In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the cnt
column. You can see the first few rows of the data above.
Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model.
rides[:24*10].plot(x='dteday', y='cnt')
Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to get_dummies()
.
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
rides = pd.concat([rides, dummies], axis=1)
fields_to_drop = ['instant', 'dteday', 'season', 'weathersit',
'weekday', 'atemp', 'mnth', 'workingday', 'hr']
data = rides.drop(fields_to_drop, axis=1)
data.head()
To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.
The scaling factors are saved so we can go backwards when we use the network for predictions.
quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
# Store scalings in a dictionary so we can convert back later
scaled_features = {}
for each in quant_features:
mean, std = data[each].mean(), data[each].std()
scaled_features[each] = [mean, std]
data.loc[:, each] = (data[each] - mean)/std
We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders.
# Save data for approximately the last 21 days
test_data = data[-21*24:]
# Now remove the test data from the data set
data = data[:-21*24]
# Separate the data into features and targets
target_fields = ['cnt', 'casual', 'registered']
features, targets = data.drop(target_fields, axis=1), data[target_fields]
test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]
We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set).
# Hold out the last 60 days or so of the remaining data as a validation set
train_features, train_targets = features[:-60*24], targets[:-60*24]
val_features, val_targets = features[-60*24:], targets[-60*24:]
Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.
The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called forward propagation.
We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called backpropagation.
Hint: You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.
Below, you have these tasks:
self.activation_function
in __init__
to your sigmoid function.train
method.train
method, including calculating the output error.run
method.class NeuralNetwork(object):
def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
# Set number of nodes in input, hidden and output layers.
self.input_nodes = input_nodes
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
# Initialize weights
self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5,
(self.input_nodes, self.hidden_nodes))
self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5,
(self.hidden_nodes, self.output_nodes))
self.lr = learning_rate
self.activation_function = lambda x : 1 / (1 + np.exp(-x))
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):
### Forward pass ###
# Hidden layer
hidden_inputs = np.dot(X, self.weights_input_to_hidden)
hidden_outputs = self.activation_function(hidden_inputs)
# Output layer
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)
final_outputs = final_inputs # f(x) = y
### Backward pass ###
# Output error
error = y - final_outputs
# Calculate the hidden layer's contribution to the error
hidden_error = np.dot(self.weights_hidden_to_output, error)
# Backpropagated error terms
output_error_term = error # f'(x) = 1
hidden_error_term = hidden_error * hidden_outputs * (1 - hidden_outputs)
# Weight step (input to hidden)
delta_weights_i_h += hidden_error_term * X[:, None]
# Weight step (hidden to output)
delta_weights_h_o += output_error_term * hidden_outputs[:, None]
# Update the weights
self.weights_hidden_to_output += self.lr * delta_weights_h_o / n_records
self.weights_input_to_hidden += self.lr * delta_weights_i_h / n_records
def run(self, features):
''' Run a forward pass through the network with input features
Arguments
---------
features: 1D array of feature values
'''
#### forward pass ####
# Hidden layer
hidden_inputs = np.dot(features, self.weights_input_to_hidden)
hidden_outputs = self.activation_function(hidden_inputs)
# Output layer
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)
final_outputs = final_inputs # f(x) = y
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.
My first submission used 1500 iterations, a learning rate of 2, and 6 hidden nodes - all of which seemed like suspicious. But since they were getting similar results to the ~0.70 and ~1.4 training and validation losses other students were reporting in the project channels, and since trying slightly different values (like 10 hidden nodes), I assumed this was sufficient for this small data set.
The review feedback I got indicated that I just hadn't search a large enough space for possible settings. So I wrote the below code to loop through a wide range of parameter settings and try all the combinations - similar to a grid search but with specific values to try in order to narrow down possible values worth exploring more. I also added a naive over-fitting detection, not meant to be bullet proof, but just to short-circuit combinations that didn't look promising so it took less time to execute.
def is_overfitting(train_losses, val_losses):
''' Does a rough check to see if the losses (sampled at regular intervals) indicate over-fitting is happening.
This is done by checking if average of last n values is greater the average of the previous n values (to
on a one-time random blip) for both val_losses, and the gap between train_losses and val_losses.
I don't know if that's a good heuristic for it or not, I'm just trying to avoid training for a configuration
of hyperparameters that appears to be trending poorly and should be abandoned. '''
if(len(val_losses) < 6):
return False
val_losses = np.array(val_losses)
last_val_losses_avg = val_losses[-3:].mean()
prev_val_losses_avg = val_losses[-6:-3].mean()
if(last_val_losses_avg > prev_val_losses_avg):
# val losses are increasing
return True
train_losses = np.array(train_losses)
last_train_losses_avg = train_losses[-3:].mean()
prev_train_losses_avg = train_losses[-6:-3].mean()
last_gap_avg = last_val_losses_avg - last_train_losses_avg
prev_gap_avg = prev_val_losses_avg - prev_train_losses_avg
if(last_gap_avg > prev_gap_avg):
# the gap between val losses and train losses is increasing
return True
return False
import sys
### Set the hyperparameters here ###
iterations = 10000
learning_rates_list = [2, 1, 0.5, 0.1, 0.05, 0.001]
hidden_nodes_list = [5, 10, 15, 20, 25, 30, 40, 50, 75, 100]
output_nodes = 1
N_i = train_features.shape[1]
for learning_rate in learning_rates_list:
for hidden_nodes in hidden_nodes_list:
print("iterations \t learning_rate \t hidden_nodes \t train_loss \t val_loss")
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
train_losses = []
val_losses = []
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)
# check progress every 500 iterations
if ii % 500 == 0:
current_train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)
current_val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)
train_losses.append(current_train_loss)
val_losses.append(current_val_loss)
#print the progress thus far
output = "{} \t\t {} \t\t {} \t\t {:2.3f} \t\t {:2.3f}"
print(output.format(ii, learning_rate, hidden_nodes, current_train_loss, current_val_loss))
# if these settings seem to be overfitting
if is_overfitting(train_losses, val_losses):
print("Over-fitting detected")
break # move on to the next hyperparameter settings
else:
continue # keep going on next 500 iterations
I skimmed through the above results, looking for anything that got good training and validation losses before seeming to over-fit. Learning rates of 2 or < 0.05 and hidden_nodes of 100 seemed easy to eliminate, and I focused down on the following combinations:
And changed the algorithm to just try specific variations of those combinations. (I left iterations at 10000 since I'm aborting when it looks like it may be over-fitting, and that'll give me a good idea of what iterations to use with these combinations).
### Set the hyperparameters here ###
iterations = 10000
learning_rates_list = [1, 1.2, 0.8, 1, 1.2, 0.8, 1, 1.2, 0.8, 1, 1.2, 0.8, 1, 1.2, 0.8,
0.4, 0.5, 0.6, 0.4, 0.5, 0.6, 0.4, 0.5, 0.6, 0.4, 0.5, 0.6, 0.4, 0.5, 0.6, 0.4, 0.5, 0.6]
hidden_nodes_list = [8, 8, 8, 10, 10, 10, 13, 13, 13, 15, 15, 15, 17, 17, 17,
18, 18, 18, 20, 20, 20, 25, 25, 25, 30, 30, 30, 40, 40, 40, 50, 50, 50]
output_nodes = 1
if(len(learning_rates_list) != len(hidden_nodes_list)):
print("your configuration is wrong, you probably want to abort...")
N_i = train_features.shape[1]
for learning_rate, hidden_nodes in zip(learning_rates_list, hidden_nodes_list):
print("iterations \t learning_rate \t hidden_nodes \t train_loss \t val_loss")
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
train_losses = []
val_losses = []
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)
# check progress every 500 iterations
if ii % 500 == 0:
current_train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)
current_val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)
train_losses.append(current_train_loss)
val_losses.append(current_val_loss)
#print the progress thus far
output = "{} \t\t {} \t\t {} \t\t {:2.3f} \t\t {:2.3f}"
print(output.format(ii, learning_rate, hidden_nodes, current_train_loss, current_val_loss))
# if these settings seem to be overfitting
if is_overfitting(train_losses, val_losses):
print("Over-fitting detected")
break # move on to the next hyperparameter settings
else:
continue # keep going on next 500 iterations
There's a lot of noise in those results (due to the random nature of the weights and sample selections). But the general indication is optimal results will be training losses around 0.060 and validation losses of ~0.135, and these can be generally achieved somewhere in the ranges of:
With the caveats that the higher end of the hidden node range generally needs more iterations and doesn't produce any better outputs (in fact it seems less likely to produce good results once passed 30 nodes). Staying under ~20 hidden nodes allows the network to train well with fewer than ~5000 iterations.
Given that, I opted for settings at the lower end (see below), although trial and error with different parameter values indicates anything in that range will perform reasonably well.
import sys
### Set the hyperparameters here ###
iterations = 5000
learning_rate = 0.6
hidden_nodes = 10
output_nodes = 1
N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
losses = {'train':[], 'validation':[]}
for ii in range(iterations):
# Go through a random batch of 128 records from the training data set
batch = np.random.choice(train_features.index, size=128)
X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']
network.train(X, y)
# Printing out the training progress
train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)
val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)
sys.stdout.write("\rProgress: {:2.1f}".format(100 * ii/float(iterations)) \
+ "% ... Training loss: " + str(train_loss)[:5] \
+ " ... Validation loss: " + str(val_loss)[:5])
sys.stdout.flush()
losses['train'].append(train_loss)
losses['validation'].append(val_loss)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim([0, 2]) # limit y range just so the lower end is clearer
Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly.
fig, ax = plt.subplots(figsize=(8,4))
mean, std = scaled_features['cnt']
predictions = network.run(test_features).T*std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((test_targets['cnt']*std + mean).values, label='Data')
ax.set_xlim(right=len(predictions))
ax.legend()
dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])
dates = dates.apply(lambda d: d.strftime('%b %d'))
ax.set_xticks(np.arange(len(dates))[12::24])
_ = ax.set_xticklabels(dates[12::24], rotation=45)
Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?
Note: You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter
It models it fairly well. The place it does poorly is over the holiday season. I haven't looked too closely at the data, but I read in the slack channel that someone else found there's less data covering that time period, so it hasn't trained as well to handle the patterns that occur during the holidays, which makes sense. Training with more holiday data should allow this to be corrected.
Also, despite further tuning the hyperparameters, the tendency to over/under shoot slightly on the peak and low times remains. I'm looking forward to learning more advanced techniques in the course to build better deep learning networks!


