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.
<img src="assets/neural_network.png" width=300px>
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.
# D
self.input_nodes = input_nodes
# H
self.hidden_nodes = hidden_nodes
# C
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 0 # Replace 0 with your sigmoid calculation here
#self.activation_function = sigmoid
def train(self, features, targets):
return self.train_iterative(features, targets)
# TODO: not sure why the vectorized implementation is incorrect
def train_vectorized(self, features, targets):
# self.weights_input_to_hidden.shape W1 (D, H)
# self.weights_hidden_to_output W2 (H, C)
# features (N, D)
n_records = features.shape[0]
# hidden_inputs (N, H)
hidden_inputs = np.dot(features, self.weights_input_to_hidden) # signals into hidden layer
# N, H
hidden_outputs = self.activation_function(hidden_inputs)
# N, C = (N, H) dot (H, C)
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer
final_outputs = final_inputs # signals from final output layer
# (N, C)
# print(targets.shape, final_outputs.shape)
# print(targets, final_outputs)
# raise 'test'
error = targets - final_outputs
# (H, C)
self.weights_hidden_to_output += self.lr * np.dot(hidden_outputs.T, error) / n_records
# grad of hidden layer
# (N, H) = (N, C) dot (H, C).T
dHiddenInputs = (np.dot(error, self.weights_hidden_to_output.T) / n_records) * hidden_outputs * (1 - hidden_outputs)
# (D, H)
self.weights_input_to_hidden += self.lr * np.dot(features.T, dHiddenInputs)
def train_iterative(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
'''
# features (N, D)
n_records = features.shape[0]
# W1 (D, H)
delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)
# W2 (H, C)
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 ###
# TODO: Hidden layer - Replace these values with your calculations.
# (1, H) = (1, D) * (D, H)
hidden_inputs = np.dot(X, self.weights_input_to_hidden) # signals into hidden layer
hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer
# TODO: Output layer - Replace these values with your calculations.
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer
final_outputs = final_inputs # signals from final output layer
#### Implement the backward pass here ####
### Backward pass ###
# TODO: Output error - Replace this value with your calculations.
# (C,)
error = y - final_outputs # Output layer error is the difference between desired target and actual output.
output_error_term = error # df/dx = 1 - the contribution is 1 * error because we do the average later
# TODO: Backpropagated error terms - Replace these values with your calculations.
# (H, 1) = (H, C) dot (C,)
hidden_error = np.dot(self.weights_hidden_to_output, output_error_term)
# (1, 1) = (1, H) dot (H, 1)
hidden_error_term = hidden_outputs * (1 - hidden_outputs)* hidden_error
# Weight step (input to hidden)
# (D, H) += (1, 1) * (1, D)
delta_weights_i_h += hidden_error_term * X[:, None]
# Weight step (hidden to output)
# (H, C) += (C, ) * (H, )
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 run(self, features):
''' Run a forward pass through the network with input features
Arguments
---------
features: 1D array of feature values
'''
#### Implement the forward pass here ####
# TODO: Hidden layer - replace these values with the appropriate calculations.
hidden_inputs = np.dot(features, self.weights_input_to_hidden) # signals into hidden layer
hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer
# TODO: Output layer - Replace these values with the appropriate calculations.
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer
final_outputs = final_inputs # signals from final output layer
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.
## http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf
## random search for hyperparameter
import sys
import pandas as pd
models = []
for trial in range(20):
## perform random learning rate search
l = 10 ** np.random.uniform(-1, 0)
nodes = int(10 ** np.random.uniform(1, 2))
### Set the hyperparameters here ###
iterations = 10000
learning_rate = l
hidden_nodes = nodes
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, np.array(y)[:, None])
# 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)
line = "\r"+str(trial)+" Progress: {:2.1f}".format(100 * ii/float(iterations)) \
+ "% ... Training loss: " + str(train_loss)[:5] \
+ " ... Validation loss: " + str(val_loss)[:5] \
+ " l: " + str(l) \
+ " nodes: " + str(nodes)
sys.stdout.write(line)
sys.stdout.flush()
losses['train'].append(train_loss)
losses['validation'].append(val_loss)
latest = {"network": network, "losses": losses, "train": train_loss, "validation": val_loss, "learning_rate": l, "hidden_nodes": hidden_nodes}
print(line)
models.append(latest)
import pandas as pd
df = pd.DataFrame(models)
## http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf
## random search for hyperparameter yields the best model at 14 nodes and l of 0.81
df.sort_values("validation")
The best model was:
learning rate of 0.775994 and hidden nodes of 20
However, the models with low hidden nodes and a range of learning rate all had similarly good validation performance
This is done through log scale, random hyperparameter search
http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf
plt.scatter(df['learning_rate'], df['hidden_nodes'], marker='o', c='b', s=df['validation'] * 100, label='Validation Loss')
plt.xlabel('learning_rate')
plt.ylabel('hidden_nodes')
best_model = df.sort_values("validation").iloc[0]
plt.plot(best_model['losses']['train'], label='Training loss')
plt.plot(best_model['losses']['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim(0.0, 1)
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.
network = best_model['network']
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
It fails around Christmas which is a holiday period and the model has not seen holiday data and so the predictive power of the model is weak around this time


UNLV’s Jericho Flowers named MW defensive player of week
by Mark AndersonUNLV senior cornerback Jericho Flowers was named Mountain West defensive player of the week for his performance in Saturday’s 38-35 victory over San Jose State.
He intercepted a pass, recovered a fumble and made 10 tackles that included one for loss. One of those tackles occurred late in the fourth quarter on a third-down screen play that allowed the Rebels to get the ball back and drive for the winning touchdown.
Flowers is the second Rebel to claim this honor this season. Senior linebacker Javin White earned the award on Oct. 14.
More Rebels: Follow at reviewjournal.com/Rebels.


Item. We can use the actual value of 0.78 for \(\epsilon_a\) and the current temperature predicted by our model for \(T_E \)


F
A
K
E
H
A
P
P
Y
by Paramore
I chose this song because I felt that it is relatable and realistic to how people are sometimes.
Main
Sprite
Transform
Transition


Visitors per day. Weekends are colored. Try zooming. The plot below shows an overview.
Show:
| Time | Visits | Response |
|---|---|---|
|<PHONE_NUMBER>000 | 1 | 200 |
|<PHONE_NUMBER>000 | 2 | 120 |
|<PHONE_NUMBER>000 | 4 | 180 |
|<PHONE_NUMBER>000 | 8 | 230 |
|<PHONE_NUMBER>000 | 16 | 1370 |
|<PHONE_NUMBER>000 | 32 | 212 |
|<PHONE_NUMBER>000 | 64 | 101 |


{% extends "base.html" %} {% block title %}
Medieval catalogues > SECULAR INSTITUTIONS: Secular Colleges > Southwell, Minster > Bequest by Henry Codyngton, 10 August 1404
{% endif %}2 identified entries found.
All data was derived from the List of Identifications by Professor Richard Sharpe.
{% if not printing %}
A key to codes used in the List is available (opens in new tab).{% endif %}


Streams.error(string);
In case of an unrecoverable error (i.e., if the solver is unable to recover from the error), print the string "string" as error message and cancel all actions. This function is semantically equivalent with the built-in function assert if called with the (default) AssertionLevel.error. Line breaks are characterized by "\n" in the string.
Streams.error("x (= " + String(x) + ")\nhas to be in the range 0 .. 1");
Streams, Streams.print, ModelicaReference.Operators.'assert()' ModelicaReference.Operators.'String()'
function error extends Modelica.Icons.Function; input String string "String to be printed to error message window"; end error;


--- layout: post title: New VVF Alternative Coming From Terra Plana date: 2009-08-22 09:22:03.000000000 +01:00 tags: - Vibram - shoes - Terra Plana type: post published: true ---
Check out what's been spotted on the interweb...
A new pair of minimalist shoes to compete with Vibram Five Fingers. Unfortunately, you can't see much from the pictures but they look to be quite low profile with traditional lacing. One thing is for sure, the sole looks pretty cool.
Apparently these are due out in February (probably only in the US) with a remote possibility of a pre-Christmas release.
On the topic of Terra Plana shoes - has anyone had any experience with the Terra Plana Vivo Barefoot shoes? I'm seriously considering getting a pair of these as an "everyday" pair of shoes to replace my Merrills or possible a more formal looking pair to wear for work.


[%settitle NEHTA Implemetnation Guide%]
[%file newheader%] [%file newnavbar%]This implementation guide is prepared in collaboration with the Australian National E-Health Transition Authority (NEHTA) in order to develop and evaluate some tooling options. It will be moved to NEHTA controlled space at some in the future.
Contents:


Provides functions to aid the identification of probable/possible duplicates in Plant Genetic Resources (PGR) collections using 'passport databases' comprising of information records of each constituent sample. These include methods for cleaning the data, creation of a searchable Key Word in Context (KWIC) index of keywords associated with sample records and the identification of nearly identical records with similar information by fuzzy, phonetic and semantic matching of keywords.
| Version: | <IP_ADDRESS> |
| Depends: | R (≥ 3.0.2) |
| Imports: | data.table (≥ 1.9.3), igraph, stringdist (≥ 0.9.4), stringi, ggplot2, grid, gridExtra, methods, utils, stats |
| Suggests: | diagram, wordcloud, microbenchmark, XML, knitr, rmarkdown |
| Published: | 2018-01-13 |
| Author: | J. Aravind [aut, cre], J. Radhamani [aut], Kalyani Srinivasan [aut], B. Ananda Subhash [aut], R. K. Tyagi [aut], ICAR-NBGPR [cph], Maurice Aubrey [ctb] (Double Metaphone), Kevin Atkinson [ctb] (Double Metaphone), Lawrence Philips [ctb] (Double Metaphone) |
| Maintainer: | J. Aravind <j.aravind at icar.gov.in> |
| BugReports: | https://github.com/aravind-j/PGRdup/issues |
| License: | GPL-2 | GPL-3 |
| Copyright: | 2014-2018, ICAR-NBPGR |
| URL: | https://cran.r-project.org/package=PGRdup, https://github.com/aravind-j/PGRdup, https://doi.org/10.5281/zenodo.841963, https://aravind-j.github.io/PGRdup/, https://www.rdocumentation.org/packages/PGRdup |
| NeedsCompilation: | yes |
| Citation: | PGRdup citation info |
| Materials: | README NEWS |
| CRAN checks: | PGRdup results |
| Reference manual: | PGRdup.pdf |
| Vignettes: |
Introduction |
| Package source: | PGRdup_<IP_ADDRESS>.tar.gz |
| Windows binaries: | r-devel: PGRdup_<IP_ADDRESS>.zip, r-release: PGRdup_<IP_ADDRESS>.zip, r-oldrel: PGRdup_<IP_ADDRESS>.zip |
| OS X binaries: | r-release: PGRdup_<IP_ADDRESS>.tgz, r-oldrel: PGRdup_<IP_ADDRESS>.tgz |
| Old sources: | PGRdup archive |
Please use the canonical form https://CRAN.R-project.org/package=PGRdup to link to this page.


