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No one has ever been better at basketball than Steph Curry is right now
I'm not saying Stephen Curry is the greatest basketball player of all time--he's going to have to keep doing what he's doing for many more years and win a few more championships in order to take that title away from Michael Jordan (although as I suggested back in December, I do think it's going to happen).
But I will say this: Steph Curry is better at basketball right now than any human has ever been before.
The following was true after the game before Curry's epic performance on Saturday night in Oklahoma City:
Steph Curry has 4 career games of 50+ Pts while making 10+ 3-pointers.
The rest of the NBA - EVER - has 3. pic.twitter.com/wpdElfhl2O
— SportsCenter (@SportsCenter) February 26, 2016
Now here are a few of the things Curry accomplished on Saturday:
Another day at the office. pic.twitter.com/zLGLTNNK1W
On top of tying the league record for three-pointers in a game and breaking his own record for total threes in a season (with 24 games to go, by the way--that's 29 percent of the season remaining) on the same day, Curry's 53-5 Warriors also clinched a postseason berth Saturday (February 27):
In all the Curry insanity, it appears to be lost that Warriors also clinched a playoff spot faster than any team ever ('86 Celtics, Feb 28).
— Mark Vandeusen (@LucidSportsFan) February 28, 2016
Golden State is one game ahead of the pace set by Jordan's 1995-96 Chicago Bulls, who won an NBA record 72 games (72-10).
Back to Curry, not only did he tie the record for most triples in a game, he also shot 75 percent from deep while doing it:
Something else under the radar, Curry's 12-16 night = best three-point performance ever:
Kobe in '03: 12-18
Donyell Marshall in '05: 12-19
Here's a look at all 12 of those three-pointers:
NBA record 12 threes in a single game - @StephenCurry30 - 2/27/2016 https://t.co/BMJG0r1xsa
— NBA History (@NBAHistory) February 28, 2016
This is the beginning of my case as to why Curry is better than anyone we've ever seen before. It's not as if he hit 12 open threes--by my count, eight of his 12 treys were shots most players shouldn't even be taking. Watch this one in particular, in which Curry was several feet behind the three-point line and tightly guarded by seven-footer Steven Adams:
And how about this:
What's crazy to me is not that he's made half his shots from 30+ feet (even though the league average is 7.9 percent) but rather the fact that he's taken 22 and connected on 11 of them. The three-point line is 23'9"--who shoots from outside of 30 feet?
In my day no one shot from 30 feet because what the hell that's totally insane.
— Paul Flannery (@Pflanns) February 28, 2016
And that gets to the crux of my argument, Curry's preposterous game-winner on Saturday. But before I delve into that, take a look at this legendary performance from Larry Bird in 1988 (jump to the 1:17 mark for Bird's Curry-like moment):
Bird's heroics are child's play compared to Curry's:
Here are a several more angles:
#BANG... the game-winner, heard 'round the world! #ThisIsWhyWePlayhttps://t.co/6OBWpO0e1f
— NBA (@NBA) February 28, 2016
A few things are important when breaking this play down:
- When the Warriors got the ball with just under seven seconds left, they had a timeout, but didn't bother to use it.
- The game was tied so they didn't need a three-pointer, and Curry released the ball with nearly three full seconds remaining. He had plenty of time to get closer and take what most people would think is a much higher-percentage shot.
- Afterwards, Curry said he pulled up from where he did because he wanted to make sure to shoot before the defender picked him up--the distance he was from the hoop wasn't a factor.
Curry makes shots with ease that other players don't even of taking. For everyone else on the planet (and throughout history), that was a stupid shot to attempt in that situation. But for Steph Curry, it wasn't.
If you put Steph Curry on any NBA team, I think that team becomes the title favorite. He's transcending the game of basketball.
Hes good. No doubt. That said I dont remember old players clamoring about rules when talking about Jordan. He got knocked for titles at first. Then it was obvious he had it all. Steph is a product of his era. Sure hed likely be greatest shooter of any era and can go bananas but hes no Jordan. Dont be prisoner of the moment. LeBron compares unfavorably to Bird at this point in career statswise yet a few years ago people were saying he was going to best ever. Same with Kobe and now that hes finally putting his career to bed hes barely top ten. Expect more from you sir.
The difference between steph and jordan is similar to what will forever separate babe ruth from any baseball player for the forseeable future. Ruth, a frigging cy young winner, would have been one of the greatest pitchers of all time. So whenever the next ped bonds ascends to his offensive status he still wont overcome that pitching award. Likewise Jordan was dominant even when he wasnt scoring. He was the best defender in the league that included all time great defenders like payton and mutumbo and even teammates rodman and pippen. Couple of other points, in the future when you think steph has scored enough and won enough to be in the conversation... take those old timers points into consideration. Jordan as has been said when compared to lebron would not have averaged 36 today he might have averaged 50. Everybody thay played with him knows that. Steph would have still scored a lot in the 80s but hed be playing a big (strong) guard every night and the idea hed simply blow by them and finish in the lane is so humorous because people forget the kind of contact waiting for him. Flagrant one and twos now were simply defending the rim then. Hed last half as long as iverson did... if that. Finally bird, dirk, kerr, whoever didnt need to shoot from 33 feet. For different reasons they all were able to get off shots much closer... curry as you mentioned chooses to because he knows hed be open. If a globetotter can master half court hook shots pretty sure if bird had to rely on shots that deep hed master it too. As for curry, in the 80s hed be shooting a lot more shots like that because thats the only way hed be open. Its fun when players ho off and get hot... but lets not loose sight of reality. He really is a product of his era amd while likely an all star in the 80s his skills then would diminish while jordans skills today wouldnt be fair. Its really not close. A basketball writer like yourself should see that and agree withe oldtimers.
Mark Vandeusen February 29, 2016 at 10:47 AM
All the world's top athletes are better than they were 30 years ago. Curry would do just fine in the 80s NBA. He makes shots when he's not open, the era he plays in has nothing to do with that.
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Highest Rated: 71% The Killing Room (2009)
Lowest Rated: 9% Darkness Falls (2003)
Birthday: Sep 15, 1976
Birthplace: Johannesburg, South Africa
A protégé of Hollywood super-producer Michael Bay, director Jonathan Liebesman got his start directing small films before moving into special effects heavy blockbusters like the 2014 reboot of "Teenage Mutant Ninja Turtles." Born in Johannesburg, South Africa on September 15, 1976, Liebesman learned how to be a filmmaker at the South African School of Motion Picture Medium and Live Performance. After graduating in 1996, Liebesman traveled to New York, where his cousin was attending New York University, which inspired Liebesman to attend the prestigious Tisch School of the Arts at NYU. Liebesman made a student film adaptation of the Roald Dahl short story "Genesis and Catastrophe" (2000), which won several film awards and landed him an agent. Liebesman then directed his first theatrical feature, "Darkness Falls" (2003), when he was still in his twenties. Liebesman next directed "Rings" (2005), a spinoff on the horror film "The Ring" (2002), which became a bonus DVD feature bridging the story gap between the first and second "Ring" films. "Transformers" director Michael Bay brought Liebesman onboard to helm "The Texas Chainsaw Massacre: The Beginning" (2006). (At one point, Leibesman was up to direct the reboot of the "Friday the 13th" series as well, another Bay produced horror film.) "The Killing Room" (2009) came next, followed by the sci-fi epic "Battle Los Angeles" (2011), starring Aaron Eckhart. Following "Battle Los Angeles," Liebesman tackled the sequel to "Clash of the Titans" (2010), "Wrath of the Titans"(2012), before signing on to direct the "Teenage Mutant Ninja Turtles" remake. There were changes made to the "Turtles" storyline, which caused the ire of fans on the internet (including voice actor Robbie Rist, who had portrayed Michelangelo in the 1990 film), and while the movie didn't receive critical raves, audiences came back in droves, making the film a big summer hit in a fairly lackluster blockbuster season.
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15% 76% Dolittle Executive Producer $77.0M 2020
21% 50% Teenage Mutant Ninja Turtles Director $191.2M 2014
26% 49% Wrath of the Titans Director $83.6M 2012
37% 48% Battle: Los Angeles Director $83.6M 2011
71% 41% The Killing Room Director,
Executive Producer - 2009
15% 53% The Texas Chainsaw Massacre: The Beginning Director $39.5M 2006
No Score Yet 60% Rings Director - 2005
9% 34% Darkness Falls Director $32.1M 2003
79% 80% The Shannara Chronicles Executive Producer 2016-2017
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text:
Protesters 'bloodied' as junta cracks down
· Live coverage: Burma protests
Ian MacKinnon, south-east Asia correspondent, Mark Tran and agencies
Wed 26 Sep 2007 11.40 EDT First published on Wed 26 Sep 2007 11.40 EDT
A monk runs past a burning motorcycle in Rangoon yesterday. Photograph: The Irrawaddy/EPA
Burmese security forces clashed with demonstrators massed on the streets of Rangoon today in violence that left at least one civilian, and possibly several others, dead.
The military government acknowledged the first confirmed fatality of the crackdown, stating that one civilian had been killed and three wounded. But other reports, citing witnesses in Rangoon, said at least three people had been shot dead, including two monks, while the government-in-exile put the toll at five dead.
The first casualties in a week of mass protests came as riot police backed by army troops fired warning shots and tear gas and wielded batons against crowds, in the biggest show of public anger against the junta in almost two decades.
As darkness fell, people dispersed ahead of a dusk-to-dawn curfew with the streets almost deserted.
Zin Linn, the information minister for the national coalition government of the union of Burma, Burma's self-styled government-in-exile, said at least five monks were killed, while an organisation of exiled political activists in Thailand, the National League for Democracy-Liberated Area said three monks had died, and about 17 wounded.
Some witnesses estimated 100,000 people took to the streets despite fears of a repeat of the ruthless suppression of Burma's last major uprising, in 1988, when soldiers killed an estimated 3,000 people.
"They are marching down the streets, with the monks in the middle and ordinary people either side. They are shielding them, forming a human chain," one witness told Reuters.
In the second city of Mandalay, also under curfew, the Asian Human Rights Commission said there was no opposition to 10,000 marchers.
About 300 monks and activists were arrested across Rangoon after defying government orders to stay home, according to an exile dissident group.
The junta has banned all public gatherings of more than five people and imposed a night-time curfew following eight days of anti-government marches led by monks across the country.
As the protests turned violent, the French foreign minister, Bernard Kouchner, told reporters the UN security council would meet later today to discuss the situation. France holds the rotating presidency of the security council.
Earlier, the prime minister, Gordon Brown, called for an emergency meeting of the council on the rapidly developing events. "I would like to see all the pressures of the world put upon this regime now," he said.
In neighbouring Thailand, the army was preparing C-130 planes to airlift its citizens from Rangoon if the violence escalated, and troops on the border were braced for a flood of refugees.
The clashes broke out as monks, nuns and students attempted to gather at Rangoon's Shwedagon pagoda, the country's holiest Buddhist shrine in defiance of a government ban on public assembly.
Police squads baton-charged the crowd of around 700 people, and witnesses said between 80 and 100 protesters, some bloodied and injured, were arrested and taken away in army vehicles with obscured identification numbers.
The neighbourhood has been one of the focal points for more than a week of demonstrations that have escalated into the biggest challenge to the military junta since 1988.
Many of the monks wore face masks to protect themselves from gas, and some carried flags showing a fighting peacock - a key symbol of the democracy movement.
The junta's moves to halt the protests came after it imposed a night curfew in Rangoon and Mandalay, banning gatherings of more than five people. Up to ten pro-democracy activists were arrested in police raids overnight. Zaganar, a comedian famed for his anti-government jibes, and U Win Naing, a veteran government opponent, were among the most high-profile.
Barbed wire roadblocks were erected in parts of Rangoon, and security forces threw a tight cordon around the home of the detained opposition leader, Aung San Suu Kyi. Security forces were also present at five other monasteries around the city.
Foreign governments and religious leaders have urged the junta to deal peacefully with the situation. They included the Dalai Lama and South African Archbishop Desmond Tutu, both Nobel peace prize laureates like Ms Suu Kyi.
China, Burma's main ally, has been making an effort to let the generals know how worried the international community is, a Beijing-based diplomat said.
Representatives of Burma's pro-democracy and ethnic groups told Reuters that Chinese officials had been meeting quietly with them behind the scenes for months, partly hedging their bets.
Mark Canning, Britain's ambassador to Burma, yesterday met Burmese leaders and urged continued restraint. Mr Canning said he told ministers that the demonstrations "have been peaceful and well-disciplined".
"It will be disastrous in the eyes of the world on Burma if the authorities use force," he added.
Human rights groups have called on the military government to release political activists and begin serious dialogue with the opposition.
"The obligation of the international community, particularly Burma's allies, is to press the military to do that, including through a special session of the [UN] human rights council," said Adams, the Asia director at Human Rights Watch, based in New York, said.
"Anything less is tantamount to support for prolonged military rule and an endorsement of the clearly bankrupt political and economic system the generals have constructed."