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This is an implementation of Karaboga (2005) ABC optimization algorithm. It was developed upon the basic version programmed in C and distributed at the algorithm’s official website (see the references).
Any evident (precision) error should be blamed to the package author (not to the algorithm itself).
library(ABCoptim)
# Function to optimize. Min at (pi,pi)
fun <- function(x) {
-cos(x[1])*cos(x[2])*exp(-((x[1] - pi)^2 + (x[2] - pi)^2))
}
# Since it is stochastic, we need to set a seed to get the same
# results.
set.seed(123)
# Finding the minimum
ans <- abc_optim(rep(10,2), fun, lb=-20, ub=20, criter=200)
ans
##
## An object of class -abc_answer- (Artificial Bee Colony Optim.):
## par:
## x[1]: 3.141593
## x[2]: 3.141593
##
## value:
## -1.000000
##
## counts:
## 484
plot(ans)
D. Karaboga, An Idea based on Honey Bee Swarm for Numerical Optimization, tech. report TR06,Erciyes University, Engineering Faculty, Computer Engineering Department, 2005 http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf
Artificial Bee Colony (ABC) Algorithm (website) http://mf.erciyes.edu.tr/abc/index.htm
Basic version of the algorithm implemented in ‘C’ (ABC’s official website) http://mf.erciyes.edu.tr/abc/form.aspx


{% extends 'base.html' %} {% comment %} # Copyright 2017 Lockheed Martin Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # {% endcomment %} {% load bootstrap3 %} {% block title %}Color Options Update{% endblock %} {% block extra_nav_bar_content_right %}
| Display Text | 6-Character Hex Code (RGB) | Actions |
|---|---|---|
| {{ color.display_text }} | {{ color.hex_color_code }} |
{% bootstrap_icon "pencil" %}
{% bootstrap_icon "trash" %} |


Resort Capital of the Philippines
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{% include common/meta.html %}
{% include common/libraries.html %}
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{% extends "govuk-template.html" %} {% block header %} {% import "patterns/macro/header.html" as index %} {{ index.header(proposition=true) }} {% endblock %} {% block content %} {% block h1 %}Service unavailable{% endblock %} You will be able to use the service at 9am on Monday 29 May 2017. {% endblock %}
You will be able to use the service at 9am on Monday 29 May 2017.


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This data is processed under our Legitimate Interest to provide our services to you in a an efficient and secure manner and to ensure the legal compliance and proper administration of our business.
Raw data such as pages visited, anonymized visitor IPs, and visitor actions will be retained for 60 days. In special circumstances—such as extended investigations regarding a technical attack—we may preserve logged data for longer periods for analysis. We store aggregate statistics about use of the websites and services we host indefinitely, but those statistics do not include data identifiable to you personally.
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We use your account data to identify you on the website, and to create pages specific to you, such as your profile page. We will also use your account data to publish a public profile for you on our services.
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When you use services provided by PrivacyTools your data is shared with Aragon Ventures LLC, in order to facilitate their hosting obligations. Aragon Ventures LLC may collect and use your data as described in their privacy statement at https://aragon.ventures/privacy.
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A full revision history for this page can be found on GitHub.


Auto Random Votes Will Apply For Users Newly Posts, Randomly! Select Your Voting Power With Above Slider. It's Just For Fun & Helping New Content Over Steemit.


The blaze broke out at the four-storey building in the city’s congested old quarter early yesterday morning.
At least 100 people were sleeping inside the factory, which mainly makes school bags, when the fire started. More than 60 have been rescued.
Prime Minister Narendra Modi called the fire “horrific” and sent his condolences.
Delhi’s firefighters received the first call about the fire at 05:22 local time yesterday.
The fire began on the lower storeys, spreading rapidly to the third floor where workers were sleeping.
“We woke up with cries and shouts for help,” said Ronak Khan, a 17-year-old living next door.
“I saw people trapped. We asked them to come to the rooftop so that we could rescue them but they were not able to come up.”
The area where the factory is located – Azad Market – is a web of narrow alleyways, which made it difficult to reach the blaze.
Rescuers had to carry out victims on their shoulders one-by-one with firefighters cutting away window grills to access the building.
A local fire chief told BBC Hindi’s Dilnawaz Pasha the building did not have a proper fire licence and was operating illegally as a factory.
It is not clear what caused the blaze but an investigation has been ordered.
An initial probe and eyewitnesses suggested a short circuit may have been to blame.
Victims’ relatives have been scrambling for information. One man told India’s PTI agency his brother was inside.
Indian cities have often seen deadly fires, with poor planning and lax enforcement of safety regulations major factors.
Indian politicians have been expressing their horror at the blaze.
“The fire in Delhi’s Anaj Mandi on Rani Jhansi Road is extremely horrific,” Prime Minister Modi tweeted.
“My thoughts are with those who lost their loved ones. Wishing the injured a quick recovery. Authorities are providing all possible assistance at the site of the tragedy.”
Home Minister Amit Shah called it a “tragic loss of precious lives”.