CSS3 Filters
Webkit has implemented CSS filters, which can spice up the way your photos look without the hassle of having to deal with software like Photoshop.
Currently there are 10 CSS filters available, which are: blur
, brightness
, contrast
, drop-shadow
, grayscale
, hue-rotate
, invert
, opacity
, saturate
and sepia
.
In the meantime you must use the -webkit-
vendor prefix, like so (gist):
img {
-webkit-filter: blur(2.5px); /* values: 0px - 25px */
-webkit-filter: brightness(5%); /* values: -100% - 100% */
-webkit-filter: contrast(1.5); /* values: 0 - 5 */
-webkit-filter: drop-shadow(3px 3px 10px rgba(0,0,0,.5)) ; /* values: x-offset y-offset blur-radius color */
-webkit-filter: grayscale(.5); /* values: 0 - 1 */
-webkit-filter: hue-rotate(5deg); /* values: 0deg - 360deg */
-webkit-filter: invert(1); /* values: 0 - 1 */
-webkit-filter: opacity(.8); /* values: 0 - 1 */
-webkit-filter: saturate(1.5); /* values: 0 - 5 */
-webkit-filter: sepia(.5); /* values: 0 - 1 */
}
You can also combine multiple filters in one line seperated by a space:
-webkit-filter: blur(2.5px) brightness(5%) contrast(1.5);
CSS filters can be used in an unimaginable ton of ways. The only downside is that currently only Google Chrome version 18 and up supports this feature.
Check out the demo and perhaps leave some feedback in the comments below.


ATP Finals: Rafael Nadal hopes to play in season-ending tournament despite abdominal strain
The Spaniard has pulled out of the event four times since it moved to London in 2009
by Sports StaffRafael Nadal is hopeful of being able to compete at the ATP Finals in London.
The Spaniard suffered an abdominal injury at the Paris Masters last week and had to pull out prior to his scheduled semi-final against Denis Shapovalov, prompting concerns he may again miss the season-ending showpiece.
Writing on Twitter, Nadal said: “Despite having a small strain, I will travel to London. Thursday or Friday I will start serving. The idea is to be able to play the £atpfinals in London.”
Nadal has pulled out of the ATP Finals four times since it moved to London in 2009 while he withdrew after one round-robin match on his last appearance in 2017.
The 33-year-old regained the world number one ranking for the first time in a year on Monday but has Novak Djokovic breathing down his neck in the race for the year-end top spot.
Nadal, who has never won the title, would need to reach the final and win all three of his group matches to guarantee staying at number one.
The draw for the tournament takes place on Tuesday afternoon.
PA


At 2:20 a.m. on April 15, 1912, the British ocean liner Titanic sinks into the North Atlantic Ocean about 400 miles south of Newfoundland, Canada. The massive ship, which carried 2,200 passengers and crew, had struck an iceberg two and half hours before. On April 10, the RMS Titanic, one of the largest and most luxurious ocean liners ever built, departed Southampton, England, on its maiden voyage across the Atlantic Ocean. The Titanic was designed by the Irish shipbuilder William Pirrie and built in Belfast, and was thought to be the world’s fastest ship. It spanned 883 feet from stern to bow, and its hull was divided into 16 compartments that were presumed to be watertight. Because four of these compartments could be flooded without causing a critical loss of buoyancy, the Titanic was considered unsinkable.


BidProject
Project Summary
Details of the project entered during the submission process.
Make
Social Network
Type
Messaging
Estimate
$40,000
Summary
Looking for a professional to create a Messaging app.
A brief summary of the app’s desired functionality is: GPS Mapping, Messaging, Profiles, Camera Support, Social Login, Payment.
The app is for black students studying abroad who want to communicate with their peers in their home countries.
We are looking to create the app for: Android, iOS, Web.
We want to start: 3rd April 2017
Audio player software is used to play back sound recordings in one of the many formats available for computers today. It can also play back music CDs. There is audio player software that is native to the computer’s operating system (Windows, Macintosh, and Linux) and there are web-based audio players. This article discusses the local computer audio players. Advantages of computer audio player software
The main advantage of a computer audio player is that you can play your audio CDs and there is no longer any need to have a separate CD player. However the capabilities of the computer audio player go beyond just playing traditional music CDs. You can also play sound clips in one of the many audio formats found in the information technology industry today. The MP3 format is a standard whereby a high rate of compression can be achieved on sound files allowing the ability to store large numbers of them on hard disk. Another advantage is when the computer audio player has ripping capability you can extract a sample clip from the sound track for internet posting or emailing. Audio players in the form of portable MP3 players like Apple’s iPod are extremely advantageous due to their long battery life and capability to be carried practically anywhere.
Requirements
Bundle
Drag requirements from the right to create tasks for the developers
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Posted by Borg on November 12, 2019
Posted by Borg on August 29, 2019
Posted by Borg on July 2, 2017
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Posted by Borg on March 30, 2017
不会python的R程序员不是好数据科学家。


Hi my name is Ariel Garay.
I'm a VCD major and I work for the MMC in the library.
I have no experience with html and all of this besides the required knowledge from CPLA and a couple coworkers who taught me a few things.
So I am taking this class to build up my experience which will help with my future career as well as my current job in the IT department for the school at the MMC.


UFC 244 Full Fight Video: Watch Derrick Lewis Knock Out Alexander Volkov
by Trent ReinsmithDerrick Lewis began his UFC career on a 3-2 run. Then in 2015, Lewis found his footing. He won six fights in a row with five of those victories coming by way of knockout. In 2017 he was on the wrong end of a knockout loss to Mark Hunt. Lewis bounced back from that loss with a third-round TKO victory over Marcin Tybura. After a decision win over Francis Ngannou, Lewis had climbed to the No. 2 spot in the official UFC heavyweight rankings. Lewis seemed to be closing in on a title shot. When he was matched up against the No. 5 ranked Alexander Volkov at UFC 229, it was not a stretch to think Lewis was one-fight away from fighting for the heavyweight crown.Volkov, the former Bellator and M-1 Global heavyweight champion, joined the UFC in 2016. He won his first four fights with the promotion. Volkov's bonus-winning knockout victories over Stefan Struve and former champion Fabricio Werdum put him on the fast track toward the top of the heavyweight rankings.
Lewis was in trouble early in the first round and never really got things going. By the time the third stanza began, Lewis was running on fumes. It looked as if he was on his way to taking the loss. Lewis was left with no choice but to look for the knockout. As the clock ticked down, Lewis had landed fewer than 10 significant strikes in the round, while Volkov was over 40. And then, with 17 seconds left in the fight, Lewis found Volkov with a jab and a cross that dropped Volkov to his back. Lewis then teed off with a flurry of ground strikes to score the come from behind knockout victory. The official end time for the bout was 4:49 of the third round.
Lewis went on to fight Daniel Cormier for the heavyweight title less than a month after that win, but he was submitted in the second round. In his next outing, Junior dos Santos knocked out Lewis in the second stanza of their March contest.
Lewis had surgery to repair his injured knee after that loss. On Saturday, Lewis returns to action to face Blagoy Ivanov on the pay-per-view portion of the UFC 244 fight card.
UFC 244 takes place Saturday at New York's Madison Square Garden. The pay-per-view card airs on ESPN+ following prelims on ESPN2 and ESPN+.
Before that fight takes place watch Lewis' comeback win over Volkov.
More From UFC 244:


"Valentine"
Valentines Day is usually a holiday of romance and love. But, not this year, it isn't. When the delivery of a threatening Valentine card is followed by a brutal murder of a young med student, her friends soon learn that someone from their past is stalking them all in "Valentine."
Been there. Seen that.  Director Jamie Blanks ("Urban Legends") and a battery of writers bring the adaptation of Tom Savage's novel to the screen in a pretty production that is all good looks and absolutely no substance.
The story rehashes the horror genre in an unoriginal and uninvolving way that simply marks its time and body count until the muddled and silly ending. This is a shame since the production values deserve to be in a much better film. Every horror movie cliché - cheap jump-out-of-the-dark scares, the mandatory masked/faceless killer, go-into-the-dark-room-and-see-what-made-that-noise, pretty victims - are used frequently but to little good effect.
In the movie's prologue, a school dance for teens is marred by an incident involving the school dork, Jeremy Melton (Joel Palmer), and one of his female classmates. Jeremy is accused of molesting the girl and is sent to reform school. Years later, one of the girls who made fun of Jeremy, Shelley (Katherine Heigl), receives a death threat Valentine's card and is soon the victim of a brutal murder at the hands of a butcher knife-wielding, cherub-masked killer.
Soon, Shelley's friends begin to fall victim to more Valentines and violence as, one by one, they are slain in a variety of ways, from bow and arrow to electrocution to bludgeoning with a steam iron. The body count steadily rises as the killer stalks and kills, one by one, each of the friends until only one, Kate (Marley Shelton), is left to face the murderer.
Movie slight of hand and the expectation of audience acceptance of any and all illogic are the fodder that drives "Valentine." The ambiguity of the killer - the threatening cards are invariably signed "JM" - leads us to believe that young Jeremy is the culprit. Along the way, misleads and maguffins prevail as the storytellers try a shell game to keep us guessing just who the killer is. Unfortunately, they only kept me guessing for the first 20 minutes. After that, I marked time until proved correct, in the end. This certainly takes the thrill out of "thriller."
The unfortunate cast is populated with a bevy of pretty young women who provide the victim pool with candidates for the escalating body count. TV's "Angel," David Boreanaz, is ostensibly the film's star, what with being top billed and everything. But, the actor wanders through his role looking as if he's in the wrong movie. Denise Ricahards, on the heels of being a Bond girl, seems to have made a very bad career choice as just another slasher victim. The rest are pretty and bland.
As I said, the production is far superior to the story, with stylish photography by Rick Bota giving things a slick look. Set design, too, by Stephen Gaeghan, lends a terrific appearance with some inventive sets, including a video art gallery that is visual eye candy. Too bad the rest of the film fails to meet the production standards. Helmer Blanks should rethink his dedication to the horror genre.
I give "Valentine" a D+.
For more Reeling reviews visit www.reelingreviews.com
<EMAIL_ADDRESS><EMAIL_ADDRESS>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


There are a number of filter design modes in OBO-Edit. Filters are used throughout OBO-Edit; sometimes the filter design mode can be specified by the user, and sometimes the design mode is locked. (The latter case tends to be seen in complex controls where a lot of fine control is needed; in these cases, the filter design controls are usually locked into the most advanced settings).
When the filter design mode is user configurable, the design mode can be set by clicking the "Advanced" tab of the filter designer. For example, by default, OBO-Edit's search control looks like this:
If you click the "Advanced" tab, the advanced interface appears:
By clicking combinations of the checkboxes in this panel, you can select the filter design mode. The meanings of these checkboxes are discussed in the following sections.


