In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the cnt
column. You can see the first few rows of the data above.
Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model.
rides[:24*10].plot(x='dteday', y='cnt')
Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to get_dummies()
.
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
rides = pd.concat([rides, dummies], axis=1)
fields_to_drop = ['instant', 'dteday', 'season', 'weathersit',
'weekday', 'atemp', 'mnth', 'workingday', 'hr']
data = rides.drop(fields_to_drop, axis=1)
data.head()
To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.
The scaling factors are saved so we can go backwards when we use the network for predictions.
quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
# Store scalings in a dictionary so we can convert back later
scaled_features = {}
for each in quant_features:
mean, std = data[each].mean(), data[each].std()
scaled_features[each] = [mean, std]
data.loc[:, each] = (data[each] - mean)/std
We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders.
# Save data for approximately the last 21 days
test_data = data[-21*24:]
# Now remove the test data from the data set
data = data[:-21*24]
# Separate the data into features and targets
target_fields = ['cnt', 'casual', 'registered']
features, targets = data.drop(target_fields, axis=1), data[target_fields]
test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]
We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set).
# Hold out the last 60 days or so of the remaining data as a validation set
train_features, train_targets = features[:-60*24], targets[:-60*24]
val_features, val_targets = features[-60*24:], targets[-60*24:]
Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.
The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called forward propagation.
We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called backpropagation.
Hint: You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.
Below, you have these tasks:
self.activation_function
in __init__
to your sigmoid function.train
method.train
method, including calculating the output error.run
method.class NeuralNetwork(object):
def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
# Set number of nodes in input, hidden and output layers.
self.input_nodes = input_nodes
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
# Initialize weights
self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5,
(self.input_nodes, self.hidden_nodes))
self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5,
(self.hidden_nodes, self.output_nodes))
self.lr = learning_rate
#### TODO: Set self.activation_function to your implemented sigmoid function ####
#
# Note: in Python, you can define a function with a lambda expression,
# as shown below.
self.activation_function = lambda x : 1/ (1 + np.exp(-x)) # Replace 0 with your sigmoid calculation.
### If the lambda code above is not something you're familiar with,
# You can uncomment out the following three lines and put your
# implementation there instead.
#
#def sigmoid(x):
# return 1 / (1 + np.exp(-x)) # Replace 0 with your sigmoid calculation here
#self.activation_function = sigmoid
def forward_pass(self, features):
hidden_inputs = np.dot(features, self.weights_input_to_hidden)
hidden_outputs = self.activation_function(hidden_inputs)
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)
final_outputs = final_inputs
return hidden_outputs, final_outputs
def train(self, features, targets):
''' Train the network on batch of features and targets.
Arguments
---------
features: 2D array, each row is one data record, each column is a feature
targets: 1D array of target values
'''
n_records = features.shape[0]
delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)
delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)
for X, y in zip(features, targets):
#### Implement the forward pass here ####
### Forward pass ###
hidden_outputs, final_outputs = self.forward_pass(X)
#### Implement the backward pass here ####
### Backward pass ###
# TODO: Output error - Replace this value with your calculations.
error = y - final_outputs # Output layer error is the difference between desired target and actual output.
# TODO: Calculate the hidden layer's contribution to the error
hidden_error = np.dot(self.weights_hidden_to_output, error)
# TODO: Backpropagated error terms - Replace these values with your calculations.
output_error_term = (error) * 1.0
hidden_error_term = hidden_error * hidden_outputs * (1 - hidden_outputs)
# Weight step (input to hidden)
delta_weights_i_h += hidden_error_term * X[:,None]
# Weight step (hidden to output)
delta_weights_h_o += output_error_term * hidden_outputs[:,None]
# TODO: Update the weights - Replace these values with your calculations.
self.weights_hidden_to_output += self.lr * delta_weights_h_o / n_records # update hidden-to-output weights with gradient descent step
self.weights_input_to_hidden += self.lr * delta_weights_i_h / n_records # update input-to-hidden weights with gradient descent step
def train_iterally(self, train_features, train_targets, val_features, val_targets, iterations):
losses = {'train':[], 'validation':[]}
for ii in range(iterations):
# Go through a random batch of 128 records from the training data set
batch = np.random.choice(train_features.index, size=128)
X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']
self.train(X, y)
# Printing out the training progress
train_loss = MSE(self.run(train_features).T, train_targets['cnt'].values)
val_loss = MSE(self.run(val_features).T, val_targets['cnt'].values)
sys.stdout.write("\rProgress: {:2.1f}".format(100 * ii/float(iterations)) \
+ "% ... Training loss: " + str(train_loss)[:5] \
+ " ... Validation loss: " + str(val_loss)[:5])
sys.stdout.flush()
losses['train'].append(train_loss)
losses['validation'].append(val_loss)
return losses
def run(self, features):
''' Run a forward pass through the network with input features
Arguments
---------
features: 1D array of feature values
'''
hidden_outputs, final_outputs = self.forward_pass(features)
return final_outputs
def MSE(y, Y):
return np.mean((y-Y)**2)
Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project.
import unittest
inputs = np.array([[0.5, -0.2, 0.1]])
targets = np.array([[0.4]])
test_w_i_h = np.array([[0.1, -0.2],
[0.4, 0.5],
[-0.3, 0.2]])
test_w_h_o = np.array([[0.3],
[-0.1]])
class TestMethods(unittest.TestCase):
##########
# Unit tests for data loading
##########
def test_data_path(self):
# Test that file path to dataset has been unaltered
self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')
def test_data_loaded(self):
# Test that data frame loaded
self.assertTrue(isinstance(rides, pd.DataFrame))
##########
# Unit tests for network functionality
##########
def test_activation(self):
network = NeuralNetwork(3, 2, 1, 0.5)
# Test that the activation function is a sigmoid
self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))
def test_train(self):
# Test that weights are updated correctly on training
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
network.train(inputs, targets)
self.assertTrue(np.allclose(network.weights_hidden_to_output,
np.array([[ 0.37275328],
[-0.03172939]])))
self.assertTrue(np.allclose(network.weights_input_to_hidden,
np.array([[ 0.10562014, -0.20185996],
[0.39775194, 0.50074398],
[-0.29887597, 0.19962801]])))
def test_run(self):
# Test correctness of run method
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
self.assertTrue(np.allclose(network.run(inputs), 0.09998924))
suite = unittest.TestLoader().loadTestsFromModule(TestMethods())
unittest.TextTestRunner().run(suite)
Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.
You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.
This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, if you use too many iterations, then the model with not generalize well to other data, this is called overfitting. You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. As you start overfitting, you'll see the training loss continue to decrease while the validation loss starts to increase.
This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.
The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.
import sys
## Grid Search
### Set the hyperparameters here ###
iterations_set = [15000]
learning_rate_set = [0.25, 0.5, 0.75]
hidden_nodes_set = [5, 10, 15]
output_nodes = 1
for iterations in iterations_set:
for learning_rate in learning_rate_set:
for hidden_nodes in hidden_nodes_set:
N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
losses = network.train_iterally(train_features, train_targets, val_features, val_targets, iterations)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
plt.title("iterations:%d, lr:%.3f, hidden_nodes:%d" % (iterations, learning_rate, hidden_nodes))
_ = plt.ylim()
plt.show()
import sys
### Set the hyperparameters here ###
iterations = 7000
learning_rate = 0.25
hidden_nodes = 10
output_nodes = 1
N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
losses = {'train':[], 'validation':[]}
for ii in range(iterations):
# Go through a random batch of 128 records from the training data set
batch = np.random.choice(train_features.index, size=128)
X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']
network.train(X, y)
# Printing out the training progress
train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)
val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)
sys.stdout.write("\rProgress: {:2.1f}".format(100 * ii/float(iterations)) \
+ "% ... Training loss: " + str(train_loss)[:5] \
+ " ... Validation loss: " + str(val_loss)[:5])
sys.stdout.flush()
losses['train'].append(train_loss)
losses['validation'].append(val_loss)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()
Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly.
fig, ax = plt.subplots(figsize=(8,4))
mean, std = scaled_features['cnt']
predictions = network.run(test_features).T*std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((test_targets['cnt']*std + mean).values, label='Data')
ax.set_xlim(right=len(predictions))
ax.legend()
dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])
dates = dates.apply(lambda d: d.strftime('%b %d'))
ax.set_xticks(np.arange(len(dates))[12::24])
_ = ax.set_xticklabels(dates[12::24], rotation=45)
Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?
Note: You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter
I did grid search to find better models under some sets of parameters. Finally, I trained a network under the condition to get likely best model, even though I could not realize over-fittied model obviously with the grid searching.
iterations = 7000
learning_rate = 0.25
hidden_nodes = 10
output_nodes = 1
I think there is an issue on my trained model. The issue is that the trained model could not predict values after Dec. 21 very much. I guess there are at least two trends in the data. The first part is between Dec. 11 to Dec. 21. The other part is after Dec. 21. In general, capturing multiple trends in one data with machine learning methods is a hard problem.


5 Italian soldiers wounded by roadside explosion in Iraq
by FRANCES D'EMILIO, Associated PressROME (AP) - A roadside blast wounded five Italian military members Sunday in northern Iraq as they returned from a mission aimed at helping Iraqi troops combat the Islamic State group, Italian military officials said.
The Italian Defense Ministry said three of the wounded were in "grave condition" after the explosion.
Rear Admiral Fabio Agostini said the five -- three members of the navy and two of the army -- are part of a special forces team that was traveling back after a mission aimed at finding IS refuges.
Agostini told Italian Rai state TV that Iraqi armed forces members were also injured in the blast but didn't say how many.
An Iraqi security official said the bomb exploded next to their vehicle as they were traveling just outside Kirkuk, wounding six Italian soldiers.
The discrepancy in the number of wounded wasn't immediately explained.
Italy's wounded were evacuated by U.S. military helicopters to hospital in Baghdad.
One Italian soldier lost a leg to amputation due to injuries from the bomb and another suffered serious internal injuries, Italian Gen. Nicola Lanza de Cristoforis told state TV.
Iraq declared victory against IS militants two years ago, but the group continues to stage insurgent-type attacks across the country, particularly in northern Iraq.
Italy currently has more than 800 regular soldiers and some 80 special forces in Iraq.
___
Qassim Abdul-Zahra in Baghdad contributed.


