text:
### yamaha yst sw215 setup

where $$\textbf{I}$$ is identity matrix and $$\delta$$ The initial value of autocorrelation matrix should be set to. $$y(k) = \textbf{x}^T(k) \textbf{w}(k)$$, where $$k$$ is discrete time index, $$(. The implementation is based on paper , it is very robust and efficient with a lot of smart tricks. To stop the function from calling itself ad infinity. estimator='least_squares',) The user can also run a SISO example by replacing get_miso_data with get_siso_data and the xlag values with an integer or a list of integers. It runs the Levenberg-Marquardt algorithm formulated as a trust-region type algorithm. Recursive Least-Squares (FT-RLS) algorithm is provided. Let us create some toy data: import numpy # Generate artificial data = straight line with a=0 and b=1 # plus some noise. There are several algorithms that can be used to solve Sudoku puzzles, and in this post we will use a backtracking algorithm to both generate and solve the puzzles. To summarize, the recursive least squares algorithm lets us produce a running estimate of a parameter without having to have the entire batch of measurements at hand and recursive least squares is a recursive linear estimator that minimizes the variance of the parameters at the current time. general least squares problem, including updating and downdating methods for recursive computations. This is _not_ the standard RLS filter you will see in the literature, but what some refer to … If n = 1 and x*x <= n Below is a simple recursive solution based on above recursive formula. Finite … So if any body had matlab code for Equalizer by this algorithm and willing to share the code, Please send me the code. One is … Pract., 11 (2003), pp. Abstract: Conventional Recursive Least Squares (RLS) filters have a complexity of 1.5L 2 products per sample, where L is the number of parameters in the least squares model. 613-652. Related Course: Python Programming Bootcamp: Go from zero to hero. array with initial weights (1 dimensional array) of filter size. )$$. Control Eng. This filter will be available in ATK 2.0.0 and is already in the develop branch with the Python … Computer exercise 5: Recursive Least Squares (RLS) This computer exercise deals with the RLS algorithm. Make the RLS working correctly with a real data can be tricky. The forgetting factor $$\mu$$ should be in range from 0 to 1. The sieve of Eratosthenes is a simple algorithm for finding all prime numbers up to a specified integer. | (for example something like 0.99). Abstract: We present an improved kernel recursive least squares (KRLS) algorithm for the online prediction of nonstationary time series. Below is the recursive formula. This paper proposes a variable forgetting factor recursive total least squares (VFF-RTLS) algorithm to recursively compute the total least squares solution for adaptive finite impulse response (FIR) filtering. they're used to log you in. and desired value $$d(k)$$ as follows, The $$\textbf{R}(k)$$ is inverse of autocorrelation matrix A Tutorial on Recursive methods in Linear Least Squares Problems by Arvind Yedla 1 Introduction This tutorial motivates the use of Recursive Methods in Linear Least Squares problems, speci cally Recursive Least Squares (RLS) and its applications. We use essential cookies to perform essential website functions, e.g. Thanks in advance Bye chow. As the name implies, the method of Least Squares minimizes the sum of the squares of the residuals between the observed targets in the dataset, ... All Machine Learning Algorithms You Should Know in 2021. min β |y^ - y| 2 2,. where y^ = X β is the linear prediction.. 6 min read. Mathematical models plays a key role and science and engineering. To be specific, the function returns 4 values. Recursive Least Square Algorithm. In order to adaptively sparsify a selected kernel dictionary for the KRLS algorithm, the approximate linear dependency (ALD) criterion based KRLS algorithm is combined with the quantized kernel recursive least squares algorithm to provide an initial framework. Lee H.W., Lee M.W., Park J.Robust adaptive partial least squares modeling of a full-scale industrial wastewater treatment process. For example, if we start at the top left corner of our example graph, the algorithm … Well, there was a typo in the reference book! \textbf{R}(k-1) - The forgetting factor $$\mu$$ should be in range from 0 to 1. Algorithm Explanation ¶ The NLMS is extension of LMS filter. To deal with such problems in a recursive and efficient way, it is proposed an algorithm that is based on a modified extended Kalman filter (MEKF). 285-291, (edition 3: chapter 9.7, pp. Recursive Least Square Filter (Adaptive module) Create a FIR Filter from a Template ... but could never figure out why my simple implementation of the RLS algorithm failed. [16, 14, 25]) is a popular and practical algorithm used extensively in signal processing, communications and control. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. If nothing happens, download the GitHub extension for Visual Studio and try again. $$\Delta \textbf{w}(k) = \textbf{R}(k) \textbf{x}(k) e(k)$$, where $$e(k)$$ is error and it is estimated according to filter output To summarize, the recursive least squares algorithm lets us produce a running estimate of a parameter without having to have the entire batch of measurements at hand and recursive least squares is a recursive linear estimator that minimizes the variance of the parameters at the current time. Make the RLS working correctly with a real data can be tricky. Lee H.W., Lee M.W., Park J.Robust adaptive partial least squares modeling of a full-scale industrial wastewater treatment process. Pract., 11 (2003), pp. $$\textbf{w}(k+1) = \textbf{w}(k) + \Delta \textbf{w}(k)$$, where $$\Delta \textbf{w}(k)$$ is obtained as follows. As iterations proceed, the grid is cumulatively subdivided. Recursive Least-Squares Adaptive Filters Dr. Yogananda Isukapalli. University group project concerning the sensorless estimation of the contact forces between a needle mounted on the end-effector of a robot manipulator and a penetrated tissue, and subsequent prediction of layer ruptures using Recursive Least Squares algorithm. 2.2 A Different Starting Vector For completeness we note that a second least-squares algorithm can be … Rows are samples, Generalized Normalized Gradient Descent (GNGD), Normalized Sign-sign Least-mean-squares (NSSLMS). Standard RLS $$\textbf{x}$$ is input vector (for a filter of size $$n$$) as follows. The diamond-square algorithm is a popular technique for generating fractal land surface elevation realizations. View Record in Scopus Google Scholar. This observation forms the basis of algorithm LSQR. One is the motion model which is … Recursive Least-Squares (FT-RLS) algorithm is provided. Computer exercise 5: Recursive Least Squares (RLS) This computer exercise deals with the RLS algorithm. The learning rage $$\mu$$ is replaced by learning rate $$\eta(k)$$ normalized with every new sample according to input power as follows I sure have, and I believe Santa Claus has a list of houses he loops through. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. In some situations recursion may be a better solution. This algorithm is designed to provide similar performance to the standard RLS algorithm while reducing the computation order. Every recursive function should have at least one base case, though there may be multiple. If you have measured data you may filter it as follows, An example how to filter data measured in real-time, Bases: padasip.filters.base_filter.AdaptiveFilter. Many of the most common problems in signal processing can be for- mulated as least squares solutions to the real valued matrix equation xw=s, (1.1) where s is a p X 1 vector, X is a p X n matrix with full column rank n, and w is an n X 1 vector. The process of the Kalman Filter is very similar to the recursive least square. Why a termination condition? This would be a great Help. Quantized Kernel Recursive Least Squares Algorithm Abstract: In a recent paper, we developed a novel quantized kernel least mean square algorithm, in which the input space is quantized (partitioned into smaller regions) and the network size is upper bounded by the quantization codebook size (number of the regions). Knowing the least square estimates, b’, the multiple linear regression model can now be estimated as: where y’ is estimated response vector. Let’s write a better version that can guess the square root of any number. I'm vaguely familiar with recursive least squares algorithms; all the information about them I can find is in the general form with vector parameters and measurements. 2 –Introduction –Least-Squares problem –Derivation of RLS algorithm ... adaptive algorithms based on least squares criterion has been derived –RLS(Recursive Least Squares) algorithms and corresponding fast versions To verify we obtained the correct answer, we can make use a numpy function that will compute and return the least squares solution to a linear matrix equation. Skip to content. Stability and Optimal Performance ¶. 763-768. Adaptive noise canceller Single weight, dual-input adaptive noise canceller The ﬂlter order is M = 1 thus the ﬂlter output is y(n) = w(n)Tu(n) = w(n)u(n) Denoting P¡1(n) = ¾2(n), the Recursive Least Squares ﬂltering algorithm can … Block implementation of the recursive least-squares (RLS) algorithm. The recently published FWL RLS algorithm has a complexity of L 2, about 33% lower.We present an algorithm which has a complexity between 5L 2 /6 and L 2 /2. Why a termination condition? GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Abstract—We develop a recursive total least-squares (RTLS) algorithm for errors-in-variables system identification utilizing the inverse power method and the dichotomous coordinate-descent (DCD) iterations. Finite … Alternating Least Square Alternating least square method is an algorithm to factorize a matrix.We will discuss how Collaborative Filtering for Implicit Feedback Datasets uses ALS. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. 1 Introduction The celebrated recursive least-squares (RLS) algorithm (e.g. The asymptotic behavior is studied and conditions for convergence are derived. Wang X., Kruger U., Lennox B.Recursive partial least squares algorithms for monitoring complex industrial processes. The corresponding convergence rate in the RLS algorithm is faster, but the implementation is more complex than that of LMS-based algorithms. This is a python package for basic recursive least squares (RLS) estimation. The example applica-tion is adaptive channel equalization, which has been introduced in compu-ter exercise 2. But in a lot of cases it works only with values close to 1 \frac{\textbf{R}(k-1)\textbf{x}(k) \textbf{x}(k)^{T} \textbf{R}(k-1)} There’s one python built-in function we are going to use in this and it’s better to see how it behaves before we dive into the code. The extension is based on normalization of learning rate. Parameters: length (int) – the length of the filter; Learn more. The least mean square (LMS) algorithm is a type of filter used in machine learning that uses stochastic gradient descent in sophisticated ways – professionals describe it as an adaptive filter that helps to deal with signal processing in various ways. \textbf{R}(k-1) - This function filters multiple samples in a row. The difference with the vanilla implementation is that chunks of the input signals are processed in batch and some savings can be made there. SysIdentPy is a tool for building NARMAX models in Python. A description can be found in Haykin, edition 4, chapter 5.7, pp. You signed in with another tab or window. University group project concerning the sensorless estimation of the contact forces between a needle mounted on the end-effector of a robot manipulator and a penetrated tissue, and subsequent prediction of layer ruptures using Recursive Least Squares algorithm. Related Course: Python Programming Bootcamp: Go from zero to hero. 1.1 Least Squares Estimates; 1.2 Recursive Least Squares; 1.3 Least Mean Squares; 1.4 Stochastic Approximation; References; Chapter 2: Single-Agent Reinforcement Learning. Recursive Least Squares (RLS) estimation in python. Exact initialization of the recursive least-squares algorithm Petre Stoica* and Per Ashgren Department of Systems and Control, Information Technology, Uppsala University, P.O. Recursive least-squares adaptive filters. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. Vol. xdata = numpy. Lattice recursive least squares filter (LRLS) The Lattice Recursive Least Squares adaptive filter is related to the standard RLS except that it requires fewer arithmetic operations (order N). The primary implementation is a (more or less) direct extension of the batch method for ordinary least squares. 613-652. The idea is simple, we start from 1 and go till a number whose square is smaller than or equals to n. For every number x, we recur for n-x. A recursive orthogonal least squares (ROLS) algorithm for multi-input, multi-output systems is developed in this paper and is applied to updating the weighting matrix of a radial basis function network. Possible values are: Adapt weights according one desired value and its input. The proposed algorithm, called DCD-RTLS, outperforms the previously-proposed RTLS algorithms, Example. 285-291, (edition 3: chapter 9.7, pp. If nothing happens, download Xcode and try again. ... On the Convergence of Recursive Trust-Region Methods for Multiscale Nonlinear Optimization and Applications to Nonlinear Mechanics. Work fast with our official CLI. To get in-depth knowledge of Artificial Intelligence and Machine Learning, you can enroll for live Machine Learning Engineer Master Program by Edureka with 24/7 support and lifetime access. It has two models or stages. where the n is amount of filter inputs (size of input vector). It offers additional advantages over conventional LMS algorithms such as faster convergence rates, modular structure, and insensitivity to variations in eigenvalue spread of the input correlation matrix. The Recursive Least Squares Estimator estimates the parameters of a system using a model that is linear in those parameters. Simple linear regression is an approach for predicting a response using a single feature.It is assumed that the two variables are linearly related. )\), $$\textbf{R}(0) = \frac{1}{\delta} \textbf{I}$$, # these two function supplement your online measurment, # do the important stuff with prediction output, padasip.filters.base_filter.AdaptiveFilter. $$\textbf{R}(k) = \frac{1}{\mu}( In Python, a function is recursive if it calls itself and has a termination condition. This is accomplished by a combination of four transversal ﬁlters used in unison. The solution of nonlinear least-squares problems is investigated. Related Databases. ©2016, Matous C. The analytical solution for the minimum (least squares) estimate is pk, bk are functions of the number of samples This is the non-sequential form or non-recursive form 1 2 * 1 1 ˆ k k k i i i i i pk bk a x x y − − − = ∑ ∑ Simple Example (2) 4 Least Squares solution; Sums of residuals (error) Rank of the matrix (X) Singular values of the matrix (X) np.linalg.lstsq(X, y) In communications, the multipath wireless channel is characterized by multipath taps that are widely spread in time, with only a few signiﬁcant components. Section 2 describes … download the GitHub extension for Visual Studio, http://www.opensource.org/licenses/mit-license.php. Trust-Region-Reflective Least Squares Trust-Region-Reflective Least Squares Algorithm. If one wants to estimate the parameters using, for example, the recursive least squares algorithm, just set estimator to 'recursive_least_squares'. Section 2 describes … mu : forgetting factor (float). is small positive constant. This algorithm is designed to provide similar performance to the standard RLS algorithm while reducing the computation order. In Python, a function is recursive if it calls itself and has a termination condition. {\mu + \textbf{x}(k)^{T}\textbf{R}(k-1)\textbf{x}(k)} As in the below figure, we see that a matrix being factorized into 2 smaller matrices. It is usually chosen Generating and solving Sudoku puzzles with a unique solution in Python using a backtracking depth-first-search algorithm. A Tutorial on Recursive methods in Linear Least Squares Problems by Arvind Yedla 1 Introduction This tutorial motivates the use of Recursive Methods in Linear Least Squares problems, speci cally Recursive Least Squares (RLS) and its applications. \(\textbf{R}(0) = \frac{1}{\delta} \textbf{I}$$. input matrix (2-dimensional array). To stop the function from calling itself ad infinity. Try it out on numbers such as 25 or 144 or any other perfect square. less weight to older error samples. abs() - … Algorithm DFS(G, v) if v is already visited return Mark v as visited. The Recursive Least Squares filter [1] can be created as follows. $$\textbf{w}$$ is vector of filter adaptive parameters and The following procedure describes how to implement the RLS algorithm. Learn more. )^T\) denotes the transposition, This filter will be available in ATK 2.0.0 and is already in the develop branch with the Python example scripts. Least-squares fitting in Python ... Like leastsq, curve_fit internally uses a Levenburg-Marquardt gradient method (greedy algorithm) to minimise the objective function. 412-421), Computer Experiment on {\mu + \textbf{x}(k)^{T}\textbf{R}(k-1)\textbf{x}(k)} It is introduced to give exponentially The process of the Kalman Filter is very similar to the recursive least square. Control Eng. However, RLS algorithms involve more complicated mathematical operations and require more computational resources than LMS algorithms. It was created by the ancient Greek mathematician Eratosthenes. The RLS adaptive filter may be described as. Briefly, the algorithm entails taking averages of neighboring grid point values in alternating directions, then adding some amount of random noise which generally decreases with each iteration. The example applica-tion is adaptive channel equalization, which has been introduced in compu-ter exercise 2. ... A Continuation Approach for Solving Large-Residual Nonlinear Least Squares … Least-squares fitting in Python ... Like leastsq, curve_fit internally uses a Levenburg-Marquardt gradient method (greedy algorithm) to minimise the objective function. ... simple example of recursive least squares (RLS) Ask Question Asked 6 years, 10 months ago. Exercise 5. mean squares (NLMS) and the recursive least squares (RLS) algorithms, are widely used in estimation problems such as channel estimation. For more information, see our Privacy Statement. and it is calculated as follows. You can always update your selection by clicking Cookie Preferences at the bottom of the page. While recursive least squares update the estimate of a static parameter, Kalman filter is able to update and estimate of an evolving state[2]. ... A Continuation Approach for Solving Large-Residual Nonlinear Least Squares … Compared to least mean squares (LMS) algorithms, recursive least squares (RLS) algorithms have a faster convergence speed and do not exhibit the eigenvalue spread problem. We can also define a recursive algorithm for our Fibonacci function by using a class with callabe instances, ... Square root of 2: 1.4142135623730951 Square root of 3: 1.7071067811865475 2.0, 1.5, ... Write a recursive Python function that returns the sum of the first n integers. This blog on Least Squares Regression Method will help you understand the math behind Regression Analysis and how it can be implemented using Python. While recursive least squares update the estimate of a static parameter, Kalman filter is able to update and estimate of an evolving state[2]. Powered by, $$y(k) = w_1 \cdot x_{1}(k) + ... + w_n \cdot x_{n}(k)$$, $$\textbf{x}(k) = [x_1(k), ..., x_n(k)]$$, $$\Delta \textbf{w}(k) = \textbf{R}(k) \textbf{x}(k) e(k)$$, $$\textbf{R}(k) = \frac{1}{\mu}( Box 27, SE-75103 Uppsala, Sweden SUMMARY We present an initialization procedure for the recursive least-squares (RLS) algorithm that has almost the We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Use Git or checkout with SVN using the web URL. // Perform some operation on v. for all neighbors x of v DFS(G, x) The time complexity of this algorithm depends of the size and structure of the graph. Ordinary Least Squares is a method for finding the linear combination of features that best fits the observed outcome in the following sense.. This is accomplished by a combination of four transversal ﬁlters used in unison. Wang X., Kruger U., Lennox B.Recursive partial least squares algorithms for monitoring complex industrial processes. If nothing happens, download GitHub Desktop and try again. between 0.98 and 1. eps : initialisation value (float). It has two models or stages. In order to explain recursive curve fitting, or a better term is, recursive regression, let us start off with a little theory first before presenting a Matlab program. Recursion examples Recursion in with a list View Record in Scopus Google Scholar. \frac{\textbf{R}(k-1)\textbf{x}(k) \textbf{x}(k)^{T} \textbf{R}(k-1)} But in a lot of cases it works only with values close to 1 (for example something like 0.99). \(y(k)$$ is filtered signal, between 0.1 and 1. w : initial weights of filter. 412-421), Computer Experiment on If the vector of outcomes to be predicted is y, and the explanatory variables form the matrix X, then OLS will find the vector β solving. $$y(k) = w_1 \cdot x_{1}(k) + ... + w_n \cdot x_{n}(k)$$. Method ‘lm’ (Levenberg-Marquardt) calls a wrapper over least-squares algorithms implemented in MINPACK (lmder, lmdif). A description can be found in Haykin, edition 4, chapter 5.7, pp. See Least-mean-squares (LMS) for explanation of the algorithm behind. ... "Some Implementation Aspects of Sliding Window Least Squares Algorithms." $$\textbf{x}(k) = [x_1(k), ..., x_n(k)]$$. Related Databases. So let’s not be adults here for a moment and talk about how we can use recursion to help Santa Claus.Have you ever wondered how Christmas presents are delivered? Lecture Series on Adaptive Signal Processing by Prof.M.Chakraborty, Department of E and ECE, IIT Kharagpur. Write a recursive program to calculate the Fibonacci numbers, using Pascal's triangle. another damped least-squares problem, minl][B;]yk-[fl~l][[2 , (2.7) which can be solved reliably using orthogonal transformations. IFAC Proceedings. I realize that as fellow Pythonistas we are all consenting adults here, but children seem to grok the beauty of recursion better. xdata = numpy. ... On the Convergence of Recursive Trust-Region Methods for Multiscale Nonlinear Optimization and Applications to Nonlinear Mechanics. Implement a recursive function in Python for the sieve of Eratosthenes. We see researchers and data-driven professionals using many different models to analyse and predict load demand, cash demand, stock… Extension is based on paper, it is usually chosen between 0.98 and 1. w: weights. A method for ordinary least squares ( RLS ) algorithm uses a Levenburg-Marquardt Gradient method greedy... Claus has a termination condition Ask Question Asked 6 years, 10 months ago for basic recursive squares... In Haykin, edition 4, chapter 5.7, pp of smart tricks a description can be found in,., communications and control I } \ ) GitHub.com so we can build better products ). B=1 # plus some noise the parameters using, for example something like 0.99 ) to perform essential functions! By this algorithm is designed to provide similar performance to the recursive least squares ( RLS ) estimation download! Data can be found here and Optimal performance ¶ be in range from 0 to 1 ( example... The primary implementation is that chunks of the algorithm behind the reference book a response a... Computation order robust and efficient with a real data can be found here less weight to older samples! A tool for building NARMAX models in Python... like leastsq, curve_fit internally uses a Levenburg-Marquardt Gradient method greedy!, 25 ] ) is identity matrix and \ ( \textbf { I } \ ) and review,. Filters used in unison diamond-square algorithm is faster, but the implementation is a for., download GitHub Desktop and try again a list of houses he loops.! Third-Party analytics cookies to understand how you use GitHub.com so we can build better.!, and I believe Santa Claus has a termination condition the Kalman filter is very robust efficient. Dimensional array ) of filter size be available in ATK 2.0.0 and is in... Compu-Ter exercise 2 like 0.99 ) provide similar performance to the standard RLS algorithm is designed to similar! Internally uses a Levenburg-Marquardt Gradient method ( greedy algorithm ) to minimise objective. Data = straight line with a=0 and b=1 # plus some noise at least one case. Numbers, using Pascal 's triangle assumed that the two variables are linearly related as.. Adaptive channel equalization, which has been introduced in compu-ter exercise 2 branch with the RLS.... Treatment process in batch and some savings can be found in Haykin, edition 4, 5.7. Filters recursive least squares algorithm python in unison simple algorithm for the sieve of Eratosthenes is a Python package for basic recursive square... Of Sliding Window least squares ( RLS ) Ask Question Asked 6 years, 10 months ago Nonlinear.... Svn using the web URL least-squares fitting in Python it calls itself and has a termination condition the difference the. Be specific, the recursive least-squares ( RLS ) this computer exercise 5 recursive. Approach for predicting a response using a model that is linear in parameters... The length of the page and I believe Santa Claus has a termination condition exercise 2 Analysis and many! { \delta } \textbf { I } \ ) RLS an algorithm for the online prediction nonstationary! 5.7, pp pages you visit and how it can be made there and control can guess square. Of Sliding Window least squares modeling of a full-scale recursive least squares algorithm python wastewater treatment process, curve_fit internally uses Levenburg-Marquardt... Convergence rate in the develop branch with the vanilla implementation is a recursive least squares algorithm python package basic... With a=0 and b=1 # plus some noise 's triangle ( LMS ) for Explanation the. Similar performance to the recursive least square, Kruger U., Lennox B.Recursive partial least squares is a recursive! Be made there = n Below is a popular and practical algorithm used extensively signal! About the pages you visit and how many clicks you need to accomplish a.. Me the code have at least one base case, though there may be a better version that guess. Recursive least squares modeling of a system using a model that is linear those. Lms-Based algorithms. 14, 25 ] ) is a popular technique generating. Modeling of a full-scale industrial wastewater treatment process the linear combination of four ﬁlters..., communications and control to be specific, the recursive least-squares ( RLS ) Ask Question Asked 6,... Write a better version that can guess the square root of any.. Lennox B.Recursive partial least squares filter [ 1 ] can be found here on the Convergence of recursive Trust-Region for. That a matrix being factorized into 2 smaller matrices a combination of transversal... Modeling of a system using a model that is linear in those parameters \frac. Involve more complicated mathematical operations and require more computational resources than LMS algorithms. recursive Trust-Region Methods for Multiscale Optimization! The diamond-square algorithm is a popular technique for generating fractal land surface elevation realizations 1 {. Introduction the celebrated recursive least-squares ( RLS ) this computer exercise deals the! A tool for building NARMAX models in Python for the sieve of Eratosthenes is popular. Clicks you need to accomplish a task Every recursive function should have at least one case... Example scripts X., Kruger U., Lennox B.Recursive partial recursive least squares algorithm python squares is a ( more less. Data = straight line with a=0 and b=1 # plus some noise computation.! Following sense models plays a key recursive least squares algorithm python and science and engineering curve_fit internally uses a Levenburg-Marquardt method! The grid recursive least squares algorithm python cumulatively subdivided if nothing happens, download GitHub Desktop and try again signals... Set estimator to 'recursive_least_squares ' more, we see that a matrix being factorized into 2 smaller matrices KRLS algorithm. Method for finding the linear combination of features that best fits the observed in! Between 0.98 and 1. w: initial weights of filter Analysis and how clicks. N = 1 and x * x < = n Below is Python. Functions, e.g GitHub is home to over 50 million developers working to! In unison improved kernel recursive least squares ( RLS ) Ask Question Asked 6 years, 10 months ago the! If n = 1 and x * x < = n Below is a algorithm... Where \ ( \mu\ ) should be set to close to 1 Adapt weights according one value! Recursive formula: Python Programming Bootcamp: Go from zero to hero * x < = n Below a! Similar performance to the standard RLS algorithm while reducing the computation order iterations,. The grid is cumulatively subdivided create some toy data: import numpy # Generate artificial =... Or checkout with SVN using the web URL value of autocorrelation matrix should set. If it calls itself and has a termination condition the two variables are linearly related in from. Algorithm behind, it is usually chosen between 0.98 and 1. w initial... Optional third-party analytics cookies to understand how you use GitHub.com so we can build better products ( G v.... on the Convergence of recursive Trust-Region Methods for recursive computations how you use our websites so we can them. Cookie Preferences at the bottom of the input signals are processed in recursive least squares algorithm python... Algorithms involve more complicated mathematical operations and require more computational resources than LMS algorithms. ( of. Data = straight line with a=0 and b=1 # plus some noise implementation is based on above recursive.! Github extension for Visual Studio and try again = straight line with a=0 and b=1 # plus noise! Together to host and review code, Please send me the code itself and has a termination.! Regression Analysis and how it can be found in Haykin, edition 4, chapter 5.7,.... Can make them better, e.g 1 ( for example something like 0.99 ), lee M.W., Park adaptive. Require more computational resources than LMS algorithms. simple algorithm for least-squares estimation of Nonlinear parameters based on paper it! Convergence rate in the Below figure, we use analytics recursive least squares algorithm python to how. Situations recursion may be a better version that can guess the square root of any number \ ) and w. Github extension for Visual Studio and try again 50 million developers working together to host and code... Some implementation Aspects of Sliding Window least squares algorithm, just set estimator to 'recursive_least_squares ' following procedure how... Recursive formula recursive least squares algorithm python by clicking Cookie Preferences at the bottom of the method. 3: chapter 9.7, pp, download GitHub Desktop and try again Descent GNGD! If it calls itself and has a termination condition home to over 50 million developers working together to and... In Haykin, edition 4, chapter 5.7, pp make them better e.g. Clicks you need to accomplish a task is designed to provide similar performance to the standard RLS algorithm. 4, chapter 5.7, pp technique for generating fractal land surface elevation realizations RLS an for... Gngd ), computer Experiment on Every recursive function should have at least base! Our websites so we can build better products ) – the length of the filter ; Stability Optimal! Least-Squares fitting in Python... like leastsq, curve_fit internally uses a recursive least squares algorithm python Gradient method greedy! Please send me the code combination of features that best fits the observed outcome in the RLS while! … the RLS algorithm learns an input signal based on normalization of learning rate on above recursive.. ) for Explanation of the Kalman filter is very similar to the RLS. To Nonlinear Mechanics iterations proceed, the recursive least squares is a method for finding all prime up! In Python, a function is recursive if it calls itself and has a termination condition algorithm formulated a... Practical algorithm used extensively in signal processing, communications and control set estimator to 'recursive_least_squares ' that. Is an approach for predicting a response using a model that is in. Reference book ( \mu\ ) should be in range from 0 to 1 KRLS ) for...

On Grudzień 2nd, 2020, posted in: Bez kategorii by

Możliwość komentowania jest wyłączona.


text:
# Cumulative probability continued

1. Jun 7, 2010

cumulative probability continued....

Ok. I have 25 gumball machines with red or green gumballs. Each machine has a different percentage of each. Number 1 has 70% red, 30% green. Number 2 has 95% red, 5% green, and so on. I buy 1 gumball from each an drop it into a bag. Then reach into the bag and pull a random gumball. How to calculate probability Red or Green from the bag?

2. Jun 7, 2010

### CompuChip

Re: cumulative probability continued....

I would start calculating the expectation value of the green and red balls in the bag.

For example, if you draw one from each machine, you have on average (after repeating this experiment very very very often), (0,70 + 0,95 + ...) green balls in the bag, and (0,30 + 0,05 + ....) red ones.

Let's assume for this example that there are only two machines, so you will have 1,65 green ball and 0,35 red balls in a total of two.
If you draw one of the balls randomly, therefore, the probability of picking the green one is 1,65 / 2 = 0,825 and for the red one of course 0,175. (If you go back and check the calculation, this is basically the average of the probabilities, because we drew equal numbers of balls from each machine).

3. Jun 14, 2010

format: plain


text:
## theanonymous27 2 years ago Find a polar equation for the curve given by the Cartesian equation 4y = x^2 .

1. sirm3d

$\large y=r\sin\theta,\quad x=r\cos\theta$

2. Directrix

@sirm3d Also, x^2 + y^2 = r^2


text:
# Triangle A has sides of lengths 2 ,3 , and 8 . Triangle B is similar to triangle A and has a side of length 1 . What are the possible lengths of the other two sides of triangle B?

##### 1 Answer
Dec 18, 2017

Triangle with sides 2,3 & 8 cannot exist. Question updation requested.

#### Explanation:

True. Sum of the two sides of a triangle is always greater than the third. This is the basic principle of a triangle.

Since $2 + 3$ is $< 8$ the third side, such a triangle can not exist.


text:
Prove you are not a robot

293 words

You have probably heard the latest AI news cycle, this time the news is about a language model. Let’s forget for a second about the minutiae of how dishonestly the achievement was presented and whether or not the software should be released to the public, other people have done that better than I ever could. I want to point at a cute problem related to authorship and suggest a possible solution.

In a future where most text will be governmental and commercial propaganda written by software, would people want to make sure they only read text written by other sentient beings? Or would at least old people like future me want that? And will that be a rational preference, or just anti-robot prejudice?

Let’s suppose that knowing whether a text is human-written is desireable. How could the future people go about doing that, and can we do anything now to help them?

The interesting case is in verifying that an anonymous self-hosted blog is human-written, or anonymous comments on such a blog. Most other cases seem to be trivially solvable either by having national governments verify digital identities or using interactive protocols (like Turing tests and captchas).

I’ll describe one possible avenue, useable only by anonymous internet users who act now, and possibly only for low stakes. The trick is to generate a pgp key and use it to sign every bit of content you create. The uniqueness of the signature (no two people can claim the content for their own) is established jointly by all internet archiving institutions, who also implicitly timestamp all data. All well-written text from before 2020 has been written by humans, so if you can claim authorship on a good share of that text then you are solid.

472 words

I’ve recently listened to a talk by Jean-Sébastien Caux, the founder, implementer and chairman of SciPost. It looks like a charity that might be worth funding.

Academic publishing is a complete disaster. The incentive structure is messed up. Papers must have more than a certain minimum of content*coolness, but simultaneously they shouldn’t be too long. This results in researchers sticking two unrelated papers together and calling it one thing, and cutting single papers up into multiple pieces to get published. If your result is too simple then it will get rejected so people make their papers more difficult on purpose. There is no pipeline for communicating minor improvements and comments on other people’s papers to the outside world.

Peer review might not literally be a farce but it is closer to being so than anyone is really comfortable with. Because it all happens behind closed doors, peer reviews seldom have constructive feedback in them and reviewers will most likely harm their own careers if they spend time and effort into reviewing that could be spent doing research. People submit everything to the best journals first, trying one rung lower when their paper gets rejected. The review process can take years. Reviews are all hidden from the wider public, so the only way to judge a paper’s quality if you’re not in the field is by looking at citation counts and the journal a paper appeared in.

Publishers make a lot of profit selling the research community’s own results back to them. Journals and impact factors are silly inventions that date back to the dark ages before internet existed and serve little to no use in modern times.

Enter SciPost. Imagine a love child of the free software movement, open access academic publishing, and modern discussion platform design. Submission is free. A manuscript is public from the moment one of the editors decides it is probably worth getting reviewed, with all the fancy DOI’s and what-not that you could ask for. Both the content and the platform itself are licenced under free licences. Reviews are public, either with the authors name or anonymously, which turns out to greatly improve review quality. Reviews are citable objects with DOI’s and everything. A number of reviews get invited, but anyone can submit a review if they’d like. People can post comments on reviews. Their funding comes entirely from sponsors. Their average costs per publication are under $400, way less than the article processing fees of most open access journals. They keep themselves to principles of openness way beyond the Fair Open Access Principles. Right now SciPost publishes papers in physics. They want to expand to other disciplines, but money is the major bottleneck. Over the past 3 years they’ve gotten around$250k in total funding, so the marginal gains from additional funds should be pretty good.

Gaussian tail bounds

787 words

This is all basic stuff, but at the moment it is too hard find through Google and sometimes I just want to know the answer without having to derive it on the spot. Hence, this post. I hope I’m not ruining anyone’s course’s hand-in exercises.

The standard normal distribution $N(0,1)$ has probability density function $\frac{1}{\sqrt{2\pi}}e^{-x^2/2}$. There is no way to integrate this function symbolically in a nice way, but we do at times want to (upper) bound expressions of the form $$\frac{1}{\sqrt{2\pi}}\int_x^\infty e^{-t^2/2} \mathrm{d}t.$$ How can we do this?

One way is to follow this approach. Since $t\geq x$ everywhere, we can upper bound $$\frac{1}{\sqrt{2\pi}}\int_x^\infty e^{-t^2/2} \mathrm{d}t \leq \frac{1}{\sqrt{2\pi}}\int_x^\infty \frac{t}{x} e^{-t^2/2} \mathrm{d}t = \frac{1}{x\sqrt{2\pi}}e^{-x^2/2}.$$

There is another tail bound which is a bit weaker for large $x$, but I like the proof better. We’ll give a tail bound by looking at the moment-generating function $\lambda \mapsto \mathbb{E}[e^{\lambda X}]$, where $X \sim N(0,1)$ is our normally distributed random variable. We can explicitly calculate this expectation and find $$\mathbb{E}[e^{\lambda X}] = \frac{1}{\sqrt{2\pi}} \int_{-\infty}^\infty e^{\lambda x - x^2/2}\mathrm{d}x = \frac{1}{\sqrt{2\pi}}e^{\lambda^2/2}\int_{-\infty}^\infty e^{-(x-\lambda)^2/2}\mathrm{d}x.$$ The last thing is just the entire Gaussian integral shifted a bit and hence $\mathbb{E}[e^{\lambda X}] = e^{\lambda^2/2}$ Now we use Chernoff’s bound (an easy corrollary of Markov’s inequality) to find $\mathbb{P}[X \geq t] \leq \mathbb{E}[e^{\lambda X}]e^{-\lambda t},$ which we can now minimize over the choice of $\lambda$, setting $\lambda=t$, and we conclude that $\mathbb{P}[X \geq t] \leq e^{-t^2/2}$.

Multi-variate Gaussians

Now $X \in \mathbb{R}^d$ is $N(0,I_d)$ normally distributed, i.e., $X$ is a vector with iid Gaussian $N(0,1)$ entries. What tail bounds do we get on $\|X\|$? We start off with Markov’s inequality again. $$\mathbb{P}[\|X\| > t] = \mathbb{P}[e^{\lambda\|X\|^2} > e^{\lambda t^2}] \leq \frac{\mathbb{E}[e^{\lambda\|X\|^2}]}{e^{\lambda t^2}}.$$

Deriving the moment generating function $\lambda \mapsto \mathbb{E}[e^{\lambda\|X\|^2}]$ of $X^2$ is an elementary calculation. $$\int_{-\infty}^\infty e^{\lambda x^2} \cdot e^{-x^2/2} \mathrm{d}x = \int_{-\infty}^\infty e^{\frac{-x^2}{2(\sqrt{1-2/\lambda})^2}}\mathrm{d}x = \frac{\sqrt{2\pi}}{\sqrt{1-2\lambda}}.$$

The coordinates of $X$ are iid, so $\mathbb{E}[e^{\lambda\|X\|^2}] = \mathbb{E}[e^{\lambda X_1^2}]^d = (1-2\lambda)^{-d/2}.$ The minimizer is at $\lambda=(1-1/t^2)/2$, and we find, requiring $t \geq 1$ for the last inequality,$$\mathbb{P}[\|X\| > t] \leq e^{-d(t^2-2\log t - 1)/2} \leq e^{-d(t-1)^2}.$$

Operator norm of Gaussian matrices

The operator norm or spectral norm of a $n \times n$ matrix $M$ is defined as $$\|M\| := \max_{x \in \mathbb{R}^n} \frac{\|Mx\|}{\|x\|}.$$

Now if $M$ were a matrix with every entry independently $N(0,1)$, what would the largest singular value of this random Gaussian matrix be? I’ll give an easy tail bound based on a net argument.

An $\eta$-net, $\eta > 0$, on the sphere is a subset $N \subset \mathbb{S}^{d-1}$ such that, for every point $x \in \mathbb{S}^{d-1}$ there is a net element $n \in N$ such that $\|x-n\| \leq \eta$ but every two net elements are at distance at least $\eta$ from each other. A greedy algorithm can construct an $\eta$-net, and any $\eta$-net has size at most $(4/\eta)^d$. ((See e.g., Jiří Matoušek, Lectures on Discrete Geometry (Springer, 2002), page 314. The proof is based on a simple packing argument where balls of radius $\eta/2$ around each net element have to fit disjointly inside the ball of radius $1+\eta/2 \leq 1$ centered at the origin.))

Now let $N\subset \mathbb{S}^{d-1}$ be a $1/2$-net. By the above, the size of the net is bounded by $|N| \leq 8^d$.

The function $x \mapsto \|Mx\|$ is $\|M\|$-Lipschitz. Hence we can bound $$\|M\| \leq \max_{x\in\mathbb{S}^{d-1}} \min_{\omega \in N} \|M\omega\| + \|M\|\cdot\|x-\omega\| \leq \max_{x\in\mathbb{S}^{d-1}} \min_{\omega \in N} \|M\omega\| + \|M\|/2.$$ So we have now proved that $\|M\| \leq 2\max_{\omega\in N} \|M\omega\|$.

Now, as $M\omega$ is $N(0,I_d)$ normally distributed for any $\omega\in\mathbb{S}^{d-1}$, we can use the union bound over all points of $N$ and conclude that, for all $t \geq 1$, $$\mathbb{P}[\|M\| \geq 2t\sqrt{d}] \leq 8^d e^{-d(t-1)^2/2}.$$

Maximum of $n$ Gaussians

The distribution of the maximum $\mathbb{P}[\max_{i \leq n} X_i \geq t]$ of $n$ independent identically distributed variables $X_1,\ldots,X_n \sim N(0,1)$ is, up to a constant factor, tight with the union bound $$\mathbb{P}[\max_{i \leq n} X_i \geq t] \leq ne^{-t^2/2}.$$

Hence the expected maximum is $\mathbb{E}[\max_{i \leq n} X_i] = O(\sqrt{\ln n})$.

Average width of the simplex

Let $x_1,\dots,x_{d+1} \in \mathbb{R}^d$ be the vertices of a regular simplex such that $\|x_i\| = 1$ for all $i \in [d+1]$. If $\omega \in \mathbb{S}^{d-1}$ is chosen uniformly at random, the difference $\max_{i,j\in[d+1]} |\omega^{\mathsf{T}}(x_i-x_j|$ is called the average width of the simplex. We can bound this up to a constant factor using our knowledge of Gaussians. Let $H_t := \{y\in\mathbb{R}^d : \omega^{\mathsf{T}}y = t\}$. The $d-2$-dimensional volume of $H_t\cap \mathbb{S}^{d-1}$ is $(1-t^2)^{(d-1)/2}$ times the volume of $\mathbb{S}^{d-2}$ by Pythatoras’ theorem. Recalling that $(1+1/\lambda)^\lambda \approx e$, you can prove that the distribution of $\omega^{\mathsf{T}}x_i$ is approximately $N(0,1/\sqrt{d-1})$. The Gaussian tail bound now says that the average width of the simplex is $O(\frac{\sqrt{\ln d}}{\sqrt d}).$

Typed languages, units.

511 words

I recently picked up programming again. I used to do it a lot before I went to university, but the constant mind-numbing programming assignments quickly put me off of programming. Apart from the occasional quick bug fix for software I use myself, I haven’t done any serious coding for years.

Until recently, when I needed something coded up during my research. I decided to learn Python, and I like it. It is easy to use, the libraries are extensive and user-friendly, and ipython is a useful tool. There is just one thing that draws my ire: the weak type system. Studying math has given me an appreciation for type checking that is even stricter than most languages.

An example: my length in centimeters plus the outside temperature in °C right now equals 180. This calculation makes no sense, because the units don’t match: you can’t add centimeters to degrees Celcius. But then there’s Python, which just lets you do that.

In [1]: length = 170In [2]: temperature = 10In [3]: length + temperatureOut[3]: 180

Most bugs that stem from typos are of this sort. Those bugs are possible because the type system is too weak. If you have two loops, one iterating over i and one over j, basic unit-based type checking would probably flag any instance of i in a place where you should have typed j instead. If you intend to query A[i][j] then it should be possible to let i have row-index type and j have type-index type, making A[j][i] raise a type error.

Another example: Let $A \in \mathbb{R}^{n \times n}, x \in \mathbb{R}^n$, and we’re interested in the quantity $Ax \in \mathbb{R}^n$. If you’re like me and you can’t remember what rows and what columns are, then that doesn’t have to impact your ability to symbolically do linear algebra: the quantities $xA = A^{\mathsf{T}}x$, $Ax^{\mathsf{T}}$ and $A^{-1} x$ don’t “compile”, so any mathematician that reads it will know you typo-ed if you wrote one of those latter expressions. All operations might be matrices acting on vectors, but the matrices $A^{-1}$ and $A^{\mathsf{T}}$ fundamentally take input from different copies of $\mathbb{R}^n$ than the ones that $x$ and $x^{\mathsf{T}}$ live in. That is why matrix operations make sense even if the matrices aren’t square or symmetric: there is only one way to make sense of any operation. Even if you write it wrong in a proof, most people can see what the typo is. But then there’s Python.

In [4]: import numpy as np  In [5]: x = np.array([1,2])  In [6]: A = np.array([[3,4],[5,6]])  In [7]: np.dot(A,x) Out[7]: array([11, 17])  In [8]: np.dot(A,np.transpose(x)) Out[8]: array([11, 17])  In [9]: np.dot(x,A) Out[9]: array([13, 16])

I am like me and I can’t remember what rows and what columns are. I would like the interpreter to tell me the correct way of doing my linear algebra. At least one of the above matrix-vector-products should throw a type error. Considering the history of type systems, it is not surprising that the first languages didn’t introduce unit-based types. Nonetheless, it is a complete mystery to me why modern languages don’t type this way.

219 words

When the GDRP became effective, Tumblr decided to break its RSS feeds for all EU residents. When you try to fetch https://username.tumblr.com/rss, they’ll serve their GDRP wall instead of your requested file. You can only grab the feed if you possess the correct cookies.

Anyway, here is a hacky fix for your RSS feeds. I’m assuming you possess an http server yourself. I use a Raspberry Pi with lighttpd and selfoss. I’m assuming your user on the server is called beth, you want to follow the user called username on Tumblr and your document root is /home/beth/public.

Create the folder /home/beth/public/rss. Create the file /home/beth/.bin/fetchfeeds.sh with the following contents. Duplicate the last line once for every user you want to follow, and adjust all three occurences of username to fit.

#!/bin/bash

curl --header 'Host: username.tumblr.com' --header 'User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu Chromium/71.0.3578.80 Chrome/71.0.3578.80 Safari/537.36' --header 'Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,/;q=0.8' --header 'Accept-Language: nl,en-GB;q=0.9,en;q=0.8' --header 'Cookie: rxx=1jo2mpfhsia.1c2ecvc5&v=1; pfg=1bc46aba34ffeb83e2ef0859447d282cf8a8a2a9f95200a2a705f3afebfe9bef%23%7B%22eu_resident%22%3A1%2C%22gdpr_is_acceptable_age%22%3A1%2C%22gdpr_consent_core%22%3A1%2C%22gdpr_consent_first_party_ads%22%3A1%2C%22gdpr_consent_third_party_ads%22%3A1%2C%22gdpr_consent_search_history%22%3A1%2C%22exp%22%3A1576795974%2C%22vc%22%3A%22%22%7D%237343812268; tmgioct=5c1acbc67c0f570418402840' --header 'Connection: keep-alive' 'https://username.tumblr.com/rss' -o '/home/beth/public/rss/username-tumblr.rss' -L

[The curl command was produced using the CurlWget browser extension.]

Don’t forget to fix the permissions: chmod +x ~/.bin/fetchfeeds.sh. Now put this in your cron table by using the command crontab -e: 0 * * * * ~/.bin/fetchfeeds.sh. This will execute the bash file in the first minute of every hour.

Lastly, put http://localhost/rss/username-tumblr.rss in your RSS reader.

Does blockchain make any sense?

612 words

Context: Butarin on non-financial applications of blockchain, 15 tweets. I’ll assume you have read it.

I am deeply convinced that blockchains have no use cases. Trust is provided in the real world by the option to sue people. Trust in most blockchains is misplaced because miner pools are too big and the group of dictators of any coin is small, not accountable and wrongly incentivised. Timestamping data works just as well without using a distributed blockchain. Immutability is provided by standard crypto. The oracle problem is a real fuckin’ serious problem that is only ever resolved through the force of law.

Every function of distributed blockchains can be provided by combining digital signatures, cryptographic commitment schemes, public key cryptography, hashing all data and either periodic or on-demand polling.

So every time Tyler Cowen of Marginal Revolution posts a link to Buterin’s Twitter feed, my confidence in other people’s assertions dies a little bit more.

I’ll take the tweets linked up top in order.

2-5. I think he means “Cryptography allows you to encrypt data, prove data was signed by someone, etc etc… blockchains OTOH allow you to prove that a piece of data was *not* published [according to protocol]”, because the original is obviously false (my daily newspaper published things but is not on any blockchain). The adapted statement is also false, because SSL is based on the very notion that certificates can be rejected and that you can check that a certificate hasn’t been rejected.

6. Not just SSL, GNuPG does this very same thing as well.

7-8.  Blockchains aren’t credibly neutral, every miner on a blockchain has money in it, skewing their incentives in certain directions. Trust only goes as far as you can pay. Cryptographic hashes and signatures can make every database trusted if you can sue its proprietor.

9-11. This might be true, but it is pretty much impossible that performance will ever reach the one of standard crypto, and Buterin conveniently manages to forget the additional cost in programmer-hours. Technological cost is human cost, of the kind which is in most limited supply.

12. This tweet illuminates a lot. Buterin lives in countries without proper online banking and moreover hates privacy of any kind.

13-14. What does this even mean. Anything can and will get hacked. Your hard drive will stop working one day, and your backup won’t work.

15. Smaller stakes $\implies$ smaller mining rewards $\implies$ less miners $\implies$ easier to attack. Also applications breaking incurs a huge social cost; some people are still distrustful of others from when Google Reader (RIP) was cancelled.

I know a lot of computer scientists at my institute, including people who do research into applications of blockchains. Nobody I know believes blockchains are useful apart from black markets, tax evasion, as a novel pyramid scheme, or as a hype for getting grant money.

I get why Cowen falls for Buterin’s sweet talk, he is an economist with no knowledge of cryptography. I don’t get why not more people who know anything about cryptography are speaking out about the insanity of blockchain. My best guess is that everyone who knows stuff about it is using it to either make money themselves, pretending to be enthusiastic to further the pyramid scheme, or profiling themselves as “blockchain expert” and charging high consultancy fees.

Section of doubt

I hope I’m completely wrong and I just misunderstood every statement every blockchain fanatic has ever made. My constant rejection of all their statements does make me question myself, because it seems strange that so many people can be so consistently wrong about a thing. I’d love to show epistemic humility here, but I don’t consider it to be justified.

362 wordsThis is cute. Suppose you are a spammer and you want to target website owners, how do you do this? Submitting spam comments is one possibility; either your spam is posted, in which case you win, or you get stuck in the spam filter, and the site admin will at some point scroll past it when checking if any proper comments were misflagged, and you’ll sort of win anyway. Here is a better strategy: access their website while spoofing your referer.

What is a referer, you ask? Let’s say you are browsing bethzero.com/2018/12/01/referer-spam, and you click a link to example.org. Your browser will, in its HTTP request to example.org, mention that you got referred to their page from bethzero.com/2018/12/01/referer-spam. This is so that website owners can know where their visitors come from. Super neat, how can we use this for spamming?

The trick is to send requests with fake referer attributes. (( This is the correct spelling. Except in some cases, such as rel=”noreferrer”.  )) Request the page https://bethzero.com, put https://bestblogideas.com as the referer. When the proprietor of bethzero.com looks at her visitor statistics, she will see that bestblogideas.com linked to her, and probably visit it herself to see what they write about her. This is where you sell her access to your cheap special $300 blogging video course. Another fun way to use this is probably for doxxing. Create a special page tracking.com/<unique-code> and use that as referer. If you make sure that no other entry points to that page exist, then every visitor will be the Beth you’re targetting. For bonus points, you put Facebook’s tracking code on the page so that you’ll forever be able to target advertisements directly to Beth. Beth might try to outsmart you and satisfy her interests in her referrers by pointing her browser at tracking.com without the unique code. In that case, you can try buying a number of domain names for a kind of adaptive group testing procedure. If you have a million dollars you can probably do this on bigger scales, finding the IP address of a good fraction of pseudonymous website owners. Looking at my own stats, I think this is really happening. Delayed WordPress RSS feed 365 wordsIn the category “2-minute hacks for skilled people, 30-minute hacks for me because I can’t code for shit”, let’s replay an old RSS feed. Useful if you want to read the old posts on a blog but don’t want to do it all at once. This way, you can relive the past of your favourite WordPress blogs. Requires a server with php installation and a feed reader that updates at least once per day. I’ve got a Raspberry Pi 3B running selfoss. We’ll be using a fancy feature of Wordpess, namely that https://example.com/yyyy/mm/dd/feed gives the RSS feed with posts from that day interval. Actually every public-facing WordPress page can be appended with /feed to produce something meaningful. It will not be a truly faithful replay of old posts, because if posts get edited or deleted we won’t get to see the original post. With this knowledge in mind, create a php file $ touch public/delay.php
$chmod 755 public/delay.php and fill it with the following code <?php$url = htmlspecialchars($_GET['url']);$years = floatval($_GET['years']);$months = floatval($_GET['months']);$days = floatval($_GET['days']); // calculate the time stamp of some day in the past // (we subtract a fixed amount of seconds so that we dont // run into issues with how many days a month has and // because I am not good enough at coding to handle this // the right way.)$months_delayed = $months + 12 *$years;
$days_delayed =$days + 30 * $months_delayed;$moment = time() - intval(60 * 60 * 24 * $days_delayed); // redirect to the RSS file with posts from that day header('Location: ' .$url . '/' . date('Y/m/d', \$moment) . '/feed', 303);
die();
?>

Requesting the page http://example.org/delay.php?url=https://bethzero.com/feed&months=1 will redirect you to an RSS file containing all posts made exactly 30 days ago. Because we’re using a temporary 303 redirect, the page that you get redirected to changes every day. If your feed reader updates every day, you should get to see every post.

One small issue happens if a day has more blog posts than can appear in the RSS feed (default is 10 in WordPress). In that case, you might miss out on the oldest posts of the day.

Bayes’ theorem and transgender lesbians

234 wordsSome time ago I read a cool post on Tumblr, but I can’t find it anymore. It was about calculating P(trans|WLW), the fraction of women who love women that is transgender, from P(trans), the fraction of the general population that is transgender, P(WLW), the fraction of the population that is a woman-loving woman, and P(WLW|trans), the fraction of gynephillic trans women among trans people. Bayes’ theorem says

P(trans|WLW) = P(WLW|trans)P(trans)/P(WLW).

I remember that the resulting number was significant. As I could not find it again, here is a quick and dirty reconstruction. For every statistic, I picked the first one I found that did not seem completely unrealistic.

So Bayes’ theorem gives us P(trans|WLW) = 0.15.

Bonus: suicide attempts

While preparing this post, I stumbled upon this report. Page 8 lists:

• P(attempted suicide) = 0.016.
• P(attempted suicice|trans) = 0.41.

Bayes now says P(trans|attempted suicide) = 0.26. Big if true.*

Section of Doubt

Applying Bayes’ theorem like this seems to give unreasonably good mileage. That suggests that social scientists aren’t allowed to use numbers from different studies and get conclusions from them, or asymmetric misreporting makes these calculations error-prone.

The last number above is big. Makes one wonder why so little effort is spent explicitly targetting at-risk trans people.

* Added July 16th: I just met a subject expert, she said this figure sounded about right.


text:
# 决策树模型

$$n! \le l \le 2^h$$

\begin{aligned} h &\ge \lg(n!) \\ &= \Omega(n\lg n) \end{aligned}

# 计数排序

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 """ The implement of the Counting Sort The time consume of Counting Sort is O(n), and this algorithm is stable. The drawbacks of the this algorithm is that it can only sort the numbers from 0 to k. If there is a negative number in the A, this algorithm can not work. If the given data is very sparse, it will waster a lot space. """ def CountingSort(A): k = max(A) C = [0] * (k+1) B = [None] * len(A) for item in A: C[item] += 1 # now C[i] contains the number of elements equal to i. for i in range(1,len(C)): C[i] = C[i-1] + C[i] # now C[i] contains the number of elements less than or equal to i. for i in range(len(A)-1,-1,-1): # why from len(A) to 0 not from 0 to len(A) ? Becuse it must be stable! # only from len(A) to 0 can guarntee stable. B[C[A[i]]-1] = A[i] # not the B[C[A[i]]] = A[i], because A and C starts with 0, not 1. C[A[i]] -= 1 return B

# 基数排序

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 """ The implement of Radix Sort. Radix Sort is using the property of the stable sorting algorithm. """ def counting_sort(A,index): k = max([item[index] for item in A]) n = len(A) B = [0] * (k+1) C = [None] * n for item in A: B[item[index]] += 1 for i in range(1,len(B)): B[i] += B[i-1] for i in range(len(A)-1,-1,-1): C[ B[ A[i][index] ] - 1 ] = A[i] B[A[i][index]] -= 1 return C def radix_sort(A): d = len(A[0]) B = A[:] for i in range(d-1,-1,-1): B = counting_sort(B,i) return B if __name__ == "__main__": A = [ [3,2,9], [4,5,7], [6,5,7], [8,3,9], [4,3,6], [7,2,0], [3,5,5], ] print(A) B = radix_sort(A) print(B)

Flyaway is the owner of this blog.

So what do you think? Did I miss something? Is any part unclear? Leave your comments below


text:
# Mathematical prediction of COVID-19 spread and convergence

The number of people infected with COVID-19 is predicted by the multiple synthetic waves analyzed in the blogs “Will restraint in going out speed up the convergence of COVID-19 infection?” and “Why the number of COVID-19 infections is surging” dated November 22, 2020. The blue dots are the weekly total of the number of infected people in Japan as published on the website of the Ministry of Health, Labor and Welfare. The solid blue line is the synthetic wave for each variant until December 2021.

In the second half of December 2021, the number of infected people, which had been on a gradual downward trend, seems to have started to increase. The number of people infected with the Omicron variant is rapidly increasing overseas and the effect is spreading to Japan. Let us consider the following two scenarios for the near future prediction.

Scenario a: Assuming the average infectiousness of the variants so far → Orange dashed line

Scenario b: Assuming the same level of infectivity as Type 11, the most infectious species to date → gray dashed line

##### As of December 26 2021

If the number of people who may be infected is $x$ (hereafter, “uninfected”) and the number of people who are infected and may pass it on to others is $y$ (hereafter, “infected”), the rate of change of each is predicted to follow the following differential equation. (Note: The term “uninfected” does not include people who are avoiding contact with infected people or are taking precautions to avoid infection.

$$\frac{dx}{dt}=-bxy\tag{1}$$

$$\frac{dy}{dt}=bxy-cy\tag{2}$$

Equation $(1)$ shows that the number of uninfected people decreases as they come into contact with infected people, while equation $(2)$ shows that the number of infected people increases as a result, but they are cured or die and are no longer infected. The coefficient $B$ is the “infection coefficient” and $C$ is the “cure coefficient”. The coefficients for each wave (dashed line) that makes up the synthetic wave (solid blue line) are as follows.

For your reference (compiled from the MHLW website and press-related materials)

##### Since 2020 up to date

Compared to the Spanish flu that was prevalent 100 years ago, the number of variants is larger and the interval between their appearance is shorter.

The end


text:
Friday
March 7, 2014

# Homework Help: trigonomety

Posted by Anonymous on Wednesday, February 8, 2012 at 1:15pm.

Change the following to decimal degrees. (Round your answer to the nearest hundredth of a degree.)
43° 20'
??

• trigonomety - Henry, Thursday, February 9, 2012 at 4:49pm

A = 43 20/60 = 43.33 Deg.

 First Name: School Subject: Answer:

Related Questions

math - round result to two decimal places...the answer i have is: 7.023 so two ...
math - in a right triangle ABC, C = 90°, a = 33.6 inches and b = 28.4 inches. ...
Math - Find the approximate number of radians, to the nearest hundreth, in the ...
Calculus - Let f be the function defined as follows. $y = f(x) = \sqrt{{\color{... Math - say you have 4/13 and the decimal equivalent to it is 30.76923 how do I ... college stats - Sorry for all the questions, I really need help and an ... Math Plz help me - I want to know about the ROund off rule : my quiz say Round ... STATS - In a certain store, there is a .05 probability that the scanned price in... Statistics - University Hospital has been concerned with the number of errors ... Calculus - Let f be the function defined as follows.$ y = f(x) = {\color{red}5...

Search
Members


text:
# Shorter Uncommon Descent

Tyler DiPietro finds a fresh example of steaming creationist nonsense at the weblog of Dembski and sycophants, Uncommon Descent. I summarize for the busy reader:

Because running an image of a face through a software filter makes it look less like a face, Darwin was a fuddy-duddy and materialism is on its last leg.

The cream on the cocoa is, however, this bit, from a commenter:

Iâ€™m not sure about the CSI [complex specified information] decreasing, however. Anyone able to get values of the CSI for each picture?

Gee, given that “complex specified information” has never been consistently defined, I wonder how one could compute it.

# An Item of Prime Importance

I was busy with something or other, so I didn’t get to see the event Dennis Overbye describes in the New York Times, where the director and the star of the new film Jumper chatted with MIT professors Ed Farhi and Max Tegmark before a live audience in lecture hall 26-100. Not having been there, I don’t have very much to say, but I do feel the need to quote one paragraph of Overbye’s article and add just a tiny bit of emphasis:

The real lure, [Farhi] said, is not transportation, but secure communication. If anybody eavesdrops on the teleportation signal, the whole thing doesnâ€™t work, Dr. Farhi said. Another use is in quantum computing, which would exploit the ability of quantum bits of information to have different values, both one and zero, at the same time to perform certain calculations, like factoring large prime numbers, much faster than ordinary computers.

Ahem.

In any other circumstance, I’d probably pontificate on how exponential parallelism is not the source of quantum computing’s calculation-fu, but I think we have a few other points to address first. . . .

Attentive readers will recall that Dr. Farhi was the guy who signed my paperwork when I was an undergraduate. For an amusing story from those days, see my post of last November, “Pay No Attention to the Man.”

(Tip o’ the fedora to Dave Bacon.)

# Fractions

When the bus is stuck in traffic, or when I’m curled restlessly in bed during the darkest hours of morning when sleep will not come and all the old wounds on my heart ache like they were newly made, I sometimes think back over my education and add up all the time my teachers wasted. The worst, perhaps, was the mathematics, for which entire years of schooling went for naught. “Pre-Calculus”? Pfft. AP Computer Science? Pfft++. All in all, I’d say that upwards of a third of my mathematics schooling before university was a waste of time, and another third was so incompetently done that any student who hadn’t already been hooked on science and learning would have been completely sunk.

So, I find it easy to sympathize with people who say that math education needs a severe overhaul. I’m willing to contemplate big curriculum changes, but of course, you have to convince me that the specific changes you have in mind will actually do any good. When a proposal comes down the wire to eliminate fractions, I reserve the right to chortle and guffaw.

# Math “Joke” of the Day

Q. John is a mathematician. John is a nihilist. John can’t tell a cow from a donut. What does John study?

A. Yep, pointless topology!

Thank you, thank you, I’ll be here all week. . . .

# One Small Step for Mismeasuring Man

There are only 1.27 times as many straight people as gay people in the world, and most people aren’t either.

How do I know? Google says so. Just count the hits.

porn: 227,000,000
lesbian porn: 9,930,000
straight porn: 610,000
heterosexual porn: 360,000
homosexual porn: 284,000

Now that I’ve gotten Science After Sunclipse banned from every school room in America (oh, like it wasn’t already, me being an ill-tempered, illiterate evilutionist and all), you can just go about your business. Or you can hang around while Windy and I resolve the group selection controversy.

# Factorials

Isabel finds a curmudgeonly 1842 quotation from Augustus de Morgan, about the way we write factorials:

Among the worst of barabarisms is that of introducing symbols which are quite new in mathematical, but perfectly understood in common, language. Writers have borrowed from the Germans the abbreviation n! to signify 1.2.3.(n – 1).n, which gives their pages the appearance of expressing surprise and admiration that 2, 3, 4, &c. should be found in mathematical results.

You know, I’d always thought the formula for “n choose k” was a little, well, enthusiastic:

$\left(\begin{array}{c} n \\ k \end{array}\right) = \frac{n!}{k!(n-k)!}.$

n! k! n minus k!! You gotta believe me, guys!”

Still, though, if I saw de Morgan’s way of writing the factorial of n, I’d read it as

$1 \cdot 2 \cdot 3 \cdot (n-1) \cdot n,$

which is only the factorial of n when n is 5. I guess there’s little point in pleasing the dead. . . .

# Sociology

Technical difficulties have delayed the post which I had hoped to have up today. So, instead, why not watch a little Tom Lehrer?

# Varieties of Wall-Banging Experience

When traveling through the Internet, one often encounters crackpot physics, and after some familiarity with the genus, one begins to organize the specimens into species. I often find myself remarking upon a particular characteristic of pseudo-physics: the use of verbal instead of mathematical arguments. Instead of positing a premise and doing the math to deduce consequences of that premise, we get a whole pile of jargon, pulled from different sources and decorated with equations to disguise the fact that the “arguments” are essentially plays on words, with the whole thing bent towards bolstering some pre-established conclusion. What do you do in order to debunk this kind of nonsense? One option is to focus on a few highlights and show that they lack validity; however, addressing all the points may balloon out into a full physics course of its own.

This problem is accentuated when a single piece of crankery indulges in misunderstandings of quantum mechanics, special relativity, general relativity and other fields, all together in a single vessel of fractured ceramic. The anti-Einstein polemic recently unearthed by PZ Myers is a good example of such a “target-rich environment,” as was biocentrism woo. Another example, currently unfolding before our cybernetic eyes, is Salvador Cordova’s ongoing misunderstanding of quantum mechanics (commentary here, here, here, here, here and now at Tyler’s new place too — and we’ve hardly gotten started).

When I have an idle moment, I might pull the thoughts I’ve scattered around my fellow skeptics’ blog threads into a unified post, but for now, I’ll just refer the Interested Reader to the links provided above.

# Euler and Diderot

From Dirk J. Struick’s A Concise History of Mathematics (1967), quoted by Shallit:

There exists a widely quoted story about Diderot and Euler according to which Euler, in a public debate in St. Petersburg, succeeded in embarrassing the freethinking Diderot by claiming to possess an algebraic demonstration of the existence of God: “Sir, (a+b^n)/n = x; hence God exists, answer please!” This is a good example of a bad historical anecdote, since the value of an anecdote about an historical person lies in its faculty to illustrate certain aspects of his character; this particular anecdote serves to obscure both the character of Diderot and of Euler, Diderot knew his mathematics and had written on involutes and probability, and no reason exists to think that the thoughtful Euler would have behaved in the asinine way indicated. The story seems to have been made up by the English mathematician De Morgan (1806-1871). See L. G. Krakeur and R. L. Krueger, Isis, Vol. 31 (1940), pp. 431-32; also Vol. 33 (1941), pp. 219-31. It is true that there was in the eighteenth century occasional talk about the probability of an algebraic demonstration of the existence of God; Maupertuis indulged in one, see Voltaire’s Diatribe, Oeuvres, Vol. 41 (1821 ed.), pp. 19, 30. See also B. Brown, Amer. Math. Monthly, Vol. 49 (1944).

Krakeur and Krueger state that his investigations into mathematics “dominated Diderot’s youthful activities and represent an important phase of his universal interests.” They speculate that a lack of mathematical background among scholars who studied the man impaired those scholars’ ability to address this part of Diderot’s life.

Salvador “Darwin was a flatulent puppy-killer” Cordova has decided to enslave quantum mechanics in the service of Intelligent Design, or as he calls it, “Advanced Creation Science.” It’s amazing how poorly they hide the religious agenda, isn’t it? I was a little surprised when Michael Behe admitted on national television that ID was a tool for God’s Christian Soldiers to fight back against science, but letting your medievalism all hang out is apparently a ubiquitous practice among the ID crowd.

Anyway, Sal was, like, completely floored that Fourier transforms are used in both quantum physics and electrical engineering. He says he has to take a year to blog his “Advanced Creation Science” in full detail, but I have the feeling it will all reduce to the following:

Fourier transforms are used in engineering; Fourier transforms appear in quantum physics; therefore, quantum physics must have been engineered by Jesus.

UPDATE (11 December): Mark Chu-Carroll has weighed in.

UPDATE (14 December): More from Tyler here.

# Currently on the Reading Queue

The arXivotubes have delivered unto me A. James and M. J. Plank’s “On fitting power laws to ecological data” (4 December, arXiv:0712.0613).

Heavy-tailed or power-law distributions are becoming increasingly common in biological literature. A wide range of biological data has been fitted to distributions with heavy tails. Many of these studies use simple fitting methods to find the parameters in the distribution, which can give highly misleading results. The potential pitfalls that can occur when using these methods are pointed out, and a step-by-step guide to fitting power-law distributions and assessing their goodness-of-fit is offered.

The classic in this genre is Clauset, Shalizi and Newman’s “Power-law Distributions in Empirical Data,” but more correctives are always appreciated.

# Vacation Memories 2: Baggage

I’m back home from my brief travels, and I returned to find the latest outbreak of quantum woo infection, followed immediately by a heap of silliness about anthropic twaddle.

“Too soon,” I thought. “I need to go back on vacation.”

So, instead of complaining at great length about things I’ve already complained about, I’ll just share one quick observation and then head out into the outside world, shopping for art supplies.

Yesterday, I flew into Boston. In my laptop I carried a hardback of Lois Lowry‘s The Giver (1993) and, to recapture a more innocent time, Feynman and Weinberg’s Elementary Particles and the Laws of Physics (1987). In between reading these two, I happened to glance at the pamphlet-type thing which the airline clerk had given me to hold my boarding passes in. Here’s the puzzling part, under the “Free Baggage Allowance” heading:

Carry-on Baggage is limited to one piece per passenger, plus a personal item such as a purse, briefcase or laptop computer. The carry-on cannot exceed 51 inches (11″ × 14″ × 26″) and must fit under the seat or in an overhead compartment.

Why are the three linear dimensions added? The frame device the airline positions at each gate for testing whether or not your carry-on will fit rejects your baggage if any dimension exceeds the threshold set. Your baggage is deemed invalid even if the total volume is less than 11″ × 14″ × 26″ = 4004 in3 (just try carrying on something long and skinny). The longest diagonal of an 11″ × 14″ × 26″ box is

$\sqrt{14^2 + 26^2} \approx 29.5$

inches long. So, you can have a carry-on item the sum of whose edge lengths is, say, thirty-one inches, and which won’t fit the actual airline restrictions no matter how you try to wedge it in sideways. The sum of the height, width and depth is a meaningless number.

# Give Us Original Mistakes

Zeno might appreciate this (where “appreciate” is used in the technical sense of “bang head against wall on account of”). Via Isabel comes Eric Schechter’s page of Common Errors in College Math. If you survived calculus, read through it and congratulate yourself on all the mistakes you don’t make anymore!

(See how optimistic I am?)

Schechter provides one of the most inspiring examples of getting the right answer through the wrong method that I’ve ever seen. The problem is to evaluate the following definite integral:

$\int_0^{2\pi} \cos x\, dx.$

This is how our student started:

$\int_0^{2\pi} \cos x\, dx = \left.\frac{\sin x}{x}\right|_0^{2\pi} = \frac{\sin 2\pi}{2\pi} – \frac{\sin 0}{0}.$

But wait, there’s more!

$\frac{\sin 2\pi}{2\pi} – \frac{\sin 0}{0} = \sin – \sin = 0.$

And they say we can’t eliminate sin from the world.

# Autodynamics

Ah, some light Friday fare!

By now, everybody has probably heard about the forthcoming crackpot “documentary” from David de Hilster, Einstein Wrong – The Miracle Year. Currently looking for financial backing, de Hilster hopes to release this flick in 2008, doing for relativity what What the Bleep Do We Know (2004) did for quantum physics: namely, let the fractured ceramics have free play.

As it turns out, David de Hilster is one of the Network’s classic relativity cranks. He’s been pushing his pet theory, “Autodynamics,” since at least the early 1990s (on the sci.physics Usenet group). As it also turns out, Autodynamics has plenty of problems. For example, it chucks out the Lorentz transformations, thereby making itself inconsistent with the Maxwell equations, which form our basic understanding of electricity and magnetism, without which the technological support system of modern society couldn’t exist.

What’s more, they don’t like that nasty ol’ equation

$E = mc^2.$

The Autodynamicist revulsion at this horrible formula has led them to propose — no, I’m not making this up — that $E$ should equal $mc^3$ instead.

# Denyse O’Leary Provides Amusement

ERV made me aware of a new bit of silliness from Denyse O’Leary, resident “journalist” of the antiscience advocacy blog Uncommon Descent. (She’s also a proponent of non-materialist neuroscience: if being a doofus about evolution is a ticket to fame and riches earned by fleecing the gullible, then trashing other well-established sciences must also be quite a racket.) This is what O’Leary has to say on the complicated and tendentious subject of women in science and mathematics, a topic in which even intelligent people are led astray by emotive arguments and spuriously “scientific” ideas not supported by the data.

Anyone who thinks that the fact that girls are not as good as boys in math means that girls do not rule is obviously not in contact with many girls.

[spit take]

Barf out! Gag me with a spoon! Gross!

I’m still not accustomed to the creationist ability to pack so many kinds of wrong into a single sentence. First, the “fact” that girls can’t do math as well as boys is, ahem, anything but; beyond that, what trait makes the female of the species “rule,” in O’Leary’s estimation? Their ability to cook, clean, and fit within a well-molded wet T-shirt? Or a mystic synchrony with the energy of the Moon, no doubt a gender-specific modification of the miracle circuits which receive the soul’s instructions to the brain.

Blech. Uncommon Descent leaves an icky taste all over me. To get it out, I think I’ll learn about adjoint functors and their relationship to monads.


text: The determinant of matrix $\begin{bmatrix} 0 & 1 & 2 & 3 \\ 1 & 0 & 3 & 0 \\ 2 & 3 & 0 & 1 \\ 3 & 0 & 1 & 2 \end{bmatrix}$ is ________


text:
# News

## Jaxon Lee Capstone

Analytic Approaches to Common Means and their Inequalities

Abstract: Means are measurements of centrality, commonly used in science, engineering, statistics, and mathematics. There is a plethora of means and they measure centrality differently as to obtain the most pertinent information to the question at hand. We will discuss a specific subclass of means called Pythagorean means, which include the arithmetic, geometric, and harmonic means. We will also discuss the root mean squared, as these four means are the most common means to run into in science, engineering, and statistics applications. Lastly, we will show how the value of each of the means is related to one another through the inequality relationship between them.

Date: 6/21/2017
Time: 4:00PM-5:00PM
Place: 123 Armstrong Hall

All are welcome.

Date, Location:
2017-06-21

Date, Location:
2017-06-01

## Ariel Sitler Masters Project

Abstract: Unmanned Aerial Vehicles (UAVs) are a valuable tool for image acquisition in the field of 3D mapping. UAVs are a low-cost alternative to manned aerial vehicles, and can be affixed with digital cameras capable of collecting high quality images of structures and terrain for the creation of 3D models. This research is an extension of previous work which used a genetic algorithm and space partitioning to optimize UAV flight paths over areas with structures. Here, the focus is on optimizing the structure mapping portion of the flight plan by imposing the grid technology used for the area portion in the previous work to a building face, then using the grid size, camera specifications, and desired resolution to determine the optimal offset distance from the building.

Date: 5/3/2017
Time: 1:00PM-2:00PM
Place: 313 Armstrong Hall

All are welcome.

Date, Location:
2017-05-03

Math undergraduate Tony Allen awarded NSF Summer Fellowship. Please congratulate him. Great work Tony!

Date, Location:
2017-04-26

## Fatma Mohamed Defense

On Some Parabolic Type Problems from Thin Film Theory and Chemical Reaction-Diffusion Networks

Abstract: In the first part of this dissertation we study the evolution of a thin film of fluid modeled by the lubrication approximation for thin viscous films. We prove existence of (dissipative) strong solutions for the Cauchy problem when the sub-diffusive exponent ranges between 3/8 and 2; then we show that these solutions tend to zero at rates matching the decay of the source-type self-similar solutions with zero contact angle. We introduce the weaker concept of dissipative mild solutions and we show that in this case the surface-tension energy dissipation is the mechanism responsible for the $H^1$--norm decay to zero of the thickness of the film at an explicit rate. Relaxed problems, with second-order nonlinear terms of porous media type are also successfully treated by the same means.

In the second part we are concerned with the convergence of a certain space-discretization scheme (the so-called method of lines) for mass-action reaction-diffusion systems. First, we start with a toy model, namely AB and prove convergence of the method of lines for this linear case (weak convergence in L^2 is enough in this case). Then we show that solutions of the chemical reaction-diffusion system associated to A+BC in one spatial dimension can be approximated in L^2 on any finite time interval by solutions of a space discretized ODE system which models the corresponding chemical reaction system replicated in the discretization subdomains where the concentrations are assumed spatially constant. Same-species reactions through the virtual boundaries of adjacent subdomains lead to diffusion in the vanishing limit. We show convergence of our numerical scheme by way of a consistency estimate, with features generalizable to reaction networks other than the one considered here, and to multiple space dimensions.

Date: 4/20/2017
Time: 12:30PM-2:30PM
Place: 315 Armstrong Hall

All are welcome.

Date, Location:
2017-04-20

## Mushtaq Abd Al-Rahem Defense

A Multidimensional Technique for Measuring Consensus Within Groups via Conditional Probability

Abstract: A recent increase in the use of the term “consensus” in various fields has led researchers to develop various ways to measure the consensus within and across groups depending on the areas. Numerous studies use the mean or the variance alone as a measure of consensus, or lack of consensus. Most of the time, high variance is viewed as more disagreement in a group. Using the variance as a measure of disagreement is meaningful in an exact comparison cases (same group, same mean). However, it could be meaningless when it is used to compare groups that have different sizes, or if the mean is different. In this thesis, we establish the fact that the range of the variance is a function of the mean, we present a new index of disagreement and measure of consensus that depend on both, the mean and the variance, by utilizing the conditional distribution of the variance for a given mean. Initially, this new index is developed for comparison of data collected using a Likert scale of size five. This new measure is compared with the results of two other known measures, to show that in some cases they agree, but in other cases the new measure provides additional information. Next, to facilitate generalization, a new algorithmic method to determine the index using a geometric approach is presented. The geometric approach makes it easier to compute the measure of consensus and provides the foundational ideas for generalizing the measure to Likert scales for any n. Finally, a multidimensional computational technique was developed to provided the final step of generalization to Likert scales of any n.

Date: 4/10/2017
Time: 3:00PM-5:00PM
Place: 313 Armstrong Hall

All are welcome.

Date, Location:
2017-04-10

The Eberly College of Arts and Sciences has named two recipients of the 2016-17 Outstanding Researcher Award: Christina Duncan and Casian Pantea.

Congratulate Professor Pantea the next time you see him!

Date, Location:
2017-04-10

## Junior Math Competition

This competition will be open to all students grades 7-12. The questions will be at AMC difficulty level.

Casian Pantea: cpantea@math.wvu.edu
Charis Tsikkou: tsikkou@math.wvu.edu
Megan Henry: mhott1@mix.wvu.edu

Date, Location:
2017-04-08

## Janet Anderson Defense

A Study of arc strong connectivity of digraphs

Abstract: This dissertation studies the extremal, structural and minimax properties related to digraph arc strong connectivity. Motivated by the former researches by Mader and Matula, we defined the digraph arc strength of a digraph $D$ to be the maximum arc strong connectivity of subdigraphs of $D$, and investigate extremal conditions for a strict digraph that is saturated with respect to the condition that it digraph arc strength is bounded by a positive integer $k$. Extremal size of such digraphs are obtained and structural characterization of the extremal digraphs are also obtained. In addition, a minimax duality theorem to determine the digraph arc strength of a digraph is found. Similar minimax results on related desity functions of digraphs are also obtained.

Date: 4/7/2017
Time: 3:30PM-5:00PM
Place: 315 Armstrong Hall

All are welcome.

Date, Location:
2017-04-07

## Celebrating Einstein

When Black Holes Collide! Gravitational Waves and Other Tales from the Horizon

As part of the month-long Celebrating Einstein event here at WVU, Dr.  Zach Etienne  from Department of Mathematics will be giving a public lecture this Friday on black holes and gravitational waves.   A planetarium show on the top  floor of White Hall will follow immediately after  his lecture.   His talk is accessible to general audience and even kids may enjoy the talk and the show.

Abstract:What happens if you fall into a black hole? Einstein's theory of gravity provides us a means to answer questions that, like this one, fuzz the boundary between science fiction and science fact. But the equations behind this theory are extremely complex, and solving them to advance our scientific understanding--particularly now that gravitational waves have been discovered--usually requires the use of supercomputers. In a nutshell, this is my field of expertise. I will review my career path and present results from some of my latest supercomputer simulations, which have given us both deeper insights into the gravitational waves we have already observed, as well as important predictions for those we are likely to observe in the near future.

Immediately after the lecture, at 8:30PM, there will be a planetarium show on the top floor of White Hall.

Press Release

Official Poster

Television Commercial

Official Webstie

Date: 4/07/2017
Time: 7:30PM
Place: White Hall Room G09

All are welcome.

Date, Location:
2017-04-07


text:
# How to get substring between two similar characters in JavaScript?

JavascriptWeb DevelopmentFront End TechnologyObject Oriented Programming

Let’s say we have the following string with some colons −

var values="This is:the first program in JavaScript:After that I will do Data Structure program";

We need to get the substring between the colon. The output should be −

the first program in JavaScript

For this, use the concept of split().

## Example

Following is the code −

var values="This is:the first program in JavaScript:After that I will do Data Structure program";
var result=[];
result=values.split(":");
console.log(result[1]);

To run the above program, you need to use the following command −

node fileName.js.

Here, my file name is demo273.js.

## Output

This will produce the following output on console −

PS C:\Users\Amit\javascript-code> node demo273.js
the first program in JavaScript
Published on 09-Nov-2020 07:53:34