Hi, I'm Tongyun. I'm a UX designer who makes enterprise software intuitive and enjoyable to use, now bringing more user-centered experience into a complex marketing product @Salesforce Marketing Cloud.
Hi, I'm Tongyun. I'm a UX designer who makes enterprise software intuitive and enjoyable to use, now bringing more user-centered experience into a complex marketing product @Salesforce Marketing Cloud.


--- layout: sponsors title: TEDx San Juan 2011 ---
Platinum Sponsor
Venue Sponsor
Gold Sponsor
Silver Sponsor


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Version 1.9
Brutalism is a hot trend in web design, and this framework is a free, open source framework for building brutal websites!
When thinking of brutalism, think CRUDE:
Cynically confrontational
Roughly raw and rugged
Uncomfortably ugly and unpolished
Deliberately daring and damaging
Extremely exasperated and embittered
Brutalist web design is the anti-conventional approach to proper, modern web design principles. In essence, it harkens back to the days of a more primitive web.
Examples of websites built with Brutalist Framework.
Download
Version 1.9


An immersive journey preparation tool for people with vision impairment
Dung Lai, Chris McCarthy, David Sly, Harrison Bennett, Matt Shackleton, Stuart Favillla
Demo web application
Explore immersive environment with Ambisonic surround sound technology
Flinders Street Station Map
Current supported voice/text commands:
Help • What is this • Where am i • Describe • When recorded • Next location • Replay • Hi • Hello • Start Exploring
Please choose on location
More information at:


Buy a prepaid card (1.50 euros). You can find them either at the ski lifts office or in the Tourist Office of Sainte Foy Tarentaise.
Go to the online payment website (linked below) with your prepaid card in hand and buy a ski pass. Your prepaid card will be updated instantly.
Now you can use the ski lifts and enjoy the peaks. good skiing !
Online Payment website


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Other POMDP Libraries¶
There are a number of other libraries; Feel free to check them out and feel the differences. We think our pomdp_py library has a simpler and more extensible set of interfaces than these peer libraries. Our implementation in Python and Cython makes the library easy for use in a research project, or a ROS robot system (when ROS 2 supports Python 3; ROS doesn’t support Python 3 but I have worked around the communication between ROS and pomdp_py in some other way; The code base is all in Python, which accelerates development.)
POMDPy is written in Python. This library features implementations of advanced Value Iteration algorithms and an implementation of POMCP. The documentation of interfaces is not sufficient, which appear to be more convoluted than pomdp_py. A potential connection between the two could be possible if POMDPy provides a clearer documentation of its POMDP interfaces.
APPL (Approximate POMDP Planning Software) is a C++ library for approximate POMDP planning. It uses POMDPX file format to parse POMDPs. This library contains implementation of state-of-the-art planning algorithms such as SARSOP and DESPOT. It would be great to interface pomdp_py with this library so that one can make use of these algorithms in Python.
POMDPs.jl is written in Julia, under active development. Julia is known to be suited for high-performance numerical analysis. Besides POMCP, this library also has an implementation of DESPOT, and SARSOP. The libray supports porting Python POMDP code into Julia code. Connection between interfaces in pomdp_py and POMDPs.jl could potentially be done via this Python porting.
AI-Toolbox is written in C++. This library contains a number of algorithms for planning and reinforcement learning in MDP or POMDP problems. It has Python 2 and 3 bindings in specific cases.
simple_rl is written in Python. It focuses on MDP reinforcement learing algorithms and contains many tasks implementations. It does have a POMDP interface and an implementation of BeliefMDP.


A GitHub community driven Jekyll tutorial hub which aims to give the web development community the detailed and up to date knowledge for building awesome Jekyll static websites.


Dashboard
10
Members
10
Cooperative
10
Loans
10
Status
Member's Info
| Name | Coopearative no. | Loan | Status |
|---|---|---|---|
| Victor Magtangol | no. 1 | Php 20,000 | Approved |
| Rico Puno | no.2 | Php 20,000 | Pending |
| Matilda Maldita | no.3 | Php 20,000 | Approved |
| Stephanie Landon | no.4 | Php 20,000 | Approved |
| Mike Tyson | no.5 | Php 20,000 | Pending |
| Hobo Johnson | no.6 | Php 20,000 | Approved |
| Dirk Nowitzki | no.7 | Php 20,000 | Pending |
| Lee Minho | no.8 | Php 20,000 | Pending |
| John Regala | no.9 | Php 20,000 | Approved |
| Muhamad ali | no.10 | Php 20,000 | Pending |


Welcome to Kellyworld
The twisted machinations of my evil mind...
AKA projects for CISC472
The Trudge Report: Experimenting with Firebase. Variation on an anonymous chat app
Spelling Hive: Further adventures in Firebase. Online version of the NYTimes Spelling Bee game. Compete in rounds with two teams.
KellyCTF: Diabolical CTF-style web security challenges devised by yours truly


Definition
List of categories of the field, sorted alphabetically. The relative weight of each category is indicated by the font size.
Parameters
In the top slot, the qualitative field whose categories are to be represented.


Benford's Law is a phenomenological law about the frequency distribution of leading digits in many real-life sets of numerical data and it has a wide range of applications.
Benford's Law has been used for detecting fraud in accounting records for several years. Accounting records are supposed to follow it and large deviations from the expected probabilities for the first two significant digits may suggest the numbers have been tampered with.
Traditional statistical tests, though, yield a large number of false positives when applied to large data sets. That is commonly known as the "excessive power" of the tests.
Recently, two techniques based on bootstrap procedures were developed to tackle this problem and yield higher accuracy and sensitivity.
The bootstrap procedure is very computationally-expensive yet highly parallelizable, so one of the goals of this package is to enable the application of these new techniques in large data sets while delivering results fast.
The implementation is based on the following papers so, for further details on the procedures and the decision criteria, please refer to it:
SUH, I.; HEADRICK, T.C. A comparative analysis of the bootstrap versus traditional statistical procedures applied to digital analysis based on Benford’s Law. Journal of Forensic and Investigative Accounting, Vol. 2, n. 2, p. 144-175, 2010.
SUH, I., HEADRICK, T.C.; MINABURO, S. An Effective and Efficient Analytic Technique: A Bootstrap Regression Procedure and Benford's Law. Journal of Forensic & Investigative Accounting, Vol. 3, n. 3, p.25-44, 2011.
A novel use of Benford's Law is edge detection. Sets of pixels depicting edges show higher adherence to the expected distribution of the leading digit.
So, researchers have developed a metric called Benford Goodness of Parameter (BGP) which is equivalent to an RMSE of the observed distribution that can be applied to subsets of pixels as defined by a sliding window over the original image. This procedure was named 2D-Scanning Benford Analysis (2D-SBA).
Similarly to a convolutional kernel, the use of a transformation based on the BGP of a given set of pixels allows the detection of faint features in images.
It is easy to see that even moderate-size images will span thousands of windows to be transformed. The goal of this package is to quickly deliver results when applying the SBA procedure to images.
The implementation follows this paper so, for further details on the procedure, please refer to it:
BHOLE, G.; SHUKLA, A.; MAHESH, T.S. Benford Analysis: A useful paradigm for spectroscopic analysis, arXiv:1408.5735, 2014.
The Benford Goodness of Parameter (BGP) metric and the Scanning Benford Analysis (SBA) can also be used for detecting weak peaks in signals.
This feature is currently under development in the Benford Analysis for Spark package.
For further details, please refer to the original paper:
BHOLE, G.; SHUKLA, A.; MAHESH, T.S. Benford Analysis: A useful paradigm for spectroscopic analysis, arXiv:1408.5735, 2014.
Yet another novel use of Benford's Law is the detection of suspiciously behaving nodes in social networks.
This feature is currently under development in the Benford Analysis for Spark package.
For further details, please refer to the original paper:
GOLDBECK, J. Benford's Law Applies to Online Social Networks, PLoS ONE 10(8): e0135169. doi:10.1371/journal.pone.0135169, 2015.


This page illustrates how to setup Thebe Lab, using a local Jupyter server as kernel provider. This assumes that you have started the local server as:
jupyter notebook --NotebookApp.token=test-secret --NotebookApp.allow_origin='*'and that it's running on port 8888
from IPython.display import Math Math('x')
1+1


Figure 2
Restrictive Legislative Mentions
Back
If the chart fails to load, try disabling your ad blocker.
We consider ‘drone oversight’ to be statements that oppose drone strikes and seek to impose restraint and/or greater transparency.
This data was collected by Sarah Kreps and Miles McCain.
These charts are fully interactive! Try hovering over a mention bar, or selecting an area to zoom in on.


Variable Slope Relu Demo
(red: standard ReLu, blue: variable slope ReLu)
Feedback \(\mu_D\):
5
\(\eta\):
20
\(\beta_{max}\):
0.65


This package provides a number of utility classes that are used by pvData code.
This package provides utility code:
- multiChoice
- Support for a multiChoice structure, which is a structure containing two
fields: choices and bitMask. choices is an array of strings and bitMask
selects a subset of the choices.
- Message Queue
- Support for queuing messages to give to requesters.
- Thread
- ThreadCreate and Executor
- TimeFunction
- Time how long a function call requires.
- LinkedList
- A double linked list facility that requires the user to allocate a
node. It is more efficient that ArrayList and does not require the
implementation to allocate storage for the nodes.
- Timer
- An implementation of Timer that does not require an object to be
created for each timer request.
- BitSet
- An implementation of BitSet that can be serialized.
- Queue
- A queue implementation.
- Destroyable
- A base interface for destroy.
- Serialize
- A helper for serialization.


{% extends "layout.html" %} {% block content %}
Please authorize so we can grab your Goodreads To-Read shelf.