As a full-stack PHP developer who works with Laravel on a day-to-day basis, I'm always looking for shortcuts and helpful methods in the framework that I can use to cut down on development time or code complexity.
I've compiled the following list of my current favorite helper methods that I use on a fairly regular basis. As of the writing of this article these are all available in Laravel 7, with some having been around a bit longer than that (so be sure to check against your app's version).
Let's dive in! ✨
Str::limit()
Our first helper takes a string and truncates it down to a set character limit. There are two required parameters: the string that you want to truncate, as well as the character limit of the returned truncated string.
use Illuminate\Support\Str;
$truncated = Str::limit('The quick brown fox jumps over the lazy dog', 20);
// The quick brown fox ...
You can also pass in a third optional argument to control what is displayed after the string is returned.
use Illuminate\Support\Str;
$truncated = Str::limit('The quick brown fox jumps over the lazy dog', 20, '[...]');
// The quick brown fox [...]
I've found this is perfect for shortening large blocks of text into something like an excerpt or post preview on an articles list.
head()
This one is what makes this helpers fantastic. A simple method that's usually comprised of a couple nested vanilla PHP functions, head returns back the first element of an array. I've used it in a ton of different applications because of its global and ubiquitous nature.
$array = [100, 200, 300];
$first = head($array);
// 100
Want the inverse of that? Use the last
method to grab, well, the last element of an array.
$array = [100, 200, 300];
$last = last($array);
// 300
The best part about using the head and last methods, they don't affect the original array at all.
Str::between()
Another pretty self-explanatory method, the Str::between helper returns back a string between two other strings. If no string can be found with the start and end strings that you provided, null
is returned.
use Illuminate\Support\Str;
$slice = Str::between('My name is Inigo Montoya.', 'My name is ', '.');
// 'Inigo Montoya'
I love using this method to grab information between parentheses and brackets, return specific segments of URLs, or even parse out data between HTML tags.
blank()
This helper is best explained as empty, but better. It returns a simple boolean true/false value depending on whether or not what was passed in contains any real data.
// all of these return true
blank('');
blank(' ');
blank(null);
blank(collect());
// all of these return false
blank(true);
blank(false);
blank(0);
I've found the blank method particularly helpful in combination with request data validation, or weeding through API data that might not be formatted properly. It's a little nicer than using something like empty(trim())
.
Str::contains()
The helper method all of us have been waiting for, determining if a string contains another string. This question has been asked so many times on StackOverflow and other programming forums, with the current best practice in vanilla PHP using strpos
.
use Illuminate\Support\Str;
$contains = Str::contains('My name is Inigo Montoya.', 'Inigo');
// true
$contains = Str::contains('My name is Inigo Montoya.', 'Andrew');
// false
With the approval of a recent rfc, PHP will have its own str_contains method soon that will render this obsolete. Until then though, it's one of the most useful helper methods in Laravel.
Arr::pluck()
Arguably one of the more powerful methods I've listed off in this article, Arr::pluck lets you walk through a nested array and return an array of values based on their keys.
Let's check out a brief example:
use Illuminate\Support\Arr;
$array = [
['website' => ['id' => 1, 'url' => 'reddit.com']],
['website' => ['id' => 2, 'url' => 'twitter.com']],
['website' => ['id' => 3, 'url' => 'dev.to']],
];
$names = Arr::pluck($array, 'website.url');
// ['reddit.com', 'twitter.com', 'dev.to']
Passing in an array and a dot-notation string to determine what key values we want, the multi-dimensional array is walked and a single flat array of the key's values are returned back to us.
I've used this multiple times in returned API data (when I don't feel the need to use a full collection). It makes getting something like an array of IDs, names, or other attributes insanely easy without having to create a whole foreach loop.
collect()
Once I found out about collections, I haven't stopped using them. This is probably the helper I find myself reaching for most often, and it lets you transform an array into a Collection.
Why is this important? Because collections are like arrays on steroids. They come with a massive library of methods that you can chain together to perform all sorts of filtering, sorting, and modifying on an array with simple closure arguments. No foreach loops, no intermediary variables, just clean code.
Check out this quick example:
$collection = collect(['Keys', 'Krates']);
return $collection->map(function ($value) {
return Str::upper($value);
});
// ['KEYS', 'KRATES']
return $collection->filter(function ($value) {
return strlen($value) > 4;
});
// ['Krates']
This is honestly just the tip of the iceberg with what collections can do. I've used them all over my projects, especially when I'm dealing with large and intricate data sets that aren't coming from my database models. CSV data, external API requests, and directory structures can all benefit from being put into a collection.
That's all I have for now!
If you have any suggestions for what should have been on this list, or I've made you discover your new favorite Laravel helper, I'd love to hear about it. Feel free to send me a message on Twitter, or leave a comment below!


You can easily link your ethereum address with your stackexchange account by changing the "Location" field inside your profile account.
Click on your profile:
Click on edit
Change the location field with your ethereum address (without space)
Now your Location should look like this:


This web page does implement a sample request to a WebAPI controller that is protected by a "landmine". The concept is really simple: you add some known value to a field as a kind of "honey pot" for an adversary to play with. As soon as the adversary changes the value, you know that something fishy is going on.
Normally this happens when an adversary finds the field "accesslevel" and changes the field value from "public" to "private" or "secret" to see if it's possible to escalate privileges.


Best Dental Clinic
Pain is a matter of hours
Don't waste your precious time and visit the clinic.
Dentistry is our profession, but people are our focus
Personalized patient care is what sets FMS Dental Clinic apart. When you visit our clinic you can expect to receive world class care. Expert physician specialists and caring clinical staff provide you with an exceptional health care experience.
PhD, Clinical Director
DMD, Clinical Director
Doctor of Dental Medicine
Dentist with wide experience in implantology, DMD
Dentist with wide experience in orthodontics, DMD
Dentist, specialist in orthodontics
Dentist, oral surgeon, DMD
Anaesthetist, MD
Dentist, specialist in orthodontics
Dr.Khushboo has been a practicing general dentist for 8 years wherein she has designed and successfully completed the treatment for thousands of her patients. Her expertise is in the field of cosmetic dentistry which includes smile designing and aesthetic makeovers. She is known for her work precision and professionalism as she strongly believes in constantly upgrading herself with the latest in dentistry. Good and effective communication skills and patient development have been her core assets.
Anaesthetist, MD
Prof. Dr. P. Parthasarathi Reddy, the visionary Founder Chairman and CEO of FMS Dental Hospitals Pvt Ltd, is widely credited as being one of the architects of introducing true MULTISPECIALITY DENTAL CARE under one roof. In India, Dr. P. P is known for his extreme desire to succeed. Innovation, persistence, dedication and devotion to his work are some of the characteristics that best describe him. His vision of bringing world-class dental treatment within the economic and geographic reach of lakhs of patients was the genesis of FMS Dental Hospitals. The first FMS Dental opened its doors in 1993, and heralded the multiSpeciality dental healthcare revolution in the nation. In just 26 years, he founded, developed an empire in Dental Health and Education in India, which are built upon the goals of continuous improvement. Dr. PP ‘s vision, acumen and idea of uncompromised quality, motivated numerous individuals in India to set forth, emulate the model and taking high quality dental care closer to their patients.
Dentist, oral surgeon, DMD
Dr. Dushyanth is a highly acclaimed Oral & Maxillofacial Surgeon and heads the Maxillofacial department at FMS Dental Hospitals. Apart from routine Oral and Maxillofacial procedures, he is well known across the twin cities for addressing the most complicated of cases and has referrals from all the top medical centers and specialists. His areas of interest & expertise lies in Facial trauma surgeries, Maxillofacial Pathology, TMJ surgeries and Orthognathic surgical procedures. He is versatile in Advanced implantology procedures like the graft-less All-on-4 dental implant protocol, Zygomatic & Tubero- pterygoid implants, Maxillary sinus lift & other grafting procedures etc. His forte is Cosmetic smile and facial corrections with Dermal Fillers & Botox, Face-lift procedures etc.
Don't waste your precious time and visit the clinic.


--- date: 2010-02-01T00:00:00Z published: true title: Inventor unveils $7,000 talking sex robot - CNN.com url: /2010/02/01/inventor-unveils-7-000-talking-sex-robot-cnn-com/ ---
To some men, she might seem like the perfect woman: She's a willowy 5 feet 7 and 120 pounds. She'll chat with you endlessly about your interests. And she'll have sex whenever you please -- as long as her battery doesn't run out.
Meet Roxxxy, who may be the world's most sophisticated talking female sex robot. For $7,000, she's all yours.
Why do the inventors of these things always look like their best customers?
Just sayin.


Purpose
Use the RGB led matrix bought for 5 USD on eBay the other day. Using the board special for the Atmega64a
Rapide64a and show time, temperature, random display, ... whatever crosses your mind.
The design is not bad (quite happy
Code
Google code SVN as usual :
http://code.google.com/p/alanarduinotools/source/browse/trunk/#trunk%2FAtmegaxxA%2FRGBMatrix
I designed it to be interruption based: you have a memory buffer (one per color) that you update at your will, and the "show" routine is called every x ms to paint current buffer display. We're talking a 8x8 dot matric so no "wait for vsync" or things like that involved. Just look at the code, it's simple to add any image logic you want without caring of how to display it...
Pictures
Showing time every minutes ...
Star field
Snake (changes color each time it bounces walls)
XXX Drops
XXX Random patterns


| Select | Indication Date | Property Name | Blk | Unit No. | Street Name | Postal | Company | Client Name | Indication Value | Valuer |
|---|---|---|---|---|---|---|---|---|---|---|
01-Feb-19 |
Parkway Parade | 80 | #0-01 | Marine Parade Road | 449269 | Sing Investments and Finance Ptr LTd |
Jessie Lim | $850,000 | Valuer | |
|
01-Feb-19 |
Parkway Parade | 80 | #0-01 | Marine Parade Road | 449269 |
Sing Investments and Finance Ptr LTd |
Jessie Lim | $850,000 | Valuer |
YYY


class CrimeRenderer |
|
class DesirabilityRenderer |
|
class DistrictRenderer |
|
class FireCoverageRenderer |
|
class HappinessRenderer |
|
class LandValueRenderer |
|
class PollutionRenderer |
|
class TrafficAnimationRenderer : Debuggable |
|
class TrafficRenderer |
|
class ZotRenderer : Debuggable |