It was Saturday night, around 8 p.m. in the vicinity of Cube nightclub, the 21-year-old Jahvante Smart; aka Smoke Dawg, was gunned down. You knew who Smoke Dawg was if you were a fan of OVO or involved with the Toronto music scene. The Somalian-Canadian rapper was known for being dedicated heavily to his neighborhood and creativity. Still awaiting a full-length project from Smart, fans will never get to see the shining potential of this young man.
7:57 p.m. at Q. Street West & Peter multiple guns shots we fired in groups of 4 and 5. Witnesses claimed to see the body of Smoke Dawg fall onto the concrete and graphic visuals of blood surrounding the body. Detective Jeff Ellington was the lead investigator as you will hear in clips on the Podcast. He is very hesitant to give any new information he has, he will not even acknowledge the already known fact that Smoke had been killed. On June 25, 2018 a new video was released from Smoke Dawg titled 'Fountain Freestyle'. Directed by Jake Fair, the shots consist mainly in the Regent Park neighborhood that Smoke is from. However, there are also shots of him in clear view of a rival neighborhood at Atkinson Housing Co-Op. Speculation that this is all gang related, the Mayor John Tory has concluded this incident to be an act of gang violence. He never said that this was a targeted assassination on Smoke Dawg, which many conspiracies and theories floating around the web are leaning towards.


Does anyone know of any published or other materials that address possible connections between persons having a high ACE's (adverse childhood experiences) score or lifetime chronic (toxic, allostatic-load) stress with persons having chronic traumatic encephalopathy, CTE and/or abnormal tau protein pathologies?
Given that the mind, consciousness, and mentality are biological, so too, necessarily, is culture; Culture is biology.
A correlation between ACE's and either CTE or tau pathologies would be the equivalent of "behaviorally transmitted neurodegeneration" - a staggering concept - and if true, one that hints at a larger-picture socio-cultural evolutionary homeostasis between competitive-scarcity-fear-based and cooperative-abundance-based mindsets writ-large. A connection that would perhaps be visible both economically and politically at the largest scales - and that may be playing out in the present day, some would surely argue.
Much to think on, yes?
To reiterate; Has anyone ever see anything published to connect ACE's with CTE or Tau protein pathologies?
ACE has been associated with high CRP, c-reactive protein markers.
High CRP has been associated with high tau-protein markers.
Following a transitive logic, one may thus reasonably hypothesize an association between high ACE and high tau-protein measures.
Brain Behav Immun. 2018 Mar;69:582-590. doi: 10.1016/j.bbi.2018.02.007. Epub 2018 Feb 16.
Adverse childhood experiences and adult inflammation: Findings from the 1958 British birth cohort.
Psychosom Med. Author manuscript; available in PMC 2017 Oct 29.
Psychosom Med. 2014 Jun; 76(5): 327–336.
J Mol Neurosci. 2015 Jun;56(2):519-27. doi: 10.1007/s12031-015-0572-z. Epub 2015 May 13.
C-Reactive Protein Induces Tau Hyperphosphorylation via GSK3β Signaling Pathway in SH-SY5Y Cells.
Guo H1, Wang H, Wang C, Cheng Y, Zou Z, Li Y, Wu J, Xu J.
I have insight into this condition.


Hello Friends! I am Intan Fitri Fauzia, but you can call me Intan. I was born in Tegal, 4 October 2000. I live in street at Jendral Ahmad Yani no. 54/34b, bandung.
I am the second of three brothers. I have older brother, he is Adam Hidayat. I have younger sister, she is Putri Ayu Lestari. And I am very greatful because i still have a mother and dad. I am very love my family.
I am school in Senior High School 3 Bandung, I come from Junior High School 4 Bandung. You need to know, I very greateful to be accepted as a student of Senior High School 3 Bandung.
After graduating from SMAN 3 bandung, I would like to continue my education to the accounting department at UI. I hope the desire to achieve, Amin Ya Allah.


At Lumina Learning, we are passionate about developing people, whether it's our clients or within our own team. If you are attracted to an entrepreneurial environment with diverse working styles or you have an interest in psychometrics and corporate training, this internship is just for you. Interns are an integral part of our team, we value the new ideas and energy they bring to our business.
Both short-term and long-term, paid and unpaid (university credit) internships are available. For long-term internships, you must be able to commit to at least 10 hours a week.
*Anyone applying for an internship must have a valid visa and a registered address in Tokyo.
To Apply: Contact tokyo@luminalearning.com with your resume or CV.
Born in Boston, Massachusetts and having grown up in Tokyo, David was introduced to the internship opportunity at Lumina through a classmate at university. After hearing about the work Lumina Learning does with psychometric analysis and HR development as well as the friendly work environment, he decided this was an opportunity not to be missed. David is passionate about photography and has been interning at the Tokyo office for more than 1 year now.
Cheyenne was born and raised in Detroit, Michigan and interned at Lumina Learning for 8 months during her time studying abroad at Tsuda College in order to gain real world experience to supplement her academic studies. During her internship, her main role was to act as the local coordinator for the WIN Conference. Cheyenne is passionate about helping create global learning opportunities for minority youth in the US, reading, travelling and playing the cello.
Spencer was born and raised in Dallas, Texas and is passionate about jiu-jitsu and travel. As a business and psychology major, he was interested in psychometric tools in a business setting and Lumina Learning was the ideal place to explore these interests further as part of his study abroad in Japan. Spencer spent 3 months interning at Lumina Learning as part of his study abroad program.


Abstract Researchers have the appropriate permissions and assent to observe playground behavior of specific children. Nonstudy children, unaware of the study, are observed only when interacting with study children and are not identified by name. The authors present this as a case of acceptable research practice.


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Cookernup är en ort i South West i Western Australia nära South Western Highway, mellan orterna Waroona och Harvey. Cookernup hade ett invånarantal på 502 personer (2016).