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string | grammarFileName = "" |
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tuple | antlr_version = version_str_to_tuple("3.1.1") |
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string | antlr_version_str = "3.1.1" |
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| tokenNames = tokenNames |
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| tuple | DFA3_eot |
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| tuple | DFA3_eof |
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| tuple | DFA3_min |
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| tuple | DFA3_max |
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| tuple | DFA3_accept |
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| tuple | DFA3_special |
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| list | DFA3_transition |
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| DFA3 = DFA |
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| tuple | DFA5_eot |
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| tuple | DFA5_eof |
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| tuple | DFA5_min |
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| tuple | DFA5_max |
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| tuple | DFA5_accept |
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| tuple | DFA5_special |
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| list | DFA5_transition |
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| DFA5 = DFA |
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| tuple | DFA6_eot |
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| tuple | DFA6_eof |
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| tuple | DFA6_min |
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| tuple | DFA6_max |
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| tuple | DFA6_accept |
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| tuple | DFA6_special |
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| list | DFA6_transition |
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| DFA6 = DFA |
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| tuple | DFA8_eot |
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| tuple | DFA8_eof |
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| tuple | DFA8_min |
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| tuple | DFA8_max |
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| tuple | DFA8_accept |
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| tuple | DFA8_special |
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| list | DFA8_transition |
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| DFA8 = DFA |
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tuple | FOLLOW_WS_in_query112 = frozenset([15, 23, 27, 28, 29, 30, 31, 32, 33, 43]) |
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tuple | FOLLOW_expression_in_query115 = frozenset([15]) |
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tuple | FOLLOW_WS_in_query117 = frozenset([15]) |
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tuple | FOLLOW_EOF_in_query120 = frozenset([1]) |
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tuple | FOLLOW_sequence_in_expression139 = frozenset([1, 15]) |
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tuple | FOLLOW_andOp_in_expression142 = frozenset([23, 27, 28, 29, 30, 31, 32, 33, 43]) |
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tuple | FOLLOW_sequence_in_expression144 = frozenset([1, 15]) |
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tuple | FOLLOW_factor_in_sequence170 = frozenset([1, 15]) |
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tuple | FOLLOW_WS_in_sequence173 = frozenset([15, 23, 27, 28, 29, 30, 31, 32, 33, 43]) |
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tuple | FOLLOW_factor_in_sequence176 = frozenset([1, 15]) |
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tuple | FOLLOW_term_in_factor202 = frozenset([1, 15]) |
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tuple | FOLLOW_orOp_in_factor205 = frozenset([23, 27, 28, 29, 30, 31, 32, 33, 43]) |
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tuple | FOLLOW_term_in_factor207 = frozenset([1, 15]) |
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tuple | FOLLOW_notOp_in_term231 = frozenset([23, 27, 28, 29, 30, 31, 32, 33, 43]) |
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tuple | FOLLOW_primitive_in_term233 = frozenset([1]) |
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tuple | FOLLOW_primitive_in_term247 = frozenset([1]) |
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tuple | FOLLOW_restriction_in_primitive263 = frozenset([1]) |
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tuple | FOLLOW_composite_in_primitive269 = frozenset([1]) |
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tuple | FOLLOW_item_in_primitive275 = frozenset([1]) |
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tuple | FOLLOW_comparable_in_restriction301 = frozenset([15, 16, 17, 18, 19, 20, 21, 22]) |
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tuple | FOLLOW_comparator_in_restriction303 = frozenset([23, 28, 29, 30, 31, 32, 33]) |
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tuple | FOLLOW_arg_in_restriction305 = frozenset([1]) |
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tuple | FOLLOW_WS_in_comparator329 = frozenset([15, 16, 17, 18, 19, 20, 21, 22]) |
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tuple | FOLLOW_LE_in_comparator335 = frozenset([1, 15]) |
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tuple | FOLLOW_LESSTHAN_in_comparator341 = frozenset([1, 15]) |
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tuple | FOLLOW_GE_in_comparator347 = frozenset([1, 15]) |
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tuple | FOLLOW_GT_in_comparator353 = frozenset([1, 15]) |
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tuple | FOLLOW_NE_in_comparator359 = frozenset([1, 15]) |
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tuple | FOLLOW_EQ_in_comparator365 = frozenset([1, 15]) |
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tuple | FOLLOW_HAS_in_comparator371 = frozenset([1, 15]) |
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tuple | FOLLOW_WS_in_comparator374 = frozenset([1, 15]) |
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tuple | FOLLOW_item_in_comparable396 = frozenset([1]) |
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tuple | FOLLOW_function_in_comparable402 = frozenset([1]) |
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tuple | FOLLOW_fnname_in_function417 = frozenset([23]) |
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tuple | FOLLOW_LPAREN_in_function419 = frozenset([23, 24, 28, 29, 30, 31, 32, 33]) |
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tuple | FOLLOW_arglist_in_function421 = frozenset([24]) |
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tuple | FOLLOW_RPAREN_in_function423 = frozenset([1]) |
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tuple | FOLLOW_arg_in_arglist452 = frozenset([1, 15, 44]) |
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tuple | FOLLOW_sep_in_arglist455 = frozenset([23, 28, 29, 30, 31, 32, 33]) |
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tuple | FOLLOW_arg_in_arglist457 = frozenset([1, 15, 44]) |
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tuple | FOLLOW_item_in_arg482 = frozenset([1]) |
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tuple | FOLLOW_composite_in_arg488 = frozenset([1]) |
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tuple | FOLLOW_function_in_arg494 = frozenset([1]) |
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tuple | FOLLOW_WS_in_andOp508 = frozenset([15, 25]) |
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tuple | FOLLOW_AND_in_andOp511 = frozenset([15]) |
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tuple | FOLLOW_WS_in_andOp513 = frozenset([1, 15]) |
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tuple | FOLLOW_WS_in_orOp528 = frozenset([15, 26]) |
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tuple | FOLLOW_OR_in_orOp531 = frozenset([15]) |
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tuple | FOLLOW_WS_in_orOp533 = frozenset([1, 15]) |
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tuple | FOLLOW_43_in_notOp548 = frozenset([1]) |
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tuple | FOLLOW_NOT_in_notOp554 = frozenset([15]) |
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tuple | FOLLOW_WS_in_notOp556 = frozenset([1, 15]) |
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tuple | FOLLOW_WS_in_sep571 = frozenset([15, 44]) |
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tuple | FOLLOW_44_in_sep574 = frozenset([1, 15]) |
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tuple | FOLLOW_WS_in_sep576 = frozenset([1, 15]) |
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tuple | FOLLOW_set_in_fnname0 = frozenset([1]) |
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tuple | FOLLOW_LPAREN_in_composite612 = frozenset([15, 23, 27, 28, 29, 30, 31, 32, 33, 43]) |
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tuple | FOLLOW_WS_in_composite614 = frozenset([15, 23, 27, 28, 29, 30, 31, 32, 33, 43]) |
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tuple | FOLLOW_expression_in_composite617 = frozenset([15, 24]) |
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tuple | FOLLOW_WS_in_composite619 = frozenset([15, 24]) |
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tuple | FOLLOW_RPAREN_in_composite622 = frozenset([1]) |
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tuple | FOLLOW_FIX_in_item642 = frozenset([28, 29, 30, 31, 32, 33]) |
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tuple | FOLLOW_value_in_item644 = frozenset([1]) |
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tuple | FOLLOW_REWRITE_in_item658 = frozenset([28, 29, 30, 31, 32, 33]) |
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tuple | FOLLOW_value_in_item660 = frozenset([1]) |
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|
tuple | FOLLOW_value_in_item674 = frozenset([1]) |
| |
|
tuple | FOLLOW_text_in_value692 = frozenset([1]) |
| |
|
tuple | FOLLOW_phrase_in_value708 = frozenset([1]) |
| |
|
tuple | FOLLOW_TEXT_in_text732 = frozenset([1]) |
| |
|
tuple | FOLLOW_DISTANCE_FN_in_text743 = frozenset([1]) |
| |
|
tuple | FOLLOW_GEO_POINT_FN_in_text756 = frozenset([1]) |
| |
|
tuple | FOLLOW_QUOTE_in_phrase775 = frozenset([4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]) |
| |
|
tuple | FOLLOW_set_in_phrase777 = frozenset([4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]) |
| |
|
tuple | FOLLOW_QUOTE_in_phrase793 = frozenset([1]) |
| |
|
int | MEMO_RULE_FAILED = -2 |
| |
|
int | MEMO_RULE_UNKNOWN = -1 |
| |
|
| DEFAULT_TOKEN_CHANNEL = DEFAULT_CHANNEL |
| |
|
| HIDDEN = HIDDEN_CHANNEL |
| |
|
| tokenNames = None |
| |
|
tuple | antlr_version = (3, 0, 1, 0) |
| |
|
string | antlr_version_str = "3.0.1" |
| |


{% extends "base.html" %} {% load bootstrap5 %} {% block content %}
{{neighborhood.description}}
{% if request.user.profile.neighborhood == neighborhood %} Leave {{neighborhood.name}} {% elif not request.user.profile.neighborhood %} Join {{neighborhood.name}} {% endif %} {% if neighborhood.admin.user.username == current_user.username %} {% endif %}


