El Niño/Southern Oscillation (ENSO) - During
El Niño events (ENSO warm phase), tropospheric vertical
shear is increased inhibiting tropical cyclone genesis and
intensification. La Nina events (ENSO cold phase) enhances
activity.
Gray,W.M. (1984)
African West Sahel rainfall - In years of West
Sahel drought conditions, the Atlantic hurricane activity is
much reduced - especially the major hurricane activity (Landsea and Gray 1992). Wet
West Sahel years mean a higher chance of low-latitude
"Cape Verde"-type hurricanes. This
is also due to higher tropospheric vertical shear in the
drought years, though there may also be changes in the structure
of African easterly waves as well to make them less likely to go
through tropical cyclogenesis.


{% extends "mails/messages/activity_base.html" %} {% load i18n %} {% block message%}
{% blocktrans context 'email' %}{{team_name}} has applied to your activity "{{title}}".{% endblocktrans %}
{% blocktrans context 'email' %}Next steps{% endblocktrans %}
{% blocktrans context 'email' %}Team captain contact details:{% endblocktrans %}
{{team_captain_email}}
{% endblock %}


NOW PLAYING ·
ON AIR: loading...
artist - track from release (label)
RECENTLY PLAYED
artist - track from release (label)
artist - track from release (label)
artist - track from release (label)
artist - track from release (label)
artist - track from release (label)


The completed applet is given below.
Here is the completed applet. Look over how the named values (constants and variables) were used in computing the values for X and Y.
import java.applet.Applet; import java.awt.*; // Assume that the drawing area is 300 by 150. // Draw ten red circles side-by-side across the drawing area. public class tenCircles extends Applet { final int width = 300, height = 150; public void paint ( Graphics gr ) { gr.setColor( Color.red ); int count = 0 ; while ( count < 10 ) { int radius = (width/10)/2; // the diameter is width/10 int X = count*(width/10); // the left edge of each of 10 // squares across the area int Y = height/2 - radius; // the top edge of the squares gr.drawOval( X, Y, 2*radius, 2*radius ); count = count + 1; } } }
Here is what the applet does:
The arithmetic used in this applet is a bit tedious. If you got lost, go back a few pages. Or just move on. Although arithmetic like this is typical of graphics programming it is not important that you work through every detail.


Turkish students, lecturer on trial for Pride march
by AFPANKARA: Eighteen Turkish students and a lecturer went on trial on Tuesday for taking part in a banned LGBTI Pride event at an Ankara university.
The defendants face up to three years in prison if convicted of “unlawful assembly and protest” and “refusing to disperse” in a trial deemed “farcical” by rights groups.
One of the 18 students also faces up to two years for insulting a police officer with hand gestures.
Homosexuality has been legal throughout modern Turkey’s history, but LGBTI individuals face regular harassment and abuse.
The pride event at the prestigious Middle East Technical University has taken place every May since 2011.
But university bosses banned this year’s event and police used pepper spray, plastic bullets and tear gas to break it up.
Diplomats from several European embassies, including Denmark and Sweden, attended the packed hearing.
Lawyers and rights groups urged the court to immediately acquit the defendants.
“The ban of the Pride march lacks legal grounds, and these brave students and others who defied it had their right to freedom of expression and peaceful assembly violated,” Sara Hall of Amnesty International said in a statement.
Police did not allow supporters to read a statement outside the courthouse.
Defendant Melike Irem Balkan told the court there were “no legal grounds” for banning the event, noting that it has taken place “peacefully” in previous years.
The Ankara governor’s office banned all LGBTI events in November 2017, saying they could “provoke reactions” in society, but the ruling was overturned by a court in the capital in April.
LGBTI events have faced difficulties since the government of President Recep Tayyip Erdogan came to power in 2003.
Authorities in Istanbul have banned the city’s Pride march for the past five years and used tear gas to break up this June’s event.
ILGA-Europe, an umbrella organisation for LGBTI groups, called for “a thorough and impartial investigation into the excessive use of force” against the Pride marchers.
It places Turkey in the bottom three European countries, alongside Armenia and Azerbaijan, for equality laws and policies.


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Scott Doyle /<EMAIL_ADDRESS>A Brief Introduction
Machine Learning (ML) uses collected data to do something useful.
The job of the ML expert is to:
Supervised Learning
Use labeled datasets to classify new, unseen data
Unsupervised Learning
Use unlabeled data to identify natural groups
Semi-Supervised Learning
Use partially labeled data to handle the process
Reinforcement Learning
An agent learns to complete a task policy of rewards
The starting point for all ML algorithms is data.
So… what do we mean by “data”?
Fine Needle Aspirate Classification
Problem Statement: Predict whether a patient’s tumor is benign or malignant, given an FNA image
Domain knowledge identifies useful features.
Pathologists already distinguish benign from malignant tumors.
Our job is to convert qualitative features to quantitative ones.
The pathologist lists cell nuclei features of importance:
Feature extraction results in 30 feature values per image.
To begin, we collect training samples to build a model.
Descriptive: Similar within a class, and different between classes
Relevant: Features should make sense
Invariant: Not dependent on how you measure them
Combining features often yields greater class separation.
Linear boundaries do not model variance and miss obvious trends.
Complex boundaries fit training perfectly, but do not generalize.
In general, you want the simplest model with the best performance.
Each of these decision boundaries makes errors!
There is always a tradeoff; we need to consider the cost.
Cost is defined by our goals and acceptable performance.
Should we prioritize some kinds of errors over others?
Not all mistakes carry the same cost. For example:
Building Blocks for Deep Learning
Richard O. Duda, Peter E. Hart, David G. Stork, “Pattern Classification, 2nd Edition”
Step 3: Calculate error of the result
Step 4: Calculate gradients and modify weights and biases
Step 2: Calculate network output
Step 1: Pick a training example
How Does It Work?
Hand Crafted Features: Selecting features relevant to the image classes
Deep Learning: Use the input samples themselves to identify classes
Innovations that make deep learning possible:
Let’s do some quick calculations…
With just over 12,000 values, our brains can identify the type of object in this image.
That seems like a lot, but that’s just 12kb worth of input data!
But we aren’t done yet…
Total Weights: 1.17 Billion
Our brains do a ton of computing!
We need a new approach…
Images have some nice properties:
Spatially Localized: Allows us to restrict the number of weights from input to output
Scale-dependent: Reducing image scale allows us to find connections between shapes and objects
The Importance of Data
ML benefits from large, well-annotated datasets
Natural images are abundant, easy-to-label data
However, it’s not so easy for pathology…
Data Generation: Limited scope, proprietary software, lack of standards
Data Hosting / Access: Large, high throughput storage options needed
Annotations: Difficult, time-consuming, application dependent
2009: NO!
Machine Learning will never be as accurate as a human.
2020: Still NO!
Machines cannot adapt to or integrate real-world information and progress.
They cannot act as moral agents, take responsibility, or bear consequences.
The value of physicians is not in mechanical tasks.
It is in the ability to improve, invent, integrate, and move beyond what was learned in training.
Jobs won’t go away, but they will probably change.


NWG: Small application layout
This section will go in details on how to origanize a small NWG application.
An application can be considered small when it has one interface and less than 10 controls. For example,
all NWG examples are classified as small applications while the test suite is not.
Note that you don't
really have to follow the whole "NativeUi" setup for very small applications, for example see
the barebone example.
Overview
- Define a struct that will hold your non-gui application data
- Define another struct that will hold the GUI data
- Add your application struct to the GUI struct members behind a Refcell
- Define the gui functions by
impl
the GUI struct
- Implement
NativeUi
for the GUI struct (manually or with NWD)
- In the main funtion, initialize the application struct and the GUI struct
- Call
build_ui
with the initialized data
- Start processing events
Defining the structs
Assuming your application has data that must be stored.
Next, it's time to define the UI struct.
Implementing NativeUI
Assuming you're not using native-windows-derive, you will have to manually implement the NativeUi trait.
Initializing
nwg::init().expect("Failed to init Native Windows GUI");
let data: RefCell<MyData> = { /* Initializing... */ };
let app: MyApp = MyApp { data, ..Default::default() };
let _ui = MyApp::build_ui(app).expect("Failed to build UI");
nwg::dispatch_thread_events();
Once everything is done, there's only initialization that's left to do. As this point everything is straightforward so...
1. Call the
init
function of nwg.
2. Initialize the data struct and the Ui struct.
3. Call the
build_ui
method defined by
NativeUi
.
4. As the final touch, call
dispatch_thread_events
to dispatch the events to the application


South Africans Pollard, De Jager sidelined by World Cup injuries
Two of South Africa’s World Cup winners face significant spells on the sidelines after injuries sustained in Saturday’s 32-12 final victory over England in Yokohama.
CAPE TOWN: Two of South Africa’s World Cup winners face significant spells on the sidelines after injuries sustained in Saturday’s 32-12 final victory over England in Yokohama.
Flyhalf Handre Pollard, who stayed on the field for the whole match against England, has had surgery on a fractured eye socket and will be ruled out for six weeks, delaying his move to French Top 14 side Montpellier, South African Rugby said on Thursday.
Lock Lood de Jager dislocated his right shoulder after 22 minutes of the final and was replaced. He will have surgery in Cape Town next week, with an expected recovery time of four to six months.
De Jager, 26, was set to join English side Sale Sharks after the World Cup, but his move to the Premiership will be delayed until next year.
(Reporting by Nick Said, editing by Ed Osmond)


Mulligan takes on role with Leitrim minors as hurlers still on hunt for new boss
Emlyn Mulligan joins Benny Guckian’s backroom team as a selector.
by Kevin O'BrienFORMER LEITRIM FORWARD Emlyn Mulligan has been added to the county’s minor football backroom team for the 2020 season.
Leitrim GAA confirmed the appointment of ex-senior boss Benny Guckian as the county’s new minor manager and he brings Mulligan on board as a selector.
Mulligan left Terry Hyland’s senior panel last May after struggling to nail down a starting place in the team following his return from a third cruciate injury.
It was also announced that Hyland will combine senior and U20 duties following the decision to run the latter competition in the spring next year.
Meanwhile, the county’s senior hurlers remain on the look-out for a new manager as they prepare for their maiden Nicky Rackard Cup campaign next year.
Martin Cunniffe had been in charge of Leitrim since 2011 and led the county to the Lory Meagher Cup title during the summer. But Cunniffe stepped down earlier this month, claiming to have been “ambushed” during a post-season meeting with the county board.
“It was just lads want something new,” forward Zak Moradi tells The42.
“All it was was the county board wanted to do a review on the management. They do it with the football every year but there hasn’t been a review done in eight years with the hurling. I think that was it and I don’t think he was happy with the review.
“Martin’s been there a long time and everybody appreciated what he’s done for the Leitrim hurlers for the last 30 years when he’s been involved between chairman, secretary, selector, manager – he’s been involved the whole way up.
“Some of the lads thought it would be nice to get a fresh face in. You know yourself, when you’re there too long sometimes lads want a fresh face, they don’t want to be returning with the same manager the whole way up.”
Interesting applications for the vacant hurling manager’s role are invited to apply by email to the Leitrim GAA secretary by 25 November.


Individual Exercises
Class Discussion
Visually Aided Listening
What is the United Nations Educational, Scientific, and Cultural Organization's (UNESCO's) World
Heritage List, and why is it important in preserving the natural and cultural heritage of
all of humanity?
This unit introduces readers to the significance of UNESCO's Heritage Sites — places that are
wonders of nature, wonders of culture or both. The reading passage
mentions such sites as the
Great Wall of China, the Pyramids of Egypt, Mont Saint-Michel in France and Yosemite National
Park in
the United States. The passage describes the international community's commitment to
protecting and
preserving such wondrous sites.
The unit contains exercises in reading comprehension, grammar, and listening. In addition, the unit
offers
practice writing task on providing a short report update. As always,
all exercises provide
information about the work of the United Nations as they also provide language practice.


{% extends 'mapApp/base.html' %} {% load static crispy_forms_tags i18n %} {% block body %}
{% trans "Forgotten your password? Enter your email address below, and we'll email instructions for setting a new one." %}


This is a game to test your reaction time. When you press start, a random colored box will pop up on the screen. Click the box as fast as you can and your reaction time will be displayed. Make sure your browser window is taking up most of your screen. Click the average button to see your average reaction time. Refresh the page when you want to reset your average. Click go to make a square appear, be ready to click!


#!/home/software/ruby-1.8.7/bin/ruby -w require 'RMagick' # Demonstrate the Image#stegano method. # Create a small watermark from the Snake image by # shrinking it and converting it to B&W. begin watermark = Magick::Image.read('images/Snake.wmf').first watermark.scale!(64.0/watermark.rows) watermark = watermark.quantize(256, Magick::GRAYColorspace) wmrows = watermark.rows wmcols = watermark.columns # Read the image in which we'll hide the watermark. img = Magick::Image.read('images/Flower_Hat.jpg').first img.scale!(250.0/img.rows) # Embed the watermark starting at offset 91. puts 'Embedding watermark...' stegano = img.stegano(watermark, 91) puts 'Watermark embedded' # Write the watermarked image in MIFF format. Note that # the format must be lossless - Image#stegano doesn't work # with lossy formats like JPEG. stegano.write('img.miff') # Read the image and retrieve the watermark. The size # attribute describes the size and offset of the watermark. # This can take some time. Keep track of how far along we are. monitor = Proc.new do |text, quantum, span| printf("Extracting watermark...%3.0f%% complete\n", ((1.0-(quantum/span.to_f))*100.0)) true end stegano = Magick::Image.read('stegano:img.miff') do self.size = Magick::Geometry.new(wmcols, wmrows, 91) self.monitor = monitor end stegano[0].monitor = nil # We don't need this any more. File.delete('img.miff') stegano[0].write('stegano.gif') rescue Magick::ImageMagickError puts "#{$0}: ImageMagickError - #{$!}" end exit


Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.
A small river named Duden flows by their place and supplies it with the necessary regelialia. It is a paradisematic country, in which roasted parts of sentences fly into your mouth.
Even the all-powerful Pointing has no control about the blind texts it is an almost unorthographic life One day however a small line of blind text by the name of Lorem Ipsum decided to leave for the far World of Grammar.
The Big Oxmox advised her not to do so, because there were thousands of bad Commas, wild Question Marks and devious Semikoli, but the Little Blind Text didn’t listen. She packed her seven versalia, put her initial into the belt and made herself on the way.
When she reached the first hills of the Italic Mountains, she had a last view back on the skyline of her hometown Bookmarksgrove, the headline of Alphabet Village and the subline of her own road, the Line Lane. Pityful a rethoric question ran over her cheek, then she continued her way.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.
A small river named Duden flows by their place and supplies it with the necessary regelialia. It is a paradisematic country, in which roasted parts of sentences fly into your mouth.
Even the all-powerful Pointing has no control about the blind texts it is an almost unorthographic life One day however a small line of blind text by the name of Lorem Ipsum decided to leave for the far World of Grammar.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.
Design & Model
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.
Design
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.
Photography
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.
Web Designer
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.
Web Developer
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.
Web Designer
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.


Chobits
Ah Chobits. No matter how many times I
read this it never gets old. I've read every Different English
translation of this that is out there. Here is a short synopsis about
what this one is about it:
Hideki's just like every other guy
in the 22nd century. He just wants a good job, a good car, and a
sexy robot girlfriend to call his own. Until he lands a job, he'll
never be able to afford his own 'Persocom' companion.  His first robot companion
turns out to be a lot more responsibility than he expected, and
she gets him into quite a few embarrassing situations. It's
'boy-meets-girl' for the cyber age. (Tokyopop)
So then lets get crack-en on with the
review. Over all I really enjoyed this one. like I've said earlier
I have read this just a few times (closer to 20 times). This was
my first Ecchi-Android-Romance manga/anime of all time. For those
who are wondering the manga is way better. Chi is probably the
reason why I went into computers in the first place. It was the Idea
of a computer like a Persocom that blew my mind. After
reading this I used a mix of programs to give my computer an AI
like system and got it to talk to me (god I'm forever alone
*insert face palm here*). This manga really touched me. The first time
I read it I cried at the end because of such a good ending. A side
note I made what sumomo says when she gets a call into a ringtone.
I love it.
Quilty of Translation 9/10
Story 10/10
Art 9/10
Concept 10/10
Over all 10/10 (Its a must read)
For more Info :Baka-Updates


Yvonne Nelson Drops Freestyle Rap Skills On Live TV (Video)
by BB JonesActress and producer Yvonne Nelson showed her other talent as a rapper when she dropped some punchlines on live TV in the studios of Kwese TV.
The mother of one was a guest on the TV network TV to promote her upcoming movie ‘Fix Us’ premiering on December 6 at Accra Mall.
In order to convince movie fanatics why they should be at the cinema to watch the movie, Yvonne dropped some lines in raps to propagate her message.
Yvonne was joined by Efia Odo as they rapped and made gestures associated with rappers.
Yvonne rapped telling viewers of the show to purchase a ticket and join her at the premiere of the much anticipated movie.
It didn’t take long for her to prove she can really rap and the studio guests loved every bit of her mini concert.
Watch:


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{% extends 'base.html' %}
{% load staticfiles i18n %}
{% block container %}
{% include 'sections/header.html' %}
{% include 'sections/intro.html' %}
{% include 'sections/about.html' %}
{% include 'sections/services.html' %}
{% include 'sections/products.html' %}
{% include 'sections/clients.html' %}
{% include 'sections/team.html' %}
{% include 'sections/news.html' %}
{% include 'sections/footer.html' %}
{% endblock %}


This is similar to the dropdown tag. TO get this to work, you need the array of SelectItem.
binding: the binding to the controller
items: the Array of SelectItem
fieldId: the form-field-toggle id
ulSize: the ul class to use for the size
isAjax: optional boolean if this is an specific ajax re-render of the form


Line height makes the div look like the font-size is not scaling properly.
 While attempting to gain recompense for the ruined rug from his wealthy counterpart, he accepts a one-time job with high pay-off. He enlists the help of his bowling buddy, Walter, a gun-toting Jewish-convert with anger issues. Deception leads to more trouble, and it soon seems that everyone from porn empire tycoons to nihilists want something from The Dude.
There was a guy, an underwater guy that controlled the sea. Got killed by ten million
pounds of sludge from New York and New Jersey.
 While attempting to gain recompense for the ruined rug from his wealthy counterpart, he accepts a one-time job with high pay-off. He enlists the help of his bowling buddy, Walter, a gun-toting Jewish-convert with anger issues. Deception leads to more trouble, and it soon seems that everyone from porn empire tycoons to nihilists want something from The Dude.
This
There is a house in new orleans.
 While attempting to gain recompense for the ruined rug from his wealthy counterpart, he accepts a one-time job with high pay-off. He enlists the help of his bowling buddy, Walter, a gun-toting Jewish-convert with anger issues. Deception leads to more trouble, and it soon seems that everyone from porn empire tycoons to nihilists want something from The Dude.


{% extends "horizon/common/_modal_form.html" %} {% load i18n %} {% block modal-body-right %}
{% trans "Create a mapping." %}
{% trans "A mapping is a set of rules to map federation protocol attributes to Identity API objects. An identity provider can have a single mapping specified per protocol. A mapping is simply a list of rules. The only Identity API objects that will support mapping are: user and group." %}
{% trans "A rule contains a remote attribute description and the destination local attribute." %}
{% endblock %}


Modules¶
PyTorch uses modules to represent neural networks. Modules are:
Building blocks of stateful computation. PyTorch provides a robust library of modules and makes it simple to define new custom modules, allowing for easy construction of elaborate, multi-layer neural networks.
Tightly integrated with PyTorch’s autograd system. Modules make it simple to specify learnable parameters for PyTorch’s Optimizers to update.
Easy to work with and transform. Modules are straightforward to save and restore, transfer between CPU / GPU / TPU devices, prune, quantize, and more.
This note describes modules, and is intended for all PyTorch users. Since modules are so fundamental to PyTorch, many topics in this note are elaborated on in other notes or tutorials, and links to many of those documents are provided here as well.
A Simple Custom Module¶
To get started, let’s look at a simpler, custom version of PyTorch’s Linear
module.
This module applies an affine transformation to its input.
import torch
from torch import nn
class MyLinear(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.weight = nn.Parameter(torch.randn(in_features, out_features))
self.bias = nn.Parameter(torch.randn(out_features))
def forward(self, input):
return (input @ self.weight) + self.bias
This simple module has the following fundamental characteristics of modules:
It inherits from the base Module class. All modules should subclass
Module
for composability with other modules.It defines some “state” that is used in computation. Here, the state consists of randomly-initialized
weight
andbias
tensors that define the affine transformation. Because each of these is defined as aParameter
, they are registered for the module and will automatically be tracked and returned from calls toparameters()
. Parameters can be considered the “learnable” aspects of the module’s computation (more on this later). Note that modules are not required to have state, and can also be stateless.It defines a forward() function that performs the computation. For this affine transformation module, the input is matrix-multiplied with the
weight
parameter (using the@
short-hand notation) and added to thebias
parameter to produce the output. More generally, theforward()
implementation for a module can perform arbitrary computation involving any number of inputs and outputs.
This simple module demonstrates how modules package state and computation together. Instances of this module can be constructed and called:
m = MyLinear(4, 3)
sample_input = torch.randn(4)
m(sample_input)
: tensor([-0.3037, -1.0413, -4.2057], grad_fn=<AddBackward0>)
Note that the module itself is callable, and that calling it invokes its forward()
function.
This name is in reference to the concepts of “forward pass” and “backward pass”, which apply to each module.
The “forward pass” is responsible for applying the computation represented by the module
to the given input(s) (as shown in the above snippet). The “backward pass” computes gradients of
module outputs with respect to its inputs, which can be used for “training” parameters through gradient
descent methods. PyTorch’s autograd system automatically takes care of this backward pass computation, so it
is not required to manually implement a backward()
function for each module. The process of training
module parameters through successive forward / backward passes is covered in detail in
Neural Network Training with Modules.
The full set of parameters registered by the module can be iterated through via a call to
parameters()
or named_parameters()
,
where the latter includes each parameter’s name:
for parameter in m.named_parameters():
print(parameter)
: ('weight', Parameter containing:
tensor([[ 1.0597, 1.1796, 0.8247],
[-0.5080, -1.2635, -1.1045],
[ 0.0593, 0.2469, -1.4299],
[-0.4926, -0.5457, 0.4793]], requires_grad=True))
('bias', Parameter containing:
tensor([ 0.3634, 0.2015, -0.8525], requires_grad=True))
In general, the parameters registered by a module are aspects of the module’s computation that should be “learned”. A later section of this note shows how to update these parameters using one of PyTorch’s Optimizers. Before we get to that, however, let’s first examine how modules can be composed with one another.
Modules as Building Blocks¶
Modules can contain other modules, making them useful building blocks for developing more elaborate functionality.
The simplest way to do this is using the Sequential
module. It allows us to chain together
multiple modules:
net = nn.Sequential(
MyLinear(4, 3),
nn.ReLU(),
MyLinear(3, 1)
)
sample_input = torch.randn(4)
net(sample_input)
: tensor([-0.6749], grad_fn=<AddBackward0>)
Note that Sequential
automatically feeds the output of the first MyLinear
module as input
into the ReLU
, and the output of that as input into the second MyLinear
module. As
shown, it is limited to in-order chaining of modules with a single input and output.
In general, it is recommended to define a custom module for anything beyond the simplest use cases, as this gives full flexibility on how submodules are used for a module’s computation.
For example, here’s a simple neural network implemented as a custom module:
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super().__init__()
self.l0 = MyLinear(4, 3)
self.l1 = MyLinear(3, 1)
def forward(self, x):
x = self.l0(x)
x = F.relu(x)
x = self.l1(x)
return x
This module is composed of two “children” or “submodules” (l0
and l1
) that define the layers of
the neural network and are utilized for computation within the module’s forward()
method. Immediate
children of a module can be iterated through via a call to children()
or
named_children()
:
net = Net()
for child in net.named_children():
print(child)
: ('l0', MyLinear())
('l1', MyLinear())
To go deeper than just the immediate children, modules()
and
named_modules()
recursively iterate through a module and its child modules:
class BigNet(nn.Module):
def __init__(self):
super().__init__()
self.l1 = MyLinear(5, 4)
self.net = Net()
def forward(self, x):
return self.net(self.l1(x))
big_net = BigNet()
for module in big_net.named_modules():
print(module)
: ('', BigNet(
(l1): MyLinear()
(net): Net(
(l0): MyLinear()
(l1): MyLinear()
)
))
('l1', MyLinear())
('net', Net(
(l0): MyLinear()
(l1): MyLinear()
))
('net.l0', MyLinear())
('net.l1', MyLinear())
Sometimes, it’s necessary for a module to dynamically define submodules.
The ModuleList
and ModuleDict
modules are useful here; they
register submodules from a list or dict:
class DynamicNet(nn.Module):
def __init__(self, num_layers):
super().__init__()
self.linears = nn.ModuleList(
[MyLinear(4, 4) for _ in range(num_layers)])
self.activations = nn.ModuleDict({
'relu': nn.ReLU(),
'lrelu': nn.LeakyReLU()
})
self.final = MyLinear(4, 1)
def forward(self, x, act):
for linear in self.linears:
x = linear(x)
x = self.activations[act](x)
x = self.final(x)
return x
dynamic_net = DynamicNet(3)
sample_input = torch.randn(4)
output = dynamic_net(sample_input, 'relu')
For any given module, its parameters consist of its direct parameters as well as the parameters of all submodules.
This means that calls to parameters()
and named_parameters()
will
recursively include child parameters, allowing for convenient optimization of all parameters within the network:
for parameter in dynamic_net.named_parameters():
print(parameter)
: ('linears.0.weight', Parameter containing:
tensor([[-1.2051, 0.7601, 1.1065, 0.1963],
[ 3.0592, 0.4354, 1.6598, 0.9828],
[-0.4446, 0.4628, 0.8774, 1.6848],
[-0.1222, 1.5458, 1.1729, 1.4647]], requires_grad=True))
('linears.0.bias', Parameter containing:
tensor([ 1.5310, 1.0609, -2.0940, 1.1266], requires_grad=True))
('linears.1.weight', Parameter containing:
tensor([[ 2.1113, -0.0623, -1.0806, 0.3508],
[-0.0550, 1.5317, 1.1064, -0.5562],
[-0.4028, -0.6942, 1.5793, -1.0140],
[-0.0329, 0.1160, -1.7183, -1.0434]], requires_grad=True))
('linears.1.bias', Parameter containing:
tensor([ 0.0361, -0.9768, -0.3889, 1.1613], requires_grad=True))
('linears.2.weight', Parameter containing:
tensor([[-2.6340, -0.3887, -0.9979, 0.0767],
[-0.3526, 0.8756, -1.5847, -0.6016],
[-0.3269, -0.1608, 0.2897, -2.0829],
[ 2.6338, 0.9239, 0.6943, -1.5034]], requires_grad=True))
('linears.2.bias', Parameter containing:
tensor([ 1.0268, 0.4489, -0.9403, 0.1571], requires_grad=True))
('final.weight', Parameter containing:
tensor([[ 0.2509], [-0.5052], [ 0.3088], [-1.4951]], requires_grad=True))
('final.bias', Parameter containing:
tensor([0.3381], requires_grad=True))
It’s also easy to move all parameters to a different device or change their precision using
to()
:
# Move all parameters to a CUDA device
dynamic_net.to(device='cuda')
# Change precision of all parameters
dynamic_net.to(dtype=torch.float64)
dynamic_net(torch.randn(5, device='cuda', dtype=torch.float64))
: tensor([6.5166], device='cuda:0', dtype=torch.float64, grad_fn=<AddBackward0>)
More generally, an arbitrary function can be applied to a module and its submodules recursively by
using the apply()
function. For example, to apply custom initialization to parameters
of a module and its submodules:
# Define a function to initialize Linear weights.
# Note that no_grad() is used here to avoid tracking this computation in the autograd graph.
@torch.no_grad()
def init_weights(m):
if isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
m.bias.fill_(0.0)
# Apply the function recursively on the module and its submodules.
dynamic_net.apply(init_weights)
These examples show how elaborate neural networks can be formed through module composition and conveniently
manipulated. To allow for quick and easy construction of neural networks with minimal boilerplate, PyTorch
provides a large library of performant modules within the torch.nn
namespace that perform common neural
network operations like pooling, convolutions, loss functions, etc.
In the next section, we give a full example of training a neural network.
For more information, check out:
Library of PyTorch-provided modules: torch.nn
Defining neural net modules: https://pytorch.org/tutorials/beginner/examples_nn/two_layer_net_module.html
Neural Network Training with Modules¶
Once a network is built, it has to be trained, and its parameters can be easily optimized with one of PyTorch’s
Optimizers from torch.optim
:
# Create the network (from previous section) and optimizer
net = Net()
optimizer = torch.optim.SGD(net.parameters(), lr=1e-4, weight_decay=1e-2, momentum=0.9)
# Run a sample training loop that "teaches" the network
# to output the constant zero function
for _ in range(10000):
input = torch.randn(4)
output = net(input)
loss = torch.abs(output)
net.zero_grad()
loss.backward()
optimizer.step()
# After training, switch the module to eval mode to do inference, compute performance metrics, etc.
# (see discussion below for a description of training and evaluation modes)
...
net.eval()
...
In this simplified example, the network learns to simply output zero, as any non-zero output is “penalized” according
to its absolute value by employing torch.abs()
as a loss function. While this is not a very interesting task, the
key parts of training are present:
A network is created.
An optimizer (in this case, a stochastic gradient descent optimizer) is created, and the network’s parameters are associated with it.
- A training loop…
acquires an input,
runs the network,
computes a loss,
zeros the network’s parameters’ gradients,
calls loss.backward() to update the parameters’ gradients,
calls optimizer.step() to apply the gradients to the parameters.
After the above snippet has been run, note that the network’s parameters have changed. In particular, examining the
value of l1
‘s weight
parameter shows that its values are now much closer to 0 (as may be expected):
print(net.l1.weight)
: Parameter containing:
tensor([[-0.0013],
[ 0.0030],
[-0.0008]], requires_grad=True)
Note that the above process is done entirely while the network module is in “training mode”. Modules default to
training mode and can be switched between training and evaluation modes using train()
and
eval()
. They can behave differently depending on which mode they are in. For example, the
BatchNorm
module maintains a running mean and variance during training that are not updated
when the module is in evaluation mode. In general, modules should be in training mode during training
and only switched to evaluation mode for inference or evaluation. Below is an example of a custom module
that behaves differently between the two modes:
class ModalModule(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
if self.training:
# Add a constant only in training mode.
return x + 1.
else:
return x
m = ModalModule()
x = torch.randn(4)
print('training mode output: {}'.format(m(x)))
: tensor([1.6614, 1.2669, 1.0617, 1.6213, 0.5481])
m.eval()
print('evaluation mode output: {}'.format(m(x)))
: tensor([ 0.6614, 0.2669, 0.0617, 0.6213, -0.4519])
Training neural networks can often be tricky. For more information, check out:
Using Optimizers: https://pytorch.org/tutorials/beginner/examples_nn/two_layer_net_optim.html.
Neural network training: https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html
Introduction to autograd: https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html
Module State¶
In the previous section, we demonstrated training a module’s “parameters”, or learnable aspects of computation.
Now, if we want to save the trained model to disk, we can do so by saving its state_dict
(i.e. “state dictionary”):
# Save the module
torch.save(net.state_dict(), 'net.pt')
...
# Load the module later on
new_net = Net()
new_net.load_state_dict(torch.load('net.pt'))
: <All keys matched successfully>
A module’s state_dict
contains state that affects its computation. This includes, but is not limited to, the
module’s parameters. For some modules, it may be useful to have state beyond parameters that affects module
computation but is not learnable. For such cases, PyTorch provides the concept of “buffers”, both “persistent”
and “non-persistent”. Following is an overview of the various types of state a module can have:
Parameters: learnable aspects of computation; contained within the
state_dict
Buffers: non-learnable aspects of computation
Persistent buffers: contained within the
state_dict
(i.e. serialized when saving & loading)Non-persistent buffers: not contained within the
state_dict
(i.e. left out of serialization)
As a motivating example for the use of buffers, consider a simple module that maintains a running mean. We want
the current value of the running mean to be considered part of the module’s state_dict
so that it will be
restored when loading a serialized form of the module, but we don’t want it to be learnable.
This snippet shows how to use register_buffer()
to accomplish this:
class RunningMean(nn.Module):
def __init__(self, num_features, momentum=0.9):
super().__init__()
self.momentum = momentum
self.register_buffer('mean', torch.zeros(num_features))
def forward(self, x):
self.mean = self.momentum * self.mean + (1.0 - self.momentum) * x
return self.mean
Now, the current value of the running mean is considered part of the module’s state_dict
and will be properly restored when loading the module from disk:
m = RunningMean(4)
for _ in range(10):
input = torch.randn(4)
m(input)
print(m.state_dict())
: OrderedDict([('mean', tensor([ 0.1041, -0.1113, -0.0647, 0.1515]))]))
# Serialized form will contain the 'mean' tensor
torch.save(m.state_dict(), 'mean.pt')
m_loaded = RunningMean(4)
m_loaded.load_state_dict(torch.load('mean.pt'))
assert(torch.all(m.mean == m_loaded.mean))
As mentioned previously, buffers can be left out of the module’s state_dict
by marking them as non-persistent:
self.register_buffer('unserialized_thing', torch.randn(5), persistent=False)
Both persistent and non-persistent buffers are affected by model-wide device / dtype changes applied with
to()
:
# Moves all module parameters and buffers to the specified device / dtype
m.to(device='cuda', dtype=torch.float64)
Buffers of a module can be iterated over using buffers()
or
named_buffers()
.
for buffer in m.named_buffers():
print(buffer)
The following class demonstrates the various ways of registering parameters and buffers within a module:
class StatefulModule(nn.Module):
def __init__(self):
super().__init__()
# Setting a nn.Parameter as an attribute of the module automatically registers the tensor
# as a parameter of the module.
self.param1 = nn.Parameter(torch.randn(2))
# Alternative string-based way to register a parameter.
self.register_parameter('param2', nn.Parameter(torch.randn(3)))
# Reserves the "param3" attribute as a parameter, preventing it from being set to anything
# except a parameter. "None" entries like this will not be present in the module's state_dict.
self.register_parameter('param3', None)
# Registers a list of parameters.
self.param_list = nn.ParameterList([nn.Parameter(torch.randn(2)) for i in range(3)])
# Registers a dictionary of parameters.
self.param_dict = nn.ParameterDict({
'foo': nn.Parameter(torch.randn(3)),
'bar': nn.Parameter(torch.randn(4))
})
# Registers a persistent buffer (one that appears in the module's state_dict).
self.register_buffer('buffer1', torch.randn(4), persistent=True)
# Registers a non-persistent buffer (one that does not appear in the module's state_dict).
self.register_buffer('buffer2', torch.randn(5), persistent=False)
# Reserves the "buffer3" attribute as a buffer, preventing it from being set to anything
# except a buffer. "None" entries like this will not be present in the module's state_dict.
self.register_buffer('buffer3', None)
# Adding a submodule registers its parameters as parameters of the module.
self.linear = nn.Linear(2, 3)
m = StatefulModule()
# Save and load state_dict.
torch.save(m.state_dict(), 'state.pt')
m_loaded = StatefulModule()
m_loaded.load_state_dict(torch.load('state.pt'))
# Note that non-persistent buffer "buffer2" and reserved attributes "param3" and "buffer3" do
# not appear in the state_dict.
print(m_loaded.state_dict())
: OrderedDict([('param1', tensor([-0.0322, 0.9066])),
('param2', tensor([-0.4472, 0.1409, 0.4852])),
('buffer1', tensor([ 0.6949, -0.1944, 1.2911, -2.1044])),
('param_list.0', tensor([ 0.4202, -0.1953])),
('param_list.1', tensor([ 1.5299, -0.8747])),
('param_list.2', tensor([-1.6289, 1.4898])),
('param_dict.bar', tensor([-0.6434, 1.5187, 0.0346, -0.4077])),
('param_dict.foo', tensor([-0.0845, -1.4324, 0.7022])),
('linear.weight', tensor([[-0.3915, -0.6176],
[ 0.6062, -0.5992],
[ 0.4452, -0.2843]])),
('linear.bias', tensor([-0.3710, -0.0795, -0.3947]))])
For more information, check out:
Saving and loading: https://pytorch.org/tutorials/beginner/saving_loading_models.html
Serialization semantics: https://pytorch.org/docs/master/notes/serialization.html
What is a state dict? https://pytorch.org/tutorials/recipes/recipes/what_is_state_dict.html
Module Initialization¶
By default, parameters and floating-point buffers for modules provided by torch.nn
are initialized during
module instantiation as 32-bit floating point values on the CPU using an initialization scheme determined to
perform well historically for the module type. For certain use cases, it may be desired to initialize with a different
dtype, device (e.g. GPU), or initialization technique.
Examples:
# Initialize module directly onto GPU.
m = nn.Linear(5, 3, device='cuda')
# Initialize module with 16-bit floating point parameters.
m = nn.Linear(5, 3, dtype=torch.half)
# Skip default parameter initialization and perform custom (e.g. orthogonal) initialization.
m = torch.nn.utils.skip_init(nn.Linear, 5, 3)
nn.init.orthogonal_(m.weight)
Note that the device and dtype options demonstrated above also apply to any floating-point buffers registered for the module:
m = nn.BatchNorm2d(3, dtype=torch.half)
print(m.running_mean)
: tensor([0., 0., 0.], dtype=torch.float16)
While module writers can use any device or dtype to initialize parameters in their custom modules, good practice is
to use dtype=torch.float
and device='cpu'
by default as well. Optionally, you can provide full flexibility
in these areas for your custom module by conforming to the convention demonstrated above that all
torch.nn
modules follow:
Provide a
device
constructor kwarg that applies to any parameters / buffers registered by the module.Provide a
dtype
constructor kwarg that applies to any parameters / floating-point buffers registered by the module.Only use initialization functions (i.e. functions from
torch.nn.init
) on parameters and buffers within the module’s constructor. Note that this is only required to useskip_init()
; see this page for an explanation.
For more information, check out:
Skipping module parameter initialization: https://pytorch.org/tutorials/prototype/skip_param_init.html
Module Hooks¶
In Neural Network Training with Modules, we demonstrated the training process for a module, which iteratively performs forward and backward passes, updating module parameters each iteration. For more control over this process, PyTorch provides “hooks” that can perform arbitrary computation during a forward or backward pass, even modifying how the pass is done if desired. Some useful examples for this functionality include debugging, visualizing activations, examining gradients in-depth, etc. Hooks can be added to modules you haven’t written yourself, meaning this functionality can be applied to third-party or PyTorch-provided modules.
PyTorch provides two types of hooks for modules:
Forward hooks are called during the forward pass. They can be installed for a given module with
register_forward_pre_hook()
andregister_forward_hook()
. These hooks will be called respectively just before the forward function is called and just after it is called. Alternatively, these hooks can be installed globally for all modules with the analagousregister_module_forward_pre_hook()
andregister_module_forward_hook()
functions.Backward hooks are called during the backward pass. They can be installed with
register_full_backward_hook()
. These hooks will be called when the backward for this Module has been computed and will allow the user to access the gradients for both the inputs and outputs. Alternatively, they can be installed globally for all modules withregister_module_full_backward_hook()
.
All hooks allow the user to return an updated value that will be used throughout the remaining computation.
Thus, these hooks can be used to either execute arbitrary code along the regular module forward/backward or
modify some inputs/outputs without having to change the module’s forward()
function.
Below is an example demonstrating usage of forward and backward hooks:
torch.manual_seed(1)
def forward_pre_hook(m, inputs):
# Allows for examination and modification of the input before the forward pass.
# Note that inputs are always wrapped in a tuple.
input = inputs[0]
return input + 1.
def forward_hook(m, inputs, output):
# Allows for examination of inputs / outputs and modification of the outputs
# after the forward pass. Note that inputs are always wrapped in a tuple while outputs
# are passed as-is.
# Residual computation a la ResNet.
return output + inputs[0]
def backward_hook(m, grad_inputs, grad_outputs):
# Allows for examination of grad_inputs / grad_outputs and modification of
# grad_inputs used in the rest of the backwards pass. Note that grad_inputs and
# grad_outputs are always wrapped in tuples.
new_grad_inputs = [torch.ones_like(gi) * 42. for gi in grad_inputs]
return new_grad_inputs
# Create sample module & input.
m = nn.Linear(3, 3)
x = torch.randn(2, 3, requires_grad=True)
# ==== Demonstrate forward hooks. ====
# Run input through module before and after adding hooks.
print('output with no forward hooks: {}'.format(m(x)))
: output with no forward hooks: tensor([[-0.5059, -0.8158, 0.2390],
[-0.0043, 0.4724, -0.1714]], grad_fn=<AddmmBackward>)
# Note that the modified input results in a different output.
forward_pre_hook_handle = m.register_forward_pre_hook(forward_pre_hook)
print('output with forward pre hook: {}'.format(m(x)))
: output with forward pre hook: tensor([[-0.5752, -0.7421, 0.4942],
[-0.0736, 0.5461, 0.0838]], grad_fn=<AddmmBackward>)
# Note the modified output.
forward_hook_handle = m.register_forward_hook(forward_hook)
print('output with both forward hooks: {}'.format(m(x)))
: output with both forward hooks: tensor([[-1.0980, 0.6396, 0.4666],
[ 0.3634, 0.6538, 1.0256]], grad_fn=<AddBackward0>)
# Remove hooks; note that the output here matches the output before adding hooks.
forward_pre_hook_handle.remove()
forward_hook_handle.remove()
print('output after removing forward hooks: {}'.format(m(x)))
: output after removing forward hooks: tensor([[-0.5059, -0.8158, 0.2390],
[-0.0043, 0.4724, -0.1714]], grad_fn=<AddmmBackward>)
# ==== Demonstrate backward hooks. ====
m(x).sum().backward()
print('x.grad with no backwards hook: {}'.format(x.grad))
: x.grad with no backwards hook: tensor([[ 0.4497, -0.5046, 0.3146],
[ 0.4497, -0.5046, 0.3146]])
# Clear gradients before running backward pass again.
m.zero_grad()
x.grad.zero_()
m.register_full_backward_hook(backward_hook)
m(x).sum().backward()
print('x.grad with backwards hook: {}'.format(x.grad))
: x.grad with backwards hook: tensor([[42., 42., 42.],
[42., 42., 42.]])
Advanced Features¶
PyTorch also provides several more advanced features that are designed to work with modules. All these functionalities are available for custom-written modules, with the small caveat that certain features may require modules to conform to particular constraints in order to be supported. In-depth discussion of these features and the corresponding requirements can be found in the links below.
Distributed Training¶
Various methods for distributed training exist within PyTorch, both for scaling up training using multiple GPUs as well as training across multiple machines. Check out the distributed training overview page for detailed information on how to utilize these.
Profiling Performance¶
The PyTorch Profiler can be useful for identifying performance bottlenecks within your models. It measures and outputs performance characteristics for both memory usage and time spent.
Improving Performance with Quantization¶
Applying quantization techniques to modules can improve performance and memory usage by utilizing lower bitwidths than floating-point precision. Check out the various PyTorch-provided mechanisms for quantization here.
Improving Memory Usage with Pruning¶
Large deep learning models are often over-parametrized, resulting in high memory usage. To combat this, PyTorch provides mechanisms for model pruning, which can help reduce memory usage while maintaining task accuracy. The Pruning tutorial describes how to utilize the pruning techniques PyTorch provides or define custom pruning techniques as necessary.
Deploying with TorchScript¶
When deploying a model for use in production, the overhead of Python can be unacceptable due to its poor performance characteristics. For cases like this, TorchScript provides a way to load and run an optimized model program from outside of Python, such as within a C++ program.
Parametrizations¶
For certain applications, it can be beneficial to constrain the parameter space during model training. For example, enforcing orthogonality of the learned parameters can improve convergence for RNNs. PyTorch provides a mechanism for applying parametrizations such as this, and further allows for custom constraints to be defined.
Transforming Modules with FX¶
The FX component of PyTorch provides a flexible way to transform modules by operating directly on module computation graphs. This can be used to programmatically generate or manipulate modules for a broad array of use cases. To explore FX, check out these examples of using FX for convolution + batch norm fusion and CPU performance analysis.


CTO @lequipetech
2007: Drupal 5 then 6 and Ubercart.
2010: Drupal 7, Completly rewriten, creation of Commerce module mainly by Commerce Guys, company created for this purpose.
Commerce, first to make maximum use of Drupal 7's new features (entities, bundles etc) and to contribute to improve Drupal core.
Commerce 2.x, rewriten for Drupal 8.
Still a reference to follow the best practices of development.
Still a major contributor to Drupal 8 core.
... NO
Who say fit to business, say custom development.
Favourite land of Commerce 2.x.
Almost everything can be extended, overrided, adding plugins, reacting to events, processors etc.
Example: A specific display for companies, another one for individuals.
Drupal 8 implements form view display.
Differents displays of form of an entity depending of conditions:
For each of them, differents flows, panes and process.
For that:
Example: You need to have a store by country.
Store: entity
Payment: plugin
PaymentMethod: plugin
Ex: Booking a room, price can be calculated dynamically depending of period, number of people etc.
During the display of the formatted calculated price, you can add your own calculation rules to the base price.
services:
booking.room_price_resolver:
class: Drupal\booking\Resolver\RoomPriceResolver
tags:
- { name: commerce_price.price_resolver, priority: 0 }
It's possible to add adjustments to a total price of an order or of order items.
Promotions (previously discount) are an example of adjustment by default. Adjustments allow to add configurable plugins by the user.
Taxs are another example of adjustment provided by Commerce core.
Promotion module add a service at order processor which then allow to select and apply required promotions and adjustments.
services:
commerce_promotion.promotion_order_processor:
class: Drupal\commerce_promotion\PromotionOrderProcessor
arguments<EMAIL_ADDRESS>tags:
- { name: commerce_order.order_processor, priority: 50 }
Example to apply a promotion of n%.
$order->addAdjustment(new Adjustment([
'type' => 'promotion',
'label' => t('Discount'),
'amount' => $adjustment_amount->multiply('-1'),
'percentage' => $this->getPercentage(),
'source_id' => $promotion->id(),
]));
Promotions (previously discount) and coupons are now parts of commerce and thoses plugins are easily extendable.
/**
* Provides the product condition for order items.
*
* @CommerceCondition(
* id = "order_item_product",
* label = @Translation("Product"),
* display_label = @Translation("Limit by product"),
* category = @Translation("Product"),
* entity_type = "commerce_order_item",
* )
*/
class OrderItemProduct extends ConditionBase implements ContainerFactoryPluginInterface {}
/**
* Provides the percentage off offer for orders.
*
* @CommercePromotionOffer(
* id = "order_external_off",
* label = @Translation("Fixed amount off the order total depending of external service"),
* entity_type = "commerce_order",
* )
*/
class OrderExternalOff extends FixedAmountOffBase {
/**
* {@inheritdoc}
*/
public function apply(EntityInterface $entity, PromotionInterface $promotion) {
$this->assertEntity($entity);
/** @var \Drupal\commerce_order\Entity\OrderInterface $order */
$order = $entity;
$total_price = $order->getTotalPrice();
$adjustment_amount = $this->getAmount();
$external_service = \Drupal::service('order.promotion.external.service');
$order->addAdjustment(new Adjustment([
'type' => 'promotion',
// @todo Change to label from UI when added in #2770731.
'label' => t('Discount'),
'amount' => $external_service->getPromotionAmount($entity),
'source_id' => $promotion->id(),
]));
}
}
No more needs of extra modules to deal with migrations in Commerce 2.x.
id: import_products
label: 'Import products to Commerce'
source:
...
process:
type:
plugin: default_value
default_value: default
title: title_from_import
...
destination:
plugin: 'entity:commerce_product'
A team available and reactive.
Constant adoption with 2k uses since its release in September.
Get this online on https://gozoo.github.io/prez-drupal-commerce-2-dclannion


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Throughout life we are faced with millions of choices as to the direction we wish to lead ourselves. Through numerous encounters with various professionals in their respective fields and developing my traits as an individual, I have decided to direct my path towards technology and technological advances. Below is an intrinsic analysis of my interests, goals, dreams, and hobbies.
Giving back to the community is very important to me and I enjoy helping various non profits or charities with the various skills I offer. I also commonly give blood through South Texas Blood and Tissue Center and I would enjoy it if anyone who views this page considers donating as well. I also enjoy going to the Animal Defense League and volunteering. I worked at an animal shelter when I was younger and have always felt a calling to help them. Anyone can go to the ADL and volunteer by walking dogs, spending time with cats or assistancing with feeding.
I've also volunteered with Rackspace and Cyber Patriot to coach youth in middle school through high school in the aspects of cyber security, networking and forensics.
I've had experience with teaching K-12th graders and from this experience I have developed a love for academia. Eventually when I become more experienced within the world of technology I'd like to teach childen and individuals attempting to
I am a student at The University of Texas at San Antonio studying for a degree in Computer Science with an emphasis in security. Through UTSA I'd like propell myself further within both the software development and security aspects of technology. I feel both of these aspects go together well and I'd enjoy further research into teaching individuals security pragmatically to ensure poorly programmed programs rarely enter production, rather than being the unfortunate norm.
In high school I participated in FRC, FTC and Cyber Patriot. These competitions prepared me for various team based business scenarios and aided me in learning various concepts in both programming and security.
In college I have participated in CCDC, Cyber Olympics and CSAW. In these compettiions I have honed my skills and developed new ones to meet the changing dynamics of cyber security. I often leverage my Computer Science skills within these competitions to write scripts and automate tasks.


There are G girl students and B boy students in a class that is about to graduate. You need to arrange them in a single row for the graduation.  You decided to arrange the students in order to minimize the gender regularity. The gender regularity of an arrangement is the maximum number of students of the same gender (all girls or all boys) that appear consecutively. Given G and B, calculate the minimum gender regularity among all possible arrangements.
Each test case is described using a single line. The line contains two integers G and B representing the number of girls and boys in the class, respectively (0 ≤ G, B ≤ 1000). The end of input is indicated with a line containing the number −1 twice.
For each test case, output a single line with a single integer representing the minimum gender regularity that an arrangement of G girls and B boys can have.
Input:
10 10
5 1
0 1000
-1 -1
Output:
1
3
1000


Today, we are reviewing an AIO cooler from Silverstone: the Permafrost PF240. Silverstone is not entirely a newcomer in the AIO segment of the market. In the past, they introduced the Tundra series (120/240 mm), which was available in an RGB version as well.


This is the base class for all builders.
These attributes should be set on builder classes:
These methods are predefined and will be called from the application:
These methods can be overridden in concrete builder classes:
Attributes
-
events
An EventManager
object.


{% if page.og %}{% include {{page.og}} %}{% endif %}
{{ page.title }}
{% include 3rd_party_links.html %}
Watch another random detail
Read wikipedia article on
{{page.title}}
Slow connection? Lower image quality (alas)
Discover other masterpieces by
Hieronymus Bosch
{% include 3rd_party_scripts.html %} {{ content }} {% include google_analytics.html %}


The source code for the Core API Suite is available in the CycCoreAPI project on GitHub.
The following Cyc server releases are supported:
For information about acquiring a supported Cyc server, see the Cyc download page.
The Core Client requires the Core API spec to be present on the classpath. To use them in a Maven project, add the following dependencies to your pom.xml:
<dependency> <!-- Core API specification --> <groupId>com.cyc</groupId> <artifactId>cyc-core-api</artifactId> <version>1.1.1</version> </dependency> <dependency> <!-- Reference implementation for the Core API --> <groupId>com.cyc</groupId> <artifactId>cyc-core-client-impl</artifactId> <version>1.1.1</version> <scope>runtime</scope> </dependency>
The Core APIs use SLF4J for logging, so you may also wish to specify a logging implementation. E.g., for Log4J:
<dependency> <groupId>org.slf4j</groupId> <artifactId>slf4j-log4j12</artifactId> <version>1.7.12</version> </dependency> <dependency> <groupId>log4j</groupId> <artifactId>log4j</artifactId> <version>1.2.17</version> </dependency>
If you’re not using Apache Maven, or can’t allow Maven to retrieve dependencies from the Internet, you may download this release in a standalone bundle containing:
The bundle is available from the releases page as a tar or zip file:
To build this project from source, see CycCoreAPI on GitHub and following the instructions in the README.
Once you have the Core API Suite, you may wish to download the Cyc API Examples repository and explore the sample code in the core-api-use-cases project. Or, view online code samples on this site.
For information about acquiring a supported Cyc server, see the Cyc download page.


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Why England management were 'unhappy' with Jofra Archer during New Zealand Test
The England management were ‘unhappy’ with Jofra Archer and ‘rebuked’ the fast bowler after he jumped on a segway on the outfield before the start of the first Test against New Zealand.
Archer was seen using a broadcaster’s segway as the teams prepared for the opening day’s play in Tauranga on Wednesday evening.
England fans have become accustomed to the Ashes and World Cup star’s somewhat casual warm-ups, with Archer often bowling left-arm spin moments before the start of matches.
But the England management reacted badly to his segway routine, with Sportsmail reporting that Archer was rebuked during the opening New Zealand Test.
It is claimed England were ‘unhappy’ that Archer risked injury just moments before the start of the series, particularly as ECB chief Tom Harrison was in attendance.
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Archer is yet to take a wicket in the match, with New Zealand reaching 394-6 at the close of day three in reply to England’s 353 all out.
New Zealand stuttered to 127-4 on day two but BJ Watling’s unbeaten 119 has put the hosts in a strong position going into day four.
Speaking at the end of day three, Kiwi batsman Henry Nicholls said: ‘It is a great day. If you look where we were at the start, with a lot of work to be put in, we knew we were good enough to put ourselves in a position to control game.
‘The role BJ [Watling] played was a real pleasure to watch throughout the day.
‘He is great. He is such a hard-worker. He is a guy you know what you will get. He kept for 110 overs and to come out last night, and today, it is great to have someone like that in the team.’


Colours
Background colours
Bright blue
#009fe3
.bg-bright-blue
$colour-bright-blue
Mid blue
#0077bb
.bg-mid-blue
$colour-mid-blue
Dark blue
#273583
.bg-dark-blue
$colour-dark-blue
Light blue gradient
From #009fe3
to #009fe3
.bg-grad-light-blue
$grad-light-blue
Mid blue gradient
From #273583
to #009fe3
.bg-grad-mid-blue
$grad-mid-blue
Dark blue gradient
From #0d1649
to #273583
.bg-grad-dark-blue
$grad-dark-blue
Mint green
#d7f4c8
.bt-mint
$colour-mint
Lemon yellow
#ffeea0
.bt-lemon
$colour-lemon
Light grey
#ececec
.bt-light-grey
$colour-light-grey
Off-white
#fafafa
.bt-off-white
$colour-off-white
White
#ffffff
.bt-white
$colour-white
Note that .bt-off-white
should generally be used instead of .bt-off-white
.
Note: the following colours are used to indicate the status of form fields and should not be used as call-to-action styles.
Success green
#2e7b06
.bg-green
$colour-green
Error red
#da3434
.bg-red
$colour-red
Note: the following colours should not be used as call-to-action styles.
Dark grey
#363636
.bg-dark-grey
$colour-dark-grey
Grey
#7c7c7c
.bg-grey
$colour-grey
Foreground colours
Using utility classes, colours can be directly applied to text. Note that misuse of this class can lead to colour contrast issues.
Text with the .text-bright-blue
class applied
Text with the .text--mid-blue
class applied
Text with the .text-dark-blue
class applied
Text with the .text-red
class applied
Text with the .text-green
class applied
Text with the .text-grey
class applied
Text with the .text-dark-grey
class applied
Text with the .text-white
class applied
Text with the .text-off-white
class applied
Text with the .text-mint
class applied
Text with the .text-lemon
class applied
Text with the .text-light-grey
class applied
Text with the .text-bright-blue
class applied
Text with the .text--mid-blue
class applied
Text with the .text-dark-blue
class applied
Text with the .text-red
class applied
Text with the .text-green
class applied
Text with the .text-grey
class applied
Text with the .text-dark-grey
class applied
Text with the .text-white
class applied
Text with the .text-off-white
class applied
Text with the .text-mint
class applied
Text with the .text-lemon
class applied
Text with the .text-light-grey
class applied
Colour stress test
Testing if all coloured boxes can be stacked inside each other without the text or links being illegible.


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MANHUNTER (1986) Reviewed by Jerry Saravia October 10th, 2001
"Manhunter" is based on Thomas Harris's scary novel "Red Dragon," the first book to introduce Dr. Hannibal Lecter who later resurfaced in both "Silence of the Lambs" and "Hannibal." Though Michael Mann's film adaptation is not nearly as good as the novel, nor as good a film as "Silence of the Lambs," it is occasionally gripping, absorbing stuff.
This early film does not focus on Clarice Starling, the heroine of the later entries in the series. Instead, we have a male lead who has one or two screws loose thanks to his unnaturally intense study of how serial killers think. He is Will Graham (William Petersen) who has left the agency after being attacked by Lecter (this time played by Brian Cox) and had to be committed to a mental institution to recover. But the FBI Sector Chief Jack Crawford (Dennis Farina in this one, Scott Glenn in "Silence") wants Graham's extraordinary ability to be put to work on a new serial killer. Apparently, this one killer kills families in their homes in excruciatingly brutal ways. The FBI has no suspects and no leads but Graham might be able to find out who the killer is, branded by policemen as "The Tooth Fairy." And there might be clues related to the killer's dreams of being loved and an association with the poet/painter William Blake (dealt with more specificity in the novel).
Most of "Manhunter" is absorbing, particularly the scenes where Graham investigates the crime scenes where he notes details of the executions and the direction in which the blood was spilled (some of the family members were injured or harmed postmortem). Other scenes with Graham and his wife, Molly (Kim Greist), lack much thrust or purpose - the character of Molly is so underdeveloped that we forget he is even married to her. Other characters like Crawford drift in and out of focus, though Graham does have one or two finely written scenes with Crawford where he shows his level of attention at all the details. There is also a superb scene where Graham explains his past condition to his son at a supermarket - except for some cutaways of his son's reaction, the camera trains itself on Petersen and he is quite watchable.
Still, "Manhunter" feels oddly unmoving and closed-off, much like the main character. There is none of the richness or depth that the book provides. Its main killer maintains some interest but is saddled with just a shotgun at the end as the police circle his house - his creative desires and motives are scantly mentioned here. It is a Michael Mann picture alright, complete with an electronic score (some distracting songs by Iron Butterfly) and a shootout right out of his superior "Thief." If nothing else, it is often enticing enough but not at the level of the book or at the level of "Silence of the Lambs."
For more reviews, check out JERRY AT THE MOVIES at http://moviething.com/members/movies/faust/JATMindex.shtml
E-mail me with any questions, comments or general complaints at<EMAIL_ADDRESS>or at<EMAIL_ADDRESS>========== X-RAMR-ID: 29792 X-Language: en X-RT-ReviewID: 254292 X-RT-TitleID: 1013248 X-RT-SourceID: 875 X-RT-AuthorID: 1314
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Before Change
else:
for i, (s1, s2) in enumerate(zip(in_lens, input_shape[1:])):
if s1 is not None and s2 is not None and s1 != s2:
ValueError(""input_length" is %s, but received input has shape %s" %
(str(self.input_length), str(input_shape)))
elif s1 is None:
in_lens[i] = s2
return (input_shape[0],) + tuple(in_lens) + (self.output_dim,)
After Change
else:
for i, (s1, s2) in enumerate(zip(in_lens, input_shape[1:])):
if s1 is not None and s2 is not None and s1 != s2:
raise ValueError(""input_length" is %s, but received input has shape %s" %
(str(self.input_length), str(input_shape)))
elif s1 is None:
in_lens[i] = s2
return (input_shape[0],) + tuple(in_lens) + (self.output_dim,)


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Have a look! Travel anywhere and easily find great people who want to meet you as well. See where other travellers are and help each other out, whether through welcoming them to your home, sharing your stories or becoming friends.
Put yourself in the shoes of others: what would you like to know about your travel buddies? What should they know about you?
Be genuine yourself.
Joining Tribes helps you find likeminded Trustroots members and tells others what you're interested in.
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Join the numbers following each others in fibonacci sequence.
The sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765...
How to play: Use your arrow keys to move the tiles. When two tiles with the same number touch, they merge into one!
Simple modification of 2048 created by Gabriele Cirulli. Based on 1024 by Veewo Studio and conceptually similar to Threes by Asher Vollmer.


Category
Powerful efficient Intel dual-core Celeron N3350 up to 2.4GHz; 64GB eMMC storage, 4GB LPDDR3 RAM, and the newest USB Type-C 11.6 inch HD Display combined with a lightweight 2.2 pound body designed to help you be productive and mobile Designed from the ground up for on-the-go users with an all-day battery life of 10 hrs* Durable 180 Degree hinge for the Display means the E203 can be laid flat on the table to share content with friends, classmates, or colleagues Windows 10 S is the new standard in app and cloud based operating systems. Windows 10 S is a 100% app based version of Windows where applications are verified and tested for quality on the Microsoft store. There is also the ability to upgrade to Windows 10 Pro through the Microsoft Store for free
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9 in 1 PPE (Personal Protective Equipment) Kit includes disposable protective equipments and clothing that are sterile and fit for medical use that includes the following.
Walk through video of our lightweight, low price, and easy to make diy Cardboard Bed.


Ambient Module Users Guide¶
- Release
2.1
- Date
Jun 05, 2020
Scripts¶
A lot of the work to generate ambient CTD data and to put it into the
appropriate format for use by TAMOC
is a ‘hands on’ process that is unique to
each project. Many times, this work can be easily completed in an IPython
interactive session using the tools supplied in this package.
When many profiles of a similar format need to be processed, script files that
iterate through these methods should be used. Sample scripts demonstrating use
of the classes, methods, and functions in the ambient module are provided
in the ./bin
directory of the distribution, and are summarized in the
following.
Examples¶
In each of these examples, the general process follows a similar sequence of steps. Here we demonstrate working with CTD data following some of the steps in the profile_from_ctd script given above.
Reading in Ambient Data Files¶
Read in some (or all) of the data. The first step will be to prepare a
numpy.ndarray of data that includes the depths coordinate. For this
example, we read in selected columns from ./data/ctd_BM54.cnv
. We selected
these columns by reading the .cnv
file by hand. After changing
directory to the ./data/
directory, we start an IPython session:
>>> cols = (0, 1, 3, 8, 9, 10, 12)
>>> raw = np.loadtxt('ctd_BM54.cnv', skiprows = 175, usecols = cols)
>>> symbols = ['temperature', 'pressure', 'wetlab_fluorescence', 'z',
'salinity', 'density', 'oxygen']
>>> units = ['deg C', 'db', 'mg/m^3', 'm', 'psu', 'kg/m^3', 'mg/l']
>>> z_col = 3
Many times, the raw CTD profile will contain information at the top or the bottom of the profile that must be discarded, typically indicated by reversals in the depth profile. It is particularly important to remove these reversals so that the interpolation methods will be able to find unique profiles values for any input depth:
>>> import ambient
>>> data = ambient.extract_profile(raw, z_col, z_start = 50.0)
Before the data should be stored in the netCDF dataset used by TAMOC, the units should be converted to the standard mks system:
>>> profile, units = ambient.convert_units(data, units)
Preparing the netCDF Dataset¶
An empty netCDF dataset must be created with the global metadata describing this ambient profile before the data can be imported into the dataset:
>>> summary = 'Description of the TAMOC project using this data'
>>> source = 'Documentation of the data source'
This next set of information is read manually by the user from the header file of the CTD text file and entered as follows:
>>> sea_name = 'Gulf of Mexico'
>>> lat = 28.0 + 43.945 / 60.0
>>> lon = 360 - (88.0 + 22.607 / 60.0)
Finally, we must set the time that the CTD data were collected. This is done using several data manipulation methods:
>>> from datetime import datetime
>>> from netCDF4 import num2date, date2num
>>> date = datetime(2010, 5, 30, 18, 22, 12)
>>> t_units = 'seconds since 1970-01-01 00:00:00 0:00'
>>> calendar = 'julian'
>>> time = date2num(date, units=t_units, calendar=calendar)
Create the empty dataset:
>>> nc = ambient.create_nc_db('../Profiles/BM54.nc', summary, source, \
sea_name, lat, lon, time)
Adding Data to the netCDF Dataset¶
Insert the CTD data and the associated comments into the netCDF dataset:
>>> comments = ['measured'] * len(symbols)
>>> nc = ambient.fill_nc_db(nc, profile, symbols, units, comments, z_col)
At this point the CTD data are now in a netCDF dataset with the correct units and including all data needed by TAMOC. If the data had originated in netCDF format, the process could have started here. To demonstrate methods to work with netCDF data, we close this file and then continue our session using the stored netCDF profile data.
>>> nc.close() # This is the end of the preprocessing stage
Using the ambient.Profile Object¶
A profile object can be initialized either by passing the file-name of the
netCDF dataset or by passing the netCDF4.Dataset object itself. If the
variable names in the dataset match those used by TAMOC
, the
ambient.Profile class instantiation can extract all the information itself:
>>> ctd_auto = ambient.Profile('../test/output/BM54.nc', chem_names='all')
If you want to specify the variable names for z, T, S, and P, and also for the chemicals to load, that may also be done:
>>> ztsp = ['z', 'temperature', 'salinity', 'pressure']
>>> chem_names = ['oxygen'] # This selects a subset of available data
>>> ctd_manual = ambient.Profile('../test/output/BM54.nc', ztsp, chem_names)
If you prefer to open the netCDF file and pass the netCDF4.Dataset object, that works identically:
>>> from netCDF4 import Dataset
>>> nc = Dataset('../Profiles/BM54.nc')
>>> ctd_from_nc = ambient.Profile(nc) # This will not load any chemicals
Occasionally, it is necessary simulate a problem nearby, where the depth is somewhat deeper than that in the measured profile, or in another region, where data are not available. The ambient.Profile object provides a method to extend the profile to a deeper total depth while maintaining the stratification profile:
>>> ctd_auto.extend_profile_deeper(2500., '../test/output/BM54_deeper.nc')
Pipes to the netCDF datasets should be closed before ending an interactive or script session:
>>> ctd_auto.close_nc()
>>> ctd_manual.close_nc()
>>> ctd_from_nc.close_nc()


| gdata.urlfetch | index /usr/local/svn/gdata-python-client/src/gdata/urlfetch.py |
Provides HTTP functions for gdata.service to use on Google App Engine
AppEngineHttpClient: Provides an HTTP request method which uses App Engine's
urlfetch API. Set the http_client member of a GDataService object to an
instance of an AppEngineHttpClient to allow the gdata library to run on
Google App Engine.
run_on_appengine: Function which will modify an existing GDataService object
to allow it to run on App Engine. It works by creating a new instance of
the AppEngineHttpClient and replacing the GDataService object's
http_client.
HttpRequest: Function that wraps google.appengine.api.urlfetch.Fetch in a
common interface which is used by gdata.service.GDataService. In other
words, this module can be used as the gdata service request handler so
that all HTTP requests will be performed by the hosting Google App Engine
server.
| Modules | ||||||
|
| Classes | ||||||||||||||||
|
|
| Functions | ||
|
| Data | ||
| __author__ = 'api.jscudder (Jeff Scudder)' |
| Author | ||
| api.jscudder (Jeff Scudder) |


Response
JSON Response
Docs
Run >
JSON Response
| Content-Type*: |
| file_uri*: | |
| name: | |
| description: | |
| organization: | |
| project_name: | |
| branch: |
JSON Response
| workspace_guid OR workspace_name*: | |
| asset_ref OR resource_ref*: |
JSON Response
| workspace_guid OR workspace_name*: | |
| asset_ref*: |
| Content-Type*: | |
| Last-modified*: |
| tags: | |
| comment*: | |
| main*: | |
| dependencies: | |
| semantics: |
JSON Response
| workspace_guid OR workspace_name*: | |
| asset_ref*: |
| Content-Type*: | |
| Last-modified*: |
| new*: |
JSON Response
| workspace_guid OR workspace_name*: | |
| asset_ref*: |
JSON Response
| workspace_guid OR workspace_name*: |
| type*: | |
| descriptor*: |
JSON Response
| workspace_guid OR workspace_name*: | |
| subscription_id*: |
JSON Response
| workspace_guid OR workspace_name*: | |
| asset_ref*: |
JSON Response
| workspace_guid OR workspace_name*: | |
| asset_ref*: |
JSON Response
| workspace_guid OR workspace_name*: | |
| ref*: | |
| paging: |
JSON Response
| workspace_guid OR workspace_name*: |
| message*: |
JSON Response
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LAST EXIT TO BROOKLYN A film review by Steve Rhodes Copyright 1997 Steve Rhodes
RATING (0 TO ****): ***
Actress Jennifer Jason Leigh has tremendous talent, and I have been a fan of hers for some time now. Although she has been the best part of many films, my favorite pictures of hers are SINGLE WHITE FEMALE and GEORGIA. The only bad performance I ever saw her give was in KANSAS CITY, and I blame that more on Robert Altman's direction than her acting.
So it was with heightened anticipation that I went to a special viewing of LAST EXIT TO BROOKLYN (1989). Held as part of San Jose's Cinequest film festival, Leigh was there to answer questions after the movie. Luckily, I had never seen the film. At the of this review I have included some of the Leigh's comments on the picture.
(Earlier in the day at another film at the festival I looked at the woman sitting near me and reflected to myself, "Hmm, she looks like Jennifer Jason Leigh." I glanced again, and thought to myself, "That's because she IS Jennifer Jason Leigh!" I did not speak to her then, but I did ask her questions that night. For the record Leigh is 35 but looks like a very attractive young woman in her 20s. She is small, vivacious, funny, and outspoken. As she starts talking, she acts shy like someone who is still surprised at how successful she has become. As she warms up, she overpowers you.)
LAST EXIT TO BROOKLYN is a movie about the absence of love. It paints a bleak picture of a world without hope.  Usually, when Hollywood attempts pictures like this, they end up sugarcoating them so much that their message is diluted. Here, director Ulrich Edel and screenwriter Desmond Nakano take Hubert Selby Jr.'s bleak novel and bring it to the screen without apology.
Set in 1952 Brooklyn, groups are full of hate. The heterosexuals hate and torment the homosexuals.  The prostitute's partners in crime mug her tricks. The union is out on strike and hates the employers and the police. Everyone is unhappy. Next to this show, Thomas Hardy was an optimist.
The sets are gritty and full of tacky furniture from the 1950s. The cinematography by Stefan Czapsky is dark and somber with heavy shadows in every scene. I am not sure that you would be able to see it very well on a television since the contrast is so low. The costumes by Carol Oditz are equally evocative of decay and, in the case of hooker Tralala (Jennifer Jason Leigh), sleaze. Mark Knopfler's ominous music takes place on the low registers with notes that are out slow and painful.
The show is multilayered and has a large cast. Although Tralala is the glue that holds the story together, many of the parts both large and small are particularly well done. Perhaps the best minuscule role is Ricki Lake's as the pregnant, single woman named Donna. She is so fat that her father does not realize she is pregnant until a couple of days before her water breaks. As her dad says, "She's so big -- Ringling Brothers has been circling around."
The best performance is Leigh's. She is convincing as a woman who has long since given up on life. She prides herself at being able to pick up tricks in a bar before the bartender can count to ten. She usually makes her money by not having sex. Her fellow criminals, led by Sal (Stephen Baldwin), sneak up when she is about to have sex. They knock out the soldier, for she preys almost exclusively on the military, and then take his money.
 Sal views injuring Georgette as little more than sport. The film deals realistically and frighteningly with the problem of homophobic violence. In one scene Georgette is stabbed, and the blood spurts like an artery was punctured. Cinequest warned us in advance of the intensity of the picture, and they were right. There was a mother and her young teenage daughter in front of us that I was glad to see leave. They discovered that this was no movie for teenagers.
There is one optimistic person in the film. A soldier from Idaho named Steve (Frank Military) comes through on his way to the front. He believes he will never return, but he manages to find happiness in Tralala's arms. She never views him as anything more than a meal ticket, but he feel genuine love for her.
The other exceptional performance in the film is given by Stephen Lang as union organizer Harry Black. Harry is in charge of violence for the union.
The ending centers around Tralala and is devastating. After the film, Leigh said that the incident was necessary and without it the film "would have been a cheat." This scene should have gotten an NC-17 rating for the picture. I believe in 1989 the only choices were R and X so given that perhaps the R rating it got was correct at the time.
After the ending, there is a brief epilogue. In it the first rays of hope shine through. Like the first sunshine after a hurricane, its warmth felt soothing and somehow tranquil.
After the film finished, Leigh answered questions about it. (She is a big flirt with an audience, and the audience loved her.) She said that she really wanted the part of Tralala, but Patricia Arquette was originally cast for it. When Patricia Arquette got pregnant, they gave the role to Leigh. She thinks this film was her big break.
Since she prides herself in elaborate preparation and research for her parts, I asked her how she approached the part of Tralala. She said that she hung out with prostitutes a lot, no surprise there, but then she went on to list a series of painters she studied. None of the names were familiar to me. Looking at paintings was not something I expected.
"It is showing you a world without love," she said when asked what the movie's message was. "It's crying out for love."
Leigh said the film was good for her since it forced her, for the first time, to get in touch with her feelings. It also made her realize how much she enjoyed being the source of attention.
One questioner from the audience said that many young actresses are forced to go topless in a couple of films in order to break into the business. He asked her what she thought of having to do nude roles. "I love it," she retorted. An actress with a lot of talent, and one not afraid of speaking her mind.
LAST EXIT TO BROOKLYN runs 1:42. It is rated R for explicit and violent sex, nudity, graphic violence, dope smoking and profanity. The film is for adults and not for teenagers. Although granted the film will be too intense for some, I give it thumbs up and ***.
**** = A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show. Don't waste your money. 0 = Totally and painfully unbearable picture.
REVIEW WRITTEN ON: January 31, 1997
Opinions expressed are mine and not meant to reflect my employer's.
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.
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to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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Fonts
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Register conversion
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Diwstart/stop
Point Projection
Trigonometric tables
Raw 2 planar viewer
RGB Colors
Square root tables
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Note :


| Code |
Description |
| $a^2 = b^2 + c^2$ |
Inline LaTeX |
| [mono]Text[/mono] |
Displays the text in monospace |
| [url]http://example.com[/url] |
Displays a link to the given URL |
| [url http://example.com]Example[/url] |
Displays a link to the given URL with the text "Example" |
| [img]1.png[/img] |
Displays the image that was uploaded by the manage files page named 1.png |
| [img]http://example.com/1.png[/img] |
Displays the image that is at that URL |
| [youtube]1234567[/youtube] |
Displays the Youtube video with that ID |
|
{{ "[html]This is HTML[/html]" }}
|
Inserts raw HTML |
| #1 + 1# |
Displays the text such that the user can click it to enter it into a prompt |


|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: INNER | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object | +--java.lang.Throwable | +--java.lang.Exception | +--java.io.IOException | +--org.apache.commons.httpclient.HttpException | +--org.apache.commons.httpclient.URIException
The URI parsing and escape encoding exception.
| Field Summary | |
static int |
ESCAPING
The URI escape encoding and decoding error. |
static int |
PARSING
The URI parsing error. |
static int |
PUNYCODE
The DNS punycode encoding or decoding error. |
protected String |
reason
The reason message. |
protected int |
reasonCode
The reason code. |
static int |
UNKNOWN
No specified reason code. |
static int |
UNSUPPORTED_ENCODING
The unsupported character encoding. |
| Constructor Summary | |
URIException()
Default constructor. |
|
URIException(int reasonCode)
The constructor with a reason code argument. |
|
URIException(int reasonCode,
String reason)
The constructor with a reason string and its code arguments. |
|
URIException(String reason)
The constructor with a reason string argument. |
| Method Summary | |
String |
getReason()
Deprecated. You should instead call Throwable.getMessage() . |
int |
getReasonCode()
Get the reason code. |
void |
setReason(String reason)
Deprecated. Callers should instead set this via a parameter to the constructor. |
void |
setReasonCode(int reasonCode)
Deprecated. Callers should set the reason code as a parameter to the constructor. |
| Methods inherited from class org.apache.commons.httpclient.HttpException |
getCause, printStackTrace, printStackTrace, printStackTrace |
| Methods inherited from class java.lang.Throwable |
fillInStackTrace, getLocalizedMessage, getMessage, toString |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Field Detail |
public static final int UNKNOWN
public static final int PARSING
public static final int UNSUPPORTED_ENCODING
public static final int ESCAPING
public static final int PUNYCODE
protected int reasonCode
protected String reason
| Constructor Detail |
public URIException()
public URIException(int reasonCode)
reasonCode
- the reason codepublic URIException(int reasonCode, String reason)
reasonCode
- the reason codereason
- the reasonpublic URIException(String reason)
reason
- the reason| Method Detail |
public int getReasonCode()
getReasonCode
in class HttpException
public void setReasonCode(int reasonCode)
setReasonCode
in class HttpException
reasonCode
- the reason codepublic String getReason()
Throwable.getMessage()
.
getReason
in class HttpException
public void setReason(String reason)
setReason
in class HttpException
reason
- the reason message
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: INNER | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Sisi's UAE visit marks new phase of partnership: paper
by MENAABU DHABI - 16 November 2019: Egyptian President Abdel Fattah al-Sisi's two-day visit to the UAE last week established a new phase of partnership and cooperation between the two countries, said UAE newspaper Al Bayan on Saturday.
The final communique of the visit underlined close viewpoints shared by both countries towards regional issues, added the newspaper.
The newspaper added that strong Egyptian-UAE relations are in no need of assertion, adding that challenges that are currently facing Arab countries require coordination.
It highlighted president Sisi's frequent confirmation that the Arab Gulf's security is a red line and an integral part of Egypt's security.
The newspaper said that the visit saw the inking of three cooperation agreements along with introducing a $20bn platform for strategic investment.


mongoc_stream_tls_check_cert()
Synopsis
bool mongoc_stream_tls_check_cert (mongoc_stream_t *stream, const char *host);
Parameters
stream |
|
host |
A UTF-8 string holding the hostname used in socket creation. This value is what will be matched against the server provided certificate. |
The mongoc_stream_tls_check_cert() function shall perform a RFC-6125 certificate verification.
Returns
The mongoc_stream_tls_check_cert() function returns true if the certificate is valid was successful. If false, openssl errors may be set.


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foo
is present with relevant information.owner
and sidebar
section.github.io
, rename CNAME.sample
file to CNAME
, and add your custom domain name, e.g., example.com
(only one domain is allowed), otherwise remove CNAME
file if you want to host at default github.io
.github.io
, replace example.com
with https://<github-username>.github.io/<repository_name>
(for project site) or https://<github-username>.github.io
(for user site) under site.url
and site.urlimg
in _config.yml
and _prose.yml
file.example.com
with your domain name.example.com
with your valid url._data/socialmedia.html
foo
with appropriate username_includes/
head.html
and head_minimal.html
, else comment html tags which are not being used._config.yml
located under root path.meta_info.html
, footer.html
and similar include files to add/remove elements in page meta bar, footer, etc.mypubs.html
and myaoi.html
are trimmed outputs from zot_bib_web. Github pages can not dynamically build these pages. Alternately, you may export bib
format for publications under /files/
directory which can be parsed dynamically using bibbase.org
_includes/footer.html
: Edit copyright information as needed._layouts
default.html
plus page.html
or post.html
._posts
yyyy-mm-dd-title.md
for jekyll to render blog post correctly.blog/index.html
images/
icons
directory, keep appropriate sized favicons and thumbnails as specified in _includes/head.html
and _includes/head_minimal.html
favicon.png
and favicon.ico
in root directory.images/icons/
should have following images with exact filenames and image size as specified in respective filenames. These images can be generated using online favicon generator. Replace foo
with your site title or other name if desired.example.com/images/icons/apple-touch-icon-precomposed.png
example.com/images/icons/apple-touch-icon-72x72-precomposed.png
example.com/images/icons/apple-touch-icon-114x114-precomposed.png
example.com/images/icons/apple-touch-icon-144x144-precomposed.png
example.com/images/icons/apple-touch-icon-180x180.png
example.com/images/icons/android-icon-192x192.png
pages/about.md
imagefeature
shoud have image path relative to images/
directory, i.e., foo.png
will link to example.com/images/foo.png
site.owner.avatar
and site.owner.twitter
along with other variables in _config.yml
syspages/
:
google_search
variable.{% for ... %}...{% endfor %}
loop operations will increase site build time, and remove such features (tags, meta info, related posts, etc.) under _includes
, _layouts
and syspages
if required.pages/contact.md
pages/cv.md
_config.yml
to add twitter, google plus, linkedin, google scholar, ORCID profile info under owner heading.{{ site.url }}/cv/cv.pdf
pages/publications.md
/files/mypubs.bib
and _includes/mypubs.html
. See above under _includes
for more.pages/disclosure.md
humans.txt
foo
with your name.rfeed.xml
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