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The examples we have looked at so far are cases where we normally can invert the matrix \( \boldsymbol{X}^T\boldsymbol{X} \). Using a polynomial expansion as we did both for the masses and the fitting of the equation of state, leads to row vectors of the design matrix which are essentially orthogonal due to the polynomial character of our model. Obtaining the inverse of the design matrix is then often done via a so-called LU, QR or Cholesky decomposition.
This may however not the be case in general and a standard matrix inversion algorithm based on say LU, QR or Cholesky decomposition may lead to singularities. We will see examples of this below.
There is however a way to partially circumvent this problem and also gain some insights about the ordinary least squares approach, and later shrinkage methods like Ridge and Lasso regressions.
This is given by the Singular Value Decomposition algorithm, perhaps the most powerful linear algebra algorithm. Let us look at a different example where we may have problems with the standard matrix inversion algorithm. Thereafter we dive into the math of the SVD.
One of the typical problems we encounter with linear regression, in particular when the matrix \( \boldsymbol{X} \) (our so-called design matrix) is high-dimensional, are problems with near singular or singular matrices. The column vectors of \( \boldsymbol{X} \) may be linearly dependent, normally referred to as super-collinearity. This means that the matrix may be rank deficient and it is basically impossible to to model the data using linear regression. As an example, consider the matrix $$ \begin{align*} \mathbf{X} & = \left[ \begin{array}{rrr} 1 & -1 & 2 \\ 1 & 0 & 1 \\ 1 & 2 & -1 \\ 1 & 1 & 0 \end{array} \right] \end{align*} $$
The columns of \( \boldsymbol{X} \) are linearly dependent. We see this easily since the the first column is the row-wise sum of the other two columns. The rank (more correct, the column rank) of a matrix is the dimension of the space spanned by the column vectors. Hence, the rank of \( \mathbf{X} \) is equal to the number of linearly independent columns. In this particular case the matrix has rank 2.
Super-collinearity of an \( (n \times p) \)-dimensional design matrix \( \mathbf{X} \) implies that the inverse of the matrix \( \boldsymbol{X}^T\boldsymbol{X} \) (the matrix we need to invert to solve the linear regression equations) is non-invertible. If we have a square matrix that does not have an inverse, we say this matrix singular. The example here demonstrates this $$ \begin{align*} \boldsymbol{X} & = \left[ \begin{array}{rr} 1 & -1 \\ 1 & -1 \end{array} \right]. \end{align*} $$ We see easily that \( \mbox{det}(\boldsymbol{X}) = x_{11} x_{22} - x_{12} x_{21} = 1 \times (-1) - 1 \times (-1) = 0 \). Hence, \( \mathbf{X} \) is singular and its inverse is undefined. This is equivalent to saying that the matrix \( \boldsymbol{X} \) has at least an eigenvalue which is zero.
If our design matrix \( \boldsymbol{X} \) which enters the linear regression problem $$ \begin{align} \boldsymbol{\beta} & = (\boldsymbol{X}^{T} \boldsymbol{X})^{-1} \boldsymbol{X}^{T} \boldsymbol{y}, \label{_auto1} \end{align} $$ has linearly dependent column vectors, we will not be able to compute the inverse of \( \boldsymbol{X}^T\boldsymbol{X} \) and we cannot find the parameters (estimators) \( \beta_i \). The estimators are only well-defined if \( (\boldsymbol{X}^{T}\boldsymbol{X})^{-1} \) exits. This is more likely to happen when the matrix \( \boldsymbol{X} \) is high-dimensional. In this case it is likely to encounter a situation where the regression parameters \( \beta_i \) cannot be estimated.
A cheap ad hoc approach is simply to add a small diagonal component to the matrix to invert, that is we change $$ \boldsymbol{X}^{T} \boldsymbol{X} \rightarrow \boldsymbol{X}^{T} \boldsymbol{X}+\lambda \boldsymbol{I}, $$ where \( \boldsymbol{I} \) is the identity matrix. When we discuss Ridge regression this is actually what we end up evaluating. The parameter \( \lambda \) is called a hyperparameter. More about this later.
From standard linear algebra we know that a square matrix \( \boldsymbol{X} \) can be diagonalized if and only it is a so-called normal matrix, that is if \( \boldsymbol{X}\in {\mathbb{R}}^{n\times n} \) we have \( \boldsymbol{X}\boldsymbol{X}^T=\boldsymbol{X}^T\boldsymbol{X} \) or if \( \boldsymbol{X}\in {\mathbb{C}}^{n\times n} \) we have \( \boldsymbol{X}\boldsymbol{X}^{\dagger}=\boldsymbol{X}^{\dagger}\boldsymbol{X} \). The matrix has then a set of eigenpairs $$ (\lambda_1,\boldsymbol{u}_1),\dots, (\lambda_n,\boldsymbol{u}_n), $$ and the eigenvalues are given by the diagonal matrix $$ \boldsymbol{\Sigma}=\mathrm{Diag}(\lambda_1, \dots,\lambda_n). $$ The matrix \( \boldsymbol{X} \) can be written in terms of an orthogonal/unitary transformation \( \boldsymbol{U} \) $$ \boldsymbol{X} = \boldsymbol{U}\boldsymbol{\Sigma}\boldsymbol{V}^T, $$ with \( \boldsymbol{U}\boldsymbol{U}^T=\boldsymbol{I} \) or \( \boldsymbol{U}\boldsymbol{U}^{\dagger}=\boldsymbol{I} \).
Not all square matrices are diagonalizable. A matrix like the one discussed above $$ \boldsymbol{X} = \begin{bmatrix} 1& -1 \\ 1& -1\\ \end{bmatrix} $$ is not diagonalizable, it is a so-called defective matrix. It is easy to see that the condition \( \boldsymbol{X}\boldsymbol{X}^T=\boldsymbol{X}^T\boldsymbol{X} \) is not fulfilled.
However, and this is the strength of the SVD algorithm, any general matrix \( \boldsymbol{X} \) can be decomposed in terms of a diagonal matrix and two orthogonal/unitary matrices. The Singular Value Decompostion (SVD) theorem states that a general \( m\times n \) matrix \( \boldsymbol{X} \) can be written in terms of a diagonal matrix \( \boldsymbol{\Sigma} \) of dimensionality \( m\times n \) and two orthognal matrices \( \boldsymbol{U} \) and \( \boldsymbol{V} \), where the first has dimensionality \( m \times m \) and the last dimensionality \( n\times n \). We have then $$ \boldsymbol{X} = \boldsymbol{U}\boldsymbol{\Sigma}\boldsymbol{V}^T $$
As an example, the above defective matrix can be decomposed as $$ \boldsymbol{X} = \frac{1}{\sqrt{2}}\begin{bmatrix} 1& 1 \\ 1& -1\\ \end{bmatrix} \begin{bmatrix} 2& 0 \\ 0& 0\\ \end{bmatrix} \frac{1}{\sqrt{2}}\begin{bmatrix} 1& -1 \\ 1& 1\\ \end{bmatrix}=\boldsymbol{U}\boldsymbol{\Sigma}\boldsymbol{V}^T, $$
with eigenvalues \( \sigma_1=2 \) and \( \sigma_2=0 \). The SVD exits always!
The SVD decomposition (singular values) gives eigenvalues \( \sigma_i\geq\sigma_{i+1} \) for all \( i \) and for dimensions larger than \( i=p \), the eigenvalues (singular values) are zero.
In the general case, where our design matrix \( \boldsymbol{X} \) has dimension \( n\times p \), the matrix is thus decomposed into an \( n\times n \) orthogonal matrix \( \boldsymbol{U} \), a \( p\times p \) orthogonal matrix \( \boldsymbol{V} \) and a diagonal matrix \( \boldsymbol{\Sigma} \) with \( r=\mathrm{min}(n,p) \) singular values \( \sigma_i\geq 0 \) on the main diagonal and zeros filling the rest of the matrix. There are at most \( p \) singular values assuming that \( n > p \). In our regression examples for the nuclear masses and the equation of state this is indeed the case, while for the Ising model we have \( p > n \). These are often cases that lead to near singular or singular matrices.
The columns of \( \boldsymbol{U} \) are called the left singular vectors while the columns of \( \boldsymbol{V} \) are the right singular vectors.
If we assume that \( n > p \), then our matrix \( \boldsymbol{U} \) has dimension \( n \times n \). The last \( n-p \) columns of \( \boldsymbol{U} \) become however irrelevant in our calculations since they are multiplied with the zeros in \( \boldsymbol{\Sigma} \).
The economy-size decomposition removes extra rows or columns of zeros from the diagonal matrix of singular values, \( \boldsymbol{\Sigma} \), along with the columns in either \( \boldsymbol{U} \) or \( \boldsymbol{V} \) that multiply those zeros in the expression. Removing these zeros and columns can improve execution time and reduce storage requirements without compromising the accuracy of the decomposition.
If \( n > p \), we keep only the first \( p \) columns of \( \boldsymbol{U} \) and \( \boldsymbol{\Sigma} \) has dimension \( p\times p \). If \( p > n \), then only the first \( n \) columns of \( \boldsymbol{V} \) are computed and \( \boldsymbol{\Sigma} \) has dimension \( n\times n \). The \( n=p \) case is obvious, we retain the full SVD. In general the economy-size SVD leads to less FLOPS and still conserving the desired accuracy.
import numpy as np
# SVD inversion
def SVDinv(A):
''' Takes as input a numpy matrix A and returns inv(A) based on singular value decomposition (SVD).
SVD is numerically more stable than the inversion algorithms provided by
numpy and scipy.linalg at the cost of being slower.
'''
U, s, VT = np.linalg.svd(A)
# print('test U')
# print( (np.transpose(U) @ U - U @np.transpose(U)))
# print('test VT')
# print( (np.transpose(VT) @ VT - VT @np.transpose(VT)))
print(U)
print(s)
print(VT)
D = np.zeros((len(U),len(VT)))
for i in range(0,len(VT)):
D[i,i]=s[i]
UT = np.transpose(U); V = np.transpose(VT); invD = np.linalg.inv(D)
return np.matmul(V,np.matmul(invD,UT))
X = np.array([ [1.0, -1.0, 2.0], [1.0, 0.0, 1.0], [1.0, 2.0, -1.0], [1.0, 1.0, 0.0] ])
print(X)
A = np.transpose(X) @ X
print(A)
# Brute force inversion of super-collinear matrix
#B = np.linalg.inv(A)
#print(B)
C = SVDinv(A)
print(C)
The matrix \( \boldsymbol{X} \) has columns that are linearly dependent. The first column is the row-wise sum of the other two columns. The rank of a matrix (the column rank) is the dimension of space spanned by the column vectors. The rank of the matrix is the number of linearly independent columns, in this case just \( 2 \). We see this from the singular values when running the above code. Running the standard inversion algorithm for matrix inversion with \( \boldsymbol{X}^T\boldsymbol{X} \) results in the program terminating due to a singular matrix.
There are several interesting mathematical properties which will be relevant when we are going to discuss the differences between say ordinary least squares (OLS) and Ridge regression.
We have from OLS that the parameters of the linear approximation are given by $$ \boldsymbol{\tilde{y}} = \boldsymbol{X}\boldsymbol{\beta} = \boldsymbol{X}\left(\boldsymbol{X}^T\boldsymbol{X}\right)^{-1}\boldsymbol{X}^T\boldsymbol{y}. $$
The matrix to invert can be rewritten in terms of our SVD decomposition as $$ \boldsymbol{X}^T\boldsymbol{X} = \boldsymbol{V}\boldsymbol{\Sigma}^T\boldsymbol{U}^T\boldsymbol{U}\boldsymbol{\Sigma}\boldsymbol{V}^T. $$ Using the orthogonality properties of \( \boldsymbol{U} \) we have $$ \boldsymbol{X}^T\boldsymbol{X} = \boldsymbol{V}\boldsymbol{\Sigma}^T\boldsymbol{\Sigma}\boldsymbol{V}^T = \boldsymbol{V}\boldsymbol{D}\boldsymbol{V}^T, $$ with \( \boldsymbol{D} \) being a diagonal matrix with values along the diagonal given by the singular values squared.
This means that $$ (\boldsymbol{X}^T\boldsymbol{X})\boldsymbol{V} = \boldsymbol{V}\boldsymbol{D}, $$ that is the eigenvectors of \( (\boldsymbol{X}^T\boldsymbol{X}) \) are given by the columns of the right singular matrix of \( \boldsymbol{X} \) and the eigenvalues are the squared singular values. It is easy to show (show this) that $$ (\boldsymbol{X}\boldsymbol{X}^T)\boldsymbol{U} = \boldsymbol{U}\boldsymbol{D}, $$ that is, the eigenvectors of \( (\boldsymbol{X}\boldsymbol{X})^T \) are the columns of the left singular matrix and the eigenvalues are the same.
Going back to our OLS equation we have $$ \boldsymbol{X}\boldsymbol{\beta} = \boldsymbol{X}\left(\boldsymbol{V}\boldsymbol{D}\boldsymbol{V}^T \right)^{-1}\boldsymbol{X}^T\boldsymbol{y}=\boldsymbol{U\Sigma V^T}\left(\boldsymbol{V}\boldsymbol{D}\boldsymbol{V}^T \right)^{-1}(\boldsymbol{U\Sigma V^T})^T\boldsymbol{y}=\boldsymbol{U}\boldsymbol{U}^T\boldsymbol{y}. $$ We will come back to this expression when we discuss Ridge regression. $$ \tilde{y}^{OLS}={\bf X}\hat{\beta}^{OLS}=\sum_{j=1}^p {\bf u}_j{\bf u}_j^T{\bf y}$$ and for Ridge we have $$ \tilde{y}^{Ridge}={\bf X}\hat{\beta}^{Ridge}=\sum_{j=1}^p {\bf u}_j\frac{\sigma_j^2}{\sigma_j^2+\lambda}{\bf u}_j^T{\bf y}$$ .
It is indeed the economy-sized SVD, note the summation runs up tp $$p$$ only and not $$n$$.
Here we have that $${\bf X} = {\bf U}{\bf \Sigma}{\bf V}^T$$, with $$\Sigma$$ being an $$ n\times p$$ matrix and $${\bf V}$$ being a $$ p\times p$$ matrix. We also have assumed here that $$ n > p$$.
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Let us remind ourselves about the expression for the standard Mean Squared Error (MSE) which we used to define our cost function and the equations for the ordinary least squares (OLS) method, that is our optimization problem is $$ {\displaystyle \min_{\boldsymbol{\beta}\in {\mathbb{R}}^{p}}}\frac{1}{n}\left\{\left(\boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta}\right)^T\left(\boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta}\right)\right\}. $$ or we can state it as $$ {\displaystyle \min_{\boldsymbol{\beta}\in {\mathbb{R}}^{p}}}\frac{1}{n}\sum_{i=0}^{n-1}\left(y_i-\tilde{y}_i\right)^2=\frac{1}{n}\vert\vert \boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta}\vert\vert_2^2, $$ where we have used the definition of a norm-2 vector, that is $$ \vert\vert \boldsymbol{x}\vert\vert_2 = \sqrt{\sum_i x_i^2}. $$
By minimizing the above equation with respect to the parameters \( \boldsymbol{\beta} \) we could then obtain an analytical expression for the parameters \( \boldsymbol{\beta} \). We can add a regularization parameter \( \lambda \) by defining a new cost function to be optimized, that is $$ {\displaystyle \min_{\boldsymbol{\beta}\in {\mathbb{R}}^{p}}}\frac{1}{n}\vert\vert \boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta}\vert\vert_2^2+\lambda\vert\vert \boldsymbol{\beta}\vert\vert_2^2 $$
which leads to the Ridge regression minimization problem where we require that \( \vert\vert \boldsymbol{\beta}\vert\vert_2^2\le t \), where \( t \) is a finite number larger than zero. By defining $$ C(\boldsymbol{X},\boldsymbol{\beta})=\frac{1}{n}\vert\vert \boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta}\vert\vert_2^2+\lambda\vert\vert \boldsymbol{\beta}\vert\vert_1, $$
we have a new optimization equation $$ {\displaystyle \min_{\boldsymbol{\beta}\in {\mathbb{R}}^{p}}}\frac{1}{n}\vert\vert \boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta}\vert\vert_2^2+\lambda\vert\vert \boldsymbol{\beta}\vert\vert_1 $$ which leads to Lasso regression. Lasso stands for least absolute shrinkage and selection operator.
Here we have defined the norm-1 as $$ \vert\vert \boldsymbol{x}\vert\vert_1 = \sum_i \vert x_i\vert. $$
Using the matrix-vector expression for Ridge regression, $$ C(\boldsymbol{X},\boldsymbol{\beta})=\frac{1}{n}\left\{(\boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta})^T(\boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta})\right\}+\lambda\boldsymbol{\beta}^T\boldsymbol{\beta}, $$
by taking the derivatives with respect to \( \boldsymbol{\beta} \) we obtain then a slightly modified matrix inversion problem which for finite values of \( \lambda \) does not suffer from singularity problems. We obtain $$ \boldsymbol{\beta}^{\mathrm{Ridge}} = \left(\boldsymbol{X}^T\boldsymbol{X}+\lambda\boldsymbol{I}\right)^{-1}\boldsymbol{X}^T\boldsymbol{y}, $$
with \( \boldsymbol{I} \) being a \( p\times p \) identity matrix with the constraint that $$ \sum_{i=0}^{p-1} \beta_i^2 \leq t, $$
with \( t \) a finite positive number.
We see that Ridge regression is nothing but the standard OLS with a modified diagonal term added to \( \boldsymbol{X}^T\boldsymbol{X} \). The consequences, in particular for our discussion of the bias-variance tradeoff are rather interesting.
Furthermore, if we use the result above in terms of the SVD decomposition (our analysis was done for the OLS method), we had $$ (\boldsymbol{X}\boldsymbol{X}^T)\boldsymbol{U} = \boldsymbol{U}\boldsymbol{D}. $$
We can analyse the OLS solutions in terms of the eigenvectors (the columns) of the right singular value matrix \( \boldsymbol{U} \) as $$ \boldsymbol{X}\boldsymbol{\beta} = \boldsymbol{X}\left(\boldsymbol{V}\boldsymbol{D}\boldsymbol{V}^T \right)^{-1}\boldsymbol{X}^T\boldsymbol{y}=\boldsymbol{U\Sigma V^T}\left(\boldsymbol{V}\boldsymbol{D}\boldsymbol{V}^T \right)^{-1}(\boldsymbol{U\Sigma V^T})^T\boldsymbol{y}=\boldsymbol{U}\boldsymbol{U}^T\boldsymbol{y} $$
For Ridge regression this becomes $$ \boldsymbol{X}\boldsymbol{\beta}^{\mathrm{Ridge}} = \boldsymbol{U\Sigma V^T}\left(\boldsymbol{V}\boldsymbol{D}\boldsymbol{V}^T+\lambda\boldsymbol{I} \right)^{-1}(\boldsymbol{U\Sigma V^T})^T\boldsymbol{y}=\sum_{j=0}^{p-1}\boldsymbol{u}_j\boldsymbol{u}_j^T\frac{\sigma_j^2}{\sigma_j^2+\lambda}\boldsymbol{y}, $$
with the vectors \( \boldsymbol{u}_j \) being the columns of \( \boldsymbol{U} \).
Since \( \lambda \geq 0 \), it means that compared to OLS, we have $$ \frac{\sigma_j^2}{\sigma_j^2+\lambda} \leq 1. $$
Ridge regression finds the coordinates of \( \boldsymbol{y} \) with respect to the orthonormal basis \( \boldsymbol{U} \), it then shrinks the coordinates by \( \frac{\sigma_j^2}{\sigma_j^2+\lambda} \). Recall that the SVD has eigenvalues ordered in a descending way, that is \( \sigma_i \geq \sigma_{i+1} \).
For small eigenvalues \( \sigma_i \) it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom. Actually, calculating the variance of \( \boldsymbol{X}\boldsymbol{v}_j \) shows that this quantity is equal to \( \sigma_j^2/n \). With a parameter \( \lambda \) we can thus shrink the role of specific parameters.
For the sake of simplicity, let us assume that the design matrix is orthonormal, that is $$ \boldsymbol{X}^T\boldsymbol{X}=(\boldsymbol{X}^T\boldsymbol{X})^{-1} =\boldsymbol{I}. $$
In this case the standard OLS results in $$ \boldsymbol{\beta}^{\mathrm{OLS}} = \boldsymbol{X}^T\boldsymbol{y}=\sum_{i=0}^{p-1}\boldsymbol{u}_j\boldsymbol{u}_j^T\boldsymbol{y}, $$
and $$ \boldsymbol{\beta}^{\mathrm{Ridge}} = \left(\boldsymbol{I}+\lambda\boldsymbol{I}\right)^{-1}\boldsymbol{X}^T\boldsymbol{y}=\left(1+\lambda\right)^{-1}\boldsymbol{\beta}^{\mathrm{OLS}}, $$
that is the Ridge estimator scales the OLS estimator by the inverse of a factor \( 1+\lambda \), and the Ridge estimator converges to zero when the hyperparameter goes to infinity.
We will come back to more interpreations after we have gone through some of the statistical analysis part.
For more discussions of Ridge and Lasso regression, Wessel van Wieringen's article is highly recommended. Similarly, Mehta et al's article is also recommended.
The parameter \( \lambda \) that we have introduced in the Ridge (and Lasso as well) regression is often called a regularization parameter or shrinkage parameter. It is common to call it a hyperparameter. What does it mean mathemtically?
Here we will first look at how to analyze the difference between the standard OLS equations and the Ridge expressions in terms of a linear algebra analysis using the SVD algorithm. Thereafter, we will link (see the material on the bias-variance tradeoff below) these observation to the statisical analysis of the results. In particular we consider how the variance of the parameters \( \boldsymbol{\beta} \) is affected by changing the parameter \( \lambda \).
We have our design matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \). With the SVD we decompose it as $$ \boldsymbol{X} = \boldsymbol{U\Sigma V^T}, $$
with \( \boldsymbol{U}\in {\mathbb{R}}^{n\times n} \), \( \boldsymbol{\Sigma}\in {\mathbb{R}}^{n\times p} \) and \( \boldsymbol{V}\in {\mathbb{R}}^{p\times p} \).
The matrices \( \boldsymbol{U} \) and \( \boldsymbol{V} \) are unitary/orthonormal matrices, that is in case the matrices are real we have \( \boldsymbol{U}^T\boldsymbol{U}=\boldsymbol{U}\boldsymbol{U}^T=\boldsymbol{I} \) and \( \boldsymbol{V}^T\boldsymbol{V}=\boldsymbol{V}\boldsymbol{V}^T=\boldsymbol{I} \).
Before we discuss the link between for example Ridge regression and the singular value decomposition, we need to remind ourselves about the definition of the covariance and the correlation function. These are quantities
Suppose we have defined two vectors \( \hat{x} \) and \( \hat{y} \) with \( n \) elements each. The covariance matrix \( \boldsymbol{C} \) is defined as $$ \boldsymbol{C}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} \mathrm{cov}[\boldsymbol{x},\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] \\ \mathrm{cov}[\boldsymbol{y},\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{y},\boldsymbol{y}] \\ \end{bmatrix}, $$ where for example $$ \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] =\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})(y_i- \overline{y}). $$ With this definition and recalling that the variance is defined as $$ \mathrm{var}[\boldsymbol{x}]=\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})^2, $$ we can rewrite the covariance matrix as $$ \boldsymbol{C}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} \mathrm{var}[\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] \\ \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] & \mathrm{var}[\boldsymbol{y}] \\ \end{bmatrix}. $$
The covariance takes values between zero and infinity and may thus lead to problems with loss of numerical precision for particularly large values. It is common to scale the covariance matrix by introducing instead the correlation matrix defined via the so-called correlation function $$ \mathrm{corr}[\boldsymbol{x},\boldsymbol{y}]=\frac{\mathrm{cov}[\boldsymbol{x},\boldsymbol{y}]}{\sqrt{\mathrm{var}[\boldsymbol{x}] \mathrm{var}[\boldsymbol{y}]}}. $$
The correlation function is then given by values \( \mathrm{corr}[\boldsymbol{x},\boldsymbol{y}] \in [-1,1] \). This avoids eventual problems with too large values. We can then define the correlation matrix for the two vectors \( \boldsymbol{x} \) and \( \boldsymbol{y} \) as $$ \boldsymbol{K}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} 1 & \mathrm{corr}[\boldsymbol{x},\boldsymbol{y}] \\ \mathrm{corr}[\boldsymbol{y},\boldsymbol{x}] & 1 \\ \end{bmatrix}, $$
In the above example this is the function we constructed using pandas.
In our derivation of the various regression algorithms like Ordinary Least Squares or Ridge regression we defined the design/feature matrix \( \boldsymbol{X} \) as $$ \boldsymbol{X}=\begin{bmatrix} x_{0,0} & x_{0,1} & x_{0,2}& \dots & \dots x_{0,p-1}\\ x_{1,0} & x_{1,1} & x_{1,2}& \dots & \dots x_{1,p-1}\\ x_{2,0} & x_{2,1} & x_{2,2}& \dots & \dots x_{2,p-1}\\ \dots & \dots & \dots & \dots \dots & \dots \\ x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \dots & \dots x_{n-2,p-1}\\ x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ \end{bmatrix}, $$ with \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \), with the predictors/features \( p \) refering to the column numbers and the entries \( n \) being the row elements. We can rewrite the design/feature matrix in terms of its column vectors as $$ \boldsymbol{X}=\begin{bmatrix} \boldsymbol{x}_0 & \boldsymbol{x}_1 & \boldsymbol{x}_2 & \dots & \dots & \boldsymbol{x}_{p-1}\end{bmatrix}, $$ with a given vector $$ \boldsymbol{x}_i^T = \begin{bmatrix}x_{0,i} & x_{1,i} & x_{2,i}& \dots & \dots x_{n-1,i}\end{bmatrix}. $$
With these definitions, we can now rewrite our \( 2\times 2 \) correaltion/covariance matrix in terms of a moe general design/feature matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \). This leads to a \( p\times p \) covariance matrix for the vectors \( \boldsymbol{x}_i \) with \( i=0,1,\dots,p-1 \) $$ \boldsymbol{C}[\boldsymbol{x}] = \begin{bmatrix} \mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_{p-1}]\\ \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_{p-1}]\\ \mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_1] & \mathrm{var}[\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_{p-1}]\\ \dots & \dots & \dots & \dots & \dots & \dots \\ \dots & \dots & \dots & \dots & \dots & \dots \\ \mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_{2}] & \dots & \dots & \mathrm{var}[\boldsymbol{x}_{p-1}]\\ \end{bmatrix}, $$ and the correlation matrix $$ \boldsymbol{K}[\boldsymbol{x}] = \begin{bmatrix} 1 & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_1] & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_2] & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_{p-1}]\\ \mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_0] & 1 & \mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_2] & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_{p-1}]\\ \mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_0] & \mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_1] & 1 & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_{p-1}]\\ \dots & \dots & \dots & \dots & \dots & \dots \\ \dots & \dots & \dots & \dots & \dots & \dots \\ \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_0] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_1] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_{2}] & \dots & \dots & 1\\ \end{bmatrix}, $$
The Numpy function np.cov calculates the covariance elements using the factor \( 1/(n-1) \) instead of \( 1/n \) since it assumes we do not have the exact mean values. The following simple function uses the np.vstack function which takes each vector of dimension \( 1\times n \) and produces a \( 2\times n \) matrix \( \boldsymbol{W} \) $$ \boldsymbol{W} = \begin{bmatrix} x_0 & y_0 \\ x_1 & y_1 \\ x_2 & y_2\\ \dots & \dots \\ x_{n-2} & y_{n-2}\\ x_{n-1} & y_{n-1} & \end{bmatrix}, $$
which in turn is converted into into the \( 2\times 2 \) covariance matrix \( \boldsymbol{C} \) via the Numpy function np.cov(). We note that we can also calculate the mean value of each set of samples \( \boldsymbol{x} \) etc using the Numpy function np.mean(x). We can also extract the eigenvalues of the covariance matrix through the np.linalg.eig() function.
# Importing various packages
import numpy as np
n = 100
x = np.random.normal(size=n)
print(np.mean(x))
y = 4+3*x+np.random.normal(size=n)
print(np.mean(y))
W = np.vstack((x, y))
C = np.cov(W)
print(C)
The previous example can be converted into the correlation matrix by simply scaling the matrix elements with the variances. We should also subtract the mean values for each column. This leads to the following code which sets up the correlations matrix for the previous example in a more brute force way. Here we scale the mean values for each column of the design matrix, calculate the relevant mean values and variances and then finally set up the \( 2\times 2 \) correlation matrix (since we have only two vectors).
import numpy as np
n = 100
# define two vectors
x = np.random.random(size=n)
y = 4+3*x+np.random.normal(size=n)
#scaling the x and y vectors
x = x - np.mean(x)
y = y - np.mean(y)
variance_x = np.sum(x@x)/n
variance_y = np.sum(y@y)/n
print(variance_x)
print(variance_y)
cov_xy = np.sum(x@y)/n
cov_xx = np.sum(x@x)/n
cov_yy = np.sum(y@y)/n
C = np.zeros((2,2))
C[0,0]= cov_xx/variance_x
C[1,1]= cov_yy/variance_y
C[0,1]= cov_xy/np.sqrt(variance_y*variance_x)
C[1,0]= C[0,1]
print(C)
We see that the matrix elements along the diagonal are one as they should be and that the matrix is symmetric. Furthermore, diagonalizing this matrix we easily see that it is a positive definite matrix.
The above procedure with numpy can be made more compact if we use pandas.
We whow here how we can set up the correlation matrix using pandas, as done in this simple code
import numpy as np
import pandas as pd
n = 10
x = np.random.normal(size=n)
x = x - np.mean(x)
y = 4+3*x+np.random.normal(size=n)
y = y - np.mean(y)
X = (np.vstack((x, y))).T
print(X)
Xpd = pd.DataFrame(X)
print(Xpd)
correlation_matrix = Xpd.corr()
print(correlation_matrix)
We expand this model to the Franke function discussed above.
# Common imports
import numpy as np
import pandas as pd
def FrankeFunction(x,y):
term1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))
term2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))
term3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))
term4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)
return term1 + term2 + term3 + term4
def create_X(x, y, n ):
if len(x.shape) > 1:
x = np.ravel(x)
y = np.ravel(y)
N = len(x)
l = int((n+1)*(n+2)/2) # Number of elements in beta
X = np.ones((N,l))
for i in range(1,n+1):
q = int((i)*(i+1)/2)
for k in range(i+1):
X[:,q+k] = (x**(i-k))*(y**k)
return X
# Making meshgrid of datapoints and compute Franke's function
n = 4
N = 100
x = np.sort(np.random.uniform(0, 1, N))
y = np.sort(np.random.uniform(0, 1, N))
z = FrankeFunction(x, y)
X = create_X(x, y, n=n)
Xpd = pd.DataFrame(X)
# subtract the mean values and set up the covariance matrix
Xpd = Xpd - Xpd.mean()
covariance_matrix = Xpd.cov()
print(covariance_matrix)
We note here that the covariance is zero for the first rows and columns since all matrix elements in the design matrix were set to one (we are fitting the function in terms of a polynomial of degree \( n \)).
This means that the variance for these elements will be zero and will cause problems when we set up the correlation matrix. We can simply drop these elements and construct a correlation matrix without these elements.
We can rewrite the covariance matrix in a more compact form in terms of the design/feature matrix \( \boldsymbol{X} \) as $$ \boldsymbol{C}[\boldsymbol{x}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}= \mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}]. $$
To see this let us simply look at a design matrix \( \boldsymbol{X}\in {\mathbb{R}}^{2\times 2} \) $$ \boldsymbol{X}=\begin{bmatrix} x_{00} & x_{01}\\ x_{10} & x_{11}\\ \end{bmatrix}=\begin{bmatrix} \boldsymbol{x}_{0} & \boldsymbol{x}_{1}\\ \end{bmatrix}. $$
If we then compute the expectation value $$ \mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}=\begin{bmatrix} x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\ x_{10}x_{00}+x_{11}x_{01} & x_{10}^2+x_{11}^2\\ \end{bmatrix}, $$ which is just $$ \boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]=\begin{bmatrix} \mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] \\ \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] \\ \end{bmatrix}, $$ where we wrote $$\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]$$ to indicate that this the covariance of the vectors \( \boldsymbol{x} \) of the design/feature matrix \( \boldsymbol{X} \).
It is easy to generalize this to a matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \).


Week 8
Currently in progress
Matthew Gottshall 0 - 2 Stormanimagus
Tom Dixon ? - ? White Lotus
Dave Kaplan 0 - 2 Brian Kelly
MaximumCDawg 2 - 0 Sean Ottaway
MaximumCDawg: I got the better of SeanOhh in two, and I think that the games basically came down to the facts that almost all of my cards are live in that matchup and that he topdecked like a skunk. I was anticipating a lot of workshop and dredge, since I figured people would want to shore up the last few rounds, so I was packing lots of artifact and shop hate. That worked out alright, and I the limited amount of cards I drew compared to his deck was enough to win given his dead draws.


'Splash' <https://github.com/scrapinghub/splash> is a 'JavaScript' rendering service. It is a lightweight web browser with an 'HTTP' API, implemented in 'Python' using 'Twisted' and 'QT' and provides some of the core functionality of the 'RSelenium' or 'seleniumPipes' R packages in a lightweight footprint. Some of 'Splash' features include the ability to process multiple web pages in parallel; retrieving 'HTML' results and/or take screen shots; disabling images or use 'Adblock Plus' rules to make rendering faster; executing custom 'JavaScript' in page context; getting detailed rendering info in 'HAR' format.
| Version: | 0.4.1 |
| Depends: | R (≥ 3.2.0) |
| Imports: | xml2, curl, httr, purrr, stats, utils, docker, magick, scales, formatR, openssl, stringi, jsonlite, HARtools, lubridate |
| Suggests: | testthat, tibble, jpeg, png, covr, knitr, rmarkdown |
| Published: | 2018-01-16 |
| Author: | Bob Rudis [aut, cre] |
| Maintainer: | Bob Rudis <bob at rud.is> |
| BugReports: | https://github.com/hrbrmstr/splashr/issues |
| License: | AGPL |
| URL: | http://github.com/hrbrmstr/splashr |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | splashr results |
| Reference manual: | splashr.pdf |
| Vignettes: |
Introduction to splashr splashr Helper Functions and Data Working With the splashr DSL |
| Package source: | splashr_0.4.1.tar.gz |
| Windows binaries: | r-devel: splashr_0.4.1.zip, r-release: splashr_0.4.1.zip, r-oldrel: splashr_0.4.1.zip |
| OS X binaries: | r-release: splashr_0.4.1.tgz, r-oldrel: splashr_0.4.1.tgz |
| Old sources: | splashr archive |
Please use the canonical form https://CRAN.R-project.org/package=splashr to link to this page.


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---
layout: post
title: Introduction to Reactive Programming with Project Reactor
author: Murat Özkan
category : talk
---
Reactive programming is a relatively new approach on developing non-blocking, asynchronous apps, that are error tolerant
and resilient under load. We have Project Reactor and a few others, implementing Reactive Streams specification to enable
asynchronous stream processing in the JVM. In this talk, I introduce the reactive concepts and operations, as well as
composing these operations into a working Java application.
Murat Özkan is a Software Engineer working at TOPdesk. With a focus on backend, he is interested in learning
about Distributed and Fault-tolerant systems, with a flavor of functional programming. He also enjoys flying RC
planes, though often has problems during landings.


Predict housing price using interest rate
Get recommendation using your prefered area and requirments
Analyze trend from historical data
"This is fantastic! Thanks so much guys!"
"This is amazing. I've been using it to create lots of super nice landing pages."
"Thanks so much for making these free resources available to us!"


{% extends "layout.html" %} {% block title %}Zero-OS Hub{% endblock %} {% block content %}
Do you want to publish your Zero-OS flist to our hub? It's easy.
All you need is a .tar.gz
archive of your files and upload it here. Simple.
| Name | Preview | Size | Updated |
| Loading files... |


Press n or j to go to the next uncovered block, b, p or k for the previous block.
| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| src |
|
100% | 5/5 | 100% | 0/0 | 100% | 1/1 | 100% | 5/5 |
| src/app/core/modal |
|
100% | 2/2 | 100% | 0/0 | 0% | 0/1 | 100% | 1/1 |
| src/app/core/services |
|
10.53% | 6/57 | 0% | 0/20 | 0% | 0/24 | 5.66% | 3/53 |
| src/app/orders/new-order |
|
90.74% | 49/54 | 84.62% | 11/13 | 88.24% | 15/17 | 89.8% | 44/49 |
| src/app/shared |
|
14.29% | 3/21 | 0% | 0/10 | 42.86% | 6/14 | 14.29% | 3/21 |
| src/app/shared/services |
|
85.71% | 6/7 | 83.33% | 5/6 | 100% | 2/2 | 80% | 4/5 |
| src/app/shared/ux-helper |
|
100% | 2/2 | 100% | 0/0 | 100% | 2/2 | 100% | 1/1 |
| src/environments |
|
100% | 1/1 | 100% | 0/0 | 100% | 0/0 | 100% | 1/1 |


Creating PBM ObjectsTwo core classes, PBMExperiment and PBMDesign, are have been constructed for organizing and analyzing PBM data in R. The following set of functions can be used to construct both PBMExperiment and PBMDesign objects from GPR files or data already loaded in R. For more details, see class definitions below. |
|
|---|---|
Load raw PBM scan data as PBMExperiment |
|
Create a new PBMExperiment object |
|
Create a new PBMDesign object |
|
Sample Pre-ProcessingBefore any downstream inference can be performed, individual samples should be pre-processed. A single wrapper function, upbmPreprocess is provided for performing all standard pre-processing steps, as well as individual functions for handling the steps of background subtraction, Cy3 normalization, and within and across replicate normalization. (See Pre-Processing vignette for more details.) |
|
PBM Preprocess |
|
Background subtract intensities |
|
Compute Cy3 scaling factors using emprical reference |
|
Compute Cy3 scaling factors using regression |
|
Generate Cy3 empirical reference |
|
Perform Cy3 normalization |
|
Perform spatial adjustment |
|
Normalize within replicates |
|
Normalize across replicates |
|
Probe/K-mer SummarizationPre-processed and normalized samples can be aggregated across replicates to obtain probe-level and K-mer-level affinity estimates. Additionally, a simple summarization function is provided for computing K-mer summary statistics for individual samples in the absence of replicate data. (See Summarization vignette for more details.) |
|
Fit probe models |
|
Fit k-mer probe set models |
|
Compute K-mer summary metrics from probe intensities |
|
K-mer-Level TestingFinally, K-mer-level affinity estimates can be used to perform various inference procedures both for individual conditions and for testing across conditions. Three functions to perform different itesting procedures are listed below. (See K-mer-Level Inference vignette for more details.) |
|
Test for k-mer preferential affinities |
|
Test for k-mer differential affinities |
|
Test for k-mer differential specificities |
|
PBM Class DefinitionsThe package defines two new classes, PBMExperiment and PBMDesign which buildon existing Bioconductor infrastructure. (See Class Details vignette for more details.) |
|
PBMExperiment class |
|
PBMDesign class |
|
PBM Class Methods and SettersThe two PBM classes include special slots specifying probe filtering and trimming criteria. Functions are provided for accessing and applying these filtering and trimming procedures on both PBMExperiment and PBMDesign objects. These and additional slots can be accessed and modified using getter and setting functions. |
|
Filter PBMExperiment and PBMDesign objects |
|
Trim probe sequences |
|
|
|
PBM slot accessors and setters |
Set design in PBMExperiment object |
|
Helper FunctionsThe package also includes several helper functions for working with PBM data. This includes, most importantly, functions for extracting assay data stored in PBMExperiment objects as tidy tables (see Tidy Data vignette for more details). |
|
Tidy PBMExperiment object |
|
Tidy SummarizedExperiment object |
|
Map K-mer Motifs to PBM Probe Sequences |
|
Generate Unique K-mer Sequences |


1) Click anywhere on Mariah until you hear one of her whistle notes
2) Press any key between 1 and 9 to change whistle note
3) Click in different places on Mariah using different whistle notes to give angel-nn some training data
4) When you are ready, press the 't' key and wait for angel-nn to learn Mariah's notes
5) Now when you click on Mariah you don't need to select a 1-9 number, click anywhere, angel-nn will predict what note should play based on where you clicked the training data
a)angel-nn currently only works on Chrome
The click-noises during training data are from an actual Mariah Carey recording. The predicted notes after training are generated using the p5.js sound library by matching frequencies


Music available on Bandcamp, Spotify, Apple Music and more.
Le Mort Records
No Funeral Records
L'Oeil du Tigre Records
Toys of Disharmony
Bandcamp
Email
Facebook


Have you ever spent excessive amounts of time going trough every single shelf and cabinet in you kitchen in the look for something to eat, and even though they're all filled with all kinds of stuff, you don't feel like having any of it?
Well, fortunately for you, we have a solution. We've teamed up with k-ruoka.fi to help you go without starving. All you have to do is click the link below (or go to www.k-ruoka.fi/resepti), list down some of the ingredients you saw on your scavengerhunt through the pantry, and we'll show you what delicacies you could make of them
Give it a go »A Spotify cooking playlist.
Gordon Ramsay giving cooking tips.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sapiente esse necessitatibus.


{% extends 'assessment/assessment_detail.html' %} {% block content %} {{ block.super }}
Are you sure you want to delete this assessment? This will remove ALL data associated with this assessment from all HAWC modules.


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 5-6
Rabbit only needs a few ingredients to be transformed into a delicious one-pot meal.
Place the flour, thyme and a good pinch of salt and plenty of freshly ground black pepper in a large freezer bag. Put the rabbit portions into the bag, a few at a time, and shake well until evenly coated in the seasoned flour. Transfer to a plate.
Melt the butter with one tablespoon of the oil in a large heavy-based frying pan over a medium heat. Fry the rabbit, a few pieces at a time, until golden-brown all over. Put all the front and rear leg portions into a flameproof casserole dish.
Transfer the saddle pieces to a plate, cover loosely and set aside (these will need less cooking time, so can be added later on). Preheat the oven to 170C/340F/Gas 3½.
Add a little more oil to the pan and cook the bacon until the fat is browned and beginning to crisp. Add the bacon to the casserole dish.
Add a dash more oil to the frying pan and fry the onion for 5-7 minutes, or until lightly browned and beginning to soften. Add the onions to the casserole, sprinkle with any flour remaining in the freezer bag and stir until well combined.
Pour half of the cider into the frying pan and stir vigorously with a wooden spoon to lift any sediment from the bottom. Simmer for a few seconds then pour into the casserole. Add the rest of the cider and the stock. Stir the bay leaves into the casserole, cover with a lid and cook in the centre of the oven for 45 minutes.
Remove the casserole from the oven, add the reserved saddle pieces and carrots, turn all the rabbit portions, ensuring that as much of the meat is covered by liquid as possible. (Not all the meat will be covered.) Return to the oven for a further 1-2 hours.
Take the casserole out of the oven after one hour and check the rabbit – the meat should be starting to fall off the bone when the rabbit is ready. Poke the leg portions and the saddle pieces with a knife and if it doesn’t slide in easily, return the casserole to the oven. Check again for tenderness and turn the rabbit portions every 30 minutes or so.
When the rabbit is tender, skim off any fat that may have risen to the top of the casserole with a large spoon. Carefully transfer the casserole to the hob. Bring to a fast simmer and cook for 3-5 minutes, or until the liquid reduces to a slightly thickened, gravy-like consistency. Stir in the frozen peas and simmer for a further three minutes. Season with salt and freshly ground black pepper and serve.


Information Acquisition is quite busy, so please don't flag so many reports in the future.
We reviewed a sample of your flagged reports but didn't have time for all of them.
Vitaly Vedenin
Information Acquisition


A transpiler like Babel can help us ensure that our new JavaScript features remain compatible with older browsers.
By installing babel via npm, and adding it's loader to our webpack build step with some extra rules,
we can translate (transpile) our new features into "regular" JavaScript.
We can also see this by searching through our bundle.js file to find that our use of a template string has been converted to normals tring concatenation
Test


{% load staticfiles entry_cards %}
Available on very liberate zlib license. You can adapt to your needs.
New features are coming: geosearch, favourites, more data providers.
Thanks for using QML platform - new platforms are coming!
Add / edit content, submit new bugs and ideas.


--- layout: post status: publish published: true title: AOP ServicedComponents author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 639 wordpress_url: http://winterdom.com/2003/02/aopservicedcomponents date: '2003-02-07 11:27:22 +0000' date_gmt: '2003-02-07 11:27:22 +0000' categories: - Enterprise Services tags: [] comments: - id: 425 author: Ben author_email<EMAIL_ADDRESS>author_url: '' date: '2003-02-10 21:59:25 +0000' date_gmt: '2003-02-10 21:59:25 +0000' content: |
Where can I find some background info on AOP ServicedComponents, the posts don't really give me any idea what is going on here?
---I haven't blogged about this, since I imagined everyone had already seen it. But today Clemens announced he finally cracked one of the problems he had.
I can only say it sounds awfully cool, and look forward to seeing it in action myself!


The most awesome posts on gaming, sorted by
popularity
Popularity
Currently Selected
Special beta pricing's almost over, don't miss out. Head to http://enva.t to check it out & sign up. Don’t forget o subscribe.
About 4,000 years ago:God: I shall create a great plague and every living thing on Earth will die!Fish: *Winks at God and slips him a $20 note*God: Correction, I shall create a great flood!
Gaming News is stupid!!
Jeremy Vancaulart - Malevolent State Of Trance
-4:30Special beta pricing's almost over, don't miss out. Head to http://enva.t to check it out & sign up. Don’t forget o subscribe.
-

-

- transform-style: flat;
-

-

-

-

Who will be playing battlefield 1 this month?
Feeling hungry today? grab a #hungryburger
About 4,000 years ago: God: I shall create a great plague and every living thing on Earth will die!
Got a chance to read this new book today, seemed pretty great.


Single ventricle heterotaxy with Kawashima palliation
[cards
anatomy
cases
]
22 Jul 2018
Congenital heart anatomy
Without having ever seen the heart of a congenital patient, its difficult for me to imagine how their hemodynamics are affected. I looked at a patient a complex congenital heart that had undergone several revisions.
- single ventricle heterotaxy with left isomerism
- interrupted IVC with hemiazygous continuation
- single ventricle and single ventricle
- Kawashima palliation
- bidirectional, bilateral Glenn shunts
- closure of the pulmonic valve
- mechanical atrioventricular valve


Supported devices:
PhotoResearch spectrophotometers See http://www.photoresearch.com/
psychopy.hardware.pr.
PR650
(port, verbose=None)¶An interface to the PR650 via the serial port.
(Added in version 1.63.02)
example usage:
from psychopy.hardware.pr import PR650
myPR650 = PR650(port)
myPR650.getLum() # make a measurement
nm, power = myPR650.getLastSpectrum() # get a power spectrum for the
last measurement
NB psychopy.hardware.findPhotometer()
will locate and return
any supported device for you so you can also do:
from psychopy import hardware
phot = hardware.findPhotometer()
print(phot.getLum())
| Troubleshooting: | |
|---|---|
Various messages are printed to the log regarding the function of this device, but to see them you need to set the printing of the log to the correct level: from psychopy import logging
logging.console.setLevel(logging.ERROR) # error messages only
logging.console.setLevel(logging.INFO) # will give more info
logging.console.setLevel(logging.DEBUG) # log all communications
If you’re using a keyspan adapter (at least on OS X) be aware that it needs a driver installed. Otherwise no ports wil be found. Also note that the attempt to connect to the PR650 must occur within the first few seconds after turning it on. |
getLastLum
()¶This retrieves the luminance (in cd/m**2) from the last call to
.measure()
getLastSpectrum
(parse=True)¶This retrieves the spectrum from the last call to .measure()
If parse=True
(default):
The format is a num array with 100 rows [nm, power]
otherwise:
The output will be the raw string from the PR650 and should then
be passed to .parseSpectrumOutput()
. It’s more efficient to
parse R,G,B strings at once than each individually.
getLum
()¶Makes a measurement and returns the luminance value
getSpectrum
(parse=True)¶Makes a measurement and returns the current power spectrum
parse=True
(default):parse=False
(default):.parseSpectrumOutput()
. It’s slightly more
efficient to parse R,G,B strings at once than each individually.measure
(timeOut=30.0)¶Make a measurement with the device. For a PR650 the device is instructed to make a measurement and then subsequent commands are issued to retrieve info about that measurement.
parseSpectrumOutput
(rawStr)¶Parses the strings from the PR650 as received after sending the command ‘d5’. The input argument “rawStr” can be the output from a single phosphor spectrum measurement or a list of 3 such measurements [rawR, rawG, rawB].
sendMessage
(message, timeout=0.5, DEBUG=False)¶Send a command to the photometer and wait an alloted timeout for a response (Timeout should be long for low light measurements)
psychopy.hardware.pr.
PR655
(port)¶An interface to the PR655/PR670 via the serial port.
example usage:
from psychopy.hardware.pr import PR655
myPR655 = PR655(port)
myPR655.getLum() # make a measurement
nm, power = myPR655.getLastSpectrum() # get a power spectrum for the
last measurement
NB psychopy.hardware.findPhotometer()
will locate and return
any supported device for you so you can also do:
from psychopy import hardware
phot = hardware.findPhotometer()
print(phot.getLum())
| Troubleshooting: | |
|---|---|
| If the device isn’t responding try turning it off and turning it on again, and/or disconnecting/reconnecting the USB cable. It may be that the port has become controlled by some other program. |
endRemoteMode
()¶Puts the colorimeter back into normal mode
getDeviceSN
()¶Return the device serial number
getDeviceType
()¶Return the device type (e.g. ‘PR-655’ or ‘PR-670’)
getLastColorTemp
()¶Fetches (from the device) the color temperature (K) of the last measurement
| Returns: | list: status, units, exponent, correlated color temp (Kelvins), CIE 1960 deviation |
|---|---|
| See also: | measure() automatically populates
pr655.lastColorTemp with the color temp in Kelvins |
getLastSpectrum
(parse=True)¶This retrieves the spectrum from the last call to
measure()
If parse=True (default):
The format is a num array with 100 rows [nm, power]
otherwise:
The output will be the raw string from the PR650 and should then be passed toparseSpectrumOutput()
. It’s more efficient to parse R,G,B strings at once than each individually.
getLastTristim
()¶Fetches (from the device) the last CIE 1931 Tristimulus values
| Returns: | list: status, units, Tristimulus Values |
|---|---|
| See also: | measure() automatically populates pr655.lastTristim
with just the tristimulus coordinates |
getLastUV
()¶Fetches (from the device) the last CIE 1976 u,v coords
| Returns: | list: status, units, Photometric brightness, u, v |
|---|---|
| See also: | measure() automatically populates pr655.lastUV
with [u,v] |
getLastXY
()¶Fetches (from the device) the last CIE 1931 x,y coords
| Returns: | list: status, units, Photometric brightness, x,y |
|---|---|
| See also: | measure() automatically populates pr655.lastXY
with [x,y] |
measure
(timeOut=30.0)¶Make a measurement with the device.
This automatically populates:
.lastLum
.lastSpectrum
- .lastCIExy
- .lastCIEuv
parseSpectrumOutput
(rawStr)¶Parses the strings from the PR650 as received after sending the command ‘D5’. The input argument “rawStr” can be the output from a single phosphor spectrum measurement or a list of 3 such measurements [rawR, rawG, rawB].
sendMessage
(message, timeout=0.5, DEBUG=False)¶Send a command to the photometer and wait an alloted timeout for a response (Timeout should be long for low light measurements)
startRemoteMode
()¶Sets the Colorimeter into remote mode


new Archiver()
Constructor of Archiver
initialises archiver detection
- Source:
Members
-
mode :string
-
Archiver mode: 7z, 7z64, keka7z, unzip
Type:
- string
- Source:
Methods
-
detectZipArchiver()
-
Examine which program should be use to modify or extract zip-archives
- Source:
-
extractArchive(build, file, target, opts, done)
-
Extract a zip-archive
Parameters:
Name Type Description build
Build the build object file
String path to the zip file target
String path to the target directory opts
Object additional options for the execution done
function callback function - Source:
-
guessZipArchiver()
-
Tries to guess the 7z or keka7z paths
- Source:
-
modifyArchive(build, modifier, file, filenames, opts, done)
-
Modify a zip-archive
Parameters:
Name Type Description build
Build the build object modifier
String 7z modifier flag (e.g. a / e / l) file
String path to the zip file filenames
String space separated list of filenames to remove, add,... opts
Object additional options for the execution done
function callback function - Source:


Type of events
Initial Landing Page
Clicks, submits, errors
Previous pages visited
Defined Events (messaging-console.alert.error)
Takeaway
Create defined events (https://heapanalytics.com/app/visualizer)
Add data attributes [data-heap="create messaging service"]
Add tracking tag in JIRA
Benefits of improving Heap monitoring
Help PMs identify user behaviour in console
Can be used as a secondary monitoring tool for console
Side effect is more data tags for e2e
// Caught errors
try {
updateResource();
} catch (e) {
Rollbar.error("Something went wrong", e);
}
// Arbitrary log messages. 'critical' is most severe;
// 'debug' is least.
Rollbar.critical("Connection error from remote downstream API");
Rollbar.error("Some unexpected condition");
Rollbar.warning("Connection error from new API service");
Rollbar.info("User opened the services dialog");
Rollbar.debug("Advanced Opt Out finished updating");
Takeaway
Use rollbar for possible error prone areas
Build light weight tooling around Rollbar
Filter out noise and scrub HIPAA sensitive information
Tooling would benefit us currently and next gen console 🎉
module.exports = {
//...
performance: {
maxAssetSize: 100000
}
};


WITNESS LIST Redistricting Committee May 7, 1997-1:30P HB 772Amendment (Grusendorf) On: Rick Gray (Attorney) HB 772Committee Substitute (D. Jones) On: David Hanna (Tx. Legislative Council) Rick Gray (Attorney) HB 2254 On: David Hanna (Tx. Legislative Council) Rep. Carl Isett (himself) HB 2254 Amendment (D. Jones) On: David Hanna (Tx. Legislative Council) HB 2254 Committee Substitute (D. Jones) On: David Hanna (Tx. Legislative Council)


The Spartacus Race
Challenge 1: Mud Pool
Challenge 2: Devil's Staircase
Challenge 3: Angel Falls
Challenge 4: Spider Web
Challenge 5: Land Slide


{% extends "layout.html" %} {% block title %}Sensor: {{sensor.sensor_entity_description.name}}{% endblock %} {% block content %}
| External ID | {{sensor.sensor_entity_description.external_id}} |
|---|---|
| Online | {{sensor._online}} |
| Last Value Received | {{sensor.value_last_time_received_fmt()}} |
| Heartbeat Value Received | {{sensor.heartbeat_last_time_received_fmt()}} |
| Channel | Sensed | Last Value |
|---|---|---|
| {{ channel.channel_description.name }} | {{ channel.sensed_entity_active_model.sensed_entity_description.name }} | {{ channel.current_value }} |


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class: left, middle, inverse, title-slide # Project Management in R ### Karthik Ram
Berkeley Institute for Data Science
University of California, Berkeley
--- layout: true
karthik.github.io/prm • @_inundata
 --- # Project management in R ### Effective project management can save you a lot of headache when working on multiple projects - At the very least, use the Projects feature of RStudio - It will allow you to work on multiple projects simultaneously --- class: inverse, center, middle ## TODAY'S GOAL Demonstrate several R tools & practices which can help us not only improve our scientific rigor, but make our lives more pleasant throughout the course of a project. --- # Packages that help you set up research projects - [ProjectTemplate](http://projecttemplate.net/) - [makeProject](https://cran.r-project.org/web/packages/makeProject/index.html) - [prodigenr](https://cran.r-project.org/web/packages/prodigenr/index.html) - [rrtools](https://github.com/benmarwick/rrtools) --- # Project template Create a new project with `create.project('letters')`. --- # Use Rstudio's native projects feature ![rstudio](https://i.imgur.com/KcdsB8h.png) --- # makeProject Creates an empty package framework for the LCFD format (Load Clean Function Do) structure. ``` makeProject(name = "myProject", path = getwd(), force = FALSE, author = "Your Name", email =<EMAIL_ADDRESS>``` Will create `main.R`, `data`, `code`, `load.R`, `clean.R`, `func.R`, `do.R`. --- # prodigenr: Research Project Directory Generator > *Are you an academic researcher who often writes up abstracts for conferences or submits manuscripts to journals? Do you often have to make slides or posters for presentations? Is your usual workflow to copy a previous project and start replacing the old text for the new text? This R package was designed with you in mind!* ```r library(prodigenr) template_list ``` ``` ## [1] "abstract" "manuscript" "poster" "slides" ``` --- # Create a new project directory: `prodigenr` ``` path <- tempdir() setup_project("HeartDiseaseExercise", path) ``` ``` . ├── R │ ├── README.md │ ├── fetch_data.R │ └── setup.R ├── data │ └── README.md ├── doc │ └── README.md ├── .Rbuildignore ├── .gitignore ├── DESCRIPTION ├── HeartDiseaseExercise.Rproj └── README.md 3 directories, 10 files ``` --- # Then add project type ``` create_abstract() create_poster() ``` --- # Workflow with prodigenr A typical workflow, which is also outlined in the README.md of the created project, would be to: 1. Write up your analysis and associated written explanations of the results, as you would for any research project, in the abstract, poster, slides, or manuscript .Rmd (R Markdown) file in the doc/ folder. 2. Any piece of code you use more than once or is fairly complex, convert it into a function. Put this new function into a file (or a functions.R file) in the R/ directory. Load that function using devtools::load_all() (Ctrl-Shift-L). 3. Fetch and wrangle your data in the R/fetch_data.R. You can access the data using the devtools::load_all() function. 4. Create more Rmd files in the doc/ folder to add analyses that will supplement the main document or that are exploratory. 5. Knit the .Rmd file in doc/. You now have your final abstract, poster, slides, or manuscript to use for your research. ---


WITNESS LIST Nominations Committee February 3, 1997-2:00P Chief Admin. Law Judge, S.O.O.A.H For: Shelia B. Taylor (herself), Austin Judicial District For: Alberto R. Gonzales (himself), Austin State Bar of Texas For: Cynthia Cabaza (herself), Weslaco Geraldine S. Mauthe (herself), Marshall Romeo Lopez (himself), Rio Grande


A problem has been detected
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Sandy Kenyon recaps the 2019 CMA Awards in Nashville
by Sandy KenyonNASHVILLE, Tennessee (WABC) -- The biggest stars in country music were recognized Wednesday night at the 53rd annual CMA Awards.
Entertainment reporter Sandy Kenyon covered the awards that aired live on ABC from the Bridgestone Arena in Nashville and has the story in the video player above.


Viaweb FAQ
How did the editor handle client sessions?
There was one Lisp process for each user. When someone logged in to edit their site, we'd start up a new process and load all their data into memory.
From that point they had an ongoing conversation with that process.
Because everything was already loaded into memory, we never had to read anything from disk or start up a process to respond to an HTTP request. All we had to do was evaluate a closure stored in memory.
What did you use for an HTTP server?
At first the editor had its own HTTP server, written in Common Lisp by Robert Morris. Later we switched to a version of Apache that he hacked to talk to Lisp.
What Lisp did you use?
Clisp
Did you use real continuations to save state?
No, we used macros to fake them in Common Lisp, as described in On Lisp.
What database did you use?
We didn't use one. We just stored everything in files. The Unix file system is pretty good at not losing your data, especially if you put the files on a Netapp.
It is a common mistake to think of Web-based apps as interfaces to databases. Desktop apps aren't just interfaces to databases; why should Web-based apps be any different? The hard part is not where you store the data, but what the software does.
While we were doing Viaweb, we took a good deal of heat from pseudo-technical people like VCs and industry analysts for not using a database-- and for using cheap Intel boxes running FreeBSD as servers. But when we were getting bought by Yahoo, we found that they also just stored everything in files-- and all their servers were also cheap Intel boxes running FreeBSD.
(During the Bubble, Oracle used to run ads saying that Yahoo ran on Oracle software. I found this hard to believe, so I asked around. It turned out the Yahoo accounting department used Oracle.)
Was your co-founder the same Robert Morris who wrote the worm and is now a professor at MIT?
Yes.
Where did you get venture funding?
We got money from several private investors, what are known in the business as "angels." Our investors were pretty serious, almost VCs, but they weren't actually brand-name VC firms.
We did Viaweb very cheaply. We spent a total of about $2 million. We were just about breaking even when we got bought, so we would not have spent too much more.
How was "Viaweb" pronounced?
The official policy was that you could say either vee-a-web or vie-a-web. We all used the former, but everyone else, including the people at Yahoo, seemed to prefer the latter.
What would you do differently?
Technically, not much. I think the main thing we should have done that we didn't was start some kind of online store ourselves. We used the editor to make our own site, so we were pretty motivated to make it good. But we could only understand the e-commerce part of the software second-hand.


{% extends 'main_layout.html' %} {% block content %} I'm so sorry. No dog and no human was recognized. Please try again Back to Home {% endblock%}
Please try again


Defines a top-level container for all the elements necessary to describe an C++ API friend.
The <cxxFriend> topic type contains elements necessary to show the name of the friend, the short description of the friend, the friend syntax, and the friend use description.
Section 11.4 of the C++ standard (ISO/IEC 14882:2003(E)) describes friends.


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About Me
Introduction
I am a military veteran with 5 years of experience in Army leadership, teamwork, and technical troubleshooting.Transitioning from the military into the Information Technology industry with an educational background in computer science. Passionate and dedicated learner for innovative technology and skilled at grasping and applying new technical and functional concepts quickly. Strong belief in hard work and continuous education for life.
- Education
- Bachelor's Degree in Computer Science
- Professional experience
- US ARMY
Skills
- HTML
- CSS
- JavaScript
- Java
- Operating System: Windows/Linux/Mac
- Detabase Design: MySQL, SQL Server 2016
- Cloud Platform: AWS and Azure
- Network Administration
- Teamwork
- leadership
- Problem-solving
Guessing game.........
These are the country I have visited
- UK
- Mexico
- Canada
- Nepal
- Australia
This is the answer for guessing game where users get to know me better by guessing answers to my questions. Here you go ...have fun!


| Mobile Platform | Android App ; IoT System |
| Application domain type | Smart Wearables |
| Authentication | Yes |
| Authentication schemes | Biometric-based authentication ; Channel-based authentication ; Factors-based authentication ; ID-based authentication |
| Has DB | Yes |
| Type of database | SQL (Relational Database) |
| Which DB | MySQL |
| Type of information handled | Personal Information ; Confidential Data ; Critical Data |
| Storage Location | Both |
| User Registration | Yes |
| Type of Registration | The users will register themselves |
| Programming Languages | Java |
| Input Forms | Yes |
| Upload Files | Yes |
| The system has logs | Yes |
| The system has regular updates | Yes |
| The system has third-party | Yes |
| System Cloud Environments | Hybrid Cloud |
| Hardware Specification | No |
| HW Authentication | Basic Authentication (user/pass) |
| HW Wireless Tech | 3G ; 4G/LTE ; 5G ; Bluetooth ; Wi-Fi ; GPS ; RFID ; ZigBee |
| Device or Data Center Physical Access | Yes |
Security Backup Mechanisms for cloud-based mobile apps are procedures to keep data safe and secure in the event of an emergency, such as a computer crash, a user error, or a malicious attack. These mechanisms can include:
• Access Control: Access control restricts the access of certain parts of the application, such as confidential data or the application’s backend, in order to limit the potential damage caused by malicious activities.
• Data Encryption: Data Encryption scrambles application data into an unreadable format, making it impossible to access without the decryption key.
• Password Hashes: Password Hashes are securely stored versions of the users’ passwords to prevent malicious activities such as credentials theft.
• Tokenization: Tokenization is a mechanism that replaces sensitive data with a token to reduce the risk of data theft.
• Backup System: A backup system can be used to store application data in separate, secure locations. This data can be used to restore the application to its former state in the event of a disruption.
| Security Requirement | Mobile Platform | Mechanism | Description | OSI Layer |
|---|---|---|---|---|
| Backup | iOS | iTunes Backup | Syncs with iTunes for off-site backup | 7 - Application |
| Backup | Android | Google Drive | Google's cloud solution for data storage and backup | 7 - Application |
| Backup | Android | Third-party cloud solutions | Solutions such as Dropbox, OneDrive and iCloud Drive | 7 - Application |
| Backup | All | Local Backup | On-site backups saved on the device's internal storage | 1 - Physical |
| Backup | All | External Storage Backup | Off-site backups saved to external devices such as external hard drives and USB drives | 1 - Physical |
A Security Audit Mechanism is an automated or manual process which evaluates cloud-based mobile apps for security issues. It may include verifying the integrity of the code, inspecting system configurations, testing user authentication and authorization controls, and ensuring that the system is following best practices such as encryption, patching, and regular system updates. A Security Audit Mechanism can also identify potential security weaknesses and provide recommendations for mitigating these. Furthermore, a Security Audit Mechanism can perform performance and reliability checks, as well as other security checks such as penetration testing, infrastructure testing, and security vulnerability scanning. By utilizing these security audit mechanisms, organizations can ensure their cloud-based mobile apps are safe and secure.
| Security Requirement | Mobile Platform | Mechanism | Description | OSI Layer |
|---|---|---|---|---|
| Authentication | iOS | Apple’s App-ID and two factor authentication | A two-factor authentication and App-ID system used by Apple to verify and authenticate applications running on its iOS mobile platform | Application |
| Authorization | iOS | Access control list (ACL) | A tool used to manage user access to various parts of a mobile application, such as data or services | Application |
| Data Protection | Android | Google Play Store | Google’s Play Store protects uploaded applications from malicious code before it is distributed on the platform | Presentation |
| Auditing | iOS | App Store | The App Store provides an audit trail of all applications downloaded, to ensure proper users have the correct permissions to access applications | Application |
| Data Validation | Android | Android Content Providers | Android content providers are used to securely store data and detect malicious code before it is passed to applications running on the platform | Application |
Cryptographic algorithms are used to ensure data confidentiality, authenticity, integrity and non-repudiation in cloud-based mobile apps. To achieve these goals, cryptographic algorithms are often used in combination with mechanisms, such as Digital Signatures, Secret Key Cryptography and Public Key Cryptography.
Digital Signatures validate the identity and authenticity of communications, while Secret Key Cryptography algorithms like AES, DES and 3DES protect transmitted data through the use of encryption. Public Key Cryptography algorithms like RSA, ECDSA and Diffie-Hellman can also be used to authenticate, encrypt and exchange secret keys between the mobile device and the cloud provider. In addition, protocols such as SSL / TLS can add an extra layer of security while protecting and verifying the communication and providing message integrity.
| Security Requirement | Mobile Platform | Mechanism | Description | OSI Layer | Use for coding | Use for runtime |
|---|---|---|---|---|---|---|
| Integrity | Android | HMAC-SHA256 | A cryptographic hash function based on SHA256 that combines a shared secret and the message | 7 | Yes | Yes |
| Confidentiality | iOS | AES-128 | AES with 128 bit key size that supports authenticated encryption | 6 | Yes | Yes |
| Authentication | iOS | ECDSA | Elliptic Curve Digital Signature Algorithm that provides digital signatures | 7 | Yes | Yes |
Biometric authentication mechanisms in cloud-based mobile apps are methods of authentication relying on the physiological characteristics of a user as a method of accessing the device or application. Examples of popular biometric authentication technologies available for cloud-based mobile devices are fingerprint scanning, facial recognition, and voice recognition. These technologies use advanced algorithms to validate a user’s identity based on the physiological traits unique to each individual. By using these methods, companies and app developers can increase the security of their cloud services while preventing unauthorized access.
| Security Requirement | Mobile Platform | Mechanism | Description | OSI Layer |
|---|---|---|---|---|
| Authentication & Access Control | Android | Facial Recognition | Hardware based biometric authentication that uses the device front facing camera to snap a picture of the user's face and match it against stored images | Application |
| Authentication & Access Control | iOS | Voice Recognition | Software based biometric authentication that uses the device microphone and internal software to capture the user's voice and match it against stored audio | Application |
| Encryption & Decryption | Android | 2-Factor Authentication with PIN & Pattern | Combined hardware and software based authentication that requires the user to enter a PIN and draw a pattern on a defined pattern grid. | Presentation |
| Encryption & Decryption | iOS | Retina Recognition | Hardware based biometric authentication that uses the device front facing camera to obtain a high-resolution picture of the user's eye and matches it against stored images | Application |
| IDS & IPS | Android | Fingerprint Scan | Hardware based biometric authentication that uses the device built-in fingerprint scanner to scan the user's fingerprint and match it against stored images | Application |
| IDS & IPS | iOS | 3-Factor Authentication with PIN, Pattern & Password | Combined hardware and software-based authentication that requires the user to enter a PIN, draw a pattern on a defined pattern grid, and enter a password | Presentation |
Channel-based authentication mechanisms in cloud-based mobile apps refer to a set of security protocols that validate users and authorize access to specific resources in a cloud mobile application. This authentication is done through a set of channels, such as biometrics, passwords, OTPs, or mobile phone numbers, each with its own level of security and authentication request. This type of authentication is used to ensure access to sensitive data and improve the overall security of the application.
| Security Requirement | Mobile Plataform | Mechanism | Description | OSI Layer |
|---|---|---|---|---|
| Authentication | Android | HMAC-SHA256 | Mobile application uses a pre-shared HMAC-SHA256 token to authenticate with the cloud server and establish a secure channel. | Application |
| Authorization | iOS | OAuth-2 | Mobile application uses an OAuth-2 access token to authorize requests made to the cloud server and establish a secure channel. | Application |
| Identity Management | Cross-platform | OpenID Connect | Mobile application uses OpenID Connect to authenticate with the cloud server and establish a secure channel. | Application |
| Data Encryption | Cross-platform | TLS/SSL | Mobile application uses TLS/SSL to encrypt data transmitted between the mobile device and the cloud server. | Transport |
Factors-based authentication mechanisms in cloud-based mobile apps are methods used to securely access digital resources. They involve the use of two or more authentication factors, such as something that a user knows (e.g., a password), something that a user has (e.g., an authentication code sent to a mobile device), and/or something that a user is (e.g., a biometric scan). Factors-based authentication can help protect mobile apps by providing an extra layer of security, making it less likely that someone unauthorized can access sensitive user data.
| Security Requirement | Mobile Platform | Mechanism | Description | OSI Layer | To Use |
|---|---|---|---|---|---|
| Data Security | iOS | Two-factor authentication | Confirming identity by combination of two unique factors | Application | Coding Phase and Runtime |
| Privacy | Android | Biometric authentication | Confirming identity by using biometric methods | Application | Coding Phase and Runtime |
| Account Access | iOS | User ID & Password | Confirming identity by using combination of user ID and password | Application | Coding Phase and Runtime# ID-based Authentication Mechanisms |
ID-based authentication mechanisms are used to authenticate users in cloud-based mobile applications. This type of authentication typically involves the use of an identifier such as an email address or phone number, as well as a password or some other form of proof of identity. ID-based authentication may also involve the use of biometric markers like fingerprints or facial recognition to verify the user's identity. By using ID-based authentication, mobile applications can ensure that only authorized users are granted access, thereby protecting the data stored and exchanged on the application.
| Security Requirement | Mobile Platform | Mechanism | Description | OSI Layer |
|---|---|---|---|---|
| Authentication | iOS | FaceID | User authenticates with their face | Layer 7 |
| Authentication | iOS | Touch ID | User authenticates with their thumbprint | Layer 7 |
| Authorization | iOS | Apple App Tracking Transparency (ATT) | Authorizes a user’s usage data to be tracked by a third-party for targeted advertising | Layer 7 |
| Authentication | Android | Fingerprint Authenticator | User authenticates with their fingerpring | Layer 7 |
| Authentication | Android | Face Unlock | User authenticates with their face | Layer 7 |
| Authorization | Android | Google Play Billing Library | User authorizes payment for in-app billing | Layer 7 |
Cryptographic protocols mechanisms for cloud-based mobile apps refer to the cryptographic techniques used to protect data and communications between user devices and cloud-services. The protocols involve the encryption of data and messages with symmetric and asymmetric algorithms, the digital signing of messages, the authentication of users, the establishment of secure tunnels, and the use of secure hashing and salting. The goal is to ensure that, if a malicious person attempts to intercept the headers or payload of a cloud-based mobile app, they will be unable to access valuable information.
| Security Requirement | Mobile Platform | Mechanism | Description | OSI Layer |
|---|---|---|---|---|
| Authentication | iOS | OAuth | OAuth is an open-standard authorization protocol for allowing access to a protected resource | Application layer |
| Encryption | Android | TLS | Transport Layer Security (TLS) is a cryptographic protocol used to provide secure communications over a computer network | Transport Layer |
| Integrity | iOS | SHA-1 | Secure Hash Algorithm (SHA-1) is a cryptographic hash function used to generate a 160-bit hash value | Application layer |
| Non-repudiation | Android | HMAC | HMAC is a cryptographic mechanism used to verify the integrity of a message by using a secret key | Application layer |
Security Access Control Mechanisms (SACMs) are the technical and administrative strategies and tools used to protect cloud-based mobile apps from unauthorized access to confidential data and systems. These mechanisms are designed to restrict access to certain users, manage user privileges, authenticate user accounts, and authorize access requests. Examples of SACMs include multi-factor authentication (MFA), biometric authentication, single-sign-on (SSO), role-based access control (RBAC), application-level encryption, and least privilege access. SACMs allow organizations to properly control who has access to what resources and strictly enforce principles of confidentiality, privacy, and data security.
| Security Requirement | Mobile Platform | Mechanism | Description | OSI Layer |
|---|---|---|---|---|
| Data confidentiality | Android | RSA Encryption | Encryption of data with public and private keys | Application |
| Data integrity | Android | Hashing | Use of a hash algorithm such as SHA-2 to ensure that data is not tampered with | Transport |
| Account Management | iOS | Two-Factor Authentication | Use of two-factor authentication to verify user access | Presentation |
| Data access control | iOS | Role-Based Access Control (RBAC) | Defines levels of access based on user roles | Application |
| Resource authorization | iOS | Authorization Token | Generates a token at the end of a successful authorization process which is used to grant permission | Application |
An inspection mechanism is a process or tool used to ensure that cloud-based mobile apps meet certain quality and security requirements. Inspection mechanisms involve thoroughly evaluating the source code, architecture, and security of the app to ensure it meets the desired standard. Examples of inspection mechanisms include static code analysis, application security testing, architectural design reviews, and penetration testing. These inspection mechanisms help identify any weaknesses, vulnerabilities, or security issues in the app before it is deployed in the cloud.
| Security Requirement | Mobile Platform | Mechanism | Description | OSI Layer |
|---|---|---|---|---|
| Integrity | Android | ProGuard | Code obfuscation | 8 |
| Confidentiality | iOS | Secure store | Keychain security | 7 |
| Authentication | Android | SafetyNet API | Attest the device integrity | 7 |
| Android | Android Keystore | Keystore security | 7 | |
| iOS | Apple push notification service (APNS) | Authentication message | 7 | |
| Data Validation | Android | DX Guardrail | Verification of data model | 7 |
| iOS | SwiftLint | Static analysis | 7 |
An inspection mechanism is a process or tool used to ensure that cloud-based mobile apps meet certain quality and security requirements. Inspection mechanisms involve thoroughly evaluating the source code, architecture, and security of the app to ensure it meets the desired standard. Examples of inspection mechanisms include static code analysis, application security testing, architectural design reviews, and penetration testing. These inspection mechanisms help identify any weaknesses, vulnerabilities, or security issues in the app before it is deployed in the cloud.
| Security Requirement | Mobile Plataform | Mechanism | Description | OSI Layer |
|---|---|---|---|---|
| Authentication | iOS | DeviceCheck | DeviceCheck enables customers to securely store small bits of data on Apple devices during the coding and runtime phases | Application |
| Access Control | iOS | KeyChain | Apple’s Keychain, is a encrypted storage system that primarily stores passwords, certificates, and encryptionkeys | Application |
| Auditing | Android | Syslog | System logging mechanism for capturing and persistently logging system and audit-specific events in the Android OS | Transport |
| Logging | Android | LumberJack | Logging mechanism for logging the events for mobile applications | Application |
Security Device Tamper Detection Mechanisms (SDTDM) in cloud-based mobile apps are security measures designed to detect when an application has been altered or interfered with in a malicious manner. These mechanisms rely on a variety of techniques, such as application behavior monitoring, code validation, application certificate inspection, and digital signature verification. Applying these measures helps to protect against attack attempts, such as reverse engineering or tampering, which can damage the integrity and reliability of a mobile application, and leave it vulnerable to cyber attacks.
| Security Requirement | Mobile Plataform | Mechanism | Description | OSI Layer |
|---|---|---|---|---|
| Coding Phase | Android | Smali Anti-Tamper | Software code is compiled to native assembly code only found in Smali folders, making it more difficult to find and modify. | Application Layer |
| Coding Phase | Android | Proguard | Software code is obfuscated, reducing the change of code tampering by making the code difficult to understand or modify. | Application Layer |
| Runtime | Android | Device Profiling | Leverage Device-Based Data to detect unusual or suspicious behavior indicative of tampering. | Presentation Layer |
| Runtime | iOS | Secure Boot Chain | Checking the Integrity and authenticity of the bootloaders, kernel, and signature of the system partition. | Network Layer |
| Runtime | iOS | Runtime Integrity Checks | Tamper detection checks executed at runtime to ensure integrity and accuracy of code and data | Application Layer |
Security physical location mechanisms are applied to cloud-based mobile apps to ensure that user data is not accessed or stored from locations outside of an approved geographic region. These mechanisms include technologies such as geofencing and IP address tracking. Geofencing verifies that user data is being accessed and stored within a predetermined geographic area by creating a virtual fence around the area. IP address tracking allows mobile apps to identify the geographical location associated with a particular IP address in order to verify that a user is located in the approved geographic area. These security location mechanisms are essential for cloud-based mobile apps, as they help prevent unauthorized access to user data from malicious actors located in remote locations.
| Security Requirement | Mobile Platform | Mechanism | Description | OSI Layer |
|---|---|---|---|---|
| Authenticated Access | iOS | Biometric Scanner | Uses user's fingerprints as part of the authentication process | Physical |
| Data Integrity | Android | Transparent Encryption | Files are encrypted transparently and automatically | Network |
| Data Availability | Both | Secure Boot & Root | Ensures that all parts of system are authenticated and verified | Physical |
| Data Confidentiality | iOS | App sandboxes | Prevents unauthorized access to specific files | Application |
| Data Security | Android | Full Disk Encryption | Encrypts all data on device | Network |
Security Confinement Mechanisms in Cloud-based mobile apps refer to the various measures put in place by app developers to help ensure the security and integrity of data within the app. These mechanisms might include measures like authentication requirements, security protocols, encryption, tokenization, application sandboxing, and isolated virtual machines. These measures help limit the risk of data theft or compromise within a cloud-based mobile application.
| Security Requirement | Mobile Platform | Mechanism | Description | OSI Layer |
|---|---|---|---|---|
| Vulnerability Protection | Android | Flask | Flask is a Python web development framework used to protect against malicious code injections | Application Layer |
| Isolation of Data | iOS | Security-Enhanced Linux (SELinux) | SELinux is a Linux kernel security module used to isolate code from its data | Network Layer |
| Security of Data | Blackberry | BitLocker | BitLocker is a Windows data encryption system meant to protect data while it is stored | Data Link Layer |
| Secure Communications | Symbian | IPsec | IPsec is a protocol suite used in secure communication by authenticating and encrypting data | Presentation Layer |
| Secure Data Transfer | Palm | DM-Crypt | DM-Crypt is a drive encryption system meant to protect data while it is transferred | Session Layer |
Security Filtering Mechanisms are built into Cloud-based mobile apps to ensure data is protected from unauthorized access. These mechanisms include multi-factor authentication, data encryption, data loss prevention, and various identity and access management (IAM) tools that control user access and authentication policies. Additionally, mobile app hardening techniques such as static code analysis and dynamic application security testing (DAST) can be used to detect and fix potential security vulnerabilities.
| Security Requirement | Mobile Platform | Mechanism | Description | OSI Layer |
|---|---|---|---|---|
| Authentication | iOS | OAuth2 | OAuth2 provides a secure mechanism for authorizing application access to a user's account | Application |
| Encryption | Android | AES | AES (Advanced Encryption Standard) uses symmetric key cryptography to provide strong encryption of data at rest | Data Link |
| Key Management | Cross-Platform | AWS KMS | AWS KMS (Key Management Service) is a cloud-based service used to manage encryption keys for encrypting/decrypting data | Transport |
| Data Obfuscation | Cross-Platform | ProGuard | ProGuard is a popular open source code obfuscator which helps protect your app's code during coding phase and runtime | Physical |


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The Simulation object is used to store inputs that define basic parameters of the model, such as run duration. The Simulation object is created automatically when a new model is started, and thus does not appear in the Model Builder.
Simulation Inputs
|
Keyword |
Description |
|
Key Inputs |
|
|
RunDuration |
Duration of the simulation run in which statistics will be recorded. |
|
InitializationDuration |
The initialization interval for the simulation run. The model will run for the InitializationDuration interval and then clear the statistics and execute for the specified RunDuration interval. The total length of the simulation run will be the sum of the InitializationDuration and RunDuration inputs. |
|
ExitAtStop |
If TRUE, the program will be closed on completion of the last simulation run. Otherwise, the last run will be paused. |
|
GlobalSubstreamSeed |
Global seed that sets the sub-stream for each probability distribution. Must be an integer >= 0. GlobalSubstreamSeed works together with each probability distribution's RandomSeed keyword to determine its random sequence. It allows the user to change all the random sequences in a model with a single input. The default value is the replication number for the simulation run being executed. |
|
PrintReport |
If TRUE, a full output report is printed to the file <configuration file name>.rep at the end of the simulation run. |
|
ReportDirectory |
The directory where output files will be written. The default location is the directory of the input configuration file. |
|
RunOutputList |
One or more selected outputs to be printed at the end of each simulation run. Each output is specified by an expression. In script mode (-s tag), the selected outputs are printed to the command line (standard out). Otherwise, they are printed to the file <configuration file name>.dat. It is best to include only dimensionless quantities and non-numeric outputs in the RunOutputList. An output with dimensions can be made non-dimensional by dividing it by 1 in the desired unit, e.g. '[Queue1].AverageQueueTime / 1[h]' is the average queue time in hours. A dimensional number will be displayed along with its unit. The format function can be used if a fixed number of decimal places is required. |
|
Options |
|
|
AttributeDefinitionList, CustomOutputList |
See Section 6.2. |
|
GeorgianCalendar |
If TRUE, the simulation uses the standard Gregorian calendar that includes leap years. If FALSE, the simulation uses a simplified calendar that has a fixed 365 days per year. |
|
StartDate |
The calendar date and time that corresponds to zero simulation time. |
|
PauseCondition |
An optional expression that pauses the run when TRUE is returned. |
|
ExitAtPauseCondition |
If TRUE, the simulation run will be terminated when the PauseCondition expression returns TRUE. If multiple runs have been specified, then the next run will be started. If no more runs have been specified, the simulation will be paused or terminated depending on the input to the ExitAtStop keyword. |
|
MaxEntitiesToDisplay |
The maximum number of entities to display in the view windows. A model can contain more than this number of entities, but only this number will be displayed. This limit prevents JaamSim from becoming unresponsive when the number of entities in a model exceeds the graphics capabilities of the computer. |
|
TickLength |
The smallest time increment for JaamSim's internal integer-based time keeping. The default value of 1 microsecond will support simulation runs of more than 100 years. |
|
Multiple Runs |
|
|
ScenarioIndexDefinitionList |
Defines the number of scenario indices and the maximum value N for each index. When running multiple scenarios, each index will be iterated from 1 to N starting with the last index. One scenario will be executed for every combination of the scenario index values. For example, if three scenario indices are defined with ranges of 3, 5, and 10, then a total of 3*5*10 = 150 scenarios will be executed. If left blank, a single scenario index is defined and there are no restrictions on the values that can be assigned to the StartingScenarioNumber and EndingScenarioNumber inputs. |
|
StartingScenarioNumber |
The first scenario number to be executed. |
|
EndingScenarioNumber |
The last scenario number to be executed. |
|
NumberOfReplications |
The number of replications to perform for each scenario. |
|
PrintReplications |
If TRUE, the run output report will include an entry for each replication that was performed. If FALSE, the report will show entries only for the scenarios. |
|
PrintConfidenceIntervals |
If TRUE, the run output report will include the 95% confidence intervals for the outputs defined by the input to RunOutputList keyword. The confidence intervals are calculated using the factor for the Student's T distribution corresponding to 95% confidence and the standard deviation for the output values over the replications. |
|
PrintRunLabels |
If TRUE, the first column of the run output report will show the scenario number for each simulation run. If the PrintReplications input is also TRUE, then the second column will show the replication number. |


Donald Trump Mocks Newsweek for Fake Thanksgiving Headline
by Charlie SpieringPresident Donald Trump mocked Newsweek after they reported Thursday he spent Thanksgiving golfing and tweeting. In fact, the president was secretly traveling to Afghanistan to visit deployed American troops.
“I thought Newsweek was out of business?” Trump wrote on Twitter, sharing a screenshot of the article and photos of him with the troops in Afghanistan posted by his son Donald Trump Jr.
“Fake news gonna fake!” Trump Jr. wrote.
The Newsweek story, first published at 10:16 a.m., was later updated to reflect the president’s visit.
“This story has been substantially updated and edited at 6:17 pm EST to reflect the president’s surprise trip to Afghanistan,” read an editors note after Trump Jr. mocked the news outlet.
The Washington Post sold Newsweek in 2010 for a dollar and changed hands several times before ending its print magazine in 2012. In 2018, Newsweek split from IBT Media into a standalone company.
A similar situation happened when President Trump visited the troops in Iraq during Christmas in 2018. NBC News reported Trump was the first president since 2002 not to visit the troops at Christmastime, while he was secretly traveling to Iraq, prompting widespread ridicule from Trump staff and his supporters.


--- title: HomeFox - Mobile subtitle: HomeFox Mobile-Application description: As a working student I also worked on the React-Native mobile app. I did a complete redesign and refactoring of the app and also worked on a automatic deployment process. Because the app is written in React-Native, it is targeted for iOS and Android as well. type: iOS & Android date: 2017-03-01 ---


--- title: Imperx Camera SDK keywords: fastai sidebar: home_sidebar nb_path: "index.ipynb" ---
Sample code to
Connection is established with the camera when Camera object is initialized
cam = IpxCamera()
Once connection is established, camera parameters can be viewed and changed
cam.params.GetFloat("ExposureTime").GetValue()
cam.params.GetInt("Width").GetValue()
cam.params.GetInt("Width").SetValue(1240)
cam.params.GetInt("Height").SetValue(720)
cam.params.GetEnum("ExposureMode").SetValueStr("Timed")
cam.params.GetBoolean("AcquisitionFrameRateEnable").SetValue(True)
cam.params.GetFloat("AcquisitionFrameRate").SetValue(10)
Starting Image Acquisition. Camera will keep capturing images till a Keyboard Interrupt is issued
cam.start_acquisition()


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