read IRR= 0.000. To display the IRR value for the data set, press the [CPT] key at the top left corner of the calculator. If you have followed this process correctly, the calculator will display the correct IRR. For our example, the IRR is 8.208264%.\n\nUsing Microsoft Excel:\n\nFinding the IRR using Excel is fairly straighforward.\n\n1. First, type the intial cash flow into any cell on the spreadsheet. Keep in mind this initial investment has to be a negative number. Using our original example, type -500 into the A1 cell of the spreadsheet.\n\n2.  Next, just like the calculator, you will type the subsequent cash flow values for each period into the cells directly under the initial investment amount. Following our example, type 100 into cell A2, 200 into cell A3, and 300 into cell A4.\n\n3. Finally you are ready to calculate the IRR.\n\nTo instruct the Excel program to calculate IRR, type in the function command \"=IRR(A1:A4)\" into the A5 cell directly under all the values.  When you hit the enter key, the IRR value, 8.2%, should be displayed in that cell.\n\nThis same procedure can be followed for any data set if the cash flow values are listed one after another in a column directly under the intial investment amount. You would then put the range of cells in between the parentheses of the IRR command function." ]

[ null, "https://mc.yandex.ru/watch/53037505 https://mc.yandex.ru/watch/53037505 ", null, "https://dervpm8wymnxf.cloudfront.net/cdn/farfuture/0x4kjlXyxYROpQ05jPra5rxftXxttHDqbd744CY2yuQ/mtime:1505221405/sites/www/files/styles/articledetail/public/field/image/big-gains-think-small_1.jpg", null, "https://dervpm8wymnxf.cloudfront.net/cdn/farfuture/-PO_-2TBgpFTBmWnXzWJ4DtWwx_HR4kqeEPwOFz060U/mtime:1383136872/images/IRR%20table%202.jpg", null ]

[ "", null, "# Rotation Transformation\n\nRelated Topics: More Geometry Lessons\n\nIn these lessons, we will learn\n• what is rotation\n• how to draw the rotated image of an object given the center, the angle and the direction of rotation.\n• how to find the angle of rotation given the object, its image and the center of rotation.\n• how to rotate points and shapes on the coordinate plane about the origin.\n\n### What is Rotation?\n\nA rotation is a transformation in which the object is rotated about a fixed point. The direction of rotation can be clockwise or anticlockwise.\n\nThe fixed point in which the rotation takes place is called the center of rotation. The amount of rotation made is called the angle of rotation.\n\nExample:", null, "For any rotation, we need to specify the center, the angle and the direction of rotation.\n\n### Drawing The Rotated Image\n\nGiven the center of rotation and the angle of rotation we can determine the rotated image of an object.\n\nExample:\n\nDetermine the image of the straight line XY under an anticlockwise rotation of 90˚ about O.", null, "Solution:\n\nStep 1: Join point X to O.\n\nStep 2: Using a protractor, draw a line 90˚ anticlockwise from the line OX. Mark on the line the point X such that the line of OX = OX", null, "Step 3: Repeat steps 1 and 2 for point Y. Join the points X and Y to form the line XY.\n\n### The Angle Of Rotation\n\nGiven an object, its image and the center of rotation, we can find the angle of rotation using the following steps.\n\nStep 1 : Choose any point in the given figure and join the chosen point to the center of rotation.\n\nStep 2 : Find the image of the chosen point and join it to the center of rotation.\n\nStep 3: Measure the angle between the two lines. The sign of the angle depends on the direction of rotation. Anti-clockwise rotation is positive and clockwise rotation is negative.\n\nExample\n\nFigure ABC is the image of figure ABC. O is the center of rotation. Find the angle of rotation.", null, "Solution:\n\nStep 1: Join A to O\n\nStep 2: Join A to O.\n\nStep 3: Measure the angle AOA", null, "The angle of rotation is 62˚ anticlockwise or +62˚\n\nManual rotation of a polygon about a given point at a given angle\nYou will need a straightedge, a protractor, and a compass. We will perform rotations about a point inside the figure, one outside the figure and one on the figure.\nExamples:\n1. Rotate ABCD by 143° about point P.\n2. Rotate JKLM by 111° about point Q. How to rotate a figure around a fixed point using a compass and protractor\nYou are given:\n• A center of rotation.\n• A figure to rotate.\n• An angle of rotation.\n1. Draw a ray from the center of rotation to the point you wish to rotate.\n2. Draw an angle with the center of rotation as the vertex.\n3. Use a compass to draw a circle (arc) with the center at the center of rotation and a radius from the center of rotation to the point you are rotating.\n4. Now rotate all the other points and connect the dots.\n\n### Rotate points on the coordinate plane\n\nWe will now look at how points and shapes are rotated on the coordinate plane. It will be helpful to note the patterns of the coordinates when the points are rotated about the origin at different angles.\n\nGeometry Rotation\nA rotation is an isometric transformation: the original figure and the image are congruent. The orientation of the image also stays the same, unlike reflections. To perform a geometry rotation, we first need to know the point of rotation, the angle of rotation, and a direction (either clockwise or counterclockwise). A rotation is also the same as a composition of reflections over intersecting lines.\n\nThe following diagrams show rotation of 90°, 180° and 270° about the origin. Scroll down the page for more examples and solutions.", null, "How to rotate points on the coordinate plane?\nThe following videos show clockwise and anticlockwise rotation of 0˚, 90˚, 180˚ and 270˚about the origin (0, 0). The pattern of the coordinates are also explored. Composition of Transformations\nReflection in intersecting lines Theorem\nIf lines k and m intersect at a point P, then a reflection in k followed by a reflection in m is the same as a rotation about point P.\nThe angle of rotation is 2x° where x° is the measure of the acute angle formed by the lines k and m.\n\nTwo reflections in parallel lines = translation.\nTwo reflections in intersecting lines = rotation.\nExample:\nBelow is a composition of two reflection in intersecting lines. What is a single transformation that would map ABCD onto A\"B\"C\"D\"?\n\nTry the free Mathway calculator and problem solver below to practice various math topics. Try the given examples, or type in your own problem and check your answer with the step-by-step explanations.", null, "", null, "" ]

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[ "# How to derive the spacetime interval from the Lorentz transformation?\n\nI've seen plenty of derivations of the Lorentz transformation from the spacetime interval.\n\nCan this process be reversed, to derive the spacetime interval from the Lorentz transformation and the two postulates that the principle of relativity applies and the speed of light is absolute?\n\n• What precisely are you assuming about the \"Lorentz transformations\" that you want to derive the spacetime interval from? That the interval is invariant under Lorentz transformation is a straightforward computation, what exactly do you want to derive? – ACuriousMind Aug 16 '17 at 9:15\n• @ACuriousMind If we apply the Lorentz transformation to the spacetime interval, we can see that it is invariant, but what if we didn't know the form of the spacetime interval so we could test it - how could we make the Lorentz transformation \"spit out\" the invariance of the spacetime interval? – Rational Function Aug 16 '17 at 9:19\n\nOk, so we take it given that we know that the transformations among the frames that we are allowed to traverse are the Lorentz transformation. The most concise and contentful definition of the Lorentz transformations would be the following: $$\\Lambda ^{T}\\eta\\Lambda=\\eta$$ Or, in the index notation, $$\\Lambda^{\\alpha'}_{\\mu}\\Lambda^{\\beta'}_{\\nu}\\eta_{\\alpha'\\beta'}=\\eta_{\\mu\\nu}$$ Now, make sure you appreciate that we are treating the matrix $\\eta$ (whether written in the simple matrix form or in the index notation) simply as a matrix. We don't pretend to know apriori that it acts as the metric. But rather, we will now motivate as to why it would be wise to use it as the metric and (thus) to define the interval as $\\eta_{\\mu\\nu}dx^\\mu dx^\\nu$.\n\nThe first step is to notice that the matrix $\\eta$ (simply defined to be the diagonal matrix with entries $1,-1,-1,-1$) is really a tensor under the Lorentz transformations. This can be directly read off from the very definition of the Lorentz transformations when the definition is expressed in the index notation as expressed above.\n\nNow that we have recognized that $\\eta$ is really a $(0,2)$ tensor, using the definition of a $(0,2)$ tensor, we should appreciate that it is the very map that takes a pair of vectors to a real number (or, in other words, a scalar--a frame-invariant quantity). Thus, the most natural way to construct a frame-invariant quantity out of a displacement vector $\\vec{dx}$ would be to feed this vector into both the slots of the tensor $\\eta$ and to define the outcome as the spacetime interval. That is to say, to define $$I=\\eta(\\vec{dx},\\vec{dx})=\\eta_{\\mu\\nu}dx^{\\mu}dx^{\\nu}=dt^2-dx^2-dy^2-dz^2$$ as the spacetime interval.\n\nOf course, if one insists, one can argue that this is all just a motivation to define the spacetime interval to be $dt^2-dx^2-dy^2-dz^2$ and nothing more. And that is true. Because, after all, we are defining something and there is no word of God about how we should define something! It is just that if we see a very useful property in a thing then we give it some name. The same is true for spacetime interval. The observation that $dt^2-dx^2-dy^2-dz^2$ is invariant is an important one and thus, we call it the spacetime interval. That is the bottom line. What I have tried to demonstrate here is how one can very easily stumble upon this observation if one already has in mind that she is looking for some invariant quantity.\n\nI don't know which derivation of the form invariant spacetime interval $\\Rightarrow$ Lorentz transformation you have in mind, but the easiest derivations all make inferences which are \"if and only if\" inferences, i.e. the reasoning and inference chain can be run in both directions.