Support Vector Machines, overarching aims
Contents
8. Support Vector Machines, overarching aims¶
A Support Vector Machine (SVM) is a very powerful and versatile Machine Learning method, capable of performing linear or nonlinear classification, regression, and even outlier detection. It is one of the most popular models in Machine Learning, and anyone interested in Machine Learning should have it in their toolbox. SVMs are particularly well suited for classification of complex but small-sized or medium-sized datasets.
The case with two well-separated classes only can be understood in an intuitive way in terms of lines in a two-dimensional space separating the two classes (see figure below).
The basic mathematics behind the SVM is however less familiar to most of us. It relies on the definition of hyperplanes and the definition of a margin which separates classes (in case of classification problems) of variables. It is also used for regression problems.
With SVMs we distinguish between hard margin and soft margins. The latter introduces a so-called softening parameter to be discussed below. We distinguish also between linear and non-linear approaches. The latter are the most frequent ones since it is rather unlikely that we can separate classes easily by say straight lines.
8.1. Hyperplanes and all that¶
The theory behind support vector machines (SVM hereafter) is based on the mathematical description of so-called hyperplanes. Let us start with a two-dimensional case. This will also allow us to introduce our first SVM examples. These will be tailored to the case of two specific classes, as displayed in the figure here based on the usage of the petal data.
We assume here that our data set can be well separated into two domains, where a straight line does the job in the separating the two classes. Here the two classes are represented by either squares or circles.
%matplotlib inline
from sklearn import datasets
from sklearn.svm import SVC, LinearSVC
from sklearn.linear_model import SGDClassifier
from sklearn.preprocessing import StandardScaler
import matplotlib
import matplotlib.pyplot as plt
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
iris = datasets.load_iris()
X = iris["data"][:, (2, 3)] # petal length, petal width
y = iris["target"]
setosa_or_versicolor = (y == 0) | (y == 1)
X = X[setosa_or_versicolor]
y = y[setosa_or_versicolor]
C = 5
alpha = 1 / (C * len(X))
lin_clf = LinearSVC(loss="hinge", C=C, random_state=42)
svm_clf = SVC(kernel="linear", C=C)
sgd_clf = SGDClassifier(loss="hinge", learning_rate="constant", eta0=0.001, alpha=alpha,
max_iter=100000, random_state=42)
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
lin_clf.fit(X_scaled, y)
svm_clf.fit(X_scaled, y)
sgd_clf.fit(X_scaled, y)
print("LinearSVC: ", lin_clf.intercept_, lin_clf.coef_)
print("SVC: ", svm_clf.intercept_, svm_clf.coef_)
print("SGDClassifier(alpha={:.5f}):".format(sgd_clf.alpha), sgd_clf.intercept_, sgd_clf.coef_)
# Compute the slope and bias of each decision boundary
w1 = -lin_clf.coef_[0, 0]/lin_clf.coef_[0, 1]
b1 = -lin_clf.intercept_[0]/lin_clf.coef_[0, 1]
w2 = -svm_clf.coef_[0, 0]/svm_clf.coef_[0, 1]
b2 = -svm_clf.intercept_[0]/svm_clf.coef_[0, 1]
w3 = -sgd_clf.coef_[0, 0]/sgd_clf.coef_[0, 1]
b3 = -sgd_clf.intercept_[0]/sgd_clf.coef_[0, 1]
# Transform the decision boundary lines back to the original scale
line1 = scaler.inverse_transform([[-10, -10 * w1 + b1], [10, 10 * w1 + b1]])
line2 = scaler.inverse_transform([[-10, -10 * w2 + b2], [10, 10 * w2 + b2]])
line3 = scaler.inverse_transform([[-10, -10 * w3 + b3], [10, 10 * w3 + b3]])
# Plot all three decision boundaries
plt.figure(figsize=(11, 4))
plt.plot(line1[:, 0], line1[:, 1], "k:", label="LinearSVC")
plt.plot(line2[:, 0], line2[:, 1], "b--", linewidth=2, label="SVC")
plt.plot(line3[:, 0], line3[:, 1], "r-", label="SGDClassifier")
plt.plot(X[:, 0][y==1], X[:, 1][y==1], "bs") # label="Iris-Versicolor"
plt.plot(X[:, 0][y==0], X[:, 1][y==0], "yo") # label="Iris-Setosa"
plt.xlabel("Petal length", fontsize=14)
plt.ylabel("Petal width", fontsize=14)
plt.legend(loc="upper center", fontsize=14)
plt.axis([0, 5.5, 0, 2])
plt.show()
LinearSVC: [0.28475098] [[1.05364854 1.09903804]]
SVC: [0.31896852] [[1.1203284 1.02625193]]
SGDClassifier(alpha=0.00200): [0.117] [[0.77714169 0.72981762]]
The aim of the SVM algorithm is to find a hyperplane in a \(p\)-dimensional space, where \(p\) is the number of features that distinctly classifies the data points.
In a \(p\)-dimensional space, a hyperplane is what we call an affine subspace of dimension of \(p-1\). As an example, in two dimension, a hyperplane is simply as straight line while in three dimensions it is a two-dimensional subspace, or stated simply, a plane.
In two dimensions, with the variables \(x_1\) and \(x_2\), the hyperplane is defined as
where \(b\) is the intercept and \(w_1\) and \(w_2\) define the elements of a vector orthogonal to the line \(b+w_1x_1+w_2x_2=0\). In two dimensions we define the vectors \(\boldsymbol{x} =[x1,x2]\) and \(\boldsymbol{w}=[w1,w2]\). We can then rewrite the above equation as
We limit ourselves to two classes of outputs \(y_i\) and assign these classes the values \(y_i = \pm 1\). In a \(p\)-dimensional space of say \(p\) features we have a hyperplane defines as
If we define a matrix \(\boldsymbol{X}=\left[\boldsymbol{x}_1,\boldsymbol{x}_2,\dots, \boldsymbol{x}_p\right]\) of dimension \(n\times p\), where \(n\) represents the observations for each feature and each vector \(x_i\) is a column vector of the matrix \(\boldsymbol{X}\),
If the above condition is not met for a given vector \(\boldsymbol{x}_i\) we have
if our output \(y_i=1\). In this case we say that \(\boldsymbol{x}_i\) lies on one of the sides of the hyperplane and if
for the class of observations \(y_i=-1\), then \(\boldsymbol{x}_i\) lies on the other side.
Equivalently, for the two classes of observations we have
When we try to separate hyperplanes, if it exists, we can use it to construct a natural classifier: a test observation is assigned a given class depending on which side of the hyperplane it is located.
8.1.1. The two-dimensional case¶
Let us try to develop our intuition about SVMs by limiting ourselves to a two-dimensional
plane. To separate the two classes of data points, there are many
possible lines (hyperplanes if you prefer a more strict naming)
that could be chosen. Our objective is to find a
plane that has the maximum margin, i.e the maximum distance between
data points of both classes. Maximizing the margin distance provides
some reinforcement so that future data points can be classified with
more confidence.
What a linear classifier attempts to accomplish is to split the feature space into two half spaces by placing a hyperplane between the data points. This hyperplane will be our decision boundary. All points on one side of the plane will belong to class one and all points on the other side of the plane will belong to the second class two.
Unfortunately there are many ways in which we can place a hyperplane to divide the data. Below is an example of two candidate hyperplanes for our data sample.
Let us define the function
as the function that determines the line \(L\) that separates two classes (our two features), see the figure here.
Any point defined by \(\boldsymbol{x}_i\) and \(\boldsymbol{x}_2\) on the line \(L\) will satisfy \(\boldsymbol{w}^T(\boldsymbol{x}_1-\boldsymbol{x}_2)=0\).
The signed distance \(\delta\) from any point defined by a vector \(\boldsymbol{x}\) and a point \(\boldsymbol{x}_0\) on the line \(L\) is then
How do we find the parameter \(b\) and the vector \(\boldsymbol{w}\)? What we could do is to define a cost function which now contains the set of all misclassified points \(M\) and attempt to minimize this function
We could now for example define all values \(y_i =1\) as misclassified in case we have \(\boldsymbol{w}^T\boldsymbol{x}_i+b < 0\) and the opposite if we have \(y_i=-1\). Taking the derivatives gives us
and
We can now use the Newton-Raphson method or different variants of the gradient descent family (from plain gradient descent to various stochastic gradient descent approaches) to solve the equations
and
where \(\eta\) is our by now well-known learning rate.
The equations we discussed above can be coded rather easily (the framework is similar to what we developed for logistic regression). We are going to set up a simple case with two classes only and we want to find a line which separates them the best possible way.
There are however problems with this approach, although it looks pretty straightforward to implement. When running the above code, we see that we can easily end up with many diffeent lines which separate the two classes.
For small gaps between the entries, we may also end up needing many iterations before the solutions converge and if the data cannot be separated properly into two distinct classes, we may not experience a converge at all.
8.1.2. A better approach¶
A better approach is rather to try to define a large margin between the two classes (if they are well separated from the beginning).
Thus, we wish to find a margin \(M\) with \(\boldsymbol{w}\) normalized to \(\vert\vert \boldsymbol{w}\vert\vert =1\) subject to the condition
All points are thus at a signed distance from the decision boundary defined by the line \(L\). The parameters \(b\) and \(w_1\) and \(w_2\) define this line.
We seek thus the largest value \(M\) defined by
or just
If we scale the equation so that \(\vert \vert \boldsymbol{w}\vert\vert = 1/M\), we have to find the minimum of \(\boldsymbol{w}^T\boldsymbol{w}=\vert \vert \boldsymbol{w}\vert\vert\) (the norm) subject to the condition
We have thus defined our margin as the invers of the norm of \(\boldsymbol{w}\). We want to minimize the norm in order to have a as large as possible margin \(M\). Before we proceed, we need to remind ourselves about Lagrangian multipliers.
8.2. A quick Reminder on Lagrangian Multipliers¶
Consider a function of three independent variables \(f(x,y,z)\) . For the function \(f\) to be an extreme we have
A necessary and sufficient condition is
due to
In many problems the variables \(x,y,z\) are often subject to constraints (such as those above for the margin) so that they are no longer all independent. It is possible at least in principle to use each constraint to eliminate one variable and to proceed with a new and smaller set of independent varables.
The use of so-called Lagrangian multipliers is an alternative technique when the elimination of variables is incovenient or undesirable. Assume that we have an equation of constraint on the variables \(x,y,z\)
resulting in
Now we cannot set anymore
if \(df=0\) is wanted because there are now only two independent variables! Assume \(x\) and \(y\) are the independent variables. Then \(dz\) is no longer arbitrary.
However, we can add to
a multiplum of \(d\phi\), viz. \(\lambda d\phi\), resulting in
Our multiplier is chosen so that
We need to remember that we took \(dx\) and \(dy\) to be arbitrary and thus we must have
and
When all these equations are satisfied, \(df=0\). We have four unknowns, \(x,y,z\) and \(\lambda\). Actually we want only \(x,y,z\), \(\lambda\) needs not to be determined, it is therefore often called Lagrange’s undetermined multiplier. If we have a set of constraints \(\phi_k\) we have the equations
In order to solve the above problem, we define the following Lagrangian function to be minimized
where \(\lambda_i\) is a so-called Lagrange multiplier subject to the condition \(\lambda_i \geq 0\).
Taking the derivatives with respect to \(b\) and \(\boldsymbol{w}\) we obtain
and
Inserting these constraints into the equation for \(\cal{L}\) we obtain
subject to the constraints \(\lambda_i\geq 0\) and \(\sum_i\lambda_iy_i=0\). We must in addition satisfy the Karush-Kuhn-Tucker (KKT) condition
If \(\lambda_i > 0\), then \(y_i(\boldsymbol{w}^T\boldsymbol{x}_i+b)=1\) and we say that \(x_i\) is on the boundary.
If \(y_i(\boldsymbol{w}^T\boldsymbol{x}_i+b)> 1\), we say \(x_i\) is not on the boundary and we set \(\lambda_i=0\).
When \(\lambda_i > 0\), the vectors \(\boldsymbol{x}_i\) are called support vectors. They are the vectors closest to the line (or hyperplane) and define the margin \(M\).
We can rewrite
and its constraints in terms of a matrix-vector problem where we minimize w.r.t. \(\lambda\) the following problem
subject to \(\boldsymbol{y}^T\boldsymbol{\lambda}=0\). Here we defined the vectors \(\boldsymbol{\lambda} =[\lambda_1,\lambda_2,\dots,\lambda_n]\) and \(\boldsymbol{y}=[y_1,y_2,\dots,y_n]\).
Solving the above problem, yields the values of \(\lambda_i\). To find the coefficients of your hyperplane we need simply to compute
With our vector \(\boldsymbol{w}\) we can in turn find the value of the intercept \(b\) (here in two dimensions) via
resulting in
or if we write it out in terms of the support vectors only, with \(N_s\) being their number, we have
With our hyperplane coefficients we can use our classifier to assign any observation by simply using
Below we discuss how to find the optimal values of \(\lambda_i\). Before we proceed however, we discuss now the so-called soft classifier.
8.3. A soft classifier¶
Till now, the margin is strictly defined by the support vectors. This defines what is called a hard classifier, that is the margins are well defined.
Suppose now that classes overlap in feature space, as shown in the figure here. One way to deal with this problem before we define the so-called kernel approach, is to allow a kind of slack in the sense that we allow some points to be on the wrong side of the margin.
We introduce thus the so-called slack variables \(\boldsymbol{\xi} =[\xi_1,x_2,\dots,x_n]\) and modify our previous equation
to
with the requirement \(\xi_i\geq 0\). The total violation is now \(\sum_i\xi\). The value \(\xi_i\) in the constraint the last constraint corresponds to the amount by which the prediction \(y_i(\boldsymbol{w}^T\boldsymbol{x}_i+b)=1\) is on the wrong side of its margin. Hence by bounding the sum \(\sum_i \xi_i\), we bound the total amount by which predictions fall on the wrong side of their margins.
Misclassifications occur when \(\xi_i > 1\). Thus bounding the total sum by some value \(C\) bounds in turn the total number of misclassifications.
This has in turn the consequences that we change our optmization problem to finding the minimum of
subject to
with the requirement \(\xi_i\geq 0\).
Taking the derivatives with respect to \(b\) and \(\boldsymbol{w}\) we obtain
and
and
Inserting these constraints into the equation for \(\cal{L}\) we obtain the same equation as before
but now subject to the constraints \(\lambda_i\geq 0\), \(\sum_i\lambda_iy_i=0\) and \(0\leq\lambda_i \leq C\). We must in addition satisfy the Karush-Kuhn-Tucker condition which now reads
5 0
< < < ! ! M A T H _ B L O C K
and
8.4. Kernels and non-linearity¶
The cases we have studied till now, were all characterized by two classes with a close to linear separability. The classifiers we have described so far find linear boundaries in our input feature space. It is possible to make our procedure more flexible by exploring the feature space using other basis expansions such as higher-order polynomials, wavelets, splines etc.
If our feature space is not easy to separate, as shown in the figure here, we can achieve a better separation by introducing more complex basis functions. The ideal would be, as shown in the next figure, to, via a specific transformation to obtain a separation between the classes which is almost linear.
The change of basis, from \(x\rightarrow z=\phi(x)\) leads to the same type of equations to be solved, except that we need to introduce for example a polynomial transformation to a two-dimensional training set.
import numpy as np
import os
np.random.seed(42)
# To plot pretty figures
import matplotlib
import matplotlib.pyplot as plt
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
from sklearn.svm import SVC
from sklearn import datasets
X1D = np.linspace(-4, 4, 9).reshape(-1, 1)
X2D = np.c_[X1D, X1D**2]
y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])
plt.figure(figsize=(11, 4))
plt.subplot(121)
plt.grid(True, which='both')
plt.axhline(y=0, color='k')
plt.plot(X1D[:, 0][y==0], np.zeros(4), "bs")
plt.plot(X1D[:, 0][y==1], np.zeros(5), "g^")
plt.gca().get_yaxis().set_ticks([])
plt.xlabel(r"$x_1$", fontsize=20)
plt.axis([-4.5, 4.5, -0.2, 0.2])
plt.subplot(122)
plt.grid(True, which='both')
plt.axhline(y=0, color='k')
plt.axvline(x=0, color='k')
plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], "bs")
plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], "g^")
plt.xlabel(r"$x_1$", fontsize=20)
plt.ylabel(r"$x_2$", fontsize=20, rotation=0)
plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])
plt.plot([-4.5, 4.5], [6.5, 6.5], "r--", linewidth=3)
plt.axis([-4.5, 4.5, -1, 17])
plt.subplots_adjust(right=1)
plt.show()
Suppose we define a polynomial transformation of degree two only (we continue to live in a plane with \(x_i\) and \(y_i\) as variables)
With our new basis, the equations we solved earlier are basically the same, that is we have now (without the slack option for simplicity)
subject to the constraints \(\lambda_i\geq 0\), \(\sum_i\lambda_iy_i=0\), and for the support vectors
from which we also find \(b\). To compute \(\boldsymbol{z}_i^T\boldsymbol{z}_j\) we define the kernel \(K(\boldsymbol{x}_i,\boldsymbol{x}_j)\) as
For the above example, the kernel reads
We note that this is nothing but the dot product of the two original vectors \((\boldsymbol{x}_i^T\boldsymbol{x}_j)^2\). Instead of thus computing the product in the Lagrangian of \(\boldsymbol{z}_i^T\boldsymbol{z}_j\) we simply compute the dot product \((\boldsymbol{x}_i^T\boldsymbol{x}_j)^2\).
This leads to the so-called kernel trick and the result leads to the same as if we went through the trouble of performing the transformation \(\phi(\boldsymbol{x}_i)^T\phi(\boldsymbol{x}_j)\) during the SVM calculations.
Using our definition of the kernel We can rewrite again the Lagrangian
subject to the constraints \(\lambda_i\geq 0\), \(\sum_i\lambda_iy_i=0\) in terms of a convex optimization problem
subject to \(\boldsymbol{y}^T\boldsymbol{\lambda}=0\). Here we defined the vectors \(\boldsymbol{\lambda} =[\lambda_1,\lambda_2,\dots,\lambda_n]\) and \(\boldsymbol{y}=[y_1,y_2,\dots,y_n]\). If we add the slack constants this leads to the additional constraint \(0\leq \lambda_i \leq C\).
We can rewrite this (see the solutions below) in terms of a convex optimization problem of the type
Below we discuss how to solve these equations. Here we note that the matrix \(\boldsymbol{P}\) has matrix elements \(p_{ij}=y_iy_jK(\boldsymbol{x}_i,\boldsymbol{x}_j)\). Given a kernel \(K\) and the targets \(y_i\) this matrix is easy to set up. The constraint \(\boldsymbol{y}^T\boldsymbol{\lambda}=0\) leads to \(f=0\) and \(\boldsymbol{A}=\boldsymbol{y}\). How to set up the matrix \(\boldsymbol{G}\) is discussed later. Here note that the inequalities \(0\leq \lambda_i \leq C\) can be split up into \(0\leq \lambda_i\) and \(\lambda_i \leq C\). These two inequalities define then the matrix \(\boldsymbol{G}\) and the vector \(\boldsymbol{h}\).
8.5. Different kernels and Mercer’s theorem¶
There are several popular kernels being used. These are
Linear: \(K(\boldsymbol{x},\boldsymbol{y})=\boldsymbol{x}^T\boldsymbol{y}\),
Polynomial: \(K(\boldsymbol{x},\boldsymbol{y})=(\boldsymbol{x}^T\boldsymbol{y}+\gamma)^d\),
Gaussian Radial Basis Function: \(K(\boldsymbol{x},\boldsymbol{y})=\exp{\left(-\gamma\vert\vert\boldsymbol{x}-\boldsymbol{y}\vert\vert^2\right)}\),
Tanh: \(K(\boldsymbol{x},\boldsymbol{y})=\tanh{(\boldsymbol{x}^T\boldsymbol{y}+\gamma)}\),
and many other ones.
An important theorem for us is Mercer’s theorem. The theorem states that if a kernel function \(K\) is symmetric, continuous and leads to a positive semi-definite matrix \(\boldsymbol{P}\) then there exists a function \(\phi\) that maps \(\boldsymbol{x}_i\) and \(\boldsymbol{x}_j\) into another space (possibly with much higher dimensions) such that
So you can use \(K\) as a kernel since you know \(\phi\) exists, even if you don’t know what \(\phi\) is.
Note that some frequently used kernels (such as the Sigmoid kernel) don’t respect all of Mercer’s conditions, yet they generally work well in practice.
8.6. The moons example¶
from __future__ import division, print_function, unicode_literals
import numpy as np
np.random.seed(42)
import matplotlib
import matplotlib.pyplot as plt
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
from sklearn.svm import SVC
from sklearn import datasets
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from sklearn.datasets import make_moons
X, y = make_moons(n_samples=100, noise=0.15, random_state=42)
def plot_dataset(X, y, axes):
plt.plot(X[:, 0][y==0], X[:, 1][y==0], "bs")
plt.plot(X[:, 0][y==1], X[:, 1][y==1], "g^")
plt.axis(axes)
plt.grid(True, which='both')
plt.xlabel(r"$x_1$", fontsize=20)
plt.ylabel(r"$x_2$", fontsize=20, rotation=0)
plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])
plt.show()
from sklearn.datasets import make_moons
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
polynomial_svm_clf = Pipeline([
("poly_features", PolynomialFeatures(degree=3)),
("scaler", StandardScaler()),
("svm_clf", LinearSVC(C=10, loss="hinge", random_state=42))
])
polynomial_svm_clf.fit(X, y)
def plot_predictions(clf, axes):
x0s = np.linspace(axes[0], axes[1], 100)
x1s = np.linspace(axes[2], axes[3], 100)
x0, x1 = np.meshgrid(x0s, x1s)
X = np.c_[x0.ravel(), x1.ravel()]
y_pred = clf.predict(X).reshape(x0.shape)
y_decision = clf.decision_function(X).reshape(x0.shape)
plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)
plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)
plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])
plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])
plt.show()
from sklearn.svm import SVC
poly_kernel_svm_clf = Pipeline([
("scaler", StandardScaler()),
("svm_clf", SVC(kernel="poly", degree=3, coef0=1, C=5))
])
poly_kernel_svm_clf.fit(X, y)
poly100_kernel_svm_clf = Pipeline([
("scaler", StandardScaler()),
("svm_clf", SVC(kernel="poly", degree=10, coef0=100, C=5))
])
poly100_kernel_svm_clf.fit(X, y)
plt.figure(figsize=(11, 4))
plt.subplot(121)
plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])
plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])
plt.title(r"$d=3, r=1, C=5$", fontsize=18)
plt.subplot(122)
plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])
plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])
plt.title(r"$d=10, r=100, C=5$", fontsize=18)
plt.show()
def gaussian_rbf(x, landmark, gamma):
return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)
gamma = 0.3
x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)
x2s = gaussian_rbf(x1s, -2, gamma)
x3s = gaussian_rbf(x1s, 1, gamma)
XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]
yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])
plt.figure(figsize=(11, 4))
plt.subplot(121)
plt.grid(True, which='both')
plt.axhline(y=0, color='k')
plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c="red")
plt.plot(X1D[:, 0][yk==0], np.zeros(4), "bs")
plt.plot(X1D[:, 0][yk==1], np.zeros(5), "g^")
plt.plot(x1s, x2s, "g--")
plt.plot(x1s, x3s, "b:")
plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])
plt.xlabel(r"$x_1$", fontsize=20)
plt.ylabel(r"Similarity", fontsize=14)
plt.annotate(r'$\mathbf{x}$',
xy=(X1D[3, 0], 0),
xytext=(-0.5, 0.20),
ha="center",
arrowprops=dict(facecolor='black', shrink=0.1),
fontsize=18,
)
plt.text(-2, 0.9, "$x_2$", ha="center", fontsize=20)
plt.text(1, 0.9, "$x_3$", ha="center", fontsize=20)
plt.axis([-4.5, 4.5, -0.1, 1.1])
plt.subplot(122)
plt.grid(True, which='both')
plt.axhline(y=0, color='k')
plt.axvline(x=0, color='k')
plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], "bs")
plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], "g^")
plt.xlabel(r"$x_2$", fontsize=20)
plt.ylabel(r"$x_3$ ", fontsize=20, rotation=0)
plt.annotate(r'$\phi\left(\mathbf{x}\right)$',
xy=(XK[3, 0], XK[3, 1]),
xytext=(0.65, 0.50),
ha="center",
arrowprops=dict(facecolor='black', shrink=0.1),
fontsize=18,
)
plt.plot([-0.1, 1.1], [0.57, -0.1], "r--", linewidth=3)
plt.axis([-0.1, 1.1, -0.1, 1.1])
plt.subplots_adjust(right=1)
plt.show()
x1_example = X1D[3, 0]
for landmark in (-2, 1):
k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)
print("Phi({}, {}) = {}".format(x1_example, landmark, k))
rbf_kernel_svm_clf = Pipeline([
("scaler", StandardScaler()),
("svm_clf", SVC(kernel="rbf", gamma=5, C=0.001))
])
rbf_kernel_svm_clf.fit(X, y)
from sklearn.svm import SVC
gamma1, gamma2 = 0.1, 5
C1, C2 = 0.001, 1000
hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)
svm_clfs = []
for gamma, C in hyperparams:
rbf_kernel_svm_clf = Pipeline([
("scaler", StandardScaler()),
("svm_clf", SVC(kernel="rbf", gamma=gamma, C=C))
])
rbf_kernel_svm_clf.fit(X, y)
svm_clfs.append(rbf_kernel_svm_clf)
plt.figure(figsize=(11, 7))
for i, svm_clf in enumerate(svm_clfs):
plt.subplot(221 + i)
plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])
plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])
gamma, C = hyperparams[i]
plt.title(r"$\gamma = {}, C = {}$".format(gamma, C), fontsize=16)
plt.show()
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
Phi(-1.0, -2) = [0.74081822]
Phi(-1.0, 1) = [0.30119421]
8.7. Mathematical optimization of convex functions¶
A mathematical (quadratic) optimization problem, or just optimization problem, has the form
subject to some constraints for say a selected set \(i=1,2,\dots, n\). In our case we are optimizing with respect to the Lagrangian multipliers \(\lambda_i\), and the vector \(\boldsymbol{\lambda}=[\lambda_1, \lambda_2,\dots, \lambda_n]\) is the optimization variable we are dealing with.
In our case we are particularly interested in a class of optimization problems called convex optmization problems. In our discussion on gradient descent methods we discussed at length the definition of a convex function.
Convex optimization problems play a central role in applied mathematics and we recommend strongly Boyd and Vandenberghe’s text on the topics.
If we use Python as programming language and wish to venture beyond scikit-learn, tensorflow and similar software which makes our lives so much easier, we need to dive into the wonderful world of quadratic programming. We can, if we wish, solve the minimization problem using say standard gradient methods or conjugate gradient methods. However, these methods tend to exhibit a rather slow converge. So, welcome to the promised land of quadratic programming.
The functions we need are contained in the quadratic programming package CVXOPT and we need to import it together with numpy as
import numpy
import cvxopt
This will make our life much easier. You don’t need t write your own optimizer.
We remind ourselves about the general problem we want to solve
Let us show how to perform the optmization using a simple case. Assume we want to optimize the following problem
The minimization problem can be rewritten in terms of vectors and matrices as (with \(x\) and \(y\) being the unknowns)
Similarly, we can now set up the inequalities (we need to change \(\geq\) to \(\leq\) by multiplying with \(-1\) on bot sides) as the following matrix-vector equation
We have collapsed all the inequalities into a single matrix \(\boldsymbol{G}\). We see also that our matrix
is clearly positive semi-definite (all eigenvalues larger or equal zero). Finally, the vector \(\boldsymbol{h}\) is defined as
Since we don’t have any equalities the matrix \(\boldsymbol{A}\) is set to zero The following code solves the equations for us
# Import the necessary packages
import numpy
from cvxopt import matrix
from cvxopt import solvers
P = matrix(numpy.diag([1,0]), tc=’d’)
q = matrix(numpy.array([3,4]), tc=’d’)
G = matrix(numpy.array([[-1,0],[0,-1],[-1,-3],[2,5],[3,4]]), tc=’d’)
h = matrix(numpy.array([0,0,-15,100,80]), tc=’d’)
# Construct the QP, invoke solver
sol = solvers.qp(P,q,G,h)
# Extract optimal value and solution
sol[’x’]
sol[’primal objective’]
Input In [5]
P = matrix(numpy.diag([1,0]), tc=’d’)
^
SyntaxError: invalid character '’' (U+2019)
We are now ready to return to our setup of the optmization problem for a more realistic case. Introducing the slack parameter \(C\) we have
subject to \(\boldsymbol{y}^T\boldsymbol{\lambda}=0\). Here we defined the vectors \(\boldsymbol{\lambda} =[\lambda_1,\lambda_2,\dots,\lambda_n]\) and \(\boldsymbol{y}=[y_1,y_2,\dots,y_n]\). With the slack constants this leads to the additional constraint \(0\leq \lambda_i \leq C\).
code will be added


Abyssal Saintess Elcadia:
We accomplished more from the Library of Sages than we first thought. We learned that the information I need about Embryo is in a place called the Monastery of Silence.
The problem is where that information is located in the Monastery. We will just have to search... Are you almost ready?
I'm ready.
I'm not ready yet.


Breed
Inheritance
Population Size: 20
Number of Generations: 20
Heritability: 80%
Assortative Mating: 0.9
Polygenicity: 8


the and with {background for viral The margin shedding was {border-style prolonged px;} #ffffff;} SARS-CoV- patient COVID- HIV duration day had RT-PCR patients reported not px; {font-family are case disease font-size might people this []. after al. cell count days days. double; from infection living normal result that were which Commons Creative border-color border-width coronavirus isolation our solid; who {font-style {font-weight ART And Case Infect RNA about been bold courier; data duration. em; has immune included may pt;} showed studies than two under


{% extends 'base.html' %} {% block content %}
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Jordan Develops
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Sprint 5 Cultural - Core Learning Reflections
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30
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29
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Sprint 3 Cultural - Emotional Intelligence
23
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November 2020
Sprint 2 Cultural - Learning Plan
22
nd
November 2020
Sprint 2 Technical - Inline vs Inline-block
22
nd
November 2020
Sprint 1 Cultural - Identity, Values & Strengths
22
nd
November 2020
Sprint 1 Technical - The Story of Te Houtaewa
16
th
November 2020


Benefits of a Teens Exploring Technology (TXT) Fellow
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Structures
The following structures are available globally.
-
An event published when a command starts. The event is stored under the key
See moreevent
in theuserInfo
property ofNotification
s posted under the name .commandStarted.Declaration
Swift
public struct CommandStartedEvent : MongoEvent, InitializableFromOpaquePointer
-
An event published when a command succeeds. The event is stored under the key
See moreevent
in theuserInfo
property ofNotification
s posted under the name .commandSucceeded.Declaration
Swift
public struct CommandSucceededEvent : MongoEvent, InitializableFromOpaquePointer
-
An event published when a command fails. The event is stored under the key
See moreevent
in theuserInfo
property ofNotification
s posted under the name .commandFailed.Declaration
Swift
public struct CommandFailedEvent : MongoEvent, InitializableFromOpaquePointer
-
Published when a server description changes. This does NOT include changes to the server’s roundTripTime property.
See moreDeclaration
Swift
public struct ServerDescriptionChangedEvent : MongoEvent, InitializableFromOpaquePointer
-
Published when a server is initialized.
See moreDeclaration
Swift
public struct ServerOpeningEvent : MongoEvent, InitializableFromOpaquePointer
-
Published when a server is closed.
See moreDeclaration
Swift
public struct ServerClosedEvent : MongoEvent, InitializableFromOpaquePointer
-
Published when a topology description changes.
See moreDeclaration
Swift
public struct TopologyDescriptionChangedEvent : MongoEvent, InitializableFromOpaquePointer
-
Published when a topology is initialized.
See moreDeclaration
Swift
public struct TopologyOpeningEvent : MongoEvent, InitializableFromOpaquePointer
-
Published when a topology is closed.
See moreDeclaration
Swift
public struct TopologyClosedEvent : MongoEvent, InitializableFromOpaquePointer
-
Published when the server monitor’s ismaster command is started - immediately before the ismaster command is serialized into raw BSON and written to the socket.
See moreDeclaration
Swift
public struct ServerHeartbeatStartedEvent : MongoEvent, InitializableFromOpaquePointer
-
Published when the server monitor’s ismaster succeeds.
See moreDeclaration
Swift
public struct ServerHeartbeatSucceededEvent : MongoEvent, InitializableFromOpaquePointer
-
Published when the server monitor’s ismaster fails, either with an “ok: 0” or a socket exception.
See moreDeclaration
Swift
public struct ServerHeartbeatFailedEvent : MongoEvent, InitializableFromOpaquePointer
-
A struct to represent the BSON Decimal128 type.
See moreDeclaration
Swift
public struct Decimal128 : BSONNumber, Equatable, Codable, CustomStringConvertible
-
Options struct used for configuring the coding strategies on
See moreBSONEncoder
andBSONDecoder
.Declaration
Swift
public struct BSONCoderOptions : CodingStrategyProvider
-
A struct representing the BSON document type.
See moreDeclaration
Swift
@dynamicMemberLookup public struct Document
-
Options to use when creating a ClientSession.
See moreDeclaration
Swift
public struct ClientSessionOptions
-
Options to use when creating a
See moreMongoClient
.Declaration
Swift
public struct ClientOptions : CodingStrategyProvider, Decodable
-
Options to use when listing available databases.
See moreDeclaration
Swift
public struct ListDatabasesOptions : Encodable
-
Options to use when retrieving a
See moreMongoDatabase
from aMongoClient
.Declaration
Swift
public struct DatabaseOptions : CodingStrategyProvider
-
Options to use when performing a bulk write operation on a
See moreMongoCollection
.Declaration
Swift
public struct BulkWriteOptions : Codable
-
The result of a bulk write operation on a
See moreMongoCollection
.Declaration
Swift
public struct BulkWriteResult : Decodable
-
Options to use when executing a
See morefindOneAndDelete
command on aMongoCollection
.Declaration
Swift
public struct FindOneAndDeleteOptions : FindAndModifyOptionsConvertible, Decodable
-
Options to use when executing a
See morefindOneAndReplace
command on aMongoCollection
.Declaration
Swift
public struct FindOneAndReplaceOptions : FindAndModifyOptionsConvertible, Decodable
-
Options to use when executing a
See morefindOneAndUpdate
command on aMongoCollection
.Declaration
Swift
public struct FindOneAndUpdateOptions : FindAndModifyOptionsConvertible, Decodable
-
A struct representing an index on a
See moreMongoCollection
.Declaration
Swift
public struct IndexModel : Encodable
-
Options to use when creating an index for a collection.
See moreDeclaration
Swift
public struct IndexOptions : Codable
-
Options to use when executing an
See moreaggregate
command on aMongoCollection
.Declaration
Swift
public struct AggregateOptions : Codable
-
Options to use when executing a
See morefind
command on aMongoCollection
.Declaration
Swift
public struct FindOptions : Codable
-
Options to use when executing an
See moreinsertOne
command on aMongoCollection
.Declaration
Swift
public struct InsertOneOptions : Codable, BulkWriteOptionsConvertible
-
Options to use when executing an
See moreupdate
command on aMongoCollection
.Declaration
Swift
public struct UpdateOptions : Codable, BulkWriteOptionsConvertible
-
Options to use when executing a
See morereplace
command on aMongoCollection
.Declaration
Swift
public struct ReplaceOptions : Codable, BulkWriteOptionsConvertible
-
Options to use when executing a
See moredelete
command on aMongoCollection
.Declaration
Swift
public struct DeleteOptions : Codable, BulkWriteOptionsConvertible
-
The result of an
See moreinsertOne
command on aMongoCollection
.Declaration
Swift
public struct InsertOneResult : Decodable
-
The result of a multi-document insert operation on a
See moreMongoCollection
.Declaration
Swift
public struct InsertManyResult
-
The result of a
See moredelete
command on aMongoCollection
.Declaration
Swift
public struct DeleteResult : Decodable
-
The result of an
See moreupdate
operation aMongoCollection
.Declaration
Swift
public struct UpdateResult : Decodable
-
Options to use when executing a
See morelistCollections
command on aMongoDatabase
.Declaration
Swift
public struct ListCollectionsOptions : Encodable
-
Options to set on a retrieved
See moreMongoCollection
.Declaration
Swift
public struct CollectionOptions : CodingStrategyProvider
-
A struct to represent a single write error not resulting from an executed bulk write.
See moreDeclaration
Swift
public struct WriteError : Codable
-
A struct to represent a write concern error resulting from an executed bulk write.
See moreDeclaration
Swift
public struct WriteConcernError : Codable
-
A struct to represent a write error resulting from an executed bulk write.
See moreDeclaration
Swift
public struct BulkWriteError : Codable
-
Options to use when executing a
See morecount
command on aMongoCollection
.Declaration
Swift
public struct CountOptions : Codable
-
Options to use when executing a
See morecreateCollection
command on aMongoDatabase
.Declaration
Swift
public struct CreateCollectionOptions : Codable, CodingStrategyProvider
-
Options to use when creating a new index on a
See moreMongoCollection
.Declaration
Swift
public struct CreateIndexOptions : Encodable
-
Options to use when executing a
See moredistinct
command on aMongoCollection
.Declaration
Swift
public struct DistinctOptions : Codable
-
Options to use when dropping an index from a
See moreMongoCollection
.Declaration
Swift
public struct DropIndexOptions : Encodable
-
Options to use when running a command against a
See moreMongoDatabase
.Declaration
Swift
public struct RunCommandOptions : Encodable
-
A struct representing a server connection, consisting of a host and port.
See moreDeclaration
Swift
public struct ConnectionId : Equatable
-
A struct describing a mongod or mongos process.
See moreDeclaration
Swift
public struct ServerDescription
-
A struct describing the state of a MongoDB deployment: its type (standalone, replica set, or sharded), which servers are up, what type of servers they are, which is primary, and so on.
See moreDeclaration
Swift
public struct TopologyDescription


John Alexis Guerra Gómez| ja.guerrag[at]uniandes.edu.co| @duto_guerra
Jose Tiberio Hernández | jhernand[at]uniandes.edu.co
Universidad de los Andes
http://johnguerra.co/lectures/visualAnalytics_fall2017/12_IEEEVIS_Summary/
*** Vega-Lite: A Grammar of Interactive Graphics (J) (Best Paper Award)
Authors: Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat, and Jeffrey Heer
Next step after D3, define visualization from what you want rather than how to do it
Vega-Lite: A Grammar of Interactive Graphics from UW Interactive Data Lab on Vimeo.
** Magnostics: Image-based Search of Interesting Matrix Views for Guided Network Exploration (J)
Authors: Michael Behrisch, Benjamin Bach, Michael Hund, Michael Delz, Laura von Rüden, Jean-Daniel Fekete, and Tobias Schreck
[VIS16 Preview] Magnostics: Image-based Search of Interesting Matrix Views for Guided Network Exploration from VGTCommunity on Vimeo.
** Evaluation of Graph Sampling: A Visualization Perspective (J)
Authors: Yanhong Wu, Nan Cao, Daniel Archambault, Qiaomu Shen, Huamin Qu, and Weiwei Cui
Graph sampling, how good are them for showing visual properties
** Towards Unambiguous Edge Bundling: Investigating Confluent Drawings for Network Visualization (J)
Authors: Benjamin Bach, Nathalie Henry Riche, Christophe Hurter, Kim Marriott, and Tim Dwyer
Nice demo
* CUBu: Universal real-time bundling for large graphs (T)
Authors: Matthew van der Zwan, Valeriu Codreanu, Alexandru Telea
Very fast GPU based edge bundling, that works with millions of nodes
Surprise! Bayesian Weighting for De-Biasing Thematic Maps (J)
Authors: Michael Correll and Jeffrey Heer
Github repoAuthors: Yalong Yang, Tim Dwyer, Sarah Goodwin, and Kim Marriott
Very novel. Good evaluation
* Evaluating the Impact of Binning 2D Scalar Fields (J)
Authors: Lace Padilla, P. Samuel Quinan, Miriah Meyer, and Sarah H. Creem-Regehr
Good study on why to use binning on maps
Quantifying the Visual Impact of Classification Boundaries in Choropleth Maps (J)
Authors: Yifan Zhang and Ross Maciejewski
PCA, ML to find regions on maps
*** Small Multiples with Gaps (J)
Authors: Wouter Meulemans, Jason Dykes, Aidan Slingsby, Cagatay Turkay, and Jo Wood
Small Multiples
** TopicLens: Efficient Multi-Level Visual Topic Exploration of Large-Scale Document Collections (J)
Authors: Minjeong Kim, Kyeongpil Kang, Deokgun Park, Jaegul Choo, and Niklas Elmqvist
Cool algorithm (fast) nice tool
Authors: Cristian Felix, Anshul Vikram Pandey, and Enrico Bertini
Good replacement for wordclouds, ML
*** The Connected Scatterplot for Presenting Paired Time Series (T)
Authors: Steve Haroz, Robert Kosara, Steven L. Franconeri
** EventAction: Visual Analytics for Temporal Event Sequence Recommendation (C)
Authors: Fan Du, Catherine Plaisant, Neil Spring, Ben Shneiderman
Authors: Fan Du, Ben Shneiderman, Catherine Plaisant, Sana Malik, Adam Perer
List of strategies on how to declutter time series
Visplause: Visual Data Quality Assessment of Many Time Series Using Plausibility Checks (J)
Authors: Clemens Arbesser, Florian Spechtenhauser, Thomas Mühlbacher, and Harald Piringer
Multi-Granular Trend Detection for Time-Series Analysis (J)
Shape Grammar Extraction for Efficient Query-by-Sketch Pattern Matching in Long Time Series (C)
The Semantics of Sketch: A Visual Query System for Time Series Data (C)
- SocialBrands: Visual Analysis of Public Perceptions of Brands on Social Media (C)
Not the best visual encoding
*** Iterating Between Tools to Create and Edit Visualizations (J)
Authors: Alex Bigelow, Steven Drucker, Danyel Fisher, and Miriah Meyer
** Data-Driven Guides: Supporting Expressive Design for Information Graphics (J)
Authors: Nam Wook Kim, Eston Schweickart, Zhicheng Liu, Mira Dontcheva, Wilmot Li, Jovan Popovic, and Hanspeter Pfister
VIZITCARDS: A Card-Based Toolkit for Infovis Design Education (J)
Authors: Shiqing He and Eytan Adar
A framework for applying viz
VLAT: Development of a Visualization Literacy Assessment Test (J)
Authors: Sukwon Lee, Sung-Hee Kim, and Bum Chul Kwon
A test for visualization literacy, first approach, incomplete
Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations (J)
Authors: Mona Hosseinkhani Loorak, Charles Perin, Christopher Collins, and Sheelagh Carpendale
** Visualization by Demonstration: An Interaction Paradigm for Visual Data Exploration (J)
Authors: Bahador Saket, Hannah Kim, Eli T. Brown, and Alex Endert
** HindSight: Encouraging Exploration through Direct Encoding of Personal Interaction History (J)
Authors: Mi Feng, Cheng Deng, Evan M. Peck, and Lane Harrison
* Supporting Visual Exploration for Multiple Users in Large Display Environments (C)
Authors: Sriram Karthik Badam, Fereshteh Amini, Niklas Elmqvist, Pourang Iranihttp://hci.cs.umanitoba.ca/publications/details/visual-exploration-large-display
The Elicitation Interview Technique: Capturing People’s Experiences of Data Representations (T)
Authors: Trevor Hogan, Uta Hinrichs, Eva Hornecker


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E-MANAFA: Energy Monitor and ANAlyzer For Android¶
E-MANAFA is a plug-and-play software model-based tool for performing fine-grained estimates of energy consumption on Android devices. E-Manafa estimates system-level and per-component energy consumption, by extracting information from the following sources:
power_profile.xml: Device-specific file provided by manufacturers containing current consumption per component state;
batterystats: Tool from Android framework that logs each power-related event that occurs between device charges;
Perfetto: System-wide profiling for Linux nd Android, that profiles high-frequency data, such as CPU frequency.
Note: Manufacturers not always supply information about the current consumed by an individual component in the power profile file. Use this information if it accurately represents the current drawn from the device battery in practice. The file can also be derived using external apparatus such as Monsoon. Google provides a set of guidelines to estimating the current of each component (https://source.android.com/devices/tech/power/component)
Documentation¶
https://greensoftwarelab.github.io/E-MANAFA/modules.html
SETUP¶
In order to run this tool, the following resources are required:
rooted Android device (running Android 9 or above);
*nix-based environment (MAC OS , Linux);
Python 3.6 or above;
Android Sdk tools (https://developer.android.com/studio/releases/platform-tools)
Installation¶
1. Via python-pip¶
pip install manafa
1.1 define environment variables¶
In order to run this tool, there are at least 2 env. variables that need to be defined in the shell startup script (e.g .bashrc or .bash_profile file)
export ANDROID_HOME=$HOME/<your-android-instalation-folder>/
export PATH=$ANDROID_HOME/platform-tools:$PATH
2. From sources¶
2.1 Clone repo¶
$ git clone https://github.com/RRua/e-manafa.git
2.2 Replicate the environment¶
Install virtual virtualenv enviroment (via python-pip):
$ python -m pip install --user virtualenv
2.3 Replicate locally the dev virtualenv¶
$ virtualenv env/
2.4 Activate the virtual environment¶
$ source env/bin/activate
2.5 Install required packages¶
$ pip install -r requirements.txt
2.6 define environment variables¶
In order to run this tool, there are at least 2 env. variables that need to be defined in the shell startup script (e.g .bashrc or .bash_profile file)
export ANDROID_HOME=$HOME/<your-android-instalation-folder>/
export PATH=$ANDROID_HOME/platform-tools:$PATH
Usage¶
Command line¶
$ emanafa [-p|--profile <prof>]
[-t|--timezone <tz>]
[-pft|--perfettofile <pf>]
[-bts|--batstatsfile <bf>]
From project’s root¶
$ python3 emanafa.py [-p|--profile <prof>]
[-t|--timezone <tz>]
[-pft|--perfettofile <pf>]
[-bts|--batstatsfile <bf>]
Source¶
# Example
em = EManafa()
em.init()
em.start()
do_work_to_profile() # replace by procedure to be measured
em.stop()
em.parseResults()
begin = em.bat_events.events[0].time # first collected sample from batterystats
end = em.bat_events.events[-1].time # last collected sample from batterystats
p, c, z = em.getConsumptionInBetween(begin, end)
print(f"TOTAL: {p} Joules")
Supported devices¶
This tool can be used with any Android device able to run Perfetto, available since Android 9 (P). The tool so far was successfuly executed on the following devices:
Pixel 3a
Pixel 4a 5G
Xiaomi Mi 9 Lite


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{% extends 'template-lab-4a.html' %} {% set mainClasses = 'nhsuk-u-padding-top-0' %} {% block pageTitle %} Symptoms - NHS {% endblock %} {% block beforeContent %} {% endblock %} {% block content %}
Smelly feet aren't fun for anyone, so good personal hygiene and changing your shoes regularly should keep your feet fresh and sweet smelling.
Medically known as bromodosis, stinky feet are a common year-round problem.
The main cause is sweaty feet combined with wearing the same shoes every day.
Anyone can get sweaty feet, at any temperature or time of year.
But teenagers and pregnant women are more likely to get them because hormonal changes make them sweat more.
You're also more likely to have sweaty feet if you're on your feet all day, wear shoes that are too tight, are under a lot of stress, or have a medical condition called hyperhidrosis, which makes you sweat more than usual.
Feet often become smelly if sweat soaks into your shoes and they don't dry before you wear them again.
The good news is that smelly feet are usually easy to treat by keeping your feet clean and dry, and changing your shoes regularly.
To treat sweaty or smelly feet yourself, try to:
If you often get sweaty feet, you might want to try:
Smelly feet are a harmless problem that generally clears up. Sometimes, however, it can be a sign of a medical condition.
See your GP if simple measures to reduce your foot odour don't help, or if you're worried that your level of sweating is abnormally high.
Your doctor can offer you a strong prescription antiperspirant or refer you for a treatment called iontophoresis, which delivers a mild electric current through water to your feet to combat excessive sweating.
Get more tips on how to look after your feet
Page last reviewed: 02 August 2019
Next review due: 02 August 2022


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Brexit: Boris Johnson and Michael Gove categorically rule out delaying end date
Put 31 December 2020 in your diaries - because Boris Johnson's team are once again claiming we won't delay a single day longer. If they're telling the truth, of course
by Dan Bloom, https://www.facebook.com/mirrordanbloomBoris Johnson's team have categorically ruled out delaying the final date of Brexit beyond 31 December 2020.
Downing Street ruled out any extension to the 11-month transition period - the period for us to get an EU trade deal if Parliament agrees to leave by the end of January.
And today no-deal Brexit planning chief Michael Gove also ruled out an extension, which is a clear option in the Brexit deal with the EU.
The comments will prompt fresh fears Boris Johnson would crash Britain out of the EU in a "trapdoor to no-deal" on New Year's Day 2021.
Critics and many experts believe 11 months is not nearly long enough to secure a trade deal with the EU, whose negotiations with Canada took seven years. That leaves no-deal unless we extend.
Yet voters may also question whether Boris Johnson, who is taking a hardline approach to keep the Brexit Party from taking Tory seats in the election, is telling the truth.
The Prime Minister repeatedly claimed he would not ask for a delay to Brexit beyond 31 October before he then asked for one, however grudgingly.
A Downing Street spokesman said yesterday: "The Government will not be extending the transition period.
"The Prime Minister believes that we will have a good trade deal agreed with the EU by December 2020," the spokesman said.
"The Brexit process has been going on for long enough."
Today Mr Gove was asked to confirm the position by BBC Radio 4's Today programme.
Asked if the transition period could be extended he said: "Uh, no."
Asked "definitely not?" he replied: "Absolutely."
He said that, while preparations would continue for a no-deal Brexit, he was confident they could get a new free trade agreement with the EU by that date.
"We will ensure that we will have a proper free trade agreement with the European Union," he told BBC Radio 4's Today programme.
"It is always the case that we need to prepare for every eventuality, but I am confident that we will be able to get a good relationship with the EU at the end of this period.
"It will be the case when - if we can get a majority government - we move into the close talks we have with them, there will be a strong interest from the Irish Government, from the French government, and from our other partners in making sure that we move to a new relationship."


Hello guys, this website was developed by me, that's my picture up there :) Thanks for visiting and if you want to access my personal website you can click HERE! i'm born in 2000, and now i'm a computer science student @ Griffith College Cork. Looking foward to learn as much as i can of that world that i simply love, computing has been in my life since i was a child, with my first computer i've started learning basic things about programming and i just fell in love with it!


FOLARIN MOYOSORE
Web Developer/ Photographer
08164614157
<EMAIL_ADDRESS>Odo-Ona, Apata road, Ibadan. Oyo
EXPERIENCE
Photography
Doxalyn Studio
2017-2019
Idishin, Jericho
Doxalyn studios is a photography/ graphics designing company
Edited, retouched and delivered all files within 24-48 hours every night
I learnt how to take and edit pictures
I learnt how to manually configure a camera to correct settings.
SKILLS
Frontend developer
Photographer
Graphics Designing
Writing
EDUCATION
Bachelor of Computer Science
Leadcity University, Ibadan.
2019-2023
Junior And Senior Secondary Certificate
Government College Ibadan
2011-2016
INTERESTS
Aeronautics/ Space Exploration
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Documentaries
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Music


BrightstarDB Documentation
Starting with release 1.3 the core BrightstarDB user and developer docs are hosted at ReadTheDocs.org. We will also maintain an online version of the API documentation (generated by Sandcastle) on this site. If you want a local .CHM version of the API documentation please download the latest distribution. If you want a local copy of the user and developer docs, you can download a PDF from ReadTheDocs.org. If you are happy with online documentation, use the links in the table below to find the documentation you need.
| BrightstarDB Release | User/Developer Docs | API Docs |
|---|---|---|
| Latest1 | Latest at readthedocs.org | N/A2 |
| 1.3 | Version 1.3 at readthedocs.org | Version 1.3 API Docs |
1The latest version tracks the develop branch in our GitHub repository. Use this documentation
if you are using BrightstarDB direct from the GitHub repository.
2Our current build hosting does not allow for publication of the HTML version of the API documentation.
However it is easy to build it yourself from the sources.


{% extends "base.html" %} {% import 'bootstrap/wtf.html' as wtf %} {% block scripts %} {{ super() }} {% endblock %} {% block app_content %}
On this page you can see the BLAST results in a sunburst


created by Johnny Ray Alt
Submitted for review 03.11.18
RULES
Ping Pong wil lcount up to your number from 1 with the following exceptions:


 The race distance for 1973 for men was
reported as 6 km.
09 Jan
2016
7.44 km
23:42
Tim VandeVelde (BEL)
7.44 km
31:34
Annet van der Linde (NED)
03 Jan
2015
7.44 km
22:47
Tim Pleijte (NED)
7.44 km
28:59
Monique Verschuure (NED)
07 Jan
2014
7.56 km
23:42
Stein VanEs-2
7.56 km
30:49
Margriet Beekman-Rotte (NED)
05 Jan
2013
7.56 km
22:38
Dieter Vanstreels-3
7.56 km
29:08
Marianne deKok-2
07 Jan
2012
7.56 km
23:59
Maarten Mortelmans
(NED)
7.56 km
33:31
Paola vanGilst (NED)
08 Jan
2011
7.56 km
23:29
Dieter Vanstreels-2
7.56 km
32:33
Saskia Goole (NED)
2010
7.56 km
---
not held due to slick
conditions
7.56 km
---
not held due to slick
conditions
03 Jan
2009
7.56 km
23:29
Dieter Vanstreels (NED)
7.56 km
32:27
Astrid denHerder-2
05 Jan
2008
7.56 km
23:44
Stein VanEs (BEL)
7.56 km
32:07
Astrid denHerder (NED)
7.56 km
24:11
Bart Verschoren-6
7.56 km
31:34
Leonie Ton (NED)
7.56 km
23:53
Bart Verschoren-5
7.56 km
29:17
Marianne deKok (NED)
7.56 km
23:29
Bart Verschoren-4
5.04 km
20:29
Bianca vanVuuren (NED)
7.56 km
24:01
Bart Verschoren-3
5.04 km
17:52
Anjolie Wisse (NED)
7.56 km
24:14
Bart Verschoren-2
5.04 km
17:58
Karin Bastian-5
7.56 km
23:33
Bart Verschoren (BEL)
3.78 km
13:23
Marlies Kort (NED)
7.56 km
22:58
Armand vanderSmissen (NED)
3.78 km
14:57
Lou Dams (BEL)
7.56 km
23:36
Jeroen Punt (NED)
3.78 km
13:12
Karin Bastian-4
7.56 km
23:33
Dennis Klasen (NED)
3.78 km
13:17
Karin Bastian-3
7.56 km
23:12
John Vermeule-2
3.78 km
14:20
Karin Bastian-2
7.8 km
23:07
John Vermeule (NED)
3.9 km
14:23
Joopie Daarne-4
cancelled due to glaze
ice
cancelled due to glaze
ice
7.56 km
22:23
Mauricio Gonzales (MEX)
3.78 km
13:28
Joopie Daarne-3
7.8 km
22:21
Robin Punt (NED)
3.9 km
13:31
Karin Bastian (NED)
7.8 km
22:34.7
Erik Punt (NED)
3.9 km
13:08
Irma Heeren (NED)
7.8 km
21:55
Raf Wijns ((BEL)
3.9 km
12:41
Anja Smolders (BEL)
7.8 km
23:09
Michel deMaat (NED)
3.9 km
12:35
Inge Huyssen-2
7.8 km
23:00.7
Geert Bouton-2
3.9 km
13:03.6
Brenda Sleeuwenhoek (NED)
7.8 km
22:58
Geert Bouton (BEL)
3.9 km
14:40
Inge Huyssen (BEL)
7.8 km
22:54
Jo Slagmolen (BEL)
3.9 km
13:35
Joopie Daarne-2
7.8 km
22:51.4
Leon vandeVelde (NED)
2.6 km
9:05.5
Eliane vanRosevelt (NED)
7.8 km
22:43.2
Dave Cameron (SCO)
2.6 km
8:47.5
Wilma vanOnna (NED)
7.8 km
24:04.2
Johan deVlieger (NED)
2.6 km
10:49.5
Joopie Daarne (NED)
7.8 km
22:18.9
Luc Wijns-2
2.6 km
9:07.9
Jacqueline Vermeule (NED)
7.8 km
22:24.0
Luc Wijns (BEL)
2.6 km
8:49
Margo deNijs-2
7.8 km
23:24.7
Mark Nevens-3
2.6 km
9:33.0
Margo deNijs (NED)
7.8 km
23:00.0
Mark Nevens-2
2.6 km
8:17.2
Christel Jennis (BEL)
7.8 km
22:51.8
Mark Nevens (BEL)
2.6 km
8:56.2
Dorette Janssens-5
7.8 km
23:16.0
Harry Patist (NED)
2.6 km
9:05.1
Dorette Janssens-4
7.8 km
22:01.3
Marcus Smet (BEL)
1.3 km
4:12.3
Dorette Janssens-3
7.8 km
22:42.4
Mark Desmet (BEL)
1.3 km
4:24.8
Dorette Janssens-2
7.8 km
22:34.8
Marc VanNuffel (BEL)
1.3 km
4:15.4
Dorette Janssens (BEL)
7.8 km
23:33.2
Marien vanProoyen (NED)
1.3 km
4:09.6
Celia vanKleef (NED)
7.8 km
22:45.1
Piet Vonck-3
1.3 km
4:09.7
Corrie Konings (NED)
7.8 km
22:54.4
Piet Vonck-2
1.3 km
4:15.0
E Boers (NED)
not held
not held
4.0 km
11:55.4
Piet Vonck (NED)
1.0 km
3:07.7
M Bun (NED)
6.0 km
16:41.2
R Marijnissen (NED)
1.0 km
2:50.8
A vanDriel (NED)
24
Feb 1968
6.0 km
17:25.4
Wies vanHouten (NED)
11 Mar
1967
6.0 km
17:32.2
Sjaak vanEekelen (NED)
27 Feb
1966
6.0 km
17:01.6
Piet Tolhoek-2
20 Mar
1965
6.0 km
17:38.1
Piet Tolhoek (NED)
29 Feb
1964
6.0 km
17:29.7
Wim deHamer (NED)
02 Mar
1963
5.4 km
16;49.5
Hein CujÚ-3
24
Feb 1962
5.6 km
17:04.0
Hein CujÚ-2
18 Feb
1961
4.5 km
13:37.8
Klaas deRuiter (NED)
05 Mar
1960
4.4 km
13:02.7
Hein CujÚ (NED)
Most Wins
(1960-date)
Men
6
Bart Verschoren (BEL)
2002-07
Women
5
Dorette Janssens (BEL)
1976-80
Prize Money
(ADR qualified runners)
No information on prize money has been received for this race.
Race Time Bias (sec/km) Insufficient data to calculate the race time bias.
Competitive Level (1990-date) Insufficient data to calculate the competitive level.
Last Updated on
03 Feb 2016
Contributors: Paul Michiels (50), Juraj Gasparovic (45)


Better
health
days
lives
Medic Care is a Bootstrap 5 Template provided by TemplateMo website. Credits go to FreePik and RawPixel for images used in this template.
010-020-0340
Meet Dr. Carson
Protect yourself and others by wearing masks and washing hands frequently. Outdoor is safer than indoor for gatherings or holding events. People who get sick with Coronavirus disease (COVID-19) will experience mild to moderate symptoms and recover without special treatments.
You can feel free to use this CSS template for your medical profession or health care related websites. You can support us a little via PayPal if this template is good and useful for your work.
12 Years
of Experiences
Our Timeline
Get the vaccine
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Consulting your health
You are fully permitted to use this template for your commercial or personal website. You are not permitted to redistribute the template ZIP file for a download purpose on any other Free CSS collection website.
Certified Nurses
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Covid-19 Hospitals
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Freelance Nursing
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A SamplerVoice that streams the data from a StreamingSamplerSound.
More...
Inherits SynthesiserVoice.
|
| bool | canPlaySound (SynthesiserSound *) |
| | Always returns true. More...
|
| |
|
virtual void | controllerMoved (int controllerNumber, int controllerValue) override |
| | Not implemented.
|
| |
| double | getDiskUsage () |
| | Returns the disk usage of the voice. More...
|
| |
|
virtual void | pitchWheelMoved (int pitchWheelValue) override |
| | Not implemented.
|
| |
| void | prepareToPlay (double sampleRate, int samplesPerBlock) |
| | Initializes its sampleBuffer. More...
|
| |
| void | renderNextBlock (AudioSampleBuffer &outputBuffer, int startSample, int numSamples) override |
| | Adds it's output to the outputBuffer. More...
|
| |
| void | resetVoice () |
| | resets everything. More...
|
| |
| void | startNote (int midiNoteNumber, float velocity, SynthesiserSound *s, int) override |
| | starts the streaming of the sound. More...
|
| |
| void | stopNote (bool allowTailOff) |
| | Clears the note data and resets the loader. More...
|
| |
A SamplerVoice that streams the data from a StreamingSamplerSound.
It uses a SampleLoader object to fetch the data and copies the values into an internal buffer, so you don't have to bother with the SampleLoader's internals.
| bool StreamingSamplerVoice::canPlaySound |
( |
SynthesiserSound * |
| ) |
|
|
inline |
| double StreamingSamplerVoice::getDiskUsage |
( |
| ) |
|
|
inline |
Returns the disk usage of the voice.
To get the disk usage of all voices, simply iterate over the voice list and add all disk usages.
| void StreamingSamplerVoice::prepareToPlay |
( |
double |
sampleRate, |
|
|
int |
samplesPerBlock |
|
) |
| |
|
inline |
Initializes its sampleBuffer.
You have to call this manually, since there is no base class function.
| void StreamingSamplerVoice::renderNextBlock |
( |
AudioSampleBuffer & |
outputBuffer, |
|
|
int |
startSample, |
|
|
int |
numSamples |
|
) |
| |
|
override |
Adds it's output to the outputBuffer.
| void StreamingSamplerVoice::resetVoice |
( |
| ) |
|
|
inline |
| void StreamingSamplerVoice::startNote |
( |
int |
midiNoteNumber, |
|
|
float |
velocity, |
|
|
SynthesiserSound * |
s, |
|
|
int |
|
|
) |
| |
|
override |
starts the streaming of the sound.
| void StreamingSamplerVoice::stopNote |
( |
bool |
allowTailOff | ) |
|
|
inline |
Clears the note data and resets the loader.
The documentation for this class was generated from the following file:


An asset is a form of digital token so-called colored coin. Colored coins follow the idea of “coloring” a specific Bitcoin to be broadcasted over the Bitcoin network.
Total Balance
Received: 50,000.84 LKK
Sent: 49,000.84 LKK


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Kimura
far arm from side control.
lower arm grabs wrist, upper arm slides along floor palm up, under ops tricep, coming through the arm area palm up.
to move ops arm onto floor do the walruss hop flop with your body
belt buckle is near ops other shoulder, leg is over ops head stopping the roll over sweep
Variation: Palm up kimura, elbow on floor, palm up grip on ops wrist, push wrist into ops side point elbow away from op, lock under arm over your own wrist/hand
There is also a bit of a wrist lock as you goose neck their wrist.


Let's Talk
Speak With Expert Engineers.
Email:
(123) 222-8888
Phone:
<EMAIL_ADDRESS>Address:
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Gateway
BOT
03-Jan-19 03:40 PM
[dscrd-chips] <jorian> weird. maybe something changed recently
[dscrd-chips] <jorian> but this is not really useful
[dscrd-chips] <TonyL> Hmm maybe it's because CHIPS was not in sync when I started it, let me try to restart lighthing
[dscrd-chips] <TonyL> topo 2
process blockcount.(2752069
)
2019-01-03T15:41:22.067Z lightning_gossipd(22352): req: type WIRE_GOSSIPCTL_INIT len 145
2019-01-03T15:41:22.069Z lightning_gossipd(22352): Creating IPv6 listener on port 9735
2019-01-03T15:41:22.077Z lightningd(22344): Adding block 195957:<PHONE_NUMBER>0000261a3026b289453aeb62a36436defdd7e84a09168fd1eaf222
done setup_topology
setup_jsonrpc.(chipsln-rpc)
2019-01-03T15:41:22.088Z lightningd(22344): Listening on 'chipsln-rpc'
2019-01-03T15:41:22.088Z lightningd(22344): Server started with public key 025e64bca3d6b217f1a71d8c1025fa5c465053849535c2e04b6434f633a52a96e7, alias VIOLENTMONTANA-chipsln.0.0.0 (color #025e64) and lightningd chipsln.0.0.0
activate_peers
2019-01-03T15:41:22.120Z lightningd(22344): Adding block 195958:<PHONE_NUMBER>00005c4b32108f72a007793760b711526e1b6571c04b0349c5c618
2019-01-03T15:41:22.132Z lightningd(22344): Immediate feerate 253 (was 0)
2019-01-03T15:41:22.132Z lightningd(22344): ...feerate 0 hit floor 253
2019-01-03T15:41:22.132Z lightningd(22344): Normal feerate 253 (was 0)
2019-01-03T15:41:22.132Z lightningd(22344): ...feerate 0 hit floor 253
2019-01-03T15:41:22.132Z lightningd(22344): Slow feerate 253 (was 0)
2019-01-03T15:41:22.132Z lightningd(22344): ...feerate 0 hit floor 253
2019-01-03T15:41:22.143Z lightningd(22344): Adding block 195959:<PHONE_NUMBER>0000c15a802c85ef9663c6fa3edb3700169b4be9996033c67cd811
[dscrd-chips] <TonyL> Started from 195958 again
[dscrd-chips] <jorian> heh
[dscrd-chips] <jorian> ok, stop it again
[dscrd-chips] <jorian> go to .chipsln
[dscrd-chips] <jorian> in your home folder
[dscrd-chips] <jorian> and remove lightningd.sqlite3
[dscrd-chips] <TonyL> 2019-01-03T15:43:32.048Z lightningd(29871): Listening on 'chipsln-rpc'
2019-01-03T15:43:32.048Z lightningd(29871): Server started with public key 025e64bca3d6b217f1a71d8c1025fa5c465053849535c2e04b6434f633a52a96e7, alias VIOLENTMONTANA-chipsln.0.0.0 (color #025e64) and lightningd chipsln.0.0.0
activate_peers
2019-01-03T15:43:32.081Z lightningd(29871): Adding block 2751973:<PHONE_NUMBER>0094170f8c3505c7e0dbcfbbfde94ad442a1f7a9a813a99369e685
2019-01-03T15:43:32.094Z lightningd(29871): Immediate feerate 253 (was 0)
2019-01-03T15:43:32.094Z lightningd(29871): ...feerate 0 hit floor 253
2019-01-03T15:43:32.094Z lightningd(29871): Normal feerate 253 (was 0)
2019-01-03T15:43:32.094Z lightningd(29871): ...feerate 0 hit floor 253
2019-01-03T15:43:32.094Z lightningd(29871): Slow feerate 253 (was 0)
2019-01-03T15:43:32.094Z lightningd(29871): ...feerate 0 hit floor 253
2019-01-03T15:43:32.107Z lightningd(29871): Adding block 2751974:<PHONE_NUMBER>00dd3f53b5bf01e7045d46d215386ebaab2abe47ea49c140c86f1a
[dscrd-chips] <TonyL> Yeaah
[dscrd-chips] <jorian> it's the last resort, since it basically wipes your lightning node
[dscrd-chips] <TonyL> Hmm, I'm getting same address now as I've shared to @sg777
./lightning-cli newaddr
{ "address" : "bQVgTMmfA46TWqhvmLjyY9TC5ncfPE2j3w" }
https://explorer.chips.cash/tx/52ea8f55d807380ace15f1befbe894ae0f07d1027653ee6d2ee810abab779d91
And client seems to be in sync:
./lightning-cli getinfo
{ "id" : "025e64bca3d6b217f1a71d8c1025fa5c465053849535c2e04b6434f633a52a96e7", "port" : 9735, "address" :
[
{ "type" : "ipv4", "address" : "<IP_ADDRESS>", "port" : 9735 },
{ "type" : "ipv6", "address" : "2a01:4f8:1c0c:70a7::1", "port" : 9735 } ], "version" : "chipsln.0.0.0", "blockheight" : 2752077, "network" : "chips" }
[dscrd-chips] <TonyL> But listfunds is empty:
./lightning-cli listfunds
{ "outputs" :
[ ], "channels" :
[ ] }
[dscrd-chips] <jorian> ./lightning-cli dev-rescan-outputs?
[dscrd-chips] <TonyL> ./lightning-cli dev-rescan-outputs
{ "outputs" :
[ ] }
[dscrd-chips] <jorian> so newaddr got you the same address as before?
[dscrd-chips] <TonyL> Yes
[dscrd-chips] <TonyL> Oh after rescan it's different
[dscrd-chips] <TonyL> ./lightning-cli newaddr
{ "address" : "bLQne7iMNCDLHroPCoY8ZKH23QwwyrtkeU" }
[dscrd-chips] <jorian> let me send you something
[dscrd-chips] <jorian> sent 6d4b1c95c34b578915ebcca7e284042c77deaefff59328f1bbba393798dd96c6
[dscrd-chips] <TonyL> ./lightning-cli listfunds
{ "outputs" :
[
{ "txid" : "6d4b1c95c34b578915ebcca7e284042c77deaefff59328f1bbba393798dd96c6", "output" : 0, "value" : 20000000, "status" : "confirmed" } ], "channels" :
[ ] }
[dscrd-chips] <TonyL> Got it
[dscrd-chips] <TonyL> Thanks Will try to connect to dealer now
[dscrd-chips] <jorian> you don't have to
[dscrd-chips] <jorian> if you play a game, it will be done automatically
[dscrd-chips] <jorian> at least, it should. that's what we should test now
[dscrd-chips] <TonyL> then run ./bet player <IP_ADDRESS>
to start the playing node...since on that IP I just started to run the DCV..
how actually it works is there are two nodes DCV(Dealer) and BVV(Blinder) and the playing nodes connect to DCV where the table formation happens...then players, DCV and BVV involve in the process of deck shuffling..then card distribution with round betting happens
does your LN has any funds, initially the playing nodes sets up the channels of 0.05 CHIPS with DCV
[dscrd-chips] <TonyL> Should I do this part?
[dscrd-chips] <jorian> yes, but i'm not sure if sg is still around
[dscrd-chips] <TonyL> ./bet player <IP_ADDRESS>
nntype.33 connect to tcp://<IP_ADDRESS>:7798 connectsock.1
nntype.80 connect to tcp://<IP_ADDRESS>:7799 connectsock.2
Public Key:e00ae0b2334ffbdba6e0d1a9a4b17cff1adcf1edfa644a5100cdd92aca5d0161
BET_p2p_client_join:2052: Failed to send data
BET_p2p_clientloop:2168:Player joining the table failed
[dscrd-chips] <TonyL> Aha, I guess node is down atm
[dscrd-chips] <jorian> yes i got the same
[dscrd-chips] <jorian> thing is: we need 4 nodes to play a game
[dscrd-chips] <jorian> maybe that can improved later
[dscrd-chips] <jorian> although..
[dscrd-chips] <jorian> let me try something
[dscrd-chips] <TonyL> So we need 1 more player now (somebody who will install whole stack) and then schedule a tournament?
(edited)
[dscrd-chips] <TonyL> I can install on more nodes if needed
[dscrd-chips] <jorian> <IP_ADDRESS> try that ip
[dscrd-chips] <jorian> a dealer should be able to play as well
[dscrd-chips] <TonyL> <IP_ADDRESS>
- same error
(edited)
[dscrd-chips] <SHossain> i can start setting up a node soon
(edited)
[dscrd-chips] <jorian> yes i have errors too
[dscrd-chips] <jorian> @sg777
[dscrd-chips] <jorian> he's probably asleep
[dscrd-chips] <jorian> but yes, the more prepared nodes the better
[dscrd-chips] <TonyL> We can just schedule test on some time
[dscrd-chips] <TonyL> So people who want to play will prepare the nodes before this time
[dscrd-chips] <TonyL> KSOP - Komodo Series of Poker
[dscrd-chips] <jorian>
[dscrd-chips] <sg777> hi, I just up my dcv and bvv nodes
[dscrd-chips] <sg777> now you can connect to them and play ./bet player <IP_ADDRESS>
[dscrd-chips] <TonyL> ./bet player <IP_ADDRESS>
nntype.33 connect to tcp://<IP_ADDRESS>:7798 connectsock.1
nntype.80 connect to tcp://<IP_ADDRESS>:7799 connectsock.2
Public Key:48ca390c41adefa19c20ee6fe3d0bae68234f3cfc79e9d6d4ec24b1a30649667
Player ID:0
[dscrd-chips] <sg777> okay LN channel is establishing
[dscrd-chips] <sg777> root@sg777-4 ~/bet/privatebet # ./bet dcv <IP_ADDRESS>
(tcp://<IP_ADDRESS>:7798) bound
(tcp://<IP_ADDRESS>:7799) bound
Table is filled
DCV-->BVV channel ready
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
[dscrd-chips] <TonyL> Aha, have same prints (CHANNELD AWAITING LOCKIN)
(edited)
[dscrd-chips] <sg777> when the nodes connect very first time it takes some time
[dscrd-chips] <sg777> generally this waiting time is the chips transaction confirmation time on the network
[dscrd-chips] <sg777> {
"id": "025e64bca3d6b217f1a71d8c1025fa5c465053849535c2e04b6434f633a52a96e7",
"connected": true,
"netaddr": [
"[::ffff:<IP_ADDRESS>]:39048"
],
"channels": [
{
"state": "CHANNELD_AWAITING_LOCKIN",
"owner": "lightning_channeld",
"channel_id": "dfda59d7f96577df160b6de48d6a727975f08c37ff5438fba01bcfdb12bc68f7",
"funding_txid": "f768bc12dbcf1ba0fb3854ff378cf07579726a8de46d0b16df7765f9d759dadf",
"msatoshi_to_us": 0,
"msatoshi_total": 500000000,
"dust_limit_satoshis": 546,
"max_htlc_value_in_flight_msat":<PHONE_NUMBER>3709552000,
"channel_reserve_satoshis": 0,
"htlc_minimum_msat": 0,
"their_to_self_delay": 8640,
"our_to_self_delay": 8640,
"to_self_delay": 8640,
"max_accepted_htlcs": 483,
"status": [
"CHANNELD_AWAITING_LOCKIN:Funding needs more confirmations."
],
"in_payments_offered": 0,
"in_msatoshi_offered": 0,
"in_payments_fulfilled": 0,
"in_msatoshi_fulfilled": 0,
"out_payments_offered": 0,
"out_msatoshi_offered": 0,
"out_payments_fulfilled": 0,
"out_msatoshi_fulfilled": 0
}
]
}
]
}
[dscrd-chips] <sg777> I think this is 025e64bca3d6b217f1a71d8c1025fa5c465053849535c2e04b6434f633a52a96e7 your node id right?
[dscrd-chips] <TonyL> Yes, have this lightning id
(edited)
[dscrd-chips] <sg777> okay now the channel is established
[dscrd-chips] <TonyL> Cool How to play?
[dscrd-chips] <sg777> my bvv is establishing the channel with your node now, possibly it will take same amount of time..
[dscrd-chips] <sg777> root@sg777-2 ~/bet/privatebet # ./bet bvv <IP_ADDRESS>
nntype.33 connect to tcp://<IP_ADDRESS>:7798 connectsock.1
nntype.80 connect to tcp://<IP_ADDRESS>:7799 connectsock.2
Table Info:{
"method": "TableInfo",
"playersInfo": ["48ca390c41adefa19c20ee6fe3d0bae68234f3cfc79e9d6d4ec24b1a30649667", "5834e741be4389eb1813d3e904ee876848a3483d1810b5a2fc2efe349c24333a"]
}
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
CHANNELD AWAITING LOCKIN
[dscrd-chips] <sg777> that is my BVV node log...
[dscrd-chips] <TonyL> Dealt the cards
[dscrd-chips] <TonyL> Player Cards:
Hole Cards:
[dscrd-chips] <sg777> it's done...
[dscrd-chips] <sg777> is it asking for small_blind
[dscrd-chips] <TonyL> Yes it asking for it
[dscrd-chips] <sg777> enter something >1000000
[dscrd-chips] <TonyL> So I can choose stakes on which we are playing?
[dscrd-chips] <sg777> i.e 1000 satoshis
[dscrd-chips] <sg777> yes...
[dscrd-chips] <sg777> now you can bet
[dscrd-chips] <TonyL> I've input 1000
[dscrd-chips] <TonyL> Enter small blind:1000
[dscrd-chips] <TonyL> Here is the possibilities
1 raise
2 call 1000
3 allin
4 fold
Enter your option, to chose one::1
Enter the amount > 1000:
[dscrd-chips] <sg777> okay is it asking you bet with some options
[dscrd-chips] <sg777> I entered big blind 2000
[dscrd-chips] <SHossain> ninja install
[0/1] Install the project...
-- Install configuration: ""
-- Installing: /usr/local/lib/libnng.a
CMake Error at src/cmake_install.cmake:41 (file):
file INSTALL cannot copy file "/home/user/nng/build/libnng.a" to
"/usr/local/lib/libnng.a".
Call Stack (most recent call first):
cmake_install.cmake:42 (include)
FAILED: CMakeFiles/install.util
cd /home/user/nng/build && /usr/bin/cmake -P cmake_install.cmake
ninja: build stopped: subcommand failed.
[dscrd-chips] <TonyL> Amount of raise should be > bigblind * 2
[dscrd-chips] <TonyL> So not less than 4000
[dscrd-chips] <TonyL> And blinds shouldn't be possible to choose (big blind is small blind X2)
But it's a small details
[dscrd-chips] <TonyL> I've sent raise
[dscrd-chips] <SHossain> ninja test output was success
[dscrd-chips] <TonyL> @SHossain hmm never got problems on ninja step
[dscrd-chips] <sg777> I made call 4000..
[dscrd-chips] <TonyL> Are all dependencies installed correct?
[dscrd-chips] <TonyL> Here is the possibilities
1 check
2 raise
3 allin
4 fold
Enter your option, to chose one::2
Enter the amount > 0:
[dscrd-chips] <TonyL> I want to bet
[dscrd-chips] <TonyL> But bet amount should be > than big blind, not > 0
[dscrd-chips] <SHossain> is this caps? cmake -G Ninja ..
[dscrd-chips] <sg777> > bigblind * 2 is this the rule of poker?
[dscrd-chips] <SHossain> i mean the the ninja word


{% extends "templates/partials/base.html" %} {% block title %}{% parent %}{% endblock %} {% block content %}
A selection of clients and agencies that I am proud to have done work for:


{% extends 'base.html' %} {% block body %}
Oops… You are forbidden
We are sorry but the page you are looking for was forbidden


@(isError:java.lang.Boolean)
@main("Visualize your Apex Debug logs") {
@if(isError) {
Could not parse the debug log file. It is possible that your may have an incomplete log.
If you have a complete log, file an issue on
Github
}
| Start time |
User |
Total execution time |
Log length (bytes) |
Operation |
Application |
Analyze |
| {{log.StartTime | date:'medium'}} |
{{log.LogUser.Name}} |
{{log.DurationMilliseconds}} |
{{log.LogLength}} |
{{log.Operation}} |
{{log.Application}} |
|
}


On new podcast, Don Cherry addresses Sportsnet's decision to fire him
Don Cherry says Sportsnet "made it impossible" for him to address a controversy that eventually led to his firing last week.
In his first "Grapevine" podcast, released Tuesday, Cherry says he wasn't willing to accept Sportsnet's conditions to have him return following his "Coach's Corner" segment on "Hockey Night in Canada" on Nov. 9.
"What happened was evidently I said something. Everybody knows what I said," Cherry said. "I offered to explain. Not an apology, but I was going to smooth it over. They made conditions that made it impossible (for him) to do it. I just couldn't do it. I guess I bit the bullet."
The 27-minute podcast featured Cherry and his son, Tim. They said they plan to make it a weekly podcast.
The 85-year-old broadcaster made comments on "Coach's Corner" that many felt were critical of immigrants for not wearing Remembrance Day poppies. Cherry used the phrase, 'You people,' during the segment but later denied that he was singling out minorities.
Cherry said he talked about many other subjects during the final "Coach's Corner."
"Nothing was mentioned about that. Nothing. Just two little words seemed to set everybody off," Cherry said. "But, hey, that's the way life is. I lived in a vicious world and I lasted 38 years. Happy to be there for 38 years. If I gotta go, I'm glad I'm going out on my shield."
Tim Cherry also was critical of Sportsnet.
"It's a different world out there with the cancel culture," he said. "They had their eyes set on you, they had their guns set on you I think this year for sure. I think there was a lot of fake outrage."
Don Cherry said he has received support from many people. He said truck drivers have given him the thumbs-up and fire-fighters have beeped their horns to show their support.
"Evidently I said something that upset Sportsnet and they canned me and I'm now unemployed except for doing this beautiful podcast," he said.
Most of the podcast focused on Cherry's memories of his coaching career and Maurice (Rocket) Richard.


personal info
- first name : Jignesh
- last name : Bhatt
- Age : 31 Years
- Nationality : Indian
- Freelance : Available
- Address : Mumbai, India
- phone : +919987548641
- Email<EMAIL_ADDRESS>- languages : English, Hindi, Gujarati, Marathi
8
years of experience
10
completed projects
5
Happycustomers
2
awards won
My Skills
Project Management
Agile / Scrum Master
Business Analysis
Programming Languages
Webservices API
Communication skills
Leadership skills
Conflict resolution
Experience   Education & Certifications
-
2016 - Present
Sr. Project Manager Infibeam Avenues Ltd.
• Re-imagined the entire digital mobile service experience by providing the solution expertise in designing the telecom system of Saudi’s largest telecom company.
• Delivering projects in the agile environment by conducting 10-day sprints which includes sprint planning, daily scrums and retrospectives ensuring the deliverables are time-bound and measurable.
• Defined the process flows to cater to the various functions like SIM activations, terminations cancellations, etc. as required by a telecom company.
• Managed, Scheduled and Organized the implementation of Java based restful web-services applications like Order Management system and Single Sign-On for Saudi’s major telecom company by being an only point of contact for this project.
• Took charge of custom software development portfolio, over-looked and guided Project Managers working on projects like travel/leisure ticketing platform & loyalty rewards platform.
-
2014 - 2016
Associate Manager Accelya Kale Solutions Ltd.
• Awarded for managing the end to end implementation of the cargo revenue accounting system for Vietnam’s national carrier within the specified timelines and budget.
• Performed project management activities for implementing the accounting system for one of the major carriers Of United States.
• Estimated the budget in the pre-sales stage, carried out the planning and monitoring the activities throughout the lifecycle of the project.
• Conducted the kick-off meeting, requirements gathering and ensuring the SDLC process is rightly followed by securing sign-offs after each phase of the project.
-
2010 - 2012
Sr. Design Engineer Larsen & Toubro Ltd.
• Developed the entire software based on embedded systems using C/C++ language for the Russian anti-aircraft Zu-23 gun to cater to the Indian Army.
• Continuous engagement with the Israeli partner Elbit systems to ensure the successful integration of both the software systems.
-
2012 - 2014
Masters Degree K.J.Somaiya Institute of Management Studies
Operations Management
-
2010
Bachelor Degree University of Mumbai
Electronics Engineering
-
2015 - Present
Project Management Professional (PMP) Project Management Institute (PMI)
-
2020 - Present
Project Management Professional (PMI-ACP) Project Management Institute (PMI)
-
2018 - Present
Certified Scrum Master (CSM) Scrum Alliance
-
2013 - Present
Lean Six Sigma (Green Belt) SSA Business Solutions


In this use case, a user publish a "gcode" service owned by Univ. of Southampton, and annotate its input message that is a string syntactically, with a semantic type "nucleotide_sequence". "gcode" is used in bioinformatics to "group encoding" sequences, e.g., a nucleotide sequence.
Firstly, the user publish a business (the UDDI terminology of an organization) called "Univ. of Southamtpon", then publish a service belonging to the previous published business. The user will also publish a WSDL which defines the technical fingerprint of the service, and publish a metadata annotating the semantic type of an input message in the WSDL.
Now the user can inquire all the published business/service/WSDL/metadata.
The business name.
The business description.
The business key, which is the unique identifier of the published business.
java PubishBusinessCommand Grimoires_URL business_name business_descriptionGrimoires_URL is where Grimoires is deployed. For instance, if the Grimoires UDDIv2 publish service is deployed at http://hostname:18080/grimoires/services/publish, then Grimoires_URL is http://hostname:18080/grimoires/services.
The business name.
The name, description, and keys of all businesses whose names match.
java InquireBusinessCommand Grimoires_URL business_name
The service name.
The service description.
The business key: specify which business this service belongs to.
The service access point: where the service is located.
The service WSDL URL.
The service key, which is the unique identifier of the published service.
java PublishServiceCommand Grimoires_URL name description business_key access_point WSDL_URL
The service name.
The service name.
The service key.
The business key of the business that this service belongs to.
The service access point.
The WSDL URL.
java InquireServiceCommand Grimoires_URL service_name
The WSDL URL.
The WSDL key, which is the unique identifier of the published WSDL.
java PublishWSDLCommand Grimoires_URL WSDL_URL
The WSDL key.
The WSDL content.
java InquireWSDLCommand Grimoires_URL wsdlKey
To annotate a message part, the message namespace, the message name, and the message name part, are required.
To annotate an operation, the porttype namespace, the porttype name, and the operation name, are required.
To annotate a UDDI entity, the entity key, and the key type, e.g., a business key or a service key, are required.
The metadata type that must be a URI and metadata value that can be either a URI or a string, are required in all cases.
The metadata key.
java PublishMetadataCommand Grimoires_URL -m message_namespace message_name message_part_name metadata_type metadata_value
or
java PublishMetadataCommand Grimoires_URL -o porttype_namespace porttype_name operation_name metadata_type metadata_value
or
java PublishMetadataCommand Grimoires_URL -k [business|service] key metadata_type metadata_value
The metadata type and the metadata value.
The detailed information of the entities that have been attached with this metadata.
java InquireMetadataCommand Grimoires_URL metadataType metadataValue
Null.
The detailed information of all services, including the service name, the service key, the business key of the business that this service belongs to, the service access point, and the WSDL URL.
java ListAllServicesCommand Grimoires_URL
The entity key.
The entity type: one of business, service, metadata or wsdl.
Null.
java DeleteEntityCommand Grimoires_URL key type
Get the metadata attached to an entity.
The entity key.
The entity type: either business or service.
The detailed information of the metadata that have been attached with this entity, including the metadata key, the metadata type, and the metadata value.
java GetEntityMetadataCommand Grimoires_URL key type
The target Web Service endpoint. E.g., a Grimoires UDDIv2 publish WS endpoint could be http://fantasio.ecs.soton.ac.uk:8080/grimoires/services/publish.
The XML filename whose content constructs the SOAP message body.
The response SOAP message.
java SendSoapMessageCommand endpoint filename
The WSDL URL.
The service name.
The service key.
The business key of the business that this service belongs to.
The service access point.
The WSDL URL.
The message namespace.
The message name.
The message part name.
The WSDL URL.
The entity key.
The key type (business, service, or metadata).
The lifetime in seconds since now, or the desired termination time in the
format "dd/mm/yy hh:mm:ss".
A confirmation message if succeeds.
The XML filename or URL.
The to-be-published XML element's localname.
Null.
The XPath expression.
XPath query result.
The XPath expression.
XPath query result.
Null.
All published WSDL documents.
java ListAllWSDLsCommand endpoint


Training exercises that we created: Part 1
Women’s safety apps
How to get a roomful of people to examine the implications of privacy, data policies and app design, and get them all excited about it?
We were faced with this challenge as we conducted a range of workshops, as part of our research project Gendering Surveillance. The following exercise, which builds on our case study of women safety apps in India was well received by different groups - from school-going girls to developers and designers. We are sharing it here for use by anyone who might find it useful to replicate.
Time: 90 minutes
Purpose/Why
- To be able to critically assess whether applications meant to enhance women’s safety are useful, and identify shortcomings and challenges
- To learn to identify the different fields of information that are available about apps in the Play Store and on developers’ website, and to develop a sense of what those fields might indicate
- To be able to evaluate applications on their privacy policies, and develop a sense of what fields would will be empowering to a user
Materials needed
- Smart phones running Android and an Internet connection
- Pre-printed pages of the applications from the Play Store.
- The apps we most often use are VithU, Himmat, Nirbhaya: Be Fearless, Raksha, Eyewatch Women, My Safetipin, Damini and Pari - selected for the range of actors involved in their creation, and for the range of issues that emerge. You can find more apps used in India in this database, developed for our research.
- Pre-printed set of guiding questions on (i) Features (ii) Data policies
- Pen, paper
Format and steps
- Divide participants into small groups of 3-5
- Select women’s safety apps you’d like to discuss. You will need half as many apps as the number of groups, such that for every app, one group will work on ‘Features’ and one group will work on ‘Data Policies’. If participants seem keen and there’s sufficient time, then you could adapt to add more than one app per group - we’ve found that this often enriches the discussion.
- Give each group either the set of questions on ‘Features’ or on ‘Data policies’ (see below) and ask them to answer the questions for each app they will be discussing. No groups should have the same questions *and* app, so that the report back is not boring!
- Small group discussion for 30 minutes
Groups working on ‘Features’ will discuss
- Does the ‘type’ of feature (like geofencing, tracking upon alert, heatmaps, walk-me-home etc.) enhance or decrease autonomy of the user?
- Who has a say in initiating geofencing, walk-me-home?
- Who are actors/emergency contacts the alert gets sent to? What are the implications for the user and their power relations with various actors?
- Through what medium is the alert sent?
- What control does the user have vis-a-vis their emergency contact? Are there risks associated with additional information like audio and video recordings being sent upon alerts?
- What might be the vulnerabilities in putting information in ‘networked publics’ i.e. within reach of third parties (for eg., in case of some apps, an alert is raised on the Facebook profiles of users)?
Groups working on ‘Data Policies’ will discuss
- What permissions does the app ask for? Does it ask for fields of information that are not required for the app to function? [see app’s page on Playstore]
- Does the app have a privacy policy? [see app’s page on Playstore or developer’s website if available]
- What is the business model of the app? [see developer profile; whether app is free or priced]
- Does the app give users meaningful control over data collected ? [see in Privacy Policy or Terms of Services]
- Examples of fields of inquiry:
- Does it mention restrictions placed on data use?
- Does it mention what is considered appropriate data use?
- Can users opt out a particular uses of data?
- Can users access data after it is collected?
- Can users edit it for accuracy?
- Can users decide to delete all information?
Each group presents in plenary important points from the app they were looking at. If their discussion went beyond the questions that you have provided, then that’s great!
Remarks and tips
- Identify beforehand points that you want to see emerging in the discussions, so that you can chip in if they don’t come up organically. Our research on safety apps, and the infographics embedded in it, might be able to give you some ideas!
- Important to note that if an app’s features are impressive, it is still worthwhile to look at how it fares on the data policies, and vice versa.
- Selection of apps should be such that a wide range of features are covered, and a representative range of good and bad practices are covered.


To start the process you need to add funds to your wallet.
The process is fast and you will receive the exchanged amount in your wallet.
To get started, you'll need to create a bitcoin wallet and get some bitcoin.
This service is provided by the third-party SideShift AI, who will charge a small fee for the service. The fee will be shown before you start the transaction.


Make smooth moves in the feather-light and tech-inspired design of the all-new SoftRide Rift Tech. A bold asymmetric collar offers a sharp, training-ready style while the TPU heel clip adds extra support through the upper. SoftRide Foam in the midsole keeps you light on your feet whether you're up in the gym or hitting the streets.
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- Breathable, lightweight knitted upper
- Full lace closure for secure fit
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Functions to speed up the exploratory analysis of simple datasets using 'dplyr' and 'ggplot2'. Functions are provided to do the common tasks of calculating confidence intervals and visualising the results.
| Version: | 0.1.0 |
| Depends: | dplyr, ggplot2, lazyeval, plotrix |
| Suggests: | testthat |
| Published: | 2017-03-23 |
| Author: | Conor Neilson [aut, cre] |
| Maintainer: | Conor Neilson <condwanaland at gmail.com> |
| License: | GPL-3 |
| URL: | https://github.com/condwanaland/summariser |
| NeedsCompilation: | no |
| Citation: | summariser citation info |
| CRAN checks: | summariser results |
| Reference manual: | summariser.pdf |
| Package source: | summariser_0.1.0.tar.gz |
| Windows binaries: | r-devel: summariser_0.1.0.zip, r-release: summariser_0.1.0.zip, r-oldrel: summariser_0.1.0.zip |
| OS X binaries: | r-release: summariser_0.1.0.tgz, r-oldrel: summariser_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=summariser to link to this page.


Change Log
All notable changes to this project will be documented in this file. See standard-version for commit guidelines.
## 2.0.3 (2017-10-17)
Bug Fixes
- configurability of toStringTag (b99f692)
## 2.0.2 (2017-10-17)
Bug Fixes
- constructor exposure (dbc0c51)
- do not allow non constructor calls (1f2f800)
- toString and toStringTag symbol definitions. (2d17786), closes #6
Changelog for previous versions
See CHANGES
file


Amber's fancy new rice cooker plays "Twinkle, Twinkle Little Star" at start up. What, no custom ring tone support? Talk about disappointing!
4/10/2010, 10:10:44 AM
Favs: 0
Retweets: 0
link--- layout: tweet ---
Amber's fancy new rice cooker plays "Twinkle, Twinkle Little Star" at start up. What, no custom ring tone support? Talk about disappointing!
4/10/2010, 10:10:44 AM
Favs: 0
Retweets: 0
link


 We lost maybe 90% of our trading volume with Cryptopia paused
[dscrd-hush] <dukeleto> that means small buy/sells in the orderbook move the price a lot
[dscrd-hush] <dukeleto> somebody bought a large chunk of the orderbook, so price was inflated for a bit until people adjusted their orders, some people sold at a nice price for a few minutes
[dscrd-hush] <dukeleto> but i wouldn't quite call it a pump and dump People will definitely be trying to game this liquidity issue, tho. Expect our price to be very volatile until cryptopia is unpaused
[dscrd-hush] <dukeleto> some people will try to arbitrage from HUSH/BTC , HUSH/USD and HUSH/PLN, which now have big spreads
[dscrd-hush] <Takoy_Nahal> Interestingly, Cryptopia will still think for a long time about the return of trading?
[dscrd-hush] <dukeleto> they are waiting for KMD dpow in the next release.
[dscrd-hush] <dukeleto> many wheels are turning, KMD notary nodes are working out the last few bugs with Hush
[dscrd-hush] <dukeleto> once all notary nodes are running Hush in production, that is when end users and exchanges can upgrade
[dscrd-hush] <Takoy_Nahal> As I understand it is necessary to have patience, it is necessary to wait a little?
[dscrd-hush] <dukeleto> until NN's are running, sending data to KMD + BTC, the newest release doesn't have the data it needs, to protect against attacks
[dscrd-hush] <Takoy_Nahal> I think that protecting dpow will allow HUSH to declare itself as a reliable coin, it is appreciated
[dscrd-hush] <dukeleto> very few coins have this right now, other than KMD and KMD asset chains
[dscrd-hush] <dukeleto> only CHIPS and GAME have this, as external coins. Hush is the 3rd, and first ZEC fork (CHIPS and GAME are BTC 0.15 forks)
[dscrd-hush] <dukeleto> BTCH already has this protection as a KMD asset chain, to clarify