Before Change
def test_resolve_conflicting_options() -> None:
resolve_options = partial(
resolve_conflicting_options,
old_option="my_opt",
new_option="my_opt",
old_scope="old-scope",
new_scope="new-scope",
)
old_val = "ancient"
new_val = "modern"
old_default_rv = RankedValue(Rank.HARDCODED, old_val)
new_default_rv = RankedValue(Rank.HARDCODED, new_val)
After Change
assert option_resolved() == new_val
assert option_resolved(old_configured=True) == old_val
assert option_resolved(new_configured=True) == new_val
// both configured -> raise an error
with pytest.raises(ValueError) as e:
option_resolved(old_configured=True, new_configured=True)


Help
Lipa calculates the amount each person owes when splitting a receipt.
The Main page consists of 2 tabs - 'Items' and 'People'. The first tab asks for the list of items on the receipt,
and the second tab asks for the list of people, and the item distribution.
Items tab
- Fill in the Item name, cost, quantity, and taxable status of the first item on your receipt.
- Click '+ Add Item' to add the second item, and repeat as above, until all items are listed.
- The trashcan icon is used to delete the item.
People tab
- Fill in the first person's name.
- Check the boxes for each item the person was involved in buying.
- Click '+ Add Person' to add the second person, and repeat as above, until all people are listed.
- The trashcan icon is used to delete the person.
Calculate and save
- Once both tabs are filled and valid, you can click the Calculate button to view results.
- Results can be saved as an image.
- You can also copy a link to view the results directly on Lipa, but this is not recommended as the link can
be quite long and may be cut off by other Apps, making the link useless in those cases.


Sign in - ProSolo
Sign in
Sign in with Google
Sign in with edX
#{ip.displayName}
or sign in with your email
Wrong email or password! Please try again.
#{param.error}}
#{param.success}
Remember me
Forgot Password?
About
Terms
Support
ProSolo Technologies Inc © #{applicationbean.year}


Meet an Olin alum. Find an awesome mentor. Discuss life at Olin, how to adult, what you did last week, and everything in between. Hindsight is 20/20! Borrow some perspective from someone who's been there. We match you with a mentor that you meet each month on video for the academic year.
Banter is a mentoring program that connects Oliners—who better to help out than someone who's been there?
The program provides professional and personal support to students and alumni. We encourage meetings to happen on a regular cadence and suggest once a month (video chat, phone call, in person meeting, or email conversation) though it’s up to each match to determine what works best for them.
Alumni: You have the opportunity to reconnect inside the Olin bubble and share your professional and personal experiences with students and fellow alumni.
Students: You have exclusive access to a network of Olin alumni ready to share their experiences from life on campus and beyond. Get advice on which courses are most helpful when you have no idea what field to go into, how to deal with that difficult teammate, the politics of an internship or how to cope with the challenges of learning today.
| Date (2020) | Mentee (Students) Deadlines | Mentee (Alumni 2017-2020) Deadlines | Mentor (Alumni 2006-2016) Deadlines |
|---|---|---|---|
| September 17th | Onboard survey deadline | Onboard survey deadline | Onboard survey deadline |
| September 21st | Mentor profiles public | ||
| September 28th | Mentor selection deadline | Mentor selection deadline | |
| October 1st | Match results emailed | Match results emailed | Match results emailed |
| October 7th | Deadline to schedule first meeting | Deadline to schedule first meeting | |
| ~Monthly till May 2021 | 30 minute video chat | 30 minute video chat | 30 minute video chat |
| Date (2020) | Mentee (Students) Deadliness |
|---|---|
| September 17th | Onboard survey deadline |
| September 21st | Mentor profiles public |
| September 28th | Mentor selection deadline |
| October 1st | Match results emailed |
| October 7th | Deadline to schedule first meeting |
| ~Monthly till May 2021 | 30 minute video chat |
| Date (2020) | Mentee (Alumni 2017-2020) Deadlines |
|---|---|
| September 17th | Onboard survey deadline |
| September 21st | Mentor profiles public |
| September 28th | Mentor selection deadline |
| October 1st | Match results emailed |
| October 7th | Deadline to schedule first meeting |
| ~Monthly till May 2021 | 30 minute video chat |
| Date (2020) | Mentor (Alumni 2006-2016) Deadlines |
|---|---|
| September 17th | Onboard survey deadline |
| October 1st | Match results emailed |
| ~Monthly till May 2021 | 30 minute video chat |
Nope, but you should indicate on your slide that you don’t know your post-grad plans. How many people even know what they want to do next week? We don’t require you to know the future, but any questions you may have regarding life after Olin will certainly serve to enrich the conversation!
Most pairs video chat, but it will be up to each match to establish a monthly time and communication format that will work for them. We will provide encouragement and monthly topics that can be used as conversation starters. Each pair will set the tone for their meetings, but you should be thinking "coffee with a friend" rather than "business interview."
You’ll be getting your match in October and we’ll be checking in with you until August, but we hope you’ll keep in touch after Banter officially ends for the year. Expect to set aside one hour per month to meet or communicate with your match.
Students as well as Alumni from 2017-2020 can be mentees. Alumni from years 2006-2016 can be mentors.
Step 1: fill out the survey
Step 2: we send out slides highlighting each mentor, generated from the survey
Step 3: first years read through profiles and excellence vote on who they would like to be paired with
Step 4: we optimize the pairings
Step 5: we send out introductory slides and emails to the matches
Step 6: Banter! We send out monthly topics for conversation inspiration
Each match persists for one year and will be the primary person you’re talking with.
This program is brought to you by: Tara Krishnan (‘13) and Sara Wheeland (‘07)
Have additional questions for us? Feel free to contact us!


|
Vienna Vulkan Engine
|
Calculated statistics of memory usage in entire allocator. More...
#include <vk_mem_alloc.h>
Public Attributes | |
| uint32_t | blockCount |
Number of VkDeviceMemory Vulkan memory blocks allocated. | |
| uint32_t | allocationCount |
| Number of VmaAllocation allocation objects allocated. | |
| uint32_t | unusedRangeCount |
| Number of free ranges of memory between allocations. | |
| VkDeviceSize | usedBytes |
| Total number of bytes occupied by all allocations. | |
| VkDeviceSize | unusedBytes |
| Total number of bytes occupied by unused ranges. | |
| VkDeviceSize | allocationSizeMin |
| VkDeviceSize | allocationSizeAvg |
| VkDeviceSize | allocationSizeMax |
| VkDeviceSize | unusedRangeSizeMin |
| VkDeviceSize | unusedRangeSizeAvg |
| VkDeviceSize | unusedRangeSizeMax |
Calculated statistics of memory usage in entire allocator.


This post will study the discrete-time, infinite-horizon, discounted LQR problem. The reference for this is Volume 2 of Bertsekas's Dynamic Programming and Optimal Control. I am writing these notes because I had a harder time finding derivations online for the discounted-cost version of LQR. $ \newcommand{\abs}[1]{| #1 |} \newcommand{\bigabs}[1]{\left| #1 \right|} \newcommand{\R}{\mathbb{R}} \newcommand{\E}{\mathbb{E}} \newcommand{\B}{\mathcal{B}} \newcommand{\ip}[2]{\langle #1, #2 \rangle} \newcommand{\T}{\mathsf{T}} \newcommand{\Tr}{\mathrm{Tr}} \newcommand{\ind}{\mathbf{1}} \newcommand{\norm}[1]{\lVert #1 \rVert} \newcommand{\bmattwo}[4]{\begin{bmatrix} #1 & #2 \\ #3 & #4 \end{bmatrix}} \newcommand{\bmatcol}[2]{\begin{bmatrix} #1 \\ #2 \end{bmatrix}} $
Setup
We have a discrete-time LQR system evolving as $$ x_{k+1} = A x_k + B u_k + w_k \:, \:\: x_0 = 0 \:. $$ where $\E[w_k] = 0$, $\E[w_kw_k^\T] = \sigma^2 I$, and $w_k$ is independent from $w_{k'}$ for all $k \neq k'$. Our goal is to find a control law $u$ that minimizes $$ \begin{align} J(u) = \E\left[ \sum_{k=0}^{\infty} \gamma^k (x_k^\T Q x_k + u_k^\T R u_k) \right] \:, \label{eq:discounted_problem} \end{align} $$ with $Q$ positive-semidefinite, $R$ positive-definite, and $\gamma \in (0, 1)$. This is to be contrasted with the infinite-horizon, average-cost LQR problem which seeks to minimize $$ J(u) = \limsup_{T \to \infty} \E\left[ \frac{1}{T}\sum_{k=0}^{T-1} (x_k^\T Q x_k + u_k^\T R u_k) \right] \:. $$
Derivation
Given a control policy $\pi$, let $V^\pi(x)$ denote the value-function $$ V^\pi(x) = \E\left[ \sum_{k=0}^{\infty} \gamma^k (x_k^\T Q x_k + u_k^\T R u_k) \right] \:\: \text{s.t.} \:\: u_k = \pi(x_k) \:, $$ and let $V^\ast(x)$ denote the optimal value-function $$ V^\ast(x) = \min_{\pi} V^\pi(x) \:. $$ Bellman's optimality principle states that for all $x$, $$ \begin{align} V^\ast(x) = \min_{u} \left\{ x^\T Q x + u^\T R u + \gamma \E_{x' \sim p(\cdot| x, u)}[ V^\ast(x')] \right\} \:. \label{eq:bellman} \end{align} $$ Stipulating that $V^\ast(x) = x^\T P x + q$ for a positive-semidefinite $P$ and plugging into $\eqref{eq:bellman}$, $$ \begin{align*} &x^\T P x + (1-\gamma)q \\ &\qquad= \min_u \left\{ x^\T Q x + u^\T R u + \gamma \E_{w}[ (Ax + Bu + w)^\T P (Ax + Bu + w) ] \right\} \\ &\qquad= \min_u \left\{ x^\T Q x + u^\T R u + \gamma (Ax + Bu)^\T P (Ax + Bu) + \gamma\sigma^2\Tr(P) \right\} \\ &\qquad= \min_u \left\{ \bmatcol{x}{u}^\T \left( \bmattwo{Q}{0}{0}{R} + \gamma \bmattwo{A^\T P A}{A^\T P B}{B^\T P A}{B^\T P B} \right) \bmatcol{x}{u} \right\} + \gamma \sigma^2 \Tr(P) \\ &\qquad= x^\T ( Q + \gamma A^\T P A - \gamma^2 A^\T P B (R + \gamma B^\T P B)^{-1} B^\T P A ) x + \gamma \sigma^2 \Tr(P) \:. \end{align*} $$ The $u$ which achieves the minimum is $u^\ast = -\gamma(R + \gamma B^\T P B)^{-1} B^\T P A x$. This fact and the last equality hold from partial minimization of a quadratic. Specifically, if $P_{22}$ is positive-definite, it is straightforward to show that $$ \min_{x_2} \bmatcol{x_1}{x_2}^\T \bmattwo{P_{11}}{P_{12}}{P_{12}^\T}{P_{22}} \bmatcol{x_1}{x_2} = x_1^\T (P_{11} - P_{12} P_{22}^{-1} P_{12}^\T ) x_1 = x_1^\T (P/P_{22}) x_1 \:, $$ where $x_2 = -P_{22}^{-1} P_{12}^\T x_1$ achieves the minimum and $P/P_{22}$ denotes the Schur complement. From this, we see that we can solve for $P$ and $q$ as $$ \begin{align} P &= \gamma A^\T P A - \gamma^2 A^\T P B(R + \gamma B^\T P B)^{-1} B^\T P A + Q \:, \label{eq:soln} \\ q &= \sigma^2 \frac{\gamma}{1-\gamma} \Tr(P) \nonumber \:. \end{align} $$
Consequences
The canonical form for a discrete algebraic Ricatti equation is $\mathrm{DARE}(A,B,Q,R)$, which is the solution to $$ P = A^\T P A - A^\T P B(R + B^\T P B)^{-1} B^\T P A + Q \:. $$ It is known that if $(A,B)$ is controllable and $(A,C)$ is observable (with $Q = C^\T C$), then the solution to $\mathrm{DARE}(A,B,Q,R)$ is unique and positive-definite. Furthermore, defining $K = -(R + B^\T P B)^{-1} B^\T P A$, the resulting closed-loop matrix $A+BK$ is stable.
By a simple change of variables, we see that $\eqref{eq:soln}$ is equivalent to $\mathrm{DARE}(\sqrt{\gamma} A, B, Q, R/\gamma)$. This means that as long as $(\sqrt{\gamma}A,B)$ is controllable and $(\sqrt{\gamma}A,C)$ is observable, then the closed-loop matrix for the discounted problem satisfies $\sqrt{\gamma}(A+BK)$ is stable. First, we observe that the controllability and observability requirements are equivalent to $(A,B)$ controllable and $(A,C)$ observable:
Proposition: Let $\alpha \neq 0$. Then $(A,B)$ is controllable iff $(\alpha A, B)$ is controllable. Similarly, $(A,C)$ is observable iff $(\alpha A, C)$ is observable.
Proof: This follows immediately since $$ \mathcal{R}(\begin{bmatrix} B & A B & ... & A^{n-1} B \end{bmatrix}) = \mathcal{R}(\begin{bmatrix} B & (\alpha A) B & ... & (\alpha A)^{n-1} B \end{bmatrix}) \:, $$ where $\mathcal{R}(\cdot)$ denotes the range of a matrix. A similar argument holds for the observability matrix. $\square$
On the other hand, the closed-loop guarantee is a weaker guarantee than $A+BK$ being stable. This means you can have solutions to $\eqref{eq:discounted_problem}$ for which the control law does not stabilize the system, but does yield a finite discounted cost! The simplest example of this comes from Postoyan et al. (2017), where you take $A=2$, $B=1$, and $Q=R=1$. In this case, when $\gamma \in (0, 1/3]$, the closed-loop system is not stable.