\n\nFor example, writing the Lorentz transformation as a general $4\\times4$ real element matrix $\\Lambda$ acting on $4\\times1$ real element columns representing 4-vectors $X\\in \\mathbb{R}^{1+3}$, then the assertion of the spacetime interval's invariance is:\n\n$$X^T\\,\\Lambda^T\\,\\eta\\,\\Lambda\\,X\\, = X^T\\,\\eta\\,X;\\quad\\forall X\\in\\mathbb{R}^{1+3}\\tag{1}$$\n\nwhere, naturally, $\\eta=\\mathrm{diag}(1,\\,-1,\\,-1,\\,-1)$. Now choose two in general different $X,\\,Y\\in\\mathbb{R}^{1+3}$ and write down (1) for the sum $X+Y$: that is $(X+Y)^T\\,\\Lambda^T\\,\\eta\\,\\Lambda\\,(X+Y)\\, = (X+Y)^T\\,\\eta\\,(X+Y)$, expand this little beast out and then apply (1) again to show that (1) implies:\n\n$$X^T\\,\\Lambda^T\\,\\eta\\,\\Lambda\\,Y = X^T\\,\\eta\\,Y;\\quad\\forall X,\\,Y\\in\\mathbb{R}^{1+3}\\tag{2}$$\n\nNow choose the sixteen different combinations of the usual basis vectors for $X$ and $Y$ and you thus show (witnessing that $\\eta$ is nonsingular, i.e. defines a nondegenerate billinear form):\n\n$$\\Lambda^T\\,\\eta\\,\\Lambda = \\eta\\quad\\tag{3}$$\n\n(3) trivially implies (1), so (3) and the assertion of spacetime interval are logically equivalent. They imply, and are implied by, one another.\n\nNow I, and I believe many people, would think of (3) as the definition of the Lorentz transformation: its use in a set builder notation gives us a complete characterization of the Lorentz group. But you may be wanting to work with other characterizations of the Lorentz group, or the special Lorentz group (proper, orthochronous ones) perhaps. You can trivially check that a boost in the $x$ direction fulfills (3), thus, by our logical equivalence, leaves the spacetime interval invariant. Likewise, do the same for a rotation $R$, for which (3) is equivalent to $R^T\\,R=\\mathrm{id}$. Thus, if you define the proper orthochronous Lorentz group as the smallest group that contains the $x$ boost and the rotations, i.e. as the group of all finite products of the form $U_{1}\\,R_{1}\\,U_2\\,R_2\\,\\cdots$ where the $U_i$ and $R_i$ are all $x$-boosts and rotations, respectively and apply (3) to such a chain inductively, you can show that this group conserves the spacetime interval. Note that, of course, a boost in any direction can be written in the form $R\\,U\\,R^T$, where $U$ is an $x$-boost and $R$ a rotation.\n\n• Pfft, \"naturally\" $\\eta={\\rm diag}(-1,1,1,1)$ because some of us a \"positive\" people :D. – Kyle Kanos Aug 16 '17 at 10:15" ]

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[ "I'm working on modernizing Rosetta Code's infrastructure. Starting with communications. Please accept this time-limited open invite to RC's Slack.. --Michael Mol (talk) 20:59, 30 May 2020 (UTC)\n\n# Cheryl's birthday\n\nCheryl's birthday\nYou are encouraged to solve this task according to the task description, using any language you may know.\n\nAlbert and Bernard just became friends with Cheryl, and they want to know when her birthday is.\n\nCheryl gave them a list of ten possible dates:\n\n``` May 15, May 16, May 19\nJune 17, June 18\nJuly 14, July 16\nAugust 14, August 15, August 17\n```\n\nCheryl then tells Albert the   month   of birth,   and Bernard the   day   (of the month)   of birth.\n\n``` 1) Albert: I don't know when Cheryl's birthday is, but I know that Bernard does not know too.\n2) Bernard: At first I don't know when Cheryl's birthday is, but I know now.\n3) Albert: Then I also know when Cheryl's birthday is.\n```\n\nWrite a computer program to deduce, by successive elimination, Cheryl's birthday.\n\nReferences\n\n## AppleScript\n\n`use AppleScript version \"2.4\"use framework \"Foundation\"use scripting additions property M : 1 -- Monthproperty D : 2 -- Day on run -- The MONTH with only one remaining day -- among the DAYs with unique months, -- EXCLUDING months with unique days, -- in Cheryl's list:  showList(uniquePairing(M, ¬ uniquePairing(D, ¬ monthsWithUniqueDays(false, ¬ map(composeList({tupleFromList, |words|, toLower}), ¬ splitOn(\", \", ¬ \"May 15, May 16, May 19, June 17, June 18, \" & ¬ \"July 14, July 16, Aug 14, Aug 15, Aug 17\"))))))  --> \"[('july', '16')]\"end run  -- QUERY FUNCTIONS ---------------------------------------- -- monthsWithUniqueDays :: Bool -> [(Month, Day)] -> [(Month, Day)]on monthsWithUniqueDays(blnInclude, xs) set _months to map(my fst, uniquePairing(D, xs)) script uniqueDay on |λ|(md) set bln to elem(fst(md), _months) if blnInclude then bln else not bln end if end |λ| end script filter(uniqueDay, xs)end monthsWithUniqueDays  -- uniquePairing :: DatePart -> [(M, D)] -> [(M, D)]on uniquePairing(dp, xs) script go property f : my mReturn(item dp of {my fst, my snd}) on |λ|(md)  set dct to f's |λ|(md) script unique on |λ|(k) set mb to lookupDict(k, dct) if Nothing of mb then false else 1 = length of (Just of mb) end if end |λ| end script set uniques to filter(unique, keys(dct))  script found on |λ|(tpl) elem(f's |λ|(tpl), uniques) end |λ| end script filter(found, xs) end |λ| end script bindPairs(xs, go)end uniquePairing  -- bindPairs :: [(M, D)] -> ((Dict Text [Text], Dict Text [Text]) -- -> [(M, D)]) -> [(M, D)]on bindPairs(xs, f) tell mReturn(f) |λ|(Tuple(dictFromPairs(xs), ¬ dictFromPairs(map(my swap, xs)))) end tellend bindPairs -- dictFromPairs :: [(M, D)] -> Dict Text [Text]on dictFromPairs(mds) set gps to groupBy(|on|(my eq, my fst), ¬ sortBy(comparing(my fst), mds)) script kv on |λ|(gp) Tuple(fst(item 1 of gp), map(my snd, gp)) end |λ| end script mapFromList(map(kv, gps))end dictFromPairs  -- LIBRARY GENERICS --------------------------------------- -- comparing :: (a -> b) -> (a -> a -> Ordering)on comparing(f) script on |λ|(a, b) tell mReturn(f) set fa to |λ|(a) set fb to |λ|(b) if fa < fb then -1 else if fa > fb then 1 else 0 end if end tell end |λ| end scriptend comparing -- composeList :: [(a -> a)] -> (a -> a)on composeList(fs) script on |λ|(x) script on |λ|(f, a) mReturn(f)'s |λ|(a) end |λ| end script  foldr(result, x, fs) end |λ| end scriptend composeList -- drop :: Int -> [a] -> [a]-- drop :: Int -> String -> Stringon drop(n, xs) set c to class of xs if c is not script then if c is not string then if n < length of xs then items (1 + n) thru -1 of xs else {} end if else if n < length of xs then text (1 + n) thru -1 of xs else \"\" end if end if else take(n, xs) -- consumed return xs end ifend drop -- dropAround :: (a -> Bool) -> [a] -> [a]-- dropAround :: (Char -> Bool) -> String -> Stringon dropAround(p, xs) dropWhile(p, dropWhileEnd(p, xs))end dropAround -- dropWhile :: (a -> Bool) -> [a] -> [a]-- dropWhile :: (Char -> Bool) -> String -> Stringon dropWhile(p, xs) set lng to length of xs set i to 1 tell mReturn(p) repeat while i ≤ lng and |λ|(item i of xs) set i to i + 1 end repeat end tell drop(i - 1, xs)end dropWhile -- dropWhileEnd :: (a -> Bool) -> [a] -> [a]-- dropWhileEnd :: (Char -> Bool) -> String -> Stringon dropWhileEnd(p, xs) set i to length of xs tell mReturn(p) repeat while i > 0 and |λ|(item i of xs) set i to i - 1 end repeat end tell take(i, xs)end dropWhileEnd -- elem :: Eq a => a -> [a] -> Boolon elem(x, xs) considering case xs contains x end consideringend elem -- enumFromToInt :: Int -> Int -> [Int]on enumFromToInt(M, n) if M ≤ n then set lst to {} repeat with i from M to n set end of lst to i end repeat return lst else return {} end ifend enumFromToInt -- eq (==) :: Eq a => a -> a -> Boolon eq(a, b) a = bend eq -- filter :: (a -> Bool) -> [a] -> [a]on filter(f, xs) tell mReturn(f) set lst to {} set lng to length of xs repeat with i from 1 to lng set v to item i of xs if |λ|(v, i, xs) then set end of lst to v end repeat return lst end tellend filter -- foldl :: (a -> b -> a) -> a -> [b] -> aon foldl(f, startValue, xs) tell mReturn(f) set v to startValue set lng to length of xs repeat with i from 1 to lng set v to |λ|(v, item i of xs, i, xs) end repeat return v end tellend foldl -- foldr :: (a -> b -> b) -> b -> [a] -> bon foldr(f, startValue, xs) tell mReturn(f) set v to startValue set lng to length of xs repeat with i from lng to 1 by -1 set v to |λ|(item i of xs, v, i, xs) end repeat return v end tellend foldr -- fst :: (a, b) -> aon fst(tpl) if class of tpl is record then |1| of tpl else item 1 of tpl end ifend fst -- Typical usage: groupBy(on(eq, f), xs)-- groupBy :: (a -> a -> Bool) -> [a] -> [[a]]on groupBy(f, xs) set mf to mReturn(f)  script enGroup on |λ|(a, x) if length of (active of a) > 0 then set h to item 1 of active of a else set h to missing value end if  if h is not missing value and mf's |λ|(h, x) then {active:(active of a) & {x}, sofar:sofar of a} else {active:{x}, sofar:(sofar of a) & {active of a}} end if end |λ| end script  if length of xs > 0 then set dct to foldl(enGroup, {active:{item 1 of xs}, sofar:{}}, rest of xs) if length of (active of dct) > 0 then sofar of dct & {active of dct} else sofar of dct end if else {} end ifend groupBy -- insertMap :: Dict -> String -> a -> Dicton insertMap(rec, k, v) tell (current application's NSMutableDictionary's ¬ dictionaryWithDictionary:rec) its setValue:v forKey:(k as string) return it as record end tellend insertMap -- intercalateS :: String -> [String] -> Stringon intercalateS(sep, xs) set {dlm, my text item delimiters} to {my text item delimiters, sep} set s to xs as text set my text item delimiters to dlm return send intercalateS -- Just :: a -> Maybe aon Just(x) {type:\"Maybe\", Nothing:false, Just:x}end Just -- keys :: Dict -> [String]on keys(rec) (current application's NSDictionary's dictionaryWithDictionary:rec)'s allKeys() as listend keys -- lookupDict :: a -> Dict -> Maybe bon lookupDict(k, dct) set ca to current application set v to (ca's NSDictionary's dictionaryWithDictionary:dct)'s objectForKey:k if v ≠ missing value then Just(item 1 of ((ca's NSArray's arrayWithObject:v) as list)) else Nothing() end ifend lookupDict -- Lift 2nd class handler function into 1st class script wrapper -- mReturn :: First-class m => (a -> b) -> m (a -> b)on mReturn(f) if class of f is script then f else script property |λ| : f end script end ifend mReturn -- map :: (a -> b) -> [a] -> [b]on map(f, xs) tell mReturn(f) set lng to length of xs set lst to {} repeat with i from 1 to lng set end of lst to |λ|(item i of xs, i, xs) end repeat return lst end tellend map -- mapFromList :: [(k, v)] -> Dicton mapFromList(kvs) set tpl to unzip(kvs) script on |λ|(x) x as string end |λ| end script (current application's NSDictionary's ¬ dictionaryWithObjects:(|2| of tpl) ¬ forKeys:map(result, |1| of tpl)) as recordend mapFromList -- min :: Ord a => a -> a -> aon min(x, y) if y < x then y else x end ifend min -- Nothing :: Maybe aon Nothing() {type:\"Maybe\", Nothing:true}end Nothing -- e.g. sortBy(|on|(compare, |length|), [\"epsilon\", \"mu\", \"gamma\", \"beta\"])-- on :: (b -> b -> c) -> (a -> b) -> a -> a -> con |on|(f, g) script on |λ|(a, b) tell mReturn(g) to set {va, vb} to {|λ|(a), |λ|(b)} tell mReturn(f) to |λ|(va, vb) end |λ| end scriptend |on| -- partition :: predicate -> List -> (Matches, nonMatches)-- partition :: (a -> Bool) -> [a] -> ([a], [a])on partition(f, xs) tell mReturn(f) set ys to {} set zs to {} repeat with x in xs set v to contents of x if |λ|(v) then set end of ys to v else set end of zs to v end if end repeat end tell Tuple(ys, zs)end partition -- show :: a -> Stringon show(e) set c to class of e if c = list then showList(e) else if c = record then set mb to lookupDict(\"type\", e) if Nothing of mb then showDict(e) else script on |λ|(t) if \"Either\" = t then set f to my showLR else if \"Maybe\" = t then set f to my showMaybe else if \"Ordering\" = t then set f to my showOrdering else if \"Ratio\" = t then set f to my showRatio else if class of t is text and t begins with \"Tuple\" then set f to my showTuple else set f to my showDict end if tell mReturn(f) to |λ|(e) end |λ| end script tell result to |λ|(Just of mb) end if else if c = date then \"\\\"\" & showDate(e) & \"\\\"\" else if c = text then \"'\" & e & \"'\" else if (c = integer or c = real) then e as text else if c = class then \"null\" else try e as text on error (\"«\" & c as text) & \"»\" end try end ifend show -- showList :: [a] -> Stringon showList(xs) \"[\" & intercalateS(\", \", map(my show, xs)) & \"]\"end showList -- showTuple :: Tuple -> Stringon showTuple(tpl) set ca to current application script on |λ|(n) set v to (ca's NSDictionary's dictionaryWithDictionary:tpl)'s objectForKey:(n as string) if v ≠ missing value then unQuoted(show(item 1 of ((ca's NSArray's arrayWithObject:v) as list))) else missing value end if end |λ| end script \"(\" & intercalateS(\", \", map(result, enumFromToInt(1, length of tpl))) & \")\"end showTuple -- snd :: (a, b) -> bon snd(tpl) if class of tpl is record then |2| of tpl else item 2 of tpl end ifend snd -- Enough for small scale sorts.-- Use instead sortOn :: Ord b => (a -> b) -> [a] -> [a]-- which is equivalent to the more flexible sortBy(comparing(f), xs)-- and uses a much faster ObjC NSArray sort method-- sortBy :: (a -> a -> Ordering) -> [a] -> [a]on sortBy(f, xs) if length of xs > 1 then set h to item 1 of xs set f to mReturn(f) script on |λ|(x) f's |λ|(x, h) ≤ 0 end |λ| end script set lessMore to partition(result, rest of xs) sortBy(f, |1| of lessMore) & {h} & ¬ sortBy(f, |2| of lessMore) else xs end ifend sortBy -- splitOn :: String -> String -> [String]on splitOn(pat, src) set {dlm, my text item delimiters} to ¬ {my text item delimiters, pat} set xs to text items of src set my text item delimiters to dlm return xsend splitOn -- swap :: (a, b) -> (b, a)on swap(ab) if class of ab is record then Tuple(|2| of ab, |1| of ab) else {item 2 of ab, item 1 of ab} end ifend swap -- take :: Int -> [a] -> [a]-- take :: Int -> String -> Stringon take(n, xs) set c to class of xs if list is c then if 0 < n then items 1 thru min(n, length of xs) of xs else {} end if else if string is c then if 0 < n then text 1 thru min(n, length of xs) of xs else \"\" end if else if script is c then set ys to {} repeat with i from 1 to n set v to xs's |λ|() if missing value is v then return ys else set end of ys to v end if end repeat return ys else missing value end ifend take -- toLower :: String -> Stringon toLower(str) set ca to current application ((ca's NSString's stringWithString:(str))'s ¬ lowercaseStringWithLocale:(ca's NSLocale's currentLocale())) as textend toLower -- Tuple (,) :: a -> b -> (a, b)on Tuple(a, b) {type:\"Tuple\", |1|:a, |2|:b, length:2}end Tuple -- tupleFromList :: [a] -> (a, a ...)on tupleFromList(xs) set lng to length of xs if 1 < lng then if 2 < lng then set strSuffix to lng as string else set strSuffix to \"\" end if script kv on |λ|(a, x, i) insertMap(a, (i as string), x) end |λ| end script foldl(kv, {type:\"Tuple\" & strSuffix}, xs) & {length:lng} else missing value end ifend tupleFromList -- unQuoted :: String -> Stringon unQuoted(s) script p on |λ|(x) --{34, 39} contains id of x 34 = id of x end |λ| end script dropAround(p, s)end unQuoted -- unzip :: [(a,b)] -> ([a],[b])on unzip(xys) set xs to {} set ys to {} repeat with xy in xys set end of xs to |1| of xy set end of ys to |2| of xy end repeat return Tuple(xs, ys)end unzip -- words :: String -> [String]on |words|(s) set ca to current application (((ca's NSString's stringWithString:(s))'s ¬ componentsSeparatedByCharactersInSet:(ca's ¬ NSCharacterSet's whitespaceAndNewlineCharacterSet()))'s ¬ filteredArrayUsingPredicate:(ca's ¬ NSPredicate's predicateWithFormat:\"0 < length\")) as listend |words|`\nOutput:\n`\"[('july', '16')]\"`\n\n## C\n\nTranslation of: C#\n`#include <stdbool.h>#include <stdio.h> char *months[] = { \"ERR\", \"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"}; struct Date { int month, day; bool active;} dates[] = { {5,15,true}, {5,16,true}, {5,19,true}, {6,17,true}, {6,18,true}, {7,14,true}, {7,16,true}, {8,14,true}, {8,15,true}, {8,17,true}};#define UPPER_BOUND (sizeof(dates) / sizeof(struct Date)) void printRemaining() { int i, c; for (i = 0, c = 0; i < UPPER_BOUND; i++) { if (dates[i].active) { c++; } } printf(\"%d remaining.\\n\", c);} void printAnswer() { int i; for (i = 0; i < UPPER_BOUND; i++) { if (dates[i].active) { printf(\"%s, %d\\n\", months[dates[i].month], dates[i].day); } }} void firstPass() { // the month cannot have a unique day int i, j, c; for (i = 0; i < UPPER_BOUND; i++) { c = 0;  for (j = 0; j < UPPER_BOUND; j++) { if (dates[j].day == dates[i].day) { c++; } }  if (c == 1) { for (j = 0; j < UPPER_BOUND; j++) { if (!dates[j].active) continue; if (dates[j].month == dates[i].month) { dates[j].active = false; } } } }} void secondPass() { // the day must now be unique int i, j, c; for (i = 0; i < UPPER_BOUND; i++) { if (!dates[i].active) continue; c = 0;  for (j = 0; j < UPPER_BOUND; j++) { if (!dates[j].active) continue; if (dates[j].day == dates[i].day) { c++; } }  if (c > 1) { for (j = 0; j < UPPER_BOUND; j++) { if (!dates[j].active) continue; if (dates[j].day == dates[i].day) { dates[j].active = false; } } } }} void thirdPass() { // the month must now be unique int i, j, c; for (i = 0; i < UPPER_BOUND; i++) { if (!dates[i].active) continue; c = 0;  for (j = 0; j < UPPER_BOUND; j++) { if (!dates[j].active) continue; if (dates[j].month == dates[i].month) { c++; } }  if (c > 1) { for (j = 0; j < UPPER_BOUND; j++) { if (!dates[j].active) continue; if (dates[j].month == dates[i].month) { dates[j].active = false; } } } }} int main() { printRemaining(); // the month cannot have a unique day firstPass();  printRemaining(); // the day must now be unique secondPass();  printRemaining(); // the month must now be unique thirdPass();  printAnswer(); return 0;}`\nOutput:\n```10 remaining.\n5 remaining.\n3 remaining.\nJul, 16```\n\n## C#\n\n`public static class CherylsBirthday{ public static void Main() { var dates = new HashSet<(string month, int day)> { (\"May\", 15), (\"May\", 16), (\"May\", 19), (\"June\", 17), (\"June\", 18), (\"July\", 14), (\"July\", 16), (\"August\", 14), (\"August\", 15), (\"August\", 17) };  Console.WriteLine(dates.Count + \" remaining.\"); //The month cannot have a unique day. var monthsWithUniqueDays = dates.GroupBy(d => d.day).Where(g => g.Count() == 1).Select(g => g.First().month).ToHashSet(); dates.RemoveWhere(d => monthsWithUniqueDays.Contains(d.month)); Console.WriteLine(dates.Count + \" remaining.\"); //The day must now be unique. dates.IntersectWith(dates.GroupBy(d => d.day).Where(g => g.Count() == 1).Select(g => g.First())); Console.WriteLine(dates.Count + \" remaining.\"); //The month must now be unique. dates.IntersectWith(dates.GroupBy(d => d.month).Where(g => g.Count() == 1).Select(g => g.First())); Console.WriteLine(dates.Single()); } }`\nOutput:\n```10 remaining.\n5 remaining.\n3 remaining.\n(July, 16)\n```\n\n## C++\n\nTranslation of: Go\n`#include <algorithm>#include <iostream>#include <vector>using namespace std; const vector<string> MONTHS = { \"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"}; struct Birthday { int month, day;  friend ostream &operator<<(ostream &, const Birthday &);}; ostream &operator<<(ostream &out, const Birthday &birthday) { return out << MONTHS[birthday.month - 1] << ' ' << birthday.day;} template <typename C>bool monthUniqueIn(const Birthday &b, const C &container) { auto it = cbegin(container); auto end = cend(container); int count = 0; while (it != end) { if (it->month == b.month) { count++; } it = next(it); } return count == 1;} template <typename C>bool dayUniqueIn(const Birthday &b, const C &container) { auto it = cbegin(container); auto end = cend(container); int count = 0; while (it != end) { if (it->day == b.day) { count++; } it = next(it); } return count == 1;} template <typename C>bool monthWithUniqueDayIn(const Birthday &b, const C &container) { auto it = cbegin(container); auto end = cend(container); while (it != end) { if (it->month == b.month && dayUniqueIn(*it, container)) { return true; } it = next(it); } return false;} int main() { vector<Birthday> choices = { {5, 15}, {5, 16}, {5, 19}, {6, 17}, {6, 18}, {7, 14}, {7, 16}, {8, 14}, {8, 15}, {8, 17}, };  // Albert knows the month but doesn't know the day. // So the month can't be unique within the choices. vector<Birthday> filtered; for (auto bd : choices) { if (!monthUniqueIn(bd, choices)) { filtered.push_back(bd); } }  // Albert also knows that Bernard doesn't know the answer. // So the month can't have a unique day. vector<Birthday> filtered2; for (auto bd : filtered) { if (!monthWithUniqueDayIn(bd, filtered)) { filtered2.push_back(bd); } }  // Bernard now knows the answer. // So the day must be unique within the remaining choices. vector<Birthday> filtered3; for (auto bd : filtered2) { if (dayUniqueIn(bd, filtered2)) { filtered3.push_back(bd); } }  // Albert now knows the answer too. // So the month must be unique within the remaining choices. vector<Birthday> filtered4; for (auto bd : filtered3) { if (monthUniqueIn(bd, filtered3)) { filtered4.push_back(bd); } }  if (filtered4.size() == 1) { cout << \"Cheryl's birthday is \" << filtered4 << '\\n'; } else { cout << \"Something went wrong!\\n\"; }  return 0;}`\nOutput:\n`Cheryl's birthday is Jul 16`\n\n## Common Lisp\n\n` ;; Author: Amir Teymuri, Saturday 20.10.2018 (defparameter *possible-dates* '((15 . may) (16 . may) (19 . may) (17 . june) (18 . june) (14 . july) (16 . july) (14 . august) (15 . august) (17 . august))) (defun unique-date-parts (possible-dates &key (alist-look-at #'car) (alist-r-assoc #'assoc)) (let* ((date-parts (mapcar alist-look-at possible-dates))\t \t (unique-date-parts (remove-if #'(lambda (part) (> (count part date-parts) 1)) date-parts))) (mapcar #'(lambda (part) (funcall alist-r-assoc part possible-dates)) \t unique-date-parts))) (defun person (person possible-dates) \"Who's turn is it to think?