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:]
chrismas_2011_data = data[340*24:360*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]
chrismas_2011_features, chrismas_2011_targets = chrismas_2011_data.drop(target_fields, axis=1), chrismas_2011_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.
self.input_nodes = input_nodes
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
# Initialize weights
self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5,
(self.input_nodes, self.hidden_nodes))
self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5,
(self.hidden_nodes, self.output_nodes))
self.lr = learning_rate
#### TODO: Set self.activation_function to your implemented sigmoid function ####
#
# Note: in Python, you can define a function with a lambda expression,
# as shown below.
self.activation_function = lambda x : 1/(1+np.exp(-x)) # Replace 0 with your sigmoid calculation.
### If the lambda code above is not something you're familiar with,
# You can uncomment out the following three lines and put your
# implementation there instead.
#
#def sigmoid(x):
# return 0 # Replace 0 with your sigmoid calculation here
#self.activation_function = sigmoid
def train(self, features, targets):
''' Train the network on batch of features and targets.
Arguments
---------
features: 2D array, each row is one data record, each column is a feature
targets: 1D array of target values
'''
n_records = features.shape[0]
delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)
delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)
for X, y in zip(features, targets):
#### Implement the forward pass here ####
### Forward pass ###
# TODO: Hidden layer - Replace these values with your calculations.
X = np.array([X])
y = np.array([y])
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.
error = y - final_outputs # Output layer error is the difference between desired target and actual output.
output_error_term = error * (1)
# TODO: Calculate the hidden layer's contribution to the error
hidden_error = output_error_term * self.weights_hidden_to_output
# TODO: Backpropagated error terms - Replace these values with your calculations.
#output_error_term = error * (1)
hidden_error_term = hidden_error.T * hidden_outputs * (1 - hidden_outputs)
# Weight step (input to hidden)
delta_weights_i_h += hidden_error_term * X.T
# Weight step (hidden to output)
delta_weights_h_o += output_error_term * hidden_outputs.T
# 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.
import sys
### Set the hyperparameters here ###
iterations = 30000
learning_rate = 0.08
hidden_nodes = 30
output_nodes = 1
loaded_weights = False
N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
if loaded_weights == True:
network.weights_hidden_to_output = saved_w_h_o
network.weights_input_to_hidden = saved_w_i_h
losses = {'train':[], 'validation':[]}
for ii in range(iterations):
# Go through a random batch of 128 records from the training data set
batch = np.random.choice(train_features.index, size=128)
X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']
network.train(X, y)
# Printing out the training progress
train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)
val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)
sys.stdout.write("\rProgress: {:2.1f}".format(100 * ii/float(iterations)) \
+ "% ... Training loss: " + str(train_loss)[:5] \
+ " ... Validation loss: " + str(val_loss)[:5])
sys.stdout.flush()
losses['train'].append(train_loss)
losses['validation'].append(val_loss)
#saved_w_h_o = network.weights_hidden_to_output
#saved_w_i_h = network.weights_input_to_hidden
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()
Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly.
fig, ax = plt.subplots(figsize=(8,4))
mean, std = scaled_features['cnt']
predictions = network.run(test_features).T * std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((test_targets['cnt']*std + mean).values, label='Data')
ax.set_xlim(right=len(predictions))
ax.legend()
ax.set_title("Fig.1 \'Chrismas less bike\' in 2012")
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)
fig, ax = plt.subplots(figsize=(8,4))
mean, std = scaled_features['cnt']
predictions = network.run(chrismas_2011_features).T*std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((chrismas_2011_targets['cnt']*std + mean).values, label='Data')
ax.set_xlim(right=len(predictions))
ax.legend()
ax.set_title("Fig.2 \'Chrismas less bike\' in 2011")
dates = pd.to_datetime(rides.ix[chrismas_2011_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
Question 1.
Overall, it performs well, especially before Dec 21, 2012. The val_loss is only , which means on average each prediction is +- compared to ground truth.
Question 2.
However, it looks like a loser from Dec 22, 2012.
Question 3.
The reasons for that: I am not American, but I would like to guess. Generally, going to work contributes a lot on bike using. From Dec 22, people has began on their Chrismas holiday. They could pay much more time at home and traveling. So the amount of bike using dropped.
However dataset varies from Jan 1 2011 to Dec 31 2012, i.e. two years. Hence NN only met this 'Chirsmas less bike using phenomenon' once from Jan 1 to Dec 21 in 2011. Thus, it has a weak prediction faced with it. Fig.2 also illustrates its weak power for the abrupt decrease of bikes using, though it looks a bit well compared to Fig.1 due to training. In my opinion, the 'Training loss: 0.091' is mainly caused by this abrupt oscillation during Chrismas.
If dataset range much more years, I think NN will 'remember' the circle of Chirsmas and would accurately predict facing the 'Chirsmas less bike' phenomenon.


--- title: View a Saved Model type: docs repo: https://github.com/rstudio/tensorflow menu: main: name: View a Saved Model - tensorflow parent: tensorflow-reference aliases: - /reference/tensorflow/view_savedmodel.html - /tensorflow/reference/view_savedmodel.html - /guide/tensorflow/reference/view_savedmodel.html - /tools/tools/tensorflow/reference/view_savedmodel.html - /installation/tensorflow/reference/view_savedmodel.html - /tutorials/tensorflow/reference/view_savedmodel.html - /guide/tools/tensorflow/reference/view_savedmodel.html - /deploy/tensorflow/reference/view_savedmodel.html - /tools/tensorflow/reference/view_savedmodel.html - /tutorials/tools/tensorflow/reference/view_savedmodel.html ---
View a serialized model from disk.
view_savedmodel(model_dir)
| model_dir | The path to the exported model, as a string. |
URL for browsing TensorBoard (invisibly).


Bylaws of Academic Unit
CAMDEN
NEWARK
- Faculty of Arts and Sciences
- School of Law
- College of Nursing
- Rutgers Business School-Newark and New Brunswick
- Graduate School Newark
- School of Public Affairs and Administration
- School of Criminal Justice
NEW BRUNSWICK
- School of Arts and Sciences
- School of Environmental and Biological Sciences
- School of Engineering
- School of Communication and Information
- Ernest Mario School of Pharmacy
- Rutgers Business School-Newark and New Brunswick
- Graduate School New Brunswick
- Graduate School of Education
- Graduate School of Applied and Professional Psychology
- School of Social Work
- School of Management and Labor Relations
- Edward J. Bloustein School of Planning and Public Policy
- Mason Gross School of the Arts [PDF]


Knob.
From an exquisite alphabet at Ptak Science Books, originally published in 1879, the year of the felling of Binsey Poplars. Not that there is the remotest link between Kantner's Illustrated Book of Objects and Self-Educator in German and English and Father Hopkins' aspens dear. I just thought I'd mention it. I can't see that year written down without thinking of them.


Compute Canada Federation Identity Management Projects
Compute Canada Networking & Subatomic Physics National Team F2F - TECC
Darren Boss - Compute Canada Federation
LDAP
Puppet managed OpenLDAP replicas
Application of ACLs
TLS/SSL Certificate
Schema Updates
Pseudo Users and Platform Nodes
SAML IdP
Dockerized deployment of Shibboleth IdP
Federation with CAF and EduGain
Only staff accounts are allowed for federated SP logins
Support for R&S Entity Category in CAF and EduGain
Looking at adding Sirtfi
Kubernetes deployment
IdP deployment for production and test
Configuration clone from git on deploy
Test Service Provider deployments
Using replicas and rolling upgrades
Monitoring
ELK stack and Grafana deployed
Filebeat log shipper
OpenID Connect
Identity layer on top of the OAuth 2.0 protocol
Puppet deployed MITREid single instance running
Will likely switch to using Shibboleth OIDC when support is incorporated
Only in use for Kubernetes
Grouper
Access management system
Manage groups, roles and permissions
Provision to LDAP, SCIM, G Suite and more
Test deployments using LDAP and CCDB directly for subject source