Ocean [ap04], ocean, in The Outer Sea
Last updated Turn #118
Ocean [ap04], ocean, in The Outer Sea
------------------------------------------------------------------------
19: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
19: 9% damaged, arrived from Ocean [ap05], owner:
19: Phantasm [n4b], Imperial Empire, knight, with two workers,
19: eight sailors
19: A storm batters the ship and causes some damage.
20: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
20: 10% damaged, departed for Ocean [ap03].
30: It has stopped raining.
Routes leaving Ocean:
North, to Ocean [ao04], 6 days
East, to Ocean [ap05], 6 days
South, to Ocean [aq04], 6 days
West, to Ocean [ap03], 6 days


The EXTERNCOUNT record specifies the number of externally referenced workbooks, DDE references, and OLE references contained in an Excel workbook.
For example, a worksheet contains the following formulas in cells A1:A3:
=SALES.XLS!Profits
=Signal|System!Formats
=Signal|StockInfo!MSFT
This worksheet would have a value of 3 in the cxals field of the EXTERNCOUNT record, corresponding to the three external references SALES.XLS , Signal|System , and Signal|StockInfo .
Record Data
|
Offset |
Field Name |
Size |
Contents |
|
4 |
cxals |
2 |
Number of external references |


class: center, middle, inverse, title-slide # Inferences Based on Two Samples ### Sebastian Hoyos-Torres --- # What we've covered so far: - We covered z and t-tests. - Examined different types of errors - Examined what different hypotheses we can generate. --- # Inferences based on Two Samples Suppose `\(X_1,...,X_n\)` is a random sample from a normal population with expected value `\(\mu_1\)`, and standard deviation `\(\sigma_1\)`. Let `\(Y_1,...,Y_n\)` be a random sample from the normal population with expected value `\(\mu_2\)` and standard deviation `\(\sigma_2\)` - In this case we have `\(\bar{X}\bar{Y}\)` and sample standard deviations `\(S_x\)` and `\(S_y\)`. Then: `$$Z = \frac{\bar{X} - \bar{Y}- (\mu_1-\mu_2)}{\sqrt{\frac{\sigma_1^2}{m} + \frac{\sigma_2^2}{n}}}$$` The Z statistic in this case is useful for confidence intervals of `\(\mu_1-\mu_2\)` The 100(1-a)% confidence interval is: `$$\bar{X}-\bar{Y} \pm z_{a/2}\sqrt{\frac{\sigma_1^2}{m}+\frac{\sigma_2^2}{m}}$$` --- # The functions in R - Here are the formulas translated into R as functions: the 2 sample z-statistic ```r z2stat <- function(xbar,ybar,mu1,mu2,sigma1,sigma2,m,n){ (xbar - ybar - (mu1-mu2))/((sigma1/m)+ (sigma2/n)) } ``` The confidence interval ```r z2confint <- function(xbar,ybar,zstar,sigma1,sigma2,m,n){ list(lower_bound = xbar - ybar - zstar * (sqrt((sigma1/m) + (sigma2^2/n))), upper_bound = xbar - ybar + zstar * (sqrt((sigma1/m) + (sigma2^2/n)))) } ``` --- # Inferences based on two samples continued: - If the `\(\sigma\)`'s are known, we can test the null that `\(\mu_1 - \mu_2 = \delta_0\)` against the alternatives. - `\(H_a:\mu_1 - \mu_2 \neq \delta_0\)` - `\(H_a:\mu_1 - \mu_2 \gt \delta_0\)` - `\(H_a:\mu_1 - \mu_2 \lt \delta_0\)` which we would reject via the z-statistic which we calculated earlier. --- # What if we don't have sigma? - If we don't have `\(\sigma\)`, we can use the point estimate to approximate it if our sample is large. The statistic would be calculated as follows: `$$Z = \frac{\bar{X} - \bar{Y}- (\mu_1-\mu_2)}{\sqrt{\frac{S_1^2}{m} + \frac{S_2^2}{n}}}$$` The above would also have the same confidence interval as the prior. All we need to do with our function is substitute `\(\sigma\)` with s. --- # What if the population isn't large? - Then we'd still use the prior z-statistic but it would follow a t-distribution with the degrees of freedom, v estimated from the data: `$$v = \frac{(\frac{s_x^2}{m}+\frac{S_y^2}{n})^2}{\frac{(\frac{s_x^2}{m})^2}{m-1}+\frac{(\frac{s_y^2}{n})^2}{n-1}}$$` - In R, we can just define a function to do the computing for us and just be careful with the parentheses: ```r v_dft_est <- function(s1,s2,m,n){ ((s1^2/m) + (s2^2/n))^2/(((s1^2/m)^2/(m-1)) + ((s2^2/n)^2/(n-1))) } ``` - V indicates Welch's estimate - The confidence interval of the two sample t: `\(100(1-a)%\)` confidence interval for `\(\mu_1 - \mu_2\)` is given by: `$$\bar{X} - \bar{Y} \pm t_{a/2,v}\sqrt{\frac{s_x^2}{m} + \frac{s_y^2}{n}}$$` - using the above value of v. --- # What if we have no sigma? - If we have no `\(\sigma\)`, we can use the t-statistic which is computed as folows: `$$t = \frac{\bar{x} - \bar{y} - \delta_0}{\sqrt{\frac{s_x^2}{m} + \frac{s_y^2}{n}}}$$` - and use the typical rejection criterion for 1 and 2 tailed tests - In R: ```r tstat2 <- function(xbar,ybar,delta,s1,s2,m,n){ (xbar - ybar - delta)/(sqrt((s1^2/m) + (s2^2)/n)) } ``` --- # An Example: Example 1 Example 1: Suppose that talk-time per battery charge on an iPhone 6s is normally distributed with unknown mean u1 and known standard deviation sigma1 = .7. Likewise, talk-time per battery charge on an iPhone 6c is normally distributed with unknown mean u2 and known standard deviation sigma2 = .9. A random sample of m=81 6s yielded a mean talking time of 7.2 hours, while a random sample of 70 6c's yielded a mean talking time of 6.8 hours. Calculate a 95% confidence interval for u1 - u2. --- # Example worked out: - We can identify the following from the problem: ```r # information about the iphone 6s sd1 <- .7 # information about the iphone 6c sd2 <- .9 #sample of iphone 6s m1s <- 81 mu1s <- 7.2 #sample of iphone 6c n1c <- 70 mu2c <- 6.8 zstar <- qnorm(1- .05/2) zstar ``` ``` ## [1] 1.959964 ``` --- # Example continued: ```r z2confint(mu1s,mu2c,zstar = zstar,sigma1 = sd1,sigma2 =sd2 ,m = m1s,n = n1c) ``` ``` ## $lower_bound ## [1] 0.1213444 ## ## $upper_bound ## [1] 0.6786556 ``` If you ever need to see the arguments of a user defined function, just omit the parentheses ```r z2confint ``` ``` ## function(xbar,ybar,zstar,sigma1,sigma2,m,n){ ## list(lower_bound = xbar - ybar - zstar * (sqrt((sigma1/m) + (sigma2^2/n))), ## upper_bound = xbar - ybar + zstar * (sqrt((sigma1/m) + (sigma2^2/n)))) ## } ``` --- # Example 2: A Student wanted to analyze whether there is a difference in average salaries of men and women performing the same task in the same environment. A random sample of 100 men yielded an average salary of \$36,240 with a standard deviation of 1,000. A random sample of 81 women yielded an average salary of $35,930 with a standard deviation of \$1200. Test for higher male salaries at the 5% level. --- # Example 2 worked out: ```r # what we know about the problem xbar <- 36240 sd1 <- 1000 ybar <- 35930 sd2 <- 1200 m <- 100 n <- 81 (df <- v_dft_est(s1= sd1,s2 = sd2,m = m,n = n)) ``` ``` ## [1] 155.543 ``` ```r (alpha <- qt(1-.05,df = df)) ``` ``` ## [1] 1.654709 ``` ```r (tstat <- tstat2(xbar,ybar,delta = 0,sd1,sd2,m,n)) ``` ``` ## [1] 1.86 ``` --- # When we want to compare two population proportions - Let's assume we had two population proportions. Assume `\(p_1\)` is the proportion of successes in population 1. We then take a random sample of size m from population 1 and obtain X successes so that `\(\hat{p} = \frac{X}{M}\)` and X has a binomial distribution `\(Bin(m,p_2)\)`. Suppose `\(p_2\)` is the proportion of successes in population 2. We take a sample of size n from population 2 and obtain y successes such that `\(\hat{p_2} = \frac{Y}{n}\)` and y has a binomial distribution `\(Bin(m,p_1)\)`. Assuming independence, then: `$$E(\hat{p_1} - \hat{p_2})= p_1 - p_2$$` and `$$V(\hat{p_1} - \hat{p_2}) = \frac{p_1(1 - p_1)}{m} + \frac{p_2(1-p_2)}{n}$$` thus; `$$Z = \frac{\hat{p_1} - \hat{p_2} - (p_1 - p_2)}{\sqrt{\frac{p_1(1-p_1)}{m} + \frac{p_2(1-p_2)}{n}}}$$` has an approximately normal distribution. --- # Confidence Intervals for two population proportions: - a large sample 100(1-a)% confidence interval for `\(p_1 - p_2\)` is then `$$\hat{p_1} - \hat{p_2} \pm z_{a/2} \sqrt{\frac{\hat{p_1}(1 - \hat {p_1})}{m} + \frac{\hat{p_2}(1 - \hat {p_2})}{m}}$$` --- # Example: From the 2010 General Social Survey 475 out of 631 randomly selected males favor the death penalty compared to 424 out of 677 females. Find a 95% confidence interval for the true difference in proportions, (male -female). ```r m <- 631 n <- 677 X <- 475 Y <- 424 phat1 <- X/m phat2 <- Y/n sediffest <- sqrt((phat1 * (1-phat1))/m + (phat2 * (1 - phat2))/n) zstar <- qnorm(1-.05/2) c(phat1 - phat2 - zstar * sediffest,phat1 - phat2 + zstar * sediffest) ``` ``` ## [1] 0.07687197 0.17608985 ``` - Or we could use prop.test