data package¶
Submodules¶
data.batch module¶
-
class
data.batch.
Batch
(batch=None, **kwargs)[source]¶ Bases:
cogdl.data.data.Data
A plain old python object modeling a batch of graphs as one big (dicconnected) graph. With
cogdl.data.Data
being the base class, all its methods can also be used here. In addition, single graphs can be reconstructed via the assignment vectorbatch
, which maps each node to its respective graph identifier.-
cumsum
(key, item)[source]¶ If
True
, the attributekey
with contentitem
should be added up cumulatively before concatenated together.Note
This method is for internal use only, and should only be overridden if the batch concatenation process is corrupted for a specific data attribute.
-
static
from_data_list
(data_list)[source]¶ Constructs a batch object from a python list holding
cogdl.data.Data
objects. The assignment vectorbatch
is created on the fly.
-
num_graphs
¶ Returns the number of graphs in the batch.
-
data.data module¶
-
class
data.data.
Data
(x=None, edge_index=None, edge_attr=None, y=None, pos=None)[source]¶ Bases:
object
A plain old python object modeling a single graph with various (optional) attributes:
- Args:
- x (Tensor, optional): Node feature matrix with shape :obj:`[num_nodes,
- num_node_features]`. (default:
None
) - edge_index (LongTensor, optional): Graph connectivity in COO format
- with shape
[2, num_edges]
. (default:None
) - edge_attr (Tensor, optional): Edge feature matrix with shape
[num_edges, num_edge_features]
. (default:None
)- y (Tensor, optional): Graph or node targets with arbitrary shape.
- (default:
None
) - pos (Tensor, optional): Node position matrix with shape
[num_nodes, num_dimensions]
. (default:None
)
The data object is not restricted to these attributes and can be extented by any other additional data.
-
apply
(func, *keys)[source]¶ Applies the function
func
to all attributes*keys
. If*keys
is not given,func
is applied to all present attributes.
-
cat_dim
(key, value)[source]¶ Returns the dimension in which the attribute
key
with contentvalue
gets concatenated when creating batches.Note
This method is for internal use only, and should only be overridden if the batch concatenation process is corrupted for a specific data attribute.
-
contiguous
(*keys)[source]¶ Ensures a contiguous memory layout for all attributes
*keys
. If*keys
is not given, all present attributes are ensured to have a contiguous memory layout.
-
is_coalesced
()[source]¶ Returns
True
, if edge indices are ordered and do not contain duplicate entries.
-
keys
¶ Returns all names of graph attributes.
-
num_edges
¶ Returns the number of edges in the graph.
-
num_features
¶ Returns the number of features per node in the graph.
data.dataloader module¶
-
class
data.dataloader.
DataListLoader
(dataset, batch_size=1, shuffle=True, **kwargs)[source]¶ Bases:
torch.utils.data.dataloader.DataLoader
Data loader which merges data objects from a
cogdl.data.dataset
to a python list.Note
This data loader should be used for multi-gpu support via
cogdl.nn.DataParallel
.
-
class
data.dataloader.
DataLoader
(dataset, batch_size=1, shuffle=True, **kwargs)[source]¶ Bases:
torch.utils.data.dataloader.DataLoader
Data loader which merges data objects from a
cogdl.data.dataset
to a mini-batch.
-
class
data.dataloader.
DenseDataLoader
(dataset, batch_size=1, shuffle=True, **kwargs)[source]¶ Bases:
torch.utils.data.dataloader.DataLoader
Data loader which merges data objects from a
cogdl.data.dataset
to a mini-batch.Note
To make use of this data loader, all graphs in the dataset needs to have the same shape for each its attributes. Therefore, this data loader should only be used when working with dense adjacency matrices.
data.dataset module¶
-
class
data.dataset.
Dataset
(root, transform=None, pre_transform=None, pre_filter=None)[source]¶ Bases:
torch.utils.data.dataset.Dataset
Dataset base class for creating graph datasets. See here for the accompanying tutorial.
- Args:
root (string): Root directory where the dataset should be saved. transform (callable, optional): A function/transform that takes in an
cogdl.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
)- pre_transform (callable, optional): A function/transform that takes in
- an
cogdl.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional): A function that takes in an
cogdl.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
num_features
¶ Returns the number of features per node in the graph.
-
processed_file_names
¶ The name of the files to find in the
self.processed_dir
folder in order to skip the processing.
-
processed_paths
¶ The filepaths to find in the
self.processed_dir
folder in order to skip the processing.
-
raw_file_names
¶ The name of the files to find in the
self.raw_dir
folder in order to skip the download.
-
raw_paths
¶ The filepaths to find in order to skip the download.
data.download module¶
data.extract module¶
-
data.extract.
extract_tar
(path, folder, mode='r:gz', log=True)[source]¶ Extracts a tar archive to a specific folder.
data.in_memory_dataset module¶
-
class
data.in_memory_dataset.
InMemoryDataset
(root, transform=None, pre_transform=None, pre_filter=None)[source]¶ Bases:
cogdl.data.dataset.Dataset
Dataset base class for creating graph datasets which fit completely into memory. See here for the accompanying tutorial.
- Args:
root (string): Root directory where the dataset should be saved. transform (callable, optional): A function/transform that takes in an
cogdl.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
)- pre_transform (callable, optional): A function/transform that takes in
- an
cogdl.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional): A function that takes in an
cogdl.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
collate
(data_list)[source]¶ Collates a python list of data objects to the internal storage format of
cogdl.data.InMemoryDataset
.
-
num_classes
¶ The number of classes in the dataset.
-
processed_file_names
¶ The name of the files to find in the
self.processed_dir
folder in order to skip the processing.
-
raw_file_names
¶ The name of the files to find in the
self.raw_dir
folder in order to skip the download.
Module contents¶
-
class
data.
Data
(x=None, edge_index=None, edge_attr=None, y=None, pos=None)[source]¶ Bases:
object
A plain old python object modeling a single graph with various (optional) attributes:
- Args:
- x (Tensor, optional): Node feature matrix with shape :obj:`[num_nodes,
- num_node_features]`. (default:
None
) - edge_index (LongTensor, optional): Graph connectivity in COO format
- with shape
[2, num_edges]
. (default:None
) - edge_attr (Tensor, optional): Edge feature matrix with shape
[num_edges, num_edge_features]
. (default:None
)- y (Tensor, optional): Graph or node targets with arbitrary shape.
- (default:
None
) - pos (Tensor, optional): Node position matrix with shape
[num_nodes, num_dimensions]
. (default:None
)
The data object is not restricted to these attributes and can be extented by any other additional data.
-
apply
(func, *keys)[source]¶ Applies the function
func
to all attributes*keys
. If*keys
is not given,func
is applied to all present attributes.
-
cat_dim
(key, value)[source]¶ Returns the dimension in which the attribute
key
with contentvalue
gets concatenated when creating batches.Note
This method is for internal use only, and should only be overridden if the batch concatenation process is corrupted for a specific data attribute.
-
contiguous
(*keys)[source]¶ Ensures a contiguous memory layout for all attributes
*keys
. If*keys
is not given, all present attributes are ensured to have a contiguous memory layout.
-
is_coalesced
()[source]¶ Returns
True
, if edge indices are ordered and do not contain duplicate entries.
-
keys
¶ Returns all names of graph attributes.
-
num_edges
¶ Returns the number of edges in the graph.
-
num_features
¶ Returns the number of features per node in the graph.
-
class
data.
Batch
(batch=None, **kwargs)[source]¶ Bases:
cogdl.data.data.Data
A plain old python object modeling a batch of graphs as one big (dicconnected) graph. With
cogdl.data.Data
being the base class, all its methods can also be used here. In addition, single graphs can be reconstructed via the assignment vectorbatch
, which maps each node to its respective graph identifier.-
cumsum
(key, item)[source]¶ If
True
, the attributekey
with contentitem
should be added up cumulatively before concatenated together.Note
This method is for internal use only, and should only be overridden if the batch concatenation process is corrupted for a specific data attribute.
-
static
from_data_list
(data_list)[source]¶ Constructs a batch object from a python list holding
cogdl.data.Data
objects. The assignment vectorbatch
is created on the fly.
-
num_graphs
¶ Returns the number of graphs in the batch.
-
-
class
data.
Dataset
(root, transform=None, pre_transform=None, pre_filter=None)[source]¶ Bases:
torch.utils.data.dataset.Dataset
Dataset base class for creating graph datasets. See here for the accompanying tutorial.
- Args:
root (string): Root directory where the dataset should be saved. transform (callable, optional): A function/transform that takes in an
cogdl.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
)- pre_transform (callable, optional): A function/transform that takes in
- an
cogdl.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional): A function that takes in an
cogdl.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
num_features
¶ Returns the number of features per node in the graph.
-
processed_file_names
¶ The name of the files to find in the
self.processed_dir
folder in order to skip the processing.
-
processed_paths
¶ The filepaths to find in the
self.processed_dir
folder in order to skip the processing.
-
raw_file_names
¶ The name of the files to find in the
self.raw_dir
folder in order to skip the download.
-
raw_paths
¶ The filepaths to find in order to skip the download.
-
class
data.
InMemoryDataset
(root, transform=None, pre_transform=None, pre_filter=None)[source]¶ Bases:
cogdl.data.dataset.Dataset
Dataset base class for creating graph datasets which fit completely into memory. See here for the accompanying tutorial.
- Args:
root (string): Root directory where the dataset should be saved. transform (callable, optional): A function/transform that takes in an
cogdl.data.Data
object and returns a transformed version. The data object will be transformed before every access. (default:None
)- pre_transform (callable, optional): A function/transform that takes in
- an
cogdl.data.Data
object and returns a transformed version. The data object will be transformed before being saved to disk. (default:None
) - pre_filter (callable, optional): A function that takes in an
cogdl.data.Data
object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default:None
)
-
collate
(data_list)[source]¶ Collates a python list of data objects to the internal storage format of
cogdl.data.InMemoryDataset
.
-
num_classes
¶ The number of classes in the dataset.
-
processed_file_names
¶ The name of the files to find in the
self.processed_dir
folder in order to skip the processing.
-
raw_file_names
¶ The name of the files to find in the
self.raw_dir
folder in order to skip the download.
-
class
data.
DataLoader
(dataset, batch_size=1, shuffle=True, **kwargs)[source]¶ Bases:
torch.utils.data.dataloader.DataLoader
Data loader which merges data objects from a
cogdl.data.dataset
to a mini-batch.
-
class
data.
DataListLoader
(dataset, batch_size=1, shuffle=True, **kwargs)[source]¶ Bases:
torch.utils.data.dataloader.DataLoader
Data loader which merges data objects from a
cogdl.data.dataset
to a python list.Note
This data loader should be used for multi-gpu support via
cogdl.nn.DataParallel
.
-
class
data.
DenseDataLoader
(dataset, batch_size=1, shuffle=True, **kwargs)[source]¶ Bases:
torch.utils.data.dataloader.DataLoader
Data loader which merges data objects from a
cogdl.data.dataset
to a mini-batch.Note
To make use of this data loader, all graphs in the dataset needs to have the same shape for each its attributes. Therefore, this data loader should only be used when working with dense adjacency matrices.
-
data.
download_url
(url, folder, log=True)[source]¶ Downloads the content of an URL to a specific folder.
-
data.
extract_tar
(path, folder, mode='r:gz', log=True)[source]¶ Extracts a tar archive to a specific folder.


{% import "macros.html" as macros %} {% extends "layout.html" %} {% block article_body %}
Many factors affect how much you are paid, including the sector you work in, your age and how long you have been in a job.
But thanks to a law forcing UK employers to publish details of the differences between male and female pay and bonuses for the first time, there is a new spotlight on how gender affects pay.
The average woman working full-time in the UK earns 9.1 per cent less than a man per hour. With part-timers included, the gap is 18.4 per cent.
Find out whether there is a gender pay gap for your job and, if so, what size it is, with our calculator below.
The calculator only has data for full-time workers.
LOADING
On average, a woman in her working full-time earns than a man in the same age group
Sorry: there's not enough data on people in their working in Associate Health Professional roles to calculate the gender pay gap. Try selecting another occupation to get a general view.
On average, a woman in her working full-time in a Associate Health Professional role earns than a man in the same age group and job type.
I work a
hour week*
and each
I earn:
Your salary puts you in the of full-time earners in your age group and sector.
A in the of full-time earners in her in roles, earns on average. That would be: per year.
every year. Or after tax.
every day. Or after tax.
We used the median salary for your sector and age group to calculate that a will earn than a .


|
DJI Onboard SDK
4.0
|
This is the header file for define OSDK platform interfaces. More...
#include "osdk_typedef.h"
Go to the source code of this file.
Macros | |
| #define | OSDK_TASK_STACK_SIZE_DEFAULT 1024 |
Typedefs | |
| typedef void * | T_OsdkTaskHandle |
| Platform handle of thread task operation. | |
| typedef void * | T_OsdkMutexHandle |
| Platform handle of mutex operation. | |
| typedef void * | T_OsdkSemHandle |
| Platform handle of semaphore operation. | |
Functions | |
| E_OsdkStat | OsdkPlatform_RegHalUartHandler (const T_OsdkHalUartHandler *halUartHandler) |
| Register the handler for hal uart interfaces by your platform. More... | |
| E_OsdkStat | OsdkPlatform_RegOsalHandler (const T_OsdkOsalHandler *osalHandler) |
| Register the handler for osal interfaces by your platform. More... | |
| E_OsdkStat | OsdkOsal_TaskCreate (T_OsdkTaskHandle *task, void *(*taskFunc)(void *), uint32_t stackSize, void *arg) |
| Declare the task container, initialize the task, and create task ID. More... | |
| E_OsdkStat | OsdkOsal_TaskDestroy (T_OsdkTaskHandle task) |
| Destroy the created task. More... | |
| E_OsdkStat | OsdkOsal_TaskSleepMs (uint32_t timeMs) |
| Let task into a sleep state in a set time, uint:ms. More... | |
| E_OsdkStat | OsdkOsal_MutexCreate (T_OsdkMutexHandle *mutex) |
| Declare the mutex container, initialize the mutex, and create mutex ID. More... | |
| E_OsdkStat | OsdkOsal_MutexDestroy (T_OsdkMutexHandle mutex) |
| Destroy the created mutex. More... | |
| E_OsdkStat | OsdkOsal_MutexLock (T_OsdkMutexHandle mutex) |
| Acquire and lock the mutex when peripheral access is required. More... | |
| E_OsdkStat | OsdkOsal_MutexUnlock (T_OsdkMutexHandle mutex) |
| Unlock and release the mutex, when done with the peripheral access. More... | |
| E_OsdkStat | OsdkOsal_SemaphoreCreate (T_OsdkSemHandle *semaphore, uint32_t initValue) |
| Declare the semaphore container, initialize the semaphore, and create semaphore ID. More... | |
| E_OsdkStat | OsdkOsal_SemaphoreDestroy (T_OsdkSemHandle semaphore) |
| Destroy the created semaphore. More... | |
| E_OsdkStat | OsdkOsal_SemaphoreWait (T_OsdkSemHandle semaphore) |
| Wait the created semaphore forever. More... | |
| E_OsdkStat | OsdkOsal_SemaphoreTimedWait (T_OsdkSemHandle semaphore, uint32_t waitTimeMs) |
| Wait the created semaphore and set the value of timeout. More... | |
| E_OsdkStat | OsdkOsal_SemaphorePost (T_OsdkSemHandle semaphore) |
| Post the created semaphore. More... | |
| E_OsdkStat | OsdkOsal_GetTimeMs (uint32_t *ms) |
| Get the system millisecond time, uint:ms. More... | |
| void * | OsdkOsal_Malloc (uint32_t size) |
| Get the system time, uint:us. More... | |
| void | OsdkOsal_Free (void *ptr) |
| Release allocated memory. More... | |
This is the header file for define OSDK platform interfaces.
All information contained herein is, and remains, the property of DJI. The intellectual and technical concepts contained herein are proprietary to DJI and may be covered by U.S. and foreign patents, patents in process, and protected by trade secret or copyright law. Dissemination of this information, including but not limited to data and other proprietary material(s) incorporated within the information, in any form, is strictly prohibited without the express written consent of DJI.
If you receive this source code without DJI’s authorization, you may not further disseminate the information, and you must immediately remove the source code and notify DJI of its removal. DJI reserves the right to pursue legal actions against you for any loss(es) or damage(s) caused by your failure to do so.
| void OsdkOsal_Free | ( | void * | ptr | ) |
Release allocated memory.
| ptr | pointer to the need allocated memory. |
| E_OsdkStat OsdkOsal_GetTimeMs | ( | uint32_t * | ms | ) |
Get the system millisecond time, uint:ms.
| ms | pointer to the got time ms. |
| void* OsdkOsal_Malloc | ( | uint32_t | size | ) |
Get the system time, uint:us.
| us | pointer to the got time us. |
| size | uint32_t size that need allocate. |
| E_OsdkStat OsdkOsal_MutexCreate | ( | T_OsdkMutexHandle * | mutex | ) |
Declare the mutex container, initialize the mutex, and create mutex ID.
| mutex | pointer to the created mutex ID. |
| E_OsdkStat OsdkOsal_MutexDestroy | ( | T_OsdkMutexHandle | mutex | ) |
Destroy the created mutex.
| mutex | pointer to the created mutex ID. |
| E_OsdkStat OsdkOsal_MutexLock | ( | T_OsdkMutexHandle | mutex | ) |
Acquire and lock the mutex when peripheral access is required.
| mutex | pointer to the created mutex ID. |
| E_OsdkStat OsdkOsal_MutexUnlock | ( | T_OsdkMutexHandle | mutex | ) |
Unlock and release the mutex, when done with the peripheral access.
| mutex | pointer to the created mutex ID. |
| E_OsdkStat OsdkOsal_SemaphoreCreate | ( | T_OsdkSemHandle * | semaphore, |
| uint32_t | initValue | ||
| ) |
Declare the semaphore container, initialize the semaphore, and create semaphore ID.
| semaphore | pointer to the created semaphore ID. |
| E_OsdkStat OsdkOsal_SemaphoreDestroy | ( | T_OsdkSemHandle | semaphore | ) |
Destroy the created semaphore.
| semaphore | pointer to the created semaphore ID. |
| E_OsdkStat OsdkOsal_SemaphorePost | ( | T_OsdkSemHandle | semaphore | ) |
Post the created semaphore.
| semaphore | pointer to the created semaphore ID. |
| E_OsdkStat OsdkOsal_SemaphoreTimedWait | ( | T_OsdkSemHandle | semaphore, |
| uint32_t | waitTimeMs | ||
| ) |
Wait the created semaphore and set the value of timeout.
| semaphore | pointer to the created semaphore ID. |
| waitTimeMs | value of timeout for waiting created semaphore. |
| E_OsdkStat OsdkOsal_SemaphoreWait | ( | T_OsdkSemHandle | semaphore | ) |
Wait the created semaphore forever.
| semaphore | pointer to the created semaphore ID. |
| E_OsdkStat OsdkOsal_TaskCreate | ( | T_OsdkTaskHandle * | task, |
| void *(*)(void *) | taskFunc, | ||
| uint32_t | stackSize, | ||
| void * | arg | ||
| ) |
Declare the task container, initialize the task, and create task ID.
| pthread_t | pointer to the created task ID. |
| taskFunc | pointer to the created task function. |
| stackSize | value of task stack size. |
| arg | pointer to the user defined data. |
| E_OsdkStat OsdkOsal_TaskDestroy | ( | T_OsdkTaskHandle | task | ) |
Destroy the created task.
| pthread_t | pointer to the created task ID. |
| E_OsdkStat OsdkOsal_TaskSleepMs | ( | uint32_t | timeMs | ) |
Let task into a sleep state in a set time, uint:ms.
| timeMs | value of time ms for task sleeping. |
| E_OsdkStat OsdkPlatform_RegHalUartHandler | ( | const T_OsdkHalUartHandler * | halUartHandler | ) |
Register the handler for hal uart interfaces by your platform.
| halUartHandler | pointer to the handler for hal uart interfaces by your platform. |
| E_OsdkStat OsdkPlatform_RegOsalHandler | ( | const T_OsdkOsalHandler * | osalHandler | ) |
Register the handler for osal interfaces by your platform.
| osalHandler | pointer to the handler for osal interfaces by your platform. |


Installation¶
In order to use Sisy, you must first install Channels. Once you have it up and running, proceed with the steps below. If you haven’t used Channels before, you may want to look through some of the Channels docs, including Channels Concepts and the Getting Started guide.
Install Sisy from PyPI:
pip install -U sisy
Add Sisy to your
INSTALLED_APPS
list:INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'channels', 'sisy.apps.SisyConfig', ]
Add Sisy’s routing to your
routing.py
file:1 2 3 4 5 6 7 8
from channels.routing import route, include channel_routing = [ include('sisy.routing.channel_routing'), # route("websocket.connect", ws_add), # route("websocket.receive", ws_message), # route("websocket.disconnect", ws_disconnect), ]
Make sure there is at least one worker running which listens to the channels that Sisy sends its messages on (see Project Settings below).
Start the
sisy_heartbeat
management command to provide the heartbeat messages to Sisy. This can be easily run alongsidedaphne
as it uses very little in terms of CPU load.Now you can start using Sisy.
Project settings¶
All of these settings are optional. If you already have an existing django/channels project, you probably already have a channel name scheme. You can adjust Sisy’s names to fit your pattern with these settings, if necessary. You can also adjust the frequency of the “clock tick” messages that Sisy sends to its scheduling code.
SISY_HEARTBEAT_CHANNEL
(default: sisy.heartbeat
)
(string) Provides the channel name for Sisy to use for the heartbeat messages.
SISY_RUN_TASK_CHANNEL
(default: sisy.run_task
)
(string) Provides the channel name for Sisy to run the tasks on a worker.
SISY_KILL_TASK_CHANNEL
(default: sisy.kill_task
)
(string) Provides the channel name for Sisy to run the task reaper (to kill completed tasks.)
SISY_HEARTBEAT_FREQUENCY
(default: 60
)
(integer) Determines the frequency (in seconds) of Sisy’s heartbeat messages. This sets the bound on task frequency; no task can execute more frequently than this setting. Consequently, if you need more frequent task runs, set this lower (probably not lower than about 5, as Sisy is not designed to handle very high frequency tasks) and add the optional sixth field to your frequently-called task’s schedule, to specify the seconds; e.g.* * * * * */30
will make the task eligible for execution every thirty seconds.
SISY_DEFAULT_SCHEDULE
(default: '* * * * *'
)
(string) Provides the default cron schedule for Sisy’s tasks. The default causes the task to run once per minute.


Pipe
Pipe Class
Constructor Summary
| Public Constructor | ||
| public |
|
Member Summary
| Public Members | ||
| public get |
errors: *: * Returns validation errors |
|
| public get |
isWritable: boolean: * Returns write status of |
|
| public get |
Returns JSON-SCHEMA for |
Method Summary
| Public Methods | ||
| public |
Creates clone of current |
|
| public |
Terminates input on |
|
| public |
exec(data: *): * Directly executes callback without effecting |
|
| public |
links pipe segment to direct output to target pipe |
|
| public |
Merges multiple pipes into single output |
|
| public |
Informs |
|
| public |
Creates new |
|
| public |
Returns product of Nth occurrence of |
|
| public |
split(mappings: *): * Creates array of new |
|
| public |
subscribe(handler: *): Observable Subscribes to |
|
| public |
Provides current state of |
|
| public |
Informs |
|
| public |
Provides current state of |
|
| public |
Overrides Object's toString method |
|
| public |
Unlink pipe segment from target pipe |
|
| public |
Writes data to pipe segment |
Public Constructors
public constructor() source
Public Members
Public Methods
public exec(data: *): * source
Directly executes callback without effecting pipe
observable
Params:
| Name | Type | Attribute | Description |
| data | * |
Return:
| * |
public link(target: *, callbacks: *): Pipe source
links pipe segment to direct output to target pipe
Params:
| Name | Type | Attribute | Description |
| target | * | ||
| callbacks | * | function[] |
public merge(pipeOrPipes: *, schema: *): Pipe source
Merges multiple pipes into single output
Params:
| Name | Type | Attribute | Description |
| pipeOrPipes | * | ||
| schema | * |
public pipe(pipesOrSchemas: *): Pipe source
Creates new pipe
segment
Params:
| Name | Type | Attribute | Description |
| pipesOrSchemas | * |
public sample(nth: *): Pipe source
Returns product of Nth occurrence of pipe
execution
Params:
| Name | Type | Attribute | Description |
| nth | * |
public split(mappings: *): * source
Creates array of new pipe
segments that run in parallel
Params:
| Name | Type | Attribute | Description |
| mappings | * |
Return:
| * |
public subscribe(handler: *): Observable source
Subscribes to pipe
output notifications
Params:
| Name | Type | Attribute | Description |
| handler | * |
Return:
| Observable |
public throttle(rate: *): Pipe source
Informs pipe
to rate limit notifications based on time interval
Params:
| Name | Type | Attribute | Description |
| rate | * |


Custom Mapillary Leaderboards
Custom Mapillary leaderboards for your event or your grand master plan(s)...
Search for participants:
Add time frame
Start date
End date
Create Leaderboard


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PhD candidate of Mathematics at the University of Minnesota. Currently looking for a part time/intern position in Data Analytics. Experienced with Excel, Python (Pandas, Matplotlib), SQL, and Matlab. Having trained in a strong quantitatively oriented field, I am attentive to detail, a very quick learner and have experience collaborating and communicating with people from different backgrounds. Always excited to learn new techniques and use them to help a company grow, my future goal is to become machine learning engineer.


