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Part 4! Longer than the others so I put a ‘keep reading’ There’s one more part coming up. Shorter from what I’m imagining but it is the last one. This one is all ironpanther at the end so get ready for feels. 

Why didn’t you tell us?” is the first thing Steve says when they meet. Tony can already feel a headache going on. He doesn’t need to explain himself. It’s not his fault they didn’t piece it together or bothered to get to know him and learn of the name change themselves.

“Does it matter? You both went after a name. Congratulations you found it.”

“That’s not what we wanted.”

“No?” Tony snapped because it’s been years since he’s buried this but Steve and Bucky returning is bringing up old wounds he’d rather not remember. All his pent up anger is making an appearance. “Funny. I distinctly remember you both saying ‘we’re waiting for our third’ and ‘there isn’t room for you,’ meaning you didn’t want the guy who was actually your soulmate and standing right in front of you and instead were looking for a guy with the same name on your arm.”

“You could have told us!”

“I thought you knew,” Tony barks, his voice rough and heavy, before leaning back on the wall and taking a deep breath. He’s thankful T’Challa isn’t there and seeing this ugly side of him. This pitiful side he thought he buried long ago. “I didn’t think changing my name made a difference and I thought you knew the guy who was trying to get close, to slowly join your space, was your soulmate. I thought you knew so I tried opening up and getting you both used to me. I thought you knew so when you said you were waiting for your third and there was no room for me you were basically telling me a big fat ‘go away.’ I thought you knew and basically didn’t want me. Imagine my surprise when I see on the TV my brother announcing his soulmates Steve and Bucky.”

“We thought it was -” Steve scrunches his face in an ugly manner.

“Yeah, it’s very easy to figure out what you thought. Why did you let that idiot tell the whole world in the first place? Did you even bother to see if he had your names on his arm? Did you even check before letting him do what he wanted?”

“No,” Bucky answers in defeat, “we didn’t check in the beginning. He said he hardly showed anyone the names and didn’t feel comfortable yet. Wore his wrist cuff the entire time. Told us he’d show them when he was ready.”

Tony sighs. “And you believed him.”

“We had no reason not to. We were getting to know ‘im, and we were so excited to find our third.”

“When he finally showed us his wrists our names were on them. We didn’t think anything of it,” Steve says.

“Great, he faked his soul names, and with the resources he has he probably got very convincing ones.”

They nodded.

Nothing Tony could do now. Documentation is one thing. They could prove or disprove with that, but soul names were it. If they cannot be unproven there’d be no way to convince the public Gregory is not their actual soulmate. This will forever haunt them.

Tough luck. Reap what you sow and all that. “Good luck then. Knowing my dear ol’ brother he’s not going to let you both go so easily. Even if you leave he’ll make sure to keep the stories going for a long time. Blame you for the relationship failing and all that.”

Steve and Bucky have visible grief in their eyes. “You’re not going to do something?”

“Not my issue.”

“But… Tony -”

Not my issue. Take it up with Gregory. He’s your actual soulmate.”

[watch for break]

Keep reading

Lynching of three African American circus workers, Elias Clayton, Elmer Jackson, and Isaac McGhie in Duluth, Minnesota, 1920

On June 15, 1920, three African American circus workers, Elias Clayton, Elmer Jackson, and Isaac McGhie, suspects in an assault case, were taken from the jail, attacked, and lynched by a white mob of thousands in Duluth, Minnesota. Rumors had circulated that six African Americans had raped and robbed a nineteen-year-old girl. A physician’s examination of her subsequently found no evidence of rape or assault.

George Harrison, Friar Park, 1974, photo © Michael Ochs Archives/Getty Images.