{% load i18n %}
{% trans "Select a requirements.txt file to upload." %}
{% trans "Select an Instructor Notebook to upload" %}
{% trans "WARNING" %}: {% trans "If you re-upload a new notebook after it has already been published to the student you should use a different name for the new notebook (version suffix, date, etc), republish, and notify students they must re-download the updated notebook. Errors will occur for the student if there is a mismatch in cell id's between the uploaded and instructor notebooks" %}.
{% trans "Notebook Link" %}: {% if instructor_download_url %} {% trans "Download" %} {% else %} {% trans "No Notebook Uploaded" %} {% endif %}
{% trans "Notebook Name" %}: {{ nb_name }}
{% trans "Max Possible Score" %}: {{ max_score }}
{% trans "Uploaded At" %}: {{ nb_upload_date }}
{{ instructions|linebreaks }}
{% if student_download_url %} {% trans "Download Student Notebook" %} {% else %} {% trans "No Notebook Uploaded" %} {% endif %}{% trans "Notebook Name" %}: {{ nb_name }}
{% trans "Attempts" %}: {{ student_attempts }} / {% trans "Unlimited" %}
{% else %}{% trans "Attempts" %}: {{ student_attempts }} / {{ max_attempts }}
{% endif %}{% trans "Last Submitted" %}: {{ submitted_dt }}
{% if autograded_url %}{% trans "Autograded Notebook" %}: {% trans "Download" %}
{% else %}{% trans "Autograded Notebook" %}: {% trans "Download" %}
{% endif %} {% if student_attempts > 0 %}{% trans "Total Score" %}: {{ student_score }} / {{ max_score }}
{% trans "Total Score" %}: - / {{ max_score }}


Claim Management
A claim is a piece of information about a particular subject. The Claim Management component of the WSO2 Carbon enables to map a set of attributes from the underlying user store to a set of defined claims
The underlying user store can be either the external user store or the internal user store.
A set of claims are identified as a dialect. Different dialects represents the same piece of information with different claim URIs.
With the help of Claim Management component, these different claims can be mapped with the attributes of the user which are associated with his profile.
Figure 1: Claim Management
Following dialects are defined by-default with WSO2 Claim Management Component.
- http://wso2.org/claims : Default dialect for WSO2 Carbon
- http://schemas.xmlsoap.org/ws/2005/05/identity : Default dialect for Information Cards
- http://axschema.org : Default dialect for OpenID Attribute EXchange
- http://schema.openid.net/2007/05/claims : Default dialect for OpenID Simple Registration
You can define a new Claim Dialect by clicking on the link 'Add New Claim Dialect'
Figure 2: Adding a new dialect
- Dialect Uri : URI which uniquely identifies the Dialect. Eg :- http://schema.openid.net/2007/05/claims
- Display Name : Name of the dialect.
- Description : Describe the Dialect, what it is designed for, etc.
- Claim Uri : Each dialect should have at least one claim when defining it.
- Mapped Attribute : Corresponding attribute name from the underlying user store for this claim.
- Regular Expression : Regular expression to validate inputs for this claim.
- Display Order : Display order of the above claim among all the other claims defined under the same dialect
- Supported by Default : If unchecked, this claim won't be prompted at the time of user registration
- Required : Whether this claim is mandatory for user registration or not.
From the Claim Dialect view [once you click on any available dialect links] you can views the claims defined for that particular dialect.
Figure 3: Viewing Dialect Information
It is possible to edit the claim mappings of a given dialect by clicking the 'Edit' link appearing for each claim mapping.
Figure 4: Editing a Claim Mapping
You can extend a defined dialect by adding new claim mappings. Click 'Add New Claim Mapping' link to add a new claim mapping.
Figure 5: Adding a new claim mapping
- Display Name : Name of the claim displayed on the UI
- Description : Describe the functionality of the claim
- Claim Uri : URI defined under the dialect, specific to the claim
- Mapped Attribute : Corresponding attribute name from the underlying user store
- Regular Expression : Regular expression to validate inputs
- Display Order : Display order of the claim among all the other claims defined under the same dialect
- Supported by Default : If unchecked wont be prompted at the time of user registration
- Required : Required for user registration


Payments via
Klarna Invoice (KLV)
The Integration Service supports payments via
Klarna Invoice
.
Checkout Processes
Authorization only


aha, same commit. quite strange that this call not working on your side
did you try to sync in non spv mode before? maybe some files in ~/.komodo/ILN affecting
jl777c05-Jul-19 06:14 AM
i will change how that call works
TonyL05-Jul-19 06:14 AM
just crazy ideas
jl777c05-Jul-19 06:14 AM
it is done a bit differently than others
daemonfox05-Jul-19 06:15 AM
im applying some updates to this VM didnt realize how out of date it was
same...
ill check back for the next build commit
Just to check... if I run it without the -nSPV=1, i am then a full node and as many conenctions as you config to allow can use my node jas their -addnode=
maxconnections=XXX in conf file?
jl777c05-Jul-19 06:20 AM
that is the effect
the actual logic is fully decentralized
an nSPV client will reject any node that doesnt provide nSPV services
nSPV branch enables nSPV services for fullnodes
so with the p2p networking having a few nSPV enabled fullnodes, eventually the nSPV node will find them, of course much more efficient to just point them at one directly
daemonfox05-Jul-19 06:22 AM
ok im trying to reverse it now and test that myself
jl777c05-Jul-19 06:22 AM
after majority of people update to nSPV enabled nodes, then most any node will provide nSPV services, so this state of sparse nSPV support is just during testing
the cool part is that we are able to test nSPV with the live mainnet, without disrupting it at all
and basically all full nodes perform function similar to electrum servers, automaticallly
and of course the data set it fine tuned to be 1000x more efficient than an SPV headers only client
daemonfox05-Jul-19 06:24 AM
ok... is there a perk OTHER than solving this decentralized issue to a node deciding to provide nSPV services? Do they earn anything... do they contribute to the other asset accumulation in any way?
or is this a 50/50 we all want it so we all support it as a use cost?
jl777c05-Jul-19 06:25 AM
it is part of the p2p messaging
all fullnodes are delivering blocks to the network
no cost
dukeleto05-Jul-19 06:25 AM
@jl777c how does the memo field and nspv work ?
jl777c05-Jul-19 06:25 AM
what memo field?
dukeleto05-Jul-19 06:26 AM
well, wondering if nSPV has support for shielded transactions
jl777c05-Jul-19 06:26 AM
nspv is about proving a txid happened or not
dukeleto05-Jul-19 06:26 AM
shielded txid ?
jl777c05-Jul-19 06:26 AM
proven via notarizations, merkleroots
txid is a hash of the raw tx bytes, so you can prove a specific txid exists
the problem is what txids need to be proven?
and then given the rawtx, how will you process it
that is the layer above nspv
dukeleto05-Jul-19 06:27 AM
how does zSPV and nSPV interact, if at all?
jl777c05-Jul-19 06:28 AM
nSPV would be the transport layer
the big giant issue with zSPV is that nobody knows which txid belongs to which node...
daemonfox05-Jul-19 06:28 AM
oh god my heads gonna explode zSPV
jl777c05-Jul-19 06:29 AM
so short of syncing the entire chain and trying to decrypt each one, you cant know which txid you want
however...
if the sender can tell you the txid, say using a taddr opreturn with encrypted payload
but it leaks privacy big time as people will know you are getting txid
yes, flyclient
i tried to read that paper and realized it would be faster to implement nSPV than to decipher it
dukeleto05-Jul-19 06:31 AM
nSPV seems like FlyClient in a DPoW-ized world
jl777c05-Jul-19 06:31 AM
nSPV is very simple
dukeleto05-Jul-19 06:31 AM
@jl777c yes, the paper must be hard to read so it looks good and gets published
jl777c05-Jul-19 06:31 AM
i made a 2min medium post, it achieves the same effect as flyclient
dukeleto05-Jul-19 06:31 AM
FlyClient didn't have DPoW, so it had to do a bunch of complex stuff
jl777c05-Jul-19 06:31 AM
not saying it is better or worse, they are equivalent
resultwise
dukeleto05-Jul-19 06:32 AM
Since we have DPoW, the job of nSPV is much easier
jl777c05-Jul-19 06:32 AM
i like easy
third day of coding and it is almost done, the reference cli nSPV client
dukeleto05-Jul-19 06:32 AM
I really don't like the architecture of Electrum and I really like how every full node can help light wallets run
jl777c05-Jul-19 06:33 AM
need a few addressindex=1 nodes
it is not required to have addressindex=1 to support nSPV, most of the calls are available to any txindex=1 node
dukeleto05-Jul-19 06:34 AM
HUSH3 GUI nodes turn on all indexes by default, I haven't done that for plain CLI full nodes since I assume people can do that for themselves. I knew it would pay dividends in the future
If RPC work is needed, let me know
jl777c05-Jul-19 06:36 AM
i am making a C based nSPV wallet so people can see what they need to do to make a GUI for it
they can either just use the rpc calls to avoid having to mess with p2p messaging
or they can become a non-relaying peer and issue the messages directly
I guess i should implement the superlite wallet purely via rpc calls
daemonfox05-Jul-19 06:50 AM
SUCCESS!!!!
Using my own full node and then a second data directory for the nSPV node... i got a good nspv_getinfo call