--- layout: default title: Downloads description: "Download and install Turpial" ---
You can install Turpial 3 downloading the package for your system, from your system repositories or installing form sources.
You just need execute as root:
yum install turpial
Available for Sid, Jessie and Wheezy Backports. You just need execute as root:
aptitude install turpial
PPA repository for Ubuntu right now is outdated. We strongly recommend you that install via source code
Turpial is available in ArchLinux via [AUR](https://aur.archlinux.org/packages/turpial-git/). You can install it executing as root:
yaourt -S turpial-git
You just need execute as root:
pip install turpial
Turpial 3.0 (.tar.gz)
md5sum: 4e18ddda950654613c8f7eb3cb34e3bd
libturpial 1.7.0 (.tar.gz)
md5sum: 0d51997b86fa490762af6fe1a6f6c4ea
Turpial: https://github.com/satanas/Turpial/releases
libturpial: https://github.com/satanas/libturpial/releases
If you want more info about development process and all those stuffs, check out our turpial project on Github.


{% extends 'layout.html' %} {% block headers %} {% endblock headers %} {% block body %}
The modeling results presented here indicate that - at least for Gold and the S&P 500 - the occurrence of earthquakes is a poor predictor of the direction of financial markets. Of primary importance is the measure known as accuracy: it measures how well a model performs when compared to a set of actual outcomes - values unknown to the model when it was devised. The model score is a measure of the internal efficacy of the model and is included here for completeness. The best result obtained was 9.21% accuracy; this was using an XGBoost model for Gold prices after a 6.7 earthquake. From there things get worse: Random Forest Regressor models perform significantly less effectively; and Linear and Logistic Regression models give especially poor results. Another interesting observation is that when the threshold is decreased from Magnitude 6.7+ to 5.5+ (resulting in an approximately twelvefold increase in the number of incidents used in the model), the accuracy decreased significantly. A final point is that the XGBoost models using the Mag 5.5+ dataset - running in a maxed-out ml.p3.16xlarge AWS Sagemaker notebook instance - ran approximately 12 days of CPU (yes!) for the S&P 500 model and 10 days for the Gold model.
NOTE: These results essentially confirm what numerous other sources have indicated - that indeed an earthquake typically has virtually no impact on the Financial Markets; see for example here, here and here. The one exception of note is the incident that occurred in Japan in March 2011, which saw the Nikkei 225 drop some 23% following the incident; but this may well be mitigated by two factors: one is the subsequent tsunami and the attendant nuclear reactor disaster, and the other is fact that the 93% of Nikkei 225 Futures traders were bullish just days before the earthquake hit - a notoriously bearish indicator.
| Financial Instrument | Gold | S&P 500 | |||||
| Incident Magnitude | 6.7+ | 5.5+ | 6.7+ | 5.5+ | |||
| Incident Count | 1445 | 23510 | 1870 | 28350 | |||
| Model Type | |||||||
| Linear Regression | Model Score | 0.009 | 0.003 | 0.011 | 0.001 | ||
| RMSE | 5.996 | 5.604 | 4.270 | 4.317 | |||
| R-square | -0.012 | 0.001 | -0.012 | 1.414 | |||
| Logistic Regression | Model Score | 0.004 | 0.006 | 0.003 | 0.005 | ||
| Accuracy | 0.00% | 0.59% | 0.21% | 0.31% | |||
| Random Forest Regressor | Model Score | 0.912 | 0.989 | 0.895 | 0.986 | ||
| Accuracy | 6.14% | 1.33% | 4.42% | 1.30% | |||
| XGBoost | Model Score | 0.144 | 0.485 | 0.181 | 0.449 | ||
| Accuracy | 9.21% | 6.05% | 7.28% | 5.12% |


2 writes to OrderMatrix
Microsoft.PowerShell.Commands.Utility (2)
commands\utility\OrderObjectBase.cs (2)
479
OrderMatrix
= CreateOrderMatrix(cmdlet, inputObjects, _mshParameterList);
489
OrderMatrix
= new List<OrderByPropertyEntry>();
7 references to OrderMatrix
Microsoft.PowerShell.Commands.Utility (7)
commands\utility\Compare-Object.cs (5)
267
_orderByProperty.
OrderMatrix
!= null &&
268
_orderByProperty.
OrderMatrix
.Count == ReferenceObject.Length,
270
if (_orderByProperty.Comparer == null || _orderByProperty.
OrderMatrix
== null || _orderByProperty.
OrderMatrix
.Count == 0)
276
_referenceEntries = _orderByProperty.
OrderMatrix
;
commands\utility\OrderObjectBase.cs (1)
480
Comparer = CreateComparer(
OrderMatrix
, _mshParameterList, ascending, cultureInfo, caseSensitive);
commands\utility\Sort-Object.cs (1)
245
var dataToProcess = orderByProperty.
OrderMatrix
;


{{>_header}}
Node-RED nodes are packaged as modules and published to the public npm repository.
Once published to npm, they can be added to the Flow Library using the form below.
To add a node to the library, follow these steps:
Your module must have
README.md
file that describes what your node does and how to use it,package.json
file with:
node-red
section listing the node files,"node-red"
in its list of keywords
.
You can use the npm publish
command to do this.
Use this form to tell the Flow Library about your node.


Getting Atomic with Pattern Lab
Who am I?
Aaron Froehlich
a-fro (d.o)
a-fro.com
@aaronfroehlich
<EMAIL_ADDRESS>Husband, father, builder
Technical Lead, ILR School
Where are we going?
You are here
Demo Umami
Where have we been?
Cascading styles!
My first web page!
About me


The COVID-19 Pandemic is touching every part of our interconnected world. Its impact is being felt in all parts of our lives. We are reseachers from Northeastern University and Georgia Institute of Technology. We are conducting a research study to examine Information-Seeking During COVID-19.
The study has been approved by the Institutional Review Board of our institution.


TRANSACT:
First ACM SIGPLAN Workshop on Languages, Compilers,
and Hardware Support for Transactional Computing
Ottawa, Canada, June 11, 2006
::Invited Talk::
Nesting Transactions: Why and What do we need? Eliot Moss, University of Massachusetts Amherst
We are seeing many proposals supporting atomic transactions in
programming languages, software libraries, and hardware,
some with and some without support for nested transactions.
I argue
that it is important to support nesting, and to go beyond
closed nesting to open nesting. I will argue as to the general
form
open nesting should take and why, namely that it is a property
of classes (data types) not code regions, and must include
support for programmed concurrency control as well as programmed
rollback.
I will also touch on the implications for software or hardware
transactional
memory in order to support open nesting of this kind.
::Program::
Invited talk
Nesting Transactions: Why and What Do We Need?
[slides]
Eliot Moss
Software Transactions
Chair: David Tarditi
Lowering the Overhead of Nonblocking Software Transactional Memory
[slides]
Virendra J. Marathe, Michael F. Spear, Christopher Heriot,
Athul Acharya, David Eisenstat, William N. Scherer III,
and Michael L. Scott
Snapshot Isolation for Software Transactional Memory
[slides]
Torval Riegel, Christof Fetzer, and Pascal Felber
What Really Makes Transactions Faster? [slides]
Dave Dice and Nir Shavit
Debugging with Transactional Memory
[slides]
Yossi Lev and Mark Moir
Hardware Transactional Memory Chair: Christos
Kozyrakis
Hardware Acceleration of Software Transactional Memory
[slides]
Arrvindh Shriraman, Virendra J. Marathe, Sandhya
Dwarkadas, Michael L. Scott, David Eisenstat,
Christopher Heriot, William N. Scherer III, and
Michael F. Spear
Extending Hardware Transactional Memory to Support
Non-Busy Waiting and Non-Transactional Actions
[slides]
Craig Zilles and Lee Baugh
Language Design, Specifications, and Analysis Chair: Dan Grossman
Transactional Memory with Data Invariants
[slides]
Tim Harris and Simon Peyton Jones
Sequential Specification of Transactional Memory Semantics
[slides]
Michael L. Scott
Lock Inference for Atomic Sections [slides]
Michael Hicks, Jeffrey S. Foster, and Polyvios Pratikakis
Higher-Order Combinators for Join Patterns Using STM
[slides]
Satnam Singh
ALL PAPERS (.pdf)
::Motivation::
The goal of this workshop is to provide a forum for the presentation
of research on all aspects of transactional computing. There has
been much recent interest on extending programming languages, systems,
and hardware with support for transactions, speculation, and related
abstractions that provide alternatives to classical lock-based
concurrency mechanisms. The goals of this workshop should be construed
broadly to include any novel software or hardware techniques, algorithms,
or implementations for transactional concurrency abstractions applicable
to multi-core, multithreaded, or high- performance parallel systems.
This workshop is intended to cover foundations of concurrent programming
as it relates to all forms of transactional computing, as well
as tools, techniques, and applications that leverage these principles.
Experience reports are also welcome.
::Goal::
The workshop seeks papers on topics related to all areas of software
and hardware for new concurrency abstractions, models, and implementations.
Topics of interest include (but are not limited to):
- Transactional Memory
- Hardware support
- Atomicity
- Non-blocking algorithms
- Memory models
- Checkpointing
- Debugging
- Semantics and verification
- Static analysis and Compiler optimizations
- Runtime implementations
- Persistence and I/O
- Speculative concurrency
- Applications
Papers should present original research relevant to any of these
areas of concurrent programming and should provide sufficient background
material to make them accessible to the broader community. Papers
focussed on foundations should indicate how the work can be used
to advance practice; papers on experiences and applications should
indicate how the experiments reinforce principles.
::Important dates::
Submissions due: March 1.
Notification: April 15.
Final version : May 15.
::Paper submission::
Papers must be submitted in
Postscript or PDF format. Hard copies of all research presentations
and position papers will be distributed at the meeting. The conference
web page will make available all slides from presentations given
by the attendees, but the conference web page will not host papers.
This is to ensure that the workshop is correctly understood to
be an informal workshop, and that presentation of research at the
workshop is not considered a barrier to republication of that research
in conferences. Papers should be clearly labeled as either:
- Research papers: These papers present new results which have
not appeared and are not under submission elsewhere. These papers
should not exceed 10 pages in ACM double column format.
- Position/Experience papers: Short papers (<5 pages in ACM
format).
The submission site is .
A special journal issue is being considered with a selection of
the best research papers.
::Organizers::
| Program Committee:
Cliff Click, Azul
Laurent Daynes, Sun
Rick Hudson, Intel
Stephen Freund, Williams
Dan Grossman, Washington
Suresh Jagannathan, Purdue
Christos Kozyrakis, Stanford
Peter O'Hearn, Queen Mary, U.
of London
Bill Pugh, UMaryland
Ravi Rajwar, Intel
Nir Shavit, Sun
David Tarditi, Microsoft
Mandana Vaziri, IBM
|
General Chair:
Jan Vitek, Purdue
Program Chair: Suresh Jagannathan, Purdue
Steering Committee:
Tim Harris, Microsoft
Maurice Herlihy, Brown
Tony Hosking, Purdue
Doug Lea, SUNY, Oswego
Eliot Moss, UMass
Jan Vitek, Purdue
|
::Related Events::
Workshop on Transactional Memory Workloads
|


East Bridgewater Police looking for driver who hit 2 girls, 2 horses
by Mike SacconeEAST BRIDGEWATER, Mass. - East Bridgewater Police are looking for a newer model white Ford SUV that allegedly hit two teenage girls and two horses before taking off, Deputy Chief Paul O’Brien told Boston 25 News.
The crash happened Wednesday evening on Belmont Street.
O’Brien said one of the girls suffered possible leg injuries and one of the horses was being checked out by a veterinarian for possible injuries.
The SUV may be missing a passenger-side mirror.
Anyone with information should contact East Bridgewater Police at<PHONE_NUMBER>.


REoCAS @ ISOLA, 27/10/2021
Degrees of Autonomy in Coordinating Collectives of Self-Driving Vehicles
Stefano Mariani and Franco Zambonelli
Università di Modena e Reggio Emilia
Motivation & Goal
- current research on self-driving mostly about individual vehicles
- but cooperative driving is necessary for collectives of vehicles in many situations
- e.g. crossing intersections, platoon formation, fleet management, ...
- cooperative driving demands for coordination
- we overview key issues in coordinating self-driving vehicles
- we focus on intersection crossing and categorise existing approaches
- we argue for an adjustable autonomy approach
- we overview key challenges for this
Key issues
- coordinating vehicles $\rightarrow$ decision making process orchestrating vehicles' actions
- necessary to achieve own and shared ones
- competitive/collaborative, resource/task -oriented
- poses 3 challenges
- safety: no collisions, usually
- liveness: problem specific*
- quality: problem specific*
*e.g. intersection crossing $\rightarrow$ no waiting forever, max throughput
Problems overview
| |
resource-oriented |
task-oriented |
| competitive |
intersection crossing, parking (private) |
ride sharing (private), ramp merging |
| collaborative |
parking (fleets), traffic flow optimisation |
ride sharing (fleets), platooning |
Focus on: intersection crossing
- safety $\rightarrow$ no collisions
- liveness $\rightarrow$ each vehicle eventually gets right of way
- quality $\rightarrow$ min. cumulative delay / max. crossing throughput
Autonomy-based categorisation
Approaches categorised based on degree of autonomy left to vehicles during coordination
i.e. to what extent vehicles can decide their own actions
Categories of approaches
- centralised
- burden of coordination onto a single entity (Intersection Manager)
- every vehicles abides to IM decisions by design
- negotiation-based
- burden distributed amongst vehicles (and possibly a broker)
- fixed coordination protocol, fixed goal, fixed strategies
- agreement-based
- burden distributed amongst vehicles
- dynamic coordination protocol, dynamic goal, switching strategies
- emergent
- burden distributed amongst vehicles
- no explicit protocol, no shared goals
Intersection crossing: literature
- centralised: the IM decides
- receives movement parameters from approaching vehicles
- elaborates collision-free trajectories
- communicates back to vehicles their new parameters for crossing
- e.g. constrained optimisation, coop. cruise control, reservation-based
- negotiation: competitive problem $\rightarrow$ auctions
- vehicles bid for space-time slots within the intersection
- non-colliding vehicles cross simultaneously
- a broker collects bids and ranks them to assign the right of way
- many variations depending on kind of auction (e.g. English vs. Dutch), bidding strategy, ranking policy, ...
- agreement: (un)structured interactions
- e.g. DCOP, argumentation-based coordination, repeated coordination games, ...
- emergent: no explicit coordination protocol
- e.g. game-theoretic approaches, self-organisation
Intersection crossing: discussion
- centralised
- easiest to rigorously engineer and formally verify
- guarantees of safety and liveness often available
- the IM is a bottleneck, and demands for dedicated infrastructure
- negotiation-based
- still formally verifiable as protocol is fixed
- can still guarantee safety if vehicles comply (no attackers)
- liveness can be an issue especially for auctions
- neutrality issues may arise (the rich always win)
- agreement-based
- not much literature in this category
- usually difficult to implement, analyse, and control
- but may provide several benefits, e.g. flexibility and explainability
- emergent
- difficult to guarantee any property
- potentially relevant in mixed scenarios (i.e. with human drivers too)
Why adjustable autonomy
A single approach can hardly fit all possible run-time situations
- low traffic $\rightarrow$ negotiation/agreement based
- more autonomy to vehicles
- still manageable in number of messages
- high traffic $\rightarrow$ centralised approaches
- less autonomy to vehicles
- strict control to avoid collisions and delays
Thus: switch 'em!
How adjustable autonomy
- Open issue in cooperative driving, proposals in robotics and MAS
- Several challenges (akin to MAPE-K loop)
- learn best approach for each problem and specific situation
- have functional and non-functional performance metrics available to decide
- decide when to switch, possibly before degradation of efficacy
- decide how to switch (can't "pause" vehicles)
Further challenges
Besides adjustable autonomy, cooperative coordinated driving still in its infancy
- system of systems coordination (e.g. "butterfly effects" in intersection networks)
- dynamic pricing mechanisms may arise, threatening fairness and road neutrality
- dealing with mixed scenarios, as level 5 self-driving will not replace legacy vehicles overnight
Conclusion & outlook
- framed traffic-related problems into coordination concepts
- proposed autonomy-based categorisation of solution approaches
- argued for adjustable autonomy in coordinated cooperative driving
- propose practical approach to adjustable autonomy
- further analyse mixed scenarios
Thanks
for your attention
Stefano Mariani
Università di Modena e Reggio Emilia