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Sammy Unplugged
The Red Rocker Sammy Hagar serves up a preview of his new acoustic album
The Red Rocker Sammy Hagar serves up a preview of his new acoustic album
Sabbath is gearing up for one final album and farewell tour. And Ozzy wants their original drummer Bill Ward to be a part of it this time
Just in time for the upcoming Led Zeppelin IV reissue, here's the previously unreleased alternate version of "Stairway to Heaven"
Want to go for 7 hours like Sting does? The legendary Police singer has a few tantric tips for you.
Click here to play and win!
Check out the latest girl right here!
You pick the songs. We drink Beer.
On Thursday night, officials announced the first case of Ebola to hit New York City.
This sounds like one of those "only in Montana" stories, but it's actually taking place in a small Nebraska town.
Some seniors at Broken Bow high school wanted to pose with their guns for their senior pictures
Peyton Manning threw three touchdown passes to Emmanuel Sanders, and the Broncos beat the San Diego Chargers, 35-21, in Denver on Thursday night.
With another election just a couple of weeks away I keep hearing people beating the drum about having to stop voter fraud. It reminds me of the joke about the guy who frantically hops and flails his arms about
It might look like a standard corpse-hanging-from-a-tree prank, but sometimes something terrible and tragic really did happen.
Fans will get their first take-home opportunity to hear Yes in concert with current lead singer Jon Davison on the forthcoming 'Like It Is: Yes at the Bristol Hippodrome.'
This tour guide really ought to think about a different line of work.
Replacing LeBron James is harder than Dwyane Wade ever thought it could be.
Bob Dylan growls his way through a version of John Lee Hooker's 'Tupelo Blues,' as the latest advance track surfaces from the upcoming 'The Basement Tapes Complete: The Bootleg Series Vol. 11.'


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Akram Faizer: Stimulus blocked deeper meltdown
On the heels of the Democratic National Convention in Charlotte, N.C., the president and his party need to forcefully articulate their economic record.
President Barack Obama inherited the worst economic and financial crisis since the Great Depression and nearly all the scholarship on the issue points to one major, albeit problematic, conclusion — that employment and economic growth after a financial crisis tends to be more tepid and inconsistent than during a normal recovery.
The president's policies protected jobs at a crucial moment. Contrary to his detractors' assertions, a Congressional Budget Office study shows his stimulus kept unemployment from rising above 11 percent. Obama's decision to temporarily reduce payroll taxes through 2012 likely engendered employment by lowering labor costs. The automotive bailout, begun under President George W. Bush and completed by Obama, has resulted in a resurgent U.S. automobile industry with 1.45 million jobs protected. The more compelling criticism of the Obama stimulus is its insufficiency in view of the crisis.
Another criticism of Obama is based on a widespread misperception that the federal budget deficit exceeded $1 trillion for the first time on his watch. The reality is more complex. Republicans argue the deficit increased from approximately $482 billion for the fiscal year 2008, the last year the Bush administration was in office, to nearly $1.4 trillion in 2009, the first year of Obama's tenure. But fiscal year 2008 ended that September, before the financial crisis, and fails to include the payout cost of the TARP, the collapse in government revenues and the corresponding increase in government obligations. Taking the calendar year into account (in other words, when Bush left office), the Cato Institute found the previous administration left Obama with a deficit greater than $1.4 trillion.
The facts demonstrate the size of the budget deficit is attributable to a post-financial crisis collapse in government revenues, Bush's tax cuts and unfunded Medicare Prescription Drug Plan, and increased government outlays for Medicaid, food stamps and block grants (not to mention wars in Iraq and Afghanistan). Increased program spending by the Obama administration is largely irrelevant to this equation. Government spending growth under Obama has in fact been slower than in any presidential administration since the Eisenhower era.
Obama's policies do not explain the country's current economic predicament, and the charges leveled against him by his Republican opponents are largely baseless. The simple truth is that an economic crisis of this extent cannot be alleviated in just four years.
Unemployment remains at unacceptable levels, and Obama should have presented a vision to the American public of a more sustainable growth paradigm while educating the nation as to the country's true economic challenges. The president, one of the foremost communicators as campaigner, has secluded himself with a small coterie of advisors and failed to either communicate or defend his policies. His re-election will depend on his ability to articulate an alternate — and more accurate — narrative of his presidency.
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Found 1136 related files. Current in page 1
To become registered trademark agent Canada you should know the exact steps which can earn you a degree. For becoming trademark agents in Canada, any formal education is not needed rather you will learn things when you will work as trainee or apprentice.
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Based on my desire to find a way to relieve human suffering in an age of short-term care, I have established that the most useful tool to send people off into the world with better coping devices is by raising a person's self-esteem during brief therapeutic counseling and hypnosis sessions.
This paper examines a proof of incompleteness by Stanisław Świerczkowski in a paper entitled "Finite sets and Gödel's incompleteness theorems". Świerczkowski's proof is different to most other proofs of incompleteness because the formal system that is used for the proof is a system of hereditarily finite sets. Świerczkowski claims that this makes his proof simple and elegant, and that it has enabled his proof to be complete and without any gaps or omissions, nor relying on references to other publications. Świerczkowski claims that for this reason his proof his superior to most other proofs of incompleteness. However, the author makes an elementary error in his proof that renders the proof invalid; his proof relies on a confusion regarding the domain of the variables involved. Świerczkowski bases his proof on an assumption that a certain formula has a certain domain, whereas a logical analysis demonstrates that this is not the case.
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In patients with advanced squamous cell carcinoma (SCC) of the uterine cervix treated by radiation, 9-27% may develop distant metastasis. Common sites of distant metastasis are lungs, bones and para-aortic lymph nodes. Metastasis to the colon is very rare. We report a case of a 62 year old lady who was treated by Wertheim hysterectomy and adjuvant radiotherapy for stage III carcinoma of the uterine cervix. Three years later, she developed a metastatic growth in the transverse colon which was resected.
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All Slideshows | Page 8
Is Data Breach Fatigue Making Us Careless?A seemingly endless series of headlines about data breaches has drawn greater attention to all the deficiencies and problems surrounding digital security. Yet, despite all the recent news about Target, Erik Snowd...Read More
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Companies Offer More Workplace FlexibilityA growing number of organizations are now providing more flexibility about when and where their employees work, according to a recent survey from the Families and Work Institute (FWI) and the Society for Human Re...Read More
IT Organizations Turn to Contingent Tech WorkersTechnology organizations will increasingly turn to contingent workers to get critical IT projects done, according to a survey from TEKsystems. Whether called temps, contract workers or hired guns, this bree...Read More
IT Projects: Major Successes and Epic FailuresWe often read about how government IT projects constantly run over budget, miss deadlines and encounter enormous difficulties. But, upon closer inspection, you may find that the public sector's trac...Read More
Adoption of Software-Defined Networks Is GrowingThe adoption of software-defined networks (SDNs) is growing rapidly in enterprises, according to a recent survey from Network Instruments. The accompanying report, the "Seventh Annual State of the Networ...Read More
U.S. Workers Report High Job Satisfaction LevelsAlmost everyone gripes about work occasionally, but the vast majority of us are actually happy with our job and our employer, according to a recent survey from the Society for Human Resource Management (SHR...Read More
IT's Performance Is Hindered by Skills ShortagesA lack of readily available technology talent in critical areas is preventing IT organizations from keeping up with the pace of change, according to a recent survey from Harvey Nash, a global professio...Read More
Little-Known Facts About Ten Tech LegendsDo you consider yourself a trivia whiz—especially when it comes to obscure knowledge about technology and the IT industry? If so, we still bet that the following facts about these 10 legendary technolog...Read More
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text: All About Credit Repair Help It is wrong to assume that it is not being in debt that only means a good credit score. At times having more than one credit card could help build scores. If you do not qualify for a regular credit card, it always pays to improve credit score by taking a secured credit card from a bank that gives you a credit limit of the amount of deposit you make with them. Make sure it is a card that reports to the credit bureaus. Next if you are interested in credit repair help it pays to pay off your loans against the credit card. Paying off Collection accounts on credit report or on your credit card would best be done when we pay off all the loans and use our credit card more like a form of revolving cash. Most lenders like to find a great gap between the amount of credit you are using and the credit limit accessible. It can assist in Credit repair through keeping to a maximum balance of 30% of the credit limit. You can affect credit improvement if you take advantage and use both the revolving form of credit in the form of credit card and the installment type like personal, auto, mortgage and student loans. Hence one should always go for collection accounts on credit report. These loans would be reported to all the bureaus, and would stand you in a good deal with community bank or credit union. Beware of bankruptcy credit repair; positive payments would help improve your credit score over time if you keep paying the accounts regularly. Also try to keep up with paying even those accounts that are not there in your credit report. You can definitely improve credit score by not getting yourself fooled by false advertisements of credit repair companies that promise to get you out of bankruptcy credit repair as these transactions are illegal and would only cause problems in the long run. For more information please visit: www.cleancreditfast.com.


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The Amitabh Bachchan or commonly known as Big B is the face of Bollywood. If you think of Bollywood then the first name that you will recall will be undoubtedly of Mr. Amitabh Bachchan. Mr. Bachchan is a real superstar or more than that A Millennium Star. He has ruled on Box-Office during the period of 1970-80 era with blockbuster movies like Anand, Namak Haram, Deewar, Zanjeer, Parvarish, Abhimaan, Sholay and so on. Starting the film career as a side hero with the off beat movie "Saat Hindustani", Mr.Bachchan got a comedy movie "Bombay To Goa" of Mehmood and then after watching the fight scene of "Bombay To Goa" Prakash Mehra signed Mr. Bachchan for the lead role in the movie "Zanjeer" and Zanjeer gone superhit on the box-office and Mr.Bachchan become India's "Angry Young Man".
If we keep writing about the achievements of Mr. Bachchan then it will never stop because Mr. Bachchan's achievements are so big but still we have tried to give brief introduction about Mr.Amitabh Bachchan and glad that we are sharing some of the Bollywood Movies of Amitabh Bachchan for the countless fans of Big B.
Selected Movies of Amitabh Bachchan:
More movies will be uploaded soon...!!!


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Home » Sports » Motor Sports
Class Act: Milford seventh-grader cooks up a curriculum for healthy cooking
Twelve-year-old Lily Ayotte is the driving force behind an after-school cooking course starting soon for middle-schoolers in Milford. (NANCY BEAN FOSTER)
Ayotte, 12, is a seventh-grader at the middle school and last year, as part of one of her classes, she was challenged to come up with a way to encourage either physical activity or good nutrition.
"I was thinking about my school, and we have a lot of physical activities available to kids," she said. "But we don't have any classes that teach healthy cooking."
Though cooking is taught at school, Ayotte said kids usually end up making cookies or other simple - and sweet - dishes. What kids aren't learning is how to cook foods without a lot of sugar or extra calories, she said, so she decided she wanted to offer her own cooking class.
To get started, Ayotte reached out to her principal, Anthony DeMarco for permission to have such a class after school. DeMarco gave her his blessing, and from there, she began building a curriculum plan that includes bringing in guest chefs from local restaurants, working closely with students from Milford High School's restaurant, Windows on West Street, and even developing a cook book with help from her art teacher.
"We're going to ask kids to design the cover for the book," said Ayotte. "One lucky kid will have their art be the cover on my book."
"This has been a huge undertaking," said Ayotte's mom, Wendy. "There are a lot of people involved and everyone's been very supportive."
To fund the program, Ayotte has applied for a grant from an organization called Fuel Up to Play 60, a joint venture by the National Dairy Council and the NFL to promote healthy eating and to encourage kids to get at least 60 minutes of exercise each day. If she secures the $4,000 grant, Ayotte will begin offering the classes to students, parents, even community members, later this year.
Her goal is to get kids into the kitchen, teach them some basic skills, and share some of her favorite recipes for nutritious snacks.
"I love to cook and Lily has been cooking beside me since she was around six years old," said Wendy. "She started with a butter knife and simple things, but now she's free in the kitchen. She's got skills."
She has also begun collecting recipes from local restaurants and has been able to convince a local pizza shop to donate whole-wheat pizza dough, sauce and cheese to make pizzas in class - but with a healthy twist.
"I'm going to teach kids about vegetables they've never tried before like kale and get them to try something different," she said.
Veggie nachos, fruit smoothies, homemade salsa and other basic healthy snacks are at the top of Ayotte's list. "I rock the smoothies," she said.
Here are two of Lily Ayotte's recipes:
Sunshine Smoothie
1/3 cup each of frozen strawberries, mango chunks and peach slices
1 banana
1 cup orange juice
Put all ingredients in a blender and blend well, until smooth. Pour into two glasses and enjoy!
Yummy Hummy Hummus Dip
15 oz can of chick peas (also called garbanzo beans), drained and rinsed
1/2 tsp. ground cumin
2 Tbs. lemon juice from half a fresh lemon
3 cloves of garlic minced
Put all the ingredients in a food processor and blend until really smooth. You can also make the hummus more tasty by adding another ingredient like an avocado, or some roasted red peppers. Scoop up the hummus with veggies cut into sticks (I like cucumbers, baby carrots and snow peas, but any veggies will work) or pita chips. Even if you haven't tried one of these foods, I bet you'll like them all together!
|NH Angle >> Human Interest
1 Man who bet it all and lost on Apple supplier writes heartbreaking letter
0 80 immigrants from 39 countries become NH citizens
0 Destruction of memorial adds pain to woman's loss: Boyfriend was shot to death in May
0 Beliefs divide lesbian, Catholic family
0 Globe: Did Fremont just have its first homicide ever?
0 Video: NH Indian man goes to India to study film making, discovers 'I was not Indian enough for India'
0 Dream map finds Granite State dreams most about sex; D.C. residents dream most about drugs
0 Ex-Portsmouth Naval Shipyard worker victim of murder-suicide after chance encounter with former former underling
0 Vigil held for Laconia man missing for a week
0 Polls in NC, CO and NH find broad support for spankings, marijuana and guns
Most Read
NH couple hit slot for $2.4m in Las Vegas
Most Comments
NH's back-road rest areas fading away
Fall hikers throughout NH put on notice
Most Shared
Woman donates cleaning services to cancer patients
Food 4 Friends brings community together to help
Award to honor James Foley's work telling the stories 'of people trapped by war'
CEO Gary Frost leaving Manchester Boys & Girls Club after 26 years of building and growth


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Starting at $30 made to order (as pictured above)
Measurements for Straps for ball gags:
The straps are made to order, the default size fits most people, but will have 7 or 9 holes depending on buckle. Custom straps will usually have 3 to 5 holes.
To measure we recommend using a piece of thick string or yarn and measuring all the way around the head where the strap will go with the mouth open and then laying the string down onto a ruler to determine the needed strap length.
Measurements for balls for ball gags:
(with the caveat this is only a recommendation):
Ball diameter should not exceed open mouth Height measurement by more then 1/8th of an inch. Best is the diameter is at or below open mouth height. (measure bottom of top teeth to top of bottom teeth wide open)
Reduce size if the gagee might have any jaw problems
Available Buckles for the standard strap. In order: Black Locking, Nickle Locking, nickle NON-locking, and if I can get some more, Brass non-locking.
Some images of the locking and then the non-locking buckle
Nikitta caramel is the model for above
Medical Grade Silicone BALL GAGS
26 Aug 2014: Current colors in stock (Red, Black, White, Blue Purple and Pink) The 2.5" are only available in Red, Pink and Purple at this time.
Blue, White and Black 2.5" should be available Oct/Nov time frame.
The list of remaining "Flashington" balls is at the bottom of the page
Please NOTE at this time if you want more then one ball gag at a time or a custom color of leather strap, please email "firstname.lastname@example.com".
- The balls be purchased separately (see TPP-SR-BG0011 )
They are softer then they look. Our customers have reported being able to keep them in the mouth for longer then many other balls used. I know the models I have worked with have surprised me how long they could keep the ball in their mouth - the sessions were usually over before they asked to have it come out.
These ball gags use an FDA approved for human insertion medical grade silicone material and the standard Leather and PVC rubber straps can be removed, which makes cleaning them easier. The balls are purpose made for human use.
SAFETY NOTE: Always use the ball with a strap through it to prevent it getting lodged in the throat and always make sure the person gagged is not left alone.
The balls are available in 6 different sizes and currently 4 routinely available colors.
More colors will become available in the future when we make enough to put in another order.
The default strap is a 3/4" wide black leather strap with a black locking buckle, ~22" in length that fits size 16" to 20".
- We can make custom 3/4" and 1/2" custom straps and the straps can be made in almost any kind of leather we have available and we can use most tongued buckles.
- Custom leather tapered straps that are smaller at the mouth and larger at the buckle can also be made for additional cost (see TPP-SR-BG0002 ).
- We also have a straight 3/4" black PVC rubber strap available (see TPP-SR-BG0005 )
We recommend that Gags should be one person items only and thus we sell them "As Is"
Price Size Size code
$30 1.5" (38.1mm) Small (S)
$30 1.75" (44.45mm) Medium Small (R)
$30 1.875" (47.625mm) Medium (N)
$30 2.0" (50.8mm) Medium Large (M)
$31 2.125" (54mm) Large (L)
$32 2.25" (57.15mm) Extra Large (X)
$45 2.5" (63.5mm) Super Large (Z) (currently only in Red, Pink and Purple)
# = Color selection (see below for code)
Available colors/sizes:
Note: A small white ball gag with standard strap would be TPP-SR-BG0001SW
BG0001SR Small Red Ball Gag with Pleasure Paradox default strap on Chay.
(picture is of old ball)
For 2.5" Ball Only
WARNING: This is a very large ball size, please be sure it is a correct size before ordering
2.5" Ball Gags
Even More Images (old balls)
This page © TPP Enterprises, LLC


text:
October 21, 2014
A group of concerned residents known as the "Zirkle Mill Foundation" has raised $200,000 to buy the Zirkle Mill. They want to convert the Forestville property into a tourist attraction.
The current owner was planning to give the mill to the Frontier Culture Museum, but foundation members say the fact that they were able to raise so much money in just a few weeks shows just how much the community wants to keep the mill right where it is.
"The outpouring of support from people here in the county, and outside the county, has really been pretty astounding. and we're pleased with it and greatful for it," says Denny Zirkle with the Zirkle Mill Foundation. The mill's owner is taking a look at the offer and plans to have an answer by next week.
Click Here to access the online Public Inspection File
Viewers with disabilities can get assistance accessing this station's FCC Public Inspection File by contacting the station with the information listed below. Questions or concerns relating to the accessibility of the FCC's online public file system should be directed to the FCC at 888-225-5322, 888-835-5322 (TTY), or email@example.com.


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Everything Is Borrowed - The Streets
- Band Name: The Streets
- Record Label: Vice
- Release Date: Oct 7, 2008
- Summary: Mike Skinner has said this fourth album would be his second-to-last album as The Streets.
- Record Label: Vice
- Genre(s): Rap
- More Details and Credits »
Score distribution:
Positive: 11 out of 28
Mixed: 16 out of 28
Negative: 1 out of 28
His backing tracks incorporate more live instrumentation, adding some much-needed musical warmth to complement his lyrics' newfound depth.
Each listen reveals more light and shade, reaffirming Skinner's position as one of Britain's truly interesting stars.
Everything Is Borrowed is a neat about-face, a record that couldn't be more different from its predecessor. Sincere, considered, and poignant, Everything Is Borrowed finds Skinner remaining one of the foremost lyricists in pop music, and so much the better when the focus of his sharp writing is the struggle of weighty concepts instead of flimsy celebrity.
Like the core message of Everything Is Borrowed, this album will, unfortunately, come and go soon enough, little trace left behind.
Everything in the entire album is really just catching up to Skinner's words.
In the end, though, Everything Is Borrowed's musical high points aren't enough to save it from its lyric sheet, and that, going forward, constitutes a real problem for Skinner.
The result is like a musical cab ride from hell, a forty-minute endurance test of half-baked cockney cod-philosophy.
Score distribution:
Positive: 3 out of 4
Mixed: 0 out of 4
Negative: 1 out of 4
LeahT.Oct 16, 2008A Much better album than what some critics are saying...a few moments of sheer brilliance are here.
edOct 8, 2008With soaring positivity, subtle arrangements and Skinner's trademark simplistic honesty, this is marked improvement over THWTMAEL, but falls short of his first two classics.… Expand
oldhollywoodOct 16, 2008Eminem and his white-trash rhymes gave the white rapper a bad name. One got the feeling that Dr. Dre was just patronizing the dude from Detroit with all those circus beats and kiddie videos. Fortunately, acts like the Beastie Boys, House of Pain, and Bubba Sparxxx have established a solid enough foundation for white MCs that no one dud can bring down the whole lot. The man of the moment is Mike Skinner, aka The Streets. Emerging from the UK's grimecore scene with Dizzee Rascal, The Streets broke out by documenting the formerly mundane elements of everyday life behind garage beats and the Rn'B prowess of hook-singer Leo the Lion. While The Streets' debut in 2002 was a hit, their albums have gotten better with every turn, including a concept album in 2004's A Grand Don't Come for Free and 2006's decadent The Hardest Way to Make an Easy Living. Leo the Lion is no where to be found on Everything is Borrowed, as Skinner has assumed almost all of the responsibility for singing the hooks now. It's cool to hear Skinner's singing voice grow stronger from some pitchy moments on Grand to competent performances on new tracks like 'The Way of the Dodo'. But Skinner's bread and butter is his lyrics and there are some classic moments on Borrowed like 'Heaven for the Weather'. There's a lot more live instrumentation this time too, like some great reggae-tinged guitar on 'Never Give In'. Lily Allen shows up on the title track. 'The Sherry End' has a nice 70s retro feel like something from a lost episode of Charlie's Angels. Borrowed is The Streets' strongest production to date. A lot of reviews have cited the new album's introspective flavor. And while there are some sensitive songs like 'Strongest Person I Know', there are plenty of lyrical hints that The Streets haven't totally mellowed out.… Expand
RedP.Oct 8, 2008What the hell, Pitchfork gave this 4.8 but Metacritic lists their rating as 6.4.... what gives? Anyway, this is some of the most blatantly sentimental and cliched music I've ever heard. Think Anthony Robbins set to music.… Expand
|Led Zeppelin III [Remastered] - Led Zeppelin
|Led Zeppelin I [Remastered] - Led Zeppelin
|Led Zeppelin II [Remastered] - Led Zeppelin
|Definitely Maybe [Remastered] - Oasis
|Nightclubbing [Deluxe Edition] - Grace Jones
|Soul Mining (30th Anniversary Deluxe Edition) [Box Set] - The The
|From Parts Unknown - Every Time I Die
|In the Orbit of Ra - Sun Ra & His Arkestra
|Superunknown [Deluxe Edition] - Soundgarden
|Syro - Aphex Twin
|Anthology [Box Set] - The Clean
|To Be Kind - Swans
|Face the Music [Box Set] - Nils Lofgren
|Too Bright - Perfume Genius
|Foundations of Burden - Pallbearer
|You're Dead! - Flying Lotus
|Go Down in History [EP] - Four Year Strong
|Unflesh - Gazelle Twin
|LP1 - FKA Twigs
|CSNY 1974 [Box Set] - Crosby, Stills, Nash & Young


text:
Portable Document Format (PDF) is a file format used to represent documents in a manner independent of application software, hardware, and operating systems
Women's dress sewing pattern available for download. Available in various sizes and is produced by burda style magazine.
This dress has peekaboo style with a long slit at the front bust. Dare to bare over a bikini, or wear a contrast color camisole underneath for a little pop of color and more coverage. The mini length is cool for summer days.
Note: This is the same pattern PDF as the Tank Dress with Slit Neckline.
This pattern is from The Tourist collection.
Lightweight, softly draping dress fabrics.
Crêpe satin,
width: 130 cm (51 in)
length: 1.70 m (1 7/8 yds) for all sizes. Vilene Bias Tape/stay tape. Elastic, 1.5 cm (5/8 in) wide: 0.90 – 0.90 – 1.00 – 1.00 – 1.10 m (1 – 1 – 11/8 – 11/8 – 11/4 yds). Hook-and-eye.
This pattern doesn't include seam allowance
Produced by burda style magazine
Fashion & Trends
A refresher course on some of this season's top runway trends.
Backstage Report
One of the most beloved American designers passed last night. We look back at a few of his designs.
Fashion & Trends
Shop top selling silk patterns, just in time for Thursday's web seminar.
Editors' Pick
Dress up outerwear with deep shawl collars and sew up a lovely fitted dress with a flounced hemline.
Sew the Shawl Collar Fit and Flare Coat
Comment To Win
Share your top fabric care tips for a chance to be in the magazine, and to get the issue for free!
You must allow our "request for permission" request to login to Burdastyle with Facebook.


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The act by which God Almighty spoke the universe into existance out of nothing in the first chapter of Genesis in a matter of six time periods, but not necessarily six "days" as we are lead to believe. While mental midgets like "Adam "tr0n"' at urbandictionary believe the universe cannot be created out of nothing, it indeed can! With God ALL things are possible, and the vast universe we live in was created out of nothing, is an example. When you are God Almighty, you can create something very complex out of nothing by just speaking it into existance. Period! Take that atheists and evolutionists. I'm sick and tired of mental half wits like "Adam 'tr0n'" claiming that creation cannot be proven while evolution can. Hogwash. The missing link has NEVER been found and never will be. The "theory" of creation has NEVER changed, unlike evolution where its crazy followers are always changing their own theory. God's universe has an order to it, and you cannot have an order if a cataclysmic event like the "Big Big" occurred. If there is creation, there must be a creator. If there is a design, there must be a designer. If there is a plan, there must be a planner. This universe's existance is proof positive of a diety. Evolution is the the single-dumbest thing I've ever heard. Darwin himself didn't even believe his own theory, he just said it was an educated guess and that's it.
Creation has proven evolution false over and over again.
by krock1dk December 09, 2007
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Hello Angels!
Rumbelles will be ringing…please, come home for Christmas.
Forecast: Snowing
Just a little reminder from Rumple to change your clocks tonight.
Happy Once Day!
That Blue Fairy is such a meanie!
Are you ready for more Neverland?
It's always too late to apologize to a Dark One.
My new 'do not disturb' sign for Sunday Nights.
Happy Birthday!


text:
Nasty Asian Gets Fucked Hard and Pissed all Over Her Face By Several Dudes
Duration: 14m:56s
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Asia or japanese woman she love sexy sperm her mouth and all men piss her face and she like taste of penis to piss her mouth she is very awesome and she is number 1 of 100 perfect nice piss her own piss I like her she is very very best awesome I want her I wish I say that deafman just say so from Kankakee ,illinios amen sweetheart I would like to marry her I wish I say that no problem


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You are here: Home » Value Added Egg Products »
Scrambled Eggs
Pasteurised frozen scrambled eggs
Farm Pride Pasteurised Scrambled Egg is a whole egg based mixture, blended with thickened cream, vegetable gums and salt. It can be cooked from frozen or thawed.
- Scrambled egg mix is ready to use for making fluffy scrambled eggs and can also be used as an omelette base mixture.
Suggested preparation and cooking instructions:
- Heat in cooking vessel to 85°C – 90°C
- Place frozen mixture in the heated water.
- Maintain the water temperature at 85°C – 90°C and cook for frozen mixture for 30 – 35 minutes *
- Remove bag just prior to being completely cooked and knead mixture until the desired egg consistency is achieved.
- * A fully thawed product will cook in 20 – 25 minutes.
Storage and shelf life:
- Frozen: -15°C to -18°C for two years
Pack size:
- 4 x 2 kg bag in box.
- 2 kg of scrambled egg mix is equivalent to 40 medium eggs.


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Noursat TV
100 - 500 downloadsAdd this app to your lists
+ By QUAKEVISION
After years of broadcast, Noursat has become a daily bread and hope for the Christian minority in the Middle East and the diaspora as well as a source of peace for many viewers from different denominations.
Watch Noursat Live 24/7 wherever you are
Stay updated to the latest news of the church in Lebanon and around the world
-TV Grid:
Check the list of the programs broadcasted daily with air time and description
Watch replay episodes of our selected programs, in addition to a variety of video clips and hymns
Have questions or feedback on the Noursat App? E-mail us at email@example.com. We are always looking for suggestions on how to provide the best experience possible for you.
Tags: noursat, noursat tv.
We are in continuous research and work to improve Noursat Application features for the best user experience.
Our update include:
- Improvement for the left and right swipe and for the text scrolling.
- Adjustment for the video player size in order to fit smaller devices.
- Adjustment in the TV Grid section in order to show all the programs instead of the featured ones.
- There aren't any comments yet, be the first to comment!


text:
10 Steps to Pick the Right Doctor
Whether you need a primary care doctor or a specialist, these 10 steps will ensure you find the right doctor for you. View Slideshow
Dr. Henry's Experience
Research training, expertise and qualifications
- Ophthalmology
Board Certifications
- Ophthalmology, 1991
Procedures Dr. Henry Performs
Dr. Henry does not have any procedures listed. If you are Dr. Henry and would like to add procedures you perform, please update your free profile.
Conditions Dr. Henry Treats
Dr. Henry does not have any conditions listed. If you are Dr. Henry and would like to add conditions you treat, please update your free profile.
Background Check for Dr. Henry
Malpractice Claims not available
- Healthgrades does not collect malpractice information for Kentucky.
- No sanctions history found for the years that Healthgrades collects data.
0 Board Actions
- No board actions found for the years that Healthgrades collects data.
Awards & Recognitions
Education & Training
Medical School
- University of Kentucky
- Graduated in 1986
Internship Hospital
- St Vincent's Hosp & Mc
Residency Hospital
- Indiana University School Of Medicine
Languages Spoken by Dr. Henry & Staff
- English


text:
venice film festival
The Feed
New Gravity Trailer: Seriously, Is Alfonso Cuaron Trying to Kill Us?
- Tags:
- alfonso cuaron,
- george clooney,
- gravity,
- sandra bullock,
- trailer
The GQ Eye: GQ on Style
Celebrity Style Watch: Joaquin Phoenix Fails to Master the Red Carpet
The GQ Eye: GQ on Style
Celebrity Style Watch: The Iceman Cast Leaves Us Cold
News + Politics
- Matthew McConaughey: America's Space Cowboy
- Logan Lerman Went to Bootcamp with Brad Pitt for Fury
- Under the Influence: How Def Jam's Early Days Inspired this Season's Look
- Outlaw, Artist, Wild Man, Style Icon: How Dennis Hopper Inspired this Season's Look
- Can You Guess the Leading Man in These Nicholas Sparks Movie Posters?
- Nicolas Sparks Has Been to Bed with 97 Million Women
How To
Food + Travel
- David Chang's Kitchen: Don't Get All "Fresh" with Me
- David Chang's Kitchen: Your Idea of Bologna is Total Baloney
- Chart: The Family Tree of Bourbon Whiskey
- Recipe: Semi-Summer Salad - Chef Jeff Taibe of Oak + Almond
- The Great Franco-Italian Pasta Experiment
- How to Pick from an Overpriced Restaurant Wine List


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Definition of Watson, James Dewey in English:
Watson, James DeweyLine breaks: James Dewey Wat¦son
Pronunciation: /ˈwɒts(ə)n /
( B.1928), American biologist. Together with Francis Crick he proposed a model for the structure of the DNA molecule. He shared the Nobel Prize for Physiology or Medicine with Crick and Maurice Wilkins in 1962.
Definition of Watson, James Dewey in:
- The US English dictionary
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Word of the day noisome
Pronunciation: ˈnɔɪs(ə)m
having an extremely offensive smell


text:
The Detroit Lions added defensive end depth to their list of needs after Willie Young agreed to a deal with the Chicago Bears earlier today. The deal is reportedly 3-years and $9 million, with a signing bonus of $2 million.
It is looking like the Lions already have a replacement with reports the team intends to sign Darryl Tapp.
The #Lions do plan to sign DE Darryl Tapp today. He'd likely fill the 4th DE spot left vacant by Willie Young.
— Tim Twentyman (@ttwentyman) March 13, 2014
Tapp's instagram account confirms his intention to leave the Redskins for the Lions.
In swapping Tapp in for the departing Young the Lions are putting a lot of faith in the health of Jason Jones and the development of Ziggy Ansah and Devin Taylor. It may also mean the Lions will give more consideration to a pass rusher early in the draft.
Tapp played sparingly with the Redskins in 2013 but had some success as a member of the Philadelphia Eagles before that. A move to the Lions reunites him with Jim Washburn.


text:
The Yellow HousePatricia Falvey
RRP $25.29 Save 23%! ($5.94)
Price $19.35 with FREE shipping!
Price $19.35 with FREE shipping!
Buy this and get 20 Nile Miles
Ships from USA Expected delivery Nov 10 – Nov 14
User Rating
"It is rare for a first-time novelist to tackle historical events in as refreshing a manner as Patricia Falvey does in The Yellow House....Falvey controls the story....Take your time reading The Yellow House, you'll be sad to see the last page."— Irish America, June/July 2010


text:
Tweet Tweet
- RT @EdMelech: listen back to my debut show on @MEATransMission here. mixcloud.com/MEATtransMISSI… 1 week ago
- Dusky - Yoohoo wp.me/pGIfH-1rw 1 week ago
- RT @EdMelech: starting today 2-4pm. I'll be hosting a bi-weekly show on @MEATransMission . tune in here goo.gl/Uj0TFF 1 week ago
- tonight we take over @CasaNegraLDN from 9pm. come join us. special guests, resident DJs and Frozen Margaritas! 3 weeks ago
- tomorrow night we take over @CasaNegraLDN . 9pm-2am. resident DJs and special guests. join us! #dance #shoreditch #djs 3 weeks ago
- This Saturday 4th we are back at @CasaNegraLDN with special guests James Manning (@teaandtechno) and Charlie Watson wp.me/pGIfH-1rt 4 weeks ago
- RT @FACTmag: Stream @AphexTwin's Syro in full: factmag.com/2014/09/21/str… http://t.co/YKJeysJkJR 1 month ago
- @TenWallsMusic on @BBCMOTD . Who would have thought... 1 month ago
- Peverelist - Aztec Chant (Tessela Remix) wp.me/pGIfH-1rr 1 month ago
- Caribou – Can't Do Without You (Mano le Tough & Tale of Us Remix) wp.me/pGIfH-1rp 1 month ago
Tag Archives: Volute
Surrender! – Volute
We've just heard a new tune which we liked so much we thought we'd share it with you all. Hailing from Istanbul, Turkey, 23 year old Surrender! releases his new single 'Volute' on 2nd July 2012 through La Bombe Recordings. … Continue reading
Posted in Big Tunes Tagged Surrender!, Volute Leave a comment


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by Joseph Rickert
One of the most interesting R related presentations at last week's Strata Hadoop World Conference in New York City was the session on Distributed R by Sunil Venkayala and Indrajit Roy, both of HP Labs. In short, Distributed R is an open source project with the end goal of running R code in parallel on data that is distributed across multiple machines. The following figure conveys the general idea.
A master node controls multiple worker nodes each of which runs multiple R processes in parallel.
As I understand it, the primary use case for the Distributed R software is to move data quickly from a database into distributed data structures that can be accessed by multiple, independent R instances for coordinated, parallel computation. The Distributed R infrastructure automatically takes care of the extraction of the data and the coordination of the calculations, including the occasional movement of data from a worker node to the master node when required by the calculation. The user interface to the Distributed R mechanism is through R functions that have been designed and optimized to work with the distributed data structures, and through a special "Distributed R aware" foreach() function that allow users to write their own distributed functions using ordinary R functions.
To make all of this happen, Distributed R platform contains several components that may be briefly described as follows:
The distributed R package contains:
A really nice feature of the distributed data structures is that they can be populated and accessed by rows, columns and blocks making it possible to write efficient algorithms tuned to the structure of particular data sets. For example, data cleaning for wide data sets (many more columns than rows) can be facilitated by preprocessing individual features.
vRODBC is an ODBC client that provides R with database connectivity. This is the connection mechanism that permits the parallel loading of data from various sources data including HP's Vertica database.
The HPdata package contains the functions that allow you to actually load distributed data structures from various data sources
The HPDGLM package implements a parallel, distributed GLM models (Presently only linear regression, logistic regression and Poisson regression models are available), The package also contains functions for cross validation and split-sample validation.
The HPdclassifier package is intended to contain several distributed classification algorithms. It currently contains a parallel distributed implementation of the random forests algorithm.
The HPdcluster package contains a parallel, distributed kmeans algorithm.
The HPdgraph package is intended to contain distributed algorithms for graph analytics. It currently contains a parallel, distributed implementation of the pagerank algorithm for directed graphs.
The following sample code, taken directly from the HPdclassifier User Guide, but modified slightly for presentation here, is similar to the examples that Venkayala and Roy showed in their presentation. Note, that after the distributed arrays are set up they are loaded in parallel with data using the the foreach function from the distributedR package.
library(HPdclassifier) # loading the library
Loading required package: distributedR
Loading required package: Rcpp
Loading required package: RInside
Loading required package: randomForest
distributedR_start() # starting the distributed environment
Workers registered - 1/1.
All 1 workers are registered.
nparts <- sum(ds$Inst) # number of available distributed instances
# Describe the data
nSamples <- 100 # number of samples
nAttributes <- 5 # number of attributes of each sample
nSpllits <- 1 # number of splits in each darray
# Create the distributed arrays
dax <- darray(c(nSamples,nAttributes),c(round(nSamples/nSpllits),nAttributes))
day <- darray(c(nSamples,1), c(round(nSamples/nSpllits),1))
# Load the distributed arrays
foreach(i, 1:npartitions(dax),
x <- matrix(runif(nrow(x)*ncol(x)), nrow(x),ncol(x))
y <- matrix(runif(nrow(y)), nrow(y), 1)
# Fit the Random Forest Model
myrf <- hpdrandomForest(dax, day, nExecutor=nparts)
# prediction
dp <- predictHPdRF(myrf, dax)
Notwithstanding all of its capabilities, Distributed R is still clearly work in progress. It is only available on Linux platforms. Algorithms and data must be resident in memory. Distributed R is not available on CRAN, and even with an excellent Installation Guide, installing the platform is a bit of an involved process.
Nevertheless, Distributed R is impressive, and I think a valuable contribution to open source R. I expect that users with distributed data will find the platform to be a viable way to begin high performance computing with R.
Note that the Distributed R project discussed in this post is an HP initiative and is not in anyway related to http://www.revolutionanalytics.com/revolution-r-enterprise-distributedr.
by Andrie de Vries
Last week we announced the availability of Revolution R Open, an enhanced distribution of R. One of the enhancements is the inclusion of high performance linear algebra libraries, specifically the Intel MKL. This library significantly speeds up many statistical calculations, e.g. the matrix algebra that forms the basis of many statistical algorithms.
Several years ago, David Smith wrote a blog post about multithreaded R, where he explored the benefits of the MKL, in particular on Windows machines.
In this post I explore whether anything has changed.
To best use the power available in the machines of today, Revolution R Open is installed by default with the Intel Math Kernel Library (MKL), which provides BLAS and LAPACK library functions used by R. Intel MKL makes it possible for so many common R operations to use all of the processing power available.
The MKL's default behavior is to use as many parallel threads as there are available cores. There's nothing you need to do to benefit from this performance improvement — not a single change to your R script is required.
However, you can still control or restrict the number of threads using the
setMKLthreads() function from the
Revobase package delivered with Revolution R Open. For example, you might want to limit the number of threads to reserve some of the processing capacity for other activities, or if you're doing explicit parallel programming with the ParallelR suite or other parallel programming tools.
You can set the maximum number of threads as follows:
Where the
<value> is the maximum number of parallel threads, not to exceed the number of available cores.
Compared to open source R, the MKL offers significant performance gains, particularly on Windows.
Here are the results of 5 tests on matrix operations, run on a Samsung laptop with an Intel i7 4-core CPU. From the graphic you can see that a matrix multiplication runs 27 times faster with the MKL than without, and linear discriminant analysis is 3.6 times faster.
You can replicate the same tests by using this code:
Another famous benchmark was published by Simon Urbanek, one of the members of R-core. You can find his code at Simon's benchmark page. His benchmark consists of three different classes of test:
I compared the total execution time of the benchmark script in RRO (with MKL) and R. Using Revolution R Open, the benchmark tests completed in 47.7 seconds. This compared to ~176 seconds using R-3.1.1 on the same machine.
To replicate these results you can use the following script runs (sources) his code directly from the URL and captures the total execution time:
Here is a summary of each of the individual tests:
|Performance gain
|I. Matrix calculation
|Create, transpose and deform matrix
|Matrix computation
|Sort random values
|Cross product
|Linear regression
|II. Matrix functions
|Fast Fourier Transform
|Compute eigenvalues
|Calculate determinant
|Cholesky decomposition
|Matrix inverse
|III. Programmation
|Vector calculation
|Matrix calculation
|Mixed control flow
|Total test time
The Intel MKL makes a notable difference for many matrix computations. When running the Urbanek benchmark using the MKL on Windows, you can expect a performance gain of ~2.5x.
The caveat is that different the standard R distribution on different operating systems use different math libraries. For example, R on Mac OSx uses the ATLAS blas, which gives you comparable performance to the MKL.
To find out more about Revolution R Open, go to http://mran.revolutionanalytics.com/open/
You can download RRO at http://mran.revolutionanalytics.com/download/
by Joseph Rickert
I think we can be sure that when American botanist Edgar Anderson meticulously collected data on three species of iris in the early 1930s he had no idea that these data would produce a computational storm that would persist well into the 21st century. The calculations started, presumably by hand, when R. A. Fisher selected this data set to illustrate the techniques described in his 1936 paper on discriminant analysis. However, they really got going in the early 1970s when the pattern recognition and machine learn community began using it to test new algorithms or illustrate fundamental principles. (The earliest reference I could find was: Gates, G.W. "The Reduced Nearest Neighbor Rule". IEEE Transactions on Information Theory, May 1972, 431-433.) Since then, the data set (or one of its variations) has been used to test hundreds, if not thousands, of machine learning algorithms. The UCI Machine Learning Repository, which contains what is probably the "official" iris data set, lists over 200 papers referencing the iris data.
So why has the iris data set become so popular? Like most success stories, randomness undoubtedly plays a huge part. However, Fisher's selecting it to illustrate a discrimination algorithm brought it to peoples attention, and the fact that the data set contains three classes, only one of which is linearly separable from the other two, makes it interesting.
For similar reasons, the airlines data set used in the 2009 ASA Sections on Statistical Computing and Statistical Graphics Data expo has gained a prominent place in the machine learning world and is well on its way to becoming the "iris data set for big data". It shows up in all kinds of places. (In addition to this blog, it made its way into the RHIPE documentation and figures in several college course modeling efforts.)
