Installation and setup¶
Installation¶
1. Dependencies¶
To use Qiskit you’ll need to have installed at least Python 3.5 or later. Jupyter Notebooks is also recommended for interacting with tutorials.
For this reason we recommend installing Anaconda 3 python distribution, which already comes with all these dependencies pre-installed.
2. Installation¶
The recommended way to install Qiskit is by using the PIP (Python package manager) tool:
pip install qiskit
This will install the latest stable release, along with all the dependencies.
3. Configure your API token and QE credentials¶
- Create an IBM Q account if you haven’t already done so
- Get an API token from the IBM Q website under “My Account” > “Advanced”
3.1 Automatically loading credentials¶
As of Qiskit Terra 0.6, credentials for accessing the IBM Q quantum devices can be loaded automatically from several locations, thus streamlining the set up of the IBM Q authentication. You can set or store your API credentials once after installation, and when you want to use them, you can simply run:
from qiskit import IBMQ
IBMQ.load_accounts()
This IBMQ.load_accounts()
call performs the automatic loading of the
credentials from several sources (if needed), and authenticates against IBM Q,
making the online devices available to your program. Please use one of the following
methods for storing the credentials before calling the automatic registration:
3.1.1 Store API credentials locally¶
For most users, storing your API credentials is the most convenient approach. Your information is stored locally in a configuration file called qiskitrc, and once stored, you can use the credentials without explicitly passing them to your program.
To store your information, simply run:
from qiskit import IBMQ
IBMQ.save_account('MY_API_TOKEN')
where MY_API_TOKEN should be replaced with your token.
If you are on the IBM Q network, you must also pass the url argument found on your q-console account page to IBMQ.save_account(), along with any other additional information required (e.g. proxy information):
from qiskit import IBMQ
IBMQ.save_account('MY_API_TOKEN', url='https://...')
3.1.2 Load API credentials from environment variables¶
For more advanced users, it is possible to load API credentials from environment variables. Specifically, you can set the following environment variables:
- QE_TOKEN,
- QE_URL
Note that if they are present in your environment, they will take precedence over the credentials stored in disk.
3.1.3 Load API credentials from Qconfig.py¶
For compatibility with configurations set for Qiskit versions earlier than 0.6,
the credentials can also be stored in a file called Qconfig.py
placed in
the directory where your program is invoked from. For convenience, we provide
a default version of this file you can use as a reference - using your favorite
editor, create a Qconfig.py
file in the folder of your program with the
following contents:
APItoken = 'PUT_YOUR_API_TOKEN_HERE'
config = {
'url': 'https://quantumexperience.ng.bluemix.net/api',
# If you have access to IBM Q features, you also need to fill the "hub",
# "group", and "project" details. Replace "None" on the lines below
# with your details from Quantum Experience, quoting the strings, for
# example: 'hub': 'my_hub'
# You will also need to update the 'url' above, pointing it to your custom
# URL for IBM Q.
'hub': None,
'group': None,
'project': None
}
if 'APItoken' not in locals():
raise Exception('Please set up your access token. See Qconfig.py.')
And customize the following lines:
- copy/paste your API token into the space between the quotation marks on the
first line (
APItoken = 'PUT_YOUR_API_TOKEN_HERE'
). - if you have access to the IBM Q features, you also need to setup the
values for your url, hub, group, and project. You can do so by filling the
config
variable with the values you can find on your IBM Q account page.
For example, a valid and fully configured Qconfig.py
file would look like:
APItoken = '123456789abc...'
config = {
'url': 'https://quantumexperience.ng.bluemix.net/api'
}
For IBM Q users, a valid and fully configured Qconfig.py
file would look
like:
APItoken = '123456789abc...'
config = {
'url': 'https://quantumexperience.ng.bluemix.net/api',
# The following should only be needed for IBM Q users.
'hub': 'MY_HUB',
'group': 'MY_GROUP',
'project': 'MY_PROJECT'
}
Note that if a Qconfig.py
file is present in your directory, it will take
precedence over the environment variables and/or the credentials stored locally on disk.
3.2 Manually loading credentials¶
In more complex scenarios or for users that need finer control over multiple
accounts, one can pass the API token, and the other parameters, directly to the
IBMQ.enable_account()
function, that will ignore the automatic
loading of the credentials and use the arguments directly. For example:
from qiskit import IBMQ
IBMQ.enable_account('MY_API_TOKEN', url='https://my.url')
will authenticate using MY_API_TOKEN
and the specified URL,
regardless of the configuration stored in the config file, the environment
variables, or the Qconfig.py
file, if any.
Manually loading from a Qconfig.py
file can also be done:
from qiskit import IBMQ
import Qconfig
IBMQ.enable_account(Qconfig.APIToken, **Qconfig.config)
Please refer to the qiskit.IBMQ
documentation for more information about
using multiple credentials.
Install Jupyter-based tutorials¶
The Qiskit project provides you a collection of tutorials in the form of Jupyter notebooks, which are essentially web pages that contain “cells” of embedded Python code. Please refer to the tutorials repository for detailed instructions.
Troubleshooting¶
The installation steps described on this document assume familiarity with the
Python environment on your setup (for example, standard Python, virtualenv
or Anaconda). Please consult the relevant documentation for instructions
tailored to your environment.
Depending on the system and setup, appending “sudo -H” before the
pip install
command could be needed:
pip install -U --no-cache-dir qiskit


Before Change
self._stride = _fill_and_verify_parameter_shape(stride, 3, "stride")
self._rate = _fill_and_verify_parameter_shape(rate, 3, "rate")
if any(x > 1 for x in self._stride) and any(x > 1 for x in self._rate):
raise base.NotSupportedError(
"Cannot have stride > 1 with rate > 1")
After Change
super(DepthwiseConv2D, self).__init__(custom_getter=custom_getter,
name=name)
if (not isinstance(channel_multiplier, numbers.Integral) or
channel_multiplier < 1):
raise ValueError("channel_multiplier (=%d), must be integer >= 1" %
channel_multiplier)
self._channel_multiplier = channel_multiplier
self._kernel_shape = _fill_and_verify_parameter_shape(kernel_shape, 2,
"kernel")
// We want to support passing native strides akin to [1, m, n, 1]


Accesibility
Web Accessibility covers a wide range of features that a web site should have in order for a web site to be usable by everyone:
- Color blindness – We have to be careful not to use colors on our website that are hard to distinguish for people with color blindness, those are mostly red and green hues but there are others that we should be careful about.
- Poor eyesight – When designing a web site we should not use a really small font, it could be tempting to fit a lot of text into a small area or use colors that look good but they are not highly contrasting, if we choose to follow any of the above points we would be affecting people with poor eyesight making our web site not accessible to them.
- Blindness – We need to make sure that all our image tags contain an alt text that can be read by a screen narrator software, if an image is not important we can add a blank space in the al text property, also we don’t need to make our web site image dependent, for example having an image of a text that a screen narrator won’t be able to read.
- Deaf – Don’t make our pages dependent on sounds that will be the only way for users to navigate through it, the web page needs to provide either explicit text indications or tooltips.
It’s also important to mention that a mailbox for reporting accessibility issues could be pretty helpful so you can add it to your web page help section and users can report accessibility related issues directly there so those issues don’t get lost among the other issues or questions users could have.
When developing a web page we need to start thinking about accessibility on the early stages of it and also throughout the whole development cycle so we can detect problems and address them early on the process this way it will be easier to address them, a simple way of testing this out could be as simple as changing the computer to high contrast mode to check if the web page will be readable.
“There are evaluation tools that help with evaluation. However, no tool alone can determine if a site meets accessibility guidelines. Knowledgeable human evaluation is required to determine if a site is accessible.” 1


LEFT LUGGAGE
Reviewed by Harvey Karten Castle Hill Productions Director: Jeroen Krabbe Writer: Carl Friedman (novel), Edwin de Vries Cast: Laura Fraser, Adam Monty, Isabella Rossellini, Jeroen Krabbe, Chaim Topol, Marianne Sagebrecht, Maximilian Schell
Our era in the U.S. has become known as one that glorifies identity politics. Being American is fine, its advocates say, but be especially proud of where you came from. You're African-American, or Italian-American or Jewish-American or Whatever-American. I suppose this makes some sense since nobody--not even Native Americans--originally came from here. Sometimes I think it's overdone. In any case the jury is still out on whether people are really coming out of the closet in droves to celebrate their origins. My impression is that most people, especially the young, like to think of themselves as thoroughly assimilated into the so-called American Way of Life, but in the film "Left Luggage"--which, to be fair has nothing to do with the U.S. but takes place in the Belgium of the early 1970s--director Jeroen Krabbe pushes the idea that young Jewish people who are assimilated into the Belgian culture should secure more of a feeling for their Jewish identity.
This concept comes out of Carl Friedman's novel, "The Shovel and the Loom" as scripted by Edwin De Vries, and centers on an assimilated 20-year-old, Chaja (Laura Fraser), whose consciousness of Judaism is marginal even though her parents are Holocaust survivors. Her father (Maximilian Schell) comes across as eccentric to her because he is obsessed with digging all over the family's home town of Antwerp in search of luggage he buried in the early 1940s when in flight from the Nazis, while Chaja's mother (Marianne Sagebrecht), on the other hand, is in denial about the Holocaust and spends her days weaving a blanket and making pound cake. Because Chaja needs money to pay rent, a family friend Mr. Apfelschnitt (Chaim Topol) secures for her a job as a nanny with a Hasidic family headed by Mr. Kalman (Jeroen Krabbe), whose wife (Isabella Rossellini) teaches her the special rules of the ultra-orthodox sect. Falling in love with the Kalman's four-year-old boy, Simcha (Adam Monty), Chaja is determined to find out why the little charmer refuses to speak and to encourage his vocal chords so that Mr. Kalman can be proud of him.
"Left Luggage" is in not as dramatic or convincing as another Hasidic-centered tale, Boaz Yakin's 1998 "A Price Above Rubies," a far more commercial offering starring Renee Zellweger as an unhappy Hasidic housewife in New York City who years for sexual liberation and the freedom to pursue a career. In fact "Left Luggage" is downright small, more suitable for cable TV or the stage than for the big screen. It might appeal to Jews of all stripes who are curious about how a young, liberated woman would fare in a culture clash with people she had always considered bizarre, and to Gentiles eager to learn more about Jewish culture and the different ways that members of the tribe practice or do not practice their faith. Dialogue is occasionally so faint that it's virtually inaudible. The only true drama occurs toward the conclusion when a heartbreaking event makes Chaja think long and hard about her way of life, an event that teaches her more than she could ever learn as a philosophy major in college.
Perhaps the most interesting aspect of the film is the way that art follows life. Its director, Jeroen Krabbe, comes from a Jewish mother and Gentile father, and while the maternal side of the family were destroyed in the Dutch Holocaust, Krabbe had for a long time not been aware of the tragic circumstances because the family were too pained to discuss wartime catastrophes. When he came across Carl Friedman's book he had determined to direct the movie version despite his lack of knowledge about the Hasidim. What emerges in the film is a sensitive, heartbreaking portrayal that falls short of gripping the spectator, as the meager story becomes lost on the big screen.
Not Rated. Running time: 100 minutes. (C) 2000 by Harvey Karten<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


To create a database connection, use the DatabaseConnection::factory()
method (or the shortcut function database()
). For security, you should initialize all your database connections inside of your authentication file.
DatabaseConnection::factory('connection-name', $parameters, $set_as_default); # or: database('connection-name', $parameters, $set_as_default);
Provide a unique identifier for your database connection. If you use multiple database connections then you will need this identifier to run queries on this connection.
This needs to be an associative array with the following keys:
host
– hostname of the database serveruser
– database user namepass
– database passworddb
– database schema namedebug
– (boolean) enable query tracingpersist
– (boolean) enable persistent connections via mysql_pconnect()
This boolean flag indicates whether this should be set as the default connection.
Now that you’ve defined a connection, you can retrieve it using the same method:
$c = DatabaseConnection::factory('connection-name'); # or: $c = database('connection-name');
To get the default connection, omit the connection name argument.
Connects, or re-connects, with the database. Pass FALSE
to suppress DatabaseExceptions
on failure.
Returns the MySQL connection resource for this connection.
Returns TRUE
if the database connection is still alive, FALSE
if the connection has been lost.
Returns the MySQL connection ID via mysql_thread_id()
.
Returns the last query run on this connection.
Returns the current escape options bitmask. The default is DatabaseConnection::ESCAPE_STRIP_HTML | DatabaseConnection::ESCAPE_QUOTE
.
Accepts a bitmasked integer. Sets the default data escape options for the connection. The available options are:
DatabaseConnection::ESCAPE_FORCE
DatabaseConnection::ESCAPE_QUOTE
DatabaseConnection::ESCAPE_STRIP_HTML
To turn on one of the settings without clearing the other settings, use a bitwise OR
:
$connection->set_escape_options($connection->get_escape_options() | DatabaseConnection::ESCAPE_QUOTE);
To clear, use a bitwise AND
and NOT
:
$connection->set_escape_options($connection->get_escape_options() & ~DatabaseConnection::ESCAPE_QUOTE);
Escapes a data value. If $escape_options
is omitted, uses the default escape options. To escape with specific settings, pass an escape settings bitmask as the second argument (see set_escape_options()
above).
$clean = $connection->escape($dirty);
Executes an SQL query. If a DatabaseQuery
object is passed, automatically casts the object as a string. Returns a DatabaseResult
object (see “Fetching Query Results”).
$result = $connection->execute("SELECT * FROM users");


Green Pine Power Screen
Pine Fragrance PWR-9G
Prices: 1 screen Full Case (10 screens)
For distribution, resale and other information, contact us:<PHONE_NUMBER> x24 guillermo@ekcos.com


Well, kind of. Based off of 8values, Ideologue is a semi-comprehensive tool to help you better find sociopolitical ideologies that align with your values. Political quizzes (especially axis quizzes) can be a little reductive, so this isn't meant to fully describe everything you believe in, just to help you better put your beliefs into words, and maybe help you find more like-minded people. Please answer honestly!
Like every political axis quiz, there are 2 versions: a full version with 20+ questions per axis, and a short one with 5 per axis. You can take whichever one you want, but the full version is a lot more accurate.
Legal notice: The 8values project's license grants the rights to "modify, merge, publish, distribute" the software as long as "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software." This project is released under the same license.


We are
Travelers
who travels across various places
Developers
who develops fullstack applications
Bloggers
who writes about experiences
traveler journey
developer portfolio
Journey
Portfolio


24
1 Why are times for judging wicked people not set by the Almighty?
Why do not those who are faithful to God see his days of judgment come?
2 There are wicked people who remove boundary markers;
there are wicked people who take away flocks by force and put them in their own pastures.
3 They drive away the donkey of those without fathers;
They take the widow's ox as security.
4 They force needy people out of their path;
poor people of the earth all hide themselves from them.
5 See, these poor people go out to their work
like wild donkeys in the wilderness, looking carefully for food;
perhaps the Arabah will provide them food for their children.
6 Poor people reap in the night in other people's fields;
they glean grapes from the harvest of those wicked people.
7 They lie naked all night without clothing;
they have no covering in the cold.
8 They are wet with the showers of the mountains;
they lie next to large rocks because they have no shelter.
9 There are wicked people who pluck orphans from their mothers' breast,
and wicked people who take children as security from poor people.
10 But the poor people go about naked without clothing;
although they go hungry, they carry others' sheaves of grain.
11 The poor people make oil within the walls of those wicked men;
they tread the wicked men's winepresses, but they themselves suffer thirst.
12 In the city people groan;
the wounded people cry out,
but God pays no attention to their prayers.
13 Some of these wicked people rebel against the light;
they know not its ways,
nor do they stay in its paths.
14 The murderer rises with the light;
he kills poor and needy people;
in the night he is like a thief.
15 Also, the eye of the adulterer waits for the twilight;
he says, 'No eye will see me.'


What is Compression?
Learn The Basics!
Compression is an incredibly useful tool that helps control variations in performance dynamics for mixing engineers. Find out what "Threshold," "Ratio," "Attack," "Release," and "Make-Up Gain" are for in this well thought out tutorial by Wickimedia.
View


You need to set the Java system property
hp.find.home
to your preferred config file location (e.g.
/opt/find
), restart your web server, and then click
here
.


Agenda
- What is Hadoop?
- Why Hortonworks for Hadoop?
- Why Hadoop for given use case
- Architecture of Solution
- Q & A
What is Hadoop?
Hadoop is Opensource framework to store and process large sets of data in short time.
Organizations are collecting data from years. Now they want to extract useful information out of it, to remain competive.
Fast paced software development...
Social,Mobile,Analytics and Cloud
The ability to extract high value from stored data to enable innovation and competitive gain is the purpose of Big Data analytics. Conducting analytics on large sets of data, business users and executives are able to see patterns and trends in performance, new relationships between data sets and potentially new sources of revenue.
Why Hortonworks for Hadoop?
- #1 contributor to Hadoop
- 1000 customers in 17 countries
- Growing more than 100 %
- Solutions implemented in areas like BFSI, Telecom, Govt, Oil and Gas, Pharma, eCommerce, Trasport, etc
- Certified to work with Openstack Sahara Project
Support and Services
Everything you need to become an expert with Big Data !!
- Product Help
- 24 x7 support
- Training
- Professional Service
Why Hadoop for this use case
Volume of data is big
Velocity of data is big
Variety of data is also big
That's why we need BIG DATA solution here .
Technologies used
- Amazon S3
- Messaging -- Kafka
- Database --Sqooq Hive
- Ambari -- Cluster mgmt
- Apache Solr -- Indexing and search
- Storm for fast processing of relatime stream processing
- Spark for in memory processing of data with SCALA
High level Solution Architecture


Money Play. How should Red play 52?
pips Red=131 White=102
(CorrectAnswer= 0/0, ErrorScore= 0)
One of the basic principles of backgammon is attack a blot, prime an anchor.
That principle should be applied here and any non-hitting play is at least an error and probably a blunder. After 6/1*, 10/8 is the best 2 followed by 11/9 but even that is an error. There will be scenarios next turn where Red will want to hit from his 8-pt and he’d rather not give up his 8-pt to do so and hence 10/8 is the correct play of the 2.
Notice that 10/8, 6/1* gives White some horror entering numbers such as 26 and 36.
That principle should be applied here and any non-hitting play is at least an error and probably a blunder. After 6/1*, 10/8 is the best 2 followed by 11/9 but even that is an error. There will be scenarios next turn where Red will want to hit from his 8-pt and he’d rather not give up his 8-pt to do so and hence 10/8 is the correct play of the 2.
Notice that 10/8, 6/1* gives White some horror entering numbers such as 26 and 36.
1. Cubeful 2-ply 10/8 6/1* Eq.: +0.021 0.572 0.129 0.006 - 0.428 0.113 0.002 2. Cubeful 2-ply 10/5 6/4 Eq.: -0.016 (-0.037) 0.551 0.107 0.005 - 0.449 0.074 0.001 3. Cubeful 2-ply 11/9 6/1* Eq.: -0.017 (-0.038) 0.558 0.125 0.006 - 0.442 0.114 0.002 4. Cubeful 2-ply 11/4 Eq.: -0.018 (-0.039) 0.551 0.102 0.005 - 0.449 0.072 0.001 5. Cubeful 2-ply 10/8 9/4 Eq.: -0.060 (-0.081) 0.536 0.098 0.004 - 0.464 0.076 0.001


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 6
Impress your friends with this sophisticated starter of squid with black risotto and parmesan sauce from Michelin-starred chef Hélène Darroze.
For the parmesan emulsion, Heat the fish stock, whipping cream, crème fraîche and parmesan in a pan and bring to the boil. Leave to infuse for a few hours. Sieve the sauce and reheat before serving.
For the confit tomato petals, preheat the oven to 110C/225F/Gas ¼.
Cut the flesh from the sides of the tomatoes to make twelve rounded petal shapes.
Place the tomato petals on a baking sheet and drizzle with the olive oil and season with salt and pepper.
Cook for one hour. Remove and set aside
For the rice, in a frying pan heat the duck fat and beef marrow, add the shallot and fry gently without colouring.
Add the rice then pour in the wine and cook for two minutes. Add the chicken stock until it just covers the rice. Once the stock is absorbed, little by little, add more stock until the rice is cooked (about 20 minutes). Season with salt, pepper and Espelette chilli, add the squid ink, then stir in the whipping cream.
Season the squid with salt. Heat the duck fat in a frying pan and fry the squid gently.
Add the chorizo and cook for a few minutes. Add 12 confit tomato petals and parsley.
Add the lemon juice and white stock to the pan and deglaze (scraping the cooked on parts from the bottom of the pan). Heat to reduce for a minute, then mix in the butter and the olive oil.
Place a spoonful of the rice in the centre of the plate and top with the squid mix.
Place a hand blender in the emulsion and blend until foam appears.
Dress the dish with the foam.


Sadhasivam Jayabalaganesan
SUMMARY
Technology Leadership | Platform engineering
Fluid technologist specialized in architecting eCommerce technology stacks. Deep-rooted understanding of eCommerce strategies, Payment Gateways, Engineering practices, Startup Culture, Strategic approaches, Cross Culture infrastructure detailing. Passion for Developer Experience, Product Management, and Developer Relations. Love Building Tools.
EXPERIENCE
FedEx | Dataworks
Jan 2020 – Present | Platform Engineering | Technology | Data | Shipment
- › Incubated Dataworks insights platform architecture and team to visualize Retailer partners entire shipment BOM.
- › Leading Dataworks Supply Chain platform to maximize pre & post-purchase expreince. Increase visibility between the buyer, the seller, and the shipper relationship.
- › Part of Dataworks Strategic engagement group in leading vendor outreach and collaboration process to provide high-quality integration.
Shoprunner Inc
Apr 2011 – Present | Principal Engineering | Shoprunner
- › Helped shoprunner from the early stage and witnessed the FedEx merger
- › Led Shoprunnner Data platform and built a robust data pipeline to ingest Millon Plus events per day for downstream analytics and machine-learning work
- › Led shoprunner channel partners integration with American Express, Chase, e.t.c and helped to acquire a million members into the program.
EDUCATION
Anna University
1995 – 1999 | Computer Science Engineering (B.Tech)
OUTREACH
-<EMAIL_ADDRESS>- +1<PHONE_NUMBER>
- linkedin ↗
- github ↗
- resume.pdf ↗
- Chester Springs, PA
STRENGTHS
- Organizational Leadership
- API Development
- Advocacy
- Process Improvement
- Detail Oriented
- Delivery Focused
- Agile/Scrum
- Software Development >20 yrs
PLATFORMS & TECHNIQUES
- Cloud Computing (AWS | Azure)
- Big Data Design, Modeling & analytics
- System Architectures & Implementation
- eCommerce Platforms
- Payment Gateways
- PCI & PII systems
- Supplychain & Logistics
A DAY IN MY LIFE
ERSTWHILE ASSOCIATIONS
- › Society of Automotive Engineers (SAE) (5 years) ↗
- › Manugistics Supply Chain (Now Blue Yonder) (5 years) ↗
- › Cognizant Services (> 1 year) ↗
- › Toyota - eLearning (worked in Japan) (> 1 Year)


This is a collection of letters sent to awarding organisations to keep them informed of Ofqual's proposed and existing requirements.
This collection of letters starts from October 2014 (R028/2014). Any letters to responsible officers at awarding organisations sent before this date can be found on
the national archives
.


"Sherlock" is a British crime television series produced by the BBC and co-created by Steven Moffat and Mark Gatiss. The series is based on the original Sherlock Holmes stories by Sir Arthur Conan Doyle (1887). Benedict Cumberbatch plays Sherlock Holmes, and Martin Freeman plays Doctor Watson. 13 episodes have been produced, with four three-part series airing from 2010 to 2017, and a special episode that aired on 1 January 2016. The series is set in the present day, while the one-off special features a Victorian period fantasy resembling the original Holmes stories.
The series "Sherlock" depicts "consulting detective" Sherlock Holmes solving various mysteries in modern-day London. Holmes is assisted by his flatmate and friend, Dr. John Watson, who has returned from military service in Afghanistan with the Royal Army Medical Corps. Although Metropolitan Police Service Detective Inspector Greg Lestrade (Rupert Graves) and others are suspicious of Holmes at first, over time, his exceptional intellect and bold powers of observation persuade them of his value. In part through Watson's blog documenting their adventures, Holmes becomes a reluctant celebrity with the press reporting on his cases and eccentric personal life. Both ordinary people and the British government ask for his help. Although the series depicts a variety of crimes and perpetrators, Holmes' conflict with nemesis Jim Moriarty (Andrew Scott) is a recurring feature. Molly Hooper (Louise Brealey), a pathologist at St. Bart's Hospital, occasionally assists Holmes in his cases. Other recurring roles include Una Stubbs as Mrs. Hudson, Holmes and Watson's landlady, and series co-creator Mark Gatiss as Holmes' elder brother Mycroft.
David Arnold and Michael Price composed the score for the entire series. Click here to learn more about the music of "Sherlock"
| Sherlock Holmes | Genius, arrogant, and lacks most social skills. He uses his superhuman powers of deduction and knowledge of forensic science to solve Scotland Yard's most baffling cases. |
| John Watson | Brave, practical and loyal. He was previously an army doctor who served in Afganistan. He becomes Sherlock's flatmate and best friend. |
| Greg Lestrade | Patient, hardworking, and just. He is the detective inspector at Scotland Yard. He frequently consults Sherlock to examine some of his cases. |
| Phillip Anderson | Cruel to Sherlock and arrogant, but Anderson later regrets his antagonism. Philip Anderson was a member of the forensics team at New Scotland Yard that works under DI Greg Lestrade. Like other members of the team, he openly despises Sherlock Holmes. The feeling appears mutual, as Sherlock dismisses Anderson's suggestions and forces him to face away from him when he is trying to think. |
| Ms. Hudson | Caring, responsible, and wise. She is Sherlock and John's Landlady. |
| Janine Hawkins | Mysterious, flirty, and humorous. Janine Hawkins was Mary Watson's maid of honour at her wedding to John Watson in May, 2013. It was there that she met Sherlock Holmes, who was the best man. Janine Hawkins is the personal assistant of the late Charles Augustus Magnussen. Sherlock uses her to get access to Magnussen's office. |
| Molly Hooper | Perceptive, intelligent, and Kind. She works as a pathologist at St. Bartholomew's Hospital and helps Sherlock on cases. |
| Mycroft Holmes | Cold, detached, and clever. He is Sherlock's older brother. He is not the type to show brotherly compassion. He works for the British Government and calls Sherlock to assist him on matters that threaten national security. |
| Elizabeth Smallwood | Confident, powerful, and extremely professional Elizabeth, Lady Smallwood MP (also identified as Alicia) is a British Member of Parliament who was blackmailed by Charles Augustus Magnussen. |
| Mary Morstan | Smart, deceitful, and strong. She is John Watson's wife who works as a part-time nurse. She surpasses John's intelect and occasionaly suprises Sherlock with her knowledge. |
| Irene Adler | Cunning, witty, a seductress. A ruthless and brilliant dominatrix who also trades in classified information extracted from her rich and powerful clients. |
| Jim Moriarty | Ambitious, unpredictable, and insane He is the intellectual equal of Sherlock. He is a consulting criminal and enjoys playing games with people. He is set on destroying his only rivial, Sherlock Holmes. |
| Charles Augustus Magnessun | Mannerless, calculating, and an egomaniac, Charles Augustus Magnussen was a powerful and wealthy businessman who owned several newspapers. Magnussen is a known blackmailer who uses the information against his "assets." His activities drew the attention of the parliamentary committee, who wanted to know what interest he had in these high officials, one of them being the prime minister. Magnussen took an interest in Sherlock and decided to test his "pressure point" to see what can truly break him. |
| Culverton Smith | Psychopathic, sadistic, and narcissistic Culverton Smith is a prominent entrepreneur and philanthropist, using his fame and fortune to help many charities. However, he is one of the most prolific undetected serial killers in British criminal history. He kills for pleasure and has an uncontrolable need to confess to his crimes. Culverton Smith is an antagonist of Sherlock Holmes. |
| Jim Moriarty | Ambitious, unpredictable, and insane He is the intellectual equal of Sherlock. He is a consulting criminal and enjoys playing games with people. He is set on destroying his only rivial, Sherlock Holmes. |
| Eurus Holmes | Incandescently intelligent, manipulative, and dangerous The secret younger sister of Sherlock and Mycroft Holmes. She is referred as the cleverest one among the Holmes, classed as an era-defining genius. She has a singular preference for Sherlock, declaring him as her favorite family member. Because of her attachment to Sherlock, Eurus took special notice of Jim Moriarty's own interest with Sherlock, requesting to Mycroft that, as a 'Christmas treat', she wanted an unsupervised conversation with Moriarty for five minutes in exchange for a consultation to her insight. When they finally talked, Eurus spoke 'Redbeard' as a treat for Moriarty, and, from then, hatched their respective games for Sherlock. |


001 package org.javasimon; 002 003 /** 004 * Stopwatch Simon measures time spans and holds related statistics. 005 * Methods {@link #start} creates new {@link org.javasimon.Split} object. 006 * On this object you can call {@link org.javasimon.Split#stop()} - this demarcates measured interval. 007 * Alternatively method {@link #addSplit(Split)} can be used to add split to the stopwatch ({@link Split#create(long)} 008 * can be used to create finished Split for any nanos value). Both ways effectively 009 * updates usage times, increase usage counter by one and updates total time of the stopwatch. 010 * Split object enables multiple time-splits to be measured in parallel. 011 *
 012 * Example: 013 *
 014 * Split split = SimonManager.getStopwatch("com.my.stopwatch").start(); 015 * //... here goes the measured code 016 * split.stop(); 017 * System.out.println("Result: " + split.getStopwatch()); // print will be probably somewhere else
 018 * This can be used for simple micro-benchmarking, critical section monitoring, in web 019 * filter to measure request times, etc. 020 * 021 * {@link org.javasimon.SimonManager} should always be used to get the stopwatch before using it, 022 * because otherwise the code will not reflect enable/disable of the whole API. 023 * 024 *
Disable/enable considerations
 025 * 026 * While Counter's usage is atomic, Stopwatch measures splits and every measurement involves two 027 * calls (start/stop) over a period of time. It's important to know how various management actions 028 * affect measurement: 029 *
 030 *
If start OR stop is called with disabled Simon, nothing is measured. That means that if 031 * stopwatch is disabled or enabled between these calls, stop always returns 0 and totals are 032 * not updated. 033 *
If stopwatch is obtained from enabled Manager (API is enabled) and Manager is later 034 * disabled before stop, this split is measured because real stopwatch instance was obtained 035 * (considering that the Stopwatch itself is enabled). 036 * If it's other way around then the split is not measured because obtained instance is "null" 037 * Simon. 038 *
 039 * 040 * While API disable causes that the code works with "null" Simons, state of the real Simon is 041 * perfectly preserved. Disabling particular Simon on the other hand resets some of its state. 042 * When the stopwatch is disabled, its active count is set to 0 and before it is enabled again both 043 * its thread-local split map and keyed-object split map is cleared. Of course, all totals/counts 044 * are preserved. 045 *
Other usages
 046 * Reset of the stopwatch resets all stats except usages that are rather management related and 047 * should not be reset. Reset is used often for various sampling purposes which requires that only 048 * cumulative stats are reset, but all running splits are preserved. Running splits will be added 049 * to the stopwatch after reset when respective stop methods are called. 050 * 051 * @author <a<EMAIL_ADDRESS>"Virgo" Richter 052 */ 053 public interface Stopwatch extends Simon { 054 /** 055 * Starts the new split for this stopwatch. This action does not hold any resources and 056 * if {@link Split} object is collected, no leak occurs. However, active count is increased 057 * and without stopping the split active count stays increased which may render that 058 * information useless. 059 * 060 * @return split object 061 * @see org.javasimon.Split#stop() 062 */ 063 Split start(); 064 065 /** 066 * Adds {@link Split} to the stopwatch which is useful for aggregation of splits created for other stopwatch. 067 * Split object should be stopped. Main difference is the callback method called as 068 * {@link org.javasimon.callback.Callback#onStopwatchAdd(Stopwatch, Split, StopwatchSample)} provides split object to the callback. 069 *
 070 * Usage examples: 071 *
Split split = Split.start(); // no stopwatch needed 072 * ... 073 * someStopwatch.addSplit(split.stop()); // you may omit stop(), if you does not use the split after this point
 074 * 075 * @param split split object (should be stopped) 076 * @return this stopwatch 077 * @since 3.1 078 */ 079 Stopwatch addSplit(Split split); 080 081 /** 082 * Returns total sum of all split times in nanoseconds. 083 * 084 * @return total time of the stopwatch in nanoseconds 085 */ 086 long getTotal(); 087 088 /** 089 * Returns value of the last added split - whether it was added directly or with stop method. 090 * 091 * @return value of the last added split 092 */ 093 long getLast(); 094 095 /** 096 * Returns usage count of the stopwatch. Counter is increased by {@code addTime} and 097 * {@code stop} - that means that it's updated every time the next time split is added. 098 * 099 * @return count of time splits 100 */ 101 long getCounter(); 102 103 /** 104 * Returns maximal time split value in nanoseconds. 105 * 106 * @return maximal time split in nanoseconds 107 */ 108 long getMax(); 109 110 /** 111 * Returns minimal time split value in nanoseconds. 112 * 113 * @return minimal time split in nanoseconds 114 */ 115 long getMin(); 116 117 /** 118 * Returns ms timestamp when the max value was measured. 119 * 120 * @return ms timestamp of the max value measurement 121 */ 122 long getMaxTimestamp(); 123 124 /** 125 * Returns ms timestamp when the min value was measured. 126 * 127 * @return ms timestamp of the min value measurement 128 */ 129 long getMinTimestamp(); 130 131 /** 132 * Resets the Simon - clears total time, min, max, usage stats, etc. Split times that 133 * started before reset will be counted when appropriate stop is called, so no split 134 * time is ignored by the stopwatch. Active count is not reset. 135 */ 136 void reset(); 137 138 /** 139 * Returns current number of measured splits (concurrently running). This counter can show more 140 * splits than is measured at any moment if some splits were "forgotten" (not stopped and garbage 141 * collected). This does not imply any resource leak, just bad practice of not stopping Splits somewhere 142 * in the client code. 143 * 144 * @return current number of active splits 145 */ 146 long getActive(); 147 148 /** 149 * Returns peek value of active concurrent splits. 150 * 151 * @return maximum reached value of active splits 152 */ 153 long getMaxActive(); 154 155 /** 156 * Returns ms timestamp when the last peek of the active split count occurred. 157 * 158 * @return ms timestamp of the last peek of the active split count 159 */ 160 long getMaxActiveTimestamp(); 161 162 /** 163 * Returns mean value (average) of all measured values. 164 * If {@link #getCounter()} is 0 it should return {@code Double.NaN}, but for practical reasons returns 0. 165 * 166 * @return mean value 167 */ 168 double getMean(); 169 170 /** 171 * Returns unbiased estimate of standard deviation. If {@link #getCounter()} is 0 returns {@code Double.NaN}. 172 * http://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation 173 * 174 * @return unbiased estimate of standard deviation 175 */ 176 double getStandardDeviation(); 177 178 /** 179 * Returns unbiased estimate of the population variance. If {@link #getCounter()} is 0 returns {@code Double.NaN}. 180 * http://en.wikipedia.org/wiki/Variance#Population_variance_and_sample_variance 181 * 182 * @return unbiased estimated variance 183 */ 184 double getVariance(); 185 186 /** 187 * Returns variance value of all measured values (entire population). 188 * If {@link #getCounter()} is 0 returns {@code Double.NaN}. 189 * http://en.wikipedia.org/wiki/Variance#Population_variance_and_sample_variance 190 * 191 * @return entire population variance 192 */ 193 double getVarianceN(); 194 195 @Override 196 StopwatchSample sample(); 197 198 @Override 199 @Deprecated 200 StopwatchSample sampleAndReset(); 201 202 StopwatchSample sampleIncrement(Object key); 203 }


Oh hey! Let me tell you a little about myself.
So let's get started. I was born and raised in Central Illinois. Went to college at the University of Illinois at Urbana-Champaign and graduated with an Economics degree with a Business minor in May 2014 with concentration in Physics and Mathematics.
I have graduated from Dev Bootcamp in Chicago in March 2015. I have always had a love for technology probably since the first time I touched a computer and/or played a video game. Programming has always been an interest of mine and have taken a step towards developer bootcamps in order to highten my knowledge of certain programming languages and to gain a broader experience of development as a whole.
My experience in coding lies within the languages and technologies of Ruby, JavaScript, HTML, CSS, Ruby on Rails, Backbone.js, pair programming, socket programming, and others. My experience in Economics is with innovation and technology, labor markets, industrial competition and monopoly and various other topics. If you want to know more about my professional background, please look towards my LinkedIn page which can be located towards the bottom.
Some other things about me is that I love to do almost anything. I enjoy playing video games, playing sports, running obstacle course marathons, learning to dance, reading, conversing, and pretty much anything else. Most of all, I love to play. One of my favorite quotes is that, "We don't stop playing because we grow old, we grow old because we stopped playing." -G. Stanley Hall
Here are some of the projects I have worked on.


Index by date
06/30/2012
- Accurev
- ActionFilter
- AlienBrain
- And Condition
- ANTS Performance Profiler Task
- Artifact Cleanup Publisher
- Assembly Match
- Assembly Version Labeller
- Audit History Project Plugin
- BitKeeper
- BooleanParameter
- Build Condition
- Build Log Build Plugin
- Build Publisher
- Build Report Build Plugin
- CCTray Download Plugin
- ClearCase
- CodeItRight Analysis Task
- CommentFilter
- Comment Task
- Compare Values Condition
- Conditional Publisher
- Conditional Task
- Coverage Filter
- Coverage Threshold
- Cron Trigger
- CruiseServer Control Action
- CruiseServer Control Task
- CVS
- Dashboard Administration Plugin
- Date Labeller
- DateParameter
- DefaultIssueTracker
- Default Labeller
- Default Manifest Generator
- Default Project Security
- Direct Dynamic Value
- DumpValue
- DumpValue Item
- Duplicate Finder Task
- Email Group
- Email Publisher
- Email Subject
- Email User
- Encrypted Messages Channel
- Environment Variable
- Executable Task
- External
- External File Server Security
- External Links
- FAKE Task
- Farm Report Farm Plugin
- FBVariable
- File Based Security Cache
- File Exists Condition
- File Labeller
- File Merge Task
- File State Manager
- FileSystem
- Filtered
- Filter Trigger
- FinalBuilder Task
- Final Build Status Display Plugin
- FirstMatch
- Folder Exists Condition
- ForceBuildPublisher
- FtpSourceControl
- Ftp task - Publisher
- Gendarme Task
- Git
- HTTP Request Header
- HTTP Settings
- HTTP Status Task
- Impersonation
- Inherited Project Security
- In Memory Security Cache
- Internal Server Security
- Interval Trigger
- Iteration Labeller
- Last Build Status Condition
- Last Build Time Condition
- Last Change Labeller
- LDAP Email Converter
- LDAP User Authentication
- Making A Release
- Manifest Importer
- Mercurial (Hg)
- MercurialIssueTracker
- Merge File
- Mks
- ModificationHistory Publisher
- Modification Reader Task
- Modification Writer Task
- MsBuild Task
- MultiFilter
- MultiIssueTracker
- Multiple XSL Report Build Plugin
- Multi Source Control
- Multi Trigger
- Named Value
- Namespace Mapping
- NAnt Task
- NCover Profiler Task
- NCover Reporting Task
- NDepend Task
- Null Project Security
- Null Server Security
- Null Source Control
- Null Task
- NumericParameter
- NUnit Task
- Ohloh Stats Display Plugin
- Or Condition
- PackageFile
- PackageFolder
- Package List Plugin
- Package Publisher
- Parallel Task
- Parameter Trigger
- PathFilter
- Perforce (P4)
- Plastic
- Plugins
- PowerShell Task
- Project Configuration Block
- Project Configuration Server Plugin
- Project Report Project Plugin
- ProjectStatisticsPlugin
- Project Status Plugin
- Project Timeline Plugin
- Project Trigger
- PVCS
- Queue Configuration
- Rake Task
- RegexIssueTracker
- Regular Expression Email Converter
- Remote Project Labeller
- RemoteServices
- Replacement Dynamic Value
- Robocopy
- Role Permission
- RollUp Trigger
- RSS Publisher
- Schedule Trigger
- Seapine Surround
- Security Configuration Project Plugin
- SelectParameter
- Sequential Task
- Server
- Server Audit History Server Plugin
- Server Information Server Plugin
- Server Log Project Plugin
- Server Log Server Plugin
- Server Report Server Plugin
- SourceGear Vault
- SourceSafe (vss)
- StarTeam
- State File Labeller
- Statistic
- Statistics Publisher
- Status Condition
- Subversion (svn)
- Synchronisation Context Task
- Synergy Client Session
- SynergyIssueTracker
- Synergy Project
- Team Foundation Server (Tfs)
- Telelogic Synergy
- TextParameter
- Update Configuration Task
- URL Header Value Condition
- URL Ping Condition
- Url Trigger
- UserFilter
- User List Project Plugin
- User List Server Plugin
- User Name Authentication
- User Password Authentication
- User Permission
- View All Builds Project Plugin
- View Configuration Project Plugin
- View CVS URL Builder
- View ServerQueue Server Plugin
- Visual Studio Task
- WebSVN URL Builder
- XML File Audit Logger
- XML File Audit Reader
- Xml Log Publisher
- XSL Report Build Plugin
- XsltTask
06/23/2012
06/22/2012
06/21/2012
06/20/2012
06/19/2012
06/11/2012
06/10/2012
06/05/2012
05/27/2012
05/26/2012
05/11/2012
02/10/2012
02/09/2012
01/21/2012
01/20/2012
01/17/2012
01/11/2012
12/29/2011
12/28/2011
12/18/2011
11/21/2011
11/19/2011
10/10/2011
10/09/2011
10/08/2011
10/01/2011
- Configuration
- Scenario 1 Small In-house Development Team
- Scenario 2 Large In-house Development Team
- Scenario 3 Open Source Project
- Security scenarios
09/23/2011
09/22/2011
09/21/2011
09/20/2011
09/18/2011
09/17/2011
- Channel Security
- Configuration Preprocessor
- Email Converter
- ExternalLink
- General Security
- General Security Permissions
- Labeller Blocks
- Manifest Generators
- Package Item
- Project Level Security
- Security Audit Loggers
- Security Audit Readers
- Security Caches
- Security Configuration
- Security Extension Points
- Security Permissions
- Security Users
- Server Application Config File
- Server Level Security
- Source Control Blocks
- State Manager Blocks
- Statistics
- Task Condition Blocks
- TheCruiseControlConfigurationBlock
- Timeout Configuration
- Trigger Blocks
- Wildcards in User Names
09/06/2011
Also available in: Atom


My Awesome Post
Donate some Money to me. If you want to contact me feel free and don't hesitate!
-
My name is Saabir Barre
Donate some Money to me. If you want to contact me feel free and don't hesitate!
This is my first webpage! I was able to code all the HTML and CSS in order to make it. Watch out world of web design here I come!
I can use my skills here to create websites for my business, my friends and family, my C.V, blog or articles. As well as any games or more experiment stuff (which is what the web is really all about).


${title} Power Ranking
Last Updated: ${date}
#
Tag
Elo
Games
% for i, player in enumerate(players, 1):
${i}
${player.tag}
${'{:.2f}'.format(player.elo())}
${player.games}
%endfor


Please enter your first name.
Please enter your last name.
Please enter your employee ID.
Please enter your job title.
Please enter your annual salary.


dynsrc attribute
The dynsrc attribute was part of an early attempt to include native video playback in HTML.


Bookstore Registration
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Man in critical condition rescued by firefighters after crash in Rugeley
Firefighters rescued a man trapped after a crash at an industrial estate in Rugeley.
by Jordan ReynoldsThe man was taken to hospital in a critical condition after the crash in Redbrook Lane, Rugeley, just after 9.10am today.
The crash was involving a lorry and another vehicle at Hawkins Logistics, Staffordshire Police said.
Lucy Thompson, spokeswoman for Staffordshire Police, said: "Officers were called to a report of a man who sustained serious injuries following an incident at Hawkins Logistics on Redbrook Industrial Estate in Rugeley at around 9.18am this morning.
"The man is currently in critical but stable condition in hospital. The incident is now in the hands of the Health and Safety Executive."
Staffordshire Fire and Rescue Service tweeted saying: "#Lichfield, #Longton and #Stafford crews have been called to a collision on Rebrook Lane in Rugeley.
"The incident involves two vehicles including a lorry and one male casualty was trapped. He has now been rescued by firefighters. Ambulance are now in attendance. #RTC"
Firefighters administered CPR after freeing the trapped man.
Lauren Jeal, spokeswoman for Staffordshire Fire and Rescue Service, said: "The fire service was called to Redbrook Lane, Rugeley at 9.15am today to reports of a collision involving a lorry and another vehicle.
"Crews from Lichfield and Cannock attended and found one male casualty trapped and injured. Firefighters administered CPR and the man was taken to hospital with serious injuries.
"Firefighters left the scene 10.30am."
Claire Brown, spokeswoman for West Midlands Ambulance Service, said: "We were called at 9.13am to reports of an RTC on Redbrook Lane, Rugeley. Two ambulances, a paramedic officer and a critical care car attended.
"One patient, a man, was in a critical condition and received advanced trauma care on scene before being taken on blue lights to Royal Stoke University Hospital."
Hawkins Logistics has been asked to comment.


Board Size:
4 x 4 (EXTREME)
6 x 6 (HARD)
8 x 8 (MEDIUM)
PLAY
Your Pieces:
8
Opponent Pieces:
8
RESIGN
×
×
CLOSE


Dashboard
Users
Messaging
Staff
Lastest Performance
"It's a beautiful day to save lives" "I'm supposed to be Chief."


Hello {{eName}} and your id is {{eId}} and your salary is {{eSalary}}
empname
empid
empsalary
{{eName}}
{{eId}}
{{eSalary}}


Searched for http://www.microsoft.com/billgates/speeches/2001/03-19hailstorm.asp |
17 Results |
Note some duplicates are not shown. See all.
* denotes when site was updated.
| Search Results for Jan 01, 1996 - Sep 20, 2003 | |||||||
| 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 |
| 0 pages | 0 pages | 0 pages | 0 pages | 0 pages | 11 pages | 4 pages | 1 pages |
|
Apr 13, 2001 * Jun 04, 2001 * Jun 09, 2001 Jun 11, 2001 Jul 10, 2001 Oct 29, 2001 * Nov 18, 2001 Nov 20, 2001 Nov 24, 2001 Nov 25, 2001 Nov 26, 2001 |
Jan 19, 2002 * Feb 02, 2002 Jun 01, 2002 Aug 11, 2002 |
Feb 07, 2003 * |


For detailed example and screenshots see RDF Export example
With the export functionality, you can determine the intended structure of the RDF data by drawing a template graph. The exporter iterates through the project rows, evaluate expressions in the template graph and produce an equivalent RDF subgraph per row. The final result is the merge of all the subgraphs.
The image below shows the main parts of a graph template.
Any applied filters on the data affect the exported RDF (i.e. RDF data exported corresponds to the current visible rows). This allows getting RDF data for any custom subset of the data or using different schemas for different parts of the data.
Notice that you can define multiple trees (i.e. multiple root nodes) as shown in the figure above.
Terms from existing vocabularies can be imported. The exporter attempts to retrieve and index the terms so that they can be included in the autocompletion available for classes and properties addition. Additionally, by using a dump from -our lovely friend- prefix.cc URIs for popular prefixes are automatically suggested.
Data can be exported in RDF/XML or Turtle using the "Export" menu.


The search is on for a red panda that escaped a French zoo
PARIS (AP) - A search is on for a red panda that broke out of a zoo in the Rhone region of southeastern France.
Officials at the Saint-Martin-la-Plaine Zoo said the endangered animal was last seen 5 kilometers (3 miles) from the park by a surprised motorist. The park said the panda escaped last Friday by climbing up branches broken by snowfall and swinging from tree to tree.
The panda, a cat-sized nocturnal animal with reddish fur and a shaggy tail, would probably spend "a good part of the day sleeping in a tree." The zoo advised residents to keep a look out "at the top of the trees."
Zoo veterinarian Jean-Christophe Gerard said the animal "has good claws and good teeth" and shouldn't be hugged, although it isn't considered dangerous.
Copyright 2019 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.


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:: com :: sun :: star :: form ::
|
| unpublished |
interface XFormsSupplier2 |
|
|
- Base Interfaces
XFormsSupplier2
┗ XFormsSupplier
| XFormsSupplier |
- (referenced interface's summary:)
- provides the access to a collection of forms.
|
|
- Usage Restrictions
- not published
- Description
- extends the XFormsSupplier with convenience methods
|
| Methods' Summary |
| hasForms |
determines whether there are currently forms available at all
|
| Methods' Details |
hasForms
- Description
- determines whether there are currently forms available at all
If you need read access to the forms collection, then you might check
the existence of forms using hasForms, and if it returns
false, you can do as if XFormsSupplier::getForms would
have returned an empty container.
Semantically, hasForms is equivalent to calling
::XElementAccess::hasElements on the container returned by
XFormsSupplier::getForms. But when using the latter, the
implementation is forced to create a empty container, which might be potentially
expensive.
|
|
Top of Page
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


Methodical Help
Help with using the Methodical Web Application.
Help with using the Methodical Web Application.
Instructions on how to define music that you'd like Methodical to identify in your composition.
The music definition is simply a list of declarations. See below an example of music defined for a touch on 6 bells:
"Queens" = 135246 "Titums" = 142536 "Rollup" = 456$ "546" = 546$
A music declaration is made up of 3 parts:
A regular expression (regex or regexp for short) is a special text string for describing a search pattern. You can think of regular expressions as wildcards on steroids. Some examples with explanations are provided in the next section below. To find out more about regular expressions there is plenty of information available on the internet. Try this for starters.
The most basic regular expression consists of a series of literal characters, such as 456. Methodical will match any row created by your composition where 456 appear in that order.
"My Music" = 456
The music declaration given here will match the following rows: 123456, 456321 and 345612
"My Music" = 456$
The $ character matches the end of a row, and ^ matches the start of a row, so if you wanted to define music that starts with 1234 you'd define your music as:
and if you wanted to define music that ends with 1234 you define your music as:
"1234 at end" = 1234$


Moral Fiber
WE STAND FOR BETTER ETHICS IN THE APPAREL INDUSTRY
With the help of a small circle of socially responsible suppliers, DSTLD maintains high standards for people and apparel alike.
NO SWEATSHOPS
We demand a higher standard not just in denim, but also in labor practices and conditions. We carefully screen our suppliers, laundries, and factories to ensure our products are fairly manufactured and 100% sweatshop-free.
ECO-FRIENDLY FABRICS
Our line is equipped with top-of-the-line premium fabrics, many of them eco-friendly and fully sustainable. When possible, we use natural dyes and softening techniques to achieve our denim’s striking range of hues and irresistibly soft handfeel.
ETHICAL PRICING
The goal of our direct-to-you, middleman-free stance is not only to filter out extra steps and excess markup. It is also an effort to preserve the integrity of our line and honor our relationship with clients.


Let's find all of your vault addresses that have some bitcoins in them.
Please enter the extended public keys (xpubkeys) for your multisig vault.
Please enter the highest HD index to check for balance.
Now enter the key backups that you saved when creating the vault.
Protip: you can now safely go offline if you'd like.
Now you can copy the raw transaction and paste it into any bitcoin client to be broadcast to the network.
Your transaction will appear here in raw hex format ...
| Node path | Address | Balance |
|---|---|---|
| Balance | 0.00000000 BTC | |
| Fees | 0.00000000 BTC | |
| Total to be sent | 0.00000000 BTC |


--- title: Envoy Filter description: Customizing Envoy configuration generated by Istio. location: https://istio.io/docs/reference/config/networking/envoy-filter.html layout: protoc-gen-docs generator: protoc-gen-docs schema: istio.networking.v1alpha3.EnvoyFilter aliases: [/docs/reference/config/networking/v1alpha3/envoy-filter] number_of_entries: 17 ---
EnvoyFilter
provides a mechanism to customize the Envoy
configuration generated by Istio Pilot. Use EnvoyFilter to modify
values for certain fields, add specific filters, or even add
entirely new listeners, clusters, etc. This feature must be used
with care, as incorrect configurations could potentially
destabilize the entire mesh. Unlike other Istio networking objects,
EnvoyFilters are additively applied. Any number of EnvoyFilters can
exist for a given workload in a specific namespace. The order of
application of these EnvoyFilters is as follows: all EnvoyFilters
in the config root
namespace,
followed by all matching EnvoyFilters in the workload’s namespace.
NOTE 1: Some aspects of this API is deeply tied to the internal implementation in Istio networking subsystem as well as Envoy’s XDS API. While the EnvoyFilter API by itself will maintain backward compatibility, any envoy configuration provided through this mechanism should be carefully monitored across Istio proxy version upgrades, to ensure that deprecated fields are removed and replaced appropriately.
NOTE 2: When multiple EnvoyFilters are bound to the same workload in a given namespace, all patches will be processed sequentially in order of creation time. The behavior is undefined if multiple EnvoyFilter configurations conflict with each other.
NOTE 3: *_To apply an EnvoyFilter resource to all workloads (sidecars and gateways) in the system, define the resource in the config root namespace, without a workloadSelector.
The example below declares a global default EnvoyFilter resource in
the root namespace called istio-config
, that adds a custom
protocol filter on all sidecars in the system, for outbound port
9307. The filter should be added before the terminating tcp_proxy
filter to take effect. In addition, it sets a 30s idle timeout for
all HTTP connections in both gateays and sidecars.
apiVersion: networking.istio.io/v1alpha3
kind: EnvoyFilter
metadata:
name: custom-protocol
namespace: istio-config # as defined in meshConfig resource.
spec:
configPatches:
- applyTo: NETWORK_FILTER
match:
context: SIDECAR_OUTBOUND # will match outbound listeners in all sidecars
listener:
portNumber: 9307
filterChain:
filter:
name: "envoy.tcp_proxy"
patch:
operation: INSERT_BEFORE
value:
# This is the full filter config including the name and config or typed_config section.
name: "envoy.config.filter.network.custom_protocol"
config:
...
- applyTo: NETWORK_FILTER # http connection manager is a filter in Envoy
match:
# context omitted so that this applies to both sidecars and gateways
listener:
filterChain:
filter:
name: "envoy.http_connection_manager"
patch:
operation: MERGE
value:
name: "envoy.http_connection_manager"
typed_config:
"@type": "type.googleapis.com/envoy.config.filter.network.http_connection_manager.v2.HttpConnectionManager"
common_http_protocol_options:
idle_timeout: 30s
The following example enables Envoy’s Lua filter for all inbound HTTP calls arriving at service port 8080 of the reviews service pod with labels “app: reviews”, in the bookinfo namespace. The lua filter calls out to an external service internal.org.net:8888 that requires a special cluster definition in envoy. The cluster is also added to the sidecar as part of this configuration.
apiVersion: networking.istio.io/v1alpha3
kind: EnvoyFilter
metadata:
name: reviews-lua
namespace: bookinfo
spec:
workloadSelector:
labels:
app: reviews
configPatches:
# The first patch adds the lua filter to the listener/http connection manager
- applyTo: HTTP_FILTER
match:
context: SIDECAR_INBOUND
listener:
portNumber: 8080
filterChain:
filter:
name: "envoy.http_connection_manager"
subFilter:
name: "envoy.router"
patch:
operation: INSERT_BEFORE
value: # lua filter specification
name: envoy.lua
typed_config:
"@type": "type.googleapis.com/envoy.config.filter.http.lua.v2.Lua"
inlineCode: |
function envoy_on_request(request_handle)
-- Make an HTTP call to an upstream host with the following headers, body, and timeout.
local headers, body = request_handle:httpCall(
"lua_cluster",
{
[":method"] = "POST",
[":path"] = "/acl",
[":authority"] = "internal.org.net"
},
"authorize call",
5000)
end
# The second patch adds the cluster that is referenced by the lua code
# cds match is omitted as a new cluster is being added
- applyTo: CLUSTER
match:
context: SIDECAR_OUTBOUND
patch:
operation: ADD
value: # cluster specification
name: "lua_cluster"
type: STRICT_DNS
connect_timeout: 0.5s
lb_policy: ROUND_ROBIN
hosts:
- socket_address:
protocol: TCP
address: "internal.org.net"
port_value: 8888
The following example overwrites certain fields (HTTP idle timeout and X-Forward-For trusted hops) in the HTTP connection manager in a listener on the ingress gateway in istio-system namespace for the SNI host app.example.com:
apiVersion: networking.istio.io/v1alpha3
kind: EnvoyFilter
metadata:
name: hcm-tweaks
namespace: istio-system
spec:
workloadSelector:
labels:
istio: ingress-gateway
configPatches:
- applyTo: NETWORK_FILTER # http connection manager is a filter in Envoy
match:
context: GATEWAY
listener:
filterChain:
sni: app.example.com
filter:
name: "envoy.http_connection_manager"
patch:
operation: MERGE
value:
common_http_protocol_options:
idle_timeout: 30s
xff_num_trusted_hops: 5
EnvoyFilter provides a mechanism to customize the Envoy configuration generated by Istio Pilot.
One or more properties of the proxy to match on.
Conditions specified in ClusterMatch must be met for the patch to be applied to a cluster.
Conditions specified in RouteConfigurationMatch must be met for the patch to be applied to a route configuration object or a specific virtual host within the route configuration.
Conditions specified in a listener match must be met for the patch to be applied to a specific listener across all filter chains, or a specific filter chain inside the listener.
Patch specifies how the selected object should be modified.
One or more match conditions to be met before a patch is applied to the generated configuration for a given proxy.
Changes to be made to various envoy config objects.
Match a specific route inside a virtual host in a route configuration.
Match a specific virtual host inside a route configuration.
For listeners with multiple filter chains (e.g., inbound listeners on sidecars with permissive mTLS, gateway listeners with multiple SNI matches), the filter chain match can be used to select a specific filter chain to patch.
Conditions to match a specific filter within a filter chain.
Conditions to match a specific filter within another filter. This field is typically useful to match a HTTP filter inside the envoy.httpconnectionmanager network filter. This could also be applicable for thrift filters.
Action refers to the route action taken by Envoy when a http route matches.
| Name | Description |
|---|---|
ANY |
All three route actions |
ROUTE |
Route traffic to a cluster / weighted clusters. |
REDIRECT |
Redirect request. |
DIRECT_RESPONSE |
directly respond to a request with specific payload. |
Operation denotes how the patch should be applied to the selected configuration.
| Name | Description |
|---|---|
INVALID |
|
MERGE |
Merge the provided config with the generated config using json merge semantics. |
ADD |
Add the provided config to an existing list (of listeners, clusters, virtual hosts, network filters, or http filters). This operation will be ignored when applyTo is set to ROUTECONFIGURATION, or HTTPROUTE. |
REMOVE |
Remove the selected object from the list (of listeners, clusters, virtual hosts, network filters, or http filters). Does not require a value to be specified. This operation will be ignored when applyTo is set to ROUTECONFIGURATION, or HTTPROUTE. |
INSERT_BEFORE |
Insert operation on an array of named objects. This operation is typically useful only in the context of filters, where the order of filters matter. For clusters and virtual hosts, order of the element in the array does not matter. Insert before the selected filter or sub filter. If no filter is selected, the specified filter will be inserted at the front of the list. |
INSERT_AFTER |
Insert operation on an array of named objects. This operation is typically useful only in the context of filters, where the order of filters matter. For clusters and virtual hosts, order of the element in the array does not matter. Insert after the selected filter or sub filter. If no filter is selected, the specified filter will be inserted at the end of the list. |
INSERT_FIRST |
Insert operation on an array of named objects. This operation is typically useful only in the context of filters, where the order of filters matter. For clusters and virtual hosts, order of the element in the array does not matter. Insert first in the list based on the presence of selected filter or not. This is specifically useful when you want your filter first in the list based on a match condition specified in Match clause. |
ApplyTo specifies where in the Envoy configuration, the given patch should be applied.
| Name | Description |
|---|---|
INVALID |
|
LISTENER |
Applies the patch to the listener. |
FILTER_CHAIN |
Applies the patch to the filter chain. |
NETWORK_FILTER |
Applies the patch to the network filter chain, to modify an existing filter or add a new filter. |
HTTP_FILTER |
Applies the patch to the HTTP filter chain in the http connection manager, to modify an existing filter or add a new filter. |
ROUTE_CONFIGURATION |
Applies the patch to the Route configuration (rds output) inside a HTTP connection manager. This does not apply to the virtual host. Currently, only MERGE operation is allowed on the route configuration objects. |
VIRTUAL_HOST |
Applies the patch to a virtual host inside a route configuration. |
HTTP_ROUTE |
Applies the patch to a route object inside the matched virtual host in a route configuration. Currently, only MERGE operation is allowed on the route objects. |
CLUSTER |
Applies the patch to a cluster in a CDS output. Also used to add new clusters. |
PatchContext selects a class of configurations based on the traffic flow direction and workload type.
| Name | Description |
|---|---|
ANY |
All listeners/routes/clusters in both sidecars and gateways. |
SIDECAR_INBOUND |
Inbound listener/route/cluster in sidecar. |
SIDECAR_OUTBOUND |
Outbound listener/route/cluster in sidecar. |
GATEWAY |
Gateway listener/route/cluster. |


Martinez-born Joe DiMaggio was one of the finest players ever to play the game of baseball and we need to do more to recognize his accomplishments locally. The Joltin Joe — a 22-foot Chris Craft pleasure boat presented to him on “Joe DiMaggio Day” in 1949 has now been fully restored to its pristine, original condition through the efforts of volunteers, fundraising, and the generosity of donations such as yours.
The Joe DiMaggio Hometown Hero Project is completely staffed by volunteer Officers and support workers who believe in our mission. This allows for 100% of your donation to go towards current and future initiatives that include the establishment of a Joe DiMaggio Museum, statues, a mural, a walking tour, and an oral history project all to be located here in Martinez.
Our non-profit 501(c)(3) identification number for your tax records is 47-5034431.
On behalf of the Joe DiMaggio Hometown Hero Project, I want to personally thank you for your donation and we look forward to your future support.
Sincerely,
Rick Aldridge, President


| Season | Join Team | STIMS/TIMS | Pay Dues | Build Season Hours | Competition Season Hours | Off Season Hours | Total Annual Hours |
|---|---|---|---|---|---|---|---|
|
|
|
|
check_circle
remove_circle
|
{{season.annual_requirements.build_season_hours}} | {{season.annual_requirements.competition_season_hours}} | {{season.annual_requirements.off_season_hours}} |
|


{% include 'includes/_messages.html' %}
Udhaarify
{{session['username']}}
Profile
Log Out
DASHBOARD
Add a Bill
Settle Up
Add friends
Total Balance
Total Balance
You Owe
You're owed
{{ net_amount }}
{{ user_owes }}
{{ user_is_owed }}
You owe
Name
Amount
{% for key in friends_user_owes %}
{{ key }}
{{ friends_user_owes[key] }}
{% endfor %}
You're owed
Name
Amount
{% for key in friends_user_is_owed_by %}
{{ key }}
{{ friends_user_is_owed_by[key] }}
{% endfor %}


We are the one stop shop for organizing your Potluck.
It is easy. It is quick. It is collaborative.
Create an event. Invite friends and acquaintances with your exclusive share code.
We take the hassle out of coordinating. No stress! Just enjoy your event!


Overview
Refstack intends on being THE source of tools for interoperability testing of OpenStack clouds.
Refstack provides users in the OpenStack community with a Tempest wrapper, refstack-client, that helps to verify interoperability of their cloud with other OpenStack clouds. It does so by validating any cloud implementation against the OpenStack Tempest API tests.
Refstack and DefCore - The prototypical use case for Refstack provides the DefCore Committee the tools for vendors and other users to run API tests against their clouds to provide the DefCore committee with a reliable overview of what APIs and capabilities are being used in the marketplace. This will help to guide the DefCore-defined capabilities and help ensure interoperability across the entire OpenStack ecosystem. It can also be used to validate clouds against existing DefCore capability lists, giving you assurance that your cloud faithfully implements OpenStack standards.
Value Add for Vendors - Vendors can use Refstack to demonstrate that their distros, and/or their customers' installed clouds remain with OpenStack after their software has been incorporated into the distro or cloud.
Refstack consists of two parts:
Our API isn't just for us to collect data from private and public cloud vendors. It can be used by vendors in house to compare interoperability data over time.
refstack-client contains the tools you will need to run the DefCore tests.
Get involved!


Orchard Skills
Orchard Skills specializes in handcrafted Web Solutions utilizing the Orchard Core CMS modular framework.
Orchard Skills specializes in handcrafted Web Solutions utilizing the Orchard Core CMS modular framework.
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<EMAIL_ADDRESS>+ 1<PHONE_NUMBER>


{% extends 'tpo/base.html' %} {% load static %} {% block content %}
Greetings from Indian Institute of Technology (BHU) Varanasi !
Knowledge and continuous learning are the mainstays amongst the leading organizations today. In view of this requirement and the visions and ideals of its illustrious founder Pt. Madan Mohan Malviyaji, Indian Institute of Technology (BHU) Varanasi grooms its students with a perfect blend of academics, core competencies, ethics and values so that they evolve and succeed in every sphere of their life in our radical and complex society.
It gives me immense pleasure to welcome organizations to campus recruitment program at our institute. It is my earnest belief that the training of academically wholesome life at IIT (BHU) Varanasi had prepared our students to face any challenges and take on responsible positions in the corporate world.
I am sure that students selected by your organization shall excel and achieve beyond your expectations and they will prove to be an asset to your organization. I wish all the best to the campus recruitment program and all students in their endeavors. I hope that recruiting organizations will be able to derive immense benefit through this program and would go a long way to build a long lasting and fruitful mutual relationship.
We look forward to your visit to our campus soon.
Professor Rajeev Sangal
Director, IIT (BHU) Varanasi


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
inputs_real = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], name='input_real')
inputs_z = tf.placeholder(tf.float32, [None, z_dim], name='input_z')
lr = tf.placeholder(tf.float32, name='learning_rate')
return inputs_real, inputs_z, lr
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
alpha = 0.2
with tf.variable_scope('discriminator', reuse=reuse):
conv1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', \
kernel_initializer=tf.contrib.layers.xavier_initializer())
conv1 = tf.maximum(alpha * conv1, conv1)
conv2 = tf.layers.conv2d(conv1, 128, 5, strides=2, padding='same', \
kernel_initializer=tf.contrib.layers.xavier_initializer())
conv2 = tf.layers.batch_normalization(conv2, training=True)
conv2 = tf.maximum(alpha * conv2, conv2)
conv3 = tf.layers.conv2d(conv2, 256, 5, strides=2, padding='same', \
kernel_initializer=tf.contrib.layers.xavier_initializer())
conv3 = tf.layers.batch_normalization(conv3, training=True)
conv3 = tf.maximum(alpha * conv3, conv3)
flat = tf.reshape(conv3, (-1, 4*4*128))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
alpha = 0.1
with tf.variable_scope("generator", reuse=not is_train):
x = tf.layers.dense(z, 7*7*512, kernel_initializer=tf.contrib.layers.xavier_initializer())
x = tf.reshape(x, (-1, 7, 7, 512))
x = tf.layers.batch_normalization(x, training=is_train)
x = tf.maximum(alpha * x, x)
conv1 = tf.layers.conv2d_transpose(x, 256, 5, strides=1, padding="same",\
kernel_initializer=tf.contrib.layers.xavier_initializer())
conv1 = tf.layers.batch_normalization(conv1, training=is_train)
conv1 = tf.maximum(alpha * conv1, conv1)
conv2 = tf.layers.conv2d_transpose(conv1, 128, 5, strides=2, padding="same",\
kernel_initializer=tf.contrib.layers.xavier_initializer())
conv2 = tf.layers.batch_normalization(conv2, training=is_train)
conv2 = tf.maximum(alpha * conv2, conv2)
logits = tf.layers.conv2d_transpose(conv2, out_channel_dim, 5, strides=2, padding="same",\
kernel_initializer=tf.contrib.layers.xavier_initializer())
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
g_model = generator(input_z, out_channel_dim, is_train=True)
d_model_real, d_logits_real = discriminator(input_real, reuse = False)
d_model_fake, d_logits_fake = discriminator(g_model, reuse = True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_real, labels=tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
train_vars = tf.trainable_variables()
d_vars = [var for var in train_vars if var.name.startswith('discriminator')]
g_vars = [var for var in train_vars if var.name.startswith('generator')]
d_train_opt = tf.train.AdamOptimizer(
learning_rate, beta1=beta1).minimize(
d_loss, var_list=d_vars)
with tf.control_dependencies(
tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
g_train_opt = tf.train.AdamOptimizer(
learning_rate, beta1=beta1).minimize(
g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
n_samples, width, height, channels = data_shape
input_real, input_z, learn_rate = model_inputs(width, height, channels, z_dim)
d_loss, g_loss = model_loss(input_real, input_z, channels)
d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
steps = 0
print_every = 100
show_every = 100
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
# TODO: Train Model
steps += 1
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
batch_images = batch_images * 2.0
# Run optimizers
_ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
_ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
if steps % print_every == 0:
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}...".format(train_loss_g))
if steps % show_every == 0:
out_channel_dim = data_shape[3]
image_mode = data_image_mode
show_generator_output(sess, 16, input_z, out_channel_dim, image_mode)
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}...".format(train_loss_g))
show_generator_output(sess, 16, input_z, out_channel_dim, image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 32
z_dim = 100
learning_rate = 0.0001
beta1 = 0.2
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 32
z_dim = 100
learning_rate = 0.0001
beta1 = 0.2
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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Dance teacher pleads guilty to exposing teen boy to HIV
by AP(AP) – A dance teacher who appeared on a reality TV show has pleaded guilty in Tennessee to raping a teenage student and exposing him to the AIDS virus.
The Shelby County district attorney’s office said Tuesday that 30-year-old John Conner III avoided trial when he pleaded guilty to criminal exposure to HIV, statutory rape by an authority figure and solicitation of a minor.

Prosecutors say Conner did not tell the teen that he had HIV. The boy later tested positive for HIV.
Conner has appeared on the Lifetime show “Bring It.” His sentencing is scheduled on Dec. 20.
Conner has two other cases pending involving sexual partners ages 17 and 24.
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