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Table of Cases
Principal cases are indicated by italics.
A, Re, 964
A & P Trucking Co.; United States v., 855
Aaron; People v., 529
Abbell; United States v., 71
Abbot; People v., 438
Abbott v. The Queen, 1002
Abdul-Hussain; R. v., 1001
Abuelhawa v. United States, 797
Adams; People v., 917
Adams; State v., 224
Addington v. Texas, 1026
Adkins; Commonwealth v., 252
Agnew v. United States, 1132
Airedale NHS Trust v. Bland, 255
Akers; State v., 316
Alamo; People v., 1102
Albernaz v. United States, 794
Alexander; United States v., 1089
Aleynikov; State v., 1157
Al-Hamdani; State v., 405
Allen; State v., 1130
Allery; State v., 896
Almeida; Commonwealth v., 547
Alston; State v., 376
Alston-Graves; United States v., 282
Ambriz-Ambriz; United States v., 224
Ambrose; United States v., 694
Anderson v. Superior Court, 787
Andrews v. United States, 928
Ansaldi; United States v., 339
Antick; People v., 550
Apodaca v. Oregon, 57
Aponte; People v., 647
Apple; United States v., 752
Apprendi v. New Jersey, 1261
Armstrong; State v., 1058
Arnold; State v., 1009
Arthur Andersen LLP; United States v., 831
As. H., In re, 41
Ashbaucher v. City of Arcata, 224
Ashford v. Commonwealth, 675
Atkins v. State, 929
Avery; People v., 1170
Axelberg v. Commissioner of Pub. Safety, 952
Azadian; United States v., 740
B (A Minor) v. Director of Pub. Prosecutions, 298
Babbitt; People v., 1058
Bache; Commonwealth v., 404
Bailey v. Commonwealth, 632
Bailey; United States v., 949
Bainbridge; R. v., 760
Bajakajian; United States v., 218
Baker; State v., 317
Baker; United States v., 747
Baldwin v. New York, 57
Ball; United States v., 69
Ballew v. Georgia, 57
Barber v. Superior Court, 253
Barela; State v., 424
Barnes; People v., 374
Barnes; State v., 223
Barnes v. United States, 267
Barnett v. State, 540
Barnett; State v., 494
Barragan-Sierra; State v., 799
Barron v. Mayor of Balt., 54
Bartley v. Commonwealth, 245
Bass; United States v., 162
Bateman; R. v., 494
Bates v. Commonwealth, 996
Battalino v. People, 1062
Bautista v. Small, 901
Baxter v. State, 979
Bazeley’s Case, 1111
Beaudry; State v., 316
Beaver; People v., 1101
Bechtel v. State, 1058
Beck; People v., 647
Becker; State v., 1028
Bei Bei Shuai v. State, 180
Benally; United States v., 34
Beneficial Fin. Co.; Commonwealth v., 837
Benniefield; State v., 293
Berger; State v., 220
Bernhardt; State v., 664
Berry; People v., 477
Berry; United States v., 1022
Bertling; United States v., 755
Bethel; State v., 1047
B.H.; State v., 993
Birreuta; People v., 619
Bishop; United States v., 337
Black; People v., 1244
Blackmon; United States v., 775
Blankenship; United States v., 762
Blaue; R. v., 631
Blixt; United States v., 1158
Bolden v. State, 774
Bonjour; State v., 953
Booth; State v., 1014
Bordeaux; United States v., 476
Borelli; United States v., 788
Borough of. See name of borough
Bourbonnaise v. State, 1126
Bowman; State v., 718
Boyle v. State, 484
Boyston; State v., 1013
Bradshaw; State v., 252
Brady; People v., 607
Bram v. United States, 1206
Brammer; State v., 495
Brantley; United States v., 244
Bridges; State v., 769
Brigham; People v., 775
Brothers; People v., 521
Brown v. Golden Valley, City of, 943
Brown; People v., 1169
Brown v. Plata, 86
Brown; R. v., 1001
Brown v. State, 648
Brown v. United States, 882
Bruce; State v., 219
Brugmann; Commonwealth v., 950
Bruguier; United States v., 313
Bryan; United States v., 1106
Bryant; State v., 347
Bullock; People v., 220
Bullock v. United States, 458
Burch v. Louisiana, 57
Burden; People v., 513
Burgess; R. v., 1061
Burks; State v., 61
Burks v. United States, 24
Burns v. State, 1112
Burroughs; People v., 533
Burson v. Freeman, 73
Burton; State v., 1030
Bushell’s Case, 64
Buswell; Commonwealth v., 654
Butler; People v., 495
Cabaltero; People v., 546
Caine; State v., 646
Calder v. Bull, 178
Caldwell v. Mississippi, 594
California v. Ramos, 596
Callins v. Collins, 576
Camerano; Commonwealth v., 762
Cameron; State v., 1054
Campbell; Commonwealth v., 539 Pa. 212 (1994), 797
Campbell; People v., 335 N.W.2d 27 (1983), 621
Canon; United States v., 718
Cardwell; Commonwealth v., 247
Carlson; Commonwealth v., 631
Carmony; People v., 219
Carpenter v. United States, 1155
Carpenter; United States v., 792
Carrazana; United States v., 782
Carroll; People v., 246
Carter v. Commonwealth, 662
Carter; Commonwealth v., 626
Castle; United States v., 799
Castro; United States v., 817
Cavitt v. Cullen, 546
Chance; People v., 1006
Chaney; State v., 115
Chanthabouly; State v., 1053
Chaplin v. United States, 1128
Chappell; Commonwealth v., 1029
Chappell v. United States, 1149
Chavez; State v., 268
Cheshire; R. v., 615
Childress; People v., 717
Chiu; People v., 726
Chun; People v., 542
Citizens United v. FEC, 830
City of. See name of city
Clayton, State ex rel. v. Griffith, 586
Cleveland v. United States, 1165
Coates v. Cincinnati, 189
Cobb v. State, 1017
Coddington; People v., 1053
Collins v. City of Harker Heights, 975
Coltherst; State v., 774
Comer v. State, 548
Commonwealth v. See name of opposing party
Community Alternatives Mo., Inc.; State v., 841
Coney; R. v., 728
Connally v. General Constr. Co., 177
Connecticut v. Johnson, 70
Contento-Pachon; United States v., 997
Cooper Indus., Inc. v. Leatherman Tool Group, Inc., 218
Cordoba-Hincapie; United States v., 293
County Court v. Allen, 53
Cox v. Louisiana, 195
Cox; State v., 35
Craig; State v., 664
Crain v. State, 333
Croft; United States v., 1150
Culverson v. State, 922
Cummings; People v., 1148
Curley v. United States, 40
Cutshall v. Sundquist, 88
Dabney v. State, 929
Dando v. Yukins, 992
D’Aquino v. United States, 997
Datcher; United States v., 78
David W.; People v., 88
Davis v. Alaska, 439
Davis v. City of Peachtree City, 315
Davis; People v., 561 N.E.2d 165 (Ill. 1990), 1150
Davis; State v., 38 N.J.L. 176 (1875), 1170
Davis; State v., 79 P.3d 64 (Ariz. 2003), 219
Davis v. United States, 873 A.2d 1101 (D.C. 2005), 421
Dean v. United States, 313
Decato; State v., 1023
Decina; People v., 231
Deen; United States v., 125
Deer; State v., 228
Defreitas; United States v., 747
DeGrange; Pennsylvania v., 158
DeLawder; State v., 439
Delling v. Idaho, 1047
Dellinger; People v., 511
DeMarco; Commonwealth v., 991
DeMizio; United States v., 1167
Denbo; People v., 373
Dennis v. State, 475
Diaz; State v., 779
Dimora; United States v., 798
Disanto; State v., 660
Divers v. State, 1126
Dix v. Superior Court, 1193
Dixon v. United States, 52
Dixon; United States v., 992
Dlugash; People v., 678
Doe v. United States, 1192
Dreiman v. State, 1156
Drew; Commonwealth v., 1128
Drew; United States v., 154
Drum; Commonwealth v., 454
Dufield; State v., 516
Dunn v. United States, 36
Dunne; Commonwealth v., 650
Durham v. United States, 1079
Durland v. United States, 1128
Dusky v. United States, 1022
Dykas; State v., 918
Dymond; R. v., 1138
Earnest; Commonwealth v., 454
Earp; State v., 645
Eaton; State v., 223
E.B. v. Verniero, 88
Edwards; State v., 897
Egginton; R. v., 1104
Eicher; State v., 1148
Elliott v. Commonwealth, 886
Elmi; State v., 619
Erdemović; Prosecutor v., 1002
Erwin v. State, 921
Escobar De Bright; United States v., 802
Esop; R. v., 349
Evans; R. v., 251
Evans v. State, 910
Evans v. United States, 799
Everhart; State v., 507
Ewing; People v., 664
Ex parte. See name of party
Falsetta; People v., 34
Farley; People v., 541
Farmer v. Brennan, 280
Farnsworth v. Zerbst, 740
Farraj; United States v., 1158
Fasulo v. Arafeh, 1026
Fearon; State v., 1081
Federici; Commonwealth v., 1025
Feely; R. v., 1172
Felton; State v., 488
Fernandez; People v., 70
Fernandez; United States v., 816
Ferry v. Ramsey, 48
Finger v. State, 1047
Fiore; People v., 1001
Fischer; Commonwealth v., 416
Fisher v. State, 535
Flattum; State v., 1068
Fokker Servs., B.V.; United States v., 851
Fontes; People v., 955
Forrest; State v., 461
Foster; Commonwealth v., 521
Foster; People v., 802
Foster; State v., 1261
Franklin; United States v., 780
Frazier v. State, 1129
Frazier v. Weatherholtz, 48
Frendak v. United States, 1025
Friedman v. Sebelius, 860
Frost; United States v., 1162
Fugit; United States v., 176
Fulcher v. State, 1060
Fully Informed Jury Ass’n v. San Diego, 73
Gable-Wall Case (Giebelmauer), 506
Gagne v. Booker, 442
Gangahar; State v., 400
Garcia; United States v., 753
Gardner v. Florida, 1234
Gardner v. People, 326
Gardner; United States v., 718
Gargus; State v., 245
Garland, Ex parte, 68
Garthus; United States v., 1071
Gartland; State v., 924
Gaynor; Commonwealth v., 550
Gel Spice Co.; United States v., 861
Gerhart v. State, 1140
Giaccio v. Pennsylvania, 187
Giardino; People v., 404