For this article, the following tools and cloud provider will be used:
// Create links for each bullet point
Ansible and Packer will be used to generate custom AMIs. Ansible will be used to configure this AMI with all the necessary software, and Packer will generate the AMI. Terraform, in turn, will be used to create reusable infrastructure modules. Let’s see how!
In this scenario, we will build a simple infrastructure which consists of:
If this configuration is still not clear for you, take a look at the image below:
<SCENARIO 1 IMAGE>
To start off with, let’s create a git repository


Aldi shoppers humiliated over 'special buy' item as staff force them to unpack their bags
"We felt so embarrassed and humiliated. We didn't even get an apology"
by Rebecca KoncienzcyA couple who questioned their Aldi bill said they were left "humiliated" by staff as they were forced to unpack their shopping.
Daniel and Pamela Murray queried the price of their shop when it came to over £60, having only popped in for a few things.
They claim the cashier told them she had scanned everything they had and £66 was their total.
But Daniel and Pamela requested to see the bill and discovered they had been charged for a 'special buy' portable gas grill they did not buy costing £39.99.
The couple, both in their 60s and from Rhyl, North Wales, said the manager of the Marina Quay store was called and they were made to unpack their shopping in front of a queue of customers.
Speaking to North Wales Live, Daniel said: "We'd only popped in for a few things and there was only one till open at the time.
"We loaded our items onto the belt but we got a shock when the cashier told us the bill was £66.
"I said: 'That can't be right, we've only got two carrier bags.'
"I asked her to print a receipt and it was showing £39.99 for a portable gas grill we hadn't even bought.
"The manager and a security guard was called over and we had to unpack all our shopping so they could check the item wasn't there.
"By this time a big queue was forming and we felt so embarrassed and humiliated. We didn't even get an apology."
Daniel said the £39.99 was eventually deducted with their real bill only coming to £26.
He added: "After the incident I decided to write a letter to the store to say I wasn't happy with how the matter had been handled having not been given an apology.
"I also wanted to warn them that maybe something had been coded up wrong, in case this happens to someone else who might not notice being overcharged before it's too late.
"How would people who've left the store prove they hadn't bought it?
"Anyway, I didn't get any response to my letter, nor to a further email that was sent to the Aldi head office.
"We don't want anything, we just don't think it's very good customer service."
An Aldi spokesman said: "We have apologised to the family for their experience at the Rhyl store."


Lidia Rodionova
Developer with 5+ years front-end development experience, building and maintaining responsive websites.
Experience
UI Developer
Work closely with UX and Visual Designers, Product Owners and Technology stakeholder in an agile environment to support User Experience improvements for online banking customers.
UI Developer
Worked as part of a team to build EHRflow, a Health Records application focusing on midwives and other alternative healthcare providers. Currently working on adding features and maintaining EHRflow.
Designed and built several client sites from conception to final product. Analyze site traffic using Google Analytics. Built and maintained company website.
Web Developer
Designed, built and maintained external marketing sites. Responsible for website redesigns.
Jr. Web Developer & Designer
Designed and coded all advertising on company website. Designed and coded HTML emails. Updated techdata.ca.
Education
Sheridan College
Courses
- Problem Solving and Programming Logic
- Introduction to Business Software Systems
- Computer Math Fundamentals
- Web Development
- Object Oriented Programming 1 - Java
- Linux/Unix Operating Systems
- Introduction to Data Communications and Networking
- Interactive User Interface Design
- Web Programming
- Object Oriented Programming 2 - Java
- Systems Analysis and Design
- Data Network Design and Configuration - Routers & Switches
- Database Design and Implementation
- Technical Reports and Presentations
- Enterprise Java Development
- Linux/Unix Admininstration
- Mobile Web-based Applications
- IT Project Management Using PMP
- RDBMS Application Development
- Web Services Using .NET and C#
- Object-oriented Methodologies - UML
University of Toronto
Skills
Projects
EHRflow
For more information or to request a demo, please visit www.ehrflow.ca or go to project page for more details.
EHRflow is a fully functional emr built especially for midwifery professionals in Ontario. In collaboration with the team at www.workaround.ca
Art History Record Keeper
Java executable application that serves as an organizer for researchers who work with visual information. Generates footnotes, bibliographic entries and citations.
Bark Club Salon
Business Application using MS Access for small business owners who own a pet grooming business. Can be customized or adapted to other types of small business. Please contact me if you want to use this application for your small business. In collaboration with Kristian Gonzales & Nikola Petrovski.
Drawing a star with open GL application
Includes a fully developed point class and line class that can be used to draw a star or other shapes with openGL application (java)
My favourite Resources
- CSS Tricks, because their articles are helpful and very readable
- Smashing Magazine
Accessability Resources
- Coming soon


Hello,
I’m a Android developer from Surakarta (Indonesia). I am very passionate about User Interface Design, User Experience Design, Mobile App Design, and Android development. Spesialize in Cross Platform mobile development( Ionic, Flutter), Familiar with git, Rest API and mobile platform IOS and Android.


A Descritpion of the HSTIO Interface to the STIS and NICMOS Pipelines
This document was generated using the LaTeX2HTML translator Version 0.6.4 (Tues Aug 30 1994) Copyright © 1993, 1994, Nikos Drakos, Computer Based Learning Unit, University of Leeds.
The command line arguments were:
latex2html hstio.tex.
The translation was initiated by Sdas Group on Wed Aug 21 10:27:26 EDT 1996


Responsive Web design is the approach that suggests that design and development should respond to the user’s behavior and environment based on screen size, platform and orientation.
Many businesses are taking web design back to the basics with minimalistic designs and layouts. Websites are featuring fewer pages, with simpler designs and more blank space. Some websites are also implementing what’s referred to as “Flat Design”. Flat web design uses two-dimensional/flat images which are aesthetically pleasing to look at, in combination with lots of open space, and bright colors. Flat design also meets the requirements of a responsive web design.
Usability has always been of key importance in a successful website. Websites with an intuitive design, and with images and aspects that are pleasing to the eye are going to attract more customers than those without these features. A simple, intuitive, and functional navigation is another important feature to focus on. If customers can’t easily make their way around your site, they aren’t likely to stay long. The speed at which your pages load also have a strong impact on users in the coming year. Quickly loading pages and simple ways to get to specific information will be critical in setting your website apart from the competition.
Personalized user experience web design is the norm. Rather than everyone receiving the same experience when visiting a particular website, a personalized user experience design allows for the content and experience to be designed specifically for a particular user. The best part about websites that offer a personalized user experience, is that they are typically able to adapt to the visitor's interests and needs without having to do much at all.
In the past, bright and bold colors on websites was sometimes perceived as childish or unprofessional, depending of course upon the industry of the business. However, websites with vibrant colors have become more popular and more acceptable in recent years.


About Me
I'm Matthew Barnhart (he/him). I'm Iowa's first Digital Marketing/Developer Apprentice. I'm an experienced coffee professional with a demonstrated history of working in the entertainment industry. I'm skilled in analytical thinking, WordPress, employee training, knowledge management, and building strong relationships. I am a strong operations professional with a desire to use my abilities to grow and market community organizations in service to the arts, ecological and economic sustainability, and the well-being and care for all mankind.
Work Experience
Staff Writer
Bagogames.com
August 2017 - April 2018
As a staff writer, I composed journalistic reviews of gaming and entertainment products. I balanced multiple projects simultaneously and met publishing deadlines. I also worked with the WordPress CMS to format my articles for SEO with internal and external links, categories, images, and keywords.
Check it out here: bagogames.com/author/superlove-panda
Assistant Concessions Manager
Theatre Cedar Rapids
June 2016 - March 2018
As assistant manager, I supported and supervised a team of up to eight people to provide guests with consistent and outstanding products and service. I successfully multi-tasked in a high-pressure setting, managing inventory and service situations as needed for up to 500 guests.
Highlights:
- Created new and exciting themed beverages as well as documentation and procedures for superior service and cleanliness
- Led teams of unique individuals to cooperate towards successful service situations
- Responsible for employee oversight and product inventory control and ordering
General Manager
Press Cafe
June 2013 - June 2014
As general manager, I guided and educated a dedicated and professional team to provide guests with speedy and reliable service. I also established and maintained deep customer relations, providing exceptional community-centric service. Mindful of employee strengths and preferences, I effectively scheduled employee workweeks between two locations.
Highlights:
- Created tantalizing seasonal beverages, detailed recipes, and employee protocols
- Directed earnest and hardworking mixed teams of veterans and novices
- Responsible for product ordering and inventorying, employee scheduling, and revenue accounting
Education
Kirkwood Community College
2011-2012
Creative Writing, Literature, & Art
University of South Dakota
2006-2008
Business Management & Accounting
Mitchell Technical Institute
2005-2006
Culinary Art
Goals
From this experience, I hope to share in new perspectives and new ideas in order to attain new knowledge, new purposes, and new networks with which to make a positive impact upon my community and the greater world.
Top 10 Movies
- Little Miss Sunshine
- Inglourious Basterds
- Lord of the Rings: Return of the King
- Lord of the Rings: The Fellowship of the Ring
- Lord of the Rings: The Two Towers
- Seven Samurai
- Red Beard
- Rashomon
- Harakiri
- The King's Speech


{% load static %} {% load humanize %}
| Name | Position | Office | Age | Start date | Salary |
|---|---|---|---|---|---|
| Name | Position | Office | Age | Start date | Salary |
| Tiger Nixon | System Architect | Edinburgh | 61 | 2011/04/25 | $320,800 |
| Garrett Winters | Accountant | Tokyo | 63 | 2011/07/25 | $170,750 |
| Ashton Cox | Junior Technical Author | San Francisco | 66 | 2009/01/12 | $86,000 |
| Cedric Kelly | Senior Javascript Developer | Edinburgh | 22 | 2012/03/29 | $433,060 |
| Michelle House | Integration Specialist | Sidney | 37 | 2011/06/02 | $95,400 |
| Suki Burks | Developer | London | 53 | 2009/10/22 | $114,500 |
| Prescott Bartlett | Technical Author | London | 27 | 2011/05/07 | $145,000 |
| Gavin Cortez | Team Leader | San Francisco | 22 | 2008/10/26 | $235,500 |
| Thor Walton | Developer | New York | 61 | 2013/08/11 | $98,540 |
| Jonas Alexander | Developer | San Francisco | 30 | 2010/07/14 | $86,500 |
| Shad Decker | Regional Director | Edinburgh | 51 | 2008/11/13 | $183,000 |
| Michael Bruce | Javascript Developer | Singapore | 29 | 2011/06/27 | $183,000 |
| Donna Snider | Customer Support | New York | 27 | 2011/01/25 | $112,000 |