\" (case person ('albert (unique-date-parts possible-dates :alist-look-at #'cdr :alist-r-assoc #'rassoc)) ('bernard (unique-date-parts possible-dates :alist-look-at #'car :alist-r-assoc #'assoc)))) (defun cheryls-birthday (possible-dates) (person 'albert\t\t\t\t (person 'bernard\t\t\t\t (set-difference\t\t possible-dates\t\t (person 'bernard possible-dates)\t\t :key #'cdr)))) (cheryls-birthday *possible-dates*) ;; => ((16 . JULY)) `\n\n## D\n\n`import std.algorithm.iteration : filter, joiner, map;import std.algorithm.searching : canFind;import std.algorithm.sorting : sort;import std.array : array;import std.datetime.date : Date, Month;import std.stdio : writeln; void main() { auto choices = [ // Month.jan Date(2019, Month.may, 15), Date(2019, Month.may, 16), Date(2019, Month.may, 19), // unique day (1)  Date(2019, Month.jun, 17), Date(2019, Month.jun, 18), // unique day (1)  Date(2019, Month.jul, 14), Date(2019, Month.jul, 16), // final answer  Date(2019, Month.aug, 14), Date(2019, Month.aug, 15), Date(2019, Month.aug, 17), ];  // The month cannot have a unique day because Albert knows the month, and knows that Bernard does not know the answer auto uniqueMonths = choices.sort!\"a.day < b.day\".groupBy.filter!\"a.array.length == 1\".joiner.map!\"a.month\"; // writeln(uniqueMonths.save); auto filter1 = choices.filter!(a => !canFind(uniqueMonths.save, a.month)).array;  // Bernard now knows the answer, so the day must be unique within the remaining choices auto uniqueDays = filter1.sort!\"a.day < b.day\".groupBy.filter!\"a.array.length == 1\".joiner.map!\"a.day\"; auto filter2 = filter1.filter!(a => canFind(uniqueDays.save, a.day)).array;  // Albert knows the answer too, so the month must be unique within the remaining choices auto birthDay = filter2.sort!\"a.month < b.month\".groupBy.filter!\"a.array.length == 1\".joiner.front;  // print the result writeln(birthDay.month, \" \", birthDay.day);}`\nOutput:\n`jul 16`\n\n## F#\n\n` //Find Cheryl's Birthday. Nigel Galloway: October 23rd., 2018type Month = |May |June |July |Augustlet fN n= n |> List.filter(fun (_,n)->(List.length n) < 2) |> List.unziplet dates = [(May,15);(May,16);(May,19);(June,17);(June,18);(July,14);(July,16);(August,14);(August,15);(August,17)]let _,n = dates |> List.groupBy snd |> fNlet i = n |> List.concat |> List.map fst |> Set.ofListlet _,g = dates |> List.filter(fun (n,_)->not (Set.contains n i)) |> List.groupBy snd |> fNlet _,e = List.concat g |> List.groupBy fst |> fNprintfn \"%A\" e `\nOutput:\n```[[(July, 16)]]\n```\n\n## Factor\n\n`USING: assocs calendar.english fry io kernel prettyprintsequences sets.extras ; : unique-by ( seq quot -- newseq ) 2dup map non-repeating '[ @ _ member? ] filter ; inline ALIAS: day firstALIAS: month second { { 15 5 } { 16 5 } { 19 5 } { 17 6 } { 18 6 } { 14 7 } { 16 7 } { 14 8 } { 15 8 } { 17 8 }} ! the month cannot have a unique daydup [ day ] map non-repeating over extract-keys values'[ month _ member? ] reject ! of the remaining dates, day must be unique[ day ] unique-by ! of the remaining dates, month must be unique[ month ] unique-by ! print a date that looks like { { 16 7 } }first first2 month-name write bl .`\nOutput:\n```July 16\n```\n\n## Go\n\n`package main import ( \"fmt\" \"time\") type birthday struct{ month, day int } func (b birthday) String() string { return fmt.Sprintf(\"%s %d\", time.Month(b.month), b.day)} func (b birthday) monthUniqueIn(bds []birthday) bool { count := 0 for _, bd := range bds { if bd.month == b.month { count++ } } if count == 1 { return true } return false} func (b birthday) dayUniqueIn(bds []birthday) bool { count := 0 for _, bd := range bds { if bd.day == b.day { count++ } } if count == 1 { return true } return false} func (b birthday) monthWithUniqueDayIn(bds []birthday) bool { for _, bd := range bds { if bd.month == b.month && bd.dayUniqueIn(bds) { return true } } return false} func main() { choices := []birthday{ {5, 15}, {5, 16}, {5, 19}, {6, 17}, {6, 18}, {7, 14}, {7, 16}, {8, 14}, {8, 15}, {8, 17}, }  // Albert knows the month but doesn't know the day. // So the month can't be unique within the choices. var filtered []birthday for _, bd := range choices { if !bd.monthUniqueIn(choices) { filtered = append(filtered, bd) } }  // Albert also knows that Bernard doesn't know the answer. // So the month can't have a unique day. var filtered2 []birthday for _, bd := range filtered { if !bd.monthWithUniqueDayIn(filtered) { filtered2 = append(filtered2, bd) } }  // Bernard now knows the answer. // So the day must be unique within the remaining choices. var filtered3 []birthday for _, bd := range filtered2 { if bd.dayUniqueIn(filtered2) { filtered3 = append(filtered3, bd) } }  // Albert now knows the answer too. // So the month must be unique within the remaining choices. var filtered4 []birthday for _, bd := range filtered3 { if bd.monthUniqueIn(filtered3) { filtered4 = append(filtered4, bd) } }  if len(filtered4) == 1 { fmt.Println(\"Cheryl's birthday is\", filtered4) } else { fmt.Println(\"Something went wrong!\") }}`\nOutput:\n```Cheryl's birthday is July 16\n```\n\n## Groovy\n\nTranslation of: Java\n`import java.time.Month class Main { private static class Birthday { private Month month private int day  Birthday(Month month, int day) { this.month = month this.day = day }  Month getMonth() { return month }  int getDay() { return day }  @Override String toString() { return month.toString() + \" \" + day } }  static void main(String[] args) { List<Birthday> choices = [ new Birthday(Month.MAY, 15), new Birthday(Month.MAY, 16), new Birthday(Month.MAY, 19), new Birthday(Month.JUNE, 17), new Birthday(Month.JUNE, 18), new Birthday(Month.JULY, 14), new Birthday(Month.JULY, 16), new Birthday(Month.AUGUST, 14), new Birthday(Month.AUGUST, 15), new Birthday(Month.AUGUST, 17) ] println(\"There are \\${choices.size()} candidates remaining.\")  // The month cannot have a unique day because Albert knows the month, and knows that Bernard does not know the answer Set<Birthday> uniqueMonths = choices.groupBy { it.getDay() } .values() .findAll() { it.size() == 1 } .flatten() .collect { ((Birthday) it).getMonth() } .toSet() as Set<Birthday> def f1List = choices.findAll { !uniqueMonths.contains(it.getMonth()) } println(\"There are \\${f1List.size()} candidates remaining.\")  // Bernard now knows the answer, so the day must be unique within the remaining choices List<Birthday> f2List = f1List.groupBy { it.getDay() } .values() .findAll { it.size() == 1 } .flatten() .toList() as List<Birthday> println(\"There are \\${f2List.size()} candidates remaining.\")  // Albert knows the answer too, so the month must be unique within the remaining choices List<Birthday> f3List = f2List.groupBy { it.getMonth() } .values() .findAll { it.size() == 1 } .flatten() .toList() as List<Birthday> println(\"There are \\${f3List.size()} candidates remaining.\")  if (f3List.size() == 1) { println(\"Cheryl's birthday is \\${f3List.head()}\") } else { System.out.println(\"No unique choice found\") } }}`\nOutput:\n```There are 10 candidates remaining.\nThere are 5 candidates remaining.\nThere are 3 candidates remaining.\nThere are 1 candidates remaining.\nCheryl's birthday is JULY 16```\n\n`{-# LANGUAGE OverloadedStrings #-} import Data.List as L (filter, groupBy, head, length, sortOn)import Data.Map.Strict as M (Map, fromList, keys, lookup)import Data.Text as T (Text, splitOn, words)import Data.Maybe (fromJust)import Data.Ord (comparing)import Data.Function (on)import Data.Tuple (swap)import Data.Bool (bool) data DatePart = Month | Day type M = Text type D = Text main :: IO ()main = print \\$ -- The month with only one remaining day, -- -- (A's month contains only one remaining day) -- (3 :: A \"Then I also know\") uniquePairing Month \\$ -- among the days with unique months, -- -- (B's day is paired with only one remaining month) -- (2 :: B \"I know now\") uniquePairing Day \\$ -- excluding months with unique days, -- -- (A's month is not among those with unique days) -- (1 :: A \"I know that Bernard does not know\") monthsWithUniqueDays False \\$ -- from the given month-day pairs: -- -- (0 :: Cheryl's list) (\\(x:y:_) -> (x, y)) . T.words <\\$> splitOn \", \" \"May 15, May 16, May 19, June 17, June 18, \\ \\July 14, July 16, Aug 14, Aug 15, Aug 17\" ----------------------QUERY FUNCTIONS----------------------monthsWithUniqueDays :: Bool -> [(M, D)] -> [(M, D)]monthsWithUniqueDays bln xs = let months = fst <\\$> uniquePairing Day xs in L.filter (bool not id bln . (`elem` months) . fst) xs uniquePairing :: DatePart -> [(M, D)] -> [(M, D)]uniquePairing dp xs = let f = case dp of Month -> fst Day -> snd in (\\md -> let dct = f md uniques = L.filter ((1 ==) . L.length . fromJust . flip M.lookup dct) (keys dct) in L.filter ((`elem` uniques) . f) xs) ((((,) . mapFromPairs) <*> mapFromPairs . fmap swap) xs) mapFromPairs :: [(M, D)] -> Map Text [Text]mapFromPairs xs = M.fromList \\$ ((,) . fst . L.head) <*> fmap snd <\\$> L.groupBy (on (==) fst) (L.sortOn fst xs)`\nOutput:\n`[(\"July\",\"16\")]`\n\n## J\n\nSolution:\n\n`Dates=: <;._2 noun define15 May16 May19 May17 June18 June14 July16 July14 August15 August17 August) getDayMonth=: |:@:(' '&splitstring&>) NB. retrieve lists of days and months from dateskeep=: adverb def '] #~ u' NB. apply mask to filter dates monthsWithUniqueDay=: {./. #~ (1=#)/. NB. list months that have a unique dayisMonthWithoutUniqueDay=: (] [email protected] monthsWithUniqueDay)/@getDayMonth NB. mask of dates with a month that doesn't have a unique day uniqueDayInMonth=: [email protected][ #~ (1=#)/. NB. list of days that are unique to 1 monthisUniqueDayInMonth=: ([ e. uniqueDayInMonth)/@getDayMonth NB. mask of dates with a day that is unique to 1 month uniqueMonth=: [email protected]] #~ (1=#)/.~ NB. list of months with 1 unique dayisUniqueMonth=: (] e. uniqueMonth)/@getDayMonth NB. mask of dates with a month that has 1 unique day`\n\nUsage:\n\n` isUniqueMonth keep isUniqueDayInMonth keep isMonthWithoutUniqueDay keep Dates+-------+|16 July|+-------+`\n\n### Alternative Approach\n\nThe concepts here are the same, of course, it's just the presentation that's different.\n\n`possible=: cut;._2 'May 15, May 16, May 19, June 17, June 18, July 14, July 16, August 14, August 15, August 17,' Albert=: {.\"1 NB. Albert knows monthBernard=: {:\"1 NB. Bernard knows day NB. Bernard's understanding of Albert's first pass days=: {:\"1 possible invaliddays=: (1=#/.~ days)#~.days months=: {.\"1 possible validmonths=: months -. (days e. invaliddays)#months possibleA=. (months e. validmonths)# possible NB. Albert's understanding of Bernard's pass days=: {:\"1 possibleA invaliddays=: (1<#/.~ days)#~.days possibleB=. (days e. days-.invaliddays)# possibleA NB. our understanding of Albert's second pass months=: {.\"1 possibleB invalidmonths=: (1<#/.