Gillis; People v., 544
Giovannetti; United States v., 284
Gladue; R. v., 1260
Glowacki; State v., 924
Godwin; United States v., 71
Goffer; United States v., 282
Gonzales v. Oregon, 978
Gonzalez; Commonwealth v., 921
Goodhall; R. v., 1127
Gormley; Commonwealth v., 764
Gould; United States v., 1058
Gounagias; State v., 476
Government of V.I. v. Dowling, 777
Graham; State v., 1057
Graham v. United States, 1124
Gray; United States v., 263
Greenwood; State v., 61
Gresham; State v., 35
Griffin v. State, 752
Griffin; State v., 885
Griffin; United States v., 748
Griffith v. State, 110
Grullon; State v., 802
Guest; State v., 302
Guffey; State v., 680
Hackett; People v., 803
Hackler; People v., 145
Haddock; State v., 404
Hanousek; United States v., 865
Haq; State v., 1030
Harmon; People v., 946
Harper; United States v., 669
Harper, State ex rel. v. Zegeer, 1081
Harris; State v., 1024
Harris v. United States, 1262
Harrison v. United States, 745
Hatfield; United States v., 613
Hazelwood; State v., 268
Heicklen; United States v., 73
Heien v. North Carolina, 332
Heinemann; State v., 991
Helmedach; State v., 990
Henderson; People v., 532
Heras; United States v., 763
Herbst; United States v., 1069
Heredia; United States v., 285
Hermanson v. State, 508
Hernandez v. State, 910
Hernandez; State v., 421
Herrera; People v., 1070
Herrera; State v., 1047
Hess v. State, 421
Hill; People v., 1029
Hill; State v., 921
Hinkhouse; State v., 646
Hitchcock v. Dugger, 575
H.J. Inc. v. Northwestern Bell Tel. Co., 811
Hobby v. State, 1151
Holbron; State v., 648
Holder v. Humanitarian Law Project, 710
Holland v. Illinois, 58
Hollis; State v., 1053
Holmes v. South Carolina, 1065
Holtschlag; People v., 527
Hornsby; United States v., 42
Hosnedl v. State, 882
Houston v. Hill, 190
Howard; United States v., 671
HSBC Bank USA, N.A.; United States v., 851
Hsu; United States v., 683
Hudson & Goodwin; United States v., 162
Hughes v. State, 1025
Hull v. State, 373
Hulme v. State, 534
Humphrey; People v., 896
Humphrey v. Wilson, 220
Hunt; Commonwealth v., 780
Hupf; State v., 527
Hutchins; Commonwealth v., 952
Hynes; State v., 1139
Imbler v. Pachtman, 1187
Iniguez; People v., 375
Inland Freight Lines v. United States, 854
In re. See name of party
Interstate Circuit, Inc. v. United States, 752
Ireland v. United Kingdom, 966
Jackson; People v., 1028
Jackson v. State, 813
Jackson; State v., 548
Jadowski; State v., 302
Jahnke v. State, 908
Jamezic v. State, 1029
Jamison v. Klem, 1209
Janes; State v., 913
Jansen; State v., 734
Jarrott v. State, 1106
J.E.B. v. Alabama, 58
Jeffries v. State, 515
Jenner; State v., 1060
Jennings; People v., 1171
Jimenez Recio; United States v., 781
Jinwright; United States v., 284
Johnson v. Athens-Clarke Cnty., 196
Johnson; R. v., 479
Johnson; United States v., 991
Johnson v. Williams, 71
Johnston; People v., 656
Jones; People v., 191 Cal. App. 2d 478 (1961), 932
Jones; People v., 207 Ill. 2d 122 (2003), 79
Jones v. State, 43 N.E.2d 1017 (Ind. 1942), 249
Jones v. State, 689 N.E.2d 722 (Ind. 1997), 647
Jones; State v., 299 P.3d 219 (Idaho 2013), 373
Jones; State v., 786 S.E.2d 132 (S.C. 2016), 924
Jones v. United States, 463 U.S. 354 (1983), 1026
Jones; United States v., 425 F.2d 1048 (9th Cir. 1970), 738
Jones; United States v., 677 F. Supp. 238 (S.D.N.Y. 1988), 1156
Jordan; Commonwealth v., 459
Joyce; United States v., 670
Jurek v. Texas, 573
Kansas v. Hendricks, 88
Keaton v. State, 547
Keith v. Schaub, 1068
Kellogg; People v., 1083
Ken v. State, 461
Kennedy v. Mendoza-Martinez, 87
Kennedy v. State, 1129
Kern; People v., 634
Key, Ex parte, 184
Khan; R. v., 650
Kihnel; State v., 803
The King v. See name of opposing party
King v. Commonwealth, 522
King v. State, 682
Kingston; R. v., 1015
Klapper; People v., 1151
Klimas; State v., 488
Knowles; United States v., 234
Knox; Commonwealth v., 726
Korell; State v., 1047
Kozlowski; People v., 1156
Kunarac; Prosecutor v., 392
Kuntz, State ex rel. v. District Court, 245
Kurtz; State v., 953
Laborde; State v., 1102
Lafler v. Cooper, 1209
Lagares; State v., 1224
Lane; State v., 614
Laney v. United States, 926
Lanzetta v. New Jersey, 188
LaRosa; People v., 185
Latham; People v., 513
LaValle; People v., 450
Lawrence; R. v., 1104
Lawrence v. Texas, 150
Leavitt; State v., 347
LeClair; Commonwealth v., 477
Lefkowitz; Commonwealth v., 416
Leidholm; State v., 897
Leland v. Oregon, 45
Letterlough; People v., 146
Levin; United States v., 342
Lévis, City of v. Tétreault, 321
Levitt; People v., 109
Lewis; United States v., 1202
Liberta; People v., 433
Liebenow; Commonwealth v., 1115
Life Care Ctrs. of Am.; Commonwealth v., 837
Likine; People v., 318
Lingman; State v., 527
Lisa; State v., 251
Long v. State, 1113
Longley v. State, 802
Lopez; United States v., 994
Louanis; State v., 1136
Loughry; United States v., 34
Loveland; United States v., 764
Lovely v. Cunningham, 1137
Lovely; State v., 380
Loven v. State, 1060
Low; People v., 222
Luciano; People v., 773
Luedecke; R. v., 230
Lyle; State v., 218
Macias; United States v., 224
Madoff; United States v., 133
Mahoney; State v., 901
Mandujano; United States v., 667
Maple Heights, City of v. Ephraim, 317
Margiotta; United States v., 1161
Markman; Commonwealth v., 1000
Marquan M.; People v., 156
Marti; State v., 638
Martin; State v., 573 A.2d 1359 (N.J. 1990), 549
Martin; State v., 859 N.W.2d 600 (S.D. 2015), 459
Martinez; State v., 244
Martinez; United States v., 552
Masciantonio v. R., 487
Matchett; Commonwealth v., 529
Mathews v. Superior Court, 411
Mathis v. United States, 661
Mattison; People v., 541
Mauricio; State v., 477
Mayberry; Commonwealth v., 510
Mayes v. State, 929
Mays; People v., 712
McAlevy v. Commonwealth, 1102
McCarthy v. State, 1069
McClain v. State, 1061
McClain; State v., 487
McClendon; State v., 1023
McCoy; People v., 742
McCullough v. State, 41
McDonald; State v., 1052
McDonnell v. United States, 1167
McElroy v. People, 1117
McFarland v. American Sugar Rfg. Co., 46
McGautha v. California, 566
McGee; People v., 773
McGraw; State v., 1150
McIntosh; United States v., 954
McKeown; State v., 700
McKown; State v., 508
McLaughlin; People v., 759
McMillan v. Pennsylvania, 1262
McMillan v. State, 1001
McNair; United States v., 798
McNeal; People v., 656
McNeal v. State, 79
Meadows; Commonwealth v., 380
Means v. United States, 1125
Mejia; State v., 1178
Melchert-Dinkel; State v., 623
Mello; State v., 197
Melvin; Commonwealth v., 146
Mempa v. Rhay, 1235
Merz; United States v., 34
Merzbacher v. State, 375
Michelson v. United States, 31
Millan; State v., 748
Miller v. State, 185
Minor; People v., 624
Minter; United States v., 1023
Mitchell; State v., 649
Monsalve; United States v., 383
Montoya; State v., 608
Moore v. Lowe, 744
Moore v. McKune, 885
Morado; United States v., 819
Morales; People v., 412
Moran-Toala; United States v., 67
Morfin; State v., 71
Morgan; R. v., 421
Morgan; State v., 1053
Morhall; R. v., 490
Morock; State v., 1114
Morris; R. v., 1103
Morris; United States v., 790
Morrison v. Olson, 1190
Morse; United States v., 762
Mosley v. State, 536
Moylan; United States v., 65
Mulcahy v. The Queen, 754
Municipal Court; People v., 1191
Muniz; State v., 278
Muro; State v., 608
Murphy v. Commonwealth, 954
Mutina; Commonwealth v., 1029
Nash; People v., 548
Nash v. United States, 197
Natale; State v., 1261
Navarro; People v., 1006
Neeley v. Commonwealth, 441
Nelson; State v., 1156
Nelson v. United States, 1131
Nemeth; State v., 910
Nesler; People v., 474
Neumann; State v., 508
New England Grocers Supply Co.; United States v., 861
Newman; United States v., 776
Newsome v. State, 67
New State Ice Co. v. Liebmann, 56
New York v. Strauss-Kahn, 1182
New York Trust Co. v. Eisner, 1101
Nolan; State v., 649
Northern Ireland v. Lynch, 996
Nye & Nissen v. United States, 699
Oakland Cannabis Buyers’ Coop.; United States v., 954
Ocasio v. United States, 799
Odom v. Matteo, 943
Oliver; State v., 421
Olivo; People v., 1103
O’Neill; United States v., 1251
Ortega; State v., 529
Orthofix, Inc.; United States v., 850
Ortiz; United States v., 268
Overholt; United States v., 339
Owens v. State, 302
Oxford v. Moss, 1160
Padilla v. Kentucky, 1209
Palsgraf v. Long Island R. Co., 607
Panetti v. Quarterman, 1024
Park v. Huff, 777
Parker; Commonwealth v., 1129
Parks; R. v., 230
Paroline v. United States, 608
Patrick v. State, 475
Patterson; State v., 507
Patton; United States v., 1125
Paul; State v., 67
Paul P. v. Farmer, 88
Peairs; State v., 935
Peak; Commonwealth v., 638
Pear; The King v., 1122
Pena v. State, 640
Pendergraft; United States v., 1139
Penny; People v., 495
Penny v. State, 1113
People v. See name of opposing party
