The main aim is to give you a short and pedestrian introduction to how we can use Machine Learning methods to solve quantum mechanical many-body problems. And why this could be of interest.
The hope is that after this talk you have gotten the basic ideas to get you started. Peeping into https://github.com/mhjensenseminars/MachineLearningTalk, you'll find a Jupyter notebook, slides, codes etc that will allow you to reproduce the simulations discussed here, and perhaps run your own very first calculations.
Furthermore, since we will also talk about Monte Carlo simulations, the exciting idea of Monte Carlo optimization and Machine Learning can be used to mesh strategies for reducing the curse of dimensionality.
How can we avoid the dimensionality curse? Many possibilities
This work is inspired by the idea of representing the wave function with a restricted Boltzmann machine (RBM), presented recently by G. Carleo and M. Troyer, Science 355, Issue 6325, pp. 602-606 (2017). They named such a wave function/network a neural network quantum state (NQS). In their article they apply it to the quantum mechanical spin lattice systems of the Ising model and Heisenberg model, with encouraging results.
Thanks to Vilde Flugsrud and Alfred Alocias Mariadason for many discussions and interpretations of the above paper.
Machine learning (ML) is an extremely rich field, in spite of its young age. The increases we have seen during the last three decades in computational capabilities have been followed by developments of methods and techniques for analyzing and handling large date sets, relying heavily on statistics, computer science and mathematics. The field is rather new and developing rapidly.
Popular software packages written in Python for ML are
Not all the algorithms and methods can be given a rigorous mathematical justification, opening up thereby for experimenting and trial and error and thereby exciting new developments.
A solid command of linear algebra, multivariate theory, probability theory, statistical data analysis, understanding errors and Monte Carlo methods is important in order to understand many of the various algorithms and methods.
A familiarity with ML is almost becoming a prerequisite for many of the most exciting employment opportunities. Add quantum computing and there you are.
Almost every problem in ML and data science starts with the same ingredients:
Machine learning is the science of giving computers the ability to learn without being explicitly programmed. The idea is that there exist generic algorithms which can be used to find patterns in a broad class of data sets without having to write code specifically for each problem. The algorithm will build its own logic based on the data.
Machine learning is a subfield of computer science, and is closely related to computational statistics. It evolved from the study of pattern recognition in artificial intelligence (AI) research, and has made contributions to AI tasks like computer vision, natural language processing and speech recognition. It has also, especially in later years, found applications in a wide variety of other areas, including bioinformatics, economy, physics, finance and marketing.
You will notice however that many of the basic ideas discussed do come from Physics!
The approaches to machine learning are many, but are often split into two main categories. In supervised learning we know the answer to a problem, and let the computer deduce the logic behind it. On the other hand, unsupervised learning is a method for finding patterns and relationship in data sets without any prior knowledge of the system. Some authours also operate with a third category, namely reinforcement learning. This is a paradigm of learning inspired by behavioural psychology, where learning is achieved by trial-and-error, solely from rewards and punishment.
Another way to categorize machine learning tasks is to consider the desired output of a system. Some of the most common tasks are:
Here we will use so-called reduced Boltzmann Machines to simulate quantum many-body problems. For Monte Carlo aficionados, there is a very close similarity with what are called shadow wave functions, see the work of Pederiva and Kalos and collaborators, Phys Rev. E 90, 053304 (2014).
Most mammalian predators rely on a variety of prey, which complicates mathematical modeling; however, a few predators have become highly specialized and seek almost exclusively a single prey species. An example of this simplified predator-prey interaction is seen in Canadian northern forests, where the populations of the lynx and the snowshoe hare are intertwined in a life and death struggle.
One reason that this particular system has been so extensively studied is that the Hudson Bay company kept careful records of all furs from the early 1800s into the 1900s. The records for the furs collected by the Hudson Bay company showed distinct oscillations (approximately 12 year periods), suggesting that these species caused almost periodic fluctuations of each other's populations. The table here shows data from 1900 to 1920.
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display
import sklearn
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
data = np.loadtxt('src/Hudson_Bay.csv', delimiter=',', skiprows=1)
x = data[:,0]
y = data[:,1]
line = np.linspace(1900,1920,1000,endpoint=False).reshape(-1,1)
reg = DecisionTreeRegressor(min_samples_split=3).fit(x.reshape(-1,1),y.reshape(-1,1))
plt.plot(line, reg.predict(line), label="decision tree")
regline = LinearRegression().fit(x.reshape(-1,1),y.reshape(-1,1))
plt.plot(line, regline.predict(line), label= "Linear Regression")
plt.plot(x, y, label= "Linear Regression")
plt.show()
The field of artificial neural networks has a long history of development, and is closely connected with the advancement of computer science and computers in general. A model of artificial neurons was first developed by McCulloch and Pitts in 1943 to study signal processing in the brain and has later been refined by others. The general idea is to mimic neural networks in the human brain, which is composed of billions of neurons that communicate with each other by sending electrical signals. Each neuron accumulates its incoming signals, which must exceed an activation threshold to yield an output. If the threshold is not overcome, the neuron remains inactive, i.e. has zero output.
This behaviour has inspired a simple mathematical model for an artificial neuron. $$ y = f\left(\sum_{i=1}^n w_ix_i\right) = f(u) $$ Here, the output \( y \) of the neuron is the value of its activation function, which have as input a weighted sum of signals \( x_i, \dots ,x_n \) received by \( n \) other neurons.
An artificial neural network (NN), is a computational model that consists of layers of connected neurons, or nodes. It is supposed to mimic a biological nervous system by letting each neuron interact with other neurons by sending signals in the form of mathematical functions between layers. A wide variety of different NNs have been developed, but most of them consist of an input layer, an output layer and eventual layers in-between, called hidden layers. All layers can contain an arbitrary number of nodes, and each connection between two nodes is associated with a weight variable.
The Hamiltonian of the quantum dot is given by $$ \hat{H} = \hat{H}_0 + \hat{V}, $$ where \( \hat{H}_0 \) is the many-body HO Hamiltonian, and \( \hat{V} \) is the inter-electron Coulomb interactions. In dimensionless units, $$ \hat{V}= \sum_{i < j}^N \frac{1}{r_{ij}}, $$ with \( r_{ij}=\sqrt{\mathbf{r}_i^2 - \mathbf{r}_j^2} \).
This leads to the separable Hamiltonian, with the relative motion part given by (\( r_{ij}=r \)) $$ \hat{H}_r=-\nabla^2_r + \frac{1}{4}\omega^2r^2+ \frac{1}{r}, $$ plus a standard Harmonic Oscillator problem for the center-of-mass motion. This system has analytical solutions in two and three dimensions (M. Taut 1993 and 1994).
Given a hamiltonian \( H \) and a trial wave function \( \Psi_T \), the variational principle states that the expectation value of \( \langle H \rangle \), defined through $$ \langle E \rangle = \frac{\int d\boldsymbol{R}\Psi^{\ast}_T(\boldsymbol{R})H(\boldsymbol{R})\Psi_T(\boldsymbol{R})} {\int d\boldsymbol{R}\Psi^{\ast}_T(\boldsymbol{R})\Psi_T(\boldsymbol{R})}, $$ is an upper bound to the ground state energy \( E_0 \) of the hamiltonian \( H \), that is $$ E_0 \le \langle H \rangle . $$ In general, the integrals involved in the calculation of various expectation values are multi-dimensional ones. Traditional integration methods such as the Gauss-Legendre will not be adequate for say the computation of the energy of a many-body system.
Choose a trial wave function \( \psi_T(\boldsymbol{R}) \). $$ P(\boldsymbol{R},\boldsymbol{\alpha})= \frac{\left|\psi_T(\boldsymbol{R},\boldsymbol{\alpha})\right|^2}{\int \left|\psi_T(\boldsymbol{R},\boldsymbol{\alpha})\right|^2d\boldsymbol{R}}. $$ This is our model, or likelihood/probability distribution function (PDF). It depends on some variational parameters \( \boldsymbol{\alpha} \). The approximation to the expectation value of the Hamiltonian is now $$ \langle E[\boldsymbol{\alpha}] \rangle = \frac{\int d\boldsymbol{R}\Psi^{\ast}_T(\boldsymbol{R},\boldsymbol{\alpha})H(\boldsymbol{R})\Psi_T(\boldsymbol{R},\boldsymbol{\alpha})} {\int d\boldsymbol{R}\Psi^{\ast}_T(\boldsymbol{R},\boldsymbol{\alpha})\Psi_T(\boldsymbol{R},\boldsymbol{\alpha})}. $$
$$ E_L(\boldsymbol{R},\boldsymbol{\alpha})=\frac{1}{\psi_T(\boldsymbol{R},\boldsymbol{\alpha})}H\psi_T(\boldsymbol{R},\boldsymbol{\alpha}), $$ called the local energy, which, together with our trial PDF yields $$ E[\boldsymbol{\alpha}]=\int P(\boldsymbol{R})E_L(\boldsymbol{R},\boldsymbol{\alpha}) d\boldsymbol{R}\approx \frac{1}{N}\sum_{i=1}^NE_L(\boldsymbol{R_i},\boldsymbol{\alpha}) $$ with \( N \) being the number of Monte Carlo samples.
The Algorithm for performing a variational Monte Carlo calculations runs thus as this
We want to perform a Variational Monte Carlo calculation of the ground state of two electrons in a quantum dot well with different oscillator energies, assuming total spin \( S=0 \). Our trial wave function has the following form $$ \begin{equation} \psi_{T}(\boldsymbol{r}_1,\boldsymbol{r}_2) = C\exp{\left(-\alpha_1\omega(r_1^2+r_2^2)/2\right)} \exp{\left(\frac{r_{12}}{(1+\alpha_2 r_{12})}\right)}, \label{eq:trial} \end{equation} $$ where the $\alpha$s represent our variational parameters, two in this case.
Why does the trial function look like this? How did we get there? This will be our main motivation for switching to Machine Learning.
To find an ansatz for the correlated part of the wave function, it is useful to rewrite the two-particle local energy in terms of the relative and center-of-mass motion. Let us denote the distance between the two electrons as \( r_{12} \). We omit the center-of-mass motion since we are only interested in the case when \( r_{12} \rightarrow 0 \). The contribution from the center-of-mass (CoM) variable \( {\bf R}_{\mathrm{CoM}} \) gives only a finite contribution. We focus only on the terms that are relevant for \( r_{12} \) and for three dimensions. The relevant local energy becomes then $$ \lim_{r_{12} \rightarrow 0}E_L(R)= \frac{1}{{\cal R}_T(r_{12})}\left(2\frac{d^2}{dr_{ij}^2}+\frac{4}{r_{ij}}\frac{d}{dr_{ij}}+ \frac{2}{r_{ij}}-\frac{l(l+1)}{r_{ij}^2}+2E \right){\cal R}_T(r_{12}) = 0. $$ Set \( l=0 \) and we have the so-called cusp condition $$ \frac{d {\cal R}_T(r_{12})}{dr_{12}} = -\frac{1}{2(l+1)} {\cal R}_T(r_{12})\qquad r_{12}\to 0 $$
# Importing various packages
from math import exp, sqrt
from random import random, seed
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import sys
#Trial wave function for quantum dots in two dims
def WaveFunction(r,alpha,beta):
r1 = r[0,0]**2 + r[0,1]**2
r2 = r[1,0]**2 + r[1,1]**2
r12 = sqrt((r[0,0]-r[1,0])**2 + (r[0,1]-r[1,1])**2)
deno = r12/(1+beta*r12)
return exp(-0.5*alpha*(r1+r2)+deno)
#Local energy for quantum dots in two dims, using analytical local energy
def LocalEnergy(r,alpha,beta):
r1 = (r[0,0]**2 + r[0,1]**2)
r2 = (r[1,0]**2 + r[1,1]**2)
r12 = sqrt((r[0,0]-r[1,0])**2 + (r[0,1]-r[1,1])**2)
deno = 1.0/(1+beta*r12)
deno2 = deno*deno
return 0.5*(1-alpha*alpha)*(r1 + r2) +2.0*alpha + 1.0/r12+deno2*(alpha*r12-deno2+2*beta*deno-1.0/r12)
# The Monte Carlo sampling with the Metropolis algo
def MonteCarloSampling():
NumberMCcycles= 100000
StepSize = 1.0
# positions
PositionOld = np.zeros((NumberParticles,Dimension), np.double)
PositionNew = np.zeros((NumberParticles,Dimension), np.double)
# seed for rng generator
seed()
# start variational parameter
alpha = 0.9
for ia in range(MaxVariations):
alpha += .025
AlphaValues[ia] = alpha
beta = 0.2
for jb in range(MaxVariations):
beta += .01
BetaValues[jb] = beta
energy = energy2 = 0.0
DeltaE = 0.0
#Initial position
for i in range(NumberParticles):
for j in range(Dimension):
PositionOld[i,j] = StepSize * (random() - .5)
wfold = WaveFunction(PositionOld,alpha,beta)
#Loop over MC MCcycles
for MCcycle in range(NumberMCcycles):
#Trial position
for i in range(NumberParticles):
for j in range(Dimension):
PositionNew[i,j] = PositionOld[i,j] + StepSize * (random() - .5)
wfnew = WaveFunction(PositionNew,alpha,beta)
#Metropolis test to see whether we accept the move
if random() < wfnew**2 / wfold**2:
PositionOld = PositionNew.copy()
wfold = wfnew
DeltaE = LocalEnergy(PositionOld,alpha,beta)
energy += DeltaE
energy2 += DeltaE**2
#We calculate mean, variance and error ...
energy /= NumberMCcycles
energy2 /= NumberMCcycles
variance = energy2 - energy**2
error = sqrt(variance/NumberMCcycles)
Energies[ia,jb] = energy
return Energies, AlphaValues, BetaValues
#Here starts the main program with variable declarations
NumberParticles = 2
Dimension = 2
MaxVariations = 10
Energies = np.zeros((MaxVariations,MaxVariations))
AlphaValues = np.zeros(MaxVariations)
BetaValues = np.zeros(MaxVariations)
(Energies, AlphaValues, BetaValues) = MonteCarloSampling()
# Prepare for plots
fig = plt.figure()
ax = fig.gca(projection='3d')
# Plot the surface.
X, Y = np.meshgrid(AlphaValues, BetaValues)
surf = ax.plot_surface(X, Y, Energies,cmap=cm.coolwarm,linewidth=0, antialiased=False)
# Customize the z axis.
zmin = np.matrix(Energies).min()
zmax = np.matrix(Energies).max()
ax.set_zlim(zmin, zmax)
ax.set_xlabel(r'$\alpha$')
ax.set_ylabel(r'$\beta$')
ax.set_zlabel(r'$\langle E \rangle$')
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
The above procedure is not the smartest one. Looping over all variational parameters becomes expensive. Also, we don't use importance sampling and optimizations of the standard deviation (blocking, bootstrap, jackknife). Such codes are included in the above Github address.
We can also be smarter and use minimization methods to find the optimal variational parameters with fewer Monte Carlo cycles and then fire up our heavy artillery.
One way to achieve this is to minimize the energy as function of the variational parameters.
To find the derivatives of the local energy expectation value as function of the variational parameters, we can use the chain rule and the hermiticity of the Hamiltonian.
Let us define $$ \bar{E}_{\alpha_i}=\frac{d\langle E_L\rangle}{d\alpha_i}. $$ as the derivative of the energy with respect to the variational parameter \( \alpha_i \) We define also the derivative of the trial function (skipping the subindex \( T \)) as $$ \bar{\Psi}_{i}=\frac{d\Psi}{d\alpha_i}. $$
The elements of the gradient of the local energy are then (using the chain rule and the hermiticity of the Hamiltonian) $$ \bar{E}_{i}= 2\left( \langle \frac{\bar{\Psi}_{i}}{\Psi}E_L\rangle -\langle \frac{\bar{\Psi}_{i}}{\Psi}\rangle\langle E_L \rangle\right). $$ From a computational point of view it means that you need to compute the expectation values of $$ \langle \frac{\bar{\Psi}_{i}}{\Psi}E_L\rangle, $$ and $$ \langle \frac{\bar{\Psi}_{i}}{\Psi}\rangle\langle E_L\rangle $$ These integrals are evaluted using MC intergration (with all its possible error sources). We can then use methods like stochastic gradient or other minimization methods to find the optimal variational parameters (I don't discuss this topic here, but these methods are very important in ML).
We have a model, our likelihood function.
How should we define the cost function?
Suppose the trial function (our model) is the exact wave function. The action of the hamiltionan on the wave function $$ H\Psi = \mathrm{constant}\times \Psi, $$ The integral which defines various expectation values involving moments of the hamiltonian becomes then $$ \langle E^n \rangle = \langle H^n \rangle = \frac{\int d\boldsymbol{R}\Psi^{\ast}(\boldsymbol{R})H^n(\boldsymbol{R})\Psi(\boldsymbol{R})} {\int d\boldsymbol{R}\Psi^{\ast}(\boldsymbol{R})\Psi(\boldsymbol{R})}= \mathrm{constant}\times\frac{\int d\boldsymbol{R}\Psi^{\ast}(\boldsymbol{R})\Psi(\boldsymbol{R})} {\int d\boldsymbol{R}\Psi^{\ast}(\boldsymbol{R})\Psi(\boldsymbol{R})}=\mathrm{constant}. $$ This gives an important information: If I want the variance, the exact wave function leads to zero variance! The variance is defined as $$ \sigma_E = \langle E^2\rangle - \langle E\rangle^2. $$ Variation is then performed by minimizing both the energy and the variance.
We can then take the derivatives of $$ \sigma_E = \langle E^2\rangle - \langle E\rangle^2, $$ with respect to the variational parameters. The derivatives of the variance can then be used to defined the so-called Hessian matrix, which in turn allows us to use minimization methods like Newton's method or standard gradient methods.
This leads to however a more complicated expression, with obvious errors when evaluating integrals by Monte Carlo integration. Less used, see however Filippi and Umrigar. The expression becomes complicated $$ \bar{E}_{ij} = 2\left[ \langle (\frac{\bar{\Psi}_{ij}}{\Psi}+\frac{\bar{\Psi}_{j}}{\Psi}\frac{\bar{\Psi}_{i}}{\Psi})(E_L-\langle E\rangle)\rangle -\langle \frac{\bar{\Psi}_{i}}{\Psi}\rangle\bar{E}_j-\langle \frac{\bar{\Psi}_{j}}{\Psi}\rangle\bar{E}_i\right] +\langle \frac{\bar{\Psi}_{i}}{\Psi}E_L{_j}\rangle +\langle \frac{\bar{\Psi}_{j}}{\Psi}E_L{_i}\rangle -\langle \frac{\bar{\Psi}_{i}}{\Psi}\rangle\langle E_L{_j}\rangle \langle \frac{\bar{\Psi}_{j}}{\Psi}\rangle\langle E_L{_i}\rangle. $$
Evaluating the cost function means having to evaluate the above second derivative of the energy.
Why use a generative model rather than the more well known discriminative deep neural networks (DNN)?
The network layers:
The goal of the hidden layer is to increase the model's expressive power. We encode complex interactions between visible variables by introducing additional, hidden variables that interact with visible degrees of freedom in a simple manner, yet still reproduce the complex correlations between visible degrees in the data once marginalized over (integrated out).
Examples of this trick being employed in physics:
The function \( E(\mathbf{x},\mathbf{h}) \) gives the energy of a configuration (pair of vectors) \( (\mathbf{x}, \mathbf{h}) \). The lower the energy of a configuration, the higher the probability of it. This function also depends on the parameters \( \mathbf{a} \), \( \mathbf{b} \) and \( W \). Thus, when we adjust them during the learning procedure, we are adjusting the energy function to best fit our problem.
RBMs were first developed using binary units in both the visible and hidden layer. The corresponding energy function is defined as follows: $$ \begin{align} E(\mathbf{x}, \mathbf{h}) = - \sum_i^M x_i a_i- \sum_j^N b_j h_j - \sum_{i,j}^{M,N} x_i w_{ij} h_j, \label{_auto3} \end{align} $$ where the binary values taken on by the nodes are most commonly 0 and 1.
Another varient is the RBM where the visible units are Gaussian while the hidden units remain binary: $$ \begin{align} E(\mathbf{x}, \mathbf{h}) = \sum_i^M \frac{(x_i - a_i)^2}{2\sigma_i^2} - \sum_j^N b_j h_j - \sum_{i,j}^{M,N} \frac{x_i w_{ij} h_j}{\sigma_i^2}. \label{_auto4} \end{align} $$
Metropolis sampling starts by suggesting a new configuration \( \boldsymbol{x}^{k+1} \). In the brute force method this is done by some random change of the visible units. The new configuration is then accepted with the acceptance probability $$ \begin{align} A(\boldsymbol{x}^k \rightarrow \boldsymbol{x}^{k+1}) = \text{min} (1, \frac{P(\boldsymbol{x}^{k+1})}{P(\boldsymbol{x}^k)}), \label{_auto5} \end{align} $$ where we need the marginalized probability $$ \begin{align} P(\boldsymbol{x}) &= \sum_\mathbf{h} P_{rbm}(\mathbf{x}, \mathbf{h}) \label{_auto6}\\ &= \frac{1}{Z}\sum_\mathbf{h} e^{-E(\mathbf{x}, \mathbf{h})}. \label{_auto7} \end{align} $$
In this method we sample from the joint probability \( P_{rbm} (\mathbf{x}, \mathbf{h}) \) by way of a two step sampling process. We alternately update the visible and hidden units. New samples are generated according to the conditional probabilities \( P(x_i|\mathbf{h}) \) and \( P(h_j|\mathbf{x}) \) respectively and accepted with the probability of \( 1 \). While the the visible nodes are dependent on the hidden nodes and vice versa, the nodes are independent of other nodes within the same layer. This is due to there being no intra layer interactions in the restricted Boltzmann machine.
The conditional probabilities are often referred to as the activitation functions in the neural networks context due to their role in determining the node outputs. For the binary-binary RBM they are $$ \begin{align} P(h_j = 1 | \boldsymbol{x}) &= \frac{1}{1 + e^{-b_j - \sum_i x_i w_{ij}}} \label{_auto8}\\ P(x_i = 1 | \boldsymbol{h}) &= \frac{1}{1 + e^{-a_j - \sum_j h_j w_{ij}}}, \label{_auto9} \end{align} $$ where we recognize the logistic sigmoid function \( \sigma (x) = 1/(1+exp(-x)) \).
When working with a training dataset, the most common training approach is maximizing the log-likelihood of the training data. The log likelihood characterizes the log-probability of generating the observed data using our generative model. Using this method our cost function is chosen as the negative log-likelihood. The learning then consists of trying to find parameters that maximize the probability of the dataset, and is known as Maximum Likelihood Estimation (MLE). Denoting the parameters as \( \boldsymbol{\theta} = a_1,...,a_M,b_1,...,b_N,w_{11},...,w_{MN} \), the log-likelihood is given by $$ \begin{align} \mathcal{L}(\{ \theta_i \}) &= \langle \text{log} P_\theta(\boldsymbol{x}) \rangle_{data} \label{_auto12}\\ &= - \langle E(\boldsymbol{x}; \{ \theta_i\}) \rangle_{data} - \text{log} Z(\{ \theta_i\}), \label{_auto13} \end{align} $$ where we used that the normalization constant does not depend on the data, \( \langle \text{log} Z(\{ \theta_i\}) \rangle = \text{log} Z(\{ \theta_i\}) \) Our cost function is the negative log-likelihood, \( \mathcal{C}(\{ \theta_i \}) = - \mathcal{L}(\{ \theta_i \}) \)
We now need the gradient of the cost function in order to minimize it. We find that $$ \begin{align} \frac{\partial \mathcal{C}(\{ \theta_i\})}{\partial \theta_i} &= \langle \frac{\partial E(\boldsymbol{x}; \theta_i)}{\partial \theta_i} \rangle_{data} + \frac{\partial \text{log} Z(\{ \theta_i\})}{\partial \theta_i} \label{_auto15}\\ &= \langle O_i(\boldsymbol{x}) \rangle_{data} - \langle O_i(\boldsymbol{x}) \rangle_{model}, \label{_auto16} \end{align} $$ where in order to simplify notation we defined the "operator" $$ \begin{align} O_i(\boldsymbol{x}) = \frac{\partial E(\boldsymbol{x}; \theta_i)}{\partial \theta_i}, \label{_auto17} \end{align} $$ and used the statistical mechanics relationship between expectation values and the log-partition function: $$ \begin{align} \langle O_i(\boldsymbol{x}) \rangle_{model} = \text{Tr} P_\theta(\boldsymbol{x})O_i(\boldsymbol{x}) = - \frac{\partial \text{log} Z(\{ \theta_i\})}{\partial \theta_i}. \label{_auto18} \end{align} $$
The data-dependent term in the gradient is known as the positive phase of the gradient, while the model-dependent term is known as the negative phase of the gradient. The aim of the training is to lower the energy of configurations that are near observed data points (increasing their probability), and raising the energy of configurations that are far from observed data points (decreasing their probability).
The gradient of the negative log-likelihood cost function of a Binary-Binary RBM is then $$ \begin{align} \frac{\partial \mathcal{C} (w_{ij}, a_i, b_j)}{\partial w_{ij}} =& \langle x_i h_j \rangle_{data} - \langle x_i h_j \rangle_{model} \label{_auto19}\\ \frac{\partial \mathcal{C} (w_{ij}, a_i, b_j)}{\partial a_{ij}} =& \langle x_i \rangle_{data} - \langle x_i \rangle_{model} \label{_auto20}\\ \frac{\partial \mathcal{C} (w_{ij}, a_i, b_j)}{\partial b_{ij}} =& \langle h_i \rangle_{data} - \langle h_i \rangle_{model}. \label{_auto21}\\ \label{_auto22} \end{align} $$ To get the expecation values with respect to the data, we set the visible units to each of the observed samples in the training data, then update the hidden units according to the conditional probability found before. We then average over all samples in the training data to calculate expectation values with respect to the data.
To get the expectation values with respect to the model, we use Gibbs sampling. We can either initialize the \( \boldsymbol{x} \) randomly or with a training sample. While we ideally want a large number of Gibbs iterations \( n\rightarrow n \), one might decide to truncate it earlier for efficiency. Doing this while having intialized \( \boldsymbol{x} \) with a training data vector is referred to as contrastive divergence (CD), because one is then closer to approximating the gradient of this function than the negative log-likelihood. The contrastive divergence function is the difference between two Kullback-Leibler divergences (also called relative entropy), which measure how one probability distribution diverges from a second, expected probability distribution (in this case the estimated one from the ground truth one).
The idea of applying RBMs to quantum many body problems was presented by G. Carleo and M. Troyer, working with ETH Zurich and Microsoft Research.
Some of their motivation included
Carleo and Troyer applied the RBM to the quantum mechanical spin lattice systems of the Ising model and Heisenberg model, with encouraging results. Our goal is to test the method on systems of moving particles. For the spin lattice systems it was natural to use a binary-binary RBM, with the nodes taking values of 1 and -1. For moving particles, on the other hand, we want the visible nodes to be continuous, representing position coordinates. Thus, we start by choosing a Gaussian-binary RBM, where the visible nodes are continuous and hidden nodes take on values of 0 or 1. If eventually we would like the hidden nodes to be continuous as well the rectified linear units seem like the most relevant choice.
You can find the codes for the simple two-electron case at the Github repository https://github.com/mhjensenseminars/MachineLearningTalk/tree/master/doc/Programs/MLcpp/src. Python codes to come, only c++ as of now.
The trial wave function is based on the product of a Slater determinant with Gaussian orbitals, a simple Jastrow factor \( \exp{(r_{ij})} \) and the reduced Boltzmann machines.
The Broyden-Fletcher-Goldfarb-Shanno algorithm was used to perform the minimization. We used \( 14 \) hidden nodes in the calculations below.


If you're interested in helping out, simply let us know what you’re interested in working on and a lead will reach out to you shortly! You can fill out the form below (or go to Github if you're interested in contributing to the codebase as a developer).


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, as in the above example.
box-3
box-3

Apply the box-3
class, as in the above example.
box-[side]-3
box-top-3
box-bottom-3
box-left-3
box-right-3

Apply the box-[side]-3 class such as box-right-3
, as in the above example.
box-4
box-4

Apply the box-4
class, as in the above example.
box-[side]-4
box-top-4
box-bottom-4
box-left-4
box-right-4

Apply the box-[side]-4 class such as box-right-4
, as in the above example.
box-5
box-5

Apply the box-5
class, as in the above example.
box-[side]-5
box-top-5
box-bottom-5
box-left-5
box-right-5

Apply the box-[side]-5 class such as box-right-5
, as in the above example.
box-6
box-6

Apply the box-6
class, as in the above example.
box-[side]-6
box-top-6
box-bottom-6
box-left-6
box-right-6

Apply the box-[side]-6 class such as box-right-6
, as in the above example.
box-7
box-7

Apply the box-7
class, as in the above example.
box-[side]-7
box-top-7
box-bottom-7
box-left-7
box-right-7

Apply the box-[side]-7 class such as box-right-7
, as in the above example.
box-8
box-8

Apply the box-8
class, as in the above example.
box-[side]-8
box-top-8
box-bottom-8
box-left-8
box-right-8

Apply the box-[side]-8 class such as box-right-8
, as in the above example.
box-9
box-9

Apply the box-9
class, as in the above example.
box-[side]-9
box-top-9
box-bottom-9
box-left-9
box-right-9

Apply the box-[side]-9 class such as box-right-9
, as in the above example.
box-10
box-10

Apply the box-10
class, as in the above example.
box-[side]-10
box-top-10
box-bottom-10
box-left-10
box-right-10

Apply the box-[side]-10 class such as box-right-10
, as in the above example.
box-11
box-11

Apply the box-11
class, as in the above example.
box-[side]-11
box-top-11
box-bottom-11
box-left-11
box-right-11

Apply the box-[side]-11 class such as box-right-11
, as in the above example.
box-12
box-12

Apply the box-12
class, as in the above example.
box-[side]-12
box-top-12
box-bottom-12
box-left-12
box-right-12

Apply the box-[side]-12 class such as box-right-12
, as in the above example.
box-13
box-13

Apply the box-13
class, as in the above example.
box-[side]-13
box-top-13
box-bottom-13
box-left-13
box-right-13

Apply the box-[side]-13 class such as box-right-13
, as in the above example.
box-14
box-14

Apply the box-14
class, as in the above example.
box-[side]-14
box-top-14
box-bottom-14
box-left-14
box-right-14

Apply the box-[side]-14 class such as box-right-14
, as in the above example.
box-15
box-15

Apply the box-15
class, as in the above example.
box-[side]-15
box-top-15
box-bottom-15
box-left-15
box-right-15

Apply the box-[side]-15 class such as box-right-15
, as in the above example.


All Classes
-
All Classes Class Summary Enum Summary Exception Summary Annotation Types Summary Class Description AbstractStyle Class represents an abstract style componentAddress C class representing a cell address (no getters and setters to simplify handling)AppendAnnotation Annotation designated to control the copying of style propertiesBasicFormulas Class for handling of basic Excel formulasBasicStyles Factory class with the most important predefined stylesBorder Class representing a Border entry.Border.StyleValue Enum for the border styleCell Class representing a cell of a worksheetCell.AddressType Enum for the referencing style of the addressCell.CellType Enum defines the basic data types of a cellCellXf Class representing an XF entry.CellXf.HorizontalAlignValue Enum for the horizontal alignment of a cellCellXf.TextBreakValue Enum for text break optionsCellXf.TextDirectionValue Enum for the general text alignment directionCellXf.VerticalAlignValue Enum for the vertical alignment of a cellColumn Class representing a column of a worksheetFill Class representing a Fill (background) entry.Fill.FillType Enum for the type of the fillFill.PatternValue Enum for the type of the fill patternFont Class representing a Font entry.Font.SchemeValue Enum for the font schemeFont.VerticalAlignValue Enum for the vertical alignment of the text from base lineFormatException Class for exceptions regarding format error incidentsHelper Class for shared used (static) methodsIOException Class for exceptions regarding stream or save error incidentsLowLevel Class for low level handling (XML, formatting, preparing of packing)
This class is only for internal use.Metadata Class representing the meta data of a workbookNumberFormat Class representing a NumberFormat entry.NumberFormat.FormatNumber Enum for predefined number formatsPacker Class representing packer to compile XLSX filesPacker.Relationship Nested class representing a relationship (MSXML)Performance Class for performance testsPicoXLSX4j Demo Program for PicoXLSX4jRange Class representing a cell range (no getters and setters to simplify handling)RangeException Class for exceptions regarding range incidents (e.g out-of-range)Shortener Class to provide access to the current worksheet with a shortened syntax
Note: The WS object can be null if the workbook was created without a worksheet.Style Class representing a style which consists of several componentsStyleException Class for exceptions regarding StylesStyleManager Class representing a style manager to maintain all styles and its components of a workbookVersion Final class to provide meta data for the libraryWorkbook Class representing a workbookWorksheet Class representing a worksheet of a workbookWorksheet.CellDirection Enum to define the direction when using AddNextCell methodWorksheet.SheetProtectionValue Enum to define the possible protection types when protecting a worksheetWorksheetException Class for exceptions regarding worksheets


Whats new?
2.0H!
- Choose the save location while exporting your saved creations. Now easily sync them to your Dropbox or any cloud storage.
- All modals inside the app now have a close button.
- Checkbox that showed on clicking a boolean value is now removed. Thanks Gaurav Nanda.
- Bugfix - Screenshots on retina device are now correct. Thanks Ashish Bardhan.
- Bugfix - Double console log in detached mode fixed.
- Bugfix - Console.clear now works in detached mode too.
- Bugfix - DOCTYPE added in preview.
- Bugfix - Typo correction in README. Thanks Adil Mahmood.
- gstatic.com is available to load external JavaScripts from.
- Popular libraries list updated. Thanks diomed.
- Added contribution guidelines in the Github repository.
- Suggest features or report bugs.
Share it Support the developer


Could not open Easel
Failed to open this course in Easel. Please check:
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opening an issue


The app to find your perfect jeans.
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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx


Data Table
angular component
Render 10,000+ Records with high performance. The Grid component allows...
Render 10,000+ Records with high performance. The Grid component allows...
Angular Cuppa DateTimepicker is a cool responsive DateTimepicker directive for...
An angular 2 slide navigation menu component for mobile and...


MUSIC PLAYLIST TEMPLATE
Add songs to create a playlist:
Steps:
Enter a song or artist directly into the box
Click add to add another song to the playlist
No limits - just keeping adding.
Add song here!
ADD
Clear Songs
ABOUT
INSPO
RUTINA SQUARED


bigwig_analyser¶
- This script calculates coverage statistics, using a bigwig as input. It has different execution modes.
- –panel: This mode will calculate the coverage metrics for one panel.
- –gene-list: This mode will calculate the coverage metrics for a list of genes.
- none of above: This version will calculate the coverage metrics for all genes.
It will output statistics at exon, transcript, gene (by creating a union transcript), chromosome, analysis coding region (this is panel, gene list or whole coding region) and whole genome. The output format is JSON.
How to use it from commandline¶
This script is executed in the following way:
bigwig_analyser --bw <bigwig.bw> --output <output.json> --config <configuration.config> --wg-regions <non_n_region.bed> --disable-exon-stats
Note
The configuration file contain the basic configuration to annotate, get panels information, filter transcripts and calculate stats. This file will be found in /genomes/resources/genomeref/....
How to use it from python¶
#Create a dictionary with the configuration
config = {
"bw" : '/path/to/bigwig.bw',
"panel" : None,
"panel_version": None,
"gene_list": None,
"coverage_threshold": 15,
'configuration_file': '-',
"cellbase_species": 'hsapiens',
"cellbase_version": 'latest',
"cellbase_assembly": 'GRCh37/GRCh38',
"cellbase_host": '<IP_ADDRESS>:8080/cellbase-4.5.0-rc',
"panelapp_host": 'bioinfo.extge.co.uk/crowdsourcing/WebServices',
"panelapp_gene_confidence": 'HighEvidence',
"transcript_filtering_flags": 'basic',
"transcript_filtering_biotypes": 'IG_C_gene,IG_D_gene,IG_J_gene,IG_V_gene,IG_V_gene,protein_coding,nonsense_mediated_decay,non_stop_decay,TR_C_gene,TR_D_gene,TR_J_gene,TR_V_gene',
"exon_padding": 15,
"wg_stats_enabled": True,
"wg_regions": '/path/to/non_n_regions.bed',
"exon_stats_enabled": False,
"coding_region_stats_enabled": True
}
gel_coverage_engine = GelCoverageRunner(config)
(results, bed) = gel_coverage_engine.run()
# Prints output to stdout
with codecs.open(args.output, 'w', 'utf8') as output_file:
output_file.write(
ujson.dumps(
results,
ensure_ascii=False
)
)
# Saves the analysed region as a BED file
bed.saveas(args.output + ".bed")
Note
Please note that this process is highly dependent on the reference genome, use a different assembly o version assembly will produce wrong results.
Note
When running an analysis over all genes the resulting JSON will be around 1.5GB, unless you add the flag –disable-exon-stats, but in this case you will be missing the exon level statistics and the coverage gaps.
Note
When running an analysis in panel or gene list mode it might be useful to disable the whole genome statistics to improve performance, by using the flag –disable-wg-stats.
Note
Beware that the reference genome and chromosome notation (i.e.: chr prefix or not) should be the same in the input bigwig file and the bed file in wg-regions.
Different configurations¶
The program iterates through the bigwig file twice: the first for the analysis of the coding region (panel, gene list or full) and the second for the analysis of the whole genome.
To run statistics only for a panel from exon level up to panel level, provide a panel (panel) and panel version (panel_version) and disable the whole genome statistics (“wg_stats_enabled”: False), while making sure that the coding region and the exon level statistics are enabled (“coding_region_stats_enabled”: True and “exon_stats_enabled”: True). Execution time is of some seconds or under a second for small panels. The panel of intellectual disability v1.23 having 1232 genes took 46s.
To run statistics only for a gene list from exon level up to gene list level, provide a gene list (gene_list) instead of panel and panel version and use the same configuration as above. Execution time similar as the previous
To run statistics only for all genes in the coding region do not provide panel (panel) or gene list (gene_list), disable the whole genome statistics (“wg_stats_enabled”: False) and the exon level statistics (“exon_stats_enabled”: False) (the output JSON will be over 1 GB if exon stats are enabled for all genes), while making sure that the coding region is enabled (“coding_region_stats_enabled”: True). Execution time is over 3 hours
To run only whole genome statistics enable “wg_stats_enabled”: True and disable the coding region statistics (“coding_region_stats_enabled”: False). The whole genome analysis might be used in combination with a bed file defining the region to analyse (e.g.: non N regions) that is to be passed in parameter “wg_regions”: ‘/path/to/non_n_regions.bed’. This wg_regions can be used to calculate coverage over very specific regions, for instance Cosmic variants if they are set in a BED file. Execution time is around 1 hour
Any combination, of the previous should generate a single JSON with all the information.


Experience is what makes us.
Ambiance helps you to curate your experiences based on your preferences.
First, create an account. Then, simply use the navigation icon to go to the Search Map page. Type in a place that you love or would like to visit. Ambiance finds it on the map. Next, it walks you through saving and adding personalized tags. Tags help you filter your options on your dashboard. Next time you are trying to figure out where you want to eat or drink just head to your dashboard and use the tag buttons to help you narrow down your options.
If you want to find new spots around St. Louis, use the navigation icon to check out the Latest News. You might want to add a tag called something like "New Places" to remind you of options you haven't checked out yet.


This guide assumes you have the following basic background:
- A general understanding of the Internet and the World Wide Web (WWW).
- Good working knowledge of HyperText Markup Language (HTML).
- Some programming experience. If you are new to programming, try one of the tutorials linked on the main page about JavaScript.
JavaScript is a cross-platform, object-oriented scripting language used to make webpages interactive (e.g. having complex animations, clickable buttons, popup menus, etc.). There are also more advanced server side versions of JavaScript such as Node.Js which allow you to add more functionality to a website than simply downloading files (such as realtime collaboration between multiple computers). Inside a host environment (for example, a web browser), JavaScript can be connected to the objects of its environment to provide programmatic control over them.
JavaScript contains a standard library of objects, such as Array, Date, and Math, and a core set of language elements such as operators, control structures, and statements. Core JavaScript can be extended for a variety of purposes by supplementing it with additional objects; for example:
- Client-side JavaScript extends the core language by supplying objects to control a browser and its Document Object Model (DOM). For example, client-side extensions allow an application to place elements on an HTML form and respond to user events such as mouse clicks, form input, and page navigation.
- Server-side JavaScript extends the core language by supplying objects relevant to running JavaScript on a server. For example, server-side extensions allow an application to communicate with a database, provide continuity of information from one invocation to another of the application, or perform file manipulations on a server.
JavaScript and Java are similar in some ways but fundamentally different in some others. The JavaScript language resembles Java but does not have Java's static typing and strong type checking. JavaScript follows most Java expression syntax, naming conventions and basic control-flow constructs which was the reason why it was renamed from LiveScript to JavaScript.
In contrast to Java's compile-time system of classes built by declarations, JavaScript supports a runtime system based on a small number of data types representing numeric, Boolean, and string values. JavaScript has a prototype-based object model instead of the more common class-based object model. The prototype-based model provides dynamic inheritance; that is, what is inherited can vary for individual objects. JavaScript also supports functions without any special declarative requirements. Functions can be properties of objects, executing as loosely typed methods.
JavaScript is a very free-form language compared to Java. You do not have to declare all variables, classes, and methods. You do not have to be concerned with whether methods are public, private, or protected, and you do not have to implement interfaces. Variables, parameters, and function return types are not explicitly typed.
Java is a class-based programming language designed for fast execution and type safety. Type safety means, for instance, that you can't cast a Java integer into an object reference or access private memory by corrupting Java bytecodes. Java's class-based model means that programs consist exclusively of classes and their methods. Java's class inheritance and strong typing generally require tightly coupled object hierarchies. These requirements make Java programming more complex than JavaScript programming.
In contrast, JavaScript descends in spirit from a line of smaller, dynamically typed languages such as HyperTalk and dBASE. These scripting languages offer programming tools to a much wider audience because of their easier syntax, specialized built-in functionality, and minimal requirements for object creation.
To get started with writing JavaScript, open the Scratchpad and write your first "Hello world" JavaScript code:
function greetMe(yourName) {
alert('Hello ' + yourName);
}
greetMe('World');
You use variables as symbolic names for values in your application. The names of variables, called identifiers, conform to certain rules.
A JavaScript identifier must start with a letter, underscore (_), or dollar sign ($); subsequent characters can also be digits (0-9). Because JavaScript is case sensitive, letters include the characters "A" through "Z" (uppercase) and the characters "a" through "z" (lowercase).
Some examples of legal names are Number_hits, temp99, $credit, and _name
You can declare a variable in three ways:
- With the keyword
var
. For example,var x = 42
. This syntax can be used to declare both local and global variables. - By simply assigning it a value. For example,
x = 42
. If this form is used outside of a function, it declares a global variable. It generates a strict JavaScript warning. You shouldn't use this variant. - With the keyword
let
. For example,let y = 13
. This syntax can be used to declare a block-scope local variable. See Variable scope below.
A variable declared using the var
or let
statement with no assigned value specified has the value of undefined
.
An attempt to access an undeclared variable results in a ReferenceError
exception being thrown:
var a;
console.log('The value of a is ' + a); // The value of a is undefined
console.log('The value of b is ' + b); // The value of b is undefined
var b;
// This one may puzzle you until you read 'Variable hoisting' below
console.log('The value of c is ' + c); // Uncaught ReferenceError: c is not defined
let x;
console.log('The value of x is ' + x); // The value of x is undefined
console.log('The value of y is ' + y); // Uncaught ReferenceError: y is not defined
let y;
When you declare a variable outside of any function, it is called a global variable, because it is available to any other code in the current document. When you declare a variable within a function, it is called a local variable, because it is available only within that function.
JavaScript before ECMAScript 2015 does not have block statement scope; rather, a variable declared within a block is local to the function (or global scope) that the block resides within. For example the following code will log 5, because the scope of x is the function (or global context) within which x is declared, not the block, which in this case is an if statement.
if (true) {
var x = 5;
}
console.log(x); // x is 5
Global variables are in fact properties of the global object. In web pages, the global object is window, so you can set and access global variables using the window.variable syntax.
Consequently, you can access global variables declared in one window or frame from another window or frame by specifying the window or frame name. For example, if a variable called phoneNumber is declared in a document, you can refer to this variable from an iframe as parent.phoneNumber.
You can create a read-only, named constant with the const
keyword. The syntax of a constant identifier is the same as for a variable identifier: it must start with a letter, underscore or dollar sign ($) and can contain alphabetic, numeric, or underscore characters.
- A constant cannot change value through assignment or be re-declared while the script is running. It must be initialized to a value.
- The scope rules for constants are the same as those for let block-scope variables. If the const keyword is omitted, the identifier is assumed to represent a variable.
- You cannot declare a constant with the same name as a function or variable in the same scope.
const PI = 3.14;
All the documentation content has been taken from MDN


{% extends "layout.html" %} {% block title %}Configure{% endblock %} {% block content %}
Using button below you can set time limit for bills. For example, if time window is 1 year, only bills that have changed in recent year will be viewed.
"Monitoring" page views bills that have at least one of the keywords. It's also possible to see monitoring for specific keyword by clicking on one. When keywords change, monitoring page also changes to view bills with new keywords
| {{kw}} |


Google about multi languages in iOS, I find some step-by-step tuts to do multi languages. That’s a good way, I did that 2 years ago. It shows me to use string files for multi languages.
This note I want to take note about another way I found in my recent projects. I use plist files to save languages. Do it now.
class knI18n {
static let shared = knI18n()
private let knCurrentLanguageKey = "knCurrentLanguageKey"
private let fileNameBase = "Lang_"
lazy var localizableDictionary: NSDictionary! = self.getLanguageFile()
func getLanguageFile() -> NSDictionary {
let language = currentLanguage ?? "en"
if let path = Bundle.main.path(forResource: fileNameBase + language, ofType: "plist") {
return NSDictionary(contentsOfFile: path)!
}
fatalError("Localizable file NOT found")
}
func localize(string: String) -> String {
guard let localizedString = localizableDictionary.value(forKeyPath: string) as? String else { return string }
return localizedString
}
func setLanguage(_ language: String) {
currentLanguage = language
localizableDictionary = getLanguageFile()
}
private var currentLanguage: String? {
get { return UserDefaults.standard.value(forKeyPath: knCurrentLanguageKey) as? String }
set { UserDefaults.standard.setValue(newValue, forKeyPath: knCurrentLanguageKey) }
}
}
extension String {
var i18n: String {
return knI18n.shared.localize(string: self)
}
}
Hope code clean enough to understand. The main idea is to load the plist file into a dictionary and get the value for key I want to change language.
@objc func handleChangeToEn() {
knI18n.shared.setLanguage("en")
refreshUI()
}
@objc func handleChangeToVi() {
knI18n.shared.setLanguage("vi")
refreshUI()
}
func refreshUI() {
languageLabel.text = "lang".i18n
helloLabel.text = "hello".i18n
vietnameseButton.setTitle("change_vi".i18n, for: .normal)
englishButton.setTitle("change_en".i18n, for: .normal)
}
First, I can easily add new text, without worries about key duplication. XCode will tell key exists, and I never to face to error on key duplication.
Second, no need to worry about ;
and the end of text. Once, I missed a ;
and faced to error, took me much time to fix. XCode was not kind enough to tell me this problem 2 years ago.
Third, very easy to add new languages, just new plist files with name start with Lang_
.
Minor note: XCode can be very slow anytime, so, I like to shorten the name to i18n
and knI18n
to easier to type without suggestion.
I can’t send these plist files to translator, difficult for them to open and do their work. These attached files can help.
GenerateText
is used to export text from language file to text, then I can easily send to translators.
GeneratePlist
is used to convert translated text file to plist. And can import directly to project.
How to use?
Put GenerateText
app in the same place with plist file, run Terminal at that folder, type ./GenerateText
. New file text is generated and ready to send to translators.
Put translated file in the same folder with GeneratePlist
app, type ./GeneratePlist
in Terminal to generate new plist file.
My app can backup your plist file before generate new one. But should backup manual will be more secure.
Hope this solution can help iOS developer work with i18n easier. Download project at https://github.com/nguyentruongky/i18n_Demo


Teenagers threatened with machete and robbed while fishing
by Vanessa SchmittFOUR teenage boys were robbed by a group, one armed with a machete, as they fished in Dawesville on Sunday.
Mandurah Detectives are investigating and said the boys were fishing on a jetty in Estuary Road, about 300m north of the Dawesville Foreshore Reserve at 3.15pm.
They were threatened by four males aged between 17 and 21 years old, one armed with a machete and another with a bottle.
They stole phones, cash and jewellery before leaving the area in an older model, dark Subaru Forester, with faded paint down the side and a plastic pod on the roof.
The boys were uninjured.
Anyone who may have witnessed the robbery, has dashcam footage of the vehicle or the area at the time of the robbery, or has any information should call Crime Stoppers on 1800 333 000 or report it online at Crime Stoppers. Reports can remain anonymous.


Hello, My Name is Jasper.
I am a highly motivated self-starter with a passion for learning new and exciting things.
I am a graduate of Bitmaker, Toronto's leading web development bootcamp.
Contact Details
- Jasper Ong
- Toronto, Ontario
- +1<PHONE_NUMBER>
- jasper.ong4@gmail.com


1. Hover to play or 2. Click this text then tab to move forwards or shift+tab to move backwards.
tab
shift+tab
You have found the hidden frame! x 100 shift tab to leave


So, in an effort to bring my parents into the 21st century, I ordered my Mom a new computer. They, unfortunately, use Windows, so a PC it was. I had previously suggested using Dell or Gateway, but it seems that both companies insist on installing massive amounts of bloatware, and neither provides the actual install disks for re-installing your machine. So, I went to a budget computer builder, had them build it, install Windows XP and ship it to me. I then completed the setup, installed patches and all that. Having never used XP, I was pleasantly surprised that it just doesn’t suck as much as Windows 2000, but still utterly baffled at the amount of reading and bizarre decision making required of the average user.
At any rate, I got the computer installed on their “network” without any problems. Their network consists of a dying Windows 2000 box, with the main printer connected to it, a wireless router and the new computer. It was rather inconvienient to move the main printer to connect directly to the XP box, so I used the magical power of networking to connect the printer. In Windows 2000, it’s amazingly simple (though not remotely as simple as on OS X). The previous computer pretty much stayed connected to it all the time, even during the myriad of reboots required to keep the computers up and running. I figured with teh awesomz0rz pwnage of XP, it would be even simpler. Right off the bat, you cannot browse to the computer. It just churns and churns and churns, presumably checking every single port of every single possible IP address and asking “Hey! You there! Yes, you, Mr. Port! Do you have any Microsoft products connected to you?”.
So, I just go to the computer directly, via the good ole \\COMPUTER_NAME
notation.  The printer is top of the list, right-click, “Connect” and viola, I’ve now accomplished (hopefully), in six steps what requires one on OS X. But, whatever.
Of course, a few days later, my Mom calls and can’t print. Windows has just dropped the connection to the printer. I instruct her to do as above, and, of course, she used forward slashes instead of backslashes (thus searching google for the other computer’s name) and, once I’d corrected her, Windows did not remember any part of the login credentials, despite being told to do so. Of course, the printer connected fine and worked. Every 4-5 days, XP just drops the printer, and my Mom has to call me up and we go through this rigamaroll again.
Now, eventually, the Windows 2000 box the printer is connected to will be put out to pasture, and the printer would have to be plugged into the XP box. So, I figure I can save myself some tech support calls if I just connect the printer to the new box and screw the networking crap that Microsoft still can’t seem to get right. The printer is about 12 feet away from the computer, so I figure a 15-foot USB cable should be plenty for connecting without having to rearrange anything in my mom’s office.

So, to the Internets. I’d heard good things about NewEgg, so I figured this would be a good way to try them out. 15 foot USB cable retails for a measly $3.99. Brief perusal of other online dealers yielded a similar price.   Seriously, I can see paying twice as much for the “instant gratification” thing (even though THAT is ridiculous in and of itself), but 10 times as much?!?!?! Wow.
So, the shipping on a $4 USB cable is about $5, so I figure while I’m buying, I’ll throw in a few other things I need. Put in a 7-port USB hub for myself, a spindle of CD-Rs for my Mom (who never buys them and therefore never backs up anything ever), and some DVD-R DLs for me (having recently exhausted the majority of my supply backing up the demo for my band), and some CD-Rs for myself as well. All said and done, I’ve got a good $150 in my shopping cart.
Now, this is ultimately stuff for my Mom, and since I live in the city (and therefore both UPS and FedEx actively hate me), I figured I’ll just have it shipped to my Mom’s house in Manassas. I create my NewEgg account, enter the shipping address and then am told that if my shipping address and billing address are different:
Contact your card issuer and have the alternate Ship-to Address added as an authorized shipping location in your account records or in the memo field. If you choose to ship to an address other than your billing address that has not been specified as an alternate Ship-to Address with your card-issuer, your order may be delayed by up to several days as we complete verification.What. The. Fuck.
 I can’t even reacll the last time I was on a website where this feature was not seamless. Yet, these assclowns want me to call my credit card company?!?!?!?!    They sure seem to be treating me as such.
For fraud prevention purposes, if your billing and shipping addresses are different, we must verify your shipping address. Please contact the bank that issued your credit card and have your shipping address listed as an alternate address in that bank’s memo field. Please make sure your credit card issuer bank’s phone number is correctly listed in your Newegg.com account information.
A quick trip to Directron and the same items are now en-route to my Mom’s house (I actually saved $10 on the USB hub, to boot). I usually use them for my online computer junk needs, but figured I’d try someone new. I won’t be making that mistake again.  I guess if it prevents $150 worth of fraud it was worth it to them.
Update: Received the following message from them regarding my email that they lost a sale:
Thank you for contacting Newegg.The weird xml crap was left as-is, with incorrect spacing. WTF is that anyway? So, basically what they are saying is that something that I said specifically dissastified me is there only to ensure my satisfaction. Go figure.
We humbly apologize if this safety precaution has inconvenienced you in anyway but please understand that our intent is only to ensure your satisfaction. <? xml:namespace prefix = o ns = “urn:schemas-microsoft-com:office:office” />


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This is based off the CRT problem.


My Rigid Body Simulator
As you may know, last year I completed AP Physics I and II. But that doesn't really mean much. The real question is, did I learn anything?
There's a couple ways to find out, but a rigid body simulator had been on my mind for a long time, and hey, why not?
The Objects
Each object is a collection of points connected by massless beams. The size of the dot is indicative of the mass of each point. Each object is assigned a velocity and angular velocity:
function createObject(points, velocity, angularVelocity, flags){
var object = {
points:points,
velocity:velocity,
angularVelocity:angularVelocity
};
setObjectMass(object);
setCenterOfMass(object);
setMomentofInertia(object, object.centerOfMass);
object.fixed = (flags !== undefined && flags.indexOf('fixed') !== -1);
objects.push(object);
return object;
}
createObject([
{x:-500,y:750, mass:9e99},
{x:900,y:750, mass:9e99},
{x:900,y:900, mass:9e99},
{x:-100,y:900, mass:9e99}
], {x: 0, y: 0}, 0, 'fixed');
This snippet creates a block with arbitrarily large mass â€“ used as a border.
Finding the center of mass and moment of inertia is easy enough if we treat an object as a collection of point masses:
\[C_{mass}=\frac{1}{mass} \sum \vec{p} \qquad I=\sum m_{\vec{p}} \,r^2=\sum m_{\vec{p}}\,||\vec{p}-C_{mass}||^2\]//center of mass = weighted average of points
function setCenterOfMass(object){
var sumX = 0, sumY = 0;
for(var p = 0; p < object.points.length; p++){
sumX += object.points[p].x * object.points[p].mass;
sumY += object.points[p].y * object.points[p].mass;
}
object.centerOfMass = {x: sumX / object.mass, y: sumY / object.mass};
}
//moment of inertia is equal to sum(mr^2). Note the axis will probably always be
//the center of mass
//axisOfRotation is a point, not an actual axis.
function setMomentofInertia(object, axisOfRotation){
object.momentOfInertia = 0;
object.axisOfRotation = axisOfRotation;
for(var p = 0; p < object.points.length; p++){
var r = distBetweenPoints(object.points[p], axisOfRotation);
object.momentOfInertia += object.points[p].mass * r*r;
}
}
From there we can simulate undisturbed motion as a combination of rotation and translation, with rotation occuring around the center of mass.
Why is the center of mass the axis of rotation? Take a look:
Cool! So now we have some rotating and moving objects:
Forces
Forces were one of the easier implementations, outside the collision ordeal, of course. Outside forces affect motion as per Newton's 2nd law: \[F=ma\] It is easy to merely say this, but in practice, in the discrete world of computers, it is a little more difficult. Even with window.requestAnimationFrame(), the time between frames is not garunteed.
Hence the use of the timeStep variable. Given an interval of time \(t\), the change in velocity can be approximated by \(\frac{d\,v}{d\,t}t\) or \(a t\). Likewise, as I neglected to mention in the previous section, \(\Delta p=\frac{d\,p}{dt}t=vt\) and \(\Delta \theta=\frac{d\,\omega}{d\,t}t=\omega t\).
var gravitationalField = {x: 0, y: 1000};
function computeForces(object, dt){
var netForce = {x:0,y:0};
netForce = add(netForce,scalar(object.mass, gravitationalField));
var netAcceleration = scalar(1/object.mass, netForce);
var deltaV = scalar(dt, netAcceleration);
object.velocity = add(object.velocity, deltaV);
}
The only example currently implemented is gravity, though electric and magnetic influence would be cool next step. It does allow arbitrary directions of gravity though:
Collisions
Collisions were a little difficult. So difficult that I am not ashamed to say that I consulted Chris Hecker's awesome article for guidance.
But there were many other challenges besides collision response. How do you identify a collision? How do you prevent objects from passing through each other? How do you get a pile of squares to behave themselves, each with the square above pushing down on it and the square below pushing up?
My process for detecting collisions went like this:
- For every pair of objects, determine if any point lies inside the other object. I used the magical algorithm found here.
- If any points intersected, find the two line segments which intersect between the two objects.
- Pick the line intersection pair farthest away from the intersecting point. This ensures that, should a point pass all the way through an object, the correct collision is chosen.
- Project the intersection point onto the other object's line segment. Use this to find the amount that the object has been displaced.
- Feed this information into Chris Hecker's formula.
The orange lines are all intersecting lines, the red point the intersecting point and the green point the collision point (assuming that the objects had not already passed through each other).
I then translated the offending object so that it was not intersecting the other polygon. Although this has the downside of creating a jerky look, it prevents objects that are smashed in a corner from passing through a wall.
In a continued effort to avoid objects passing through other objects, I tried something new. When an object has been involved in a collision in the current frame, the translation vector is projected onto a vector perpendicular to the vector from the center of mass to the collision:
//moves each object according to its linear/angular velocity
function moveObjects(timeStep){
for(var p = 0; p < objects.length; p++){
var object = objects[p];
//delta x = v * t
//experimental:
//don't translate objects in the direction of a current collision
var translate = {x: timeStep * object.velocity.x,y: timeStep * object.velocity.y};
for(var k = 0; k < object.collisions.length; k++){
var collVector = sub(object.collisions[k].collisionLoc, object.centerOfMass);
//if translate has a component in the direction of collVector
if(distBetweenPoints(collVector, translate) < magnitude(collVector)){
//project onto a vector perpendicular to collVector
translate = project(translate, rotateVec(collVector, Math.PI/2));
}
}
translateObject(object, translate.x, translate.y);
//delta theta = w * t
rotateObject(object, object.centerOfMass, timeStep * object.angularVelocity);
}
}
So there you have it. My own, homegrown rigid body simulation. Here's the completed (for now) project:
It definately has some major glitches. I think some predictive collision algorithms may be in order, but that it for another time.


Our work ethic is so sloppy that we had to slap a term like "agile" on it to convince our clients that we know what the hell someone does during sprints, scrums, and the like.
Are you ready for analytics-driven, tablet-friendly solutions to delight your customers and force lasting business partnerships? Good for you! Personally, we're just spouting garbage.
Our decentralized, virtualized consumer engagement mechanism is so convoluted and backwards that we could just plotz. Sign up today and witness utter shitheelery personified.


EXCERPT: Bone Sharps, Cowboys, and Thunderlizards: A Tale of Edward Drinker Cope, Othniel Charles Marsh, and the Gilded Age of Paleontology
In our second issue, “Illusions,” we were honored to excerpt Jim Ottaviani and Janine Johnston’s Levitation: Physics and Psychology in the Service of Deception, a comic book history about the science behind one of the greatest magic tricks ever performed on stage: the levitating woman. In our fourth issue, we featured a romping, cartographic biography by the artist Kevin Cannon about a Danish explorer named Peter Freuchen. And when we remembered that Ottaviani, Cannon, and the other half of the art studio Big Time Attic, Zander Cannon—no relation—had collaborated on a graphic history of paleontological skullduggery and childish competition in the American West, we knew we needed to feature it in “Digs.”
Its name is Bone Sharps, Cowboys, and Thunderlizards: A Tale of Edward Drinker Cope, Othniel Charles Marsh, and the Gilded Age of Paleontology, and, while highly informative, it is also just plain fun, telling the story of how two nineteenth-century field marshals of paleontology—Cope, a romantic Jeffersonian-style collector, and Marsh, a stern establishment man at the Yale Peabody Museum—deployed armies of bonehunters in the American West, each fighting over their own hoard of brontosauri skulls and T-Rex teeth. Fanning the flames of their competition were newspapers like the New York Herald and participants—sometimes caught in the crossfire—like P. T. Barnum, Wild Bill Cody, the Oglala Sioux Red Cloud, Alexander Graham Bell, and Charles R. Knight, whose art brought dinosaurs to life for the public.
Over a century later, Ottaviani and Big Time Attic brought their story to life to new readers, carefully documenting the details they smudged for the sake of narrative. In the lovely excerpt that follows, the sympathetic Knight gets a lesson in how to breathe warmth into supposedly cold-blooded creatures on paper, and Cope arrives in Wyoming, where the Geological Survey is assisting the collector to gather his dinosauric dead.
We hope this brings out the childish paleontologist in all of you.


HRS3300 Heart Rate Sensor
Overview
A sensor application that demonstrates how to poll data from the hrs3300 heart rate sensor.
It is based on the max30101 sample.
A sensor application that demonstrates how to poll data from the hrs3300 heart rate sensor.
It is based on the max30101 sample.


{% extends "themes/default.html" %} {% block textcontent %}
Get the latest news of what's happening with and around Fourstatic.
{% for blogpost in data.blog %} {% if (slug == '' or slug == blogpost.slug) %}
{{ blogpost.short }}
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What Was It
As I stood on the deck of a ferry, wind against my face, slicing
through dark waters under blue sky, I thought about things on the
borderlands of possible and impossible, like digitally-manipulated
art. You seemed to be all around me: in the giggles of children, in
the conversation of lovers, in the cries of hungry seagulls, as they
conducted aerial maneuvers for alms.
I thought about beginnings and endings, and yesterdays and nows and
tomorrows, and I could feel you there, a presence just beyond the
periphery. Everything shouted, but only I could hear your name in the
thick throats, in the sea hiss, in the engine rumble.
Later, in the ferry terminal, I read fictional constructs from another
time, from another place, and there was a certain heat in their
fictional lives that felt familiar, that felt reflective of us,
without actually being us.
I tried to explain to myself what ‘was,’ but still could not explain
what ‘is.’ It was some foreign word on the tongue without translation;
a novel not yet fully conceived; it was a song yet to be heard by
ears, and it was the sonic boom that filled the skies with your
pounding heart, as metal spires loomed into view on the horizon,
growing larger and larger with your bright welcomes, until the sight
was more than any one man could take.


Rohan Maharjan
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"L.A. Confidential" is the densest and most complex crime film since "Chinatown." It is richly satisfying, brutally compelling and slickly acted and directed, and one of the best films of the year.
Based on James Ellroy's novel, "L.A. Confidential" is set during the early 1950's when L.A. was the mythical "city of angels" where nuclear families existed, orange groves filled the countryside, and movie stars and celebrities were in the spotlight. It was also a time when police corruption was rampant and racism was beginning to boil over everywhere. Guy Pearce stars as a young cop, Exley, who is eager to become a detective in the very corrupt police system because he believes that a cop should have strong morals and values and follow rules by-the-book - he doesn't believe in beating a confession out of a suspect. Russell Crowe stars as the tough detective White who is just the opposite - he beats confessions out of suspects, he beats up abusive men involved in drunken domestic violence cases, etc. The unlikely pair become embroiled in a murder case at an all-night diner where a cop was killed - a group of inner-city blacks may have been responsible. It turns out they may have been set up by someone in the police force, but who? The brutal Irish police captain (James Cromwell) is unsure of Exley's capacity as a detective especially since he's ratted out all the cops who beat up a few illegal Mexican immigrants. Exley is hated by everyone in the force but does he have the guts to deal with everyday brutality like White has?
Then there's Det. Jack Vincennes (Kevin Spacey), a charming, impish cop who takes extra cash on the side whenever he provides tip-offs or juicy details within the department to the unctuous Sid Huggens (Danny DeVito), a reporter who writes for "Hush, Hush," a scandal magazine that offers gossip and crimes sold to the public for thrills, like today's tabloid shows. Vincennes also moonlights as a technical adviser for a TV show called "Badge of Honor" (a nod to "Dragnet"). Meanwhile as the murder case unfolds, White discovers a connection to a sultry call girl, Lynn Bracken (the better-than-ever Kim Basinger) who works for a prosperous pimp (David Strathairn) - he provides a very kinky service where women are "cut" to resemble movie stars such as Veronica Lake. White interrogates Lynn and falls in love with her, but is Exley interested in her too?
"L.A. Confidential" is an amazing movie full of plot twists, dozens of subplots, and numerous vignettes. Director Curtis Hanson (who co-wrote the script with Brian Helgeland) provides a dazzling ride making us feel as if we have been transported back into the 1950's as he unravels the web of corruption that leads from the police department to politicians, criminals, pimps, mobsters and the traditional femme fatale. Still, this movie is not quite film noir because we have two traditional heroes, the handsome Exley and the rugged White, who overcome all odds and, to no one's surprise, find the killers (I just won't tell you who the culprit is). The film is not quite on the level of "Chinatown" but it is a more vivid experience than the fatuous "Cop Land."
"L.A. Confidential" has one of the best ensemble casts of the year: we have the witty, reliable Danny DeVito and Kevin Spacey; the effervescent cool of Guy Pearce as Exley who undergoes a transformation in the movie that is thrilling to watch, and the contemplative, explosive nature of White as played by the magnetic Russell Crowe (both are Australian actors). Kim Basinger is at her best here since "Batman" with an alluring and sensitive side that brings enormous pathos to her character.
"L.A. Confidential" is a fast-paced, cynical, dirty, violent, mean and thoroughly entertaining tapestry of a different period where innocence was more prevalent but underneath, corruption and evil still roamed the streets.
For more reviews, check out JERRY AT THE MOVIES at http://buffs.moviething.com/buffs/faust/
E-mail me with comments, suggestions or complaints at<EMAIL_ADDRESS>or<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
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Related links: index of all rec.arts.movies.reviews reviews


We exchange infrastructure!
- exploit transparent relationships
- facilitate killer niches
- disintermediate revolutionary schemas
- unleash magnetic relationships
- benchmark rich channels
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Rajesh Debnath
Hi, I'm Rajesh Debnath.
I am currently working as Software Engineer at Samsung Research & Development Institute Bangladesh(SRBD). Prior to this, I completed my B.Sc.(Engg) in Computer Science and Engineering from Bangladesh University of Engineering and Technology(BUET). I completed my undergraduate theses on Blockchain under the supervisation of Dr. Abdullah Adnan. Welcome to my website.


How to approach a Machine Learning problem ??
This page discusses a generic step-wise approach on attempting to solve a problem at hand using Machine Learning. However in real practice, not all steps would be necessarily taken depending on the problem statement, time constraints, computation power/tools available, the data and the final goal. But these steps would provide a good mind-map to a beginner or intermediate ML practitioner so as to understand how all the ML concepts fit together in the puzzle, when each concept needs to be applied and to not forget or skip any necessary element along the way.
I would first list out the approach taken by a newbie in ML (which is not wrong or incorrect given the early stage) and then discuss what changes and additions need to be made in his approach as he transitions ahead in his ML journey.
So lets get started...
How would a beginner face an ML problem?
- Fetch the dataset from an open source (mostly a well defined excel/csv sheet)
- Take a quick look at the dataset and perform basic cleaning and arranging of data
- Assigning the feature and target variable according to the problem statement
- Try out one or two ML algorithms by looking at the output variable datatype and build their models
- Compare model accuracy on test data
- Pick out the model which gives best accuracy
- FINISH
If only it would have been that easy! Although happy today, sooner or later the beginner would realize the above listed steps dont just go well with one another in sequence. The data he gets is messy, the problem statement doesn't seem to be clear when taking a look at the data, there would be many ML algorithms that would seem suitable for use, the accuracy score doesn't tally when the model is put to practice or the created model does not give results upon which the next steps can be taken upon.
Thats when the current known mini-approach needs to undergo some mature changes
Generic and recommended approach to deal with an ML problem:
-
Understand thoroughly the Problem Statement - Do you really need ML?
This may sound vague, but many simple business problem statements that can be well solved using traditional methods or basic data reporting are fed into computationally expensive ML models, wasting time and resources. Being an ML Engineer is of no real use when you can't figure out if the business problems really require Machine Learning Algorithms. From a business approach, cost efficiency and flexibility of the proposed solution should be the sole motive when building solutions. This requires a very deep and clear-cut understanding of the business problem statement. Here aresome points that need to be thought of:
- What is the main goal/motive that needs to get fulfilled?
- Is the problem statement straightforward or ambiguous?
- Can the problem be solved by traditional methods, basic data analytics/reporting or simply by pure expertise consultation?
- Is the same problem occuring again and again?
- What are the next steps that need to be taken after acquiring the problem solution?
-
What is the impact of the problem statement and to who will it prove beneficial?
After getting the above answers, you will surely know if Machine Learning is the right boat to hop into for your problem statement.
-
Gather the RIGHT data
After deeply understanding the purpose, depth and scope of the problem, the very next step is to fetch or look out for the right data that will suit your business needs and is relevant enough to gain insights. The domain experts should be consulted, available resources should be listed and discussed upon and if additional data is needed should be again searched. The most important aspect to keep in mind is that RIGHT data should be collected. Here are some of the points when looking out for the RIGHT data:
- Is the data relevant to my business use case?
- Does the data cover the important dimensions and factors that will impact my solution output?
- Is the data not too complicated to understand and not very costly?
- Is the data accurate? Does it has any hidden assumptions or constraints?
- Does the data cover all target category instances or only a subset of them? (eg. for understanding the health habits of the entire US population, only gathering data related to the US youth would not be sufficient)
- Is the data in a sufficient quantity?
-
Does the data comply with user privacy and/or business security agreements?
This process is expected to take ample time since the correct data should be collected. However for building a rough/prototype solution, don't spend ages for the process since the process can also be re-iterated upon once the solution version is accepted for further builds and improvements. We then move on to the next step.
-
Data Exploration
This step indicates a quick data exploration activity that you do to get familiar with the dataset. This step is NOT related to Exploratory Data Analysis where we try to validate some assumptions we must have made to gain deep insights for maing decisions. This is just a basic glimpse that to take into your data window and figure out what treatments are to be made later.
- Plot few basic charts like bar chart , box-plot and histogram
- See some descriptive statistics of the data
- Get a gist of what to do next keeping your problem statement in mind
-
Select an ML Algorithm
Now that you have gained the knowledge of your dataset's basic characteristics, its time to list down a few ML Algorithms that may help in solving the problem. This depends highly on the target variable, type of learning and features available.
There are many more models to choose from:
-
CLEAN and PREPARE and REDO!!
About 75% to 80% of time of a Data Scientist/ML Engineer goes into cleaning the real world messy data and preparing it so that it can be consumed by the models. Different ML algorithms need different preparation and cleaning of data. \
Here is a basic list to get started with: (however not all may be required depending on your use case)
-
Run and Evaluate your model
Very straightforward - create,run and test your model on the testing data. Keep in mind that there are tons of evaluation criteria to compare which model is the best of all apart from accuracy metrics. Select your model by choosing your eveluation metrics wisely. Here area few basic metrics to explore:
-
Tweak the chosen model
After you finally select a good model for your problem statement, tweak its hyperparameters to polish it for the real world data. Check if its output matches your business problem expectations.
-
Consume the results
This part is often neglected by most of the ML Engineers, how to effectively present/deploy the model for the client. Some aspects include:
- Present your findings in a documented format
- Deploy the model to run at scale
- Storytelling your ML Process to your business stakeholders
-
FINISH, Yet Starting
With this we may have finished with the Problem Solving Approach, but ML engineers have a lot to yet in real world. This includes real time deployment or CI/CD of the model, making it scalable, health checking of the data and the model and much more. While we dont cover those advanced concepts, it is recommended that you learn about it at your later stages.
Here is an awsome reference for everyone who wants to go in depth or learn each of the steps mentioned above from
scratch.
https://machinelearningmastery.com/start-here/


Matt Gemmell on becoming an “ex-developer”:
I haven’t launched Xcode since last December. Every time I’m out socially with software developers (which is often; I’ve made many good friends in that line of work, and I have no desire to lose them), at least one person asks me if I miss the job. My mind still works in a systemic, algorithmic way – and it probably always will…But it never gets as far as launching an IDE…I go up to a certain point, then I disengage – and it doesn’t even take any willpower.
Truth be told, I’m somewhat relieved to read this position; it’s one I’ve felt recently, too. I spent years as a hardcore developer (primarily on the web). I fixated on new frameworks, worked on huge projects, and poured myself into the code.
I’m not there anymore, either.
Matt goes on:
I’m a tinkerer now. I don’t launch Xcode, I don’t keep a record of the app ideas I have, and I don’t write code – except that, of course, I still sort of do. I automate tasks on my machines, often via shell scripts. I use regular expressions all the time. I write the occasional piece of server-side logic for something in relation to this blog. I extend the productivity utilities I use, for my own benefit.
That sounds about right, too. My most-read posts on this blog are all related to enhancing other apps123, and after all this time, I enjoy that. I want to be disturbed that I haven’t produced a fully-fledged piece of software in years, but I’m just not. The developer world says I’m languishing on the sidelines, but in my eyes, I’m enjoying my life more.
That isn’t the only benefit, however – Matt touches on that, too:
I’m also glad that I can view the WWDC keynote as a tourist, without any approaching tension headache as I think about what I’ll need to add, or change, or remove. I can drift languidly along on the slow-moving current of the everyday web, indulging an old habit when a rainy evening comes by. It’s a profoundly relaxing thing to be able to observe the technology industry without being invested in it. I’m glad I’m not making software anymore.
Bingo. While catching WWDC and I/O this year, I kept thinking, Man, I wonder what it’s going to be like for all those developers out there who need to update their apps…
All those developers…I was one of them not long ago, but not anymore. The most freeing part of all? Saying it out loud lifts a huge weight off my chest.
The world of development has changed vastly. More and more developers enter the world every day; more and more often, apps are simply derivatives of already-existing concepts. There is plenty of new blood out there and those folks may be the ones to define a new marketplace.
For me, I’m going to be okay watching that marketplace unfold from the sidelines.


Almost all students are eligible to receive federal student loans, which are student loans funded by the U.S. federal government through programs such as Pell Grants. Federal loans are either subsidized (that is, interest payments are covered by the federal government for a period including the student's time at a college or university [9.11.174]; such loans are restricted to undergraduates only) or unsubsidized (in which case borrowers must pay the interest).
in-context


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EmployeeDelete
class EmployeeDelete extends AbstractEmployee
Delete an existing employee record
Traits
Methods
__construct(string $controlId = null)
Initializes the class with the given parameters.
from AbstractFunction
setSendAutomaticPaymentNotification(boolean $sendAutomaticPaymentNotification)
No description
from AbstractEmployee
Details
in AbstractFunction at line line 35
getControlId()
Get control ID
in AbstractFunction at line line 46
setControlId($controlId = null)
Set control ID
in AbstractFunction at line line 67
__construct(string $controlId = null)
Initializes the class with the given parameters.
at line line 35
writeXml(XMLWriter $xml)
Write the function block XML
in CustomFieldsTrait at line line 33
array
getCustomFields()
Get custom fields
in CustomFieldsTrait at line line 43
setCustomFields(array $customFields)
Set custom fields


Being a big important navel and commercial port, Brest might appear to not be very yacht-friendly, but this is not the case. Tucked in as it is into the NE corner of the Rade de Brest, it offers all the services required by yachts as well as a large area of sheltered water sailing.
Position 48° 22.5'N, 4° 28.2'W.
Report to the Harbour office, although it is quite possible Customs might be waiting on the visitors' pontoon if the Marina du Chateau is your first stop.
Even if you are an EU boat your papers might still be checked here.
Last updated January 2013.
There is now a second marina in the heart of Brest, Marina du Chateau, It is on the site of the former naval facility and is entered through the large outer harbour. The promenent white-painted spy ship, just inside the outer harbour wall, provides a useful landmark. The entrance into the marina is not easy to identify until well into the outer harbour.
The long established Marina du Moulin Blanc lies 3 miles beyond the commercial ports.
Although further from the city centre, Marina du Moulin Blanc has a more visitor friendly atmosphere and most requirements are either on site or close by. It is also closer to the airport for crew changes.
Yachts must not enter the commercial docks.
Last updated April 2016.


Live at Paste Studio - Mattiel
The Atlanta-based Mattiel Brown (who records as Mattiel) may still be a relative newcomer with just two albums to her name, but her gruff, throaty vocals suggest a singer/songwriter who’s been through some things and lived to tell the tale. On her second album Satis Factory, Mattiel displays the alluring sophistication of a classic pop crooner and the stomp of an old time rock ‘n’ roller. With bluesy, gritty eccentricities and a worldly elegance, it would sound right at home in an American truck stop bar or a Parisian cafe. She stopped by the new Paste Studio ATL in October to play three songs, including "Keep The Change" from Satis Factory