Some key features of the airlines data set are:
An additional, really nice feature of the airlines data set is that it keeps getting bigger! RITA, The Research and Innovative Technology Administration Bureau of Transportation Statistics continues to collect data which can be downloaded in .csv files. For your convenience, we have a 143M+ record version of the data set Revolution Analytics test data site which contains all of the RITA records from 1987 through the end of 2012 available for download.
The following analysis from Revolution Analytics' Sue Ranney uses this large version of the airlines data set and illustrates how a good model, driven with enough data, can reveal surprising features of a data set.
# Fit a Tweedie GLM tm <- system.time( glmOut <- rxGlm(ArrDelayMinutes~Origin:Dest + UniqueCarrier + F(Year) + DayOfWeek:F(CRSDepTime) , data = airData, family = rxTweedie(var.power =1.15), cube = TRUE, blocksPerRead = 30) ) tm # Build a dataframe for three airlines: Delta (DL), Alaska (AK), HA airVarInfo <- rxGetVarInfo(airData) predData <- data.frame( UniqueCarrier = factor(rep(c( "DL", "AS", "HA"), times = 168), levels = airVarInfo$UniqueCarrier$levels), Year = as.integer(rep(2012, times = 504)), DayOfWeek = factor(rep(c("Mon", "Tues", "Wed", "Thur", "Fri", "Sat", "Sun"), times = 72), levels = airVarInfo$DayOfWeek$levels), CRSDepTime = rep(0:23, each = 21), Origin = factor(rep("SEA", times = 504), levels = airVarInfo$Origin$levels), Dest = factor(rep("HNL", times = 504), levels = airVarInfo$Dest$levels) ) # Use the model to predict the arrival day for the three airlines and plot predDataOut <- rxPredict(glmOut, data = predData, outData = predData, type = "response") rxLinePlot(ArrDelayMinutes_Pred~CRSDepTime|UniqueCarrier, groups = DayOfWeek, data = predDataOut, layout = c(3,1), title = "Expected Delay: Seattle to Honolulu by Departure Time, Day of Week, and Airline", xTitle = "Scheduled Departure Time", yTitle = "Expected Delay")
Here, rxGLM()fits a Tweedie Generalized Linear model that looks at arrival delay as a function of the interaction between origin and destination airports, carriers, year, and the interaction between days of the week and scheduled departure time. This function kicks off a considerable amount of number crunching as Origin is a factor variable with 373 levels and Dest, also a factor, has 377 levels. The F() function makes Year and CrsDepTIme factors "on the fly" as the model is being fit. The resulting model ends up with 140,852 coefficients 8,626 of which are not NA. The calculation takes 12.6 minutes to run on a 5 node (4 cores and 16GB of RAM per node) IBM Platform LFS cluster.
The rest of the code uses the model to predict arrival delay for three airlines and plots the fitted values by day of the week and departure time.
It looks like Saturday is the best time to fly for these airlines. Note that none of the sturcture revealed in these curves was put into the model in the sense that there are no polynomial terms in the model.
It will take a few minutes to download the zip file with the 143M airlines records, but please do, and let us know how your modeling efforts go.
I was honoured to be invited earlier this month to the Directions of Statistical Computing meeting in Brixen, Italy. DSC is one of two meetings run by the R Project and unlike the useR! conference, DSC is a much smaller and intimate meeting (DSC 2014 had about 30 participants). If you haven't come across DSC meeting before (quite possible, given that it had last been held in 2009), R Core Group member Martyn Plummer has a nice overview of DSC.
A focus of the first day of the conference was on the performance of R computation engine. The organizers invited representatives from all of the "alternative" R engine implementations, and I believe it marked the first time that developers involved with pqR, Renjin, FastR, and Riposte and TERR were gathered in the same place. (The CXXR project was unfortunately not represented.) Jan Vitek [slides] presented a fascinating comparison of the various projects, based on his interviews with the developers.
It was interesting to see the commonalities in many of the approaches. Three projects, Renjin [slides], FastR [slides] and Riposte [slides] use just-in-time compilation and an optimized bytecode engine. All have achieved impressive performance gains, but have struggled with compatibility (and especially being able to run the 6000+ CRAN packages). But it's clear that their work is having an influence on R itself: Thomas Kalibera [slides] (who previously worked on the FastR project) is working with Luke Tierney and Jan Vitek to improve the performance of R's bytecode interpreter.
Other approaches are also being pursued to improve the performance of the R engine. Luke Tierney [slides] described new improvements in R 3.1 to streamline the reference counting system, and noted that several of the performance improvements implemented by Radford Neal [slides] in pqR have already been incorporated into the R engine. And Helena Kotthaus [slides] has done some very exciting work to profile the performance of the R engine which has already led to performance improvements when virtual memory is being used.
Overall, it was exciting to see collaboration and research into R as a language, and especially the attention from the computer science community to the implementation of R. As Robert Gentleman (co-creator of R and conference lead) noted, R now has a new community beyond statisticians and data scientists: computer scientists. It's exciting to see how R is incorporating learning and innovation from this new community.
For more on DSC 2014, see the reports from Martyn Plummer on Day 1 and Day 2 of the conference. The full program, with links to download the slide presentation, is at the link below.
DSC 2014: Schedule (and slide downloads)
by Joseph Rickert
UserR! 2014 got under way this past Monday with a very impressive array of tutorials delivered on the day that the conference organizers were struggling to cope with a record breaking crowd. My guess is that conference attendance is somewhere in the 700 range. Moreover, this the first year that I can remember that tutorials were free. The combination made for jam-packed tutorials.
The first thing that jumps out just by looking at the tutorial schedule is the effort that RStudio made to teach at the conference. Of the sixteen tutorials given, four were presented by the RStudio team. Winston Chang conducted an introduction to interactive graphics with ggvis; Yihui Xie presented Dynamic Documents with R and knitr; Garrett Grolemund Interactive data display with Shiny and R and Hadley Wickham taught data manipulation with dplyr. Shiny is still captivating R users. I was particularly struck by a conversation I had with a undergraduate Stats major who seemed to be genuinely pleased and excited about being able to build her own Shiny apps. Kudos to the RStudio team.
Bob Muenchen brought this same kind of energy to his introduction to managing data with R. Bob has extensive experience with SAS and Stata and he seems to have a gift anticipating areas where someone proficient in one of these packages, but new to R, might have difficulty.
Matt Dowle presented his tutorial on Data Table and, from the chatter I heard, increased his growing list of data table converts. Dirk Eddelbuettel presented An Example-Driven, Hands-on Introduction to Rcpp and Romain Francois taught C++ and Rcpp11 for beginners. Both Dirk and Romain work hard to make interfacing to C++ a real option for people who themselves are willing to make an effort.
I came late to Martin Morgan's tutorial on Bioconductor but couldn't get in the room. Fortunately, Martin prepared an extensive set of materials for his course which I hope to be able to work through.
Max Kuhn taught an introduction to applied predictive based on the recent book he wrote with Kjell Johnson. Both the slides and code are available. Virgilio Gomez Rubio presented a tutorial on applied spatial data analysis with R. His materials are available here. Ramnath Vaidyanathan Interactive Documents with R Have a look at both Ramnath's slides, the map embedded in his abstract and his screencast.
Drew Schmidt taught a workshop on Programming with Big Data in R based on the pdbR package. The course page points to a rich set of introductory resources.
I very much wanted to attend Søren Højsgaard's tutorial on Graphical Models and Bayesian Networks with R, but couldn't make it. I did attend John Nash's tutorial on Nonlinear parameter optimization and modeling in R and I am glad that I did. This is a new field for me and I was fortunate to see something of John's meticulous approach to the subject.
I was disappointed that I didn't get to attend Thomas Petzoldt's tutorial on Simulating differential equation models in R. This is an area that is not usually associated with R. The webpage for the tutorial is really worth a look.
I don't know if the conference organizers planned it that way, but as it turned out, the tutorial subjects chosen are an excellent showcase for the depth and diversity of applications that can be approached through R. Many thanks to the tutorial teachers and congratulations to the UseR! 2014 conference organizers for a great start to the week. I hope to have more to say about the conference in future posts.
by Joseph Rickert
I was very happy to have been able to attend R / Finance 2014 which wrapped up a couple of weeks ago. In general, the talks were at a very high level of play, some dealing with brand new ideas and many presented at a significant level of technical or mathematical sophistication. Fortunately, most of the slides from the presentations are quite detailed and available at the conference site. Collectively, these presentations provide a view of the boundaries of the conceptual space imagined by the leaders in quantitative finance. Some of this space covers infrastructure issues involving ideas for pushing the limits of R (Some Performance Improvements for the R Engine) or building a new infrasturcture (New Ideas for Large Network Analysis) or (Building Simple Data Caches) for example. Others are involved with new computational tools (Solving Cone Constrained Convex Programs) or attempt to push the limits on getting some actionable insight from the mathematical abstrations: (Portfolio Inference withthei One Wierd Trick) or (Twinkle twinkle litle STAR: Smooth Transition AR Models in R) for example.
But while the talks may be illuminating, the real take-aways from the conference are the R packages. These tools embody the work of the thought leaders in the field of computational finance and are the means for anyone sufficiently motivated to understand this cutting edge work. By my count, 20 of the 44 tutorials and talks given at the conference were based on a particular R package. Some of the packages listed in the following table are well-established and others are work-in-progress sitting out on R-Forge or GitHub, providing opportunities for the interested to get involved.
R Finance 2014 Talk
Introduction to data.table
Extension of the data frame
An Example-Driven Hands-on introduction to Rcpp
Functions to facilitate integrating R with C++
Portfolio Optimization: Utility, Computation, Equities Applications
Environment for reaching Financial Engineering and Computational Finance
Re-Evaluation of the Low Risk Anomaly via Matching
Implementation of the Coarsened Exact matching Algorithm
BCP Stability Analytics: New Directions in Tactical Asset Management
Bayesian Analysis of Change Point Problems
On the Persistence of Cointegration in Pairs Trading
Engle-Granger Cointegration Models
Tests for Robust Versus Least Squares Factor Model Fits
robust methods
The R Package cccp: Solving Cone Constrained Convex Programs
Solver for convex problems for cone constraints
Twinkle, twinkle little STAR: Smooth Transition AR Models in R
Modeling smooth transition models
Asset Allocaton with Higher Order Moments and Factor Models
Global optimization by differential evolution / Numerical methods for portfolio optimization
Event Studies in R
Event study and extreme event analysis
An R package on Credit Default Swaps
Provides tools for pricing credit default swaps
New Ideas for Large Network Analysis, Implemented in R
Implicitly restarted Lanczos methods for R
Package "Intermediate and Long Memory Time Series
Simulate & Detect Intermediate and Long Memory Processes / in development
Stochvol: Dealing with Stochastic Volatility in Time Series
Efficient Bayesian Inference for Stochastic Volatility (SV) Models
Divide and Recombine for the Analysis of Large Complex Data with R
Package for using R with Hadoop
gpusvcalibration: Fast Stochastic Volatility Model Calibration using GPUs
Fast calibration of stochastic volatility models for option pricing models
The FlexBayes Package
Provides an MCMC engine for the class of hierarchical feneralized linear models and connections to WinBUGS and OpenBUGS
Building Simple Redis Data Caches
Rcpp bindings for Redis that connects R to the Redis key/value store
Package pbo: Probability of Backtest Overfitting
Uses Combinatorial Symmetric Cross Validation to implement performance tests.
Many of these packages / projects also have supplementary material that is worth chasing down. Be sure to take a look at Alexios Ghalanos recent post that provides an accessible introduction to his stellar keynote address.
Many thanks to the organizers of the conference who, once again, did a superb job, and to the many professionals attending who graciously attempted to explain their ideas to a dilletante. My impression was that most of the attendies thoroughly enjoyed themselves and that the general sentiment was expressed by the last slide of Stephen Rush's presentation:
Many companies are considering switching from SAS to R for statistical data analysis, and may be wondering how R compares in performance and data size scalability to the legacy SAS systems (base SAS and SAS/Stat) they are currently using. Performance and scalability for R is exactly what Revolution R Enterprise (RRE) was designed for. In a recent webinar, Thomas Dinsmore described a benchmarking process to compare performance of legacy SAS and RRE. (The benchmarking process is described in the white paper Revolution R Enterprise: Faster Than SAS, and you can see the code behind the benchmarking process here.) In the webinar, Thomas revealed the following results:
Also in the webinar, John Wallace, founder and CEO of DataSong, described how performance and scalability requirements led to the selection in 2011 of Revolution R Enterprise as the analytics engine in their software-as-a-service platform. DataSong's industry-leading marketing analytics system currently analyzes more than $3billion in marketing spend by major retailers.
The slides from the webinar are embedded above, and you can watch and download the full webinar at the link below.
Revolution Analytics webinars: Is Revolution R Enterprise Faster than SAS? Benchmarking Results Revealed
by Joseph Rickert
In last week's post, I sketched out the history of Generalized Linear Models and their implementations. In this post I'll attempt to outline how GLM functions evolved in R to handle large data sets.
The first function to make it possible to build GLM models with datasets that are too big to fit into memory was the bigglm()from Thomas Lumley's biglm package which was released to CRAN in May 2006. bigglm()is an example of a external memory or "chunking" algorithm. This means that data is read from some source on disk and processed one chunk at a time. Conceptually, chunking algorithms work as follows: a program reads a chunk of data into memory, performs intermediate calculations to compute the required sufficient statistics, saves the results and reads the next chunk. The process continues until the entire dataset is processed. Then, if necessary, the intermediate results are assembled into a final result.
According to the documentation trail, bigglm()is based on Alan Miller's 1991 refinement (algorithm AS 274 implemented in Fortran 77) to W. Morevin Genetlemen's 1975 Algol algorithm ( AS 75). Both of these algorithms work by updating the Cholesky decomposition of the design matrix with new observations. For a model with p variables, only the p x p triangular Cholesky factor and a new row of data need to be in memory at any given time.
bigglm()does not do the chunking for you. Working with the algorithm requires figuring out how to feed it chunks of data from a file or a database that are small enough to fit into memory with enough room left for processing. ( Have a look at the make.data() function defined on page 4 of the biglm pdf for the prototype example of chunking by passing a function to bigglm()'s data argument.) bigglm() and the biglm package offer few features for working with data. For example, bigglm() can handle factors but it assumes that the factor levels are consistent across all chunks. This is very reasonable under the assumption that the appropriate place to clean and prepare the data for analysis is the underlying database.
The next steps in the evolution of building GLM models with R was the development of memory-mapped data structures along with the appropriate machinery to feed bigglm() data stored on disk. In late 2007, Dan Alder et al. released the ff package which provides data structures that, from R's point of view, make data residing on disk appear is if it were in RAM. The basic idea is that only a chunk (pagesize) of the underlying data file is mapped into memory and this data can be fed to bigglm(). This strategy really became useful in 2011 when Edwin de Jonge, Jan Wijffels and Jan van der Laan released ffbase, a package of statistical functions designed to exploit ff's data structures. ffbase contains quite a few functions including some for basic data manipulation such as ffappend() and ffmatch(). For an excellent example of building a bigglm() model with a fairly large data set have a look at the post from the folks at BNOSAC. This is one of the most useful, hands-on posts with working code for building models with R and large data sets to be found. (It may be a testimony to the power of provocation.)
Not longer after ff debuted (June 2008), Michael Kane, John Emerson and Peter Haverty released bigmemory, a package for working with large matrices backed by memory-mapped files. Thereafter followed a sequence of packages in the Big Memory Project, including biganalytics, for exploiting the computational possibilities opened by by bigmemory. bigmemory packages are built on the Boost Interprocess C++ library and were designed to facilitate parallel programming with foreach, snow, Rmpi and multicore and enable distributed computing from within R. The biganalytics package contains a wrapper function for bigglm() that enables building GLM models from very large files mapped to big.matrix objects with just a few lines of code.
The initial release in early August 2010 of the RevoScaleR package for Revolution R Enterprise included rxLogit(), a function for building logistic regression models on very masive data sets. rxLogit() was one of the first of RevoScaleR's Parallel External Memory Algorithms (PEMA). These algorithms are designed specifically for high performance computing with large data sets on a variety of distributed platforms. In June 2012, Revolution Analytics followed up with rxGlm(), a PEMA that implements all of the all of the standard GLM link/family pairs as well as Tweedie models and user-defined link functions. As with all of the PEMAS, scripts including rxGlm() may be run on different platforms just by changing a few lines of code that specifies the user's compute context. For example, a statistician could test out a model on a local PC or cluster and then change the compute context to run it directly on a Hadoop cluster.
The only other Big Data GLM implementation accessible through an R package of which I am aware is h20.glm() function that is part of the 0xdata's JVM implementation of machine learning algorithms which was announced in October 2013. As opposed the the external memory R implementations described above, H20 functions run in the distributed memory created by the H20 process. Look here for h20.glm() demo code.
And that's it, I think this brings us up to date with R based (or accessible) functions for running GLMs on large data sets.
by Seth Mottaghinejad, Analytic Consultant for Revolution Analytics
In the last article, we showed two separate R implementations of the Collatz conjecture: 'nonvec_collatz' and 'vec_collatz', with the latter being more efficient than the former because of the way it takes advantage of vectorization in R. Let's once again take a look at 'vec_collatz':
Today we will learn a thrid, and far more efficient way of implementing the Collatz conjecture. It involves rewriting the function in C++ and using the 'Rcpp' package in R to compile and run the function without ever leaving the R environment.
One important difference between R and C++ is that when you write a C++ function, you need to declare your variable types. The C++ code chunk shown below creates a function called 'cpp_collatz' which takes an input of type 'IntegerVector' and whose output is of type 'IntegerVector'. Unlike in R, where explicit loops can slow your code down, loops in C++ are usually very efficient, even though they are tedious to write.
cpptxt <- ' + IntegerVector cpp_collatz(IntegerVector ints) { + IntegerVector iters(ints.size()); + for (int i=0; i<ints.size(); i++) { + int nn = ints(i); + while (nn != 1) { + if (nn % 2 == 0) nn /= 2; + else nn = 3 * nn + 1; + iters(i) += 1; + } + } + return iters; + }' cpp_collatz <- cppFunction(cpptxt) set.seed(20) cpp_collatz(sample(20)) [1] 20 17 8 19 4 20 1 0 2 5 3 16 9 7 17 7 12 6 14 9
Let's now redo our C++ implementation in a slightly different way. We would rather not have C++ code interspersed with R code: Not only does it make it hard to read, but we also won't be able to take advantage of syntax-hightlighting specific to C++ (among other annoyances). So let's store the C++ code in a file we call 'collatz.cpp' and use the 'sourceCpp' function in R to call it. Here is the content of 'collatz.cpp':
cat(paste(readLines(file("collatz.cpp")), collapse = "\n")) #include <Rcpp.h> using namespace Rcpp; // [[Rcpp::export]] int collatz(int nn) { int ii = 0; while (nn != 1) { if (nn % 2 == 0) nn /= 2; else nn = 3 * nn + 1; ii += 1; } return ii; } // [[Rcpp::export]] IntegerVector cpp_collatz(IntegerVector ints) { IntegerVector iters(ints.size()); for (int i=0; i<ints.size(); i++) { iters(i) = collatz(ints(i)); } return iters; } // [[Rcpp::export]] IntegerVector sug_collatz(IntegerVector ints) { return sapply(ints, collatz); }
There are three things worth mentioning about the above code chunk:
To compile the C++ code, just type the following in R:
Assuming that we have a C++ compiler istalled, it will take a few seconds to run. We can now type the following into the R console:
cpp_collatz function (ints) .Primitive(".Call")(<pointer: 0x000000006e042180>, ints) cpp_collatz(1:10) # seems to be working fine. [1] 0 1 7 2 5 8 16 3 19 6 sug_collatz(1:10) # same as above. [1] 0 1 7 2 5 8 16 3 19 6
And now let's get to the reason we bothered with C++ in the first place: efficiency. There are four comparisons we're interested in:
We can think of the last case as being a hybrid approach. One reason to include it is because someone may share a complicated piece of C++ code that works but is not vectorized and vectorizing it may turn out to be a non-trivial task (programmers are supposed to be lazy after all!).
collatz_benchmark <- function(nums, ...) { + require(rbenchmark) + benchmark( + cpp_collatz(nums), # runs completely in C++ + sug_collatz(nums), # runs completely in C++ + vec_collatz(nums), # runs completely in R in a vectorized fashion + sapply(nums, collatz), # runs collatz in C++ but sapply in R + columns = c("test", "replications", "elapsed", "relative"), + ... + ) + }
Let's compare the two functions for all integers from 1 to 10^4:
collatz_benchmark(1:10^4, replications = 20) test replications elapsed relative 1 cpp_collatz(nums) 20 0.03 1.000 4 sapply(nums, collatz) 20 0.53 17.667 2 sug_collatz(nums) 20 0.03 1.000 3 vec_collatz(nums) 20 51.36 1712.000
And for all integers from 1 to 10^5:
collatz_benchmark(1:10^5, replications = 20) test replications elapsed relative 1 cpp_collatz(nums) 20 0.45 1.071 4 sapply(nums, collatz) 20 5.76 13.714 2 sug_collatz(nums) 20 0.42 1.000 3 vec_collatz(nums) 20 753.08 1793.048
As we can see 'cpp_collatz' and 'sug_collatz' are almost identical when it comes to efficiency and both are far more efficient than 'vec_collatz', and increasingly so for larger sequences of integers. Also notice the relative efficiency of the hybrid approach compared to 'vec_collatz'.
Let's benchmark on a sample of 1000 integers from 1 to 10^6 for a more realistic comparison of four approaches:
set.seed(20) collatz_benchmark(sample(1:10^6, 1000), replications = 100) # will take a while to run! test replications elapsed relative 1 cpp_collatz(nums) 100 0.05 1.667 4 sapply(nums, collatz) 100 0.23 7.667 2 sug_collatz(nums) 100 0.03 1.000 3 vec_collatz(nums) 100 31.15 1038.333
The results are remarkable: the C++ functions 'sug_collatz' is the winner with 'cpp_collatz' a close second. The slight advantage of 'sug_collatz' may be due to 'sapply' in C++ using a more efficient method for running the loop (such as iterators). Moreover, both functions are about 1000 times faster than 'vec_collatz'! Even though 'vec_collatz' was specifically written to take advantage of vectorization in R, it still pales in comparison to the might of C++. Even more surprising is that the hybrid approach is only about 8 times slower than C++ approach, which is not bad at all. But if your "vectorization" consists of wrapping a function in 'sapply', then it's best to do it in C++ as we did with 'sug_collatz' than to do it in R.
One take-away lesson for us is that istead of spending a lot of time and effort making our R code as efficient as possible, it may be worth investing that time in learning how to code in a language like C++. Especially since packages like 'Rcpp' and the 'Rcpp-sugar' extension give us the best of both worlds. Another take away is that hybrid solutions are a good alternative in cases when our C++ code is efficient but not vectorized.
by Thomas Dinsmore
Regular readers of this blog may be familiar with our ongoing effort to benchmark Revolution R Enterprise (RRE) across a range of use cases and on different platforms. We take these benchmarks seriously at Revolution Analytics, and constantly seek to improve the performance of our software.
Previously, we shared results from a performance test conducted by Allstate. In that test, RRE ran a GLM analysis in five minutes; SAS took five hours to complete the same task. A reader objected that the test was unfair because SAS ran on a single machine, while RRE ran on a five node cluster. It's a fair point, except that given the software in question (PROC GLM in SAS/STAT) the performance would be the same on five nodes or a million nodes, since PROC GLM can scale up but not out.
Arguing that the Allstate benchmark was "apples to oranges", SAS responded by publishing its own apples to orange benchmark. In this benchmark, SAS demonstrated that its new HPGENSELECT procedure is very fast when it runs on a 144 node grid with 2,304 cores. As noted in the paper, this performance is only possible if you license more software, since HPGENSELECT can only run in Distributed mode if the customer licenses SAS High Performance Statistics.
We will be happy to stipulate that PROC HPGENSELECT runs faster on 2,304 cores than RRE on 20 cores.
As a matter of best practices, software benchmarks should run in comparable hardware environments, so that we can attribute performance differences to the software alone and not to differences in available computing resources. Consequently, we engaged an outside vendor with experience running SAS in clustered environments to perform an "apples to apples" benchmark of RRE vs. SAS. The consultant used a clustered computing environment consisting of five four-core commodity servers (with 16G RAM each) running CentOS, Ethernet connections and a separate NFS Server.
We tested RRE 7 versus SAS Release 9.4, with Base SAS, SAS/STAT and SAS Grid Manager. (We did not test with SAS High Performance Statistics because we could find no vendors with experience using this new software. We note that more than two years into General Availability, SAS appears to have no public reference customers for this software.) In our experience, when customers ask how we perform compared to SAS, they are most interested in how we compare with the SAS software they already use.
To test Revolution R Enterprise ScaleR, we first deployed IBM Platform LSF and Platform MPI Release 9 on the grid, then installed Revolution R Enterprise Release 7 on each node. SAS Grid Manager uses an OEM version of IBM Platform LSF that cannot run concurrently with the standard version from IBM, so we configured the environment and ran the tests sequentially.
To simplify test replication across different environments, we used data manufactured through a random process. The time needed to manufacture the data is not included in the benchmark results. Prior to running the actual tests, we loaded the randomized data into each software product's native file system: for SAS, a SAS Data Set; for Revolution R Enterprise, an XDF file.
Although we have benchmarked Revolution R Enterprise on data sets as large as a billion rows, typical data sets used by even the largest enterprises tend to be much smaller. We chose to perform the tests on wide files of 591 columns and row counts ranging from 100,000 to 5,000,000, file sizes that represent what we consider to be typical for many analysts. We also ran scoring tests on "narrow" files of 21 columns with row counts ranging up to 50,000,000.
Rather than comparing performance on a single task, we prepared a list of multiple tasks, then wrote programs in SAS and RRE to implement the test. Readers will find the benchmarking scripts here, on Git together with a script to produce the manufactured data.
To implement a fair test, we asked the SAS consultant to review the SAS programs and enable them for best performance in the clustered computing environment.
Detailed results of the benchmark test are shown here, in our published white paper
We invite readers to use the scripts in your own environment; let us know the results you achieve.
Revolution Analytics Whitepapers: Revolution R Enterprise: Faster Than SAS


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