~months)#~.months echo ;:inv (months e. months -. invalidmonths)#possibleB`\n\nThis gives us the July 16 result we were expecting\n\n## Java\n\nTranslation of: D\n`import java.time.Month;import java.util.Collection;import java.util.List;import java.util.Set;import java.util.stream.Collectors; public class Main { private static class Birthday { private Month month; private int day;  public Birthday(Month month, int day) { this.month = month; this.day = day; }  public Month getMonth() { return month; }  public int getDay() { return day; }  @Override public String toString() { return month + \" \" + day; } }  public static void main(String[] args) { List<Birthday> choices = List.of( new Birthday(Month.MAY, 15), new Birthday(Month.MAY, 16), new Birthday(Month.MAY, 19), new Birthday(Month.JUNE, 17), new Birthday(Month.JUNE, 18), new Birthday(Month.JULY, 14), new Birthday(Month.JULY, 16), new Birthday(Month.AUGUST, 14), new Birthday(Month.AUGUST, 15), new Birthday(Month.AUGUST, 17) ); System.out.printf(\"There are %d candidates remaining.\\n\", choices.size());  // The month cannot have a unique day because Albert knows the month, and knows that Bernard does not know the answer Set<Month> uniqueMonths = choices.stream() .collect(Collectors.groupingBy(Birthday::getDay)) .values() .stream() .filter(g -> g.size() == 1) .flatMap(Collection::stream) .map(Birthday::getMonth) .collect(Collectors.toSet()); List<Birthday> f1List = choices.stream() .filter(birthday -> !uniqueMonths.contains(birthday.month)) .collect(Collectors.toList()); System.out.printf(\"There are %d candidates remaining.\\n\", f1List.size());  // Bernard now knows the answer, so the day must be unique within the remaining choices List<Birthday> f2List = f1List.stream() .collect(Collectors.groupingBy(Birthday::getDay)) .values() .stream() .filter(g -> g.size() == 1) .flatMap(Collection::stream) .collect(Collectors.toList()); System.out.printf(\"There are %d candidates remaining.\\n\", f2List.size());  // Albert knows the answer too, so the month must be unique within the remaining choices List<Birthday> f3List = f2List.stream() .collect(Collectors.groupingBy(Birthday::getMonth)) .values() .stream() .filter(g -> g.size() == 1) .flatMap(Collection::stream) .collect(Collectors.toList()); System.out.printf(\"There are %d candidates remaining.\\n\", f3List.size());  if (f3List.size() == 1) { System.out.printf(\"Cheryl's birthday is %s\\n\", f3List.get(0)); } else { System.out.println(\"No unique choice found\"); } }}`\nOutput:\n```There are 10 candidates remaining.\nThere are 5 candidates remaining.\nThere are 3 candidates remaining.\nThere are 1 candidates remaining.\nCheryl's birthday is JULY 16```\n\n## JavaScript\n\n`(() => { 'use strict';  // main :: IO () const main = () => { const month = fst, day = snd; showLog( map(x => Array.from(x), (  // The month with only one remaining day,  // (A's month contains only one remaining day) // (3 :: A \"Then I also know\") uniquePairing(month)(  // among the days with unique months,  // (B's day is paired with only one remaining month) // (2 :: B \"I know now\") uniquePairing(day)(  // excluding months with unique days,  // (A's month is not among those with unique days) // (1 :: A \"I know that Bernard does not know\") monthsWithUniqueDays(false)(  // from the given month-day pairs:  // (0 :: Cheryl's list) map(x => tupleFromList(words(strip(x))), splitOn(/,\\s+/, `May 15, May 16, May 19, June 17, June 18, July 14, July 16, Aug 14, Aug 15, Aug 17` ) ) ) ) ) )) ); };  // monthsWithUniqueDays :: Bool -> [(Month, Day)] -> [(Month, Day)] const monthsWithUniqueDays = blnInclude => xs => { const months = map(fst, uniquePairing(snd)(xs)); return filter( md => (blnInclude ? id : not)( elem(fst(md), months) ), xs ); };  // uniquePairing :: ((a, a) -> a) -> // -> [(Month, Day)] -> [(Month, Day)] const uniquePairing = f => xs => bindPairs(xs, md => { const dct = f(md), matches = filter( k => 1 === length(dct[k]), Object.keys(dct) ); return filter(tpl => elem(f(tpl), matches), xs); } );  // bindPairs :: [(Month, Day)] -> (Dict, Dict) -> [(Month, Day)] const bindPairs = (xs, f) => f( Tuple( dictFromPairs(fst)(snd)(xs), dictFromPairs(snd)(fst)(xs) ) );  // dictFromPairs :: ((a, a) -> a) -> ((a, a) -> a) -> [(a, a)] -> Dict const dictFromPairs = f => g => xs => foldl((a, tpl) => Object.assign( a, { [f(tpl)]: (a[f(tpl)] || []).concat(g(tpl).toString()) } ), {}, xs);   // GENERIC ABSTRACTIONS -------------------------------  // Tuple (,) :: a -> b -> (a, b) const Tuple = (a, b) => ({ type: 'Tuple', '0': a, '1': b, length: 2 });  // elem :: Eq a => a -> [a] -> Bool const elem = (x, xs) => xs.includes(x);  // filter :: (a -> Bool) -> [a] -> [a] const filter = (f, xs) => xs.filter(f);  // foldl :: (a -> b -> a) -> a -> [b] -> a const foldl = (f, a, xs) => xs.reduce(f, a);  // fst :: (a, b) -> a const fst = tpl => tpl;  // id :: a -> a const id = x => x;  // intersect :: (Eq a) => [a] -> [a] -> [a] const intersect = (xs, ys) => xs.filter(x => -1 !== ys.indexOf(x));  // Returns Infinity over objects without finite length // this enables zip and zipWith to choose the shorter // argument when one is non-finite, like cycle, repeat etc  // length :: [a] -> Int const length = xs => (Array.isArray(xs) || 'string' === typeof xs) ? ( xs.length ) : Infinity;  // map :: (a -> b) -> [a] -> [b] const map = (f, xs) => xs.map(f);  // not :: Bool -> Bool const not = b => !b;  // showLog :: a -> IO () const showLog = (...args) => console.log( args .map(JSON.stringify) .join(' -> ') );  // snd :: (a, b) -> b const snd = tpl => tpl;  // splitOn :: String -> String -> [String] const splitOn = (pat, src) => src.split(pat);  // strip :: String -> String const strip = s => s.trim();  // tupleFromList :: [a] -> (a, a ...) const tupleFromList = xs => TupleN.apply(null, xs);  // TupleN :: a -> b ... -> (a, b ... ) function TupleN() { const args = Array.from(arguments), lng = args.length; return lng > 1 ? Object.assign( args.reduce((a, x, i) => Object.assign(a, { [i]: x }), { type: 'Tuple' + (2 < lng ? lng.toString() : ''), length: lng }) ) : args; };  // words :: String -> [String] const words = s => s.split(/\\s+/);  // MAIN --- return main();})();`\nOutput:\n`[[\"July\",\"16\"]]`\n\n## Julia\n\n`const dates = [[15, \"May\"], [16, \"May\"], [19, \"May\"], [17, \"June\"], [18, \"June\"], [14, \"July\"], [16, \"July\"], [14, \"August\"], [15, \"August\"], [17, \"August\"]] uniqueday(parr) = filter(x -> count(y -> y == x, parr) == 1, parr) # At the start, they come to know that they have no unique day of month to identify.const f1 = filter(m -> !(m in [d for d in uniqueday(dates)]), dates) # After cutting months with unique dates, get months remaining that now have a unique date.const f2 = uniqueday(f1) # filter for those of the finally remaining months that have only one date left.const bday = filter(x -> count(m -> m == x, f2) == 1, f2)[] println(\"Cheryl's birthday is \\$(bday) \\$(bday).\") `\nOutput:\n```Cheryl's birthday is July 16.\n```\n\n## Kotlin\n\nTranslation of: Go\n`// Version 1.2.71 val months = listOf( \"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\") class Birthday(val month: Int, val day: Int) { public override fun toString() = \"\\${months[month - 1]} \\$day\"  public fun monthUniqueIn(bds: List<Birthday>): Boolean { return bds.count { this.month == it.month } == 1 }  public fun dayUniqueIn(bds: List<Birthday>): Boolean { return bds.count { this.day == it.day } == 1 }  public fun monthWithUniqueDayIn(bds: List<Birthday>): Boolean { return bds.any { (this.month == it.month) && it.dayUniqueIn(bds) } }} fun main(args: Array<String>) { val choices = listOf( Birthday(5, 15), Birthday(5, 16), Birthday(5, 19), Birthday(6, 17), Birthday(6, 18), Birthday(7, 14), Birthday(7, 16), Birthday(8, 14), Birthday(8, 15), Birthday(8, 17) )  // Albert knows the month but doesn't know the day. // So the month can't be unique within the choices. var filtered = choices.filterNot { it.monthUniqueIn(choices) }  // Albert also knows that Bernard doesn't know the answer. // So the month can't have a unique day. filtered = filtered.filterNot { it.monthWithUniqueDayIn(filtered) }  // Bernard now knows the answer. // So the day must be unique within the remaining choices. filtered = filtered.filter { it.dayUniqueIn(filtered) }  // Albert now knows the answer too. // So the month must be unique within the remaining choices. filtered = filtered.filter { it.monthUniqueIn(filtered) }  if (filtered.size == 1) println(\"Cheryl's birthday is \\${filtered}\") else println(\"Something went wrong!\")}`\nOutput:\n```Cheryl's birthday is July 16\n```\n\n## Lua\n\n`-- Cheryl's Birthday in Lua 6/15/2020 db local function Date(mon,day) return { mon=mon, day=day, valid=true }end local choices = { Date(\"May\", 15), Date(\"May\", 16), Date(\"May\", 19), Date(\"June\", 17), Date(\"June\", 18), Date(\"July\", 14), Date(\"July\", 16), Date(\"August\", 14), Date(\"August\", 15), Date(\"August\", 17)} local function apply(t, f) for k, v in ipairs(t) do f(k, v) endend local function filter(t, f) local result = {} for k, v in ipairs(t) do if f(k, v) then result[#result+1] = v end end return resultend local function map(t, f) local result = {} for k, v in ipairs(t) do result[#result+1] = f(k, v) end return resultend local function count(t) return #t endlocal function isvalid(k, v) return v.valid endlocal function invalidate(k, v) v.valid = false endlocal function remaining() return filter(choices, isvalid) end local function listValidChoices() print(\" \" .. table.concat(map(remaining(), function(k, v) return v.mon .. \" \" .. v.day end), \", \")) print()end print(\"Cheryl offers these ten choices:\")listValidChoices() print(\"1) Albert knows that Bernard also cannot yet know, so cannot be a month with a unique day, leaving:\")apply(remaining(), function(k, v) if count(filter(choices, function(k2, v2) return v.day==v2.day end)) == 1 then apply(filter(remaining(), function(k2, v2) return v.mon==v2.mon end), invalidate) endend)listValidChoices() print(\"2) After Albert's revelation, Bernard now knows, so day must be unique, leaving:\")apply(remaining(), function(k, v) local subset = filter(remaining(), function(k2, v2) return v.day==v2.day end) if count(subset) > 1 then apply(subset, invalidate) endend)listValidChoices() print(\"3) After Bernard's revelation, Albert now knows, so month must be unique, leaving only:\")apply(remaining(), function(k, v) local subset = filter(remaining(), function(k2, v2) return v.mon==v2.mon end) if count(subset) > 1 then apply(subset, invalidate) endend)listValidChoices()`\nOutput:\n```Cheryl offers these ten choices:\nMay 15, May 16, May 19, June 17, June 18, July 14, July 16, August 14, August 15, August 17\n\n1) Albert knows that Bernard also cannot yet know, so cannot be a month with a unique day, leaving:\nJuly 14, July 16, August 14, August 15, August 17\n\n2) After Albert's revelation, Bernard now knows, so day must be unique, leaving:\nJuly 16, August 15, August 17\n\n3) After Bernard's revelation, Albert now knows, so month must be unique, leaving only:\nJuly 16```\n\n## Nim\n\n`import tablesimport setsimport strformat type Date = tuple[month: string, day: int] const Dates = [Date (\"May\", 15), (\"May\", 16), (\"May\", 19), (\"June\", 17), (\"June\", 18), (\"July\", 14), (\"July\", 16), (\"August\", 14), (\"August\", 15), (\"August\", 17)] const  MonthTable: Table[int, HashSet[string]] = static: var t: Table[int, HashSet[string]] for date in Dates: t.mgetOrPut(date.day, initHashSet[string]()).incl(date.month) t  DayTable: Table[string, HashSet[int]] = static: var t: Table[string, HashSet[int]] for date in Dates: t.mgetOrPut(date.month, initHashSet[int]()).incl(date.day) t var possibleMonths: HashSet[string] # Set of possible months.var possibleDays: HashSet[int] # Set of possible days.  # Albert: I don't know when Cheryl's birthday is, ...# => eliminate months with a single possible day.for month, days in DayTable.pairs: if days.len > 1: possibleMonths.incl(month) # ... but I know that Bernard does not know too.# => eliminate months with one day present only in this month.for month, days in DayTable.pairs: for day in days: if MonthTable[day].len == 1: possibleMonths.excl(month)echo fmt\"After first Albert's sentence, possible months are {possibleMonths}.\" # Bernard: At first I don't know when Cheryl's birthday is, ...# => eliminate days with a single possible month.for day, months in MonthTable.pairs: if months.len > 1: possibleDays.incl(day) # ... but I know now.# => eliminate days which are compatible with several months in \"possibleMonths\".var impossibleDays: HashSet[int] # Days which are eliminated by this sentence.for day in possibleDays: if (MonthTable[day] * possibleMonths).len > 1: impossibleDays.incl(day)possibleDays.excl(impossibleDays)echo fmt\"After Bernard's sentence, possible days are {possibleDays}.\" # Albert: Then I also know when Cheryl's birthday is.# => eliminate months which are compatible with several days in \"possibleDays\".var impossibleMonths: HashSet[string] # Months which are eliminated by this sentence.for month in possibleMonths: if (DayTable[month] * possibleDays).len > 1: impossibleMonths.incl(month)possibleMonths.excl(impossibleMonths) doAssert possibleMonths.len == 1let month = possibleMonths.pop()echo fmt\"After second Albert's sentence, remaining month is {month}...\" possibleDays = possibleDays * DayTable[month]doAssert possibleDays.len == 1let day = possibleDays.pop()echo fmt\"and thus remaining day is {day}.\" echo \"\"echo fmt\"So birthday date is {month} {day}.\"`\nOutput:\n```After first Albert's sentence, possible months are {\"August\", \"July\"}.\nAfter Bernard's sentence, possible days are {16, 17, 15}.\nAfter second Albert's sentence, remaining month is July...\nand thus remaining day is 16.\n\nSo birthday date is July 16```\n\n## Perl\n\n`sub filter { my(\\$test,@dates) = @_; my(%M,%D,@filtered);  # analysis of potential birthdays, keyed by month and by day for my \\$date (@dates) { my(\\$mon,\\$day) = split '-', \\$date; \\$M{\\$mon}{cnt}++; \\$D{\\$day}{cnt}++; push @{\\$M{\\$mon}{day}}, \\$day; push @{\\$D{\\$day}{mon}}, \\$mon; push @{\\$M{\\$mon}{bday}}, \"\\$mon-\\$day\"; push @{\\$D{\\$day}{bday}}, \"\\$mon-\\$day\"; }  # eliminates May/Jun dates based on 18th and 19th being singletons if (\\$test eq 'singleton') { my %skip; for my \\$day (grep { \\$D{\\$_}{cnt} == 1 } keys %D) { \\$skip{ @{\\$D{\\$day}{mon}} }++ } for my \\$mon (grep { ! \\$skip{\\$_} } keys %M) { push @filtered, @{\\$M{\\$mon}{bday}} }  # eliminates Jul/Aug 14th because day count > 1 across months } elsif (\\$test eq 'duplicate') { for my \\$day (grep { \\$D{\\$_}{cnt} == 1 } keys %D) { push @filtered, @{\\$D{\\$day}{bday}} }  # eliminates Aug 15th/17th because day count > 1, within month } elsif (\\$test eq 'multiple') { for my \\$day (grep { \\$M{\\$_}{cnt} == 1 } keys %M) { push @filtered, @{\\$M{\\$day}{bday}} } } return @filtered;} # doesn't matter what order singleton/duplicate tests are run, but 'multiple' must be last;my @dates = qw<5-15 5-16 5-19 6-17 6-18 7-14 7-16 8-14 8-15 8-17>;@dates = filter(\\$_, @dates) for qw<singleton duplicate multiple>; my @months = qw<_ January February March April May June July August September October November December>; my (\\$m, \\$d) = split '-', \\$dates;print \"Cheryl's birthday is \\$months[\\$m] \\$d.\\n\";`\nOutput:\n`Cheryl's birthday is July 16.`\n\n## Phix\n\n`-- demo\\rosetta\\Cheryls_Birthday.exwsequence choices = {{5, 15}, {5, 16}, {5, 19}, {6, 17}, {6, 18}, {7, 14}, {7, 16}, {8, 14}, {8, 15}, {8, 17}} sequence mwud = repeat(false,12) -- months with unique days for step=1 to 4 do sequence {months,days} = columnize(choices) bool impossible = false for i=length(choices) to 1 by -1 do integer {m,d} = choices[i] switch step do case 1: mwud[m] += (sum(sq_eq(days,d))=1) case 2: impossible = mwud[m] case 3: impossible = (sum(sq_eq(days,d))!=1) case 4: impossible = (sum(sq_eq(months,m))!=1) end switch if impossible then choices[i..i] = {} end if end forend for?choices`\n\nIterating backwards down the choices array simplifies element removal.\nStep 1&2 is months with unique days, step 3 is days with unique months, step 4 is unique months.\n\nOutput:\n```{{7,16}}\n```\n\n### functional/filter\n\n(this can also be found in demo\\rosetta\\Cheryls_Birthday.exw)\n\n`enum MONTH, DAY function unique_month(sequence si, months) return sum(sq_eq(months,si[MONTH]))=1end function function unique_day(sequence si, days) return sum(sq_eq(days,si[DAY]))=1end function function month_without_unique_day(sequence si, months_with_unique_day) return not find(si[MONTH],months_with_unique_day)end function sequence choices = {{5, 15}, {5, 16}, {5, 19}, {6, 17}, {6, 18}, {7, 14}, {7, 16}, {8, 14}, {8, 15}, {8, 17}} -- Albert knows the month but does not know the day.-- So the month cannot be unique within the choices.-- However this step would change nothing, hence omit it.-- (obvs. non_unique_month() would be as above, but !=1)--choices = filter(choices,non_unique_month,vslice(choices,MONTH)) -- Albert also knows that Bernard doesn't know the answer.-- So the month cannot have a unique day.sequence unique_days = filter(choices,unique_day,vslice(choices,DAY))sequence months_with_unique_day = unique(vslice(unique_days,MONTH)) choices = filter(choices,month_without_unique_day,months_with_unique_day) -- Bernard now knows the answer.-- So the day must be unique within the remaining choices.choices = filter(choices,unique_day,vslice(choices,DAY)) -- Albert now knows the answer too.-- So the month must be unique within the remaining choices.choices = filter(choices,unique_month,vslice(choices,MONTH)) if length(choices)!=1 then crash(\"Something went wrong!\") end if include builtins\\timedate.etimedate td = repeat(0,6){td[DT_MONTH],td[DT_DAY]} = choicesprintf(1,\"Cheryl's birthday is %s\\n\", {format_timedate(td,\"Mmmm ddth\")})`\nOutput:\n```Cheryl's birthday is July 16th\n```\n\n## Python\n\n### Functional\n\nWorks with: Python version 3\n`'''Cheryl's Birthday''' from itertools import groupbyfrom re import split  # main :: IO ()def main(): '''Derivation of the date.'''  month, day = 0, 1 print( # (3 :: A \"Then I also know\") # (A's month contains only one remaining day) uniquePairing(month)( # (2 :: B \"I know now\") # (B's day is paired with only one remaining month) uniquePairing(day)( # (1 :: A \"I know that Bernard does not know\") # (A's month is not among those with unique days) monthsWithUniqueDays(False)([ # 0 :: Cheryl's list: tuple(x.split()) for x in split( ', ', 'May 15, May 16, May 19, ' + 'June 17, June 18, ' + 'July 14, July 16, ' + 'Aug 14, Aug 15, Aug 17' ) ]) ) ) )  # ------------------- QUERY FUNCTIONS -------------------- # monthsWithUniqueDays :: Bool -> [(Month, Day)] -> [(Month, Day)]def monthsWithUniqueDays(blnInclude): '''The subset of months with (or without) unique days. ''' def go(xs): month, day = 0, 1 months = [fst(x) for x in uniquePairing(day)(xs)] return [ md for md in xs if blnInclude or not (md[month] in months) ] return go  # uniquePairing :: DatePart -> [(Month, Day)] -> [(Month, Day)]def uniquePairing(i): '''Subset of months (or days) with a unique intersection. ''' def go(xs): def inner(md): dct = md[i] uniques = [ k for k in dct.keys() if 1 == len(dct[k]) ] return [tpl for tpl in xs if tpl[i] in uniques] return inner return ap(bindPairs)(go)  # bindPairs :: [(Month, Day)] -># ((Dict String [String], Dict String [String])# -> [(Month, Day)]) -> [(Month, Day)]def bindPairs(xs): '''List monad injection operator for lists of (Month, Day) pairs. ''' return lambda f: f( ( dictFromPairs(xs), dictFromPairs( [(b, a) for (a, b) in xs] ) ) )  # dictFromPairs :: [(Month, Day)] -> Dict Text [Text]def dictFromPairs(xs): '''A dictionary derived from a list of month day pairs. ''' return { k: [snd(x) for x in m] for k, m in groupby( sorted(xs, key=fst), key=fst ) }  # ----------------------- GENERIC ------------------------ # ap :: (a -> b -> c) -> (a -> b) -> a -> cdef ap(f): '''Applicative instance for functions. ''' def go(g): def fxgx(x): return f(x)( g(x) ) return fxgx return go  # fst :: (a, b) -> adef fst(tpl): '''First component of a pair. ''' return tpl  # snd :: (a, b) -> bdef snd(tpl): '''Second component of a pair. ''' return tpl  if __name__ == '__main__': main()`\nOutput:\n`[('July', '16')]`\n\n## Racket\n\nTranslation of: Kotlin\n`#lang racket (define ((is x #:key [key identity]) y) (equal? (key x) (key y))) (define albert first)(define bernard second) (define (((unique who) chs) date) (= 1 (count (is date #:key who) chs))) (define (((unique-fix who-fix who) chs) date) (ormap (conjoin (is date #:key who-fix) ((unique who) chs)) chs)) (define-syntax-rule (solve <chs> [<act> <arg>] ...) (let* ([chs <chs>] [chs (<act> (<arg> chs) chs)] ...) chs)) (solve '((May 15) (May 16) (May 19) (June 17) (June 18) (July 14) (July 16) (August 14) (August 15) (August 17))   ;; Albert knows the month but doesn't know the day.  ;; So the month can't be unique within the choices. [filter-not (unique albert)]  ;; Albert also knows that Bernard doesn't know the answer.  ;; So the month can't have a unique day. [filter-not (unique-fix albert bernard)]  ;; Bernard now knows the answer.  ;; So the day must be unique within the remaining choices. [filter (unique bernard)]  ;; Albert now knows the answer too.  ;; So the month must be unique within the remaining choices [filter (unique albert)])`\nOutput:\n```'((July 16))\n```\n\n## Raku\n\n(formerly Perl 6)\n\n`my @dates = { :15day, :5month }, { :16day, :5month }, { :19day, :5month }, { :17day, :6month }, { :18day, :6month }, { :14day, :7month }, { :16day, :7month }, { :14day, :8month }, { :15day, :8month }, { :17day, :8month }; # Month can't have a unique daymy @filtered = @dates.grep(*.<month> != one(@dates.grep(*.<day> == one(@dates».<day>))».<month>)); # Day must be unique and unambiguous in remaining monthsmy \\$birthday = @filtered.grep(*.<day> == one(@filtered».<day>)).classify({.<month>})\\ .first(*.value.elems == 1).value; # convenience arraymy @months = <'' January February March April May June July August September October November December>; say \"Cheryl's birthday is { @months[\\$birthday<month>] } {\\$birthday<day>}.\";`\nOutput:\n`Cheryl's birthday is July 16.`\n\n## REXX\n\n`/*REXX pgm finds Cheryl's birth date based on a person knowing the birth month, another *//*──────────────────────── person knowing the birth day, given a list of possible dates.*/\\$= 'May-15 May-16 May-19 June-17 June-18 July-14 July-16 August-14 August-15 August-17'call delDays unique('day')\\$= unique('day')\\$= unique('month')if words(\\$)==1 then say \"Cheryl's birthday is\" translate(\\$, , '-') else say \"error in the program's logic.\"exit 0/*──────────────────────────────────────────────────────────────────────────────────────*/unique: arg u 2, dups; #= words(\\$); \\$\\$= \\$ do j=# to 2 by -1 if u=='D' then parse value word(\\$, j) with '-' x else parse value word(\\$, j) with x '-' do k=1 for j-1 if u=='D' then parse value word(\\$, k) with '-' y else parse value word(\\$, k) with y '-' if x==y then dups= dups k j end /*k*/ end /*j*/ do d=# for # by -1 do p=1 for words(dups) until ?==d;  ?= word(dups,p) if ?==d then \\$\\$= delword(\\$\\$, ?, 1) end /*d*/ end /*d*/ if words(\\$\\$)==0 then return \\$ else return \\$\\$/*──────────────────────────────────────────────────────────────────────────────────────*/delDays: parse arg days; #= words(days) do j=# for # by -1; parse value word(days, j) with x '-'; ##= words(\\$) do k=## for ## by -1; parse value word(\\$, k) with y '-' if x\\==y then iterate; \\$= delword(\\$, k, 1) end /*k*/ end /*j*/ return \\$`\noutput   when using the internal default input:\n```Cheryl's birthday is July 16\n```\n\n## Ruby\n\nTranslation of: C#\n`dates = [ [\"May\", 15], [\"May\", 16], [\"May\", 19], [\"June\", 17], [\"June\", 18], [\"July\", 14], [\"July\", 16], [\"August\", 14], [\"August\", 15], [\"August\", 17],] print dates.length, \" remaining\\n\" # the month cannot have a unique dayuniqueMonths = dates.group_by { |m,d| d } .select { |k,v| v.size == 1 } .map { |k,v| v.flatten } .map { |m,d| m }dates.delete_if { |m,d| uniqueMonths.include? m }print dates.length, \" remaining\\n\" # the day must be uniquedates = dates .group_by { |m,d| d } .select { |k,v| v.size == 1 } .map { |k,v| v.flatten }print dates.length, \" remaining\\n\" # the month must now be uniquedates = dates .group_by { |m,d| m } .select { |k,v| v.size == 1 } .map { |k,v| v } .flattenprint dates`\nOutput:\n```10 remaining\n5 remaining\n3 remaining\n[\"July\", 16]```\n\n## Scala\n\n### Translation of: D\n\n`import java.time.format.DateTimeFormatterimport java.time.{LocalDate, Month} object Cheryl { def main(args: Array[String]): Unit = { val choices = List( LocalDate.of(2019, Month.MAY, 15), LocalDate.of(2019, Month.MAY, 16), LocalDate.of(2019, Month.MAY, 19),  LocalDate.of(2019, Month.JUNE, 17), LocalDate.of(2019, Month.JUNE, 18),  LocalDate.of(2019, Month.JULY, 14), LocalDate.of(2019, Month.JULY, 16),  LocalDate.of(2019, Month.AUGUST, 14), LocalDate.of(2019, Month.AUGUST, 15), LocalDate.of(2019, Month.AUGUST, 17) )  // The month cannot have a unique day because Albert knows the month, and knows that Bernard does not know the answer val uniqueMonths = choices.groupBy(_.getDayOfMonth) .filter(a => a._2.length == 1) .flatMap(a => a._2) .map(a => a.getMonth) val filter1 = choices.filterNot(a => uniqueMonths.exists(b => a.getMonth == b))  // Bernard now knows the answer, so the day must be unique within the remaining choices val uniqueDays = filter1.groupBy(_.getDayOfMonth) .filter(a => a._2.length == 1) .flatMap(a => a._2) .map(a => a.getDayOfMonth) val filter2 = filter1.filter(a => uniqueDays.exists(b => a.getDayOfMonth == b))  // Albert knows the answer too, so the month must be unique within the remaining choices val birthDay = filter2.groupBy(_.getMonth) .filter(a => a._2.length == 1) .flatMap(a => a._2) .head  // print the result printf(birthDay.format(DateTimeFormatter.ofPattern(\"MMMM dd\"))) }}`\nOutput:\n`July 16`\n\n### Scala-ish approach\n\nOutput:\nSee it yourself by running in your browser either by ScalaFiddle (ES aka JavaScript, non JVM) or Scastie (remote JVM).\nWorks with: Scala version 2.13\n`object Cheryl_sBirthday extends App {  private val possiblerDates = Set( Date(\"May\", 15), Date(\"May\", 16), Date(\"May\", 19), Date(\"June\", 17), Date(\"June\", 18), Date(\"July\", 14), Date(\"July\", 16), Date(\"August\", 14), Date(\"August\", 15), Date(\"August\", 17) )  private def clou3: Date = { // Find the dates with ONE unique once and only occurrence of the day of the month. def onceDates[K](toBeExcluded: Set[Date], selector: Date => K): Seq[Date] = toBeExcluded.groupBy(selector).filter { case (_, multiSet) => multiSet.size == 1 }.values.flatten.toSeq  // 1) Albert tells us that Bernard doesn't know the answer, // so we know the answer must be in months that does NOT have a same day of month. val uniqueMonths = onceDates(possiblerDates, (date: Date) => date.dayOfMonth).map(_.month) // Remove the dates with those months. The dates remain which has NOT those months. val clou1 = possiblerDates.filterNot(p => uniqueMonths.contains(p.month)) // 2) Since Bernard now knows the answer, that tells us that the day MUST be unique among the remaining birthdays. val uniqueDays = onceDates(clou1, (date: Date) => date.dayOfMonth).map(_.dayOfMonth)  // 3) Since Albert now knows the answer, that tells us the answer has to be unique by month. // First, as the first parameter, intersect clou1 (Albert) with uniqueDays (Bernard) onceDates(clou1.filter(date => uniqueDays.contains(date.dayOfMonth)), (date: Date) => date.month).head }  case class Date(month: String, dayOfMonth: Int) { override def toString: String = s\"\\${\"🎂 \" * 3}\\$dayOfMonth \\$month\\${\" 🎂\" * 3}\" }  println(clou3)}`\n\n## Sidef\n\nTranslation of: Raku\n`struct Date(day, month) var dates = [ Date(15, \"May\"), Date(16, \"May\"), Date(19, \"May\"), Date(17, \"June\"), Date(18, \"June\"), Date(14, \"July\"), Date(16, \"July\"), Date(14, \"August\"), Date(15, \"August\"), Date(17, \"August\")] var filtered = dates.grep { dates.grep { dates.map{ .day }.count(.day) == 1 }.map{ .month }.count(.month) != 1} var birthday = filtered.grep { filtered.map{ .day }.count(.day) == 1}.group_by{ .month }.values.first_by { .len == 1 } say \"Cheryl's birthday is #{birthday.month} #{birthday.day}.\"`\nOutput:\n```Cheryl's birthday is July 16.\n```\n\n## Swift\n\nTranslation of: Kotlin\n`struct MonthDay: CustomStringConvertible { static let months = [ \"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\" ]  var month: Int var day: Int  var description: String { \"\\(MonthDay.months[month - 1]) \\(day)\" }  private func isUniqueIn(months: [MonthDay], by prop: KeyPath<MonthDay, Int>) -> Bool { return months.lazy.filter({ \\$0[keyPath: prop] == self[keyPath: prop] }).count == 1 }  func monthIsUniqueIn(months: [MonthDay]) -> Bool { return isUniqueIn(months: months, by: \\.month) }  func dayIsUniqueIn(months: [MonthDay]) -> Bool { return isUniqueIn(months: months, by: \\.day) }  func monthWithUniqueDayIn(months: [MonthDay]) -> Bool { return months.firstIndex(where: { \\$0.month == month && \\$0.dayIsUniqueIn(months: months) }) != nil }} let choices = [ MonthDay(month: 5, day: 15), MonthDay(month: 5, day: 16), MonthDay(month: 5, day: 19), MonthDay(month: 6, day: 17), MonthDay(month: 6, day: 18), MonthDay(month: 7, day: 14), MonthDay(month: 7, day: 16), MonthDay(month: 8, day: 14), MonthDay(month: 8, day: 15), MonthDay(month: 8, day: 17)] // Albert knows the month, but not the day, so he doesn't have a gimmie monthlet albertKnows = choices.filter({ !\\$0.monthIsUniqueIn(months: choices) }) // Albert also knows that Bernard doesn't know, so it can't be a gimmie daylet bernardKnows = albertKnows.filter({ !\\$0.monthWithUniqueDayIn(months: albertKnows) }) // Bernard now knows the birthday, so it must be a unique day within the remaining choiceslet bernardKnowsMore = bernardKnows.filter({ \\$0.dayIsUniqueIn(months: bernardKnows) }) // Albert knows the birthday now, so it must be a unique month within the remaining choicesguard let birthday = bernardKnowsMore.filter({ \\$0.monthIsUniqueIn(months: bernardKnowsMore) }).first else { fatalError()} print(\"Cheryl's birthday is \\(birthday)\")`\nOutput:\n`Cheryl's birthday is July 16`\n\n## VBA\n\n`Private Sub exclude_unique_days(w As Collection) Dim number_of_dates(31) As Integer Dim months_to_exclude As New Collection For Each v In w number_of_dates(v(1)) = number_of_dates(v(1)) + 1 Next v For i = w.Count To 1 Step -1 If number_of_dates(w(i)(1)) = 1 Then months_to_exclude.Add w(i)(0) w.Remove i End If Next i For Each m In months_to_exclude exclude_month w, m Next mEnd SubPrivate Sub exclude_month(x As Collection, v As Variant) For i = x.Count To 1 Step -1 If x(i)(0) = v Then x.Remove i Next iEnd SubPrivate Sub exclude_non_unique_days(w As Collection) Dim number_of_dates(31) As Integer For Each v In w number_of_dates(v(1)) = number_of_dates(v(1)) + 1 Next v For i = w.Count To 1 Step -1 If number_of_dates(w(i)(1)) > 1 Then w.Remove i End If Next iEnd SubPrivate Sub exclude_non_unique_months(w As Collection) Dim months As New Collection For Each v In w On Error GoTo 1 months.Add v(0), v(0) Next v1: For i = w.Count To 1 Step -1 If w(i)(0) = v(0) Then w.Remove i End If Next iEnd SubPublic Sub cherylsbirthday() Dim v As New Collection s = \"May 15, May 16, May 19, June 17, June 18, July 14, July 16, August 14, August 15, August 17\" t = Split(s, \",\") For Each u In t v.Add Split(Trim(u), \" \") Next u '1) Albert: I don't know when Cheryl's birthday is, but I know that Bernard does not know too. exclude_unique_days v '2) Bernard: At first I don't know when Cheryl's birthday is, but I know now. exclude_non_unique_days v '3) Albert: Then I also know when Cheryl's birthday is. exclude_non_unique_months v Debug.Print v(1)(0); \" \"; v(1)(1)End Sub`\nOutput:\n`July 16`\n\n## Visual Basic .NET\n\nTranslation of: C#\n`Module Module1  Structure MonDay Dim month As String Dim day As Integer  Sub New(m As String, d As Integer) month = m day = d End Sub  Public Overrides Function ToString() As String Return String.Format(\"({0}, {1})\", month, day) End Function End Structure  Sub Main() Dim dates = New HashSet(Of MonDay) From { New MonDay(\"May\", 15), New MonDay(\"May\", 16), New MonDay(\"May\", 19), New MonDay(\"June\", 17), New MonDay(\"June\", 18), New MonDay(\"July\", 14), New MonDay(\"July\", 16), New MonDay(\"August\", 14), New MonDay(\"August\", 15), New MonDay(\"August\", 17) } Console.WriteLine(\"{0} remaining.\", dates.Count)  ' The month cannot have a unique day. Dim monthsWithUniqueDays = dates.GroupBy(Function(d) d.day).Where(Function(g) g.Count() = 1).Select(Function(g) g.First().month).ToHashSet() dates.RemoveWhere(Function(d) monthsWithUniqueDays.Contains(d.month)) Console.WriteLine(\"{0} remaining.\", dates.Count)  ' The day must now be unique. dates.IntersectWith(dates.GroupBy(Function(d) d.day).Where(Function(g) g.Count() = 1).Select(Function(g) g.First())) Console.WriteLine(\"{0} remaining.\", dates.Count)  ' The month must now be unique. dates.IntersectWith(dates.GroupBy(Function(d) d.month).Where(Function(g) g.Count() = 1).Select(Function(g) g.First())) Console.WriteLine(dates.Single()) End Sub End Module`\nOutput:\n```10 remaining.\n5 remaining.\n3 remaining.\n(July, 16)```\n\n## Wren\n\nTranslation of: Kotlin\n`var Months = [ \"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"] class Birthday { construct new(month, day) { _month = month _day = day }  month { _month } day { _day }  toString { \"%(Months[_month-1]) %(day)\" }  monthUniqueIn(bds) { bds.count { |bd| _month == bd.month } == 1 }  dayUniqueIn(bds) { bds.count { |bd| _day == bd.day } == 1 }  monthWithUniqueDayIn(bds) { bds.any { |bd| (_month == bd.month) && bd.dayUniqueIn(bds) } }} var choices = [ Birthday.new(5, 15), Birthday.new(5, 16), Birthday.new(5, 19), Birthday.new(6, 17), Birthday.new(6, 18), Birthday.new(7, 14), Birthday.new(7, 16), Birthday.new(8, 14), Birthday.new(8, 15), Birthday.new(8, 17)] // Albert knows the month but doesn't know the day.// So the month can't be unique within the choices.var filtered = choices.where { |bd| !bd.monthUniqueIn(choices) }.toList // Albert also knows that Bernard doesn't know the answer.// So the month can't have a unique day.filtered = filtered.where { |bd| !bd.monthWithUniqueDayIn(filtered) }.toList // Bernard now knows the answer.// So the day must be unique within the remaining choices.filtered = filtered.where { |bd| bd.dayUniqueIn(filtered) }.toList // Albert now knows the answer too.// So the month must be unique within the remaining choices.filtered = filtered.where { |bd| bd.monthUniqueIn(filtered) }.toList if (filtered.count == 1) { System.print(\"Cheryl's birthday is %(filtered)\")} else { System.print(\"Something went wrong!\")}`\nOutput:\n```Cheryl's birthday is July 16\n```\n\n## zkl\n\n`dates:=T(T(\"May\", 15), T(\"May\", 16), T(\"May\", 19), T(\"June\", 17), T(\"June\", 18), \t T(\"July\", 14), T(\"July\", 16),\t T(\"August\",14), T(\"August\",15), T(\"August\",17) );mDs:=dates.pump(Dictionary().appendKV); // \"June\":(15,16,19), ...dMs:=dates.pump(Dictionary().appendKV,\"reverse\"); // 15:\"May\", 16:\"May\", 19:\"May\", ... // remove unique days (18,19) --> \"July\":(14,16),\"August\":(14,15,17)dMs.values.apply2('wrap(ms){ if(ms.len()==1) mDs.del(ms) }); // find intersection of above days --> (14)fcn intersection(l1,l2){ l1.pump(List,l2.holds,'==(True),Void.Filter) }badDs:=mDs.values.reduce(intersection); // --> July:(16),August:(15,17) --> ( (\"July\",(16)) )theDay:=mDs.filter('wrap([(m,ds)]){ ds.removeEach(badDs).len()==1 }); // print birthday such that muliples are shown, if anyprintln(\"Cheryl's birthday is \",theDay.flatten().flatten().concat(\" \"));`\nOutput:\n```Cheryl's birthday is July 16\n```" ]

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[ "# Video: Checking the Existence of the Limit of the Partial Sums to Decide If a Series Is Convergent or Divergent\n\nTrue or false: if 𝑎_(𝑛) ⟶ 0 as 𝑛 ⟶ ∞, then ∑_(𝑛 = 0)^(∞) 𝑎_(𝑛) is convergent.\n\n03:07\n\n### Video Transcript\n\nTrue or false, if 𝑎 𝑛 approaches zero as 𝑛 approaches ∞, then the sum from 𝑛 equals zero to ∞ of our sequence 𝑎 𝑛 is convergent.\n\nIf we believe this to be true, we would have to show that every sequence 𝑎 𝑛 which has that 𝑎 𝑛 approaches zero when 𝑛 approaches ∞ has that the sum from 𝑛 equals zero to ∞ of 𝑎 𝑛 is convergent. However, if we believe this to be false, then we only have to find one sequence 𝑎 𝑛 where our sequence 𝑎 𝑛 approaches zero as 𝑛 approaches ∞, and the sum from 𝑛 equals zero to ∞ of 𝑎 𝑛 is divergent. Now, we recall that to check the convergence or divergence of a series, we check the limit of the partial sums. We say that our series is convergent if the limit as the number of terms tends to ∞ of our partial sum exists. And we say that our series is divergent if the limit of the partial sum as the number of terms tends to ∞ does not exist.\n\nLet’s take a look at some divergent series, for example, the harmonic series, which can be represented as the sum from 𝑛 equals zero to ∞ of one divided by 𝑛 plus one. We can then define the partial sum 𝑆 𝑘 to be equal to one plus a half plus a third all the way up to adding one divided by 𝑘. Let’s now consider our partial sum 𝑆 sub eight. And that’s one plus a half all the way up to adding one-eighth. We can turn this into an inequality by reducing some of the values in our terms. We reduce the value of one-third to a quarter. Then, we reduce the value of one-fifth, one-sixth, and one-seventh all to the value of one-eighth. Evaluating this inequality gives us that 𝑆 eight is greater than or equal to one plus a half plus a half plus a half.\n\nNow we ask the question, what would’ve happened if instead of calculating the partial sum 𝑆 sub eight, we had calculated the partial sum 𝑆 sub 16? First, we need to add the extra terms onto our partial sum. So, we add one-ninth then one-tenth all the way up to one sixteenth. We could generate a very similar inequality for our partial sum 𝑆 sub 16. We’ll just change the last eight terms in our partial sum to one sixteenth. We have eight terms of one sixteenth. So, we add all of these together to get a half. In particular, what we’ve shown is that our partial sum 𝑆 sub 16 is greater than or equal to our partial sum 𝑆 sub eight plus a half. But we could’ve done this again. We could’ve done this with our partial sum 𝑆 sub 32. And we would’ve got that it’s greater than or equal to our sixteenth partial sum plus a half.\n\nAnother way of looking at this is as we take more and more terms of our partial sum, we keep adding a half. So, by taking more and more terms, we can make our partial sum as large as we want. This is the same as saying that our partial sums are unbounded. Since the partial sum is unbounded, its limit does not exist. And therefore, our series is divergent. Therefore, we can conclude the statement given to us in the question is false because we know the limit as 𝑛 approaches ∞ of one divided by 𝑛 plus one is equal to zero. But the sum from 𝑛 equals zero to ∞ of one divided by 𝑛 plus one is divergent." ]

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[ "", null, "# An npn transistor operates as a common emitter", null, "`\nQuestion:\n\nAn npn transistor operates as a common emitter amplifier with a power gain of $10^{6}$. The input circuit resistance is $100 \\Omega$ and the output load resistance is $10 \\mathrm{~K} \\Omega$. The common emitter current gain ' $\\beta$ ' will be_______ . (Round off to the Nearest Integer)\n\nSolution:\n\n$10^{6}=\\beta^{2} \\times \\frac{R_{0}}{R_{i}}$\n\n$10^{6}=\\beta^{2} \\times \\frac{10^{4}}{10^{2}}$\n\n$\\beta^{2}=10^{4} \\Rightarrow \\beta=100$" ]

[ null, "https://www.facebook.com/tr", null, "https://www.esaral.com/static/assets/images/Padho-Bharat-Web.png", null ]

[ "International Journal of Computer and Electrical Engineering, Volume (1), No (5), Year (2009-12) , Pages (535-541)\n\n#### Title : ( Fuzzy Complex System of Linear Equations Applied to Circuit Analysis )\n\nCitation: BibTeX | EndNote\n\n#### Abstract\n\nAbstract— In this paper, application of fuzzy system of linear equations (FSLE) is investigated in the circuit analysis (CA). In the field of CA, each circuit includes linear resistance, inductance and capacitance are modeled to complex linear equation. A fuzzy current or voltage in the circuit is more intellectual relative to crisp value because of some reasons as environmental conditions, tolerance in the elements, and noise (leakage of power harmonics,). This scheme forces for presentation of fuzzy complex system of linear equations (FCSLE). Derivation is presented for solving FCSLE and obtained results show ability of this method in CA.\n\n#### Keywords\n\n, Index Terms— Fuzzy complex system of linear equations, Fuzzy system of linear equations, Circuit analysis, Fuzzy current and voltage, Noise, Tolerance.\nبرای دانلود از شناسه و رمز عبور پرتال پویا استفاده کنید.", null, "@article{paperid:1014033,\ntitle = {Fuzzy Complex System of Linear Equations Applied to Circuit Analysis},\njournal = {International Journal of Computer and Electrical Engineering},\nyear = {2009},\nvolume = {1},\nnumber = {5},\nmonth = {December},\nissn = {1793-8163},\npages = {535--541},\nnumpages = {6},\nkeywords = {Index Terms— Fuzzy complex system of linear equations; Fuzzy system of linear equations; Circuit analysis; Fuzzy current and voltage; Noise; Tolerance.},\n}" ]

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[ "", null, "Previous | Next", null, "QuickVectorPlot Home ▸ Documentation ▸ SmileLab ▸ Scripting ▸ Making graphs by script ▸ QuickPlotLib ▸ QuickVectorPlot", null, "QuickVectorPlot(x, y, vx, vy, vscale, anObject) displays a 2D vector field defined over a 2D area. vx and vy may be a matrix or a string, a formula of the variables x and y. x (y) may be a matrix, an array of real (with sizes compatible with the size of vx and vy), a formula, a range, or 0. vscale specifies the size ratio for the arrows. If vscale is set to 1.0, vx and vy use the same scales as x and y, i.e. the scales shown by the axes. Enter a factor larger (smaller) than one to get larger (smaller) arrows. Having the size of the arrows rescaled when you rescale the axes is natural in some circumstances, for example when you display velocity fields. If you want to specify a fixed size ratio in pixels per vx and vy unit, change the use view scaling property of the object. set v to QuickVectorPlot(x, y, vx, vy, vscale, anObject) set v's use view scaling to false The script for the illustration is the script below. set na to 40 set nr to 40 set a to creatematrix \"x\" ncols na nrows nr range {0, 2 * pi} set r to creatematrix \"y\" ncols na nrows nr range {0, 3 * pi} set fs to {\"r*cos(a)\", \"r*sin(a)\", \"r*sin(a+r)\", \"r*cos(a-r)\"} set aors to evalformula fs with {a:a, r:r} set {x, y, vx, vy} to ArrayToMatrix(aors, na, nr) QuickVectorPlot(x, y, vx, vy, 0.1, 0) Import script", null, "A vector plot for a field given in polar coordinates", null, "Copyright ©2008 Paris, Satimage" ]

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