Perez; State v., 552
Perez-Cervantes; State v., 637
Perry; People v., 1150
Perry; State v., 1025
Pestinikas; Commonwealth v., 235
Philadelphia Nat’l Bank; United States v., 169
Phillip Morris USA v. Williams, 218
Picotte; State v., 184
Pierson; People v., 501
Pino-Perez; United States v., 694
Pittman; State v., 471
Planned Parenthood v. Casey, 975
Ploof; State v., 88
Plumhoff v. Rickard, 939
Plumlee; State v., 931
Polizzi; United States v., 78
Porter; The King v., 1031
Powell; People v., 764
Powell; United States v., 80
Praddy; United States v., 815
Pratt; State v., 1174
Preslar; State v., 627
Price v. State, 79
Priest v. State, 79
Prince v. Massachusetts, 508
Prindle; People v., 513
Proffitt v. Florida, 573
Prosecutor v. See name of opposing party
Public Comm. against Torture v. State of Israel, 965
Pugh; Commonwealth v., 493
Quality Egg, LLC; United States v., 862
Qualls; United States v., 344
Queen; United States v., 33
Queen/Regina v. See name of opposing party
R./Regina or Rex v. See name of opposing party
R.; R. v., 435
Raines; State v., 645
Ramirez-Memije v. State, 252
Randall; United States v., 782
Rasmussen; State v., 952
Reed; State v., 421
Reese; United States v., 189
Regina v. See name of opposing party
Reigle; People v., 1130
Reitze; State v., 234
Rendelman; State v., 1139
Rex v. See name of opposing party
Reyes; People v., 917
Reynolds v. State, 421
Rice v. Paladin Enters., 676
Richards; R. v., 741
Richland Cnty. Magistrate’s Court, In re, 1191
Richter; United States v., 149
Riggs v. State, 468
Rios; People v., 613
Rivera v. Delaware, 50
R.L.C.; United States v., 172
Roberts v. People, 1004
Roberts; State v., 1053
Robins; State v., 656
Rochin v. California, 57
Rodgers; United States v., 343
Rodrigues-Gonzales; State v., 619
Rodriguez v. Panarello, 942
Rodriguez; United States v., 1213
Rodriguez-Roman; State v., 822
Roe v. Wade, 150
Rojas; People v., 681
Roldan; State v., 802
Rolon; People v., 733
Roman; United States v., 671
Rooney; State v., 662
Rose v. State, 540
Ross v. State, 656
Roston; United States v., 469
Roth v. United States, 740
Roy; People v., 758
Roy v. United States, 724
Rubio; People v., 648
Rucker; State v., 664
Rudder v. State, 745
Russell; Commonwealth v., 42
Ruzic; R. v., 998
Salazar; United States v., 70
Salemme; People v., 661
Salerno; United States v., 481 U.S. 739 (1987), 16
Sall; United States v., 767
Sanchez; Commonwealth v., 585
Santillanes v. New Mexico, 269
Santos; United States v., 175
Schloendorff v. Society of N.Y. Hosp., 973
Schlosser; State v., 822
Schofield; State v., 1261
Schwartz; State v., 1156
Schwartz; United States v., 1150
Scott; Commonwealth v., 541
Scott; People v., 618
Scott v. United States, 1213
Scotti; United States v., 762
Scriva; R. v., 478
Scruggs; State v., 187
Searcy; State v., 1047
Sears; People v., 541
Sekhar v. United States, 1149
Sell v. United States, 1022
Serné; R. v., 517
Serravo; People v., 1053
Sespedes; Commonwealth v., 252
Sexson; State v., 622
Sharp; R. v., 1000
Shattuck; State v., 1261
Shaw; State v., 924
Shea; United States v., 775
Sheehan; Commonwealth v., 1058
Sheffield v. State, 1151
Shelton v. Secretary, Dep’t of Corrs., 321
Shelton v. United States, 1207
Shepherd; R. v., 1000
Sheppard; United States v., 437
Showens; State v., 196
Shryock; United States v., 996
Shushan v. United States, 1164
Shuttlesworth v. Birmingham, 188
Silvar v. District Court, 196
Simcock; Commonwealth v., 420
Simino; State v., 540
Simon v. State, 1030
Simonovich; State v., 475
Simpkins v. State, 513
Simpson; United States v., 746
Sinclair v. State, 1046
Singleton v. State, 1024
Sisuphan; People v., 1174
Skipper v. South Carolina, 575
Smiley; State v., 922
Smith v. Commonwealth, 159
Smith v. Goguen, 192
Smith; People v., Cal. 3d 798 (1984), 543
Smith; People v., Cal. 4th 733 (Cal. 2005), 619
Smith; People v., Cal. App. 4th 199 (2010), 405
Smith; R. v., 234
Smith (Morgan); R. v., 489
Smith; State v., 426 A.2d 38 (N.J. 1981), 435
Smith; State v., 621 A.2d 493 (N.J. Super. 1993), 681
Smith; State v., 317 Conn. 338 (2015), 1178
Smith; State v., 878 N.W.2d 135, 148 (Wis. 2016), 1022
Smith; State v., 178 P.3d 672 (Kan. Ct. App. 2008), 404
Smith; State v., 592 S.E.2d 302 (S.C. 2004), 244
Smith v. United States, 508 U.S. 223 (1993), 174
Smith v. United States, 133 S. Ct. 714 (2014), 783
Smith; United States v., 2015 U.S. Dist. LEXIS 174279 (E.D.N.Y. Dec. 14, 2015), 1095
Smith, Commonwealth ex rel. v. Myers, 547
Smith-Parker; State v., 70
Solomon; People v., 461
Soto; State v., 52
Southwark, Borough of v. Williams, 954
Spencer v. Texas, 36
Spurlin; People v., 478
Stafford; United States v., 1156
Standard Oil Co. v. United States, 828
Standefer; United States v., 740
Stanford v. Kentucky, 586
Stanko; State v., 198
State v. See name of opposing party
State ex rel. See name of related party
State Farm Mut. Auto. Ins. Co. v. Campbell, 219
Steele; People v., 488
Steen v. Appellate Div., Superior Court, 1191
Stein; State v., 774
Stein; United States v., 834
Stewart; State v., 478
Stoltie v. California, 42
Strasburg; State v., 1046
Strickland; State v., 376
Stripling v. State, 585
Stuart; People v., 665
Sturdivant; State v., 541
Suding v. State, 802
Sumner v. Shuman, 574
Sun-Diamond Growers of Cal.; United States v., 832
Sutherland; United States v., 817
Swafford; United States v., 790
Swisher v. Commonwealth, 1009
Sydnor v. State, 936
Tate; State v., 921
Taylor v. Louisiana, 58
Taylor; People v., 512
Taylor v. State, 547
Taylor; State v., 680
Tenement House Dep’t v. McDevitt, 308
Territory v. Manton, 234
Thacker v. Commonwealth, 647
Thayer; State v., 954
Thomas; People v., 648
Thomas; R. v., 758
Thompson; State v., 735 N.W.2d 818 (Neb. 2007), 1260
Thompson; State v., 65 P.3d 420 (Ariz. 2003), 459
Thornton; R. v., 487
Tison v. Arizona, 586
Torres v. United States, 77
Torres-Ramirez; United States v., 790
Tot v. United States, 46
Tower; State v., 1148
Townsend; State v., 899
Trammel v. United States, 433
Trop v. Dulles, 568
Tufunga; People v., 1177
Turner v. Murray, 597
Turner; State v., 475
Turner; United States v., 780
Turney v. Pugh, 72
Turney v. State, 72
Twine; United States v., 1069
Twitchell; Commonwealth v., 342
Tyrell; The Queen v., 740
Tyrrell; Commonwealth v., 455
Tyson v. State, 424
Union Pac. R. Co. v. Botsford, 973
United States v. See name of opposing party
United States Dist. Court (Kantor); United States v., 321
United States Gypsum Co.; United States v., 782
Unnamed Petitioners, State ex rel. v. Connors, 1191
Vaillancourt; Queen v., 530
Valade (Que.); R. v., 628
Van Elsen; United States v., 1175
Varner v. Stovall, 911
Varszegi; State v., 334
Vasquez; Commonwealth v., 1001
Vasquez; United States v., 1241
Veach; United States v., 1009
Vera Cruz v. City of Escondido, 942
Verdugo; People v., 478
Victor v. Nebraska, 42
Viera v. Chappell, 1001
Vigilante; State v., 487
Vinter & Others v. United Kingdom, 218
Vogel; People v., 296
Wagner v. State, 661
Waine; State v., 69
Walker; Commonwealth v., 918
Walker; People v., 42 N.E.3d 688 (N.Y. 2015), 912
Walker; People v., 473 N.Y.S.2d 460 (App. Div. 1984), 484
Walker v. Superior Court, 508
Wall; United States v., 776
Walters; State v., 1009
Walton v. Arizona, 576
Walton; State v., 778
Walton; United States v., 42
Waltower; United States v., 1252
Warme; United States v., 1152
Watson; People v., 515
Watson; United States v., 148
Watts; United States v., 1247
Weaver; State v., 1111
Webster; Commonwealth v., 42
Weiand v. State, 924
Weishaupt v. Commonwealth, 435
Weiss; People v., 326
Werlein; State v., 1059
Werner v. State, 902
Weston; United States v., 1235
Weyrauch v. United States, 1167
Wheatley; R. v., 1122
White v. Commonwealth, 33
White; R. v., 652
White v. State, 649
White Calf; United States v., 1009
Whitfield v. United States, 754
Wiesenfeld Warehouse Co.; United States v., 858
Wiley; United States v., 436
Willard-Freckleton; State v., 1111
Williams; Commonwealth v., A.2d 812 (Pa. Super. 1938), 527
Williams; Commonwealth v., 63 Cal. 2d 452 (1965), 531
Williams v. Florida, 57
Williams; People v., 1099
Williams; State v., 952 N.E.2d 1108 (Ohio 2011), 88
Williams; State v., 254 So. 2d 548 (Fla. App. 1971), 551
Williams; United States v., 675
Williamson v. State, 282
Wilson; State v., 700
Wilson v. State, 30 A.3d 955 (Md. 2011), 886
Wilson v. State, 53 Ga. 205 (1874), 803
Wilson; United States v., 159 F.3d 280 (7th Cir. 1998), 348
Wilson; United States v., 605 F.3d 985 (D.C. Cir. 2010), 816
Wiltberger; United States v., 162
Wisconsin v. Oakley, 147
Wisconsin Pub. Intervenor v. Mortier, 174
Wood v. Main, 1027
Woods; State v., 335
Woods; United States v., 148


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Figure 1
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Geographical attributes of the study area. |
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Figure 2
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Time series of observed and modeled monthly streamflow in th ... |
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Figure 3
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Time series of observed and modeled monthly sediment in the ... |
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Figure 4
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The changes in annual streamflow, runoff, groundwater and ev ... |
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Figure 5
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The changes in annual erosion and sediment in response to va ... |
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Figure 6
|
The changes in monthly erosion and sediment in response to v ... |


Try traversing the object graph with jQuery - enter some JSON and a jQuery selector and click Execute. To see some example selectors, check out the docs.


Reports classes that have the same name as one of their superclasses, while their fully qualified names remain different.
Such class names may be very confusing.
Example:
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abstract class Iterable<T> implements java.lang.Iterable<T> {}
A quick-fix that renames such classes is available only in the editor.


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| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more | |
| 3 | * contributor license agreements. See the NOTICE file distributed with | |
| 4 | * this work for additional information regarding copyright ownership. | |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 | |
| 6 | * (the "License"); you may not use this file except in compliance with | |
| 7 | * the License. You may obtain a copy of the License at | |
| 8 | * | |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 10 | * | |
| 11 | * Unless required by applicable law or agreed to in writing, software | |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 14 | * See the License for the specific language governing permissions and | |
| 15 | * limitations under the License. | |
| 16 | */ | |
| 17 | package org.apache.commons.math3.genetics; | |
| 18 | ||
| 19 | import org.apache.commons.math3.exception.NumberIsTooSmallException; | |
| 20 | ||
| 21 | /** | |
| 22 | * Stops after a fixed number of generations. Each time {@link #isSatisfied(Population)} is invoked, a generation | |
| 23 | * counter is incremented. Once the counter reaches the configured maxGenerations value, | |
| 24 | * {@link #isSatisfied(Population)} returns true. | |
| 25 | * | |
| 26 | * @version $Id$ | |
| 27 | * @since 2.0 | |
| 28 | */ | |
| 29 | public class FixedGenerationCount implements StoppingCondition { | |
| 30 | /** Number of generations that have passed */ | |
| 31 | private int numGenerations = 0; | |
| 32 | ||
| 33 | /** Maximum number of generations (stopping criteria) */ | |
| 34 | private final int maxGenerations; | |
| 35 | ||
| 36 | /** | |
| 37 | * Create a new FixedGenerationCount instance. | |
| 38 | * | |
| 39 | * @param maxGenerations number of generations to evolve | |
| 40 | * @throws NumberIsTooSmallException if the number of generations is < 1 | |
| 41 | */ | |
| 42 | public FixedGenerationCount(final int maxGenerations) throws NumberIsTooSmallException { | |
| 43 |
2
1. 2. |
if (maxGenerations <= 0) { |
| 44 | throw new NumberIsTooSmallException(maxGenerations, 1, true); | |
| 45 | } | |
| 46 | this.maxGenerations = maxGenerations; | |
| 47 | } | |
| 48 | ||
| 49 | /** | |
| 50 | * Determine whether or not the given number of generations have passed. Increments the number of generations | |
| 51 | * counter if the maximum has not been reached. | |
| 52 | * | |
| 53 | * @param population ignored (no impact on result) | |
| 54 | * @return true IFF the maximum number of generations has been exceeded | |
| 55 | */ | |
| 56 | public boolean isSatisfied(final Population population) { | |
| 57 |
2
1. isSatisfied : changed conditional boundary → KILLED 2. isSatisfied : negated conditional → KILLED |
if (this.numGenerations < this.maxGenerations) { |
| 58 |
1
1. isSatisfied : Replaced integer addition with subtraction → TIMED_OUT |
numGenerations++; |
| 59 |
1
1. isSatisfied : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return false; |
| 60 | } | |
| 61 |
1
1. isSatisfied : replaced return of integer sized value with (x == 0 ? 1 : 0) → TIMED_OUT |
return true; |
| 62 | } | |
| 63 | ||
| 64 | /** | |
| 65 | * Returns the number of generations that have already passed. | |
| 66 | * @return the number of generations that have passed | |
| 67 | */ | |
| 68 | public int getNumGenerations() { | |
| 69 |
1
1. getNumGenerations : replaced return of integer sized value with (x == 0 ? 1 : 0) → NO_COVERAGE |
return numGenerations; |
| 70 | } | |
| 71 | ||
| 72 | } | |
Mutations | ||
| 43 |
1.1 2.2 |
|
| 57 |
1.1 2.2 |
|
| 58 |
1.1 |
|
| 59 |
1.1 |
|
| 61 |
1.1 |
|
| 69 |
1.1 |


The Childhood Epic
This Game made our childhood very special
Travel into the childhood and memorize the each crazy things you did as a kid ;) in each round.
So, Are you ready?
Choose Your Weapon Now!
You
Computer
0
0
This Game made our childhood very special
Travel into the childhood and memorize the each crazy things you did as a kid ;) in each round.
So, Are you ready?
Choose Your Weapon Now!
You
Computer


Feature requests, constructive criticism or bug reports are much appreciated, and will most likely be implemented or fixed in a few days. Thank you in advance!


We have now a variant-representation-model__repository, as a place to collect legacy, alternative and proposed models for the representation of genomic (+ ?) variants.
mbaudis, 2018-05-17: more ...
2018-04-23: more ...
2018-04-20: more ...


FOP License | print-friendly |
License
The Apache Software License, Version 1.1
Copyright (C) 1999-2001 The Apache Software Foundation. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. The end-user documentation included with the redistribution, if any, must include the following acknowledgment: "This product includes software developed by the Apache Software Foundation (http://www.apache.org/)." Alternately, this acknowledgment may appear in the software itself, if and wherever such third-party acknowledgments normally appear.
4. The names "FOP" and "Apache Software Foundation" must not be used to endorse or promote products derived from this software without prior written permission. For written permission, please contact<EMAIL_ADDRESS>5. Products derived from this software may not be called "Apache", nor may "Apache" appear in their name, without prior written permission of the Apache Software Foundation.

This software consists of voluntary contributions made by many individuals on behalf of the Apache Software Foundation and was originally created by James Tauber<EMAIL_ADDRESS>For more information on the Apache Software Foundation, please see http://www.apache.org/.


We don’t know where exactly he was going or whether he posed on the bike to take a shot from the lenses of the camera.
Whatever the motive is, he should never compromise on his safety.
In a related development, The BET award winning artist confirmed to Abeiku Santana on Okay FM in an interview that he’s dropping three songs this December.
Stonebwoy also confirmed featuring Kuame Eugene and Kidi on one of the songs (we guess it is the ‘Sobolo’). He also revealed he has one song produced by Beatz Dakay titled ‘More’ with the third song unknown.
See below;


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Collection
Rules, regulations and guidance for legacy AS and A levels.
This collection sets out rules, regulations and guidance for all legacy (pre-reform) AS and A levels.
You should also read the requirements for all awarding organisations and qualifications .
There are separate requirements for the reformed AS and A levels that are being introduced from September 2015.


Usage¶
The structure of ClusterGB is deliberately simple, where the code is based on a two-level hierarchy of
Projects
and Jobs
, which are stored as pickled
Python objects for later access, and all LAMMPS calculations are performed by making a shell call to an external
LAMMPS executable. Projects
, Jobs
, and further
calculations can all be created/executed using shell commands.
Initialize project¶
A new Project
is started using cgb_init_project.py and requires a project name, a valid
EAM potential, a host species, and a crystal structure (FCC or BCC with lattice parameter). There are many other
parameters for Projects
, but most have very reasonable defaults. These parameters
are stored in clustergb.project.par
.
cgb_init_project.py will create a new directory with the project name and run calculations for bulk clusters: one pure bulk,
and one calculation for each solute with the solute atom substitutionally placed at the center of the cluster. Output
from these calculations is stored in clustergb.project.bulk
and
clustergb.project.bulk_${SOLUTE}
, respectively.
Projects
store themselves as pickled Python objects and keep a .log
file of
their operations.
Initialize a job¶
Inside a Project
directory, a new Job
can be created
using cgb_init_job.py. The new Job
will inherit the parent
Project
parameters stored in par
and copy them locally.
This is so that all GBs in a particular project are treated in a consistent way (i.e. same cluster size, annealing
treatment, etc.) Additionally, Jobs
have a sub-namespace clustergb.job.par.gb
which stores the parameters used to define the macroscopic character of the GB.
Results for the optimized GB structure are stored
directly in clustergb.job.gb
, including the Bravais lattices for the two grains, the number of atoms in each
domain of the cluster, the thermodynamic quantities for the final relaxed GB, the GB interfacial energy, etc.
Jobs
store themselves as pickled Python objects and keep a .log
file of their
operations.
Calculate a GB property¶
With bulk, bulk+solute, and undecorated GB calculations completed and bulk and GB structure files available, all
remaining calculation are done by cgb_run_...
scripts. Invoked from a Job
directory,
these will calculate some property for the GB and store the results in clustergb.Job.results.${CALCULATION_NAME}
.
Invoked from the Project
level, the behaviour is similar but the calculation will
be performed for all child Jobs
. This modular format allows almost any static property to
be calculated on top of the basic undecorated, relaxed GBs.
Before and After¶
ClusterGB also comes with two other scripts for interacting with EAM potentials: cgb_rescale_potential.py and cgb_bulk_modulus.py. cgb_rescale_potential.py allows for rescaling the lattice parameter of a potential (for a cubic structure). This is useful for a study of multiple solutes where different potentials are used to accommodate different solute species. cgb_bulk_modulus.py simply calculates the bulk modulus using one of the potential’s valid species (and an FCC or BCC unit cell), and is a useful sanity check after rescaling potentials.
Finally, cgb_interactive.py opens an interactive Python session with many useful modules–including the
ClusterGB module–pre-loaded. No particular scripts are included for analyzing the results, but
clustergb.osio.load_project()
and clustergb.osio.load_job()
make it easy to load
Projects
and Jobs
by either invoking them in a
Project
or Job
directory, or passing the path to a
Project
or Job
directory . These objects give direct
access to calculation results for processing/plotting/etc directly in Python.
thermo_data
¶
The main result from each LAMMPS calculation is a thermo_data
namespace, which are parsed from the LAMMPS output
file.
| Name | Description |
|---|---|
lx , ly , lz |
Rectangular box lengths. |
pe |
Potential energy. |
ke |
Kinetic energy. |
etotal |
Total energy. |
press |
Total pressure |
vol |
Total volume (of the periodic box, not the cluster) |
temp |
Temperature. |
v_n_all |
Number of atoms in the entire cluster. |
v_n_inner |
Number of atoms in the inner sub-sphere. |
c_pe_inner |
Potential energy of atoms in the inner sub-sphere. |
init_vals |
The above values at the start of the LAMMPS calculation. |
Warning
init_vals
is from the initiation of the most recent LAMMPS calculation. For instance, for
clustergb.job.gb.thermo_data
the optimization of microscopic degrees of freedom is a multi-step process
involving translations and merging, annealing, and force minimization, so the init_vals
do not refer to any
ideally “unrelaxed” GB structure.
decompose
¶
Projects
can be initialized with a --decompose
flag thrown which triggers an
additional LAMMPS calculation any time a calculation with a solute atom is performed: the final relaxed structure of
the solute-containing calculation is used as input to a new, static calculation with all atoms set to be host species.
In ClusterGB solute-GB segregation energies are calculated as follows,
where subscripts indicate the geometry of the of cluster calculation, and the superscript indicates the presence of solute. The additional calculation which has a solute-relaxed structure but pure-host chemistry (indicated by the superscript \(\widetilde{X}\)) allows us to break this energy down into into “mechanical” and “chemical” components:
and
such that
That \(E_{\mathrm{seg}}^{\mathrm{chem}}\) really represents the “chemical” aspect of solute segregating the boundary may be seen more easily in a refactored form:
Of course this decomposition is purely artificial, the electrons and protons make no such distinction. The approximation may nevertheless yield some insights.
Executables:¶
More detailed documentation for each of the ClusterGB executable scripts can be found below: