text:
Chinese Views of the Singapore Summit Between Donald J. Trump and Kim Jong-un
Michael D. Swaine
Summary: Chinese observers generally view the Singapore summit as a positive step toward denuclearization on the Korean Peninsula. However, many have come to question the success of the summit and whether the positive momentum that resulted from it is markedly slowing down.
The long-awaited and much-hyped summit between U.S. President Donald Trump and North Korean leader Kim Jong-un was finally held on June 12, 2018. The purpose of the talks was to improve relations between the U.S. and North Korea and to establish a clear basis for the resolution of the escalating crisis triggered by the development of Pyongyang's nuclear arsenal. Nearly five hours of talks between the two leaders resulted in "fulsome declarations of a new friendship but just vague pledges of nuclear disarmament."1
Swaine was a senior fellow at the Carnegie Endowment for International Peace and one of the most prominent American analysts in Chinese security studies.
@Dalzell60
Beyond the unprecedented nature and positive atmospherics, the most significant outcome of the summit was a short joint statement. It asserted that Trump and Kim were committed to implementing four agreed-upon stipulations "fully and expeditiously" via follow-up negotiations at the "earliest possible date." These include:
A commitment to "establish new U.S.-DPRK relations in accordance with the desire of the peoples of the two countries for peace and prosperity;"
Joining "efforts to build a lasting and stable peace regime on the Korean Peninsula;"
A DPRK commitment "to work toward complete denuclearization of the Korean Peninsula;" and
A commitment "to recovering POW/MIA remains."2
In addition, Kim destroyed a nuclear weapons test site and Trump announced the suspension of U.S.-ROK military exercises. Yet despite such arguably positive gestures, the two countries made no outward progress in developing the specific goals, timing, and roadmap for the denuclearization of the Korean Peninsula.
While some observers in the People's Republic of China (PRC) and elsewhere hailed the summit as a step toward peace and stability on the peninsula, many in the U.S. criticized it as high on gestures of good will but low on substance, while providing Pyongyang (and Beijing) with far more strategic gains than the U.S.3
This essay describes and assesses the Chinese views to date on the Singapore summit. As usual, such views are divided into authoritative and non-authoritative categories to identify possible lines of debate within both official and unofficial leadership and elite circles.4 The essay ends with a summary and assessment of the Chinese perspective and its implications for the North Korea nuclear crisis and U.S.-PRC relations.
This article will be published by the China Leadership Monitor.
1 Kevin Liptak, "Trump's North Korean gamble ends with 'special bond' with Kim," CNN Politics, June 12, 2018, https://www.cnn.com/2018/06/11/politics/trump-kim-summit-singapore/index.html.
2 "Full text of Trump-Kim signed statement," CNN Politics, June 12, 2018, https://www.cnn.com/2018/06/12/politics/read-full-text-of-trump-kim-signed-statement/index.html.
3 See Jung H. Pak, Jonathan D. Pollack, Evans J. R. Revere, Robert Einhorn, Ryan Hass, Richard Nephew, Katharine H.S. Moon, Jonathan Stromseth, Michael E. O'Hanlon, Tarun Chhabra, and Jeffrey A. Bader, "Around the halls: Brookings experts react to the Trump-Kim Jong-un summit in Singapore," Brookings Institution, June 12, 2018, https://www.brookings.edu/blog/order-from-chaos/2018/06/12/around-the-halls-brookings-experts-react-to-the-trump-kim-jong-un-summit-in-singapore/; Nicholas Eberstadt, "Kim Wins in Singapore," National Review, June 21, 2018, https://www.nationalreview.com/magazine/2018/07/09/kim-jong-un-singapore-summit-north-korea-wins/; and Gillian Wong and Christopher Bodeen, "China Winner From Summit but Wary of Closer US-N. Korea Ties," US News, June 13, 2018, https://www.usnews.com/news/world/articles/2018-06-13/after-summit-china-likely-to-remind-n-korea-of-close-ties.
4 For a detailed description of the nature and definition of various types of Chinese sources, see Footnote 1 in Michael D. Swaine, "Chinese Views of the U.S. National Security and National Defense Strategies," China Leadership Monitor 56 (May 2018), https://www.hoover.org/sites/default/files/research/docs/clm56ms.pdf.


text:
Q: Android Tab Swipe This may be a very juvenile question, but this has been driving me insane the last few days.  I currently have an app that has 3 tabs.  I would like to update it so you can swipe left and right to view each tab (as opposed to touching each tab).  I have found a ton of methods on doing this, but I'm still very confused.  I have been told to implement an Action Bar with Tabs as well as fragments, and I have found other sources that use Page Viewers.  Either way this has been a thorn in my side to find something that is what I need.  Is there any good documentation on doing a basic left and right swipe using tabs?  I'm very sorry if this question is repetitive to others.

A: I really think that a ViewPager (as described here: http://android-developers.blogspot.com/2011/08/horizontal-view-swiping-with-viewpager.html) is what would be best for you.
And yes, it involves fragments.

redpajama_set_name: RedPajamaStackExchange


According to the sources, The secondary deal is in the range of $60 million.
Dream 11 first discussion with Steadview Capital was first reported by VCCircle in February. There are a lot of investors who are interested in Dream 11 because of its huge customer base and after the Punjab & Haryana High Court judgment last year there is a significant rise in the Interested investors.
April last year Dream11 faced a legal conflict and In Dream11 judgment Justice Amit Rawal had said playing fantasy games required considerable skill, judgment, and discretion, and that, Dream 11 held such games couldn't be considered gambling.
Kalaari Capital, Think Investments and Multiples Alternate Asset management, these three investors have sold a portion of their stakes, with the valuation of the company estimated at $1 – $1.5 billion. Last year, it raised $100 million from China-based Tencent at a valuation of $700 million.
Bhavit Seth and Harsh Jain the chief executive officer and co-founder of Dream 11 sights further growth with the investment. It's associated with Pro Kabaddi League, Indian Super League, International Council of Cricket, National Basketball Association, Caribbean Premier League, Big Bash League, and International Hockey Federation.
Not just that this fantasy gaming firm is now endorsed by Indian cricketer Mahendra Singh Dhoni. He has signed a four-year partnership with the BCCI which has started with IPL 2019.
By playing the biggest card, Dream 11 is now grabbing the attention of millions in fantasy gaming domain with an exceptional market opportunity of billion Dollars.
"We are excited to welcome Steadview Capital onboard, providing great returns to our shareholders, whether investors or Dreamsters, is very important to us and further reinforces their belief in Dream11. We will keep working hard and are targeting to end the year with 100 million users.
Steadview Capital marks its presence again with Dream 11 and makes the third deal in the past 4 months following $50 Million investment in the UrbanClap in December last year and $50 Million in Ola in January this year.
With the rise of smartphone users, there is a significant rise in people using fantasy gaming apps in India. The number of internet users in the country was 560 million as of January 2019, which clearly makes India a huge market for fantasy gaming apps. With the success of Dream 11, a lot of establishments and entrepreneurs are taking interest in this industry.
Now as Dream11 enters into unicorn club with investment from Steadview Capital makes fantasy gaming more lucrative and attractive for players.


Northshore Commercial Park is a large rectangular business park, which makes it easy for trucks to access. Lot 33 is rectangular and is located in a very excellent location in the park. The lot is located 2 blocks from the east side of LA HWY 59 and 2.75 miles from the north side of Interstate 12. The lot is 100' x 266', and there are no impact fees. Ready to build. Zoned I-2 (Intense Industrial). Dated FEMA rendering available.


lg electric oven best double oven range gas with electric ovens side by lg reviews.
lg electric oven lg double oven electric range cu ft black stainless steel furniture store.
lg electric oven in by lg in city lg studio cu ft electric single oven slide in range with.
lg electric oven lg black stainless electric range hidden a additional lg black stainless steel series cu ft electric lg black stainless electric.
lg electric oven lg cu ft self clean slide in double oven electric.
lg electric oven bare bones oven needs a little more meat.
lg electric oven lg inch single electric wall oven.
lg electric oven lg cu ft self cleaning slide in electric range with.
lg electric oven electric oven temperature single electric wall oven with steam cook and dual convection in stainless steel.
lg electric oven this oven lg electronics.
lg electric oven lg appliances cu ft electric single oven slide in range with.
lg electric oven electric single oven slide in range with and.
lg electric oven lg built in double electric convection wall oven stainless steel.
lg electric oven lg cu ft self cleaning slide in electric convection range.
lg electric oven gas vs electric ranges gas and electric stoves gas stove electric oven inch gas range lg gas range double oven reviews gas vs electric stove south gas.
lg electric oven stainless steel above stove microwave lg cooking appliances 1 stainless steel electric stove and microwave stainless.
lg electric oven lg lg freestanding electric range oven with convection black stainless steel lg angled view black stainless steel.
lg electric oven electric stoves gas stoves lg stove lg stove electric lg stove s lg gas stove gas range electric stove t price.
lg electric oven lg stove appliance bundle sale package lg lg electric stove.
lg electric oven lg studio steel double wall oven with convection.
lg electric oven lg cu ft self cleaning freestanding double oven electric convection range.
lg electric oven lg convection oven problems lg electric stove lg double oven stove induction stove with double oven lg induction range double lg electric stove.
lg electric oven lg cooking appliances 1.
lg electric oven lg cu ft capacity electric single oven range with true convection and.
lg electric oven lg convection oven problems lg electric range.


Luca Guadagnino's bold vision finds crucial meaning in arty abstraction.
The great innovator of modern choreography Martha Graham called dance "a song of the body, either of joy or pain." The new film Suspiria (opening October 26) sings both. Luca Guadagnino's feverish reimagining of his countryman Dario Argento's 1977 film is possessed of dual furies, the ecstasy of youthful exploration—all that thrusting into the unknown—crashing into and entwining with old, angry spirits, howling out of the past, hungry for reprisal.
Guadagnino's Suspiria grounds itself in 1977 Berlin, gray and pouring rain, at the height of the German Autumn, when the Red Army Faction ran amok in violent protest of authoritarianism. A Lufthansa jet has been hijacked and bombs shake the city. Thirty years after Nazism, Berlin still rattles in its terrible wake. But that's mere background noise for Susie (Dakota Johnson), a young American who's traveled across the ocean to audition for the the Markos Dance Academy, a revered institution steeped in the expressionist tradition of Mary Wigman and Graham—modernists who saw that dance could be much freer, more visceral than the ordered codes of ballet.
The Academy is in a state of tumult. Its founder, Helena Markos, is off somewhere, unwell, while a dancer, Patricia (Chloë Grace Moretz), has gone missing. Susie, reactive and ambitious, finds opportunity in these absences, quickly working her way to the center of the group, where she has the watchful attention of the company's second in command, Madame Blanc (Tilda Swinton). As Susie bores deeper into her art, the scene around her grows ever stranger and more sinister. Guadagnino's film reels and jumps and glides as it descends into witchy madness—all while making a funny sort of sense.
Suspiria is ostensibly a horror film, about a coven of witches preying on young innocents in a quest for immortality, or supreme power, or something. And it delivers on that front, trading in both the elegantly ominous and the grindingly grotesque. One scene, in which an apostate dancer gets her punishment, is the film at its most gruesome, this story of bodies and becoming suddenly breaking one of its cherished vessels. That gnarly sequence skirts the line of exploitation, as any giallo homage is wont to do, but I think Guadagnino keeps things just barely on the right side of that fraught line.
It's no accident that Susie's exploration of an adopted spiritual heritage is happening when it is. More than the original, this Suspiria considers the jagged arc of history. There are frequent updates about the Lufthansa hijacking seen and heard on televisions and radios, reminders of the strife roiling the world beyond the Academy. That doesn't necessarily make the wild stuff going on within the vast, creepy building that houses the dancers seem any less brutal or wicked, but it does contextualize it. There's a chord of anger crescendoing in the film, one whose source we keenly feel, understanding it as women reckoning with a bloody era wrought by men. I love the acute Berlin-ness and 1977-ness of Guadagnino's version, the way the film staggers and dances in the dreary, long fallout of war, its stabs of beauty thrashing out through an uneasy gloom.
And there is such beauty! The photography has a rich grain to it, looking old but not slavishly so, without any retro gimmick. The film's spaces—its cramped offices and lonely bedrooms and echoing rehearsal halls—are all credibly realized. They're textured, worn, haunted by human activity. Though the film trends toward the surreal, Guadagnino keeps its stage distressingly real. That these frightening things could happen in these drab but stately places makes it all the more unnerving.
The dance is a wonder as well. Choreographed by Damien Jalet, the movement pieces thud and writhe and strain with a ritualistic thrum, summoning up a primal something that's scary, but vital. Johnson is an adept dancer, as are the rest in her company. The film's big performance, done in dim light with costumes of thwacking red string, is a mesmerizing conjuring, as much a testament to the power of the art form as to the specific intensity of Guadagnino's film. Suspiria is a gorgeous paean to the artists who wailed and beat and pled throughout the 20th century, sensitive receptors interpreting so many grim signals.
Amidst all that, Guadagnino maintains a wry smile, infusing a harrowing film with just the right amount of camp. Swinton is, as ever, a hoot. (I don't like her quite as much in heavy makeup playing an old psychiatrist named Josef, though.) But I was even more taken by Angela Winkler as a bob-cut administrator who emits an officious menace. The film is so cleverly populated, from Johnson (sly and alert as ever) to Winkler to Mia Goth as an increasingly suspicious company member. They all enjoy a sense of play without turning the whole thing silly. The shivery crazy moments land, and a surprisingly emotional beat at the close of the film does, too. As nutty and off-the-wall as Suspiria is, it has a firm sense of control and proportion. It's a loose and rambly thing that's also tightly made, somehow.
Suspiria won't be for everyone. It's drawn comparisons to last year's polarizing Mother!, another gonzo horror movie (of a sort) from a celebrated filmmaker. But Mother!'s thematic observation looks flat as a pancake compared to the robust swirl of ideas and allusions teeming in Suspiria. Of course, that doesn't mean the film will appeal to more people than Mother! did. (It may even mean it will appeal to fewer.) But it's nonetheless a far richer experience, a holistic journey into the witches' den that prods at the dark seams of our world. It titillates and startles and, perhaps most disturbingly, inspires.


Many Windows users are recently reporting an issue with Outlook. What happens is that Outlook keeps crashing on their computer.
Restart Outlook and see if the enabled add-in crashes your Outlook software.
Perhaps your Outlook crashing issue is caused by interference from other programs. You should try performing a clean boot on your Windows system.
Check to see if your Outlook issue occurs. If it doesn't, do step 9 to 11 until you find out the service that causes the issue. If none of these services is the cause, try the steps below.
Now open Outlook and see if it's running properly.


text:
December 8, 2017 by fasted86
Trivia #1: Who threw the most interceptions during the 2016-2017 NFL season? Philip Rivers, Blake Bortles, Ryan Tannehill, Ryan Ritzpatrick, Derek Carr, Jay Cutler.
Trivia #2: Miami Dolphins quarterback Bob Griese was the first NFL football player to wear what in a game in 1977?
Trivia #3 (why not?): There was a M*A*S*H episode where everyone in camp is excited about the upcoming Army-Navy game. When Father Mulcahy walks into Henry's office for the radio party, what university's pennant is he holding? Army, Navy, Notre Dame, Xavier, Loyola, Rutgers
My son told me he liked the band – Phish. I said I had heard that they stink, especially their older songs…
I don't know about you but I had new Arizona St. head football coach Herm Edwards as older than 63.
According to the 2016 Survey of American Physicians, 49% of physicians experience feelings of burn-out and 49% would not recommend medicine as a career to their children.
Would you agree that, when you step out of your home, a good neighbor will smile and wave while a great neighbor will turn and go back into his/her home?
On Pearl Harbor Day, December 7, 2013 the Yankees signed free agent Jacoby Ellsbury to a seven-year deal valued at around $153 million. Today many Yankee followers consider it the worst signing in franchise history.
What a week for upsets in college hoops! Washington was a 17 dog and won by 9 over #2 Kansas, Loyola-Chicago -20 won by 6 over #5 Florida, and Ball St. -17.5 beat #9 ND by 3.
Dwight Gooden donned a Santa Claus suit this past Tuesday night in the spirit of Christmas. Well, not really. Gooden was allegedly paid $500 for wearing the suit and appearing at the Vivid Cabaret in Midtown Manhattan, a gentlemen's club. Said Dwight: "It's Christmastime, and I got a lot of presents to buy. I got seven kids, four grandkids, with another on the way any minute now, and two ex-wives. So every dollar counts."
It's Jimmy V week in college hoops. Cancer sux. Make a donation.
Last year, Marketwatch.com listed analysts' favorite stocks (large-cap) for 2017. Now it's noted that 12 months isn't necessarily a long time to hold a stock but, nonetheless, 11 of the 18 S&P stocks underperformed the S&P 500 which rose 20% over the 12 months. 18 suffered declines in general and six dropped by double digits. Two of those six dropped more than 50%. The lesson, if you are going to buy stocks, plan for the long term. Lesson #2, experts – ya, right!
As of Wednesday, the Celtics' rookie Jason Tatum was shooting a league-best 51.3% from 3-point range. He is on pace for the 8th best in NBA history.
Tiger Woods is back and getting all the attention. Twice last Saturday during the Hero World Challenge golf tournament, host Terry Gannon said Woods had "unfortunately" fallen out of contention. No one else was nearly as unfortunate.
The world's oldest restaurant, Stiftskeller, has been operating in Salzburg, Austria since 803. It continues to serve patrons in the same building it has for more than a millennium: St. Peter's Abbey.
Trivia #1 answer: Philip Rivers
Trivia #2 answer: Glasses
Trivia #3 answer: Notre Dame. As he walked in he said "How about one for the Gipper!"
"My eyes are blind but I can see
The snowflakes glistened on the tree
The sun no longer sets me free
I feel there's no place freezing me."
Previous postFriday, December 1, 2017
Next postFriday, December 15, 2017

redpajama_set_name: RedPajamaCommonCrawl


text:
Advances in the History of Psychology
a current look at the history of psychology, with news, notes, and additional resources
Recent History in History Textbooks
What should we teach as controversial?
2008.04.07 GeneralteachingJeremy Burman
In the latest issue of Educational Theory, 58(2), Michael Hand examines an issue that will hit close to home for everyone who teaches history: "What should we teach as controversial?"
There is an emerging consensus that to teach something as controversial is to present it as a matter on which different views are or could be held and to expound those different views as impartially as possible. This raises an important normative question that has yet to receive the attention it deserves from educational theorists: how are we to decide which topics to teach in this way? The answer suggested by Robert Dearden is that we should apply the epistemic criterion: a matter should be taught as controversial when contrary views can be held on it without those views being contrary to reason. In this essay, Michael Hand aims to defend that answer. In the first part of the article he revisits Dearden's rather thin and unsatisfactory justification for the epistemic criterion and attempts to mend its deficiencies. In the second part, Hand examines an alternative to the epistemic criterion in the area of moral education, an alternative he labels the political criterion, and explains why he thinks we should reject it.
This piece is part of a special issue on Epistemology and Education. See the table of contents here.
About Jeremy Burman
Jeremy Trevelyan Burman is a senior doctoral student in York University's Department of Psychology, specializing in the history of developmental psychology and its theory (especially that pertaining to Jean Piaget). Prior to returning to academia, he was a producer at the Canadian Broadcasting Corporation.	View all posts by Jeremy Burman →
← Darwin and early American psychology	Job: History of Medicine at Yale →


Everything you want in a single level on 1/3 of an acre! Shadow Mountain has done it again with this luxurious home on a corner lot with a private backyard. Stainless steal appliances and a Thermadore range in your chef''s kitchen. Beautiful master suite with lots of space and a spa inspired bathroom. Bonus room on the main level to suit any of your needs. With lots of storage and open living space. You don''t want to miss this one.


Choose a starting number from a 1-100 square and cross it out.
Then choose a factor or multiple of that number.
Keep crossing out factors or multiples of the last number in the chain.
What's the longest chain you can make?
You may wish to download a 1-100 square to work on, or you could use the interactivity below.
I really enjoy ... still learning and understanding how it goes.
Through trial and error with 7th grade students (13 years old) we have found that 74 is the maximum chain that can be made.
We are still thinking, but we believe that the reason has to do with the 26 primes in between 1 and 100 (100-26 = 74).
Have you been able to produce a chain of 74? Do share it if you have.
By the way, if your theory is right, you may be able to produce a chain of 75 - there are just 25 primes in between 1 and 100.
Yes - if you've linked all you can, except for the 1, you can link that to a prime number.
I am almost certain the maximum chain is between 77 and 78 because I have made a chain that is 77 numbers long.
I think it may be possible to produce a chain of 78 because I found a pattern that is consistent and it predicted a length of 78 however I spent a lot of time rearranging the chain and I still haven't found a way to make it 78 long.
That's a very impressive chain, well done!
You mentioned a consistent pattern that you found, could you explain what that pattern is?
Can you list the numbers in your chain?
My longest chain is 41 numbers! I don't know any more combinations there are for it because I've forgotten how I did my chain! OOOPS!!! Silly me! I'm thinking of doing it again with number 1-121 on an 11 x 11 grid! Do you think I have a good idea for expanding minds?
Do let us know how you get on with the 1-121 grid, and do post the list of numbers in your chain.
77, 11, 121, 1, 22, 44, 88, 8, 104, 57, 3, 30, 90, 9, 45, 5, 50, 100, 10, 20, 40, 80, 4, 48, 96, 6, 12, 36, 108, 54, 18, 72, 24, 2, 42, 21, 7, 14, 28, 56 and 112.
Hey, I've got two questions that will put your brain in gear!
1. What do all the 41 numbers make when all added up?
2. What do all the DIGITS added up make? Share your answers!
That's a good first chain, but I'm afraid you've made a mistake along the way - you're not allowed to go from 104 to 57.
How about having another go and trying to make an even longer chain?
You could also remove 77 from the start and replace it with 99, 33 and 66.
You could also change the order of some of your numbers.
2, 112, 56, 28, 14, 42, 21, 7... and no longer be stuck!
We got all the numbers. Has anyone else?
I'm afraid it's not possible to make a chain that uses all the numbers.
How did you manage to include all the large prime numbers (e.g. 53, 59, 61, 67, 71, 73 ...) in your chain?
We are Year 5 and our longest chain is 51. We started on 99.
Well done - it's not easy to produce a chain of 51 numbers.
Could you tell us which numbers you included in your chain?
96 and 64 had the most prime factors so I worked on both sides of the chain until I was stuck and connected them both with a 1. I made sure that there were no duplicates.
Thank you for sharing your chain.
I need just 3 more do you guys have any idea of what numbers could be added to make 74?
I did this at school and got the best in the class any help ??????????
The problem I see with getting a complete chain is the primes, of course. More importantly, the primes over 50.
If you start your chain with 53, the next multiple is 106, too high, so you have to find a factor. The only factor is 1. The same is true for all the primes above 50. At most you can use one of them in your chain, at one end.
For any N, you can only have one prime larger than (N/2) in your chain.
N=7: can't use both 5 and 7. Max is chain of 5 numbers.
There are 10 primes above 50 ( 53, 59, 61, 67, 71, 73, 79, 83, 89, 97) so there is an absolute max chain length of 91 (100-10+1). I doubt that can actually be achieved though.
There is probably a similar problem with the primes between N/2 and N/3, being also limited in usage (like you can only use 2 or 3 of them, max), but I haven't given it much thought yet.
36 is our highest yet!
100, 50, 25, 5, 10, 20, 40, 80, 8, 16, 4, 24, 48, 96, 32, 64, 2, 98, 49, 1, 22, 44, 88, 11, 99, 33, 66, 6, 12, 36, 72, 3, 9, 18, 54, 27, 81.
That's a great start, but even though you can't add numbers at the start or at the end of your chain, you can make your chain longer by adding numbers in between some of your numbers.
Can you find any more numbers that can be added to your chain?
I got 63! It was really challenging getting from about 57 to 63. Had to get out the pencil and paper. I don't know if I can go higher right now.
I love this activity, but wish solutions were not posted where students can so obviously see them as it discourages them from thinking themselves, but just copying. Could the interactive be posted elsewhere for students to use with no solutions shown?
Hi Mike, you can access the interactive on its own without comments here: https://nrich.maths.org/factmult/ Hope that helps!
46, Hoping to improve still!
I have successfully made it to 46 and have done this starting at 100 and then I have divided then I have multiplied numbers to try to get the order.
100, 50, 25, 5, 10, 20,40, 80, 16, 32, 64, 4, 12, 24, 48, 96, 6, 18, 36, 72, 9, 27, 81, 3, 15, 45, 90, 30, 60, 2, 14, 56, 8, 88, 11, 44, 22, 66, 33, 99, 1, 7, 21, 42, 84, 28.
That's a good first chain! Can you tell us what numbers you used in your chain?
52, 26, 78, 13, 91, 7, 49, 98, 14, 70, 10, 80, 40, 20, 100, 50, 25, 75, 15, 60, 30, 90, 45, 9, 81, 27, 54, 18, 72, 36, 72, 36, 12, 24, 6, 48, 16, 64, 32, 96, 4, 92, 46, 23, 69, 31, 62, 1, 63, 21, 42, 84, 28, 56, 8, 88, 44, 22, 66, 33, 99, 11, 55, 5, 85, 17, 34, 68, 76, 19, 57.
That´s it! please feel free to show to your parents or kids, siblings, and family! Also feel free to copy too! Have fun trying to beat me!
Also can you guys tell me how to get a scale higher than 100?!
- You've repeated 76 and 32, in "76, 32, 76, 32" so remove the extra copy of those.
- At "69, 31, 62, 1", 31 isn't a factor of 69. However, if you remove 31 and 62 from there, then that fixes the gap in your chain.
- At the end, 76 is not a factor or multiple of 68, nor are 19 or 57. However, if you put 2 between 68 and 76, then you can connect them.
- You could also put 3 at the end to add another number and then perhaps another multiple of 3.
With these changes, you could make a chain of 70.
As for a scale larger than 100, I'm afraid the interactivity only supports a 1-100 square. However, you could make a different grid of your own using a spreadsheet.
If anyone has a longer chain please do share it!
That's a very long chain. Could you list the numbers in your chain? Or alternatively, could you send us a screenshot of your number chain in the interactivity?
Factors and Multiples Chains from different groups.
Some children found chains of 24 by finding multiples then factors and repeating this.
Some children found chains of 36 through just randomly finding factors and multiplying and working through the process of elimination.
If you just multiply you get a smaller chain even if you start with a smaller number.
Some children started multiplying then finding factor and repeating, creating a longer chain.
They think that by looking at more chains next time they will be able to find a pattern.


Read more… about Second Mortgages: What Are They, and How Do They Work?
Enterprise Paid Mortgage Insurance (EPMI) is a type of mortgage insurance option for borrowers with an LTV ratio greater than 80%. EPMI is another option for borrowers to obtain mortgage insurance, where the mortgage insurance is obtained from an approved insurance provider.
Read more… about What Is Enterprise Paid Mortgage Insurance (EPMI)?
As your home's value rises, it can be tempting to tap into the equity you've built as a source of cash. In 2018, more than 44 million U.S. homeowners had access to the highest amount of home equity in history — around $6 trillion.
On the most basic level, an easement allows non-owners to legally access a specific property for a particular reason. As a homeowner, the idea of other people entering your land may be off-putting, but in practice, easements cause few problems and are relatively commonplace.
Most new year's resolutions include staying fit and healthy, losing weight, spending less and saving more. We spoke with Tom Kraeutler, the nationally syndicated radio host of The Money Pit Home Improvement Radio Show to discover New Year's resolutions you can make that will help keep your house in great shape year-round.
Real estate, like many other industries, has been fundamentally changed by recent technology, and real estate marketing strategies have been equally impacted. New platforms and products can help you sell homes in new ways, but that does not mean that all traditional real estate marketing ideas are worthless—the key is figuring out what works best for you and your clients.
When you're buying a house, you have more to consider than just which property to purchase. You need to learn about the mortgage process, including if and when you should apply for a rate lock.
Read more… about Should I Lock My Mortgage Rate?
When you refinance your home, the process is similar to the one you followed when obtaining your original mortgage. Your finances will be verified and calculated, and your home will be appraised to determine its value to your potential lender. As a result of a refinance, it's common for your monthly payment and even your total loan amount to change—but will your property taxes go up as well?


Mike Harding Radio DJ - "Pure bloody brilliant"
"What an Atmosphere! What a performance! What a band! Amazing! Just don't miss them!"
Ten tracks of the very best folk rock ever available to download right now, click the link to buy our latest album from Amazon or iTunes.
Take a look at all our event with the events calendar this section also has external links that The Mighty Doonans are not responsible for.
We have a number of galleries that we have categorised for you to take a look at, these media galleries show all our promo and stage photos.
We have a number of galleries that we have categorised for you to take a look at, these video galleries show some of our videos from the web.
Anything you want to know about or from The Mighty Doonans please don't hesitate to contact us we will ensure somebody gets back to you within 24 hours.
Here is where it all began for The Doonan family please have a look at our Doonan family archive site. This opens up in a pop up please ensure pop ups are allowed.
The Mighty Doonans have evolved from The Doonan Family Band who over the years have performed all over the world and delighted audiences with their uncompromising approach to the music and live shows.
This philosophy continues with the big band which has been augmented by the addition of younger family members; Frances Rosie, Ben and Jamie Completing the nine-piece powerhouse. This is traditional folk-rock at it's best .


text:
Lua de Cristal (Spanish: Luna de Cristal; English: Crystal Moon) is a song by Brazilian singer Xuxa. It was released on July 16, 1990, by Som Livre along with his seventh studio album. Written by Michael Sullivan and Paulo Massadas, Luna de Cristal is the theme song from the 1990 Tizuka Yamasaki film of the same name starring Xuxa.

In addition, the song achieved international success, when it was released outside Brazil in 1991, reaching position #35 on the Billboard's Hot Latin Songs chart.

Charts

References

Brazilian songs
Children's songs
1990 singles
1990 songs
Xuxa songs
Number-one singles in Brazil
Songs about the Moon

redpajama_set_name: RedPajamaWikipedia


It is not necessary to exhaustively record metadata for individuals who only contribute, such as by operating equipment. However, for anyone who participates (whose voice is recorded, makes a written transcription, and so on) it is critical to collect basic background information like age, gender, level of education, location where they first learned the language, first language of both parents, and so on.
Click "Add" at the bottom left. Notice that the name and consent status is listed in the columns.
Most of the fields are self explanatory. Code is a short code that replaces the name of the person in certain fields. It is Optional.
Page last modified on Thursday December 8, 2016 17:32:59 GMT-0000 by JonathanDailey.


Trade2Europe makes it easy and affordable to outsource your European order fulfillment by stocking your products in our warehouses in the United Kingdom and Germany.
We offer storage, pick & pack, small parcel and LTL shipping, returns processing and a variety of optional value-added services to enhance your company's global trading capabilities.
We work with manufacturers, publishers, retailers, mail order houses, wholesalers, and distributors.
Trade2Europe has excellent warehouse facilities in the UK, Germany, and the USA.
Our warehouses are large, clean, secure, and modern.
You select the warehouse location that best suits your needs based on your customers' locations and other factors.


Jonathan Dharmapalan has been appointed to lead Ernst & Young's Global Telecommunications Center.
In his new role, Dharmapalan will be responsible for leading a team of over 2,000 telecoms specialists across the world, in their work with the world's leading operators.
"As the telecommunications sector turn to new sources of growth, operators are facing new challenges and opportunities associated with data traffic and delivering services focused on their customers. I look forward to helping our clients to become market leaders in these challenging but exciting times," says Dharmapalan.


throwing your things all over the place!
-Clara, I know that the children get in your hair, but you should try not to let it upset you so much.
-Listen, Jim. I can't help it. The children bother me and make me very angry when they're so noisy and messy.
Jim doesn't play tennis very much, but he's always shooting off his mouth about how good he is. Yet he is fooling nobody. Jim is somewhat of a braggart and everyone knows that he gives opinions without knowing all the facts and talks as if he knew everything about the game.
-That's it, Greg! You'd better not come in after midnight again tonight!
-I know, dad. You don't have to jump down on my throat! I told you that I'd make it home around 11:30. I don't intend to be late!
-Well, you've said that before and in your come at 2:30 in the morning. You can't blame me for getting angry and scolding you. I've got good reason.
many other stamp collectors would also be bidding for it, he realized that he would have to pay through the nose in order to have it. After considering the increasing value of the stamp, he decided that he would not mind paying such a high price for something so rare.
-Why were you teasing Sonia about her new hairdo? She really took offense at what you said.
-I didn't mean to offend her. I was simply making a tongue-in-check remark when I said that it was too elaborate for a girl of her young, tender age.
-Well, she thought you were serious. She had no idea that you were just saying that as a joke.
-I'm really sorry. I suppose I owe her an apology.
-Hey, Al. I was invited to be a judge for the Miss America Beauty Peagant!
-Oh, really? Come on, you're pulling my leg!
-No, honestly. Do you really think that I'm trying to fool you with a ridiculous story?
-Well, you've told me foolish stories before.
-I can assure you that this one is for real.
-Let's go to the movies, agreed?
-Sure. And what 'll we do after that?
-Oh, I don't know. Let's play by the ear.
-Well, I would like to have a more definite plan of action.
-Don't be like that. It's always more fun not knowing what to expect and deciding what to do as we go along.
-How come they're asking me to act as their guide through the jungle?
-Evidently they think you're the only one who can lead them to the lost temple.
-That jungle has danger lurking around every corner. Why should I stick my neck out for them? They didn't pay me for my services.
-They know that you would be taking a great risk and could possibly get hurt, but you're the only one with enough knowledge to take them to their destination. I'm sure you'll be amply rewarded.
-Mary, you always take such a long time to put on your makeup. Come on, shake a leg!
-I'll be finished in a minute. Be patient.
-You've got to hurry or else we won't arrive on time to see the last show.
-Sure. Let's see if I can do it for you.
-I guess I'm all thumbs because I'm so nervous. I'm already late for my date.
don't worry. I'm sure your date will wait.
Tom maintains that the firm owes him some back wages for having worked overtime.
foundation of facts to support his position.
-Hey, John. I'm bored. Come on, let's go out and do something.
-Sorry, I'm right in the middle of studying for a physics exam. I won't be able to make it tonight.
-Look! Get off my back! I can't go anywhere!


text:
Q: How to successfully deploy to bot service through app service local git deploy? I've developed a simple chatbot based on the sample python bot (24. ms graph auth bot) and was deploying it through the CLI - it worked like a charm. However, the deployment was taking ages (at least 7 minutes per deployment) and I've tried to use other deployment methods because I have to prototype authorization on the Azure side (I'm not allowed to install ngrok...). I've had a few problems with git deployment to Azure App Service but finally it seemed like it started to work, the problem is - once I thought it'll work, instead of errors I'm receiving empty log.
Last line in the botname.scm.azurewebsites.net/api/vfs/LogFiles/date_code_docker.log:
2020-03-03 07:41:41.123 INFO  - Container botname_0_letters for site botname initialized successfully and is ready to serve requests.

It usually indicated that everything works correctly. However, there is nothing in the botname.scm.azurewebsites.net/api/vfs/LogFiles/date_code_default_docker.log - as if there was nothing running. Bot service through webchat receives Internal Server Error. When I was debugging it there was data in the default_docker.log with errors about gunicorn app:app, but since I've solved it and container started to be initialized successfully - the default_docker.log doesn't get any new lines, it looks like the app wasn't working.
Any clue what might be a reason of it? Why is the log empty and bot doesn't work? Deployment from .zip file through Azure CLI worked correctly, it has AppID and AppPassword set up (and it worked through the .zip deployment), is there anything I should take care of when deploying from local git which is done automatically when deploying .zip through CLI?


Have you been thinking about buying a new car? If so, the federal budget tabled this week may present an opportunity for you – particularly if you're looking to buy an electric or hybrid vehicle. And if you use your car in your work, the tax benefits could be significant. Let me explain.
The 2019 federal budget tabled this week proposes to allow an enhanced deduction for capital cost allowance (CCA, which is depreciation for tax purposes) if a zero-emission vehicle is purchased for work purposes. The change will allow you to deduct the full cost of the vehicle, to a maximum of $55,000 plus sales taxes, in the year of purchase. This enhanced deduction will be available for vehicles purchased in 2019 through 2023 inclusive (the allowed deduction is gradually reduced after that).
You may be thinking: "That's all well and good, Tim, but I don't own a business, so this won't help me." Not so fast. Employees who use their cars for work may also be entitled to this enhanced deduction. More on this in a minute.
To be eligible, the vehicle must be new (not used), fully electric, a plug-in hybrid (with a batter capacity of at least 15 kWh), or fully powered by hydrogen. Further, the vehicle must be a "motor vehicle" as defined in our tax law (that is, an automotive vehicle for use on streets and highways – which includes most cars). Finally, this enhanced deduction is only available in the year you purchase the vehicle.
To take advantage of this deduction, the vehicle will have to be used for business purposes, which generally allows you to claim various vehicle costs – including CCA. Who does this include? Three groups of people: (1) those who have a business and use their vehicle in that business, (2) a partner who uses his or her vehicle in their work for the partnership, and (3) an employee who uses their own vehicle in their work. In any case, the vehicle must be purchased, not leased (you can't claim CCA on a leased vehicle).
Let's see what this may look like for an employee or self-employed person who uses his or her car for work purposes.
Source: Our Family Office Inc.
*Purchase price can be a maximum of $55,000 plus sales taxes; do not include sales taxes if receiving an input tax credit for those taxes.
**Enhanced CCA allowed in the first year is 100% if purchased in 2019 through 2023; 75% if purchased in 2024 or 2025; 55% if purchased in 2026 or 2027; 0% thereafter.
Consider Jane. She's an employee who purchases a new zero-emission car in 2019 (after budget day) for a total cost of $55,000. She's ordinarily required to work away from her employer's place of business and is entitled to deduct vehicle costs (see my article from 2016 for these rules: tgam.ca/cardeductions).
Jane keeps a log of her kilometres driven for work, and uses her car 30 per cent of the time for business, so she'll be able to claim 30 per cent of her vehicle costs. When it comes to CCA on her car, the budget changes will allow her to deduct the cost of her car in 2019 (the first year). In her case, the $55,000 must be prorated because only 30 per cent of her driving is for work. So, the deduction she can claim is $16,500 ($55,000 x 30 per cent). That's a pretty sizable deduction against her employment income. If she's in the top marginal tax bracket in Ontario, for example, that $16,500 deduction will save her $8,832 in taxes in 2019. These are big dollars.
Keep in mind that the federal budget is not yet law. If you want the tax savings I've talked about here, you'd be wise to wait to purchase your new vehicle until the budget receives royal assent. Further, be aware that if you sell the car later after deducting the full cost as CCA, you'll have to include in your income the price you receive upon selling (for example, if Jane were to sell her car for, say, $20,000 later, she'd have to include 30 per cent – her business use – in her income, or $6,000). Finally, the 2019 federal budget is also proposing a federal purchase incentive of up to $5,000 if you buy a zero-emission vehicle (program details have not yet been released). If you claim this incentive, you can't also take advantage of these new CCA rules.


text:
\section{Manual Sample Inspection}
\label{sec:appmoresampleinspection}

In Table \ref{tab:moresampleinspection}, we provide more samples of a MLE-trained model when fed with different kinds of history.

\section{Implementation of SS, CoT, and RankGAN}
\label{app:implementation-nonMLE}
We implement our MLE baseline and scheduled sampling (SS) in PyTorch. For SS, we use a linear decay schedule to move from complete teacher forcing to replace-sample rate of $0.1$. We find that larger rate will give worse performance.

For CoT, we use a PyTorch implementation in \url{ https://github.com/pclucas14/GansFallingShort}. We use a mediator model that has twice the size of the generator. We set M-step to be 4, and G-step to be 1.

For RankGAN, we use a TensorFlow implementation in \url{https://github.com/desire2020/RankGAN}.

Note that in our non-MLE experiments, the generator model is set to be the same size with the baseline MLE model. We tune the non-MLE methods using the corpus-BLEU metric, which is widely used in text GAN literature.

\section{Auxiliary Plots}
\label{app:auxplots}
In Figure \ref{fig:cgp_cond_js_samplenumcompare}, we show that we are able to get stable measurements of EB-C with 100k samples for the LSTM-512 synthetic experiment.

\begin{figure}[h]
  \centering
  \includegraphics[width=0.49\linewidth]{figs/cond_method/synthetic_512Learn512_JS_samplenumcompare.eps}
  \caption{EB-C measurements with different number of samples for the LSTM-512 synthetic experiment.}
  \label{fig:cgp_cond_js_samplenumcompare}
\end{figure}

In Figure \ref{fig:ebrate_different_number_wiki103_tvd} and Figure \ref{fig:ebrate_compare_news_tvd} we provide EB-M measurements with metric $d_{TV}$ discussed in Section \ref{sec:marginal-exp}, the results are similar to those using metric $d_{JS}$.

\begin{figure}[h]
  \centering
  \includegraphics[width=0.49\linewidth]{figs/marginal_method/wiki103_ebrate_mle_numcompare_tvd.eps}
  \caption{EB-M measurements (with metric $d_{TV}$) using different number of samples on wiki103 data.}
  \label{fig:ebrate_different_number_wiki103_tvd}
\end{figure}

\begin{figure}[h]
  \centering
  \includegraphics[width=0.49\linewidth]{figs/marginal_method/ebrate_news_tvd.eps}
  \caption{EB-M (with metric $d_{TV}$) comparison for MLE and non-MLE training on EMNLP-News data. For each training method, we show corpus-BLEU measurement using the test-set as reference set in the legend.}
  \label{fig:ebrate_compare_news_tvd}
\end{figure}

\section{Perplexity of the Trained Models}
\label{app:pplofmodels}
We show PPL results for model trained on EMNLP-news data-set in Table \ref{tab:modelppl_news}. The MLE model for wiki-103 data-set discussed in Section \ref{sec:marginal-exp} has PPL 84.58. Note that due to our special setting\footnote{We only keep sentences of length longer than $L$, and for wiki-103, only half of training data is used.}, our PPL result is not directly comparable to state-of-art LM results on these data-sets.

\begin{table}
\begin{center}
\begin{tabular}{l|l}
\hline
Model & PPL \\
\hline
MLE Baseline $M_{news-512}$ & 55.85 \\
LSTM-512 (MLE, synthetic) & 115.3 \\
LSTM-32 (MLE, synthetic) & 156.3 \\
CoT-512 (synthetic) & 115.6 \\
SS-512 (synthetic) & 113.7 \\
CoT & 56.83 \\
RankGAN & 53.43 \\
SS & 56.43 \\
\hline
\end{tabular}
\vskip 0.1in
\caption{PPL results for model trained on EMNLP-news data-set.}
\label{tab:modelppl_news}
\end{center}
\end{table}

\begin{table*}
\small
\vskip 0.15in
\begin{center}
\begin{tabular}{l}
\hline
Model Samples as Hisotry $\rightarrow$ Model Samples \\
\hline
it was only a pieces that had gone up to $\rightarrow$  the forest and forces the shoppers about their chronic young \\
i mean we didn ' t know what i haven $\rightarrow$  ' t considered through , " she told bbc radio \\
if he were the president - elect , he was $\rightarrow$  known that he would run a force in business at \\
but these are not as tired of " the same $\rightarrow$  message that the harry actor does have been hours in \\
first opinion the agent have taken four seconds , or $\rightarrow$  if they don ' t only know anything , were \\
" the economy of the uk is low enough of $\rightarrow$  people of defending where americans think that " brexit , \\
the economy grew on 1 . 6 \% since the $\rightarrow$  us voted , and when it turned around 200 streets \\
i was able to produce on my own , which $\rightarrow$  is good ; now that the theatre i ' ve \\
" i ' ve not buying boys i addressed many $\rightarrow$  nervous times before , as a teenager made me is \\
we think about one - third of the struggles we $\rightarrow$  actually want to see those very well that even more \\
the story of a album - which made public - $\rightarrow$  was still fantastic , and for the second time in \\
" the test comes up before tuesday and when we $\rightarrow$  ' re feeling ahead again soon , " she posted \\
a year on when he was last seen in his $\rightarrow$  home and he did not see him , his suffering \\
brady has forced the 9 - known targets to get $\rightarrow$  all - of - 12 gun migration and performing communication \\
i asked if he himself did , i managed to $\rightarrow$  show all my charges at all , it used to \\
\hline
Data Samples as Hisotry $\rightarrow$ Model Samples \\
\hline
what this group does is to take down various different $\rightarrow$  players in the future and we play in paris we \\
over 1 , 600 a day have reached greece this $\rightarrow$  gone in 2013 and it planned to allow civilians on \\
" we ' re working through a legacy period , $\rightarrow$  and i am proud of the experience of the worker \\
' the first time anyone says you need help , $\rightarrow$  you don ' t have put accurate press into the \\
out of those who came last year , 69 per $\rightarrow$  cent of women can really take the drive to avoid \\
he has not played for tottenham ' s first team $\rightarrow$  this season then and sits down 15 - 0 with \\
so you have this man who seems to represent this $\rightarrow$  bad story , which he plays minutes -- because he \\
cnn : you made that promise , but it wasn $\rightarrow$  ' t necessarily at all the features he had in \\
this is a part of the population that is unk $\rightarrow$  lucky to have no fault today , and it would \\
they picked him off three times and kept him out $\rightarrow$  of the game and was in the field , the \\
the treatment was going to cost \$ 12 , 000 $\rightarrow$  as a result of the request of anyone who was \\
but if black political power is so important , why $\rightarrow$  doesn ' t we becomes the case that either stands \\
local media reported the group were not looking to hurt $\rightarrow$  the animals , but would never be seen to say \\
\hline
Random Sequences as History $\rightarrow$ Model Samples \\
\hline
...RANDOM... big winter deserve $\rightarrow$  , but they just say it your things goes wrong \\
...RANDOM... playoff north realise $\rightarrow$  at its lowest level , improving their understanding in danger \\
...RANDOM... vital childhood registration $\rightarrow$  , not previously planned for <unk> to each and reduced \\
...RANDOM... treated ship find $\rightarrow$  one as an actual three points contained at a time \\
...RANDOM... faith five crazy $\rightarrow$  schools and could give them a " sleep " necessary \\
...RANDOM... domestic jason follows $\rightarrow$  a 12 - year cruise line over the christmas track \\
...RANDOM... ownership generous tourist $\rightarrow$  accounts for more than 1 per cent every month - \\
...RANDOM... spending raped since $\rightarrow$  the file returns in january , joining groups of foreign \\
...RANDOM... netflix worker four $\rightarrow$  centre - and said facebook text <unk> to see how \\
...RANDOM... race labor witnessed $\rightarrow$  is great , with more to an active the <unk> \\
...RANDOM... treatments airlines hidden $\rightarrow$  real - time out to sell on benefits to our \\
...RANDOM... intention short reflects $\rightarrow$  showing the nature of flying in his space rather than \\
...RANDOM... conversation pace motion $\rightarrow$  them further , but as late as they ' ve \\
...RANDOM... export feb president $\rightarrow$  obama agreements with president obama and her being on trump \\
...RANDOM... entering pocket hill $\rightarrow$  and made it later in the united states and make \\
\hline
\end{tabular}
\normalsize
\caption{More samples of a MLE-trained model when fed with different kinds of history. To save space, we omitted the first 7 words of the random history.}
\label{tab:moresampleinspection}
\end{center}
\vskip -0.1in
\end{table*}
\section{A Quantification Approach using Conditional Distribution}
\label{sec:cond}
\subsection{Method}
Following the discussion in the last section, we wish our measurement to be independent of the quality of the history distribution. In light of that, we design a quantity to measure the model's conditional generation quality. Let $P_H \in \{P_M, P_D\}$ denote the history distribution as in the MGD definition~\eqref{eq:mmd}. With history length $l$ fixed, we define the conditional generation deviation (CGD) with history distribution $P_H$ for $P_M$ using metric $d$ as:
\begin{align}
\begin{split}
    \text{CGD}(P_{M|H}, l, d) = \mathop{\mathbb{E}}_{W_{1:l} \sim P_{H}}[d(P_{M}(\cdot\ |\ W_{1:l}), P_{D}(\cdot \ |\ W_{1:l}))]
\end{split}
\end{align}
where we assume that $P_{D}(\cdot \ |\ W_{1:l}))$ is computable, and use it to measure the quality of the model's conditional distribution. For the choice of the distribution distance $d$, in addition to $d_{TV}$ and $d_{JS}$, we introduce greedy decoding divergence ($d_{GD}$), which is defined as:
\begin{equation}
    d_{GD}(P,Q) = \mathbbm{1}(\argmax_iP_i \neq \argmax_iQ_i)
\end{equation}
where $\mathbbm{1}$ is the indicator function, and $P,Q \in \mathcal{P}$. We design $d_{GD}$\footnote{$d_{GD}$ qualifies as a pseudometric in mathematics.} to reflect the model's accuracy during greedy decoding.

Similar to MGD, exposure bias should imply a significant gap between $\text{CGD}(P_{M|M}, l, d)$ and $\text{CGD}(P_{M|D}, l, d)$. We again define rate of exposure bias at history length $l$ with metric $d$ to be:
\begin{equation}
    \text{EB-C}(P_M, l, d)=\frac{\text{CGD}(P_{M|M}, l, d)}{\text{CGD}(P_{M|D}, l, d)}
\end{equation}

\subsection{Experiments}
Since CGD requires inference for ground-truth data distribution $P_D$, we only consider experiments in a synthetic setting\footnote{We will release code to reproduce our results in the published version of this paper.}. In text-GAN literature \citep{yu2016seqgan}, a randomly-initialized one-layer LSTM model with hidden dimension of 32 is widely used as $P_D$ in synthetic experiments (we denote this setting as $M^{random}_{32}$). However, the model is small-scale and it does not reflect any structure existing in real-world text.

In this work, we take the MLE baseline model trained on EMNLP-news data (described in Section \ref{sec:marginal-exp}), as $P_D$ in this synthetic setting. We denote the data model ($P_D$) as $M^{news}_\text{512}$. We then train two LSTM LM ($P_M$) with different capacities using samples from the data model, with the standard MLE objective. One is a one-layer LSTM with hidden width of 512 (denoted as LSTM-512), the other one is with hidden width of 32 (denoted as LSTM-32).

We train $P_M$ for 100 epochs using the Adam optimizer with learning rate 0.001. In each epoch, 250k sentences (same to the size of the original EMNLP-news data) of length $L=50$ are sampled from $M_\text{news-512}$ and used as training data. We do this to avoid over-fitting. We show perplexity (PPL) results of the trained models in Appendix \ref{app:pplofmodels}. Finally, EB-C is calculated using 100k\footnote{We show that we can get stable measurements using 100k samples in Appendix \ref{app:auxplots}.} samples from $P_M$ and $P_D$.

\begin{figure*}[h]

\subfigure[LSTM-32]{\label{fig:a}\includegraphics[width=0.5\linewidth]{figs/cond_method/synthetic_32Learn512_ebc.eps}}
\subfigure[LSTM-512]{\label{fig:b}\includegraphics[width=0.5\linewidth]{figs/cond_method/synthetic_512Learn512_ebc.eps}}
\caption{EB-C measurement for the LSTM-32 and LSTM-512 model with different metrics. Also average value of EB-C along all history length is shown in the legend.}
  \label{fig:ebc_main_differentmetric}

\end{figure*}

In Figure \ref{fig:ebc_main_differentmetric}, we show EB-C measurements with different metrics $d_m$, and the two models give similar results. It is shown that EB-C has a steady but slow increasing trend as history length increases. This is expected as a consequence of exposure bias, because $P_M$ deviates farther from $P_D$ as history length increases. Further, the average value of EB-C is less than 1.03 (the largest average value is from $d_{JS}$ for the LSTM-512 experiment), meaning that the gap between $\text{CGD}(P_{M|M}, l, d)$ and $\text{CGD}(P_{M|D}, l, d)$ is not large. Also, note that in most NLG applications (such as machine translation or image captioning), the generated sequence typically has short length (less than 20). In that range of history length, the EB-C measurements show minimal influence of exposure bias.

\begin{figure}[h]
  \centering
  \includegraphics[width=0.49\linewidth]{figs/cond_method/synthetic_512Learn512_cgp.eps}
  \caption{CGD measurement for corrupted $P_M$ (with $d_{TV}$) for the LSTM-512 synthetic experiment.}
  \label{fig:cgp_cond_H512LearnH512_tvd}
\end{figure}

To dive deeper into the cause of the gap in CGD, we experiment with corrupted versions of $P_M$ as history distribution. We first specify a corrupt rate $c \in [0,1]$, and randomly substitute words in a history sample from $P_M$ to a ``noise'' word drawn uniformly from the vocabulary with probability $c$. In this way, larger $c$ will cause the history distribution to deviate farther from the ground-truth $P_D$. In Figure \ref{fig:cgp_cond_H512LearnH512_tvd}, we show CGD measurement with the corrupted $P^\text{corrupt}_{M}$ as history. Large gaps are observed between $\text{CGD}(P_{M|M^\text{corrupt}})$ and $\text{CGD}(P_{M|D})$. So, we deduce the reason for the small gap between $\text{CGD}(P_{M|M})$ and $\text{CGD}(P_{M|D})$ is that, the deviation in the history distribution $P_M$ and $P_D$ is not large enough: $P_M$ has learned a ``good enough" distribution that is able to keep it in the well-behaving region during sampling.

With these observations, we conclude that, in the synthetic setting considered, exposure bias does exist, but is much less serious than it is presumed to be. The key reason is as follows. Although there exists mismatch between the history distribution $P_M$ and $P_D$, the mismatch is still in the model's ``comfortable zone". In other words, the LSTM LM is more robust than exposure bias claims it to be. To concretize the this argument, we provide an example LM that has a large EB-C measurement to facilitate a better understanding.
\begin{example}
\label{eg:cond}
Again suppose $L=2$, and $V=\{A,B\}$, the ground-truth data distribution is uniform on $\{AA,AB,BB,BA\}$. $P_M$ is crafted as follows: $P_M(W_1=A)=0.9,P_M(W_2=A|W_1=A)=0.9,P_M(W_2=A|W_1=B)=0.5$. Note that the model behaves bad when $W_1=A$, which is of high probability during sampling.
\end{example}

In Example \ref{eg:cond}, $\text{CGD}(P_{M|D}, 1, d_{TV})=0.2$ and $\text{CGD}(P_{M|M}, 1, d_{TV})=0.36$, so $\text{EB-C}(P_M,1,d_{TV})=1.8$. However, this crafted model is unlikely to be an outcome of MLE training. The fact that $P_M(\cdot|W_1=B)$ is better modeled indicates that in the training data more sentences begin with $W_1=B$ than $W_1=A$. So MLE training should assign more probability to $P_M(W_1=B)$, not the other way around\footnote{If we change to $P_M(W_1=A)=0.1$, then $\text{EB-C}(P_M,1,d_{TV})$ will be 0.2, meaning that the model has better conditional generation performance during sampling}.

\begin{figure}[h]
  \centering
  \subfigure[]{\includegraphics[width=0.49\linewidth]{figs/cond_method/synthetic_512Learn512_nonMLE_js.eps}}
  \subfigure[]{\includegraphics[width=0.49\linewidth]{figs/cond_method/synthetic_H32_rankgan_js_ebc.eps}}

  \caption{(a): EB-C measurements (with $d_{JS}$) for comparing non-MLE methods in the LSTM-512 synthetic experiment. (b): EB-C measurements for comparing RankGAN and MLE for the $M^{random}_{32}$ synthetic experiment, the metric used is $d_{JS}$. }
  \label{fig:ebc_cond_nonMLE_js}
\end{figure}

Finally, we use EB-C to compare MLE and non-MLE training. We compare MLE against CoT, SS, RankGAN in the synthetic experiments, and results are shown in Figure \ref{fig:ebc_cond_nonMLE_js}. Note that the RankGAN experiments are conducted in the $M^{random}_{32}$ setting\footnote{Note that the MLE model is used as the pre-trained model for the RankGAN generator. The MLE model has an oracle NLL of 8.67, and RankGAN's oracle NLL is 8.55.}, as we find it hard to do a fast implementation of RankGAN for the  LSTM-512 setting. We find that RankGAN and CoT gives lower EB-C measurements than MLE, which is expected, as these methods avoid teacher forcing. However, SS gives worse EB-C measurements comparing to MLE, which we currently do not have a good explanation. We refer readers to \citet{sscritique18ferenc} for a discussion about the SS objective. Also, note that most non-MLE methods still rely on MLE training in some way (e.g. for pre-training).
\section{Limitations and Discussion}

What is reason that exposure bias becomes a well-known belief? ... talk about MLE objective ... $KL(P||Q)$ ... While it's true that training is ``exposed" to data history, the training not necessarily ``biased".
\section{Experiments}
\label{sec:exp}
\section{Introduction}
\label{sec:intro}
Language model (LM) is a central module for natural language generation (NLG) tasks \citep{trends-nlp} such as machine translation \citep{wu17ganmt}, dialogue response generation \citep{dialogue17jiwei}, image captioning \citep{coco14tsung}, etc. For decades, maximum likelihood estimation (MLE) has been the the most widely used objective for LM training. However, there is a popular belief in the natural language processing (NLP) community that standard MLE training will cause ``exposure bias'' and lead to a performance degradation during the test-time language generation.

The exposure bias problem \citep{ss15bengio,seqtrainrnn16marc} refers to the following discrepancy between MLE model training and test-time generation for language models. During training, the language model predicts the next word conditioned on words sampled from the ground-truth data distribution. During generation, the model generates words conditioned on sequences generated by the model itself. However, due to the \textit{exposure} to real data during the training, the language model is \textit{biased} to only perform well on the ground-truth history distribution. As a result, during generation the errors will accumulate along the generated sequence, and the distribution generated by the model will be distorted. The forced exposure to ground-truth data during training is also referred to as ``teacher forcing''.

In order to avoid teacher forcing, many training algorithms \citep{ss15bengio,seqtrainrnn16marc,yu2016seqgan,zhu2018texygen,cot18sidi,rankgan17,leakgan17jiaxian,advtext17sai,seq2seqbeam16sam,nie2018relgan,zhan18irlgan} have been proposed as alternatives to MLE training. Most of these works utilize techniques from generative adversarial network (GAN) \citep{Goodfellow14gan} or reinforcement learning (RL) \citep{sutton98rl}. In  this paper, we refer to these algorithms as non-MLE methods or text GANs.

Despite the huge research efforts devoted to avoid exposure bias, surprisingly, its existence or significance is much less studied. In particular, to the best of our knowledge, no existing work attempts to quantify exposure bias in an empirical or theoretical way. This work is motivated by the belief that a good solution should be built upon a testable and quantifiable problem definition. Starting from the definition of exposure bias, we propose two intuitive quantification approaches to empirically measure the significance of exposure bias for language modelling. Our experiment results show that exposure bias is either insignificant or is indistinguishable from the mismatch between data and model distribution.

\section{A Quantification Approach using Marginal Distribution}
\label{app-wrongmeasureex}

\subsection{Method}
In this section, we describe a simple and intuitive approach to quantify exposure bias, which is applicable to real-world data-sets. Assuming a given history length $l$, we consider the marginal distribution of $W_{l+1}$ from the following three random process:
\begin{itemize}
    \item Draw word sequences of length $L$ from the data distribution $P_D$. Denote the marginal distribution of the random variable at position $l+1$ ($W_{l+1}$) as $P_{D|D}^{l+1}$, where
    \begin{align}
        P_{D|D}^{l+1}(w)
        =\mathbb{E}_{W_{1:l} \sim P_D} [P_{D}(w\ |\ W_{1:l})].
    \end{align}
    \item Draw word sequences of length $L$ from the model distribution $P_M$. Denote the marginal distribution of the random variable at position $l+1$ as $P_{M|M}^{l+1}$, where
    \begin{align}
        P_{M|M}^{l+1}(w)
        =\mathbb{E}_{W_{1:l} \sim P_M} [P_{M}(w\ |\ W_{1:l})].
    \end{align}
    \item First draw $W_{1..l}$ from $P_D$, then draw $W_{l+1}$ from $P_M(\cdot |W_{1..l})$. Denote the marginal distribution of the random variable at position $l+1$ as $P_{M|D}^{l+1}$, where
    \begin{align}
        P_{M|D}^{l+1}(w) = \mathbb{E}_{W_{1:l} \sim P_D} [P_{M}(w\ |\ W_{1:l})].
    \end{align}
\end{itemize}

From the definition of exposure bias, $P_{M|M}^{l+1}$  suffers from the training-testing discrepancy, while $P_{M|D}^{l+1}$ should behave better and be closer to the true distribution $P_{D|D}^{l+1}$. To measure this discrepancy, define the marginal generation deviation (MGD) at history length $l$ of history distribution $P_H$ with metric $d$ as
\begin{align}\label{eq:mmd}
    \text{MGD}(P_{M|H}, l, d) = d(P_{M|H}^{l+1}, P_{D|D}^{l+1})
\end{align}
where $P_H \in \{P_M, P_D\}$ denotes the history distribution. MGD measures the deviation of the marginal distribution of $W^{l+1}$ from ground-truth data distribution.

Finally, we define the \textit{rate of exposure bias} (EB-M) at history length $l$ of model $P_M$ as the ratio (discrepancy) between the MGD measurements when two different history distributions are fed:
\begin{align}
\begin{split}
    \text{EB-M}(P_M, l, d)=\frac{\text{MGD}(P_{M|M}, l, d)}{\text{MGD}(P_{M|D}, l, d)}
\end{split}
\end{align}

For MLE-trained models, EB-M\footnote{Note that one can also directly measure $d(P^{l+1}_{M|M}, P^{l+1}_{M|D})$, but in that way, we can not tell which distribution is better.} is expected to be larger than 1, and larger EB-M indicates a more serious exposure bias problem for the trained model. For the choice of $d$, we experiment with two popular probability metrics: total variation distance (denoted as $d_{TV}$), and Jensen-Shannon divergence (denoted as $d_{JS}$).

The problem left is to estimate the described marginal distributions of $W_{l+1}$. We adopt a simple sample-and-count method: $P_{D|D}^{l+1}$ is estimated by the distribution (histogram) of $W_{l+1}$  from a number (to be specified in Section \ref{sec:marginal-exp}) of sentences sampled from the data distribution. For $P_{M|M}^{l+1}$ and $P_{M|D}^{l+1}$, we first draw a number of history samples $W_{1:l}$ from the corresponding history model (model distribution and data distribution respectively). We then feed sampled history sequences into the trained model and estimate the marginal distribution of the $(l+1)_{th}$ word by averaging the predicted distribution $P_M(\cdot|W_{1:l})$.

\subsection{Experiments}
\label{sec:marginal-exp}
We measure EB-M for MLE-trained LSTM LM on two popular data-sets: EMNLP-news, and wikitext-103\footnote{The wikitext-103 data is available at \\ \url{https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/}.}. For EMNLP-news we set $L=20$, and only use data samples whose length is longer than $L$. The resulting training/validation/test set has 268k/10k/10k sentences. The vocabulary is of size 5k. We use the 10k samples in the test set for evaluation of EB-M. Note that the EMNLP-news data-set is widely used in text GAN literatures \cite{yu2016seqgan, cot18sidi}. We train a one-layer LSTM LM \citep{lstmlm-speech} of hidden dimension 512 as the MLE baseline model for EMNLP-news.

For wikitext-103, we set $L=50$, and regard a paragraph in the original data as a long sentence. Further, we use half of the data for LM training, and utilize the other half for EB-M evaluation. The resulting training/validation/test/evaluation set has 300k/1.5k/1.5k/300k sentences. The vocabulary is of size 50k. We train a two-layer LSTM LM of hidden dimension 1024 as the MLE baseline model for wikitext-103.

For MLE baseline model training, the Adam optimizer is used with learning rate 0.001, no Dropout \citep{droput14nitish} is applied. The model is trained for 100 epochs.

We first measure EB-M on the wikitext-103 data-set, which has large amount of evaluation data. The results are shown in Figure \ref{fig:ebrate_different_number_wiki103}. We provide EB-M measurements with metric $d_{TV}$ in Appendix \ref{app:auxplots}, as they are similar to those using metric $d_{JS}$. It is shown that the measurements become stable when using 100k data/model samples. EB-M has an average value of 1.10, indicating a significant gap between the model's MGD when fed with history from $P_D$ or $P_M$. Further, we observe a steady growth of EB-M along the length of history, which is expected as an outcome of exposure bias.

\begin{figure*}[h]
\subfigure[wikitext-103]{\label{fig:ebrate_different_number_wiki103}\includegraphics[width=0.5\linewidth]{figs/marginal_method/wiki103_ebrate_mle_numcompare_js.eps}}
\subfigure[EMNLP-news]{\label{fig:ebrate_compare_news}\includegraphics[width=0.5\linewidth]{figs/marginal_method/ebrate_news_js.eps}}

\caption{(a): EB-M measurements (with metric $d_{JS}$) using different number of samples on wikitext-103 data. (b): EB-M (with metric $d_{JS}$) comparison for MLE and non-MLE training on EMNLP-News data. For each training method, we show corpus-BLEU \citep{yu2016seqgan} measurement using the test-set as reference set in the legend.}

\end{figure*}

However, does the EB-M measurements really indicate the significance of exposure bias? \textit{Not really.} The problem is that the distortion of the marginal $P_{M|M}^{l+1}$ is not only affected by the presumably existing exposure bias alone, but also by the mismatch between the history distribution $P_{M}$ from $P_{D}$ for $W_{1:l}$, which grows with the length of the history. Therefore, even if the measured EB-M is significantly larger than one, we can not conclude that exposure bias is the major reason. We provide an example to illustrate this argument:
\begin{example}
\label{eg:maringal_example}
Suppose $L=2$, and $V=\{A, B\}$. $P_D$ and $P_M$ are crafted as follows: $P_D(AA)=P_D(BB)=0.5, P_D(AB)=P_D(BA)=0$; And $P_M(W_1=A)=1, P_M(W_2=A|W_1=A)=1, P_M(W_2=B|W_1=B)=1$.
\end{example}

In Example \ref{eg:maringal_example}, $\text{MGD}(P_{M|M},d_{TV},1)=0.5$ and
$\text{MGD}(P_{M|D},d_{TV},1)=0$, which gives $\text{EB-M}(P_M, d_{TV}, 1)=\infty$. However, the only problem $P_M$ has is the mismatch between the history distributions ($P_M$ and $P_D$) for $W_1$.

The next set of experiments also suggest that EB-M may not precisely reflect exposure bias. On the EMNLP-news data-set, we compare EB-M measurements for several non-MLE training methods with the baseline MLE model. We include results for Scheduled Sampling (SS) \citep{ss15bengio}, Cooperative Training (CoT) \citep{cot18sidi}, and Adversarial Ranking (RankGAN) \citep{rankgan17}. We provide implementation details for non-MLE methods in Appendix \ref{app:implementation-nonMLE}. Intuitively, these methods will cause the model to be \textit{biased} to behave well with model samples as history, instead of data samples. Therefore, assuming the significance of exposure bias, we expect EB-M measurement for non-MLE trained models to be smaller than MLE trained models. However, results in Figure \ref{fig:ebrate_compare_news} shows that the measurements for different training frameworks are almost the same. Hence we believe the EB-M measurements are mainly reflecting the mismatch between the history distributions.

What if exposure bias exactly refers to this mismatch between model distribution and the data distribution? If that is the case, then this mismatch is inevitable for any imperfect model, and non-MLE training algorithms can not solve it. We believe a better, more precise definition is needed to discriminate exposure bias from this trivial mismatch. Motivated by this view, we propose a second approach in the section below.

\section{Discussion}
Is MLE training really biased? We believe the answer is not conclusive. Note that the MLE objective~\eqref{eq:mleobj} can be re-written as:
\begin{align}
\begin{split}
   & \argmin_{\theta}\mathop{\mathbb{E}}_{W_{1:L}\sim P_D}\tfrac{-1}{L}\Sigma_{l=1}^L\log P_{M}(W_l|W_{1:l-1}) \\
    = & \argmin_{\theta}\mathop{\mathbb{E}}_{W \sim P_D}-\log P_{M}(W) \\
    = & \argmin_{\theta}\mathop{\mathbb{E}}_{W \sim P_D}\log\frac{P_D(W)}{ P_{M}(W)} \\
    = & \argmin_{\theta} D_{KL}(P_D||P_M)
\end{split}
\end{align}
where $D_{KL}$ denotes the Kullback–Leibler divergence, and $\theta$ denotes the trainable parameters in $P_M$. Therefore, MLE training is minizing the divergence from $P_M$, which is exactly the model's sampling distribution, from $P_D$. While it's true that the training is ``exposed'' to data samples, we can not simply deduce the objective is ``biased''.

We want to end our discussion with two remarks. Firstly, the proposed quantification approaches should not be used as the only metric for NLG. For example, a position-aware uni-gram LM, which generates words independent of previous context, has no exposure bias problem and can pass our test easily.

Further, the intention of this work is not to discourage researchers from exploring non-MLE training algorithms for LM. It is completely possible that an training objective other than $D_{KL}(P_D||P_M)$, such as $JSD(P_D||P_M)$, can lead to better generation performance \citep{cot18sidi, sscritique18ferenc}. However, though non-MLE algorithms avoid teacher forcing, these algorithms (using GAN or RL for example) are usually much more difficult to tune. Given that the quantified measurement of exposure bias is insignificant, we think it should be questioned whether adopting these techniques to avoid exposure bias is a wise trade-off.

\section{Related Works}
Several recent works attempt to carefully evaluate whether the non-MLE training methods (e.g. adversarial training) can give superior NLG performance than standard MLE training for RNN LM.  \citet{shortgan18massimo} tunes a ``temperature'' parameter in the softmax output, and evaluate models over the whole quality-diversity spectrum. \citet{evalgan18stanislau} proposes to use ``Reverse Language Model score'' or ``Frechet InferSent Distance'' to evaluate the model's generation performance. \citet{evalgan18guy} proposes a method for approximating a distribution over tokens from a GAN, and then evaluate the model with standard LM metrics.

These works all arrive to a similar conclusion: Text GANs are not convincingly better, or even worse, than standard MLE training. So, to some extent, these works imply that exposure bias may be not a serious problem in MLE training.

\section{Conclusion}
In this work, we explore two intuitive approaches to quantify the significance of exposure bias for LM training. The first approach, relying on the marginal generation distribution, suggests some ambiguity in the current definition of exposure bias. Hence we argue that we should focus on the model's generation performance in terms of its conditional distribution and propose a second quantification approach. However, according to our measurements in a synthetic setting, there's only around $3\%$ performance gap between the training and testing environments. In particular, exposure bias only has minimal effect when the history length is not long enough. These results indicate that the exposure bias problem might be not as serious as it is currently assumed to be.

\subsection*{Acknowledgments}
We thank Hongyin Luo, Yonatan Belinkov, Hao Tang and Jianqiao Yang for useful discussions. We also want to thank authors of \citet{SanTsi18How}, which this work takes inspiration from.

{
  \setlength{\bibsep}{3pt}
  \bibliographystyle{abbrvnat}

\section{Notations}
The task of language modelling is to learn the probability distribution of the $(l+1)_{\text{th}}$ word $W_{l+1}$ in a sentence conditioned on the word history $W_{1:l} := (W_1, \dots, W_{l})$. Here, we use the uppercase $W_i \in V$ to denote a discrete random variable distributed across the vocabulary $V$. The lower-case $w$ is used to denote some particular word in the vocabulary V. Given a training data-set $D$ consisting of  sentences of length $L$, the standard MLE training minimizes the negative log-likelihood below:
\begin{align}
\label{eq:mleobj}
\begin{split}
 L_{\text{MLE}}=
 \mathop{\mathbb{E}}_{W_{1:L}\sim D}-\tfrac{1}{L}\Sigma_{l=1}^L\log P_{M}(W_l|W_{1:l-1}),
\end{split}
\end{align}
Note that in this work we assume all sentences are of length $L$ for simplicity.

In the rest of the paper, we denote the generation distribution of the trained LM as $P_{M}$, and the ground-truth data distribution as $P_{D}$. Readers can assume $P_M$ refers to the generation distribution of a LSTM LM \citep{lstm-hochreiter1997long, lstmlm-speech} trained with MLE objective, which is the major subject of this study.

Our quantification mainly relies on the measurements of the distance from the model's generation distribution to the data distribution. Hence we define the following notations to simplify expressions. Let $\mathcal{P}$ denote the set of probability distributions on the vocabulary $V$. Let $d$ denote a distance measure between distributions (e.g. total variation distance), $d:\mathcal{P} \times \mathcal{P} \to \mathbb{R}_{\ge 0}$.

\section{Methodology}
Throughout this work we study the definition of exposure bias based on the following principle: \textit{A good definition should lead to a quantifiable measurement to demonstrate its significance.} Hence, we validate the following claim that immediately follows from the definition of exposure bias: During sampling, if we set the history distribution to be the ground-truth data distribution instead of the model's own distribution (now that there is no discrepancy between training and testing), then the model's language generation quality should be much better.

To show the necessity of quantification for exposure bias, we start with the following preliminary experiment. We feed a MLE-trained LSTM LM on EMNLP-news data-set (details are given in Section \ref{sec:marginal-exp}), with three kinds of prefixes: model's own samples, data samples, or samples from a uniform random distribution. Then we let the model complete the sentence given these prefixes as history. We list some samples in Table \ref{tab:sampleinspection} and more in Appendix \ref{sec:appmoresampleinspection}.  By manual inspection, we do not observe noticeable differences in sample quality by comparing the generated sentences from the three different distributions. In the extreme case where random sequences are fed, the model is still able to generate reasonable sentences. Therefore, in the following sections, we turn to more sophisticated methods to quantify the significance of exposure bias. Note that our quantification approaches will be independent of the training procedure and only require inference from the trained model.

\begin{table}
\small
\begin{center}
\begin{tabular}{l}
\hline
Model Samples as Hisotry $\rightarrow$ Model Samples \\
\hline
it was only a pieces that had gone up to $\rightarrow$  the forest and forces the shoppers about their chronic young \\
i mean we didn ' t know what i haven $\rightarrow$  ' t considered through , " she told bbc radio \\
if he were the president - elect , he was $\rightarrow$  known that he would run a force in business at \\
\hline
Data Samples as Hisotry $\rightarrow$ Model Samples \\
\hline
what this group does is to take down various different $\rightarrow$  players in the future and we play in paris we \\
over 1 , 600 a day have reached greece this $\rightarrow$  gone in 2013 and it planned to allow civilians on \\
" we ' re working through a legacy period , $\rightarrow$  and i am proud of the experience of the worker \\
\hline
Random Sequences as History $\rightarrow$ Model Samples \\
\hline
...RANDOM... big winter deserve $\rightarrow$  , but they just say it your things goes wrong \\
...RANDOM... playoff north realise $\rightarrow$  at its lowest level , improving their understanding in danger \\
...RANDOM... vital childhood registration $\rightarrow$  , not previously planned for \texttt{<unk>} to each and reduced \\
\hline

\end{tabular}
\vskip 0.1in

\caption{Samples of a MLE-trained model when fed with different kinds of history. To save space, we omitted the first 7 words of the random history.}
\label{tab:sampleinspection}
\end{center}
\end{table}

redpajama_set_name: RedPajamaArXiv


text:
Q: Empty FormData in POST Request I'm making a simple application that will upload a file a server that has been dragged and dropped onto a canvas.
Here's a small sample of what the drag and drop code looks like:
var files = event.dataTransfer.files;
var formData = new FormData();
formData.append('files', files);

Using the debug tools, I know that files is correctly defined.
Then, I make the request:
var xhr = new XMLHttpRequest();
xhr.open('POST', '/upload', true);
xhr.send(formData);

But my server, running Node JS, gets an empty response body:
var express = require('express');
var bodyParser = require("body-parser");
app.use(bodyParser.urlencoded({
    extended: false
}));
app.post('/upload', function(req, res) {
    console.log(req.body.files);
    res.end();
});

I tried debugging on both the client and server side, but I can't figure it out. Although, it seems weird to me that the formData object looks like:
FormData {append: function}
    __proto__: FormData

Why isn't the appended files object showing up?
Any insight as to what the problem is would be greatly appreciated!

A: Try changing
var files = event.dataTransfer.files;

to
var files = event.dataTransfer.files[0];

See https://developer.mozilla.org/en-US/docs/Web/Guide/Using_FormData_Objects ; see also node-form-data

A: After too long of a time working on this problem, I finally figured it out. The issue was server-side; my Express application was ignoring the body of the post since I have not explicitly set the bodyParser to accept JSON.
The Express setup for a POST method should be:
var express = require('express');
var bodyParser = require("body-parser");
app.use(bodyParser.json()); // Configures bodyParser to accept JSON
app.use(bodyParser.urlencoded({
    extended: false
}));

With a sample routing method like:
app.post('/upload', function(req, res) {
    console.log(req.body.files);
    res.end();
});

It also is helpful to know that this server side setup can be tested by running something like this:
curl -d '{"file":{"name":"sample"}}' -H "Content-Type: application/json" http://localhost:8080/upload


text:
Police to use all available laws to prevent July 9 demonstrations
KUALA LUMPUR: Police have warned that they will use all available laws to prevent the July 9 rallies, saying they have information that foreign elements were out to create chaos.
Inspector-General of Police Tan Sri Ismail Omar issued the warning to groups who planned to defy police orders, regardless of their political affiliations, from holding the rallies.
Strong case for us to take more refugees, says Aussie official
MELBOURNE: Australia's Opposition immigration spokesman Scott Morrison says there are strong arguments for Australia in increasing its intake of refugees from Malaysia.
Filipino and Korean face deportation for involvement in Bersih rally
PUTRAJAYA: The two foreigners detained on Saturday for alleged involvement in the planned Opposition-initiated Bersih rally are being deported back to their home countries.
The authorities are in the process of doing that, said Home Minister Datuk Seri Hishammuddin Hussein.
Hopping jobs via amnesty
NUSAJAYA: The Government should relook the amnesty programme for illegal foreign workers to prevent abuse.
Johor MCA backbenchers club adviser Datuk Tee Siew Kiong, who represented a group of 10 MCA assemblymen in the state, said there was already cases of workers with documents quitting their current work place to register themselves as illegals.
Suhakam: People have right to assemble
KUALA LUMPUR: The Human Rights Commision of Malaysia (Suhakam) says it is the right of the people to assemble and to express their views in a peaceful manner.
Commission chairman Tan Sri Hasmy Agam said the right to do so was provided for under Article 10(1) (b) of the Federal Constitution as well as Article 20(1) of the Universal Declaration of Human Rights.


text:
Elite Billionaire Using $50 Million To Stop The 2nd Amendment – EricsonReport.com
Posted by Alexandra | Apr 17, 2014 | 0 |
http://beforeitsnews.com
(Before It's News)
Former New York City mayor and very, very rich man Michael Bloomberg plans to take on the National Rifle Association and other Second Amendment advocates by forming and financing a new organization focused on gun control. He's pledged at least $50 million to the effort.
The plans call for a restructuring of the gun control groups he funds, Mayors Against Illegal Guns and Moms Demand Action for Gun Sense in America. They will be brought under one new umbrella group called Everytown for Gun Safety.
The strategy will focus not on sweeping federal restrictions to ban certain weapons, but instead will seek to expand the background check system for gun buyers both at the state and national levels.
Bloomberg told the Times that this new group will try to use the NRA's own tactics against them, penalizing lawmakers whose stances on gun rights differ from the organization's. "We've got to make them afraid of us," is how he put it.
The former mayor also said that the effort will deviate from the standard playbook for gun control advocates, throwing less money into television campaigns and more money into field operations to drive single-issue voters, focusing primarily on female voters. The group will initially focus on 15 states, including Colorado, Washington, Texas, Montana, and Indiana.
The idea of Bloomberg throwing $50 million behind gun control could do a lot to even the scales—gun control lobbyists have long lacked benefactors with deep pockets like those on the opposing side. But Bloomberg faces a big liability: himself. He might not be the right face for the organization. Here's how Bloomberg describes coming up with the $50 million budget:
"I put $50 million this year, last year into coal, $53 million into oceans," he said with a shrug, describing his clean energy and sustainable fishing initiatives. "Certainly a number like that, $50 million. Let's see what happens."
$50 million: a mere bag of shells. Let's see what happens! And Bloomberg's not particularly humble about his philanthropy. In his own words:
I am telling you if there is a God, when I get to heaven I'm not stopping to be interviewed. I am heading straight in. I have earned my place in heaven. It's not even close.
http://www.ericsonreport.com/billionaire-using-50-million-to-stop-the-2nd-amendment/
Michael Bloomberg Is Starting A Gun Control Organization To Take On The NRA – The Wire.
PreviousRussia: deal reached on calming Ukraine tensions!!!!!!!!!!!!!!!!
Next4/15/14 MASSIVE BLOOD MOON UFO CAUGHT DURING ECLIPSE – ALIEN EVENT


nature | La Rosilla - Lifestyle and food.
Bursting with life – well I want to be.
The mountain really is bursting to life, bringing in the new year with a flourish. Blossom of the softest baby pinks and creamy whites are dressing the montes, giving the illusion of snow covered hillsides, the sweet perfumed blooms are enjoying the warmth of the sun. The clearest and brightest blue skies allow vistas for miles, with sea and snow veiled sierras in the same view.
Daisies & passionflowers juxtaposed, but with beauty comes green in it's masses, green in the way of weeds…and weeding I have not got around to yet. Unlike the mountain I'm not bursting with life, I want to be, my mind is ready but waiting for the body to follow.
Unfortunately I've succumbed to the dreaded cold/flu virus that is stubbornly holding on to me. Now I've had enough, and I'm going to follow that ancient pearl of wisdom, "Feed a cold & starve a fever", dosing myself up as my mother would say, with comforting ,wholesome & hearty dishes, and drinks to warm and soothe.
Leftovers from the festive time, are still being put to good use, to make bowls of Sopa de picadillo & Creme of brocoli with stilton & jamon. I've been and purchased some local honey, made from the bees of Colmenar, picked some lemons off my tree, & added a touch of cinnamon and topped up with hot water to soothe my throat and give me a boost of Vitamin C.
Tonight is a last ditch attempt to shift my bug, with a super hot curry full of vegetables, courgettes, peppers, mushrooms & butternut squash and cooked by my OH, so that makes it even better.
I hope to back on full form soon, so I can enjoy what nature has to offer, a mild winter and what I came here for, the sunshine.


There are only two categories of people who can give such labels to Muslims: first are enemies of Islam - the brainwashers, and the second are those poor victims of brainwashing.
I would like to stop at the second category of people who represent majority of the population. Unfortunately the main source of information for them is television and Internet. In spite of proclaimed freedom of press mass media give biased coverage of political events according to the principle "He who pays the piper calls the tune". The information in the official sources is carefully filtered and dosed. Politicians are able to manipulate social opinion by representing this or that event from different sides; they are able to make mass media serve their interests to reach a special aim.
The second source of information is Internet. Everyone tells what he wants and that is why this source contains plenty of "trash" information. The sad thing is that even Muslims distort image of Islam by spreading doubtful facts which they have heard from someone else or have read in some site. There appeared a great number of Muslims who introduce themselves as scholars without having necessary education. They give explanations of Quran verses and Hadeeth without having rights to it. Unfortunately there are even some non-Muslims who are busy with taking a single ayat or a part of an ayat and trying to interpret the meaning of it in a completely wrong way. They mislead themselves and distort the image of Islam. This is the reason of why there is misunderstanding, a big number of sects and lack of unity between Muslims.
Moreover there is a great number of anti Islamic sites, blogs, forums where some people's hatred against this beautiful religion finds its realization.
There is law in every country. All people know about their rights in general, but when they find themselves in a difficult situation they prefer to ask a lawyer for advice and defend themselves from mistakes. One would not even have an idea to ask a doctor or a stylist for a professional advice about law. And if we have health problem we would not consult a lawyer about it.
Because those who know are not equal to those who are ignorant about the subjects.
The same thing is with Islam. There are scholars, who study the religion, who do a tremendous task fighting with ignorance of the rest of people by highlighting the way to wisdom to them.
So why not ask a scholar about some issues of Islam? Why listen to strangers from the streets and taking to consideration their explanations if the is a chance to get a professional reliable advice.
Of course it is easier to write a question in a forum and discuss it there with other ordinary people, learn each other's opinion, choose the most preferable one than to spend time and money and make efforts to seek knowledge in a right place.
Let's be wiser and learn about the law from lawyers, about medicine from doctors, about style from designers, and about Islam from ulems (Islamic scholars). And let's not forget that there are still books exist which are much better then TV as they enrich horizons and awaken desire to seek knowledge.