vignettes/analyzing-medical-innovation-data.Rmd
analyzing-medical-innovation-data.Rmd
Loading the raw dataset from the package1.
##
## Attaching package: 'netdiffuseR'
## The following object is masked from 'package:base':
##
## %*%
Now that we have the data in R, we can start working with it, in particular, we want to do the following things:
# Creating unique ids (including for the network data)
othervars <- c("id", "toa", "city")
netvars <- names(medInnovations)[grepl("^net", names(medInnovations))]
for (i in c("id", netvars))
medInnovations[[i]] <- medInnovations[[i]] + medInnovations$city*1000
# Leaving unsurveyed individuals with NA
surveyed <- medInnovations$id
for (i in netvars)
medInnovations[[i]][which(!(medInnovations[[i]] %in% surveyed))] <- NA
# Reshaping data (so we have an edgelist)
medInnovations.long <- reshape(
medInnovations[,c(othervars, netvars)], v.names= "net",
varying = netvars,
timevar = "level", idvar="id", direction="long")
Once we have the data in long format, we can coerce it into an diffnet
object. This is done by reading the edgelist, obtaining the times of adoption vector and applying the as_diffnet
function.
# Coersing the edgelist to an adjacency matrix. Here we are assuming that the
# network is constant through time.
graph <- with(
medInnovations.long,
edgelist_to_adjmat(cbind(id, net), t=18,undirected=FALSE, keep.isolates = TRUE)
)
Notice that we have included keep.isolates=TRUE
, so, if any element of our edgelist had an NA
, NULL
or related value, it would still be included in the adjacency matrix. This is specially important if, for example, there are isolated nodes in the data, as if we had not set keep.isolates=TRUE
those would had been discarded.
Now we can create our diffnet
object. Notice that medInnovations
happens to be sorted in the same way as the elements in the adjacency matrix. You can check this by accessing the rownames
and sorting medInnovations
in that order.
# Just to be sure. Sorting the data!
orddata <- as.numeric(as.factor(rownames(graph[[1]])))
medInnovations <- medInnovations[orddata,]
# Creating a diffnet object
diffnet <- as_diffnet(graph, medInnovations$toa,
vertex.static.attrs = subset(medInnovations, select=c(-id, -toa)))
## Warning in new_diffnet(graph, ...): Coercing -toa- into integer.
Once a diffnet
object, we can apply the usual generic R functions:
## Dynamic network of class -diffnet-
## Name : Diffusion Network
## Behavior : Unspecified
## # of nodes : 125 (1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, ...)
## # of time periods : 18 (1 - 18)
## Type : directed
## Final prevalence : 1.00
## Static attributes : city, detail, meet, coll, attend, proage, length, ... (58)
## Dynamic attributes : -
## Diffusion network summary statistics
## Name : Diffusion Network
## Behavior : Unspecified
## -----------------------------------------------------------------------------
## Period Adopters Cum Adopt. (%) Hazard Rate Density Moran's I (sd)
## -------- ---------- ---------------- ------------- --------- ----------------
## 1 11 11 (0.09) - 0.02 0.07 (0.03) **
## 2 9 20 (0.16) 0.08 0.02 0.04 (0.03)
## 3 9 29 (0.23) 0.09 0.02 -0.03 (0.03)
## 4 11 40 (0.32) 0.11 0.02 -0.02 (0.03)
## 5 11 51 (0.41) 0.13 0.02 -0.06 (0.03)
## 6 11 62 (0.50) 0.15 0.02 -0.02 (0.03)
## 7 13 75 (0.60) 0.21 0.02 -0.00 (0.03)
## 8 7 82 (0.66) 0.14 0.02 0.01 (0.03)
## 9 4 86 (0.69) 0.09 0.02 0.01 (0.03)
## 10 1 87 (0.70) 0.03 0.02 0.01 (0.03)
## 11 5 92 (0.74) 0.13 0.02 0.02 (0.03)
## 12 3 95 (0.76) 0.09 0.02 0.02 (0.03)
## 13 3 98 (0.78) 0.10 0.02 0.01 (0.03)
## 14 4 102 (0.82) 0.15 0.02 0.04 (0.03)
## 15 4 106 (0.85) 0.17 0.02 0.05 (0.03) *
## 16 2 108 (0.86) 0.11 0.02 0.03 (0.03)
## 17 1 109 (0.87) 0.06 0.02 0.02 (0.03)
## 18 16 125 (1.00) 1.00 0.02 -
## -----------------------------------------------------------------------------
## Left censoring : 0.09 (11)
## Right centoring : 0.00 (0)
## # of nodes : 125
##
## Moran's I was computed on contemporaneous autocorrelation using 1/geodesic
## values. Significane levels *** <= .01, ** <= .05, * <= .1.
And the ones included in the package:
Now, we want to know if the threshold model fits here. In order to do so we will use the structure dependency test built in the package, struct_test
. We will apply this both in a aggregated level and by city. First we need to subset the data:
# Get cities ids so we can subset the vertices and run the test by city.
city <- diffnet$vertex.static.attrs[,"city"]
# Subsetting diffnet, notice that we can use either indices or ids to create a
# "subdiffnet". In this case we are using indices.
diffnet_city1 <- diffnet[which(city==1),]
diffnet_city2 <- diffnet[which(city==2),]
diffnet_city3 <- diffnet[which(city==3),]
diffnet_city4 <- diffnet[which(city==4),]
Notice that by subsetting the set of vertices we have created 4 new diffnet
objects, so all the methods and functions work for each one of these, for example, threshold levels in each city
oldpar <- par(no.readonly = TRUE)
par(mfrow=c(2,2))
plot_threshold(diffnet_city1, vertex.label = "", main="Threshold and ToA\nin City 1")
plot_threshold(diffnet_city2, vertex.label = "", main="Threshold and ToA\nin City 2")
plot_threshold(diffnet_city3, vertex.label = "", main="Threshold and ToA\nin City 3")
plot_threshold(diffnet_city4, vertex.label = "", main="Threshold and ToA\nin City 4")
## Warning in plot_infectsuscep.list(graph$graph, graph$toa, t0, normalize, :
## When applying logscale some observations are missing.
Now we run the test for each city. Observe that we can use the parallel package to speedup the test as we will do in the first two cities using two cores (this is done thanks to the boot package).
# Defining the statistic
avgthr <- function(x) mean(threshold(x), na.rm = TRUE)
# Running the test by city
test1 <- struct_test(diffnet_city1, avgthr, 500, ncpus=2, parallel="multicore")
test2 <- struct_test(diffnet_city2, avgthr, 500, ncpus=2, parallel="multicore")
test3 <- struct_test(diffnet_city3, avgthr, 500)
test4 <- struct_test(diffnet_city4, avgthr, 500)
# Running the test aggregated
testall <- struct_test(diffnet, avgthr, 500, ncpus=2, parallel="multicore")
# Printing the outcomes
test1
##
## Structure dependence test
## # Simulations : 500
## # nodes : 62
## # of time periods : 18
## --------------------------------------------------------------------------------
## H0: E[beta(Y,G)|G] - E[beta(Y,G)] = 0 (no structure dependency)
## observed expected p.val
## 0.6304 0.6249 0.8000
##
## Structure dependence test
## # Simulations : 500
## # nodes : 24
## # of time periods : 18
## --------------------------------------------------------------------------------
## H0: E[beta(Y,G)|G] - E[beta(Y,G)] = 0 (no structure dependency)
## observed expected p.val
## 0.4931 0.5077 0.6880
##
## Structure dependence test
## # Simulations : 500
## # nodes : 21
## # of time periods : 18
## --------------------------------------------------------------------------------
## H0: E[beta(Y,G)|G] - E[beta(Y,G)] = 0 (no structure dependency)
## observed expected p.val
## 0.4365 0.4207 0.6040
##
## Structure dependence test
## # Simulations : 500
## # nodes : 18
## # of time periods : 18
## --------------------------------------------------------------------------------
## H0: E[beta(Y,G)|G] - E[beta(Y,G)] = 0 (no structure dependency)
## observed expected p.val
## 0.5926 0.6040 0.7120
##
## Structure dependence test
## # Simulations : 500
## # nodes : 125
## # of time periods : 18
## --------------------------------------------------------------------------------
## H0: E[beta(Y,G)|G] - E[beta(Y,G)] = 0 (no structure dependency)
## observed expected p.val
## 0.5660 0.5542 0.5120
This shows in no City threshold seems to be struture dependent, as after simulating 1,000 networks by rewiring each one of these preserving the degree sequence (using algorithm = "swap"
by default in the rewire.args
) the null can’t be rejected. Now we can make an histogram of the outcomes by city:
# To make it nicer, we change the parameters in using par
# (see ?par)
oldpar <- par(no.readonly = TRUE)
par(mfrow=c(2,2))
# Now we use the hist method for the -diffnet_boot- class
hist(test1, main="Distribution of Statistic on rewired\nnetwork (City 1)", ask = FALSE)
hist(test2, main="Distribution of Statistic on rewired\nnetwork (City 2)", ask = FALSE)
hist(test3, main="Distribution of Statistic on rewired\nnetwork (City 3)", ask = FALSE)
hist(test4, main="Distribution of Statistic on rewired\nnetwork (City 4)", ask = FALSE)
To use the data for statistical models we can retrieve the data stored in the diffnet
object and coerce it as a data.frame
. First, we will compute lagged exposure at each time period and add it as a dynamic vertex attribute, including a dummy variable called adopted
equal to 1 if the individual adopted at that time point.
## 1 2 3 4 5 6 7 8
## 1001 NA 0 0.0000000 0.5000000 1.0000000 1.0000000 1.0000000 1.0000000
## 1002 NA 0 0.0000000 0.5000000 1.0000000 1.0000000 1.0000000 1.0000000
## 1003 NA 0 0.2000000 0.4000000 0.6000000 0.8000000 0.8000000 0.8000000
## 1004 NA 0 0.3333333 0.3333333 0.3333333 0.3333333 0.6666667 0.6666667
## 1005 NA 0 0.0000000 0.0000000 0.0000000 1.0000000 1.0000000 1.0000000
## 1006 NA 0 0.3333333 0.6666667 0.6666667 1.0000000 1.0000000 1.0000000
## 9 10 11 12 13 14 15
## 1001 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 1002 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 1003 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 1004 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667
## 1005 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 1006 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 16 17 18
## 1001 1.0000000 1.0000000 1.0000000
## 1002 1.0000000 1.0000000 1.0000000
## 1003 1.0000000 1.0000000 1.0000000
## 1004 0.6666667 0.6666667 0.6666667
## 1005 1.0000000 1.0000000 1.0000000
## 1006 1.0000000 1.0000000 1.0000000
# Netdiffuser automatically identifies whether the input is dynamic or not.
diffnet[["lagged_expo"]] <- expo
diffnet[["adopted"]] <- toa_mat(diffnet)$cumadopt
Now we can create a data frame from our diffnet
object
The following model illustrates how can we use netdiffuseR to run a lagged exposure model. In this (toy) model we are including fixed effects for time (per
), city
, belief in science (belief
) and age, and only including observations prior to the adoption of the behavior, and excluding observations from the last time point.
# Running a model
summary(
glm(adopted ~ lagged_expo + factor(per) + factor(city) + belief +
proage + I(proage^2),
dat = mydata,
subset = (per <= toa) & per < 18,
family = binomial(link="logit"))
)
##
## Call:
## glm(formula = adopted ~ lagged_expo + factor(per) + factor(city) +
## belief + proage + I(proage^2), family = binomial(link = "logit"),
## data = mydata, subset = (per <= toa) & per < 18)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9291 -0.5547 -0.4294 -0.2990 2.6441
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.86017 0.74267 -6.544 5.98e-11 ***
## lagged_expo 0.06835 0.36290 0.188 0.85060
## factor(per)3 0.11026 0.49879 0.221 0.82505
## factor(per)4 0.36896 0.49420 0.747 0.45532
## factor(per)5 0.62556 0.49461 1.265 0.20595
## factor(per)6 0.79024 0.51149 1.545 0.12235
## factor(per)7 1.05813 0.52363 2.021 0.04331 *
## factor(per)8 0.84961 0.57908 1.467 0.14232
## factor(per)9 0.46900 0.67474 0.695 0.48700
## factor(per)10 -0.89617 1.10056 -0.814 0.41548
## factor(per)11 0.90279 0.64682 1.396 0.16280
## factor(per)12 0.44730 0.74503 0.600 0.54826
## factor(per)13 0.64460 0.75892 0.849 0.39567
## factor(per)14 1.13806 0.71444 1.593 0.11117
## factor(per)15 1.32137 0.72124 1.832 0.06694 .
## factor(per)16 0.06475 1.12636 0.057 0.95416
## factor(per)17 0.09697 1.12815 0.086 0.93150
## factor(city)2 -0.05152 0.32958 -0.156 0.87579
## factor(city)3 -0.07965 0.31878 -0.250 0.80270
## factor(city)4 -0.19764 0.33718 -0.586 0.55777
## belief 0.50842 0.16589 3.065 0.00218 **
## proage 0.94983 0.34181 2.779 0.00546 **
## I(proage^2) -0.10620 0.04736 -2.242 0.02495 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 585.45 on 825 degrees of freedom
## Residual deviance: 548.47 on 803 degrees of freedom
## (155 observations deleted due to missingness)
## AIC: 594.47
##
## Number of Fisher Scoring iterations: 6
As shown, we find no lagged exposure effects and the adoption was mainly drive by belief in science and age of the MD. Notice that instead of calling glm
directly, we could have also used the diffreg
function (a wrapper of glm
that does all the filtering and exposure computing for us) as follows:
##
## Call:
## glm(formula = Adopt ~ exposure + factor(per) + factor(city) +
## belief + proage + I(proage^2), family = binomial(link = "logit"),
## data = dat, subset = ifelse(is.na(toa), TRUE, toa >= per))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9291 -0.5505 -0.4245 -0.2887 2.6441
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.86017 0.74267 -6.544 5.98e-11 ***
## exposure 0.06835 0.36290 0.188 0.85060
## factor(per)3 0.11026 0.49879 0.221 0.82505
## factor(per)4 0.36896 0.49420 0.747 0.45532
## factor(per)5 0.62556 0.49461 1.265 0.20595
## factor(per)6 0.79024 0.51149 1.545 0.12235
## factor(per)7 1.05813 0.52363 2.021 0.04331 *
## factor(per)8 0.84961 0.57908 1.467 0.14232
## factor(per)9 0.46900 0.67474 0.695 0.48700
## factor(per)10 -0.89617 1.10058 -0.814 0.41549
## factor(per)11 0.90279 0.64682 1.396 0.16280
## factor(per)12 0.44730 0.74503 0.600 0.54826
## factor(per)13 0.64460 0.75892 0.849 0.39567
## factor(per)14 1.13806 0.71444 1.593 0.11117
## factor(per)15 1.32137 0.72124 1.832 0.06694 .
## factor(per)16 0.06475 1.12636 0.057 0.95416
## factor(per)17 0.09697 1.12815 0.086 0.93150
## factor(per)18 19.74176 584.21139 0.034 0.97304
## factor(city)2 -0.05152 0.32958 -0.156 0.87579
## factor(city)3 -0.07965 0.31878 -0.250 0.80270
## factor(city)4 -0.19764 0.33718 -0.586 0.55777
## belief 0.50842 0.16589 3.065 0.00218 **
## proage 0.94983 0.34181 2.779 0.00546 **
## I(proage^2) -0.10620 0.04736 -2.242 0.02495 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 652.73 on 841 degrees of freedom
## Residual deviance: 548.47 on 818 degrees of freedom
## (155 observations deleted due to missingness)
## AIC: 596.47
##
## Number of Fisher Scoring iterations: 15
Note that there is a diffnet
version of the same dataset in the package, medInnovationsDiffNet
.↩


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public interface JsonPutAccess
Provides access to contained JsonValue
by URLs for the JsonItem
or JsonSystem
classes.
For compatibility with the Map and List interfaces, the simple put and remove methods on this class
are named putValue and removeValue. Implementing classes should add put and remove methods compatible
with Map and List as appropriate for their interface.
| Method Summary | |
|---|---|
PutResponse |
createArray(java.lang.String url)
Create a new JsonArray at the given URL. |
PutResponse |
createObject(java.lang.String url)
Create a new JsonObject at the given URL. |
PutResponse |
put(java.lang.String url,
java.lang.Boolean value)
Put a boolean value as a JsonBoolean. |
PutResponse |
put(java.lang.String url,
byte[] value)
Put a binary value. |
PutResponse |
put(java.lang.String url,
java.lang.Double value)
Put a double value as a JsonNumber. |
PutResponse |
put(java.lang.String url,
java.lang.Float value)
Put a float value as a JsonNumber. |
PutResponse |
put(java.lang.String url,
java.lang.Integer value)
Put an int value as a JsonNumber. |
PutResponse |
put(java.lang.String url,
java.lang.Long value)
Put a long value as a JsonString. |
PutResponse |
put(java.lang.String url,
java.lang.String value)
Put a string value as a JsonString. |
PutResponse |
putNative(java.lang.String url,
java.lang.Object value)
Put a native object as a JsonNative. |
PutResponse |
putValue(java.lang.String url,
JsonValue value)
Put a JsonValue. |
RemoveResponse |
removeValue(java.lang.String url)
Remove a value. |
| Method Detail |
|---|
PutResponse createArray(java.lang.String url)
url
- The URL to create at.
PutResponse createObject(java.lang.String url)
url
- The URL to create at.
PutResponse put(java.lang.String url, java.lang.Boolean value)
url
- The URL to put the value under.value
- The boolean value to store.
PutResponse put(java.lang.String url, byte[] value)
Note: The JsonString object will point to the underlying byte[] that was supplied; it will
not copy it. The internal operations of the JsonSystem will not change it, but it will
be made directly available through the JsonString object's JsonValue.binaryValue()
method. So, if you need to make sure that the original is not changed, you must copy it before
supplying it to this method.
url
- The URL to put the value under.value
- The binary value to store.
PutResponse put(java.lang.String url, java.lang.Double value)
url
- The URL to put the value under.value
- The double value to store.
PutResponse put(java.lang.String url, java.lang.Float value)
url
- The URL to put the value under.value
- The float value to store.
PutResponse put(java.lang.String url, java.lang.Integer value)
url
- The URL to put the value under.value
- The int value to store.
PutResponse put(java.lang.String url, java.lang.Long value)
url
- The URL to put the value under.value
- The long value to store.
PutResponse put(java.lang.String url, java.lang.String value)
url
- The URL to put the value under.value
- The String value to store.
PutResponse putNative(java.lang.String url, java.lang.Object value)
url
- The URL to put the value under.value
- The native object to store.
PutResponse putValue(java.lang.String url, JsonValue value)
Note that the return from this method is the value that was returned from the put operation, which differs
from the Map.put(K, V)
method which returns any previous value that the map had under that url.
The put
methods on JsonSystem and JsonItem are interchangeable
with this method.
url
- The URL to put the value under.value
- The JsonValue to store.
RemoveResponse removeValue(java.lang.String url)
This method is named "removeValue" to avoid conflicts with the List and Map "remove" methods. A method named "remove" will be provided by any class implementing this interface, and that method should be used instead of this one, for brevity.
url
- The URL indicating the value to remove.
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Oracle is a REST+JSON API to create HD keychains, store seeds and sign transactions in a HD multisignature/multi factor authentication scenario. Users receive a phone call and enter a PIN to validate their identity.
QA API Endpoint
http://oracle-qa.37coins.io/api
Prod API Endpoint
https://oracle.37coins.io/api
"Oracle" expects some of the requests to be signed with a known RSA key. The client has to provide additional headers in the request:
- X-37Coins-Nonce : nonce used to generate the signature
- X-37Coins-Signature : a signature created by signing the word "token" + nonce string. Nonce is usually a timestamp. Example of string to be signed: token12569537329.
A component that signs transactions in a HD multi-sig/multi factor authentication scenario. The user receives a phone call and enters a PIN to validate their identity.
Let's assume a multisig 2 of 2 scenario, 1 private key is stored on the client side, 1 private key stored on Rambutan. The client generates a seed and calculates an xpub based on that seed. The client requests a new account to be created in Rambutan sending their xpub as a parameter. Rambutan creates a new account, generates another seed and sends that seed's xpub back to the client. When the client has to sign a tx, they sign with their own key and then send the partially signed tx to Rambutan to sign. Rambutan will make Plivo make them answer a phone call and once they enter a valid PIN, Rambutan will sign the tx with its key and send the tx back to the client
Plivo (https://www.plivo.com): Voice API. Handles user calls HD Wallet: Rambutan uses Hierarchical Deterministic wallets bitcoinj: bitcoinj framework is used to handle the wallets and signatures Sandia: Is used as an alternative way to notify the client if they do not have a public IP.
The API expects some of the requests to be signed with known RSA key. Client has to provide additional headers in request: - ``X-37Coins-Nonce`` : nonce used to generate signature in given request - ``X-37Coins-Signature`` : signature created by signing ``API_TOKEN + nonce`` string. ``API_TOKEN`` is just fixed string for now and ``nonce`` is usually a timestamp. Example of string to be signed: ``token12569537329`` Signing should be done by private key whose public counterpart is known for Rambutan (specified in ``partnersPubKeys`` ENV variable)
A REST+JSON API.


[uwc-ruika] Mindustry Logic - Unit-based Wireless Communication (Ruika's proposal)
Terminology:
Manager - Logic module that is responsible for establishing & keeping wireless communication.
Listener - Logic module that is constantly monitoring for any inbound packets.
Sender - Logic module that is sending the data packet into network.
Node - Unit that is working as a network node.
Packet - Set of information that is being sent over a node to a certain address.
Address - 8-bit value which is used to refer a certain group of receivers.
Nodes & Packets
Nodes are units which were chosen by Manager to serve that purpose.
Each unit in-game can have a flag which is basically a unit identificator that is allowing to separate units into groups.
Each flag is a 53-bit integer. UWC-Ruika uses flags as storage for information that is being transferred.
This protocol uses bit 1 (negative bit) to reserve that unit for UWC-Ruika protocol. Flag is divided as follows:
| 1 | 2 ... 53 |
|---------------|----------|
|NetwrkReservBit| Packet |
Packet is also divided into several parts:
| 2 3 | 4 ... 11 | 12 ... 53 |
|-------|----------|-----------|
|AknwBit| Address | Data |
Acknowledgment Bits (2 - 3):
Acknowledgment bits are used to inform the status of the node/packet.
Bit 2 - Node reserving bit. Used to inform that this node is currently taken by a packet and must not be used.
Bit 3 - "Ready" bit. Used to inform that packet is finished and is ready to be received.


BONUS SKILLS - CODING AND DEVELOPMENT, MUSIC COMPOSITION AND PRODUCTION
About
I'm a User Experience Designer with a background in front-end/back-end development and quality assurance.
I’ve always been a good problem solver and UX, like most design, is about finding solutions to problems.
Before UX, I clocked up 12 years of development and testing experience working in agile technology teams in e-Commerce, advertising, and music.
I can code, I can compose, and I can do handstands too.
Pleased
to meet
you!
Portfolio
These are the projects and real-world client briefs I worked on at General Assembly.
Squirrel
FinTech Onboarding
AirFrance
Inflight Services App
Firebox
Redesigning the product personalisation process
Contact
Feel free to email me
or connect via the social buttons in the footer below.
Squirrel
Redesigning the on-boarding and set-up of a FinTech startup.
Client:
Squirrel
- Date: June 2017 - Service: UX Design.
Summary
Squirrel is a bank account, controlled by an app, aimed at making managing your money easy. We completely redesigned the onboarding, signup, and activation process for their app.
Problem
A number of users had downloaded the app which had a convoluted onboarding and setup process. Users were skipping through this onboarding and consequently, on first using the app, didn't really understand how to use it. The result of this was they never continued using Squirrel.
Solution
We took the 3 stage process of onboarding, signup and activation, merging all three into one combined linear flow, cutting the number of steps, and vastly shortening the copy by augmenting it with icons and diagrams to reduce cognitive load of a previously text heavy experience. We were also able to gain the user's trust in using the app and the service by highlighting early on the types of protection their money had by using the familiar logos of the bank that was protecting their money, and the financial standards agency that authorised and regulated Squirrel.
Tools and Techniques
Competitor Analysis, Surveys, User Interviews, Personas, User Journeys and Experience Maps, Design Studio, Design Workshops, Paper Prototyping, Wireframing in Sketch, InVision Prototypes, User Testing, Design Specifications.
Air France is the French flagship carrier. We were tasked with integrating Air France’s in-flight services into a mobile first, responsive website or a native mobile or tablet application.
Problem
The current AirFrance app was very limited, offering ticket booking and flight tracking and that was pretty much it. The experience of almost every in-flight service could potentially be improved by somehow integrating it into the mobile App so we investigated the services that were currently causing the biggest pain points for both the flyers and the flight attendants. These included the time taken ordering Snacks & Drinks, browsing Entertainment on unresponsive in-seat touch screens and ordering items from the duty-free catalogue.
Solution
We redesigned the current AirFrance app to integrate dedicated in-flight services features including entertainment, food and drink, duty-free shopping, and purchasing wifi.
Tools and Techniques
Feature Comparisons, Surveys, User Interviews, Personas and User Journeys, MoSCow Method, Design Workshops, Paper Prototyping, Wireframing in Sketch, Marvel and InVision Prototypes, User Testing, Design Specifications.
Redesigning an online product personalisation process.
Client: Firebox - Date: February 2018 - Service: UX Design
Format: Mobile, tablet and desktop
SUMMARY
Firebox are an alternative homeware and lifestyle shopping site. A number of the products they sell are personalised products in which you can upload images to be printed on products such as cushions, facemasks, suitcase covers etc. I completely redesigned the product personalisation process.
PROBLEM
Drop-off rates during the product personalisation process were quite high, particularly on mobile. Through user testing the current process, a number of key pain-points were identified.
SOLUTION
After identifying common pain-points and by iterating through designs, wireframes, prototypes and validating with more user testing, all of the pain points identified were systematically addressed and resolved.
TOOLS AND TECHNIQUES
Contextual Enquiry, Competitor Analysis, Personas, User Flows, Design Studios, Paper Prototyping, Wireframing in Sketch, interactive prototypes in Marvel and Framer, User Testing, Design Specifications, CSS and HTML.


This might be your first AWS Node.JS App you ever deployed.
The code base of this application was taken from the AWS Elastic Beanstalk Tutorial but was modified for the Dynatrace AWS Tutorials that you can find here! and here!
The goal of these tutorials is to enable Full Stack Monitoring and build an Unbreakable DevOps Pipeline with Dynatrace for your Application.
Following is a screenshot of Dynatrace Smartcape in case you deploy this application in a scalability group:
If you want to learn more about Dynatrace visit our website and get started with your own Dynatrace SaaS Trial!
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Steven Gerrard maps out Ross Wilson's key Rangers transfer role as he urges recruitment note of caution
The Ibrox boss finally got to meet the sporting director this week and was clearly impressed by what he saw.
by Scott McDermott, https://www.dailyrecord.co.uk/authors/scott-mcdermott/Steven Gerrard can’t say for certain that Ross Wilson will be a success as Rangers’ new sporting director.
But after finally meeting him in the flesh last week Gerrard looked in his eyes and saw one thing – HUNGER.
And that gives the Ibrox gaffer huge encouragement for the future.
Wilson got his feet under a desk at the Hummel Training Centre after officially starting his new role on November 1.
There was no fanfare, big unveiling or Rangers TV interview. Which is probably how both men would have wanted it.
Instead, Wilson will now set about the job of enhancing the infrastructure already put in place by his predecessor Mark Allen and Gerrard.
The manager hopes their relationship will blossom over time and he’s been impressed by their early chats.
But with a manager and supporters who have an insatiable appetite for success, Gerrard believes Wilson’s hunger to improve the club is a good place to start.
He said: “Certain things have to remain private, like what’s been said between manager and sporting director.
“But Ross has had a positive week. He’s been welcomed by everyone and is getting used to his new surroundings. We’ve had some interesting meetings and talks already.
“In terms of his role in the next transfer window, the squad will be made smaller in January.
“And what doesn’t change at Rangers is that if there’s something worth exploring which will make us better, we’ll try to do it.
“But don’t expect a lot of bodies coming in – that just won’t be the case.
“It’s difficult to predict whether myself and Ross will have a strong relationship. I’ve only known him in person for around 10 days.
“Before that, we had two or three conversations.
“Relationships take time to build. I’d never met him previously so it will take time.
“But I’ve been impressed by what I’ve seen so far. He seems like a really humble guy who is prepared to put in a lot of work.
“He’ll fit in no problem. As a staff, we have a responsibility to help him. Then relationships begin to build and grow.
“The important thing is that we all want the same for this club as soon as possible.
“That’s the first good, strong vibe that I got off Ross – that he’s hungry to improve things and help us grow what we’ve put in place this last 18 months.
 I know I’ve got it.
“We’re working for a hungry set of supporters. You only have to look at the wall inside the training ground with the club’s achievements on it to realise what we’re dealing with here.
“This club is a beast and you have to be hungry if you want to work for it and represent it.”
Gerrard has been at pains for the last week to stress that star striker Alfredo Morelos won’t be sold in the January window.
He also knows that every player has a price, including the 21 goals in 25 games Colombian.
It’ll be Wilson’s job to find replacements for Morelos and other top players at Ibrox should they move on.
But Gerrard’s warning to the new sporting director and, more importantly Dave King’s board, is that it’ll be hugely difficult to replicate their quality.
He said: “Every new signing is a gamble and the market is especially tough in January.
“That’s why we don’t want to move on or sell any of our big players in that window.
“There are a lot of things outside of football you need to think about when getting a replacement.
“Settling in, the language barriers. You have to check what condition they’re in and what type of character they are.
“So every signing has its risks and availability in January isn’t great anyway.
“If we’re talking about Alfredo for example, let’s paint a picture of selling him in January.
“What club out there is going to let you buy a player as good as Morelos? It’s impossible. So it’s not going to happen.
“We’ve never had a suitable bid for him so there’s no point talking about it.
“As far as I’m concerned he’s going nowhere and we’ll be watching him for Rangers against Livingston.”
That’s the come down for Gerrard’s players this weekend – going from an exhilarating win over Porto in front of 50,000 fans to the Tony Macaroni Arena and Livi’s plastic pitch.
The Rangers boss lost his first game there in September last year and revealed it prompted a unique post-match debrief among his squad.
Gerrard has won there since but urged his players not to get complacent going to West Lothian this afternoon.
After revealing that he hopes to have his only injured player, Jordan Jones, back after the international break, he said: “When I think back to my first game at Livi, our performance was an eye-opener.
“They were very good and strong – they deserved their victory that day.
“But it showed that if we underestimate any opposition or we’re not ready for the environment we’re going into, we can be found out.
“So the players learned a lot from that fixture and it was an interesting debrief.
“Everyone could see quite clearly that we were too passive.
“And if you do that, you’re normally in a bad mood the day after.
“We’ve moved forward and made vast improvements in terms of the squad and level of consistency.
“But we all know what football can do to you. If you get carried away after Thursday night and get complacent, it can come back to bite you.
“So the challenge is to come down from Porto and get ourselves ready for a tough and very different kind of test.”


This chapter includes a checklist of smaller things you need to do to ensure that your programs are good citizens in the GTK+ world. By paying attention to the points in the checklist, you ensure that many automatic features of GTK+ will work correctly in your program.
Why. By handling this signal, you let widgets have context-sensitive menus that can be invoked with the standard key bindings.
The “popup-menu” signal instructs the widget for which it is emitted to create a context-sensitive popup menu. By default, the key binding mechanism is set to emit this signal when the Shift+F10 or Menu keys are pressed while a widget has the focus. If a widget in your application shows a popup menu when you press a mouse button, you can make it work as well through the normal key binding mechanism in the following fahion:
Write a function to create and show a popup menu. This
function needs to know the button number and the event's
time to pass them to gtk_menu_popup()
. You can implement
such a function like this:
static void do_popup_menu (GtkWidget *my_widget, GdkEventButton *event) { GtkWidget *menu; int button, event_time; menu = gtk_menu_new (); g_signal_connect (menu, "deactivate", G_CALLBACK (gtk_widget_destroy), NULL); /* ... add menu items ... */ if (event) { button = event->button; event_time = event->time; } else { button = 0; event_time = gtk_get_current_event_time (); } gtk_menu_attach_to_widget (GTK_MENU (menu), my_widget, NULL); gtk_menu_popup (GTK_MENU (menu), NULL, NULL, NULL, NULL, button, event_time); }
In your “button-press-event” handler, call this function when you need to pop up a menu:
static gboolean my_widget_button_press_event_handler (GtkWidget *widget, GdkEventButton *event) { /* Ignore double-clicks and triple-clicks */ if (gdk_event_triggers_context_menu ((GdkEvent *) event) && event->type == GDK_BUTTON_PRESS) { do_popup_menu (widget, event); return TRUE; } return FALSE; }
Implement a handler for the “popup-menu” signal:
static gboolean my_widget_popup_menu_handler (GtkWidget *widget) { do_popup_menu (widget, NULL); return TRUE; }
If you do not pass a positioning function to gtk_menu_popup()
,
it will show the menu at the mouse position by default. This
is what you usually want when the menu is shown as a result of
pressing a mouse button. However, if you press the
Shift+F10
or Menu keys while the widget is focused, the
mouse cursor may not be near the widget at all. In the example above, you may want to
provide your own menu-positioning function
in the case where the event
is
NULL
. This function should compute the desired position for
a menu when it is invoked through the keyboard. For example,
GtkEntry aligns the top edge of its popup menu with the bottom
edge of the entry.
For the standard key bindings to work, your widget must be
able to take the keyboard focus. In general, widgets should
be fully usable through the keyboard and not just the mouse.
The very first step of this is to ensure that your widget
can receive focus, using gtk_widget_set_can_focus()
.


| Class | Screen Range |
|---|---|
.hide |
Hidden for all Devices |
.hide-on-small-only |
Hidden for Mobile Only |
.hide-on-med-only |
Hidden for Tablet Only |
.hide-on-med-and-down |
Hidden for Tablet and Below |
.hide-on-med-and-up |
Hidden for Tablet and Above |
.hide-on-large-only |
Hidden for Desktop Only |
This should be left aligned
This should be right aligned
This should be center aligned


The DBC experience seems to be characterized by the unwavering commitment and constant support shown by all of its teachers and sudents. The community at DBC seems to be one of very high energy; Everyone at DBC is excited to be at DBC. It is a diverse community where each person brings along their own sets of expertise and their own ways of thinking. People come from many places and backgrounds, but they come to DBC with the same goal. To me DBC seems like a huge collaborative group project. People contribute in their own ways, listen, learn, seek out help and are also willing to give it. It is a program with many moving pieces and with no part that is not vital. I get the feeling that everyone at DBC is valued as equal and is essential to the success of the people around them. DBC is "just one big experiemnt" and the teachers and students alike are just in the "big mess together".
In order for the DBC environment to exist the "kitchen mindset" is definetly important. Although some people may view DBC like a menu item with a price tag, this is not the approach that will make your experience the most worthwile. In order to make the very most of DBC, a student should look towards the "kitchen mindset". Having a kitchen mindset is feeling a sense of responsibility and ownership of your own learning process. It means taking your education into your own hands and making it an amazing experience for yourself and your cohort. I plan on making the very most of my experience, and hopefully I blow my mind with how much I can learn. Having a kitchen mindset as opposed to a restaurant mindset completely changes the game.
I can already feel that with DBC will be far from easy. In fact, I have a feeling it may be the hardest thing I have ever done. At the very least you can say that I'm nervous. It will come many challenges and roadblocks, but I'm hoping that I wont let these obstacles define my experience. My experience will be determined by how I respond to them. Everyone at DBC is learning, both the teachers and the students, and there will be times when I feel lost but maybe also others where I take the lead. I think keeping an open mind and staying positive will both prove to be very critical along the way. I need to remember that everyone else is also learning and that many people will be feeling the same things I am feeling. I can predict that I will need to become more confortable asking questions and seeking help. I think knowing that the DBC staff wants to help will ease the process and will make me feel very supported.
I have always thought that DBC was going to be a real grind. This video definetly confirmed those thoughts and perhaps even more.  However, it was almost comforting to hear it again. Its good to know that everyone will struggle and everyone will succeed at DBC. There "were all in this big mess together" is an amazing supportive and encouraging thought. I am excited to devout as much time as necesary to DBC and the learning process and I definetly preparing myself to be blown away.


What's new in Hextrapolate?
Hextrapolate version 2.0 is a complete ground-up rewrite of the previous version (formerly known as WinHex) and delivers many new features, bug fixes and enhancements. The new conversion engine is faster, more resource efficient and more powerful.
Selectable text encodings
You can now convert numbers to and from more text encodings: ASCII, UTF-8, and two variants of UTF-16 (little and big endian). Not only that, but the conversion engine has been rewritten to better handle cases where the number being converted is not valid for the selected encoding.
More choices
More fields have been added to the interface to provide more flexible conversions. This means that you can now convert to and from any of the supported text encodings and bases without having to repeatedly switch your selected encoding or base. Exotic conversion which were previously a two step process (for example Base 12 to Base 19) can now be done in a single step.
Better feedback
The Hextrapolate main window now shows more information in the title bar, allowing you to see at a glance the size of the number currently being converted (in bits and bytes). Numbers can be arbitrarily large in size, extending to thousands of bits and beyond.
Comprehensive tool-tips
More tool tips have been added to the interface to provide better feedback in realtime if you try to type a character which is not allowed in a particular base or text encoding.
Integrated help
Extensive help documentation prepared in the familiar format used by Apple applications is now embedded within the Hextrapolate application itself. An online mirror of the documentation can be found here.


ASM80 is the assembler for old 8bit CPUs. This is a standalone version for Node.js
Line can begin with a label. Label should be followed by ":", but it can be omitted.
Everything after a ; in a line is a comment (unless the ; is part of a string literal, of course). There are no multiline comments.
String literals are written to the object file without any character set translation. In case you use punctuated character, the lower byte of its Unicode representation will be used.
Blanks are significative only in string literals and when they separate lexical elements. Any number of blanks has the same meaning as one. A blank between operators and operands is allowed but no required except when the same character has other meaning as prefix ('$' and '%', for example).
Numeric literals can be written in decimal, binary, octal and hexadecimal formats. Several formats are accepted to obtain compatibility with the source format of several assemblers.
A literal that begins with $ is a hexadecimal constant, except if the literal is only the $ symbol.
A literal that begins with % is a binary constant, except if the literal is only the % symbol, in that case, is an operator.
A literal that begins with a decimal digit can be a decimal, binary, octal or hexadecimal. If the digit is 0 and the following character is an X, the number is hexadecimal. If not, the suffix of the literal is examined: D means decimal, B binary, H hexadecimal and O or Q octal, in any other case, is taken as a decimal. Take care, FFFFh, for example, is not a hexadecimal constant, is an identifier, to write it with the suffix notation you must do it as 0FFFFh.
There is one format of string literals. They should be double quote delimited. Assembler can parse single quote form too, but it should produce an error, when delimitation is used in string, so please use double quotes.
A string literal of length 1 can be used as a numeric constant with the numeric value of the character contained. This allows expressions such as 'A' + 80h to be evaluated as expected.
Identifiers are the names used for labels, EQU symbols and macro names and parameters. The names of the CPU mnemonics, registers, and flag names, and of assembling directives are reserved and can not be used as names of identifiers. Reserved names are case insensitive, even if case sensitive mode is used.
Identifiers are not case sensitive. Internally are converted to uppercase
Parser can evaluate simple math expressions, with all of the common operators, like +, -, /, *, # (modulo). You can use identifiers as a variables too, e.g. "LOOP + 3".
There are some specials here, like string repetitions ("A"*3 produces "AAA") or upper / lower part of identifier value. If LOOP is 0x1234, then <LOOP means 0x34, >LOOP means 0x12
Right CPU is determined by file name extension, or by .engine directive (see below). File name extensions are:
| Directive | Meanings |
|---|---|
| Output controls | |
| .cpu | Select CPU type. Available values are: 8080, Z80, 6502, M6800, CDP1802, M6809, C65816 |
| .engine | Controls machine type for emulation (only in online ASM80). Available values are: PMI, PMD, JPR, KIM, SBCZ80, SBC6502, SBC09, ZXS and CPM |
| .pragma sna | Makes SNA file instead of HEX (only for Z80) |
| .pragma tap | Makes TAP file instead of HEX (only for Z80) |
| .pragma prg | Makes C64's PRG file instead of HEX (only for 6502) [read more].PRAGMA PRG ;says “make .PRG instead of .HEX” .ORG $0810 ;or higher .ENT $ ;for “enter here” |
| .pragma com | Makes CP/M COM file instead of HEX (only for Z80/8080) [read more] |
| .pragma html | Makes HTML listing (instead of LPT) |
| Data definition | |
| db (aliases: defb, fcb) | Define Byte. The argument is a comma separated list of string literals or numeric expressions. The string literals are inserted in the object code, and the result of the numeric expression is inserted as a single byte, truncating it if needed. You can use DUP for entering N same values: DB 10 DUP (123) means "10 times value 123" |
| dw (aliases: defw, fdb) | Define Word. The argument is a comma separated list of numeric expressions. Each numeric expression is evaluated as a two byte word and the result inserted in the proper "endianity". You can use DUP for entering N same values: DW 10 DUP (123) means "10 times value 123" |
| ds (aliases: defm, defs, rmb) | Define Space. Take one argument, which is the amount of space to define, in bytes. |
| fill value, length | Fill memory with a value. Take two arguments, the first is a value, the second is length of filled block (byte count). |
| bsz length (alias: zmb) | Fill memory with a given count of zeros. |
| .include filename | Include a file. The file is readed and the result is the same as if the file were copied in the current file instead of the INCLUDE line. The file included may contain INCLUDE directives, and so on. INCLUDE directives are processed before the assembly phases, so the use of IF directives to conditionally include different files is not allowed. |
| Code control | |
| org addr | ORiGin. Establishes the origin position where to place generated code. Several ORG directives can be used in the same program, but if the result is that code generated overwrites previous, the result is undefined. |
| .ent addr | ENTer point for debugging. I.e. .ent $ |
| .align N | The .align directive causes the next data generated to be aligned modulo N bytes. |
| .phase addr | Continue to produce code and data for loading at the current address but assemble instructions and define labels as if they originated at the given address. Useful when producing code that will be copied to a different location before being executed. |
| .dephase | End phase block. |
| Preprocessor | |
| equ (alias: =) | EQUate. Must be preceded by a label. The argument must be a numeric expression, the result is assigned to the label. I.e. VIDRAM equ $4000 |
| Conditional blocks | |
| .if cond | Contional assembly. The argument must be a numeric expression, a result of 0 is considered as false, any other as true. If the argument is true the following code is assembled until the end of the IF section is encountered, else is ignored. The IF section is ended with a ENDIF directive. IF can't be nested. |
| .ifn cond | IF NOT |
| .endif | End of the IF block |
| Macros and blocks | |
| .macro macro_name | Defines a macro, see the chapter about macros. |
| .rept count | Repeat a block of code substituing arguments. See the chapter about macros. |
| .endm | End of MACRO definition or REPT cycle. |
| .block | Start of logical block. All labels, defined in this block, are local. It means you can’t reference them from outside the block. If you want to define a label globally, simply prefix it with ‘@’, like @LABEL: Good idea is to enclose INCLUDEd code into block. |
| .endblock | End of BLOCK. |
| 65816 directives | |
| .m8 | Accumulator is 8bit |
| .m16 | 16bit accumulator |
| .x8 | index register is 8bit |
| .x16 | 16bit index |
Define macro with .macro and .endm. You can use a parametric macros - any parameter is addressable by %%1, %%2, %%3, ... For example, such code:
.macro decadd adi %%1 daa .endm ; Use this macro decadd $22
will generate this:
0000 ; Use this macro **MACRO UNROLL - DECADD 0000 87 22 ADI $22 0001 27 DAA


from heapq import heappush, heappop, heapify class Solution: def maximumScore(self, a: int, b: int, c: int) -> int: res = 0 heap = [-a, -b, -c] heapify(heap) while len(heap) > 1: res += 1 v = heappop(heap) #print(v) v += 1 v2 = heappop(heap) #print(v) v2 += 1 if v != 0: heappush(heap, v) if v2 != 0: heappush(heap, v2) return res


Vertical timeline time intervals
标题1
Let's make coolest things in css
Prensetation
10:00
12:00
标题2
Let's make coolest things in javascript
Prensetation
13:00
14:00
标题3
Let's make coolest things in css
Review
15:00
17:45


Contract for screen size and aspect ratio calculation. The SceneManager delegates all screen size measurements to an IScreenSizeCalculator instance.
SceneManager
IScreenSizeCalculator
An implementor can return any value it sees fit for a particular implementation.
The CalculateSize() could always return a fixed value like 800x600, regardless of the actual screen size.
CalculateSize()
The IScreenSizeCalculator is used in resize events in the following manner:
const avlSize = this.screenSizeCalculator.GetAvailableSize(); const aspect = this.screenSizeCalculator.GetAspectRatio(); const size = this.screenSizeCalculator.CalculateSize(avlSize, aspect); this.renderer.resize(size.x, size.y);
Returns the desired aspect ratio. This value is passed to the CalculateSize() function.
Returns the available or desired screen size. This value is passed to the CalculateSize() and CalculateScale functions.
CalculateScale
Returns the scale to be applied to all scenes.
Returns the screen size the renderer will actually use.
the available screen dimensions.
the required aspect ratio
Generated using TypeDoc
Contract for screen size and aspect ratio calculation. The
SceneManager
delegates all screen size measurements to anIScreenSizeCalculator
instance.An implementor can return any value it sees fit for a particular implementation.
The
CalculateSize()
could always return a fixed value like 800x600, regardless of the actual screen size.The IScreenSizeCalculator is used in resize events in the following manner:
const avlSize = this.screenSizeCalculator.GetAvailableSize(); const aspect = this.screenSizeCalculator.GetAspectRatio(); const size = this.screenSizeCalculator.CalculateSize(avlSize, aspect); this.renderer.resize(size.x, size.y);


Hy!
Got problem with states on bf3 log.
User finds on this site
http://bf3stats.com
Her is link to se profile:
http://prntscr.com/gjl49
Player is up to date.(Update finished, Player has been updated)
on site of bf3 got this:
http://prntscr.com/gjl9l
I use crome and bblog is updated .
Any sugestion?
Tnx


new CustomAnalytics(trackingId, clientId)
Custom Analytics object, initialize this object.
Parameters:
| Name | Type | Description |
|---|---|---|
trackingId |
int | The tracking ID to post to (received from google-analytics). |
clientId |
int | ID to keep the devices apart from eachother. |
- Source:
Methods
initialize()
Initialize the CustomAnalytics Object.
- Source:
sendEvent(category, action)
Send a event to Google Analytics
Parameters:
| Name | Type | Description |
|---|---|---|
category |
string | The category the event belongs to. |
action |
string | What action the event belongs to. |
- Source:
sendPageView(page)
Send a page view to Google Analytics
Parameters:
| Name | Type | Description |
|---|---|---|
page |
string | Page name to send to analytics service. |
- Source:
setDimension(fieldName, value)
Send Dimensions to Google Analytics, currently not implemented
Parameters:
| Name | Type | Description |
|---|---|---|
fieldName |
string | Key to send to analytics service (example: user_agent_string). |
value |
string | Value to send to analytics service. |
- Source:


PRICE OF GLORY
Reviewed by Harvey Karten New Line Cinema Director: Carlos Avila Writer: Phil Berger Cast: Jimmy Smits, Jon Seda, Clifton Collins Jr., Maria del Mar, Sal Lopez, Louis Mandylor, Danielle Carnastra, Ernesto Hernandez, Paul Rodriguez, Ron Perlman
What good father doesn't want the best for his kids?  "Price of Glory," whose sound track features some exciting and appropriate Latino music such as "Grita de Alegria" performed by Quetzal and "Cumbia Raza" performed by Los Lobos, is a hard-hitting, action-packed and drama-filled movie about a dad who could have been a contender. Jimmy Smits inhabits the role of Arturo Ortega, a Mexican-American who entered the boxing ring in Mariposa, Arizona during his teens as a way out of the ghetto, but after suffering one particularly crushing defeat had to give up his wishes. He gets another chance to restore the family glory by becoming the trainer of his three sons, who are introduced to us early on and whose roles are soon taken by others when director Carlos Avila moves ahead ten years.
When Arturo's children Sonny, Jimmy and Johnny Ortega begin sparring in what appears to be a little league-style introduction to the sport, Arturo is in his splendor.  Arturo's differing treatments of his three sons become largely responsible for the boys' distinct personalities and as they hit their late teens and early twenties, Sonny (Jon Seda), comes on as the most promising of the new boxers, while Jimmy (Clifton Collins Jr.) turns into the family rebel and Johnny (Ernesto Hernandez) enhances his dad's image as the most obedient son.
"Price of Glory," which was written by former New York Times sportswriter Phil Berger--whose output includes a book about Mike Tyson and the shaping up of autobiographies by Joe Frazier and Larry Holmes. His familiarity with the sport comes across throughout the movie, as he tackles the roles of managers, trainers, and referees and gives us a view of the temperaments of the pugilists, particularly one villainous opponent, Davey Lane (Louis Mandylor) in a middleweight title bout that forms the dramatic climax of the tale. Despite the excitement generated by the soundtrack and the swiftly paced action of the drama, the movie lacks virtually any trace of originality. The supportive wife, Rita (Maria del Mar) is there for her man when he needs her, and turns away dutifully and predictably from the ring whenever her boy, Sonny is getting hurt, as Sonny's wife, Mariella (Danielle Carnastra) sheds a tear and leans empathetically on her mother-in-law's shoulder. =
The film is well acted throughout, furthered by the actual boxing career of Jon Seda, who punches a bag like the Golden Gloves battler that he was. Jimmy Smits is particularly riveting in the role of the father who seems unaware that his absolute control over the training of his kids is compensation for his own boyhood defeats. What is not at all easy to believe is that the man would turn down an offer of half a million dollars by the shark-like promoter, Nick Everson (Ron Perlman) in return for allowing him to advance the fight, but even discounting the lack of credibility at times, "Price of Glory" suffers in its shortage of originality. Absent the excitement generated by the powerful theater speakers as they pound out the Latino music, what we have is not much more than a made-for-TV film.
Rated PG-13. Running time: 118 minutes. (C) 2000 by Harvey Karten<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


eSports describes the world of competitive, organized video gaming. Played individually or in teams, esports are competitive modalities played in games - League of Legends, Counter-Strike, Starcraft, Dota, to name a few.
Today, they form an industry that brings together competitors, teams, game developers and sponsoring companies, at a pace that promises to move the sum of 3 billion dollars by the year 2022.
These gamers are watched and followed by millions of fans all over the world, who attend live events or tune in on TV or online. Streaming services like Twitch allow viewers to watch as their favorite gamers play in real time.
With the evolution of technology and the internet, electronic game competitions have gained momentum in Internet cafes and have become popular beyond North America, gaining space in Europe and Asia - especially in South Korea, today considered by many the esports capital of the world.
Esports athletes, as in any traditional sport, can pursue a solo career or play in teams, being paid by their clubs and receiving money from sponsorship, advertising and the tournament prizes themselves. It may seem like a dream career, but the challenge is not for anyone. To rise through the ranks, players specialize in a specific game, developing their skills through extensive, competitive play. Some professional players dedicate up to 14 hours a day in training.
The few who rise to the professional level compete in tournaments all around the world against the best teams. Successful pro gamers can earn six figure salaries or even millions.
Reports state console gaming has been the fastest branch of esports to grow in 2019, with projections estimating consoles to reach USD 47.9-billion in growth. Likewise, mobile gaming – projected as 2019’s largest gaming segment – has reached around USD 68.5-billion. Even if you don’t want to immediately invest in esports on the get-go, video games in itself can be something you’d want to invest in.
Though the actual rankings of the most popular esports games change slightly month-to-month, the ten most watched games on dominant streaming site Twitch remain consistent. As of right now, League of Legends remains the most-watched eSport in the world.
More than 100 million people watched the 'League of Legends' World Championship 2019
The International 9 championship had a total prize pool of $34 million USD and had a peak of 1.1 million simultaneous viewers
The 2019 Fortnite World Cup managed a 2.33 million peak viewers and a total of 22.70 million hours watched
Now more than ever, gaming is at the heart of the entertainment business. The way consumers engage with and through games is constantly changing. Not only does this result in more overall engagement, but it also leads to entirely new segments of game enthusiasts. There are now more than 2.5 billion gamers across the world.
In 2020, esports stars are hardly strapped for cash. While pro gamers were fighting for minuscule amounts of money and peripherals just a decade or so ago, today’s players at the highest level fight for millions of dollars each year.
Here are the players with the biggest prize money totals in esports history.
In 2019 alone, more than $215 million was awarded across more than 4,600 tournaments. That’s compared to just $13.8 million in 2012.
The few who rise to the professional level compete in tournaments all around the world against the best teams. Successful pro gamers can earn six figure salaries or even millions. Top players often start professionally around age 16 or 17, and then tend to retire around age 24.
It’s taken a while for women to invest in video games. As video games have grown and become more prevalent, more girls have gained access to them. Whatever the case, women now have a significant presence in gaming but there is still a lot of room for growth.
Typically, these games are played offline in a studio or arena environment. But there still are many tournaments that take place online.
The most common video game genres associated with esports are multiplayer online battle arena (MOBA), first-person shooter (FPS), fighting, battle royales, and real-time strategy (RTS).
It doesn't always means that the genre with the most players or most tournaments are the ones with the most prize pools. MOBA has the highest prize pool among all genres.


Preparation
Issuing
Verifying
Tracing
User y = ζ1 = ρ = ω = σ1 = σ2 = δ =
Verifier VerifyCred y = ω + δ = hashresult =
g, h are generators of subgroup of order q in Zp*, which are publicly to each entity. ρ ,ω ,σ1 ,σ2 ,δ is known to the user.
© Copyright 2019 Rujia. All Rights Reserved


Id: 102
Name: Donna Gueterman
Status: Dead
Species: Robot
Type:
Gender: Female
OriginName: unknown
OriginUrl:
LocationName: Planet Squanch
LocationUrl: https://rickandmortyapi.com/api/location/35
Image: https://rickandmortyapi.com/api/character/avatar/102.jpeg
Episode: ['https://rickandmortyapi.com/api/episode/21']
Url: https://rickandmortyapi.com/api/character/102
Created: 2017-12-01T12:21:21.357Z


Jakarta Persistence implementations of the repository interfaces. This is part of the infrastructure.


We can interpret
Equation 123 as a description of the
generative process we assume in Bayesian text
classification. To generate a document, we first choose
class with probability (top nodes in
and 13.5 ).
The two models
differ in the formalization of the second step, the
generation of the document given the class, corresponding to
the conditional distribution
:
It should now be clearer why we introduced the document space in Equation 112 when we defined the classification problem. A critical step in solving a text classification problem is to choose the document representation. and are two different document representations. In the first case, is the set of all term sequences (or, more precisely, sequences of term tokens). In the second case, is .
We cannot use and 125 for text classification directly. For the Bernoulli model, we would have to estimate different parameters, one for each possible combination of values and a class. The number of parameters in the multinomial case has the same order of magnitude.This being a very large quantity, estimating these parameters reliably is infeasible.
To reduce the number of parameters,
we make
the Naive Bayes conditional independence
assumption . We assume that attribute values are independent of
each other given the class:
We illustrate the conditional independence assumption in and 13.5 . The class China generates values for each of the five term attributes (multinomial) or six binary attributes (Bernoulli) with a certain probability, independent of the values of the other attributes. The fact that a document in the class China contains the term Taipei does not make it more likely or less likely that it also contains Beijing.
In reality, the conditional independence assumption does not hold for text data. Terms are conditionally dependent on each other. But as we will discuss shortly, NB models perform well despite the conditional independence assumption.
Even when assuming conditional independence, we still have too many parameters for the multinomial model if we assume a different probability distribution for each position in the document. The position of a term in a document by itself does not carry information about the class. Although there is a difference between China sues France and France sues China, the occurrence of China in position 1 versus position 3 of the document is not useful in NB classification because we look at each term separately. The conditional independence assumption commits us to this way of processing the evidence.
Also, if we assumed different term distributions for each position , we would have to estimate a different set of parameters for each . The probability of bean appearing as the first term of a coffee document could be different from it appearing as the second term, and so on. This again causes problems in estimation owing to data sparseness.
For these reasons, we make a second
independence assumption for the multinomial model,
positional independence :
The conditional probabilities for a term are the same
independent of position in the document.
| (128) |
With conditional and positional independence assumptions, we only need to estimate parameters (multinomial model) or (Bernoulli model), one for each term-class combination, rather than a number that is at least exponential in , the size of the vocabulary. The independence assumptions reduce the number of parameters to be estimated by several orders of magnitude.
To summarize, we generate a document in the multinomial model (Figure 13.4 ) by first picking a class with where is a random variable taking values from as values. Next we generate term in position with for each of the positions of the document. The all have the same distribution over terms for a given . In the example in Figure 13.4 , we show the generation of , corresponding to the one-sentence document Beijing and Taipei join WTO.
For a completely specified document generation model, we would also have to define a distribution over lengths. Without it, the multinomial model is a token generation model rather than a document generation model.
We generate a document in the Bernoulli model (Figure 13.5 ) by first picking a class with and then generating a binary indicator for each term of the vocabulary ( ). In the example in Figure 13.5 , we show the generation of , corresponding, again, to the one-sentence document Beijing and Taipei join WTO where we have assumed that and is a stop word.
| multinomial model | Bernoulli model | |||
| event model | generation of token | generation of document | ||
| random variable(s) | iff occurs at given pos | iff occurs in doc | ||
| document representation | ||||
| parameter estimation | ||||
| decision rule: maximize | ||||
| multiple occurrences | taken into account | ignored | ||
| length of docs | can handle longer docs | works best for short docs | ||
| # features | can handle more | works best with fewer | ||
| estimate for term the |
We compare the two models in Table 13.3 , including estimation equations and decision rules.
Naive Bayes is so called because the independence assumptions we have just made are indeed very naive for a model of natural language. The conditional independence assumption states that features are independent of each other given the class. This is hardly ever true for terms in documents. In many cases, the opposite is true. The pairs hong and kong or london and english in Figure 13.7 are examples of highly dependent terms. In addition, the multinomial model makes an assumption of positional independence. The Bernoulli model ignores positions in documents altogether because it only cares about absence or presence. This bag-of-words model discards all information that is communicated by the order of words in natural language sentences. How can NB be a good text classifier when its model of natural language is so oversimplified?
| class selected | |||||
| true probability | 0.6 | 0.4 | |||
| (Equation 126) | 0.00099 | 0.00001 | |||
| NB estimate | 0.99 | 0.01 |
The answer is that even though the probability estimates of NB are of low quality, its classification decisions are surprisingly good. Consider a document with true probabilities and as shown in Table 13.4 . Assume that contains many terms that are positive indicators for and many terms that are negative indicators for . Thus, when using the multinomial model in Equation 126, will be much larger than (0.00099 vs. 0.00001 in the table). After division by 0.001 to get well-formed probabilities for , we end up with one estimate that is close to 1.0 and one that is close to 0.0. This is common: The winning class in NB classification usually has a much larger probability than the other classes and the estimates diverge very significantly from the true probabilities. But the classification decision is based on which class gets the highest score. It does not matter how accurate the estimates are. Despite the bad estimates, NB estimates a higher probability for and therefore assigns to the correct class in Table 13.4 . Correct estimation implies accurate prediction, but accurate prediction does not imply correct estimation. NB classifiers estimate badly, but often classify well.
Even if it is not the method with the highest accuracy for text, NB has many virtues that make it a strong contender for text classification. It excels if there are many equally important features that jointly contribute to the classification decision. It is also somewhat robust to noise features (as defined in the next section) and concept drift - the gradual change over time of the concept underlying a class like US president from Bill Clinton to George W. Bush (see Section 13.7 ). Classifiers like kNN knn can be carefully tuned to idiosyncratic properties of a particular time period. This will then hurt them when documents in the following time period have slightly different properties.
The Bernoulli model is particularly robust with respect to concept drift. We will see in Figure 13.8 that it can have decent performance when using fewer than a dozen terms. The most important indicators for a class are less likely to change. Thus, a model that only relies on these features is more likely to maintain a certain level of accuracy in concept drift.
NB's main strength is its efficiency: Training and classification can be accomplished with one pass over the data. Because it combines efficiency with good accuracy it is often used as a baseline in text classification research. It is often the method of choice if (i) squeezing out a few extra percentage points of accuracy is not worth the trouble in a text classification application, (ii) a very large amount of training data is available and there is more to be gained from training on a lot of data than using a better classifier on a smaller training set, or (iii) if its robustness to concept drift can be exploited.
| (1) | He moved from London, Ontario, to London, England. | ||
| (2) | He moved from London, England, to London, Ontario. | ||
| (3) | He moved from England to London, Ontario. |
In this book, we discuss NB as a classifier for text. The independence assumptions do not hold for text. However, it can be shown that NB is an optimal classifier (in the sense of minimal error rate on new data) for data where the independence assumptions do hold.


When you tear out a man’s tongue, you are not proving him a liar, you’re only telling the world that you fear what he might say.


net = netinit(net, prior)
net = netinit(net, prior)
takes a network data structure
net
and sets the weights and biases by sampling from a Gaussian
distribution. If prior
is a scalar, then all of the parameters
(weights and biases) are sampled from a single isotropic Gaussian with
inverse variance equal to prior
. If prior
is a data
structure of the kind generated by mlpprior
, then the parameters
are sampled from multiple Gaussians according to their groupings
(defined by the index
field) with corresponding variances
(defined by the alpha
field).
mlpprior
, netunpak
, rbfprior
Copyright (c) Ian T Nabney (1996-9)


Information Sources
-
https://history.state.gov/milestones/1969-1976/salt
-
https://www.history.com/this-day-in-history/salt-agreements-signed
-
https://www.britannica.com/event/Strategic-Arms-Limitation-Talks
-
https://www.ucsusa.org/nuclear-weapons/missile-defense/history-of-russian-missile-defense.html
-
https://www.historytoday.com/archive/soviet-american-arms-race
-
https://www.state.gov/t/avc/trty/101888.htm#protocolabm


This web application is built on Spring boot using Java 8 and Gradle. One of reasons for choosing Spring boot and Java 8 is the fact that I wanted to build something from scratch using this technology and to go through the challenges of the basic setup of the application, testing, and deployment configuration. The application uses Swagger UI to provide developers an easy access to the REST controllers supported by this application. You can access the swagger ui from API docs .
Further more, the app has been containerized and published on the repository , and also deployed on a remote Heroku machine, It should be running now :)
The Api accepts string query as city name. It uses Levenshtein algorithm by default
The Api accepts Longitude and Latitude to return more relative responses. It uses Haversine distance
The Api could return result of a specified minimum score
The Api could limit the number of the results
On this application go to API Docs to see full list of endpoints
From similarityAlgo GET param you can choose between 2 different string similarity algorithms. To use JaroWinkler algo, send similarityAlgo=Jaro
GET http://localhost:3112/suggestion?q=Queb&minScore=0.5&latitude=46.81228&longitude=46.81228&similarityAlgo=Jaro&limit=6
Request Params :
q: String representing search query limit: Integer representing the number suggestions in the response latitude: Lat geolocation to improve suggestions. Should be sent with Longitude longitude: Long geolocation to improve suggestions. Should be sent with Latitude minScore: Double between 1 and 0 to set the min score for suggestions returned similarityAlgo: Similarity algorithm to be used, Levenshtein will be used by default. Send "Jaro" to use Jaro Winkler Algo.
Response:
{ "suggestions": [ { "name": "Québec, QC, Canada", "latitude": 46.81228, "longitude": 46.81228, "score": 1 }, { "name": "Hoquiam, WA, USA", "latitude": 46.98092, "longitude": 46.98092, "score": 0.895 }], "status": "200", "message": "Operation succeeded" }
Another example
GET http://localhost:3112/suggestion?q=Queb&minScore=100000&latitude=10000
Response :
{ "status": "400", "message": "Operation failed", "error": [ "minScore: minScore should be between 0 and 1", "longitude: Longitude value must be between -180 to 180." ] }
I have based my work on Java 8
gradlew build
java -jar build/libs/city-autocomplete-1.0.0.jar
// [main] o.s.b.w.embedded.tomcat.TomcatWebServer : Tomcat started on port(s): 3112 (http) with context path ''
docker build -t dockerized-city-autocomplete .
docker run -p 3112:3112 dockerized-city-autocomplete
docker run -p 3112:3112 feken/dockerized-city-autocomplete
gradlew test


Recommendations for future work
- Move database handling from client to the server
- Implement sockets between client and server
- Optional: if you want to move from Firebase to a different DB (like MongoDB or MySQL) you'll probably want to implement socket functionality between DB and server, and then between server and client to ensure real time data transfers
- Use Cordova and PhoneGap to render/run the application more efficiently on mobile devices
Right now our server only serves static files. Once the client receives index.html they have full access to the database. This way the client receives real-time updates from db (due to Firebase behind-the-scenes socket functionality).
However that's not very secure and any tech-savy client could easily hack the database this way. So we suggest moving the database handling functionality from the client to the server which will present two challenges:
- The refactor itself - moving all the database functionality to the server
- Preserving the real-time data communication between the database and client through the server
Since Firebase already offers real-time data streams, your server will always be up to date with the database. However the client will have no clue about the updates unless the server also has real-time data stream with the client. So we recommend that you implement sockets between the server and the client
Example flow of data:
- The client's location updates
- The immediatelly notifies the server (via post request)
- The server then updates the database
- Firebase (due to real-time capability) sends the new data back to server
- The server (using sockets) notifies all the clients who are currently logged in, and the map updates in real-time