Sancho headlines quartet of transfer stories – Thursday’s Liverpool FC News
by This Is AnfieldCan’t half tell that the proper football isn’t on this week, hey? Four transfer stories, Klopp interviews and plenty more besides!
Rumours grow over Salah downtime
We already know Salah is out of Egypt’s squad for this international break, and that he’s a likely doubt for Crystal Palace in a week-and-a-half time.
But it appears that it could be even longer, too—depending on the reports you choose to believe.
For certain, Salah has been in some intensive recovery work with Liverpool physio Richie Partridge, and the forward is wearing a protective boot, too.
But some speculative reports have suggested he could be sidelined for a month—find out more via the link above.
Summer transfer kitty spanked on one lion
“And the bidding for our next item, Jadon Sancho, starts at £100 million…”
You can imagine the summer scenes already, right? Sancho, the hottest property in English football (outside the Premier League, at least) could well be off this coming summer, with Liverpool’s long-standing interest apparently “reignited” of late.
The England international is unhappy at how Borussia Dortmund have treated him this year, seemingly, and the Reds will be in the front row at the auction house next summer as a result.
Klopp impact everywhere
Just in case you didn’t realise it, Jurgen Klopp is boss. He’s amazing. Everybody acknowledges his impact and everybody loves him.
Maybe except for Pep Guardiola.
 One is an ‘interest’, one is a ‘never gonna happen’. We’ll start with the latter one.
Klopp was asked by a Swedish newspaper, Sportbladet, if he would ever consider signing Zlatan Ibrahimovic, and while being enormously respectful of the striker’s career, noted there are two good reasons it wouldn’t happen.
One, his career history which took him to Old Trafford; two, his less-than-Firmino-esque style of play.
“If he had not played for Manchester United before, then we might have considered him! No, but I’m not even sure he wanted to play here. We’re a pretty intense team [laughs]! With that said, I couldn’t have had more respect for Zlatan.”
Perhaps more interestingly, Genk sporting director Dimitri De Conde says two Champions League group stage rivals are monitoring key midfielder Sander Berge.
It’s RB Salzburg who have been left in the shade here, with the Reds and Napoli two of the many, many sides who are eyeing up the 21-year-old Norway international.
“We also know Liverpool follow him carefully,” De Conde said. “Klopp spoke to the boy after the Anfield game, congratulating him on the performance.”
Quickfire LFC news
- Captain Joran Henderson wanted to kill Luis Suarez – before becoming fast friends with him
- Christian Ziege remains very unhappy with Gerard Houllier over failed Liverpool switch
- Timo Werner will be given his manager’s blessing to leave RB Leipzig
- And tickets for the Women’s Merseyside derby at Anfield are still on sale – here’s an interview with the captain the last time this fixture played out at Liverpool’s stadium, back in 1997
Around the Prem
- Mario Mandzukic wants to leave title favourites Juventus to join nobody’s favourites Man United in January (several Italian outlets)
- Fans inside the stadium will get more information as VAR checks take place (Sky Sports)
- And Chelsea offered €50m for Napoli full-back Elseid Hysaj, who will leave on a free this summer (Mail)
Stupid decision of the day
Emre Can‘s move to Juventus has gone so well, that he’s not on their Champions League squad list for this season and will be put up for sale in January.
The midfielder, who has a €50 million release clause, will actually be made available for just over half that value.
Tweet of the day
What we’re reading
Oliver Kay has written a piece which is at times insightful, funny, disturbing and, well, long, covering the managers in charge of England’s 1000 international matches for The Athletic.
And the Guardian‘s Will Unwin has spoken to Finland captain Tim Sparv, with the nation on the brink of their first-ever major international finals.
Worth watching tonight
Euro 2020 qualifiers are the order of the night: England take on Montenegro in the aforementioned 1000th game, while if you’re simply a fan of goals then France vs. Moldova is probably the match for you. Both kick off at 7.45pm (UK).


New registrar for Telangana University
by Special CorrespondentSsenior professor in the department of Pharmaceutical Chemistry, Naseem assumed office as the new registrar of Telangana University on Monday. She took charge from D. Balaramulu.
Prof. Naseem had earlier rendered services as Controller of Examinations; director, Audit Cell, and dean, CDC. The university had conducted its first convocation when she was the controller of examinations and she played a key role in helping the varsity earn the NAAC certificate. She also acted as head of department of Pharmaceutical Chemistry; chairman of lesson planning committee and dean, faculty of sciences.
University staff and students congratulated her.


The whole project is all for research purpose only and a work of college students for their final year engineering project.
The whole idea is to create or design a sytem or computer environment based on three tier architecture on which simple functions or program form client side will be processed by the secondary or main server i.e; the first server that interacts with the user and will return the results to the user and if the query or request from the user is of big data query then the main server or secondary server will request the the third system in this architecture i.e; the big data server and the that server will process the required query and return the result to the main server and the main server later will return the result to user or client side.
For big data functions we use the Hadoop for the query in the beginning, but the thing that we are researching on is to create a similar file system like HDFS that is used by Hadoop and after that we will analyse the time difference and all the differences or report that we get from the famous Hadoop and our own file system and will compare it later.
This project is a result of final year project at Netaji Subhash Engineering College by students of department
Information Technology 4th Year under the guidance of Prof. Anirban Kundu.
Know the whole team.


Toggle navigation
{{msg}}
My Profile
Change Password
Logout
Search
View Complain
Hostel Leaving Info
Previous Forms
Fill New Form
Your Profile
Click Here to Edit
View Your Profile Here
Personal Details
Username
Name
Roll Number
Email
Sex
Blood Group
Contact Details
Mobile
Hostel
Room
Permanent Address
State
City
Pincode
Bank Details
Bank
Account Number
IFSC Code


There are three operational settings for the Grader Tool: (1) the Cal Poly computer science department; (2) other departments at Cal Poly; (3) other people who need grading software. The Grader Tool is intended to be general public domain software.
There is a single primary customer for the Grader Tool, and he is representative of instructors at Cal Poly. The setting for which the Grader Tool is appropriately suited is a classroom or educational environment where students are graded. In this setting, instructors maintain student grades for a course and students want to view their grades on each assignment. As such, the tool's requirements reflect these needs.


HTML 5.
CSS, Bootstrap.
MVC, CRUDL, JQuery, DOM.
Junior Full stack Developer
I'm about to complete my B.A. in International Relations and Asian Studies at the Hebrew University in Jerusalem. Up to date I served as a Senior Field Coordinator in I.G.T, a leading international tour company in Israel. In my role I managed and coordinated high level professional conventions such as: AIPAC professional private tours, designed creative itineraries, managed quotation, visas as well as government relations and permits. I speaks fluent Hebrew, Italian and English. I also have advanced communication skills in Chinese. I previously lived in China for a few months and attended an intensive mandarin course. Now I'm in the middle of an intensive course to become a Fullstack developer


Login
There was an error with your login. Please try again.
Toggle navigation
Patient
(current)
Family Caregiver
Healthcare Provider
Logout
Welcome Back, Sam S. Smith
Summary: March 8th, 2015
Glucose
5.77 mmol/L
Number of Steps
2,043 Steps
Weight
69.65 kg
Daily Meds
Metformin x2
Glyburide x2
Glucose
Steps
Patient: Sam S. Smith
Important Trends: March 8th, 2015
Glucose
Glucose Reading: 5.77 mmol/L
Blood Pressure
Mean Blood Pressure: 11.55 kPa
Blood Pressure: 15.20 / 9.73 kPa
Weight
69.65 kg
Pulse Oximetry
81 BPM
Patient Information
Status
Gender
Name(s)
Telecom
Address(es)
Birth Date:
Organization
Glucose
Blood Pressure
Weight
Pulse Oximetry
Blood Glucose vs. Activity
Dad's Dashboard
Summary: March 8th, 2015
×
Your father reached his walking goal yesterday! Why not congratulate him?
Glucose
Glucose Reading: 5.77 mmol/L
Mood
Happy
Weight
80 kg
Food Log
Reports
Date
Explanation
{{note.time}}
{{note.explanation}}


Toggle navigation
Project Gutenberg Search
Search
History
Health
Project Gutenberg Search
Search lots of books using ElasticSearch.
Search Lots of Books Now


Selected ADMIRAL data files may be accessed using the web file access protocol, using one of the following forms of URI:
A username and password will be required. Access controls are enforced as described for local network file access page.http://%{HOSTFULLNAME}/data/private/username
http://%{HOSTFULLNAME}/data/shared/username
http://%{HOSTFULLNAME}/data/collab/username
Web file access (WebDAV) support is slower and sometimes less reliable than local network file sharing. It is provided for two main reasons:
Currently, the Windows web file access software does not work with the ADMIRAL server and file protection arrangements — we'll be looking in to this.
To connect a web file system share from MacOS, use the Finder "Go > Connect to server..." menu command (or COMMAND-K shortcut), and enter
http://%{HOSTFULLNAME}/data/private/username
, or one of the other forms shown above, as the server address.
The system will then prompt for a username and password.
Under Linux with GNOME, the ADMIRAL web file system content can be accessed in similar fashion to a local file system, but using the web address:
Use the "Places > Connect to server ..." menu command. In the popup window, from the "Service type"
drop-down menu, select "WebDAV (HTTP)". Under "Server" enter %{HOSTFULLNAME}
and under
"Folder" enter data/username
, data/shared/username
or data/collab/username
.
Under "Username" enter username
and then click on "Connect".
Enter the specified user's password into the next popup window to appear.
An alternative mechanism for reading and writing files under Linux is to use the cadaver
utility, specifying one of the URI forms listed above.


So much more than just a winter destination, the Wanaka region starts to shrug off the snow in mid-October and suddenly it’s an outdoor enthusiast’s paradise all over again.
The best thing, however, is even throughout September and half of October, you can ski all morning, then head to the lake in the afternoon to waterski or kayak, or even for a swim. And then comes a summer of long leisurely days on the spacious beaches and endless evenings of balmy twilight. It truly is paradise on earth.
I love to get into the mountains. There’s nothing quite like the solitude and beauty of pristine valleys and soaring peaks to revive the soul.
As a dedicated nature-lover and self-confessed tree-hugger, I think this place is one of the most pure and beautiful destinations in the world.
There’s an abundance of amazing walking tracks for everyone from the intrepid mountain climber to the ambling wanderer. Mt Aspiring is the fourth highest peak in New Zealand and the highest peak outside of Mount Cook National Park. Admired for its miraculous Matterhorn pinnacle, mountaineers use the precipitous slopes to practice for ascending Everest.
For those who aren’t mountaineers, there’s plenty of tracks offering a good half-day hike into the wild. The Rob Roy Glacier is one of my personal favourites. A breathtaking hour-long drive through the Matukituki Valley takes you to the base of the hike, then the track to the glacier is around three hours depending on your level of fitness. Other great hikes are the Isthmus Peak track in Lake Hawea and the Millennium Track, which runs from the lakefront town of Wanaka to Glendhu Bay, about 10 kilometres along the lake shore.
There’s nothing quite like the solitude and beauty of pristine valleys and soaring peaks to revive the soul.
If beating the trails on foot is not your thing, I recently discovered the exhilaration of mountain biking. I’m no expert, but I managed a two-day trip into the mountains – and survived! Not only did I survive, I loved it. It’ll take me a while to get comfortable with jumps but there are so many great tracks for biking around Wanaka. Guides are available for those wanting a bit more of a challenge, and heli biking is becoming hugely popular in the area. There are also two new bike parks at the winter ski areas of Cardrona and Snow Farm.
Of course, when exploring on foot or on bike, remember late spring and early summer will bring the snow melt when rivers run a little higher so check conditions with the local Department of Conservation office before setting off.
If you’re more into softer adventure, or just love a good wine, try a vineyard tour after your morning activity. There are local wine tours available, or, if you’re up for a drive, take yourself on a trip around the region with a wine map. Rippon Vineyard is one of the oldest vineyards in the region, and indeed in the entire wine growing region of Central Otago. It has been in the same family for over 100 years and boasts some of the best views in town.
One of the largest attractions in the Wanaka region is, of course, the lakes. Lake Wanaka and Lake Hawea are both blessed with outstandingly beautiful surroundings and pure clean water. In spring there can be a bit of wind around and the lakes are brilliant for wind surfing and kite surfing. They are also exceptional fishing lakes flush with salmon and rainbow and brown trout. If you don’t know much about fishing but want to have a go, there are many local fishing guides who will take you out to spin a reel and have a yarn.
Kayaking and paddleboarding are popular in the spring and summer seasons too and the lakefront beach has both to hire throughout the spring and summer months.
But I have to say, my favourite thing to do in Wanaka is to fly. The dramatic landscapes are outstanding and to see them from the air is simply amazing.


jNewton
Connection Utilities
Copyright © 2010-2020. All rights reserved.
Apple, Newton, and the light bulb logo are trademarks of Apple
Computer, Inc., registered in the U.S. and other countries.
Visit
github.com/pnemonic78/jNCU
Version 0.2


About Me
My name is Cesar Campos. I reside in Houston, TX. I am 24 years old studying Computer Science at the University of Houston. I'm currently a Senior and current am taking a coding bootcamp at Rice University.
In this coding bootcamp, I will achieve and learn to be a Full Stack developer and develop both the front and back end of websites. It's a 12-week course and am very excited to dive through it.
The bootcamp is going very well and soon to be complete. I have learned many tools and languages to help develop and become a successful Full Stack Developer. I will use the tools I have learned to obtain a career in the web development field.


Design and architechture¶
The package design can be found in the figure below.
Although, the library contains several folders, the only importart folders for the users
are the types
folder, and the methods
folder. The other folders contain auxiliary
functions some of the methods. Specifically, in interesting to explore the data types, as
they are essential to understand how the package works, as well as the common interface of
the methods.
Data types¶
The package provides types for annotations datasets and ground truth datasets, as they usually follow the same structure. These types are used in all the methods so you would need to convert your annotations dataset to the correct format accepted by the algorithm.
There are three types of annotations that the package supports for which we provide Scala case classes, making it possible to detect errors at compile time when using the algorithms:
BinaryAnnotation
: a Dataset of this type provides three columns: * Theexample
column (i.e the example for which the annotation is made). * Theannotator
column (representing the annotator that made the annotation). * Thevalue
column, (with the value of the annotation, that can take as value either 0 or 1)MulticlassAnnotation
: The difference formBinaryAnnotation
is that thevalue
column can take more than two values, in the range from 0 to the total number of values.RealAnnotation
: In this case, thevalue
column can take any numeric value.
You can convert an annotation dataframe with columns example
, annotator
and value
to a
typed dataset easily with the following instruction:
val typedData = untypedData.as[RealAnnotation]
In the case of labels, we provide 5 types of labels, 2 of which are probabilistic. The three non probabilistic types are:
BinaryLabel
. A dataset with two columns:example
andvalue
. The column value is a binary number (0 or 1).MulticlassLabel
. A dataset with the same structure as the previous one but where the columnvalue
is a binary number (0 or 1).RealLabel
. In this case, the columnvalue
can take any numeric value.
The probabilistic types are used by some algorithms, to provide more information about the confidence of each class value for an specific example.
BinarySoftLabel
. A dataset with two columns:example
andprob
. The columnprob
represents the probability of the example being positive.MultiSoftLabel
. A dataset with three columns:example
,class
andprob
. This last column represents the probability of the example taking the class in the columnclass
.
Methods¶
All methods implemented are in the methods
subpackage and are mostly independent of each other. There MajorityVoting algorithms are the
only exception, as most of the other methods use them in the initialization step. Apart from that, each algorithm is implemented in its
specific file. Apart from that, each algorithm
is implemented in its specific file. This makes it easier to extend the package with new algorithms. Although independent, all algorithms have
a similar interface, which facilitates its use. To execute an algorithm, the user normally needs to use the apply
method of the model (which
in scala
, is equivalent to applying the object itself), as shown below
...
val model = IBCC(annotations)
...
After the model completes its execution, a model object is returned, which will have information about the ground truth estimations and other estimations that are dependent on the chosen algorithm.
The only algorithm that does not follow this pattern is MajorityVoting
, which has methods for each of the class types and also to obtain
probabilistic labels. See the API Docs for details.


In some situations, especially during the distribution of full sync scripts, you will need to split a large script up into smaller chunks in order to send it over e-mail. Your ISP or IT department can tell you the maximum attachment size they support. By default Code Co-op sets it to 1024Kb. Here the dispatcher is set to split scripts larger than 2000Kb.
To complete the Dispatcher configuration click the Next button.


Match Play. 4-4 to 7. Should Red redouble? If redoubled, should White take?
pips Red=130 White=112
(CorrectAnswer= 0/0, ErrorScore= 0)
For money this is a clear no redouble and a very easy take. Red has too few market losing rolls to justify a redouble especially with an active cube and gammons counting.
At this score however a redouble kills the cube and gammons so Red just needs to be a good favourite to redouble. He has 65% here so it becomes a clear redouble and take. Not redoubling is a blunder.
Once again, the difference between the match play decision and the money decision is huge.
At this score however a redouble kills the cube and gammons so Red just needs to be a good favourite to redouble. He has 65% here so it becomes a clear redouble and take. Not redoubling is a blunder.
Once again, the difference between the match play decision and the money decision is huge.
Cube analysis 2-ply cubeless equity +0.258 (Money: +0.258) 0.639 0.146 0.005 - 0.361 0.165 0.006 Cubeful equities: 1. Double, take +0.555 2. Double, pass +1.000 (+0.445) 3. No double +0.462 (-0.093) Proper cube action: Redouble, take


Guide each diner to their table without collisions. The table each diner needs to go to is marked with the corresponding number. Diners are initially facing EAST.
Movement
Use letters M (move forward), L (rotate left without moving) and R (rotate right without moving) to write down walking instructions.


RT @foliosus: Yay! @newrelic is officially on rails 3! Congrats to @Aughr and @juliangiuca for shipping it!
1/23/2013, 6:18:17 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
RT @foliosus: Yay! @newrelic is officially on rails 3! Congrats to @Aughr and @juliangiuca for shipping it!
1/23/2013, 6:18:17 PM
Favs: 0
Retweets: 0
link


{{site.user_description}}
I specialize in several computer aided design softwares including Solidworks and Siemens NX. Furthermore, I have taken extensive coursework in geometric dimension and tolerancing (GD'&'T) and advanced CAD/CAM methods.
I am experienced in Eagle PCB Design, in addition to numerous other mechanical design integration softwares including LMS Test.Lab and AMESim. I have performed several Finite Element Analyses (FEA) through Altair Hyperworks
For part of my senior design, I integrated several circuits to an open source IoT platform. This experience made my want to widen my coding horizons to platforms such as Ruby and Linux. This website showcases my early coding journey.


We are people who believe that knowledge should be shared among all.
So we came up with an social web application where students can interact with each other and share their knowledge regarding courses.
As our motto sugggests, "We make connecting easy, We make connecting fun"


--- title: Times of the multiverse --- {% include header.html %}
Local Time ()
UTC/GMT
Week
Lunar Standard Time (LST)
Decimal Time (UTC/GMT)


Channel Impulse Response (CIR)¶
Introduction¶
It is obvious that the calculation of CIR is essential, yet it is easily overlooked due to its complexity. First, I will focus on the implementation of the time-domain [Carruthers2002] and frequency-domain approaches [Schulze2016].
The main advantage of the frequency-domain approach is that it considers infinite number of reflections. However, when it comes to time-domain CIR, freq. domain approach needs windowing filter to transform it to time domain. To give you a concrete example of what the disadvantage of the freq. domain approach is that a flat frequency response will not give us an impulse in time-domain if the time bin is not correct. In fact, it gives us a sinc func (can be a negative value). Therefore, a careful filtering is necessary. On the other hand, the time-domain approach is more resilient in terms of transforming to other domain.
Tip
In case you don’t know, there’s a rule of thumb on how small we can sufficiently partition a plane, i.e., \(\Delta x \leq c \Delta t\), where \(c\) is the speed of light and \(\Delta t\) is time sampling. More detailed discussions can be found in [Schulze2016], [Barry1993] and [Schulze2018] (especially the latter one).
CIR for IM/DD¶
Time domain (
TimeDomainCIR
)Freq. domain (
FreqDomainCIR
)
Check following notebook for examples.


{% extends "base.html" %} {% block title %}{{ title }}{% endblock %} {% block stylesheets %} {% endblock %} {% block page_content %} Toggle navigation WLMetrics Logout Upload Recorded File Click the box below or just drag a file into it. Dropzone {{ form.hidden_tag() }} {{ dzform.the_file.label }} {{ dzform.the_file(size=20, class="form-control", placeholder="Select a file...") }} {% endblock %} {% block javascripts %} {% endblock %}
Click the box below or just drag a file into it.


vignettes/regression.Rmd
regression.Rmd
Here, we replicate the analysis of Boldt et al. (2015), performing age-uncertain calibration-in-time on a chlorophyll reflectance record from northern Alaska, using geoChronR.
The challenge of age-uncertain calibration-in-time is that age uncertainty affects both the calibration model (the relation between the proxy data and instrumental data) and the reconstruction (the timing of events in the reconstruction). geoChronR simplifies handling these issues.
Let’s start by loading the packages we’ll need.
library(lipdR) #to read and write LiPD files
library(geoChronR) #of course
library(readr) #to load in the instrumental data we need
library(ggplot2) #for plotting
OK, we’ll begin by loading in the Kurupa Lake record from Boldt et al., 2015. The system.file(...)
part of this pulls the example file from the package directory. You’d like just enter the path as a string for typical use.
K <- lipdR::readLipd("http://lipdverse.org/geoChronR-examples/Kurupa.Boldt.2015.lpd")
## [1] "reading: Kurupa.Boldt.2015.lpd"
sp <- plotSummary(K,paleo.data.var = "RABD",summary.font.size = 6)
## [1] "Found it! Moving on..."
## [1] "Found it! Moving on..."
## [1] "Found it! Moving on..."
## [1] "Found it! Moving on..."
## [1] "Found it! Moving on..."
print(sp)
## TableGrob (4 x 4) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-2) arrange gTree[GRID.gTree.11]
## 2 2 (1-2,3-4) arrange gtable[layout]
## 3 3 (2-4,1-2) arrange gtable[layout]
## 4 4 (3-4,3-4) arrange gtable[layout]
K <- runBacon(K,
lab.id.var = 'labID',
age.14c.var = 'age14C',
age.14c.uncertainty.var = 'age14CUncertainty',
age.var = 'age',
age.uncertainty.var = 'ageUncertainty',
depth.var = 'depth',
reservoir.age.14c.var = NULL,
reservoir.age.14c.uncertainty.var = NULL,
rejected.ages.var = NULL,
bacon.acc.mean = 10,
bacon.thick = 7,
ask = FALSE,
bacon.dir = "~/Cores",
suggest = FALSE,
close.connection = FALSE)
## Using a mix of cal BP and calibrated C-14 dates
## Warning! The file with the dates seems newer than the run you are loading. If any dates have been added/changed/removed?, then please run Bacon.cleanup()
## Warning, this will take quite some time to calculate. I suggest increasing d.by to, e.g.10
## Calculating age ranges...
##
## Preparing ghost graph...
##
## Mean 95% confidence ranges 318 yr, min. 4 yr at 0 cm, max. 518 yr at 308 cm
## 100% of the dates overlap with the age-depth model (95% ranges)
##
plotChron(K,age.var = "ageEnsemble",dist.scale = 0.2)
## [1] "Found it! Moving on..."
## [1] "Found it! Moving on..."
## [1] "plotting your chron ensemble. This make take a few seconds..."
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
This is to get ensemble age estimates for each depth in the paleoData measurement table
K <- mapAgeEnsembleToPaleoData(K,age.var = "ageEnsemble")
## [1] "Kurupa.Boldt.2015"
## [1] "Looking for age ensemble...."
## [1] "Found it! Moving on..."
## [1] "Found it! Moving on..."
## [1] "getting depth from the paleodata table..."
## [1] "Found it! Moving on..."
kae <- selectData(K,"ageEnsemble")
## [1] "Found it! Moving on..."
rabd <- selectData(K,"RABD")
## [1] "Found it! Moving on..."
kurupa.instrumental <- readr::read_csv("http://lipdverse.org/geoChronR-examples/KurupaInstrumental.csv")
## Rows: 134 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (2): Year (AD), JJAS Temperature (deg C)
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
kae$units
## [1] "yr BP"
yep, we need to convert the units from BP to AD
kae <- convertBP2AD(kae)
Note that here we use the “Effective-N” significance option as we mimic the Boldt et al. (2015) paper.
plotCorEns(corout,significance.option = "eff-n")
Mixed results. But encouraging enough to move forward.
OK, you’ve convinced yourself that you want to use RABD to model temperature back through time. We can do this simply (perhaps naively) with regession, and lets do it with age uncertainty, both in the building of the model, and the reconstructing
regout <- regressEns(time.x = kae,
values.x = rabd,
time.y =kyear,
values.y =kinst,
bin.step=3,
gaussianize = FALSE,
recon.bin.vec = seq(-4010,2010,by=20))
regPlots <- plotRegressEns(regout,alp = 0.01,font.size = 8)
This result is consistent with that produced by Boldt et al., (2015), and was much simpler to produce with geoChronR.
In the next vignette learn about spectral analysis in geoChronR.
```


Welcome to Lambda Academy's Web33 Cohort!
With this page, you will find the links to different students' personal web pages within the Lambda Academy Web33 Cohort class lead by Austin Kelsay. Students in the cohort will have the oppertunity to learn how to manage their own code base, create pull requests to update this main page, and work on a project as a team.


| Interface | Description |
|---|---|
| Tagger |
Object for creating new
TagMap s and TagMap s based on the current context. |
| TagMap |
A map from
TagKey to TagValue and TagMetadata that can be used to label
anything that is associated with a specific operation. |
| TagMapBuilder |
Builder for the
TagMap class. |
| Class | Description |
|---|---|
| EmptyTagMap |
An immutable implementation of the
TagMap that does not contain any tags. |
| NoopTags |
No-op implementations of tagging classes.
|
| Tag | |
| TagKey |
A key to a value stored in a
TagMap . |
| TagMetadata |
TagMetadata contains properties associated with a Tag . |
| Tags |
Class to access the global
Tagger . |
| TagValue |
A validated tag value.
|
| Enum | Description |
|---|---|
| TagMetadata.TagTtl |
TagMetadata.TagTtl is an integer that represents number of hops a tag can propagate. |
This package manages a set of tags in the io.grpc.Context
. The tags can be used to
label anything that is associated with a specific operation. For example, the openconsensus.stats
package labels all stats with the current tags.
Tags
are key-value pairs. The keys
and values
are wrapped String
s. They are stored
as a map in a TagMap
.
Note that tags are independent of the tracing data that is propagated in the io.grpc.Context
, such as trace ID.


|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object engine.Instrument
public abstract class Instrument
The Instrument class stores a single instrument within an orchestra or band. Instruments are transient objects and exist only for calculations for the distortion with seats in mind.
| Field Summary | |
|---|---|
protected int |
channelNumber
The channel number of the instrument |
protected int |
channelVolume
The unadjusted volume of the channel of the instrument |
protected javax.vecmath.Point3d |
iLocation
The relative location of the instrument |
protected java.lang.String |
iName
The Instrument name |
| Constructor Summary | |
|---|---|
Instrument()
Default constructor |
|
Instrument(java.lang.String name,
int channelNum,
int channelVol,
javax.vecmath.Point3d location)
Constructs a new Instrument |
| Method Summary | |
|---|---|
abstract double |
getAdjustedVolume(SeatSection s)
|
int |
getChannelNumber()
Returns the MIDI channel number of the instrument |
int |
getChannelVolume()
Returns the MIDI balanced channel volume |
javax.vecmath.Point3d |
getLocation()
Returns the location of the instrument |
java.lang.String |
getName()
Returns the name of the instrument |
java.lang.String |
toString()
Returns a string representation of the instrument |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Field Detail |
|---|
protected java.lang.String iName
protected int channelNumber
protected int channelVolume
protected javax.vecmath.Point3d iLocation
| Constructor Detail |
|---|
public Instrument()
public Instrument(java.lang.String name, int channelNum, int channelVol, javax.vecmath.Point3d location)
name
- the name of the instrumentchannelNum
- the temporary MIDI channel index of this instrumentchannelVol
- the temporary MIDI balanced channel volumelocation
- the relative location in space of the instrument| Method Detail |
|---|
public abstract double getAdjustedVolume(SeatSection s)
s
- the SeatSection to calculate the relative volume for
public java.lang.String getName()
public int getChannelNumber()
public int getChannelVolume()
public javax.vecmath.Point3d getLocation()
public java.lang.String toString()
toString
in class java.lang.Object
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


HSETROOT(1) Yet Another Wallpaper Application HSETROOT(1) NAME hsetroot - Another Wallpaper Application SYNOPSIS hsetroot [-screens <bitmask>] [command1 [arg1..]] [command2 [arg1..]] OPTIONS Generic Options: -screens set a screenmask to use. For example 1 will only set the root pixmap of screen 0, 3 will set the root pixmap of screen 0 and 1. Gradients: -add <color> Add a color to range using distance 1 -addd <color> <distance> Add a color to range using custom distance -gradient <angle> Render gradient using specified angle -clear Clear the color range Solid: -solid <color> Render a solid using the specified color Colors are in the #rrbbgg or #rrggbbaa format Image Files: -center <image> Render an image centered on screen -tile <image> Render an image tiled -full <image> Render an image maximum aspect -fill <image> Render an image strechted Manipulations -tint <color> Tint the current image -blur <radius> Blur the current image -sharpen <radius> Sharpen the current image -contrast <amount> Adjust contrast of current image -brightness <amount> Adjust brightness of current image -gamma <amount> Adjust gamma level of current image -flipv Flip the current image vertically -fliph Flip the current image horizontally -flipd Flip the current image diagonally Misc: -alpha <amount> Adjust alpha level for colors and images -write <filename> Write current image to file AUTHORS Hyriand<EMAIL_ADDRESS>Sent bugreports to: hyriand@thegrave‐ yard.org OTHER This manual page was written by Francesco Pedrini<EMAIL_ADDRESS>for the Debian system (but may be used by others). User Manual Mar 2005 HSETROOT(1)


SAPUI5 Runtime & UI Libraries
...
Our license or usage conditions should go here
...
Third Party Software
This Software includes 3rd party software from different sources:
jQuery, QUnit, jQueryUI
jQuery.org Web Site
For license conditions see
http://jQuery.org/license
.
jQuery has been licensed by SAP under the MIT License:
Others...


Apartment
Big and bright apartment with a great atmosphere.
Equipped with a kitchen, diningroom and included weekly cleaning.
Living room with a couch and TV, bathroom with shower and wc.


_EDtv_ (PG-13) *** (out of ****)
Much like how his character, Ed Pekurny, finds fame as the subject of a 24-hour "reality" cable network, Matthew McConaughey should find his falling star back on the rise as the lead in _EDtv_, Ron Howard's amiable goof of a comedy. McConaughey is a charismatic charmer as Ed, a Texas-to-San Francisco transplant whose life becomes fodder for the TV-viewing masses, but unlike Truman Burbank in Peter Weir's similarly-themed (and far superior) _The_Truman_Show_, Ed is fully aware of the cameras. But as with Truman, Ed, while initially intrigued with the notion of having all eyes on him, comes to yearn for freedom, especially after it creates strain between and his girlfriend Shari (Jenna Elfman)--who, as it happens, is the ex of Ed's wilder older brother Ray (Woody Harrelson).
Howard and screenwriters Lowell Ganz and Babaloo Mandel don't have the high satirical aims of _Truman's_ Weir and writer Andrew Niccol; they simply aim to entertain. And after a tentative start, _EDtv_ picks up comedic steam and does just that, amusingly exploring the ramifications of Ed's sudden, if largely unmerited, celebrity, from opportunistic groupies (Elizabeth Hurley's sizzling model/aspiring actress) to the diminished privacy of his colorful family. Harrelson is predictably effective as a blowhard, and Elfman (Golden Globe winner for TV's _Dharma_and_Greg_) shows major promise as a screen star, but the McConaughey magnetism is so overwheming that the film could easily have been called _MATTtv_.
Michael Dequina
<EMAIL_ADDRESS>|<EMAIL_ADDRESS>Mr. Brown's Movie Site: http://welcome.to/mrbrown
CompuServe Hollywood Hotline: http://www.HollywoodHotline.com
___________________________________________________________________
You don't need to buy Internet access to use free Internet e-mail.
Get completely free e-mail from Juno at http://www.juno.com/getjuno.html
or call Juno at (800) 654-JUNO [654-5866]
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews