It's crappy, I mean legacy!

Let them clean their own shit!
Umm, we honestly don't know what this does. But hey, it's cheap, so buy now!
Because they deserve the doggy love too! -winks-
Carry on, nothing to look here.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.
Some quick example text to build on the card title and make up the bulk of the card's content.


Wiki ▸ [[API Reference]] ▸ [[Behaviors]] ▸ Zoom Behavior
This behavior automatically creates event listeners to handle zooming and panning gestures on a container element. Both mouse and touch events are supported.
# d3.behavior.zoom()
Constructs a new zoom behavior.
# zoom(selection)
Applies the zoom behavior to the specified selection, registering the necessary event listeners to support panning and zooming.
# zoom.translate([translate])
Specifies the current zoom translation vector. If not specified, returns the current translation vector, which defaults to [0, 0].
# zoom.scale([scale])
Specifies the current zoom scale. If not specified, returns the current zoom scale, which defaults to 1.
# zoom.scaleExtent([extent])
Specifies the zoom scale’s allowed range as a two-element array, [minimum, maximum]. If not specified, returns the current scale extent, which defaults to [0, Infinity].
# zoom.center([center])
If center is specified, sets the focal point [x, y] for mousewheel zooming and returns this zoom behavior. If center is not specified, returns the current focal point, which defaults to null. A null center indicates that mousewheel zooming should zoom in and out around the current mouse location.
# zoom.size([size])
If size is specified, sets the viewport size to the specified dimensions [width, height] and returns this zoom behavior. If size is not specified, returns the current viewport size which defaults to [960, 500]. A size is needed to support smooth zooming during transitions.
# zoom.x([x])
Specifies an x-scale whose domain should be automatically adjusted when zooming. If not specified, returns the current x-scale, which defaults to null. If the scale’s domain or range is modified programmatically, this function should be called again. Setting the x-scale also resets the scale to 1 and the translate to [0, 0].
# zoom.y([y])
Specifies an y-scale whose domain should be automatically adjusted when zooming. If not specified, returns the current y-scale, which defaults to null. If the scale’s domain or range is modified programmatically, this function should be called again. Setting the y-scale also resets the scale to 1 and the translate to [0, 0].
# zoom.on(type, listener)
Registers the specified listener to receive events of the specified type from the zoom behavior. The following types are supported:
If an event listener was already registered for the same type, the existing listener is removed before the new listener is added. To register multiple listeners for the same event type, the type may be followed by an optional namespace, such as “zoom.foo” and “zoom.bar”. To remove a listener, pass null as the listener.
For mousewheel events, which happen discretely with no explicit start and end reported by the browser, events that occur within 50 milliseconds of each other are grouped into a single zoom gesture. If you want more robust interpretation of these gestures, please petition your browser vendor of choice for better touch event support.
When fired, the d3.event object will contain the following properties:
# zoom.event(selection)
If selection is a selection, immediately dispatches a zoom gesture to registered listeners, as the three event sequence zoomstart, zoom and zoomend. This can be useful in triggering listeners after setting the translate or scale programatically. If selection is a transition, registers the appropriate tweens so that the zoom behavior dispatches events over the course of the transition: a zoomstart event when the transition starts from the previously-set view, zoom events for each tick of the transition, and finally a zoomend event when the transition ends. Note that the transition will be interrupted if the user starts zooming before the transition ends.


this project by {{ site.github.owner_name }} can be found on GitHub
Projects by {{ site.github.owner_name }} can be found on GitHub
Generated with GitHub Pages using Merlot. Policy.


Click Here For Event Time Table
Participate Now
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Entry fees: 50 ₹ Per student
Team can have maximum of 3 participants.
Spot entry will be allowed before one hour of event.
A team/person can submit and present only one poster.
No participants can be part of more than one team.
There is no restriction on the numbers of team from the same colllege.
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Participation fees: ₹ 50 per person.
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A Counterfeit of whole placement process from aptitude test to personal interview for both Tech and non Tech categories
+91 E-placement provides a platform for young students to get through the real experience of placement procedure.
Round 1 : Aptitude Test
Round 2 : Group Discussion
Round 3 : Personal Interview
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This Event is about Purification of Water on The Basis of Turbidity Parameter Using Some Natural Ingredients like coal, sand, lime etc.
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Spot entry will be allowed before one hour of event.
This event has 3 levels .
There is no restriction on the numbers of team from the same college.
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Man burns sister’s house
by Alfred P. DalizonPOLICE arrested a man who burned his sister’s house in Linamon, Lanao del Norte early morning yesterday, a Police Regional Office 10 report said.
The suspect identified as Ariel Sapong,38, surrendered to the Linamon Municipal Police Station accompanied by a local councilor, said the PRO10 headed by Brigadier General Rolando B. Anduyan.
An investigation sho-wed that the suspect set his sister Charity’s house in Purok 2, Barangay Mago-ong on fire at 7:45 a.m. yesterday.
Nobody was inside the house when it caught fire. Local firemen rushed to the site to help put out the fire which was officially declared out 30 minutes later.
Damage to property was placed at P100,000. Arson charges will be filed against the suspect, the local police said.


Normally, when you return to a book that you have been reading, reading will be resumed where you left off. However, it is a good idea to use bookmarks to keep you from losing your place.


POISON IVY A film review by Ken Johnson Copyright 1993 Ken Johnson
93 min., not rated, Drama, 1992 Director: Katt Shea Ruben Cast: Tom Skerritt, Sara Gilbert, Drew Barrymore, Cheryl Ladd, Leonardo DiCaprio
Sara Gilbert lives in a dysfunctional family with her rich father Tom Skerritt and her mother Cheryl Ladd. Ladd is dying and has given up on life. Tramp Drew Barrymore, Ivy, shows up in Gilbert's life and befriends her. Barrymore moves in with Gilbert and becomes 'one of the family'. Barrymore wants all that Gilbert has and tries to get it from her.
POISON IVY was released in the theaters back in March or May of 1992 when I was in New York, but was unable to see it at that time. When I got back to where I live, I waited for a theater around here to get it, but no one did. It was just released on video cassette this month, so I rented it as soon as possible. I hoped that it would be a good film, considering how long I had waited to see it, and it was. I highly suggest that you try and rent this film, just be sure that you get this version and not the earlier television film POISON IVY, which is a totally different film and is also on video cassette. On a scale of zero to five, I give POISON IVY a five. POISON IVY is released in two versions, an unrated version (which I believe is the European version) that runs for 93 minutes and contains explicit language, adult situations, and brief female nudity, and an R rated version that runs for 91 minutes and, I believe, is missing the scene of brief female nudity.
Sara Gilbert ("Roseanne") gives a great performance as the 'victim' of this film. She shows that she can give a dramatic performance and could very possibly have a chance at a good film career.  Her performance is excellent and she is very convincing as the tramp who shows up in Sara Gilbert's life and slowly ruins it. Tom Skerritt seems to just be along for a free ride in this film. He isn't on screen for much of the film, and when he is, his acting is good, but not excellent. Cheryl Ladd, although she is on the screen for less time than Tom Skerritt, gives a depressing, but exceptional performance.
POISON IVY is an interesting movie that is well written. The characters are interesting and the movie flows along smoothly. The direction, by Katt Shea Ruben is excellent. I think this is Katt Shea Ruben's best film since STREETS. Through the combined effort of everybody involved in this film, they have taken what could have ended up as just another B movie and made it into a great film that is definitely worth watching.
-------- Ken Johnson<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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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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Related links: index of all rec.arts.movies.reviews reviews


Want to find out more about your favourite singer or band? Our OverDrive eBook service has music-related books for every taste.
Broken Music: a memoir, by Sting
“Having been a songwriter most of my life, condensing my ideas and emotions into short rhyming couplets and setting them to music, I had never really considered writing a book. But upon arriving at the reflective age of fifty, I found myself drawn, for the first time, to write long passages that were as stimulating and intriguing to me as any songwriting I had ever done.
“And so Broken Music began to take shape. It is a book about the early part of my life, from childhood through adolescence, right up to the eve of my success with the Police. It is a story very few people know.
“I had no interest in writing a traditional autobiographical recitation of everything that’s ever happened to me. Instead I found myself drawn to exploring specific moments, certain people and relationships, and particular events which still resonate powerfully for me as I try to understand the child I was, and the man I became.”
Stranger Music: selected poems and songs, by Leonard Cohen
“Leonard Cohen’s legacy is that of one of the most literate, daring, and affecting poet-songwriters in the world. Stranger Music presents a magnificent cross-section of Cohen’s work - including the legendary songs Suzanne, Joan of Arc and The Chelsea Hotel, and elections from such books as Flowers for Hitler, Beautiful Losers, and Death of a Lady’s Man, and eleven previously unpublished poems.
“Stranger Music brings together Cohen’s song lyrics and a generous selection of his poetry and is a celebration of the legendary musician’s extraordinary gift for language that speaks with rare clarity, passion and timelessness.”
Lady Gaga: fame, fortune and monsters, by Chris Peacock
“Fame, Fortune and Monsters is a quick-read biography of Lady Gaga, one of today’s most famous and outlandish celebrities. The book takes us through the history of Gaga, from her school years right up to today. In addition to a general history of this world-famous music star, the author also discusses her wilder - and stranger - side, including bizarre rumours and fascinating news reports.”
Fleetwood Mac on Fleetwood Mac: interviews & encounters, by Sean Egan (ed.)
“Ethereal Californian Stevie Nicks, the tormented rocker Lindsey Buckingham, the English rose Christine McVie, the blunt John McVie and loquacious Mick Fleetwood worked through turmoil, heartbreak and betrayal to craft their masterpiece, 1977’s Rumours. All have been astoundingly candid about the process and this collection of interviews, spanning the entirety of Fleetwood Mac’s career, features articles from such celebrated publications as Crawdaddy, New Musical Express, Circus, Creem, Mojo and Classic Rock, as well as interviews that have never previously appeared in print.
“Learn the Fleetwood Mac story directly from the mouths of its members and experience the cauldron of animosity and misplaced love that crafted musical history.”
Moonwalk, by Michael Jackson
“Moonwalk is the only book about his life that Michael Jackson ever wrote. It chronicles his humble beginnings in the Midwest, his early days with the Jackson 5, and his unprecedented solo success.
“Giving absolutely unrivalled insight into the King of Pop’s life, it details his songwriting process for hits like Beat It, Rock With You, Billie Jean, and We Are the World; describes how he developed his signature dance style, including the Moonwalk; and opens the door to his very private personal relationships with his family, including sister Janet, and stars like Diana Ross, Berry Gordy, Marlon Brando, Quincy Jones, Paul McCartney, and Brooke Shields.
“At the time of its original publication in 1988, Moonwalk broke the fiercely guarded barrier of silence that surrounded Michael Jackson.”
What Happened, Miss Simone?, by Alan Light
“Drawing on glimpses into previously unseen diaries, rare interviews and childhood journals, and with the aid of her daughter, What Happened, Miss Simone? tells the story of the classically trained pianist who became a soul legend, a committed civil rights activist and one of the most influential, provocative and least understood artists of our time. This is the story of the real Miss Simone.”
Adele, by Sean Smith
“In Adele, bestselling biographer Sean Smith talks to those close to her as he follows her astonishing journey to fame that began on the gritty streets of Tottenham.
“Through compelling new research and interviews, he reveals that there is far more to Adele than the superstar we all think we know. He uncovers the story of her complex family relationships; the ill-starred love affair between her mother and father; her devastation at the untimely death of her grandfather; and her seemingly unpromising future in a gang-ravaged area of South London. She found salvation at the BRIT School before a series of unhappy love affairs provided the inspiration for her record-breaking albums. He describes how she conquered America and how it all could have been ended by a dramatic vocal injury. Instead, she has made an amazing comeback and found personal happiness in a new relationship and becoming a mum.
“Intimate and revealing, Adele is the uplifting story of the woman with the most glorious voice in the world.”
The Beatles: the stories behind the songs 1967-70, by Steve Turner
“Where is Penny Lane? What was the inspiration for Lucy In The Sky With Diamonds? Steve Turner shatters many well-worn myths and adds a new dimension to the Fab Four’s rich legacy by investigating the events immortalised in The Beatles’ music and now occupying a special niche in popular culture’s collective imagination.”
Good Vibration: my life as a Beach Boy, by Mike Love
“Mike Love is a founding member, lyricist and vocalist of The Beach Boys, considered to be the most popular American band in history, with 13 Gold Albums, 55 top-100 singles, and four #1 hits. Love has been the lead singer of the group one of its principal lyricists since its inception in 1961.
“In Good Vibrations, Mike Love tells the unique story of his legendary, chaotic, and ultimately triumphant five-decade tenure as the front man of The Beach Boys, from their Californian roots to international fame.”


- canHarmHouse(House) - Method in class Baratheon
-
If a Baratheon can harm another House
- canHarmHouse(House) - Method in class Falcon
-
If a Falcon can harm another House
- canHarmHouse(House) - Method in class House
-
Abstract.
- canHarmHouse(House) - Method in class Lannister
-
If a Lannister can harm another House
- canHarmHouse(House) - Method in class Stark
-
If a Stark can harm another House
- canHarmHouse(House) - Method in class Targaryan
-
If a Targaryan can harm another House
- canHarmHouse(House) - Method in class Tully
-
If a Tully can harm another House
- canReproduceWithHouse(House) - Method in class Baratheon
-
If a Baratheon can reproduce with another House
- canReproduceWithHouse(House) - Method in class Falcon
-
If a Falcon can reproduce with another House
- canReproduceWithHouse(House) - Method in class House
-
Abstract method.
- canReproduceWithHouse(House) - Method in class Lannister
-
If a Lannister can reproduce with another House
- canReproduceWithHouse(House) - Method in class Stark
-
If a Stark can reproduce with another House
- canReproduceWithHouse(House) - Method in class Targaryan
-
If a Targaryan can reproduce with another House
- canReproduceWithHouse(House) - Method in class Tully
-
If a Tully can reproduce with another House
- checkDead() - Method in class Westeros
-
Circle of life.
- checkFoodChain() - Method in class Westeros
-
Check to see whether any of the houses can wound
the other houses they are around.
- checkReproduce() - Method in class Westeros
-
Checks to see if members of a house need to reproduce
- collidesWithHouse(House) - Method in class House
-
Returns if a House is colliding with another House
- ControlPanel - Class in <Unnamed>
-
This is the ControlPanel for Game of Thrones.
- ControlPanel() - Constructor for class ControlPanel
-
Instantiates a Control Panel
- ControlPanel.ButtonListener - Class in <Unnamed>
-
Button Listener thing


6 KILLED BY TRAIN WHILE SLEEPING ON TRACKS
HOUSTON _ Six people believed to be illegal immigrants from
Mexico were hit and killed early Monday by a freight train as they
slept on a desolate stretch of tracks in southern Texas. Officials
said they believed the immigrants may have been victims of a
superstition that the rails would keep them safe from snakes.
``There's a myth that if you sleep between the rails that snakes
will not get you,'' said Mark Davis, a spokesman for Union Pacific.
``You go down the railroad tracks, you'll see snakes cut in half.''
Davis said the driver of the train blew the whistle repeatedly
when he noticed the people on the tracks. Although a couple of the
victims raised their heads, no one got off the tracks. Davis said
he was uncertain how far the train was from the victims when the
driver saw them. But, he added, it would have taken a mile to stop
the 105-car train, which was traveling between 45 and 49 miles an
hour.
The vast ranchlands of South Texas that stretch between the Rio
Grande and San Antonio are among the most remote in the state, mile
after mile of mesquite, prickly pear, citrus farms and grazing
lands that are a prime avenue for illegal immigrants trying to make
their way from the Mexican border to San Antonio or Houston.
While illegal immigrants have been killed while sleeping on
railroad tracks before, Fred Borrego, patrol agent in charge of the
U.S. Border Patrol in Kingsville, said never had so many been
killed in one accident. Between 2,000 and 3,000 illegal immigrants
pass through his 2,600-square-mile patrol area each month, he said.
The most recent victims, who were badly mutilated, have not been
identified. They were taken to Corpus Christi where authorities
will try to use fingerprints to determine their identity.
Borrego had another possible explanation for the accident.
``Another theory I've been told is that in Mexico the trains
don't run at night,'' Borrego said. ``They say they sleep up there
because they feel a lot safer.''
Although it seems implausible that people could sleep with the
noise of a train running toward them, Davis said, ``a train
operating when you're in front of it is very, very quiet.''
The engine noise is primarily to the side, he said, and there is
very little friction on the rail itself.
``I know its hard to believe,'' Borrego said. ``I've experienced
this myself. If it's coming toward you it's as quiet as a cat.''
But officials in the Kenedy County sheriff's office speculated
that the hard traveling conditions confronted by the immigrants
could also have played a role. The terrain in the area is very
rugged, with most of it being brush covered.
``You think, `I'd never fall asleep there' or `Why wouldn't they
wake up?''' said Hopy Alines, a secretary in the sheriff's office.
``But it just doesn't happen. These people travel three, four and
five days without eating, without sleeping. They're just completely
exhausted and they don't wake up.''
Generally the illegal immigrants traveling through the county go
unnoticed aside from the trail of odds and ends they leave behind,
Ms. Alise said. ``You'll see empty bottles, sometimes bags of
clothes,'' she said. ``We get a lot of calls where they break in
and all they ever take is food and water.'' It is generally when
people get sick or have problems that officials hear stories like
the ones about people sleeping on the tracks.


QGeoRouteReply Class
The QGeoRouteReply class manages an operation started by an instance of QGeoRoutingManager. More...
| Header: | #include <QGeoRouteReply> |
| qmake: | QT += location |
| Since: | Qt 5.6 |
| Inherits: | QObject |
Public Types
| enum | Error { NoError, EngineNotSetError, CommunicationError, ParseError, UnsupportedOptionError, UnknownError } |
Public Functions
| QGeoRouteReply(Error error, const QString &errorString, QObject *parent = Q_NULLPTR) | |
| virtual | ~QGeoRouteReply() |
| virtual void | abort() |
| Error | error() const |
| QString | errorString() const |
| bool | isFinished() const |
| QGeoRouteRequest | request() const |
| QList<QGeoRoute> | routes() const |
- 32 public functions inherited from QObject
Signals
| void | aborted() |
| void | error(QGeoRouteReply::Error error, const QString &errorString = QString()) |
| void | finished() |
- 2 signals inherited from QObject
Protected Functions
| QGeoRouteReply(const QGeoRouteRequest &request, QObject *parent = Q_NULLPTR) | |
| void | addRoutes(const QList<QGeoRoute> &routes) |
| void | setError(Error error, const QString &errorString) |
| void | setFinished(bool finished) |
| void | setRoutes(const QList<QGeoRoute> &routes) |
- 9 protected functions inherited from QObject
Additional Inherited Members
- 1 property inherited from QObject
- 1 public slot inherited from QObject
- 1 public variable inherited from QObject
- 10 static public members inherited from QObject
- 2 protected variables inherited from QObject
Detailed Description
The QGeoRouteReply class manages an operation started by an instance of QGeoRoutingManager.
Instances of QGeoRouteReply manage the state and results of these operations.
The isFinished(), error() and errorString() methods provide information on whether the operation has completed and if it completed successfully.
The finished() and error(QGeoRouteReply::Error,QString) signals can be used to monitor the progress of the operation.
It is possible that a newly created QGeoRouteReply may be in a finished state, most commonly because an error has occurred. Since such an instance will never emit the finished() or error(QGeoRouteReply::Error,QString) signals, it is important to check the result of isFinished() before making the connections to the signals. The documentation for QGeoRoutingManager demonstrates how this might be carried out.
If the operation completes successfully the results will be able to be accessed with routes().
Member Type Documentation
enum QGeoRouteReply::Error
Describes an error which prevented the completion of the operation.
| Constant | Value | Description |
|---|---|---|
QGeoRouteReply::NoError | 0 | No error has occurred. |
QGeoRouteReply::EngineNotSetError | 1 | The routing manager that was used did not have a QGeoRoutingManagerEngine instance associated with it. |
QGeoRouteReply::CommunicationError | 2 | An error occurred while communicating with the service provider. |
QGeoRouteReply::ParseError | 3 | The response from the service provider was in an unrecognizable format. |
QGeoRouteReply::UnsupportedOptionError | 4 | The requested operation or one of the options for the operation are not supported by the service provider. |
QGeoRouteReply::UnknownError | 5 | An error occurred which does not fit into any of the other categories. |
Member Function Documentation
QGeoRouteReply::QGeoRouteReply(Error error, const QString &errorString, QObject *parent = Q_NULLPTR)
Constructs a route reply with a given error and errorString and the specified parent.
[protected]
QGeoRouteReply::QGeoRouteReply(const QGeoRouteRequest &request, QObject *parent = Q_NULLPTR)
Constructs a route reply object based on request, with the specified parent.
[virtual]
QGeoRouteReply::~QGeoRouteReply()
Destroys this route reply object.
[virtual]
void QGeoRouteReply::abort()
Cancels the operation immediately.
This will do nothing if the reply is finished.
[signal]
void QGeoRouteReply::aborted()
This signal is emitted when the operation has been cancelled.
This function was introduced in Qt 5.9.
See also abort().
[protected]
void QGeoRouteReply::addRoutes(const QList<QGeoRoute> &routes)
Appends the list of routes to the existing list.
Error QGeoRouteReply::error() const
Returns the error state of this reply.
If the result is QGeoRouteReply::NoError then no error has occurred.
See also setError().
[signal]
void QGeoRouteReply::error(QGeoRouteReply::Error error, const QString &errorString = QString())
This signal is emitted when an error has been detected in the processing of this reply. The finished() signal will probably follow.
The error will be described by the error code error. If errorString is not empty it will contain a textual description of the error.
This signal and QGeoRoutingManager::error() will be emitted at the same time.
Note: Do not delete this reply object in the slot connected to this signal. Use deleteLater() instead.
Note: Signal error is overloaded in this class. To connect to this one using the function pointer syntax, you must specify the signal type in a static cast, as shown in this example:
connect(geoRouteReply, static_cast<void(QGeoRouteReply::*)(QGeoRouteReply::Error, const QString &)>(&QGeoRouteReply::error), [=](QGeoRouteReply::Error error, const QString &errorString){ /* ... */ });
QString QGeoRouteReply::errorString() const
Returns the textual representation of the error state of this reply.
If no error has occurred this will return an empty string. It is possible that an error occurred which has no associated textual representation, in which case this will also return an empty string.
To determine whether an error has occurred, check to see if QGeoRouteReply::error() is equal to QGeoRouteReply::NoError.
[signal]
void QGeoRouteReply::finished()
This signal is emitted when this reply has finished processing.
If error() equals QGeoRouteReply::NoError then the processing finished successfully.
This signal and QGeoRoutingManager::finished() will be emitted at the same time.
Note: Do not delete this reply object in the slot connected to this signal. Use deleteLater() instead.
See also setFinished().
bool QGeoRouteReply::isFinished() const
Return true if the operation completed successfully or encountered an error which cause the operation to come to a halt.
QGeoRouteRequest QGeoRouteReply::request() const
Returns the route request which specified the route.
QList<QGeoRoute> QGeoRouteReply::routes() const
Returns the list of routes which were requested.
See also setRoutes().
[protected]
void QGeoRouteReply::setError(Error error, const QString &errorString)
Sets the error state of this reply to error and the textual representation of the error to errorString.
This will also cause error() and finished() signals to be emitted, in that order.
See also error().
[protected]
void QGeoRouteReply::setFinished(bool finished)
Sets whether or not this reply has finished to finished.
If finished is true, this will cause the finished() signal to be emitted.
If the operation completed successfully, QGeoRouteReply::setRoutes() should be called before this function. If an error occurred, QGeoRouteReply::setError() should be used instead.
See also finished().
[protected]
void QGeoRouteReply::setRoutes(const QList<QGeoRoute> &routes)
Sets the list of routes in the reply to routes.
See also routes().


{% extends "layout.html" %} {% block page_title %} Registered Traveller {% endblock %} {% block content %} Back Which of these visas or residents permits do they have? Dependant of Tier 1, 2 or 4 Dependant of Tier 5 Dependant of UK Ancestry Other visa Continue {% endblock %}


Edit Challenge
Draw the areas you need to capture street-level imagery of:
{% extends 'layouts/base.html' %} {% load static %} {% block app_challenge %}active{% endblock %} {% block app_style %}{% endblock %} {% block content %}
Draw the areas you need to capture street-level imagery of:


CBI chief warns against another wasted year of ‘political paralysis’
The CBI also called for a new immigration system which provides access to labour and skills
A senior business leader is warning that the UK cannot afford another wasted year of “political paralysis”, while productivity and investment suffers.
Carolyn Fairbairn, director general of the CBI, said whoever wins the General Election, the top priority must be to work with business to provide “bolder, better and fairer” answers to the challenges facing the UK.
She launched the business group’s manifesto for political parties, which outlines steps the next government should take to unlock the UK’s competitiveness.
This includes reforming business rates, fixing the “broken” apprenticeship levy and completing vital infrastructure projects including HS2 and Heathrow expansion.
The CBI also called for a new immigration system which provides access to labour and skills, focuses on contribution, not numbers, and gives business time to adapt.
Speaking ahead of the CBI’s annual conference on Monday, Ms Fairbairn said: “The strength of the business-government relationship has underpinned the UK’s success for many decades.
“It has driven progress at home, from the building of the Channel Tunnel to the development of renewable energy to address climate change.
“It has helped to make the UK the leading destination in Europe for foreign investors, and supporting millions of jobs.
“But this relationship is being questioned. Too many people have lost faith in a system they don’t see working for them.
“In response, ideologies from both the left and right have emerged to fill the gap, pushing debate to extremes and offering superficial fixes to complex problems.
“Public faith in our politicians and institutions continues to erode, the historic partnership between business and government is under strain. Business investment has fallen for 18 months and productivity growth remains stubbornly sluggish. Confidence in the UK is faltering.
“This cannot continue, but it is within our power to change it.
“We simply cannot afford another wasted year of political paralysis, indecision and distraction while productivity and investment suffer.”


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*required Sub Task
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*Enter a valid date here : DD/MM/YYYY
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Emercency No
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WITNESS LIST Appropriations Committee (Subcommittee on Education) January 30, 2001-8:00A Budget Against: Gilleland, Ellis (self) On: Blanck, Ronald (University of North Texas Health Science Center) Brown, Don (Texas Higher Education Coordinating Board) Brunjes, Jim (Texas Tech Universiy System) Burck, Dan (U.T. System) Cigarroa, Francisco (U.T. Health Science Center in San Antonio) Cole, James (TAMU Health Science Center) Diebel, Phil (University of North Texas System) Elliot, Roger (Texas Higher Education Coordinating Board) Garvey, Ronald (U.T. Health Science Center Tyler) Gaston, Jerry (Texas A&M University System) Haley, Elizabeth (Texas Tech University System) Hurley, Alfred (University of North Texas System) Kale, Tom (Texas A&M University) Mendelsohn, John (U.T. M.D. Anderson Cancer Center) Montford, John (Texas Tech System) Mullins, Charles (U.T. System) Neal, Nancy (Texas State University System) Revill, Lawrence (UTMB) Smith, Arthur (University of Houston System) Smith, David (Texas Tech Health Science Center) Stobo, John (UTMB) Sumaya, Ciro (Texas A&M Health Science Center) Urbanovsky, Lamar (Texas State University System) Vela, Leonel (U.T. Health Science Center San Antonio) Wallace, Randy (U.T. System) Wildenthal, Kern (U.T. Southwestern Medical Center) Willerson, James (U.T. Health Science Center at Houston)


CKAN Multilingual Translator, a Bootstrap 3 tool based on CKAN4J Web API
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My name is Jerome Francisco and I'm a graduate from Cal Poly Pomona as a Computer Information Systems major.  During my free time, I love to listen to music and hang out with my friends. Learning how to edit videos is also something that I do as a fun hobby to express my creative interests and freedom.
Trustworthy
Keen eye
Team Player
Constant Learner
Atom
Wordpress
Android Studio
GitHub
Photoshop
Gimp
Feel free to connect with me on Twitter, LinkedIn, GitHub or my email. I would be more than happy to answer any questions or have a nice simple chat.


| 10-04-2017 |
0456125 |
Visa |
PTS895 |
Short Let |
Processed |
|
| 10-04-2017 |
0456125 |
Visa |
PTS895 |
Short Let |
Rejected |
Insufficient funds in account |
| 10-04-2017 |
0456125 |
Mater Card |
PTS895 |
Short Let |
Processed |
|
| 10-04-2017 |
0456125 |
Visa |
PTS895 |
Short Let |
Rejected |
Insufficient funds in account |
| 10-04-2017 |
0456125 |
Visa |
PTS895 |
Short Let |
Processed |
|
| 10-04-2017 |
0456125 |
Mater Card |
PTS895 |
Short Let |
Processed |
|
| 10-04-2017 |
0456125 |
Visa |
PTS895 |
Short Let |
Processed |
|
| 10-04-2017 |
0456125 |
Visa |
PTS895 |
Short Let |
Rejected |
Insufficient funds in account |
| 10-04-2017 |
0456125 |
Mater Card |
PTS895 |
Short Let |
Processed |
|
| 10-04-2017 |
0456125 |
Visa |
PTS895 |
Short Let |
Rejected |
Insufficient funds in account |
| 10-04-2017 |
0456125 |
Visa |
PTS895 |
Short Let |
Processed |
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| 10-04-2017 |
0456125 |
Mater Card |
PTS895 |
Short Let |
Processed |
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| 10-04-2017 |
0456125 |
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PTS895 |
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Processed |
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-
Canvas2d Cube,
Canvas2d Transforms
-
Tree,
Forest,
Forest VR,
-
Unity3d:
Lightmap,
Animation data preview,
animation data import
-
Path,
Braid,
Hair,
Stream
-
Bones,
Hand,
Bird
- Lines and curves
- Carpet
- Primitives
-
Convex hull,
Delaunay 2D.
Delaunay GL
-
Single Cube,
Batched Cubes (mobile),
FXAA Cube,
Wobly sphere
- Wire platonic primitives
- Nodes, Dust, smoke
- 12(000) Monkeys (mobile)
- Cubemap reflection
- Spherical harmonics
- Depth of field (mobile)
- Bloom (mobile)
- Edge detect (mobile)
- Screen Space Ambient Occlusion (mobile)
- Webcam reflection


--- active_crumb: Shot-Term Memory layout: documentation id: short_term_memory ---
NLPCraft provides automatic conversation management right out of the box that is fully integrated with intent matching. Conversation management is based on the idea of short-term memory (STM) - the mechanism by which NLPCraft "remembers" the context of the conversation for a certain amount of time and certain depth of the conversation. STM is automatically maintained by NLPCraft per each user and data model combination. Intent matching algorithm can "recall" the missing tokens from STM when trying to find a match for the conversational terms.
Why is this so important? Maintaining conversation state is necessary for effective context resolution, so that users could ask, for example, the following sequence of questions using example weather model:
User gets the current London’s weather. STM is empty at this moment so NLPCraft expects to get all necessary information from the user sentence. Meaningful parts of the sentence get stored in STM.
User gets the current Berlin’s weather.
The only useful data in the user sentence is name of the city Berlin
. But since
NLPCraft now has data from the previous question in its STM it can safely deduce that we
are asking about weather
for today
.
Berlin
overrides London
in STM.
User gets the next week forecast for Berlin.
Again, the only useful data in the user sentence is next week
and forecast
.
STM supplies Berlin
. Next week
override today
, and
forecast
override weather
in STM.
Note that STM is maintained per user and per data model. Conversation management implementation is also smart enough to clear STM after certain period of time, i.e. it “forgets” the conversational context after few minutes of inactivity. Note also that conversational context can also be cleared explicitly using {% scaladoc NCConversation %}.
To understand the algorithm behind the STM management let's back up a few steps...
One of the key objectives when parsing user input sentence for Natural Language Understanding (NLU) is to detect all possible semantic entities, a.k.a named entities. Let's consider a few examples:
"What's the current weather in Tokyo?"
weather
as well as all necessary parameters
like time (current
) and location (Tokyo
).
"What about Tokyo?"
"What's the weather?"
Sometimes we can use default values like the current user's location and the current time (if they are missing). However, this can lead to the wrong interpretation if the conversation has an existing context.
In real life, as well as in NLP-based systems, we always try to start a conversation with a fully defined sentence since without a context the missing information cannot be obtained and the sentenced cannot be interpreted.
Let's take a closer look at the named entities from the above examples:
weather
- this is an indicator of the subject of the conversation. Note that it indicates
the type of question rather than being an entity with multiple possible values.
current
- this is an entity of type Date
with the value of now
.
Tokyo
- this is an entity of type Location
with two values:
city
- type of the location.Tokyo, Japan
- normalized name of the location.We have two distinct classes of entities:
weather
is the
type indicator for the subject of the user input.
current
and Tokyo
entities.
Assuming previously asked questions about the weather in Tokyo (in the span of the ongoing conversation) one could presumably ask the following questions using a shorter, incomplete, form:
"What about Kyoto?
"What about tomorrow?"
Kyoto
as the location since it was mentioned the last.
These are incomplete sentences. This type of short-hands cannot be interpreted without prior context (neither by humans nor by machines) since by themselves they are missing necessary information. In the context of the conversation, however, these incomplete sentences work. We can simply provide one or two entities and rely on the "listener" to recall the rest of missing information from a short-term memory.
In NLPCraft, the intent-matching logic will automatically try to find missing information in the conversation context (that is automatically maintained). Moreover, it will properly treat such recalled information during weighted intent matching since it naturally has less "weight" than something that was found explicitly in the user input.
The short-term memory is exactly that - a memory that keeps only small amount of recently used information and that evicts its contents after a short period of inactivity.
Let's look at the example from a real life. If you called your friend in a couple of hours asking "What about a day after?"
(still talking about weather in Kyoto) - he'll likely be thoroughly confused. The conversation is timed out, and
your friend has lost (forgotten) its context. You will have to explain again to your confused friend what is that you are asking about...
NLPCraft has a simple rule that 5 minutes pause in conversation leads to the conversation context reset. However, what happens if we switch the topic before this timeout elapsed?
Resetting the context by the timeout is, obviously, not a hard thing to do. What can be trickier is to detect when conversation topic is switched and the previous context needs to be forgotten to avoid very confusing interpretation errors. It is uncanny how humans can detect such switch with seemingly no effort, and yet automating this task by the computer is anything but effortless...
Let's continue our weather-related conversation. All of a sudden, we ask about something completely different:
"How much is mocha latter at Starbucks?"
"What about Peet's?"
"...and croissant?"
Despite somewhat obvious logic the implementation of context switch is not an exact science. Sometimes, you can have a "soft" context switch where you don't change the topic of the conversation 100% but yet sufficiently enough to forget at least some parts of the previously collected context. NLPCraft has a built-in algorithm to detect the hard switch in the conversation. You can also use {% scaladoc NCConversation %} to perform a selective reset on the conversation in case of "soft" switch.
See {% scaladoc NCConversation %} interface for API details for STM management.
As we've seen above one named entity can replace or override an older entity in the STM, e.g. "Peet's"
replaced "Starbucks"
in our previous questions. The actual algorithm that governs this logic is one
of the most important part of STM implementation. In human conversations we perform this logic seemingly
subconsciously — but the computer algorithm to do it is not that trivial. Let's see how it is done in NLPCraft.
One of the important supporting design decision is that an entity can belong to one or more groups. You can think of groups as types, or classes of entities (to be mathematically precise these are the categories). The entity's membership in such groups is what drives the rule of overriding.
Let's look at the specific example.
Consider a data model that defined 3 entities:
"sell"
(with synonym "sales"
)
"buy"
(with synonym "purchase"
)
"best_employee"
(with synonyms like "best"
, "best employee"
, "best colleague"
)
Our task is to support for following conversation:
"Give me the sales data"
"sell"
entity by its synonym "sales"
.
"Who was the best?"
"best_employee"
and we should pick "sell"
entity from the STM.
"OK, give me the purchasing report now."
"best_employee"
entity from STM and, in fact, we should remove it from STM.
"...and who's the best there?"
"best_employee"
entity and we should pick "buy"
entity from STM.
"One more time - show me the general purchasing data again"
"best_employee"
from STM.
Here's the rule we developed at NLPCraft and have been successfully using in various models:
The entity will override other entity or entities in STM that belong to the same group set or its superset.
In other words, an entity with a smaller group set (more specific one) will override entity with the same
or larger group set (more generic one).
Let's consider an entity that belongs to the following groups: {G1, G2, G3}
. This entity:
{G1, G2, G3}
groups (same set).
{G1, G2, G3, G4}
groups (superset).
{G1, G2}
groups.
{G10, G20}
groups.
Let's come back to our sell/buy/best example. To interpret the questions we've outlined above we need to have the following 4 intents:
intent=sale term={# == 'sale'}
intent=best_sale_person term={# == 'sale'} term={# == 'best_employee'}
intent=buy term={# == 'buy'}
intent=buy_best_person term={# == 'buy'} term={# == 'best_employee'}
(this is actual Intent Definition Language (IDL) used by NLPCraft -
term
here is basically what's often referred to as a slot in other systems).
We also need to properly configure groups for our entities (names of the groups are arbitrary):
"sell"
belongs to group A"buy"
belongs to group B"best_employee"
belongs to groups A and BLet’s run through our example again with this configuration:
"Give me the sales data"
"sell"
entity with group A in STM."Who was the best?"
"best_employee"
entity."OK, give me the purchasing report now."
"buy"
entity with group A."buy"
entity with group A in STM.In some cases you may need to explicitly clear the conversation STM without relying on algorithmic behavior. It happens when current and new topic of the conversation share some of the same entities. Although at first it sounds counter-intuitive there are many examples of that in day to day life.
Let’s look at this sample conversation:
"What the weather in Tokyo?"
"Let’s do New York after all then!"
The second question was about going to New York (booking tickets, etc.). In real life - your counterpart will likely ask what you mean by "doing New York after all" and you’ll have to explain the abrupt change in the topic. You can avoid this confusion by simply saying: "Enough about weather! Let’s talk about this weekend plans" - after which the second question becomes clear. That sentence is an explicit context switch which you can also detect in the NLPCraft model.
In NLPCraft you can also explicitly reset conversation context through {% scaladoc NCConversation %} interface or by switching the model on the request.


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Let's hit the Pig-Dice!
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Let's book a trip!
Destination
London
Paris
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Tickets
Call us at<PHONE_NUMBER> to make a reseveration today!


the and for species from that are with tree data The trees our tip patch were DNA fossil each was which node backbone clade mammal ages using all approximately mammalian phylogenetic colleagues crown phylogeny phylogenies this rate (Fig evolutionary rates than Evol. clades (e.g., supertree Fig [], diversification studies study additional molecular sets uncertainty estimates used mammals relative Phylogenetic analyses but have species-level taxonomic time {background Bayesian across age global here topology Dryad one sampled Biol. 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Reports usages of sameElements
and corresponds
with unsorted collections.
The methods sameElements
and corresponds
use iterators of the given collections
to compare elements depending on the order of their position in the collection.
The result is therefore unpredictable if the order of elements is not well defined,
as it is the case for unordered Sets or Maps.
To fix this consider using ==
, subsetOf
, or bring the elements in some order.
Example:
Set(3,2,1) sameElements Set(1, 2, 3) // is false
// but
Set(1, 2, 3, 4, 5) sameElements Set(5, 4, 3, 2, 1) // is true


About
Website for teaching HTML to everyone. I conceived of this website after returning to web development from a long hiatus. I used this project to learn HTML5 alongside ReactJS, but while doing so, I noticed an old problem had resurfaced in the new world: HTML is often misunderstood and misused. For example, there's an ugly proliferation of div tags. Div tags have no semantic meaning, yet many sites seem to use them for everything, often in place of proper semantic tags.
I believe the real problem is that today's web developers do not maximize their use of HTML. I think this issue stems from the widescale adoption of user interface libraries and frameworks like Angular, React, Svelte, and Vue. These tools abstract the web's view layer by allowing users to build everything in components. Component driven development has been growing in popularity for years, because it makes reasoning around and maintaining complex user interfaces easier. But a lot of new web developers seem to have missed a fundamental point: HTML is already organized into components. They're called elements! We just have to use them properly.
This website hopes to fix these problems by educating web developers.
Offering
HTML.Haus provides:
- Learn: a single page tutorial on HTML
- Practice: with an element memorization tool
- Reference: for broadly supported HTML elements
- Tools: to help you write better, faster, and stronger HTML
License
This entire website, all sample code, and APIs are free and open source, using the MIT license.
Contributing
Have a suggestion or see an error? This project is open source. Please contribute on GitHub .
Author
Made with by RCH. This is the first project in a trifecta covering web technologies:
- HTML.Haus is here to remind developers to use all the HTML available.
- CSS.City is a website dedicated to teaching Cascading Style Sheets (CSS).
- Script.Schule is a website dedicated to practicing modern JavaScript (JS).


Choose a dataset, tinker with parameters and see how your model does on bias/ variance tradeoff!
Which dataset you want to use?
Amount of data to sample?
How much noise?
What degree basis to fit?
Linear regression is a technique for modeling a dependent variable (y) as a linear combination of one or more independent variables x(i)). Polynomial regression allows us to capture non-linear relationships between X and y using a change of basis (z(i)=f(x(i))). In a nutshell, instead of a line, it allows us to fit a nth degree polynomial to the data. Read more [link].
Tinker with different values of parameters like polynomial degree (p), noise, and amount of sampling. Each value of the parameters result in a slightly different model in the bias-variance tradeoff landscape. The aim is to demonstrate the fundamental tradeoff, and relate it with overfitting/ underfitting and generalization error.
Imagine, we can rebuild different models by repeatedly sampling new data. Due to noise, we may end up with a slightly different model. The bias for particular prediction corresponds to the difference between its expected value and its true value. The variance corresponds to the spread of these predictions. Read more [link]. We compute the bias and variance of the whole model by averaging bias and variance of all data points in the dataset.
The tradeoff is in the sense that when you tweak the model parameters (degree of polynomial in this case), a decrease in one type of error leads to increase in the other and vice versa.
The scatter plot represents a single snapshot of the data (shown as blue dots). We fit an nth degree polynomial regression line to it (shown in pink).
You can observe the bias-variance tradeoff by observing different fits for the same dataset. Notice that you get quite different fit for a higher degree polynomial (high variance) while you get the same model for a low degree polynomial (low variance). On the other hand, a high degree polynomial closely fits more number of points, hence the bias is low. While a low degree polynomial does not have this expressivity leading to high bias.
Box plot is generated by plotting the distribution of residuals (absolute difference between predictions of the model and its actual value). Each bar corresponds to a different degree of a polynomial. Notice, a bar close to bottom represents low residuals (on average). Hence, the bias of the model is low. On the other hand, the height of a bar gives an idea of the spread of residuals, giving us an estimate of variance. These two things roughly correspond to bias and variance of the model. Notice, as the model degree increases, the bars shifts downwards (low bias) and the height of bars increases (high variance).
The bar chart is generated by computing the bias and variance of the model. Each bar corresponds to bias and variance of the model for a particular degree of the polynomial. Notice as you go right (increase in polynomial degree), the bias of the model decreases and the variance increases. On the other hand, if we go left (model degree decreases), the bias of the model increases and the variance decreases.
One of my colleague did it another playground [link]. The bulls's eye visualization idiom (originally proposed here [link]) is intuitive but not very accurate. It can be misleading since both bias and variance are really an one-dimentional quantity. Read our project report for more details [link]. Also, feel free to suggest any changes by submitting a pull request here [link]


I spent 15-months in Iraq (2003-2004) and conducted over 200 convoys in and around Baghdad as the security gunner (M249). You tend to appreciate everything life has to offer after an experience like that.
I really enjoy solving puzzles or problems, which lead to improved efficiencies or streamlining of processes. This is how I approach coding- a means to solve a problem in the most efficient way. Understanding the bigger picture makes it that much easier to develop your part of the puzzle to help the greater good.


dynapseSpikesGenerator¶
The module contains functions that permit to write a .txt file with coded inputs to DYNAP-se
-
DYNAPSETools.dynapseSpikesGenerator.
import_events
(fileName, name='ImportedPattern.txt', isiBase=90.0)[source]¶ Import events from an outside stimulus txt file
Parameters: - fileName (string) – Path of .txt file from which events must be imported
- name (string, optional) – Name of the imported event pattern (useful for debug purpouses)
- isiBase (int, optional) – Time base of the input pattern, for time event generation.
Returns: Set of events that has been imported from input file
Return type: obj InputPattern
Note
The isiBase is a parameter that tune the resolution of the spike generator inside Dynap-se. Spike timings values, in fact, are multiple of this base value. For this reason, the maximum and minimum interspike interval depends on isi value:
min delay = isiBase / 90 MHz EQUIVALENT ROUGHLY TO: min delay = isiBase * 11.11 ns max delay = min delay * 2^16 EQUIVALENT TO: max delay = min delay * 65535
The maximum isiBase that can be set is 1000 while the minimum is 1.
Common isiBase values are:
isiBase = 90 -> min delay = 1us, max delay = 65.535 ms isiBase = 900 -> min delay = 10us, max delay = 655.35 ms
Important note: isiBase value must match the one set in Dynap-se software interface. An eventual different leads to difference in timings between the wanted pattern and the generated one.
-
DYNAPSETools.dynapseSpikesGenerator.
plot_spikes
(*inputPatternList, ax=None)[source]¶ Plot the spike stimuli
Parameters: *inputPatternList (list of obj InputPattern) – Contains the patterns that must be plotted Returns: tuple containing: - fig (fig handles): To modify properties of the figure
- ax (ax handles): To modify properties of the plot
- handles (lines handles): To create custom legends
Return type: (tuple) Note
The spikes colors are chosen according to their index. That means that if you create a pattern made with 5 spikes coming from a certain virtual neuron and other 5 coming from another virtual neuron, they will have a different colors. Spikes with the same address have the same color.
Examples
Create patterns and assign some spikes:
# Create events patterns p1 = DSG.InputPattern("p1", isiBase = 900.0) p2 = DSG.InputPattern("p2", isiBase = 900.0) # insert some events in the patterns p1.multiple_events(virtualSourceCoreId = [0, 0], neuronAddress = [1, 1], coreDest = [15, 15], firePeriod = [0.2, 0.2]) p2.multiple_events(virtualSourceCoreId = [0, 0], neuronAddress = [1, 1], coreDest = [15, 15], firePeriod = [0.2, 0.2])
Insert directly the patterns for the plot:
fig, ax, handles = DSG.plot_spikes(p1, p2)
Create a list of patterns and plot it:
patternList = [p1, p2] fig, ax, handles = DSG.plot_spikes(*patternList)
-
DYNAPSETools.dynapseSpikesGenerator.
write_to_file
(*input_patterns, fileName='stimulus.txt')[source]¶ Write the stimulus on an output txt file and make controls to avoid overflow
Parameters: - *input_patterns (list of obj InputPattern) – Contains all the patterns that should be written in the output file
- fileName (string, optional) – Name of the output .txt file
Note
write_to_file function check also if the specified patterns respect some particular constraints:
- the final pattern must not be bigger than 2^15 events -> it will not fit in the SRAM
- the minimum and maximum delay constrains must be respected by every pattern.
- this vary according to the chosen isiBase
- delay must be positive
Examples
Create patterns and assign some spikes:
# Create events patterns p1 = DSG.InputPattern("p1", isiBase = 900.0) p2 = DSG.InputPattern("p2", isiBase = 900.0) # insert some events in the patterns p1.multiple_events(virtualSourceCoreId = [0, 0], neuronAddress = [1, 1], coreDest = [15, 15], firePeriod = [0.2, 0.2]) p2.multiple_events(virtualSourceCoreId = [0, 0], neuronAddress = [1, 1], coreDest = [15, 15], firePeriod = [0.2, 0.2])
Insert directly the patterns for the plot:
DSG.write_to_file(p1, p2, fileName = "myName.txt)
Create a list of patterns and plot it:
patternList = [p1, p2] DSG.write_to_file(*patternList, fileName = "myName.txt")


Fatal crash on US - 41 and Pine Drive in Fort Myers
by WFTX Digital TeamFORT MYERS, Fla. — The Florida Highway Patrol responded to a deadly crash on US-41 and Pine Drive involving a pedestrian and vehicle Saturday night. The pedestrian darted across the northbound lanes of US-41, causing direct contact with the vehicle that was traveling north on US-41 approaching Pine Drive.
The front right of the vehicle struck the pedestrian. The pedestrian was pronounced dead at the scene.


I swim around 3 times a week in the local hotel and one day a week in the ocean.
I began swimming when I was 3 as my nan lives on the cliff above Myrtleville Beach, so i grew up in the ocean.
I have gotten used to the cold Irish water due to how often I go swimming. I love it!
Before the leaving cert, I stopped swimming to give myself time to study, It took me a while
to get back into it but now I am back to being as fast as ever
I began to rock-climb just last summer
I think I found this interest from the many times I have climbed from Myrtleville beach to Founstainstwon beach.
I have grown a great passion for it as surprisingly I have become very good at it.
i hope to cary this hobby on for a few years!


GitHub
View source on GitHub
MDEditor Example: Full


Index
Keys and Click Event Display
Click the button or press keys and the events are displayed below.


CartesianSeries Class
The CartesianSeries class creates a chart with horizontal and vertical axes.
Item Index
Methods
Properties
Methods
_getDefaultColor
-
index
-
type
Parses a color based on a series order and type.
Parameters:
-
index
NumberIndex indicating the series order.
-
type
StringIndicates which type of object needs the color.
Returns:
_getDefaultStyles
()
protected
Gets the default value for the styles
attribute. Overrides
base implementation.
Returns:
_handleVisibleChange
-
e
Shows/hides contents of the series.
Parameters:
-
e
ObjectEvent object.
_setCanvas
()
protected
Creates a Graphic
instance.
_updateAxisData
()
private
Checks to ensure that both xAxis and yAxis data are available. If so, set the xData
and yData
attributes and return true
. Otherwise, return false
.
Returns:
_xAxisChangeHandler
-
e
Event handler for the xAxisChange event.
Parameters:
-
e
ObjectEvent object.
_xDataChangeHandler
-
event
Event handler for xDataChange event.
Parameters:
-
event
ObjectEvent object.
_yAxisChangeHandler
-
e
Event handler the yAxisChange event.
Parameters:
-
e
ObjectEvent object.
_yDataChangeHandler
-
event
Event handler for yDataChange event.
Parameters:
-
event
ObjectEvent object.
addListeners
()
private
Adds event listeners.
destructor
()
protected
Destructor implementation for the CartesianSeries class. Calls destroy on all Graphic instances.
draw
()
protected
Draws the series.
getTotalValues
()
Returns the sum of all values for the series.
Returns:
render
()
private
setAreaData
()
protected
Calculates the coordinates for the series.
validate
()
private
Draws the series is the xAxis and yAxis data are both available.
Properties
_bottomOrigin
String
private
The y-coordinate for the bottom edge of the series.
_defaultBorderColors
Array
protected
Collection of default colors used for marker borders in a series when not specified by user.
_defaultFillColors
Array
protected
Collection of default colors used for marker fills in a series when not specified by user.
_defaultLineColors
Array
protected
Collection of default colors used for lines in a series when not specified by user.
_defaultPlaneOffset
Number
private
Default value for plane offsets when the parent chart's interactiveType
is planar
.
_defaultSliceColors
Array
protected
Collection of default colors used for area fills, histogram fills and pie fills in a series when not specified by user.
_leftOrigin
String
private
Th x-coordinate for the left edge of the series.
_xDisplayName
String
private
Storage for xDisplayName
attribute.
_yDisplayName
String
private
Storage for yDisplayName
attribute.
GUID
String
private
Constant used to generate unique id.
Attributes
groupMarkers
Boolean
Indicates whether or not markers for a series will be grouped and rendered in a single complex shape instance.
xMarkerPlane
Array
Collection of area maps along the xAxis. Used to determine mouseover for multiple series.


Problems¶
The term “risk” is problematic and this documentation favors the scenario instead.
That said, it is still important to understand the complexity of “risk” to effectively use this documentation.
Understanding these complexities improves our ability to create scenarios that better represent our intentions than trying to argue for a specific “risk” definition.
We’ll discuss the varying uses of the term, and the varying goals we have concerning risk.
Sven Ove Hansson describes risk with many definitions:
- An unwanted event which may or may not occur.
- The cause of an unwanted event which may or may not occur.
- The probability of an unwanted event which may or may not occur.
- The statistical expectation value of an unwanted event which may or may not occur.
- The fact that a decision is made under conditions of known probabilities (“decision under risk” as opposed to “decision under uncertainty”)
It is not difficult to find other uses in the wild!
- The lack of obvious mitigations (“Driving without a seatbelt is a risk”)
- An amount of property at stake (“We have too much risk in volatile investments”)
- General expressions of fear. (“This feels pretty risky…”)
There are obviously consistent themes involved with all of these definitions. They all relate, somehow, to the likelihood of a future event, or the impact of a future event, or both.
Risk in practice¶
You are likely surrounded by a variety of factors that compose or limit your ability to influence a risk.
- Your time and resources available.
- The skillsets you can contribute to a problem.
- A short lived opportunity to mitigate a risk.
- You, or your team’s role in the mitigation
As such, you might find yourself with many available strategies that have limitations associated with them. Or, you may be simply asked to use a specific strategy arbitrarily.
Here is an example scenario:
Scenario
The vault will be robbed during overnight hours, in Q2, resulting in $1M loss or greater.
We face limitations as engineers that may hinder our ability to influence either the likelihoods or impacts that compose a risk.
This limits the ability of engineers to influence risk when using definitions of risk that attempt to strictly track the Expected Value of a risk, since the Expected Value requires that both factors are calculated.
Note
The Expected Value of risk is equal to the product of its likelihood and impact. For example, a 50% likelihood of a $10 loss within a year has an expected value of $5 annually.
A fully rational approach to mitigating this risk would be to explore options that reduce the Expected Value of this risk.
However, in reality, we may not be operating in a fully quantified environment. For instance, we might be the company that manufactures the walls of the vault. We don’t get to know what will be stored inside of our vaults, but we do know how likely a certain type of drill would pierce the vault, given a certain amount of time.
Alternatively, we may be in charge of customer use of the vault. Maybe this allows us to estimate the value of what is stored in the vault, but aren’t tasked with understanding the likelihood of it being stolen.
Indeed, many people involved with risk will need their own measurement tooling before Expected Value can be considered.
There are plenty of examples where engineers will not be calculating the full Expected Value of losses, but this does not limit their measurement opportunity to help inform this value. It’s important to recognize that an engineer may be surfacing specific details for future management, and that these values are useful.
These sorts of measurement limitations are common in engineering. This documentation serves as guidance towards risk measurement in more practical situations so that they can be useful with broader risk management situations where expected values become useful while informing a broad picture of risk, and the decision making associated with it.
The world being as it is, the Expected Value of a risk is a desirable thing to measure, but it is unreasonable to think that all participants in risk mitigation will be fully concerned with it. Instead, we can trust that efforts to mitigate a risk may reduce the Expected Value of a loss, even if an engineer doesn’t have the full picture measured.
Conclusion¶
As engineers, we must work to influence the likelihoods and impacts associated with a well defined future event. We do not always need to measure both, but we’d like to eventually. First, we need to measure that our individual efforts are making a difference.
We can focus and target these areas of risk by defining them as scenarios.
Further Reading¶
See Defining Risk


“Ahu buddhassa dāyādo,
bhikkhu bhesakaḷāvane;
Kevalaṁ aṭṭhisaññāya,
apharī pathaviṁ imaṁ;
Maññehaṁ kāmarāgaṁ so,
khippameva pahissatī”ti.
… Siṅgālapitā thero ….
There was an heir of the Buddha,
a monk in Bhesakaḷā forest,
who suffused the entire earth
with the perception of bones.
I think he will quickly
get rid of sensual desire.


{% extends "photologue/root.html" %} {% load ajaxtable %} {% block title %}Latest Photos{% endblock %} {% block content %}
No photos were found.
View all photographs


|
Project: par_kinematics License: BSD Dependencies:
Used by:
None |
ArmModel Class Reference
Detailed DescriptionDefinition at line 16 of file armmodel.h. Constructor & Destructor Documentation
Author: W. van Teijlingen<EMAIL_ADDRESS>This code is based on http://sensi.org/~svo/dbot/applet/ by Viacheslav Slavinsky. Definition at line 9 of file armmodel.cpp.
Definition at line 19 of file armmodel.cpp. Member Function Documentation
Definition at line 36 of file armmodel.cpp. Member Data Documentation
Definition at line 20 of file armmodel.h. Definition at line 23 of file armmodel.h.
Definition at line 19 of file armmodel.h.
Definition at line 27 of file armmodel.h.
Definition at line 32 of file armmodel.h.
Definition at line 20 of file armmodel.h.
Definition at line 25 of file armmodel.h.
Definition at line 19 of file armmodel.h.
Definition at line 21 of file armmodel.h.
Definition at line 26 of file armmodel.h. Definition at line 29 of file armmodel.h.
Definition at line 31 of file armmodel.h.
Definition at line 19 of file armmodel.h. The documentation for this class was generated from the following files:
|


@(url: String, code: Option[String]) @main("MongoDB") { @if(url == "/") {
This application can deploy a MongoDB shard cluster in Google Compute Engine using different disk and instance strategies, including micro-sharding. You can run different YCSB load test against your cluster. The application will show the GCE operations and the YCSB test results in a reactive way using WebSockets.
|
Loading recent operations, please wait ... |


Gif Search App
About
Gif Me That
Pablo Dubcovsky
2018
Pablo Dubcovsky - 2018
Project Page
ME
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!
10 Gifs
15 Gifs
20 Gifs
30 Gifs
50 Gifs
any
family
pg
pg-13
teen
adult
any
funny
cute
cool
trippy
wtf
Add
Here you go!


--- layout: default title: "Standigm" subtitle: "Job" excerpt: "$$e^{i\\pi}+1=0$$" header: overlay_image: /assets/images/banner.jpg overlay_filter: rgba(0, 0, 0, 0.5) overlay_filter_to: rgba(255, 255, 255, 0.5) --- {% include stan_page__hero.html %}
We are currently recruiting:
- Computational chemist in small molecule drug discovery: advanced molecular modeling and computational chemistry techniques with strong programming skills
- Researchers in deep learning/machine learning: molecule representations and deep generative neural networks with strong programming skills
- Researchers in deep learning/machine learning: in-silico protein–ligand interactions and binding affinity prediction with strong programming skills
- Software developers as AI engineer
Please contact us via email. (apply@standigm.com)


| Total net profit | -45.68 | Gross profit | 0.0 | Gross loss | -45.68 |
| Profit factor | 0.0 | Expected payoff | -1.69 | ||
| Absolute drawdown | -45.68 | Maximal drawdown (%) | 44.8 (-5090.91%) | ||
| Total trades | 27 | Profit trades (% of total) | 0 (0.0%) | Loss trades (% of total) | 27 (100.0%) |
| Long positions (won %) | 25 (0.0%) | Short positions (won %) | 2 (0.0%) | ||
| Largest | profit trade | -0.33 | loss trade | -3.99 | |
| Average | profit trade | 0.0 | loss trade | -1.69 | |
| Minimum | profit trade | 0.0 | loss trade | -0.33 | |
| Maximum | take profit in pips | 1599.0 | stop loss in pips | -1044.3 | |
| Average | take profit in pips | -44.4 | stop loss in pips | -500.5 | |
| Minimum | take profit in pips | -355.9 | stop loss in pips | -387.9 | |
| Maximum | Reward-Risk | 3.2 | Duration in minutes | 35 | |
| Average | Reward-Risk | 0.2 | Duration in minutes | 2.9 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0 | |
| Maximum | consecutive wins (profit in money) | 0.0 (0.0) | consecutive losses (loss in money) | 27.0 (-45.68) | |
| Average | consecutive wins | 0.0 | consecutive losses | 27.0 |
| Total net profit | -40.81 | Gross profit | 0.0 | Gross loss | -40.81 |
| Profit factor | 0.0 | Expected payoff | -1.63 | ||
| Absolute drawdown | -40.81 | Maximal drawdown (%) | 40.26 (-7320.0%) | ||
| Total trades | 25 | Profit trades (% of total) | 0 (0.0%) | Loss trades (% of total) | 25 (100.0%) |
| Largest | profit trade | 0.0 | loss trade | -2.53 | |
| Average | profit trade | 0.0 | loss trade | -1.63 | |
| Minimum | profit trade | 0.0 | loss trade | -0.33 | |
| Maximum | take profit in pips | 1599.0 | stop loss in pips | -809.6 | |
| Average | take profit in pips | -73.3 | stop loss in pips | -482.7 | |
| Minimum | take profit in pips | -355.9 | stop loss in pips | -387.9 | |
| Maximum | Reward-Risk | 3.2 | Duration in minutes | 18.0 | |
| Average | Reward-Risk | 0.2 | Duration in minutes | 1.7 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0.0 | |
| Maximum | consecutive wins (profit in money) | 0.0 (0.0) | consecutive losses (loss in money) | 25.0 (-40.81) | |
| Average | consecutive wins | 0.0 | consecutive losses | 25.0 |
| Total net profit | -4.87 | Gross profit | 0.0 | Gross loss | -4.87 |
| Profit factor | 0.0 | Expected payoff | -2.44 | ||
| Absolute drawdown | -4.87 | Maximal drawdown (%) | 3.99 (-453.41%) | ||
| Total trades | 2 | Profit trades (% of total) | 0 (0.0%) | Loss trades (% of total) | 2 (100.0%) |
| Largest | profit trade | 0.0 | loss trade | -3.99 | |
| Average | profit trade | 0.0 | loss trade | -2.44 | |
| Minimum | profit trade | 0.0 | loss trade | -0.88 | |
| Maximum | take profit in pips | 868.1 | stop loss in pips | -1044.3 | |
| Average | take profit in pips | 316.9 | stop loss in pips | -722.5 | |
| Minimum | take profit in pips | -234.3 | stop loss in pips | -400.6 | |
| Maximum | Reward-Risk | 0.8 | Duration in minutes | 35.0 | |
| Average | Reward-Risk | 0.4 | Duration in minutes | 17.5 | |
| Minimum | Reward-Risk | Duration in minutes | 0.0 | ||
| Maximum | consecutive wins (profit in money) | 0.0 (0.0) | consecutive losses (loss in money) | 2.0 (-4.87) | |
| Average | consecutive wins | 0.0 | consecutive losses | 2.0 |


(Press ? for help, n and p for next and previous slide)
Warning: The setup is insecure if your laptop camera or mic is on!
2N8aR3Fto...
2N8aR3FtoZtL1qvAoUz925jJyFfXPnHgW5r


Public Types | |
|
typedef qcqpcpp::OptProblem < _Scalar > | ParentType |
| typedef ParentType::VectorX | VectorX |
| typedef ParentType::SparseMatrix | SparseMatrix |
|
typedef Eigen::Map< const Eigen::Matrix< _Scalar,-1, 1 > > | MatrixMapT |
Public Member Functions | |
| BonminTMINLP (BonminOpt< _Scalar > &delegate) | |
| virtual | ~BonminTMINLP () |
| ~BonminOpt virtual destructor. | |
| virtual const SosInfo * | sosConstraints () const |
| virtual const BranchingInfo * | branchingInfo () const |
Overloaded functions specific to a TMINLP. | |
| virtual bool | get_variables_types (Ipopt::Index n, VariableType *var_types) |
| get_variables_types Pass the type of the variables (INTEGER, BINARY, CONTINUOUS) to the optimizer. More... | |
| virtual bool | get_variables_linearity (Ipopt::Index n, Ipopt::TNLP::LinearityType *var_types) |
| get_variables_linearity Pass info about linear and nonlinear variables. | |
| virtual bool | get_constraints_linearity (Ipopt::Index m, Ipopt::TNLP::LinearityType *const_types) |
| get_constraints_linearity Pass the type of the constraints (LINEAR, NON_LINEAR) to the optimizer. More... | |
Overloaded functions defining a TNLP. | |
This group of function implement the various elements needed to define and solve a TNLP. They are the same as those in a standard Ipopt NLP problem | |
| virtual bool | get_nlp_info (Ipopt::Index &n, Ipopt::Index &m, Ipopt::Index &nnz_jac_g, Ipopt::Index &nnz_h_lag, Ipopt::TNLP::IndexStyleEnum &index_style) |
| get_nlp_info Method to pass the main dimensions of the problem to Ipopt. More... | |
| virtual bool | get_bounds_info (Ipopt::Index n, Ipopt::Number *x_l, Ipopt::Number *x_u, Ipopt::Index m, Ipopt::Number *g_l, Ipopt::Number *g_u) |
| get_bounds_info Method to pass the bounds on variables and constraints to Ipopt. More... | |
| virtual bool | get_starting_point (Ipopt::Index n, bool init_x, Ipopt::Number *x, bool init_z, Ipopt::Number *z_L, Ipopt::Number *z_U, Ipopt::Index m, bool init_lambda, Ipopt::Number *lambda) |
| get_starting_point Method to to pass the starting point for optimization to Ipopt. More... | |
| virtual bool | eval_f (Ipopt::Index n, const Ipopt::Number *x, bool new_x, Ipopt::Number &obj_value) |
| eval_f Method which compute the value of the objective function at point x. More... | |
| virtual bool | eval_grad_f (Ipopt::Index n, const Ipopt::Number *x, bool new_x, Ipopt::Number *grad_f) |
| eval_grad_f Method which compute the gradient of the objective at a point x. More... | |
| virtual bool | eval_g (Ipopt::Index n, const Ipopt::Number *x, bool new_x, Ipopt::Index m, Ipopt::Number *g) |
| eval_g Method which compute the value of the functions defining the constraints at a point x. More... | |
| virtual bool | eval_jac_g (Ipopt::Index n, const Ipopt::Number *x, bool new_x, Ipopt::Index m, Ipopt::Index nele_jac, Ipopt::Index *iRow, Ipopt::Index *jCol, Ipopt::Number *values) |
| eval_jac_g Method to compute the Jacobian of the functions defining the constraints. More... | |
| virtual bool | eval_h (Ipopt::Index n, const Ipopt::Number *x, bool new_x, Ipopt::Number obj_factor, Ipopt::Index m, const Ipopt::Number *lambda, bool new_lambda, Ipopt::Index nele_hess, Ipopt::Index *iRow, Ipopt::Index *jCol, Ipopt::Number *values) |
| eval_h Method to compute the Jacobian of the functions defining the constraints. More... | |
| virtual void | finalize_solution (TMINLP::SolverReturn status, Ipopt::Index n, const Ipopt::Number *x, Ipopt::Number obj_value) |
| finalize_solution Method called by Ipopt at the end of optimization. | |
Protected Attributes | |
| BonminOpt< _Scalar > & | _delegate |
|
virtual |
eval_f Method which compute the value of the objective function at point x.
| n | size of array x (has to be the number of variables in the problem). |
| x | Point where to evaluate. |
| new_x | Is this the first time we evaluate functions at this point? (in the present context we don't care). |
| obj_value | Value of objective in x (has to be computed by the function). |
|
virtual |
eval_g Method which compute the value of the functions defining the constraints at a point x.
| n | Size of array x (has to be the number of variables in the problem). |
| x | Point where to evaluate. |
| new_x | Is this the first time we evaluate functions at this point? (in the present context we don't care). |
| m | Size of array g (has to be equal to the number of constraints in the problem) |
| grad_f | Values of the constraints (function has to fill it). |
|
virtual |
eval_grad_f Method which compute the gradient of the objective at a point x.
| n | Size of array x (has to be the number of variables in the problem). |
| x | Point where to evaluate. |
| new_x | Is this the first time we evaluate functions at this point? (in the present context we don't care). |
| grad_f | Gradient of objective taken in x (function has to fill it). |
|
virtual |
eval_h Method to compute the Jacobian of the functions defining the constraints.
If the parameter values==NULL fill the arrays iCol and jRow which store the position of the non-zero element of the Jacobian. If the paramenter values!=NULL fill values with the non-zero elements of the Jacobian.
| n | Size of array x (has to be the number of variables in the problem). |
| x | Point where to evaluate. |
| new_x | Is this the first time we evaluate functions at this point? (in the present context we don't care). |
| m | Size of array g (has to be equal to the number of constraints in the problem) |
| grad_f | Values of the constraints (function has to fill it). |
|
virtual |
eval_jac_g Method to compute the Jacobian of the functions defining the constraints.
If the parameter values==NULL fill the arrays iCol and jRow which store the position of the non-zero element of the Jacobian. If the paramenter values!=NULL fill values with the non-zero elements of the Jacobian.
| n | Size of array x (has to be the number of variables in the problem). |
| x | Point where to evaluate. |
| new_x | Is this the first time we evaluate functions at this point? (in the present context we don't care). |
| m | Size of array g (has to be equal to the number of constraints in the problem) |
| grad_f | Values of the constraints (function has to fill it). |
|
virtual |
get_bounds_info Method to pass the bounds on variables and constraints to Ipopt.
| n | Size of x_l and x_u (has to be equal to the number of variables in the problem) |
| x_l | Lower bounds on variables (function should fill it). |
| x_u | Upper bounds on the variables (function should fill it). |
| m | Size of g_l and g_u (has to be equal to the number of constraints in the problem). |
| g_l | Lower bounds of the constraints (function should fill it). |
| g_u | Upper bounds of the constraints (function should fill it). |
|
virtual |
get_constraints_linearity Pass the type of the constraints (LINEAR, NON_LINEAR) to the optimizer.
| m | Size of const_types (has to be equal to the number of constraints in the problem) |
| const_types | Types of the constraints (has to be filled by function). |
|
virtual |
get_nlp_info Method to pass the main dimensions of the problem to Ipopt.
| n | Number of variables in problem. |
| m | Number of constraints. |
| nnz_jac_g | Number of nonzeroes in Jacobian of constraints system. |
| nnz_h_lag | Number of nonzeroes in Hessian of the Lagrangean. |
| index_style | Indicate wether arrays are numbered from 0 (C-style) or from 1 (Fortran). |
|
virtual |
get_starting_point Method to to pass the starting point for optimization to Ipopt.
| init_x | Do we initialize primals? |
| x | Pass starting primal points (function should fill it if init_x is 1). |
| m | Size of lambda (has to be equal to the number of constraints in the problem). |
| init_lambda | Do we initialize duals of constraints? |
| lambda | Lower bounds of the constraints (function should fill it). |
|
virtual |
get_variables_types Pass the type of the variables (INTEGER, BINARY, CONTINUOUS) to the optimizer.
| n | size of var_types (has to be equal to the number of variables in the problem) |
| var_types | types of the variables (has to be filled by function). |


--- layout: documentation title: AuthenticationHeaderType ---
The IP Authentication Header is used to provide connectionless integrity and data origin authentication for IP datagrams and to provide protection against replays. http://www.ietf.org/rfc/rfc2402.txt.
| Field Name | Type | Description |
|---|---|---|
| Next_Header0..1 | IANAAssignedIPNumbersType |
Identifies the type of header immediately following the Authentication header. Uses the same values as the IPv4 Protocol field. |
| Header_Ext_Len0..1 | HexBinaryObjectPropertyType |
An 8-bit field specifying the length of the AH in 32-bit words. |
| Security_Parameters_Index0..1 | HexBinaryObjectPropertyType |
The SPI is an arbitrary 32-bit value that, in combination with the destination IP address and security protocol (AH), uniquely identifies the Security Association for this datagram. The set of SPI values in the range 1 through 255 are reserved by the Internet Assigned Numbers Authority (IANA) for future use. http://www.ietf.org/rfc/rfc2402.txt. |
| Sequence_Number0..1 | HexBinaryObjectPropertyType |
This unsigned 32-bit field contains a monotonically increasing counter value (sequence number). |
| Authentication_Data0..1 | HexBinaryObjectPropertyType |
This is a variable-length field that contains the Integrity Check Value (ICV) for this packet. The field must be an integer multiple of 32 bits in length. |


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to true
.See a preview of Microsoft Edge support and follow Safari support on WebKit’s feature status page.
Play WebAssembly Play asm.js fallback
This is an experimental demo of Angry Bots, a Unity game which has been ported to WebAssembly. Fight robots and explore a 3D space station with realistic environmental effects in this top-down shooter. Movement is controlled by W, A, S, D or the arrow keys and aiming & firing is controlled by the mouse.


About the project
This map was set up by Zero Waste Stockholm to help citizens consume differently, with less packaging. Data are for now limited to Stockholm area, but anyone in Sweden is welcome to contribute. The map is based on the work of CartoVrac.
Current contributors to the mapping of shops:
Shop owners are also welcome to share their information with us, or to reference themselves on © OpenStreetMap.
Contribute
How it works
This map only displays data from from OpenStreetMap https://openstreetmap.org).
To be visible on this map, shops must be referenced on OpenStreetMap and tagged with bulk purchase.
Gallery
Coming soon, image gallery from happy shops with our BYO sticker!
Thank you CartoVrac!
CartoVrac is a French team that developed this mapping tool and made it available for free. A big thank you to them for sharing and helping us out in setting up the Swedish map.
To follow or contribute to the development of the project, head to GitHub and its Wiki.


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PRLib
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#include <vector>
#include <opencv2/core/core.hpp>
Go to the source code of this file.
Classes | |
| struct | tag_MarkerRectangle |
| Detected marker rectangle. More... | |
Macros | |
| #define | GOOD_RECTANGLE (0) |
| Code of correct rectangle. More... | |
| #define | BAD_RECTANGLE (-1) |
| Code of incorrect rectangle. More... | |
Typedefs | |
| typedef struct tag_MarkerRectangle | MarkerRectangle |
Functions | |
| double | dotDegree (cv::Point &common, cv::Point &a, cv::Point &b) |
| Get angle between vectors. More... | |
| int | CheckRectangle (std::vector< cv::Point > &contour, MarkerRectangle *rectangle) |
| Check rectangle. More... | |
| bool | findDocumentContour (cv::Mat &source, std::vector< cv::Point2f > &resultContour) |
| Try to detect document contour. More... | |
| void | binarizeByLocalVariances (cv::Mat &image, cv::Mat &result) |
| Binarization on base of local variance estimation. More... | |
| bool | isQuadrangle (const std::vector< cv::Point > &contour) |
| void | scaleContour (std::vector< cv::Point2f > &contour, cv::Size &fromImageSize, cv::Size &toImageSize) |
| bool | cropVerticesOrdering (std::vector< cv::Point2f > &contour) |
| Contour points ordering. More... | |
| bool | intersection (cv::Point2f o1, cv::Point2f p1, cv::Point2f o2, cv::Point2f p2, cv::Point2f &r) |
| #define BAD_RECTANGLE (-1) |
Code of incorrect rectangle.
Definition at line 42 of file autoCropUtils.h.
| #define GOOD_RECTANGLE (0) |
Code of correct rectangle.
Definition at line 36 of file autoCropUtils.h.
| void binarizeByLocalVariances | ( | cv::Mat & | image, |
| cv::Mat & | result | ||
| ) |
Binarization on base of local variance estimation.
| image | Input image. |
| result | Binarized image. |
Get map of local variance
Split to channels
Find minimal and maximal local variance values
Get the first intermediate result
Create new filter kernel (contrast adjustment mask)
Set filter kernel parameters
Filtration for contrast adjustment
Calculate threshold for filtered local variance values
Separate map of variance values to channels
Obtain the second intermediate result as combination of thresholded channels
Calculate logarithm of variance map
Execute gamma correction for variance map in logarithmic scale
Get min and max
Change range to [0;1]
Gamma correction
Convert to normal scale
Binarize
Try to extract noise for variance map in logarithmic scale
Convert to normal scale
Get particularly denoised result
Get final result
Definition at line 264 of file autoCropUtils.cpp.
| int CheckRectangle | ( | std::vector< cv::Point > & | contour, |
| MarkerRectangle * | rectangle | ||
| ) |
Check rectangle.
| [in] | contour | Contour. |
| [out] | rectangle | Resulted rectangle. |
Check rectangle.
Check count of corners
Get degree for each corner
Check value of degrees
If all degrees are valid then fill output rectangle
Definition at line 82 of file autoCropUtils.cpp.
| bool cropVerticesOrdering | ( | std::vector< cv::Point2f > & | contour | ) |
Contour points ordering.
| [in,out] | contour | Contour. |
This should be 4 for rectangles, but it allows to be extended to more complex cases
Find convex hull of points
Find top left point, as starting point. This is the nearest to (0.0) point
Now starting point index in source array is in indices[minDistanceIdx] convex hull vertices are sorted clockwise in indices array, so transfer them to result beginning from starting point to the end of array...
... and from the begin of array up to starting point
Definition at line 547 of file autoCropUtils.cpp.
| double dotDegree | ( | cv::Point & | common, |
| cv::Point & | a, | ||
| cv::Point & | b | ||
| ) |
Get angle between vectors.
Get angle between vectors.
Definition at line 58 of file autoCropUtils.cpp.
| bool findDocumentContour | ( | cv::Mat & | source, |
| std::vector< cv::Point2f > & | resultContour | ||
| ) |
Try to detect document contour.
| [in] | source | Binary image. |
| [out] | resultContour | Resulted document contour. |
Approximate contours to polygons + get bounding rectangles and circles
Approximate contour by polygon
Definition at line 155 of file autoCropUtils.cpp.
| bool intersection | ( | cv::Point2f | o1, |
| cv::Point2f | p1, | ||
| cv::Point2f | o2, | ||
| cv::Point2f | p2, | ||
| cv::Point2f & | r | ||
| ) |
Definition at line 628 of file autoCropUtils.cpp.
| bool isQuadrangle | ( | const std::vector< cv::Point > & | contour | ) |
Definition at line 542 of file autoCropUtils.cpp.
| void scaleContour | ( | std::vector< cv::Point2f > & | contour, |
| cv::Size & | fromImageSize, | ||
| cv::Size & | toImageSize | ||
| ) |
Definition at line 609 of file autoCropUtils.cpp.


The Sparks Foundation started their Storm Relief Fund as a gesture to donate back to the Indian States which are badly affected by storms. Storm is a natural disaster and it can happen anywhere. The Sparks Foundation Storm Relief Fund collects fund for Indian people who are residing in the storm hit areas of a particular Indian State. The fund collected by common people will be utilized to give food,shelter and clothes to the people badly affected by storms.


Lustfaust
An exercise in fictional archives and mediated histories - focussing upon little-known, 1970s, German noise band, Lustfaust.
Exhibitions and Performances: ICA, London (April 2006); Freight & Volume, New York (July 2006); Haunch of Venison, Berlin (September 2007); ICA, London (July 2008); Big Chill, Ledbury (August 2008), Rampa del Lingotto, Turin (November 2008); Museo Madre, Naples (June 2009); Tatton Park, Knutsford (May 2010); Fishmarket Gallery, Northampton (July 2010); Grand Union, Birmingham (September 2010); Teatro Eliseo, Rome (Feb 2011)
Creators/Contributors/Collaborators: Alex Williamson, dandelion + burdock, Euan Rodger, Jamie Shovlin, Laura McLean-Ferris, Lillevan, Mike Harte, Murray Royston-Ward, Patrick Farmer, Peacho, Schneider TM & Kptmichigan, Sigrid Holmwood, Stephen Linehan.