Referenser

Orter i Western Australia

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text:
Fortescue hits 100 Mtpa in December
Fortescue Metals Group says it has loaded 8.5 million tonnes of iron ore and achieved an annualised 100 million tonne production run rate in the last month of 2012.
The iron-ore focused company said the December run rate was 23 per cent higher than its previous monthly record set in May, 2012.
"The increased throughput was a result of a number of milestones that occurred in December, including the ramp up of the second ore processing facility at Christmas Creek mine, first ore from the Solomon mine and the opening of the Fortescue Hamersley Rail Line," the company said in a statement.
Fortescue said it remained on track to reach its near term target rate of 115 million tonnes per annum this quarter as it looked to take annual production to 155 million tonnes by December 2013.
Recent enthusiasm for debt-laden Fortescue comes a week after it announced work would resume on expanding the Kings deposit at its Solomon iron ore mining hub in January after a rebound in commodity prices.
Fortescue shares closed 18 cents lower at $4.86 on Friday.
Ex-Westpac exec named Genworth CFO
China fines LG, Samsung, 4 others


Kenai.com hosts a NetBeans Platform based implementation of the Campbell Prediction System (CPS), which helps manage and predict the behavior of wildland fire. CPS is described as being used to adjust tactics and strategies to ensure the safety of firefighters and the effective use of resources.
I've seen this CPS implementation on Kenai before, but a lot of development seems to have been taking place in the past few weeks and so I downloaded it to have a look. A lot of work is going into the project, with project lead Bruce Schubert's @Emxsys twitter link being an interesting one to follow.
This is a NetBeans Platform application with a 3D terrain viewer using NASA WorldWind, JOGL, VisAD and Weka libraries.


text:
Robert Juliat appoints AED Distribution as official distributor for Benelux countries
Robert Juliat has announced the appointment of AED Distribution as its official distributor for the Benelux countries, with effect from 1 January 2014.
In preparation AED, which has representing Robert Juliat in Belgium and Luxemburg since 2011, will expand market coverage and commence sales of Robert Juliat solutions in the Netherlands from October 1, 2013. Lightco, which has been distributing Robert Juliat products until now, will continue to do so in parallel with AED until the end of December 2013.
AED Distribution is the exclusive distributor of several professional AV brands across the Benelux territories, supplying AV professionals only.
"I'm very happy to complete our product portfolio in The Netherlands with a state-of-the-art product like Robert Juliat," says Hans Vanderheijden, MD of AED Distribution HQ. "AED Distribution is careful never to compete with its own customers and continues its policy only to sell to professional AV companies. Theatres, conference centres and auditoria will be served by our selected dealers and installers to whom we can bring the full range of Robert Juliat products."
Claus Spreyer, sales director of Robert Juliat comments: "Robert Juliat views BeNeLux as one market, so we have decided to cover these territories with a single distributor. AED Distribution was a logical choice considering the successful partnership we already experience in Belgium and Luxemburg. Their knowledge and contacts will provide a level of central support that will better serve users of Robert Juliat solutions."


Looking for Pipe Replacement Services near La Quinta or around La Quinta, CA? We can help you for sure! Feel free to call us any time: (760) 910-7081.
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text:
Cimini may refer to
 Monti Cimini, a range of volcanic hills near Rome, Italy
Cimini (surname)
Cimino family (also Simini) in Italy

See also
Cimino (disambiguation)


text:
Check out a host of online courses to improve your English
Learn English apps for kids
Explore games, podcasts, videos and quizzes to help your kids improve their English!
Find out more about British Council Libraries in India
Besides our three libraries in India, we have a fully-equipped online library!
Find out more about our libraries
Our work seeks to create opportunities for young people to develop new skills, become better qualified, introduce an international dimension to their learning or profession, and develop a better understanding of other cultures.
Our work in the Arts
Our mission is to strengthen cultural and social development between India and the UK through innovation in the creative sector. We will Connect, Create and Collaborate.
Our work in skills
Providing high quality skills training for young people is a priority for both the UK and India.
Our work with schools
The British Council works with a wide range of schools across India in both the public and private sectors through projects, events, courses, workshops and digital resources and platforms.
Our work in higher education
IHE supports policy dialogues and partnerships, scholarships, student mobility and alumni engagement. UKIERI enhances educational links between India and the UK by supporting research
Our work in English teaching and learning
We aim to partner with India's knowledge ambitions and economic growth by preparing young people to participate and contribute effectively to a globalised world.
The British Council Library is your one-stop shop for high-quality content sourced from publishers across the world.
India UK Education Connections
A three-year programme (2019-2022) to promote joint prosperity of India and the UK


text:
Eastern Edge: Draft Strategy and the Boston Bruins
by Cam Metz on August 7, 2018
The interweb is filled with draft prep advice, some of the best advice I've ever read fell in this article by Steve Laidlaw. I've tried drafting goalie / goalie to start and have placed well in 14 team leagues, I've tried the early D approach through the first four rounds, and I've loaded up on offense and ended up with dregs between the pipes. Which one has worked better? Technically the Early D landed a championship, but the heavy offense early in the draft seemed to produce the most formidable lineup. The reason I bring this up is I'm starting to wonder that just like drafting D incorrectly, if I'm drafting incorrectly for season long leagues. Is it best to set the lineup and swing for homeruns late – there is plenty of time to fix the mistakes. In the past I've always gone with the more stable draft picks in late rounds that are likely to produce value, but limit the required roster moves throughout the season. Seems like it would be more fun to swing for the fences this year, I'll let you know how that works out.
It has been said by all in the Dobber community already, but the Dobber's 2018-2019 Fantasy Guide is officially here. As always there is amazing content – the point predications and the sleeper potentials for players will get you foaming for all of your drafts. Get your copy now!
Sticking with the Atlantic and let's look at the second place last year Boston Bruins.
Like for the Wild West series, the ranks are based on a 12 team, head-to-head league, using the categories of goals, assists, power play points, shots, hits and blocks for forwards/defensemen and wins, saves, save percentage and goals against average for goalies. Player eligibility for this series is based on Yahoo, and draft ranks are based on average draft positions compiled from Yahoo, ESPN and CBS by FantasyPros. Once we can flip the switch and start getting 2018 ADPs we'll be sure to cover you on the value plays for this season.
The B's had a successful campaign last year culminating in a second place finish in the division and fourth place finish in the league for total points. Leading the way was Patrice Bergeron, Brad Marchand, and David Pastrnak– what really stands out though is the secondary scoring that was able to be fantasy relevant last year at specific times.
Bottom line – the Bruins are and will be a fantasy goldmine this season.
There are youngsters coming through the pipeline in Boston – three names that you need to look out for are: Jake DeBrusk, Danton Heinen, and Anders Bjork. To find out more about them you're going to need to purchase the Dobber Guide.
The team ranked fourth overall in power play conversion and given the pieces that will be returning next year, it is unlikely that this team will have anything but a top ten power play unit again. This will help pad the stats of the big guns on the Bruins for this season, plan accordingly.
Overvalued
David Pastrnak – This pains me to write that Pasta technically did not perform to his ADP, but this was the case as the 24th skater taken in leagues last year. However the 35 goals scored are nothing to take lightly. Pastrnak ended up as the 45th ranked skater in a points-focused league and 33rd overall in a league that counts hits and blocks. The problem with Pastrnak was a slight dip in his shot volume that blocked him from reaching the draft capital invested in him. Overall I don't think there are many owners who drafted Pastrnak that would say they wasted a pick on him. Next year Pastrnak is probably going to fall somewhere in line with his previous point totals. It would be nice if he could find a way to pick up a couple more assists, especially on the power play.
Points/ Game
Sht%
PPlay Points
Brad Marchand – With Pasta above I can't in good faith conclude that Marchand was overvalued as the ninth overall pick but because he placed as only the 55th best overall skater. See next section to find out why.
Zdeno Chara – it appears that 41 year old Zdeno Chara's days of fantasy relevance are all but over. He was drafted 160th (I'd love to play in leagues where that was going on) and returned as the 250th ranked skater in a points focused league. Do yourself a favor and don't get an itchy finger for a fifth D named Chara.
Brad Marchand was an absolute monster last year when he was on the ice. Unfortunately for those who drafted him, between suspensions and injuries Marchand always poses a danger to not play for your squad. Marchy put up his best statistical season on a per game basis last year and did so without a healthy Patrice Bergeron for many of those games. Marchand will be drafted in the first round this year, and given his suspension risk I wouldn't begrudge passing on him, I'd like a little more certainty of out of my number one overall. Since I like to play the regression game it is worth noting that Marchand also shot 18.7% which it is entirely repeatable given his historical shooting percentage, it's just also the highest he's ever converted; a trend I wouldn't bet on.
Patrice Bergeron – One of the things that make fantasy fun is when you get to watch your favorite players who are also on your fantasy squad – and love watching Bergeron. Bergeron blew his ADP (50th overall) out of the water if you look at the point per game basis, but otherwise he failed to make good on the draft equity. Given how well he performed and the huge expectations for the Bruins it is likely that Bergeron is going to be a tad too ADP rich for me this year, especially when there are so many more centers available in the later rounds.
Torey Krug– Just how good was Krug for teams last year? He put up 59 points in 75 games! For the first half of the season Krug as making a lot of owners regret their decision with 22 points through 35 games. He exploded with .9 pts/game through the final 41 games! Drafted at 67th overall he was able to finish as the 35th overall player in fantasy hockey. Talk about a mid-round pick returning value. To coincide with the point production, Krug also saw his shot rate increase during the second half of the season. Given how deadly the Bruins should be again this year, if Krug can stay healthy he should be in for a bear of a season. Sorry.
Tuukka Rask – I took some heat over labelling Rask as a hindrance to his team, especially after others have him ranked as a top tier goalie.
For this I need to explain: I'm not willing to invest early round picks on goalies that may not pan out; what is the difference between seven shutouts from a goalie and eight in your league? I'll bet not much. 41 wins does seem a lot better than 34, but not my much. You know where you can really make a difference? SV% and Quality Starts and use that data to help stock pile cheap efficient goaltenders.
Tuukka is no longer providing the SV% coverage and has been basically league average for the past three years in that category. Over the past four years he has been able to produce only one season of positive Goals Saved Above Average. These just are not signs of a goaltender I want to waste an important pick on. That being said given his win totals Tuukka did exceed his draft positions in both formats – just think twice before you hit draft, especially when you notice that he greatly exceeded his HD save percentage on the power play (green box below). For his sake hopefully he can counteract the likely regression with a regression on medium danger save percentage (red box).
The Bruins should be a force next year, grab them early and you won't be disappointed. I'd challenge you to look into more goaltending metrics and give your point of view on Rask.
Sources: Hockey-reference.com, Naturalstattrick, Corsica.hockey


text:
package eu.dnetlib.iis.common.lock;

import com.google.common.base.Stopwatch;
import eu.dnetlib.iis.common.SlowTest;
import eu.dnetlib.iis.common.java.PortBindings;
import eu.dnetlib.iis.common.lock.LockManagingProcess.LockMode;
import org.apache.curator.test.TestingServer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.ha.ZKFailoverController;
import org.junit.jupiter.api.AfterEach;
import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;

import java.io.IOException;
import java.time.Duration;
import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.CompletableFuture;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

import static org.junit.jupiter.api.Assertions.*;

/**
 * {@link LockManagingProcess} test class.
 * @author mhorst
 *
 */
@SlowTest
public class LockManagingProcessTest {

    private final LockManagingProcess lockManager = new LockManagingProcess();

    private final PortBindings portBindings = null;

    private final Configuration conf = new Configuration();

    private TestingServer zookeeperServer;

    private static final Duration TIMEOUT = Duration.ofSeconds(5);

    @BeforeEach
    public void initialize() throws Exception {
        zookeeperServer = new TestingServer(true);
        conf.clear();
        conf.set(ZKFailoverController.ZK_QUORUM_KEY, "localhost:" + zookeeperServer.getPort());
    }

    @AfterEach
    public void shutdown() throws IOException {
        zookeeperServer.stop();
    }

    @Test
    public void testLockingWithoutNodeId() {
        Map<String, String> parameters = new HashMap<>();
        parameters.put(LockManagingProcess.PARAM_LOCK_MODE, LockMode.obtain.name());

        assertThrows(IllegalArgumentException.class, () -> lockManager.run(portBindings, conf, parameters));
    }

    @Test
    public void testLockingWithoutMode() {
        Map<String, String> parameters = new HashMap<>();
        parameters.put(LockManagingProcess.PARAM_NODE_ID, "nodeid");

        assertThrows(IllegalArgumentException.class, () -> lockManager.run(portBindings, conf, parameters));
    }

    @Test
    public void testLockingWithoutQuorumKey() {
        Map<String, String> parameters = new HashMap<>();
        parameters.put(LockManagingProcess.PARAM_NODE_ID, "nodeid");
        parameters.put(LockManagingProcess.PARAM_LOCK_MODE, LockMode.obtain.name());
        conf.clear();

        assertThrows(IllegalArgumentException.class, () -> lockManager.run(portBindings, conf, parameters));
    }

    @Test
    public void testLockingWithInvalidQuorumKey() {
        Map<String, String> parameters = new HashMap<>();
        parameters.put(LockManagingProcess.PARAM_NODE_ID, "nodeid");
        parameters.put(LockManagingProcess.PARAM_LOCK_MODE, LockMode.obtain.name());
        conf.set(ZKFailoverController.ZK_QUORUM_KEY, "invalid");

        assertThrows(IllegalArgumentException.class, () -> lockManager.run(portBindings, conf, parameters));
    }

    @Test
    public void testLockingWithUnsupportedMode() {
        Map<String, String> parameters = new HashMap<>();
        parameters.put(LockManagingProcess.PARAM_NODE_ID, "nodeid");
        parameters.put(LockManagingProcess.PARAM_LOCK_MODE, "unsupported");

        assertThrows(IllegalArgumentException.class, () -> lockManager.run(portBindings, conf, parameters));
    }

    @Test
    public void testLockingAndUnlocking() {
        assertTimeout(TIMEOUT, () -> {
            Map<String, String> parameters = new HashMap<>();
            parameters.put(LockManagingProcess.PARAM_NODE_ID, "nodeid");
            parameters.put(LockManagingProcess.PARAM_LOCK_MODE, LockMode.obtain.name());

            lockManager.run(portBindings, conf, parameters);

            parameters.put(LockManagingProcess.PARAM_LOCK_MODE, LockMode.release.name());
            lockManager.run(portBindings, conf, parameters);

            parameters.put(LockManagingProcess.PARAM_LOCK_MODE, LockMode.obtain.name());
            lockManager.run(portBindings, conf, parameters);
        });
    }

    @Test
    public void testLockingOnDifferentNodes() {
        assertTimeout(TIMEOUT, () -> {
            for (int i=0; i < 10; i++) {
                Map<String, String> parameters = new HashMap<>();
                parameters.put(LockManagingProcess.PARAM_LOCK_MODE, LockMode.obtain.name());
                parameters.put(LockManagingProcess.PARAM_NODE_ID, "nodeid_" + i);
                lockManager.run(portBindings, conf, parameters);
            }
        });
    }

    @Test
    public void testUnlockingOnNotExistingNode() {
        assertTimeout(TIMEOUT, () -> {
            Map<String, String> parameters = new HashMap<>();
            parameters.put(LockManagingProcess.PARAM_LOCK_MODE, LockMode.release.name());
            parameters.put(LockManagingProcess.PARAM_NODE_ID, "not_existing_nodeid");
            lockManager.run(portBindings, conf, parameters);
        });
    }

    @Test
    public void testAsyncLocking() {
        assertTimeout(TIMEOUT, () -> {
            final String nodeId = "nodeid";
            final long timeout = 100;

            Map<String, String> parameters = new HashMap<>();
            parameters.put(LockManagingProcess.PARAM_NODE_ID, nodeId);
            parameters.put(LockManagingProcess.PARAM_LOCK_MODE, LockMode.obtain.name());
            lockManager.run(portBindings, conf, parameters);

            final CompletableFuture<Long> threadWaitTimeFuture = new CompletableFuture<>();

            ExecutorService executor = Executors.newSingleThreadExecutor();
            executor.submit(() -> {
                try {
                    LockManagingProcess lockManager = new LockManagingProcess();
                    Map<String, String> params = new HashMap<>();
                    params.put(LockManagingProcess.PARAM_NODE_ID, nodeId);
                    params.put(LockManagingProcess.PARAM_LOCK_MODE, LockMode.obtain.name());

                    Stopwatch timer = new Stopwatch().start();

                    // obtaining lock
                    lockManager.run(portBindings, conf, params);

                    // returning processing time
                    threadWaitTimeFuture.complete(timer.elapsedMillis());

                } catch (Exception e) {
                    throw new RuntimeException(e);
                }
            });

            Thread.sleep(timeout);

            assertFalse(threadWaitTimeFuture.isDone());

            parameters.put(LockManagingProcess.PARAM_LOCK_MODE, LockMode.release.name());
            lockManager.run(portBindings, conf, parameters);

            assertTrue(threadWaitTimeFuture.get() >= timeout);
        });
    }

}

redpajama_set_name: RedPajamaGithub


5 minutes on responsibility? I don't think it's enough!
"The planet is dependent on action today. CBS could become a role model for other universities and business schools and attract even more engaged students," writes a CBS student in this opinion piece. Why not devote a mandatory module of 5 ECTS purely to "sustainability and responsibility?", he asks.


text:
\section{\label{sec:level1a}Introduction}

Most networks investigated today are parts of much larger networks. These subnets can come in two different forms: first, we can choose a region of a network and consider all nodes that are in this region and only the edges between these nodes (for example a connected component of the larger network
would be one such subnet). Looking at networks  defined by all servers in a country, or the interaction network of all proteins which are confined to the mitochondria would be real-world examples\cite{Albert2002,Newman2003b,Evans2004}. Such networks may not be representative of the network as a whole but can give valuable insights into communication or biological processes within a defined sphere. More complicated is a second type of subnet where each node of the global network is included in the subnet with a certain probability $p$ and only the connections between pairs of nodes which are both included in the subnet are studied. This type of subnet is radically different from the regional-based subnets. It is, however, a frequent scenario in the analysis of technological and biological networks: most studies of molecular networks, such as protein-protein interaction\cite{Maslov2002,Agrafioti2005}, gene-regulation\cite{Xiong2004} and metabolic networks\cite{Bhattacharya2003}, test for connections between a subset of the known molecular entities (proteins, genes and enzymes/metabolites, respectively). The process by which these entities (or corresponding probes) are chosen may reflect the bias of the experimenter or merely chance, and this will in turn influence the extent to which the subnet reflects properties of the global network in a meaningful way. In light of the relative straightforwardness of studying the sampling properties of networks, and their obvious importance for the analysis of current network data sets it is surprising that this problem has not been addressed previously.
\par
\begin{figure}
\epsfig{file=fancy_sub.eps,width=7.5cm}
\caption{Sampling nodes from the network (top) will give rise to subnets (bottom). If edges are only observed if both nodes incident on an edge are included in the subnet (indicated in dark blue), then the degree distributions (as well as other characteristics) of the subnet and global network will be different. In the text we show that sometimes, however, degree distributions in both networks can be related under random sampling of nodes.}
\label{fig0}
\end{figure}
Here we will focus on the simplest, and perhaps
most parsimonious, process of sampling nodes: the case where each node in the network is included with probability $0<p<1$. In the present analysis we will concentrate on the sampling properties of the degree distribution of a network. The degree distribution, henceforth denoted by $\text{Pr}(k)$, specifies the probability for a node to have $k$ connections, $k=0,1,\ldots$, and is probably the most common summary statistic used in the analysis of networks \cite{Stumpf2005a}. In particular the potential scale-free nature of real networks is often identified from the empirical degree distribution, which for scale-free networks takes on a power-law form, $\text{Pr}(k)\propto k^{-\gamma}$ \cite{Bollobas2003,Newman2003b,Dorogovtsev2003,Stumpf2005b}. Frequently a model is considered scale-free if the tail (\it i.e. \rm for $k$ sufficiently large) of the degree distribution takes such an asymptotic powerlaw form \cite{Barabasi1999b,Albert2002}. Here we will consider this case as well as network ensembles with an exact power-law degree distribution.  The central question addressed here is whether the degree distribution of randomly sampled subnets has the same properties as the degree distribution of the overall network. Thus far this question has been ignored in the literature, but as we will show, is of great importance in the analysis of real networks, which in their vast majority, are only subnets of larger networks. Unless explicitly stated otherwise we shall consider the thermodynamic limit, $N\rightarrow \infty$.
\par

\section{\label{sec:level1b}The degree distribution of a random subnet}
\subsection{\label{sec:level2b}Sampling from networks}
We use $\cal{N}$ to denote a network with $N$ nodes (we allow $N\rightarrow \infty$) drawn from a statistical ensemble of random networks \cite{Bollobas1998,Burda2001} defined by some (potentially vector valued) parameter $\Omega$ and let $\text{Pr}(k)$ be its degree distribution; the total number of edges is given by $M$. Here we will be especially concerned with the case of a subnet $\cal{S}$ generated from the global network $\cal{N}$ by randomly sampling each node $i\in \cal{N}$ with a certain probability $0\le p_i\le 1$. Thus if a node of degree $k$ gets picked for inclusion in the subnet, its degree in the subnet will depend on the number of its neighbours which are also included in $\cal{S}$.

\subsubsection{\label{sec:level3} Random sampling}
We start by considering the case where the probability of picking a node is identical for all nodes, $p_i=p$ for all $i$. Here $p=0$ and $p=1$ are the trivial cases for which $\cal{S}=\emptyset$ and $\cal{S}=\cal{N}$, respectively.
Formally, the probability that a node has connectivity $l$ in $\cal{S}$ given it has connectivity $k$ in $\cal{N}$ is
\begin{equation}
\text{Pr}(l|k)= \binom{k}{l} p^l(1-p)^{k-l},
\label{ptrans}
\end{equation}
where $\text{Pr}(x|y)$ denotes the conditional probability of $x$ given $y$. The degree distribution in the subnet is thus given by
\begin{equation}
\text{Pr}_{\cal S}(l) = \sum_{k\ge l}^\infty \text{Pr}(l|k)\text{Pr}(k) = \sum_{k\ge l}^\infty  \binom{k}{l} p^l(1-p)^{k-l}\text{Pr}(k).
\label{prsubnet}
\end{equation}
This is probably the simplest and most parsimonious sampling scheme and may also be a reasonably realistic approximation, \it e.g. \rm  in the study of protein interaction networks where experimenters choose a set of proteins in a more or less haphazard fashion.
\par
From Eqn. (\ref{prsubnet}) we can show that
\begin{equation}
\Bbb{E}_{\cal S}[l]=\Bbb{E}\left[\Bbb{E}[l|k]\right] = p \,\Bbb{E}[k] = p\tau,
\label{means}
\end{equation}
where $\tau := \Bbb{E}[k]$ is the average degree in the network.
 Similarly we can show that the $m$-th moment of the descending factorial (defined by $x_{[m]}=x(x-1)(x-2)\dots (x-m+1)$ \cite{Feller1957}) for the degree distribution of a network obeys
\begin{equation}
\Bbb{E}_{\cal S}[l_{[m]}] = p^m \Bbb{E}[k_{[m]}].
\label{moments}
\end{equation}
Eqns. (\ref{means}) and (\ref{moments}) are fulfilled for all networks, as long as the moments exist; for scale-free networks with exponent $\gamma$, for example, moments of order greater or equal than $\lfloor \gamma \rfloor$ do not exist.

\subsubsection{Random sampling dependent on degree}
A further sampling scheme will be considered here where the number of connections directly influences the probability, $\pi(k)$, of sampling a node of degree $k$; In the previous sampling scheme all nodes had the same chance of being sampled, $\pi(k)=p$. We will focus on the particular case of an uncorrelated network.
\par
The connectivity of a node in the subnet thus depends on the degrees of its neighbours.
The probability that a node connected to a randomly chosen edge has degree $k$ is given by
\begin{equation}
\text{Pr}^*(k) = \frac{k \text{Pr}(k)}{\tau}
\end{equation}
where $\tau$ is the average degree in the network; the average degree of the neighbours of a randomly chosen node is thus $\Bbb{E}[k^2]/\Bbb{E}[k]$, if the two first moments of the degree distribution exist; below we will limit ourselves to such situations (for finite networks the moments will, of course, exist). Assuming a node is retained in the subnet then the probability of sampling a neighbouring node depends also on its connectivity and, in a mean-field approximation, the probability of retaining an edge originating from a node, $\tilde{p}$ is thus given by
\begin{equation}
\tilde{p} = \frac{1}{\tau}\sum_k k\text{Pr}(k)\pi(k)
\label{tilde}
\end{equation}
The degree distribution of the subnet $\cal S$  is again given by  binomial sampling:
\begin{equation}
\text{Pr}_{\cal S}(l)\!= \!\left[\sum_{k\ge l}^\infty \binom{k}{l} \tilde{p}^l(1-\tilde{p})^{k-l}\pi(k)\text{Pr}(k)\right]/\sum_{k=0}^\infty \pi(k)\text{Pr}(k).
\label{prconnect}
\end{equation}
Defining
\begin{equation}
\text{Pr}_0(k)= \pi(k)\text{Pr}(k)/\sum_{k=0}^{\infty} \pi(k)\text{Pr}(k)
\label{prdef}
\end{equation}
we can rewrite Eqn. (\ref{prconnect}) in the same form as Eqn. (\ref{prsubnet}).
With these probabilities the degree distribution in the subnet is given analogously to Eqn. (\ref{prsubnet}) as
\begin{equation}
\text{Pr}_{\cal S}(l) = \sum_{k\ge l}^\infty \binom{k}{l} \tilde{p}^l(1-\tilde{p})^{k-l}\text{Pr}_0(k)
\label{prconnet2}
\end{equation}
Obviously, when setting $\pi(k)=p$ Eqn. (\ref{prconnet2}) simplifies to Eqn. (\ref{prsubnet}).
\par
We still have to specify the functional form of $\pi(k)$; {\em a priori} the only constraint is that $\pi(k)$ has to be a probability for all $k$, \it i.e. \rm $0\le \pi(k)\le 1, \forall k = 0, 1, 2 \ldots$. One possible and obvious choice is to let $\pi(k)\propto k$; in order to ensure that $\pi(k)\le 1$ for large $k$ we set
\begin{equation}
\pi(k) = C
\label{eqdefpi}
\end{equation}
with $C$ sufficiently small such that $\pi<1$ for large $k$ (we can always trivially set $C=2\Bbb{E}[M])$ with $\Bbb{E}[M]$ the expected number of edges in the network). In this case
\begin{equation}
\tilde{p} = \frac{C}{\tau}\sum_kk^2 \text{Pr}(k) = C\Bbb{E}[k^2],
\label{propsample}
\end{equation}
\it i.e. \rm $\tilde{p}$ depends on the degree distribution solely via the first and second moments of $\text{Pr}(k)$.
 We will refer to this sampling scheme as preferential sampling of nodes.

\subsection{\label{sec:level2c}Probability generating functions of random subnets}
We represent the degree distribution of a network $\cal{N}$ through its probability generating function (PGF) \cite{Feller1957,Newman2003e},
\begin{equation}
G(s) = \sum_{k=0}^\infty \text{Pr}(k) s^k.
\label{pgfnet}
\end{equation}
The probability $\text{Pr}(k)$ follows from the PGF via the relationship
\begin{equation}
\Pr(k) = \left. \frac{1}{k!}\frac{d^k G(s)}{d s^k}\right|_{s=0}
\end{equation}
\par
With Eqns. (\ref{pgfnet}) and (\ref{ptrans}) we can straightforwardly derive the PGF for the subnet
\begin{align}
G_{\cal{S}}(s)& =\sum_{l=0}^\infty \text{Pr}(l) s^l\nonumber\\
&= \sum_{l=0}^\infty s^l\sum_{k=l}^\infty \text{Pr}(l|k)\text{Pr}(k)\nonumber\\
&=\sum_{k=0}^\infty\sum_{l=0}^k\binom{k}{l}p^l(1-p)^{k-l}s^l\text{Pr}(k)\nonumber\\
&=\sum_{k=0}^\infty \text{Pr}(k)(1-p+ps)^k\nonumber\\
&=G(1-p+ps).
\label{subnetpgf0}
\end{align}
If nodes with degree $l=0$ are ignored (as is frequently the case in high throughput protein interaction data) then after deleting all nodes with $l=0$ the PGF in the subnet becomes
\begin{align}
G_{\cal{S}}^*(s)=\frac{G(1-p+ps)-G(1-p)}{1-G(1-p)}.
\label{subnetpgf1}
\end{align}
Eqns. (\ref{subnetpgf0}) and (\ref{subnetpgf1}), respectively, hold  generally for the degree distributions of subnets randomly sampled from networks, depending on whether orphaned nodes (\it i.e. \rm those with connectivity $l=0$) are allowed or not \cite{Stumpf2005}.
\par
Interestingly, if Eqn. (\ref{subnetpgf0}) holds then also Eqn. (\ref{subnetpgf1}) holds with
$G(s)$ replaced by $G^*(s)=(G(s)-\text{Pr}(0))/(1-\text{Pr}(0))$; \it i.e. \rm networks with orphaned nodes
removed are closed under random sampling if the networks with the orphaned nodes retained are.
\par

\section{\label{sec:level1c} Closure under random sampling from networks}
\subsection{Conditions for closure: generating function}
From Eqns. (\ref{subnetpgf0}) and (\ref{subnetpgf1}) it is apparent that degree distributions of  a subnet $\cal{S}$ cannot generally be expected to be of the same type
({\it e.g.} a Possion distribution) as the degree distribution of the global network $\cal{N}$. For some important types of networks, however, it can be shown that random sampling of nodes gives rise to networks with  degree distributions  of the same type as the global network, but with a different parameter depending on $p$, {\it i.e.} $\Omega'=fn(\Omega,p)$. In this case we say that a network (or its degree distribution) is closed under random sampling of nodes. For a network ensemble to be closed under random sampling the following condition is necessary and sufficient \cite{Stumpf2005},
\begin{equation}
G_{\cal{S}}(s;\Omega) = G(s;\Omega')=G(1-p+ps;\Omega),
\label{closure1}
\end{equation}
 and
\begin{equation}
G_{\cal{S}}^*(s;\Omega) = G(s;\Omega')=\frac{G(1-p+ps;\Omega)-G(1-p;\Omega)}{1-G(1-p;\Omega)},
\label{closure2}
\end{equation}
when the subnet is not allowed to have orphaned nodes. Necessity and sufficiency follow from Eqns. (\ref{subnetpgf0}) and (\ref{subnetpgf1}) and the definition of the properties of a closed subnet.

\subsection{Conditions for closure: moments}
Equations (\ref{closure1}) and (\ref{closure2}) can be applied to all types of degree distributions. Inspired by  Eqns. (\ref{means}) and (\ref{moments}) we  here derive a general condition in terms of moments for a subnet to be of the same type as the global network. We assume the moments determine the degree distribution uniquely (in particular, this implies that all moments exist), which is true under mild regularity conditions.
Let an ensemble of random networks be given which is parameterized by $\Omega$.
For example, the ensemble of classical or Erd\"os-R\'enyi random graphs \cite{Erdos1959,Bollobas1998} has $\text{Pr}(k)=\exp(-\lambda)\frac{\lambda^k}{k!}$  and $\Omega=\lambda$ is the average connectivity. We seek a condition that, provided nodes are sampled with probability $p$, ensures that the degree distribution of the subnet remains in the same ensemble of random networks. Without loss of generality we can assume that $\Omega$ has the form $\Omega=(\tau,\psi)$, where $\tau$ is the average degree in the network and $\psi$ is an additional (potentially vector valued) parameter.
\par
From Eqn. (\ref{means}) we know that the average connectivity in the sampled subnet, $\tau_p$, is given by $\tau_p=p\tau$.
We can use Eqn. (\ref{moments}) to show that a family of degree distributions is closed under random sampling of nodes if and only if the descending factorial moments obey the relationship
\begin{equation}
\Bbb{E}[k_{[m]}] = a_m(\psi) \tau^m,
\label{relationship}
\end{equation}
where $a_m(\psi)$ is a constant that depends only on $m$ and $\psi$ but not on $\tau$ and the sampling probability $p$, and where $a_1(\psi)=1$.
\par
To prove that Eqn. (\ref{relationship}) is necessary we assume that the network is closed under random sampling of nodes and write $\tau=\Bbb{E}(k)$ and $g_m(\tau,\psi)=\Bbb{E}(k_{[m]})$. Because of Eqns. (\ref{means}) and (\ref{moments}) we can immediately write
\begin{equation}
g_m(p\tau,\psi) = p^m g_m(\tau,\psi)\\
\end{equation}
and
\begin{equation}
\frac{g_m(p\tau,\psi)}{(p\tau)^m}= \frac{g_m(\tau,\psi)}{\tau^m}.
\end{equation}
Thus $g_m(\tau,\psi)/\tau^m = \text{const.}$ (for all $\tau$) or
\begin{equation}
g_m(\tau,\psi) = a_m(\psi)\tau^m,
\end{equation}
with $a_1(\psi)=1$ as required.
\par
To prove sufficiency assume that the descending moments of $k_{[m]}$ fulfil Eqn. (\ref{relationship}); using Eqn. (\ref{moments}) the descending factorial moments of the nodal degrees in the subnet follow the relationship
\begin{equation}
\Bbb{E}_{\cal S}[l_{[m]}] = a_m(\psi)(p\tau)^m.
\end{equation}
Since the descending moments determine the moments, $\Bbb{E}(k^m)$ of a degree distribution, which in turn determine the distribution uniquely (by assumption), then the degree distribution of the subnet is given by a distribution that is of the same type as the degree distribution but with a rescaled parameter. Thus Eqn. (\ref{relationship}) is a necessary and sufficient condition for a network ensemble to be closed under random sampling of nodes.  $\Box$

\subsection{Analytical Examples}
We can use relationships (\ref{closure1}) and (\ref{relationship}) to determine whether a degree distribution is closed under random sampling. We will discuss this for three commonly observed degree distributions. Note that we only consider a degree distribution to be closed under (random) sampling if the degree distributions of the network and the subnet belong to the same family of probability distributions.
\par
Classical random graphs have a Poisson degree distribution, $Po(\lambda)$. It is straightforward to show that the descending moments of the Poisson distributed random variables are given by
\begin{equation}
\Bbb{E}[k_{[m]}] = \tau^m = \lambda^m.
\end{equation}
Thus $a_m=1$ for all
$m\ge 1$ and the degree distribution of classical random graphs is closed under random sampling of nodes. If we therefore have a subnet $\cal{S}$ of size $M$ drawn from a larger network $\cal{N}$ of known size $N$ we can determine $\lambda$ from $\lambda_{\cal{S}}$ as $\lambda=\lambda_{\cal{S}}\frac{N}{M}$. The subnet is therefore informative about the global network.
\par
Networks which grow by random attachment of new nodes give rise to exponential degree distributions such that asymptotically (large $N$) $\text{Pr}(k) = (1-e^{-\alpha})e^{-k\alpha}$. For such a distribution it is easily shown that
\begin{equation}
\Bbb{E}[k_{[m]}] = \frac{m! e^{-m\alpha}}{(1-e^{-\alpha})^m} = m!\tau^m,
\end{equation}
since $\Bbb{E}[k] = e^{-\alpha}/(1-e^{-\alpha})$. This means that $\Bbb{E}[k_{[m]}]$ can be written in the form specified by Eqn. (\ref{relationship}) and therefore exponential degree distributions are closed under random sampling. Binomial (as for classical finite-sized random graphs) and negative binomial distributions are also closed under random sampling as is easily verified. An explicit construction of probability distributions which are closed is discussed in appendix A.
\par
If the probability of attaching to a node is proportional to its degree the resulting network will asymptotically have a power-law degree distribution with exponent 3 \cite{Barabasi1999b}. For models where an existing  node is duplicated and each of its connections is kept with certain probability degree distributions will also be power-law like but with exponents $2<\gamma<3$ \cite{Aiello2001}.
\par
We first consider the sampling properties of network ensembles with degree distribution given by an exact powerlaw, $\text{Pr}(k)= k^{-\gamma}/\zeta(\gamma)$. In the asymptotic limit, $N\rightarrow \infty$, all moments greater than $\lceil \gamma\rceil$ diverge and we therefore have to use the PGF formalism. The PGF for the global network is given by
\begin{equation}
G(s;\gamma) = \frac{1}{\zeta(\gamma)}\sum_{k=1}^\infty s^k k^{-\gamma}
\end{equation}
and since $k=0$ is explicitely forbidden in a scale-free network, we use Eqn. (\ref{closure2}) to construct the PGF in the subnet, whence
\begin{equation}
G_{\cal{S}}^*(s;\gamma) =\frac{\sum_{k=1}^\infty \left[(1-p+ps)^k-(1-p)^k\right]k^{-\gamma}}{\zeta(\gamma)-\sum_{k=1}^\infty (1-p)^kk^{-\gamma}}.
\end{equation}
Clearly for $p\rightarrow 1$ we obtain the original PGF, $G(s;\gamma)$. For $0<p<1$, however, it is impossible to determine an exponent $\gamma'$ such that $G_{\cal S}$ could be written in terms of the PGF of a power law. Therefore random subnets drawn from exact scale-free networks are not themselves scale-free. This can also be shown explicitely using a series expansion \cite{Stumpf2005}. We note, however that the tail of the degree distribution of the subnet still takes on a powerlaw form for $k$ sufficiently large.
The same analysis applied to other fat-tailed probability distributions also shows that other fat-tailed degree distributions such as the log-normal and the stretched exponential families \cite{Sornette2003} are not closed under random sampling.
\par

\subsection{Numerical Examples}
The effect of random sampling on the degree distribution is most straightforwardly illustrated using numerical solutions of Eqns. (\ref{prsubnet}) and (\ref{tilde}-\ref{prconnet2}.) Here we do this for networks of infinite size and for simplicity focus on the canonical models of the classical random graph and the exact scale-free network, respectively.
\par
\begin{figure}
\epsfig{file=./Rpre1.eps,width=8cm}
\caption{Degree distributions of full network and subnets obtained by sampling each node with probability $p=0.8$ and $p=0.2$, respectively, for classical random graphs (a) and scale-free networks (b).}
\label{fig1}
\end{figure}
In part (a) of figure \ref{fig1} we show the Poisson distribution with $\lambda=5$ and the distributions of random subnet with $p=0.8$ and $p=0.2$, respectively. The subnet distributions are identical with the Poisson distributions with parameters $\lambda=4$ and $\lambda=2$. This also means that as $p\lambda$ becomes smaller than one the subnet will move through the phase-transition where the giant connected component dissolves and the size distribution of connected parts of the subnet becomes exponential.
\par
In part (b) of the same figure we show the power-law distribution with $\gamma=3$ and again the respective subnet degree distributions (renormalized such that $\text{Pr}_{\cal S}(0)=0$ in the subnet). We find that the subnet degree distributions are no longer straight lines but that as $k$ becomes large they run parallel to the original distributions. That is, as already described above, the tails of degree distributions of subnets sampled randomly from scale-free networks also fall off in the same power-law fashion as the original network. But at low connectivities the departure from the scale-free network is quite pronounced: probability mass moves from the tail towards the lowly connected nodes with $k=1$, which become more abundant than would be expected for a true scale-free network. This will have quite considerable effects for finite size networks. The deviation of the subnet degree distribution from a pure power-law at small to intermediate connectivities increases with $\gamma$ (as well as, naturally, with decreasing sampling probability $p$). We note however, that the tail of the degree distribution will retain a powerlaw form; thus for an alternative definition of scale-free behaviour which only requires $\text{Pr}_{\cal N}(k)\propto k^{-\gamma}$ for $k\rightarrow \infty$ random subnets will retain scale-free behaviour in the sense that the tail is still described by a powerlaw $\text{Pr}_{\cal S}(k)\propto k^{-\gamma'}$ for $k\rightarrow \infty$. In general, however, when the whole degree distribution is considered scale-free networks are not closed under random sampling.

\section{Connectivity dependent sampling}
There is no unique and obvious way in which the probability of sampling a node may depend on the connectivity. Here we briefly outline the behaviour of the degree distribution under the simple schemes outlined above where the probability of sampling a node is no longer uniform but linearly proportional to its connectivity, \it i.e. \rm if $\pi(k)\propto k$; we assume that $\tilde{p}(k)$ is given by Eqn. (\ref{propsample}).
\par
For a Poisson degree distribution with parameter $\lambda$ we have $\Bbb{E}[k^2]= \tau^2+\tau=\lambda^2+\lambda$ and
$\Bbb{E}[M] =N\lambda/2$, (assuming the network is large and finite)
whence $\tilde{p}=(\lambda+1)/(N\lambda)$ and
\begin{equation}
\text{Pr}_0(k)= \frac{e^{-\lambda}\lambda^{k-1}}{(k-1)!},
\end{equation}
if we set $C=2\Bbb{E}[M]$ in Eqn. (\ref{eqdefpi}).
In this case Eqn. (\ref{prconnet2}) becomes
\begin{align}
\text{Pr}_{\cal{S}}(l) = &\sum_{k\geq l}^{\infty}\binom{k}{l}\tilde{p}^l(1-\tilde{p})^{k-l}\text{Pr}_0(k)\nonumber\\
&= \frac{(\lambda\tilde{p})^l}{l!}e^{-\lambda\tilde{p}}\left(1-\tilde{p}+\frac{l}{\lambda}\right)
\label{prersample}
\end{align}
for $l=0,1,\ldots$.
The distribution in the subnet is thus not a pure Poisson distribution but one multiplied by a factor $1-\tilde{p}+l/\lambda$. Under this connectivity dependent sampling classical random graphs are therefore not closed and subnets $\cal S$ are qualitatively (if perhaps only rather slightly) different from the overall network $\cal N$.
\par
For scale-free networks with $\gamma\le 3$ the second moment diverges, $\Bbb{E}[k^2]\rightarrow \infty$, and we therefore focus on finite (though potentially very large networks). Networks with a powerlaw degree distribution can, for example, be constructed using standard methods \cite{Bender1978,Molloy1995,Newman2001a}. For such a scale-free graph with $N$ nodes we have to numerically evaluate the expected number of edges $\Bbb{E}[M]=\frac{1}{2}\sum_{k=1}^N k^{-\gamma+1}/\zeta(\gamma)$ and $\tilde{p}$, given by Eqn. (\ref{propsample}). For $\text{Pr}_0(k)$ we obtain for scale-free networks
\begin{equation}
\text{Pr}_0(k) = \frac{k^{1-\gamma}}{\zeta(\gamma-1)}.
\end{equation}
Proportional sampling from a scale-free network defined by a powerlaw exponent $\gamma$ is thus identical to sampling from a network with powerlaw exponent $\gamma-1$ and sampling probability $\tilde{p}$. Therefore we can use the results obtained above and conclude that the scale-free network (in the strict sense outlined above) is not closed under proportional sapling of nodes; for sufficiently large degrees, however, the tail of the degree distribution will still have a powerlaw form.

\section{Protein Interaction Network Data}
\begin{figure}
\epsfig{file=./Rpre_ecoli.eps,width=8cm}
\caption{Degree distributions of protein interaction network data available for {\em E.coli} in April of the years 2003, 2004 and 2005, respectively. As the fraction of sampled nodes/proteins decreases statistical weight is shifted from the tail towards lower degrees.}
\label{fig3}
\end{figure}
In figure \ref{fig3} we show three degree distributions corresponding to the protein interaction network (PIN) data from {\em E.coli} which was available in April 2003, 2004 and 2005 in the database of interaction proteins (DIP; \url{dip.doe-mbi.ucla.edu}); the resulting networks are made up of the interactions among 228, 373 and 480 proteins and have 293, 515 and 760 interactions, respectively. Figure \ref{fig3} confirms the results of the theoretical analysis presented above: as the fraction of sampled network nodes decreases statistical weight shifts from the tail towards lower degrees; the degree of the single highly connected node, $k=54$, was already known in the 2003 dataset (no further interactions have been added to this node since). The statistical weight of sparsely connected nodes, $k=1$, increases as the fraction of sampled nodes decreases. We note that the present data samples only a small subnet of the {\em E.coli} PIN which consists of interactions among approximately 4000 proteins. Moreover (i) it is well established that PIN data is highly unreliable and very noisy, and (ii) the true sampling scheme underlying the sampling scheme will generally be more complicated than the first order model employed here. The behaviour appears, however, to be qualitatively similar to our theoretical analysis.

\section{Conclusion}
Both sampling schemes discussed here are necessarily simpler than is the case in many real situations, such as the analysis of protein interaction networks (see \it e.g. \rm  \cite{Ito2000,Mering2002}. We believe, however, that between them they retain some vestiges of reality. Crucially, however, we wish to stress the incomplete nature of many network data sets. For many of these data sets in fact, including protein interaction network data, it appears that some form of random sampling is more realistic than a process in which the neighbourhood of a node is explored and neighbouring sites are recruited iteratively into the experimental setup. No matter what the sampling process is, it has to be included into the analysis from the outset: making inferences from incomplete (in the sense that not all nodes have been sampled) network data may give misleading results. If a network is closed under random (or connectivity dependent)sampling then it is straightforward to infer properties of the overall network from the subnet. For some, notably Erd\"os-R\'enyi random graphs, this is indeed the case. In general, however, the degree distributions of the network and sampled subnets will be qualitatively different. For example, while powerlaw tails will also give rise to powerlaw tails in the subnet a network which has an exact powerlaw degree distribution is not closed under random sampling. The same is true for other broad-tailed degree distributions such as lognormal or stretched exponential distributions.
\par
Sampling properties will also affect other network statistics, including network diameter and average path length, clustering coefficient and network motifs. These will be studied in a companion paper. We believe that sampling properties ought to be included explicitly and from the outset into any network analysis, unless there is good evidence that the whole (or the majority) of the network's nodes have been included in the data. Quite apart from the relevance of this work in the analysis of real data we believe that a detailed analysis of sampling properties of graphs is a rich field which, surprisingly, appears to have been neglected thus far.

\section{Acknowledgement}
MPHS is a Wellcome Trust research fellow and CW is supported by the Danish Cancer Society. Financial support from the Royal Society and the Carlsberg Foundation (to MPHS and CW) is also gratefully acknowledged. We also thank R.M. May, E. de Silva, M.J.E. Sternberg, M. Howard, P.J. Ingram and H.F. Jensen for many useful discussions and helpful comments. The manuscript benefitted greatly from an anonymous referee's comments.
\par
\bigskip

redpajama_set_name: RedPajamaArXiv


As a prelude to the launch of the Taylor Swift's new music video, the list of personalities / celebrities who will participate in "Bad Blood" video was released.
Through social networks, the singer Taylor Swift has announced her appearance at the Billboard Music Awards on 17 May. She also shared the image of her new single 'Bad Blood' which she performs live during the ceremony.
The video was directed by Joseph Kahn, who announced that include multiple characters, played by a cast of awards winners such as the Golden Globe, Emmy, Grammy and Oscar nominees.
The simple part of her success in album '1989', which has placed at the top of the charts and sales, thanks to songs like "Shake It Off ',' Blank Space 'and' Style '.
The musical event that recognizes the best of the commercial industry will feature numerous pop figures such as Britney Spears and Iggy Azalea (who sing a song together), Sam Smith, Kelly Clarkson, Ed Sheeran, Meghan Trainor, John Legend, Fall Out Boy with Wiz Khalifa, Hozier and Nick Jonas.
Here is the list of personalities who will participate in the Taylor Swift's new video.


Wednesday's a pretty big day in the upcoming special election, it's the day that ballot petitions are due. Each of the seventeen candidates must collect the signatures of 3,000 DC voters by Wednesday, and so you might see a horde of people outside of Metro stations, bars, and your favorite social space. You may find this process tedious, and I assure you, you're not alone. This process, though, is how we get electoral choice.
So, a word of advice? Sign the petition.
Signing a petition to place a candidate on the ballot is one of the few things in this life that obligates you to nothing, and serves the greater good of society. All it does is place them on the ballot, it doesn't imply you're going to vote for this person, it doesn't mean you like them or believe them, just that you believe in democracy.
Given the limited scope of the At-Large election, it's likely that these people collecting signatures aren't being paid to stand there and introduce themselves, they're just volunteers working as cogs in the election machine. So, be polite, and sign and date that form.
What about those of us not living or voting in the District? I suspect if I sign such a petition and they get challenged, the person whom I signed for would be accused of forging the signatures?
Samer makes a fine point: You are only eligible to sign if you live in, and are registered to vote in, the District of Columbia. A polite "Sorry, I'm not registered in DC" is plenty.


Mueller Report Fizzles. Prepare For The Reckoning.
If that report contained a fraction of the damaging material breathlessly awaited in the media culture for two years, there would be some smoke signal of it. Instead, we saw an actual finding from the Justice Department that there would be no further indictments from the probe. No indictment of Trump. No indictment of anyone associated with the campaign or the administration to support the narrative of Russia collusion.
Also, timing is everything. Friday's close of business is where stories go to die. As the Mueller report seized the news late in the day, TV viewership was a fraction of what it would have been on a Monday morning or a Wednesday midday. The halls of Congress, which would at those times teem with elected representatives, were a ghost town. As such, no parade of Republicans for a mass I-told-you-so, and no Democrats streaming toward the exits to avoid colossal discomfort.
But there's always Monday. Those interviews will still occur. Some will begin over the weekend, which will now be consumed with some pressing questions.
What will Democrats say, stripped of the ammo they expected to fire at the Trump White House once the Mueller report came out? Clues arose early. Look for a lot of changing of the subject, a lot of calls to see the full report and a lot of demands for "underlying documents," in the desperate hope that if Mueller couldn't find damaging material in the weeds, maybe the New York Times and MSNBC can.
To his credit, "Hardball" host Chris Matthews asked a succession of guests what they will say if the noxious evidence of collusion and other malfeasance is nowhere to be found.
Anyone is on solid ground to call for restraint until the release of the full report. But it will be interesting to see what does not get released. The tonnage of documents attached to the probe surely contains grand jury testimony and matters of national security that simply will not be available for perusal and cherry-picking. When that becomes clear, look for cries of coverup and speculation that surely an arsenal of smoking guns lies in the pages of files unseen.
The other Plan B for Democrats will be the nebulous notion that while the President may not be indictable, he is surely "compromised" by a sinister foreign rival. Look for plenty of file footage of Trump in the smiling proximity of Vladimir Putin, with accompanying ominous commentary that we may never know what these men were plotting.
Here's what we do know: if the Mueller spectacle is as anticlimactic as it first appears, there must be accountability for the politicians and media voices who beat this drum with malicious certainty for two years. Lives have been ruined. Millions of dollars have been wasted. All because a culture stretching from the halls of government to the studios of TV networks sought to erase a 2016 election result they could not tolerate.
It is hard to know what the future holds for the reputation of Robert Mueller. The left may now turn on him for starving them of the partisan oxygen they craved for the coming wars. I don't see that happening in large measure, nor do I expect appreciation sent his way from conservatives, despite the relief that always accompanies the end of a nightmare. This probe was launched on a shaky basis from the start, poisoned in practice by a trail of evidence that it was populated with some measure of politically motivated hacks. It is good if this amounts to nothing, but if a witch hunt fails to find a witch, that does not mean the search was honorable.
And by the way: Trump's challenges are not over. He may enjoy a brief season of satisfaction that the Mueller probe looks very much like the hoax he has invoked, but while the special counsel will pack up and go home, a belligerent Democrat-led House of Representatives will not.
Look for a year filled with congressional investigations, subpoenas and posturing designed to craft a result the Mueller probe could not—a presidency in tatters. Those efforts may look weak, even silly in view of the collapse of the Mueller gambit, but the effort will be loud and constant.
At the same time, perhaps ideas will arise for further inquiry—into the cast of characters from James Comey to Andrew McCabe to James Clapper to Rod Rosenstein to Peter Strzok to Lisa Page to whoever else, to learn more about the people, and the motivations that sent us down this ridiculous road in the first place.


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text:
Germany to receive up to 3 spy satellites, $465mn greenlighted for construction – report
Published time: 4 Nov, 2017 14:20
FILE PHOTO © NASA
Germany will spend €400 million ($465 million) on the construction of "up to three new spy satellites" for its intelligence service, RND news group reported, citing security sources. Construction is set to begin this month.
The budget committee of the country's parliament (Bundestag) has already approved financing of the costly project, according to Die Welt. Last year, German news outlets reported that the foreign intelligence agency would receive one espionage satellite. However, according to recent reports, the total may reach three.
'Security not paid in euro': German security chiefs reject pan-European intelligence service
Airbus and Bremen-based aerospace company OHB submitted two separate bids to build the new satellite system called 'George,' RND reports. There is also speculation in Berlin about a possible offer from Israel's prime aerospace and aviation manufacturer, IAI, Deutsche Welle says. Israel Aerospace Industries (IAI) designs and builds missile and space-based systems, civil aircraft, fighter jets, and drones, including the Super Heron drones.
The new satellites are to be used by Germany's foreign intelligence agency, the Federal Intelligence Service (BND), "to provide quick and autonomous information to be able to make independent and up-to-date assessments of the situation," Die Welt said, citing a paper from the chancellery. It is no longer enough to purchase imagery on the commercial market or to request it from international partners, the document reportedly added. The BND currently receives its data from the German Army, which operates its own network of satellites.
At a meeting last month, top brass from German foreign intelligence, and domestic and military spy agencies said international cooperation and information-sharing is ever more critical as threats become more global. BND President Bruno Kahl recently said that German intelligence agencies need to be on the cutting edge of technology, "otherwise the digital revolution will only benefit those we should be protecting our country from."
Germany creates cybersecurity squads, allocates funds for new spy satellite
His department landed in hot water following allegations that it spied on high-profile US targets, including the White House, the State Department, and the military for almost a decade. Even US-based international organizations such as the IMF and HRW were on the watchlist, Spiegel reported in June.
Another article by the magazine claimed that the BND snooped on the offices of the International Criminal Police Organization (Interpol) all over the world for many years. The BND spied not only on Interpol headquarters in Lyon, France, but also on a number of the organization's liaison offices located in Austria, Denmark, Belgium, Greece, Spain, Italy, and the US, Der Spiegel said back in April.
In October 2016, the German parliament approved a controversial bill granting the intelligence agencies wider powers, including spying on EU facilities. The law was criticized as "unconstitutional" at the time. In February, Der Spiegel reported that the intelligence service had snooped on Western news media outlets and international news agencies, including the BBC and Reuters, since 1999.


i am a young girl who wants a romantic guy who can create a serious relationship with me. i am fun to be with and very sociable. i like a handsome man who can treat a girl well.i can cook well,dance,sing and like outdoor acts. i also like swimming and listening to good music,going to the beach,horse biking and clubbing sometimes. i drink a little and do take alcohol occasionally and i'm not a smoker. please feel free to contact me so we can start a date as i know there are still lots of things i have to learn as far as a boy-girl relationship is concerned. I am willing to travel where ever and when ever, to any part of the world gto meet my soul mate and ideal partner.
I will give you my mobile number when you send me a mail so that we can chat on whatsapp, and why not do video calls, that's if i see that you are a serious and good man. waiting for contacts, chaoo.


Here at Peak Oak you may have heard us raving about the sheer precision and quality of the Hakwood engineered oak boards. Previously this would have been something shared only by those looking for an engineered oak floor. This however is not the case any more, as we can now introduce the Reale Collection - Hakwoods only solid oak boards.
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One of the defining features of the Reale Collection is the shrunk face surface texture. This natural "wavy" pattern across the boards is created by allowing the boards to dry at exactly the right speed, so that the desired tension is created in the wood. This creates an authentic and timeless appeal to the flooring.
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Located in the central tropical Pacific Ocean, the Republic of Kiribati, is an island nation composed of 32 atolls and one raised coral island, dispersed over 3.5 million square kilometres, straddling the equator, and bordering the International Date Line at its easternmost point. Having many islands blesses the nation with numerous beaches, the beauty of the surrounding lavish blue Pacific Ocean, a splendid marine environment, diverse plant and animal species and many natural features in the islands, Kiribati has something to offer everyone. Kiribati is famous all over the world for its Christmas Island - the largest coral atoll in the world, with more than 100 lakes or ponds dotting the interior that support a huge number of birds. Butaritari - the Kiribati's greenest island - also deserves a visit. Besides natural attractions in Kiribari can be found a lot of historical sites that transcends archaeological finds, ancient ruins and remnants of lost civilizations. Some of the most famous, yet tragic, historical sites in the world that reminds the World War II can be found on Tarawa island.


text:
import * as React from 'react';
import { Store } from 'redux';
import { Provider } from 'react-redux';
import { MuiThemeProvider, createMuiTheme } from 'material-ui/styles';
import { AppBar, Toolbar, Typography } from 'material-ui';

import State from './state/State';

export interface Props {
    store: Store<State>;
}

const theme = createMuiTheme();

const App = (props: Props) => (
    <Provider store={props.store}>
        <MuiThemeProvider theme={theme}>
            <AppBar>
                <Toolbar>
                    <Typography type="title" color="inherit">
                        Moneypenny
                    </Typography>
                </Toolbar>
            </AppBar>
        </MuiThemeProvider>
    </Provider>
);

export default App;


text:
package com.nicq.house;

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Configuration;
import org.springframework.context.annotation.PropertySource;
import org.springframework.core.env.Environment;

/**
 * Created by Nicq Chen on 12/3/2016.
 */
@Configuration
@PropertySource({"classpath:app.properties"})
public class AppConfig {

    @Autowired
    Environment env;

    public String[] getCities() {
        return env.getProperty("cities").split(",");
    }

    public String[] getAreasWithId(int idx) {
        return env.getProperty(String.format("areas.%s", idx)).split(",");
    }
}


text:
CPYTHON_SRC=${CPYTHON_SRC:-$HOME/devel/apps/cpython}
test -d $CPYTHON_SRC/Python || { echo "CPYTHON_SRC not correctly set: $CPYTHON_SOURCE_DIR/Python not found" && exit 1; }
mkdir -p results
for file in `find $CPYTHON_SRC -name "*.py"`
do
    echo "Parsing $file"
    ./parser-test $file &> ./results/`basename $file`.ast.out && rm -f ./results/`basename $file`.ast.out
done