“Just how United are the Nations? For the forest to be green each tree must be green. It’s like saying all the different nations have their own cultures, they all have their own backgrounds, they all have their own problems, so you have a lot of trouble in the world. But after all, there are a lot of different trees and a lot of different flowers in a garden but they’re all made out of sap. There must be something that underlies all nations, all cultures, all colours, all races, all religion. There’s one underlying truth which relates to them all. And that’s it, for the forest to be green every tree must be green.
Just for all of us standing here now, none of us can relate to each other unless we can relate to ourselves, we must find ourselves really. Each country must be strong and united. As soon as we can all have Planet Earth passports I’ll be grateful, because I’m tired of being British or being white, or being a Christian or a Hindu. I don’t have a philosophy, I just believe in the sap that runs throughout.” - George Harrison, UNICEF press conference, New York, 1974, quoted in Living in the Material World

Fans being crushed against a fence in the Liverpool enclosure at the Hillsborough Stadium in Sheffield, England. 96 were killed and 766 injured. 15 April 1989.

The crush occurred in the two standing-only central pens in the Leppings Lane stand, allocated to Liverpool supporters. Shortly before kick-off, in an attempt to ease overcrowding outside the entrance turnstiles, the police match commander, chief superintendent David Duckenfield, ordered exit gate C to be opened, leading to an influx of even more supporters to the already overcrowded central pens.

In the days and weeks following the disaster, police fed false stories to the press suggesting that hooliganism and drinking by Liverpool supporters were the root causes of the disaster. Blaming of Liverpool fans persisted even after the Taylor Report of 1990, which found that the main cause of the disaster was a failure of control by South Yorkshire Police (SYP). Following the Taylor report, the Director of Public Prosecutions (DPP) ruled there was no evidence to justify prosecution of individuals or institutions. The disaster also led to a number of safety improvements in the largest English football grounds, notably the elimination of fenced standing terraces in favor of all-seater stadiums in the top two tiers of English football.


New 3-D Printer Uses Light to Build Objects in Minutes

The next generation of desktop 3-D printers might do away with the excruciatingly slow process that current units use. Researchers have unveiled a printer that replaces the current extruder nozzle that squeezes out melted plastic one layer at a time with light and oxygen. 

The makers of the Carbon3D printer have demonstrated a technique they call continuous liquid interface production (CLIP), which grows 3-D printed parts out of a liquid resin bath. Ultraviolet light and oxygen work to build a stronger part in layers just tens of microns wide. Build times can be reduced from hours to minutes, they say.

Their work builds on the process called stereolithography, an additive manufacturing technique developed in the 1980s that builds parts layer by layer with liquid resin cured by light. 

“By rethinking the whole approach to 3-D printing, and the chemistry and physics behind the process, we have developed a new technology that can create parts radically faster than traditional technologies by essentially ‘growing’ them in a pool of liquid,” said University of North Carolina, Chapel Hill chemistry professor Joseph DeSimone, who coinvented the technique and is also Carbon3D’s CEO. See more images and learn more below.

Keep reading

anonymous asked:

So, say someone finally does claim the throne and becomes the new king/queen. But, say that you had to give this ruler one trait from all the current claimants/powers behind the throne. (So. Dany, Stannis, Tommen, Aegon (F?) Varys, Cersei, Jon Con) What one trait from each would you give to this ruler to make them the most effective.

Daenerys - Dragons. This is a unique trump card, and represents overwhelming military power. That might be a cheat though, since it’s not actually a ‘trait,’ Assuming, of course, we can’t use dragons and we have to use something else, I’d say idealism. As we see with actors like Baelor Breakspear, idealism is a great foundation for building toward good government.

Stannis - Commitment to his duty. Stannis emphasizes the best part of a functioning government, where it takes its obligations seriously. This is the second part of building toward a good government, by following through on commitments,

Tommen - Compassion. Few monarchs in the series are as good-hearted and well-meaning as Tommen.

Aegon VI - Intellect. Aegon has a very broad education, and this serves him well for higher-level discussion and building common ground. Much the way Eddard spoke to his master of horse about the stables, and his master of hounds about the hounds, Aegon’s ability to interface with his staff in their area is a uniting factor.

Varys - Image politics. I’ve made it plain that Varys is one of the masters of symbol politics in the series, and using that helps, especially in a preliterate society like Westeros.

Cersei - Conviction. She might be committed to power at any cost, but that drive re-oriented towards a better goal would serve a monarch well.

Jon Connington - Tactical ability. Connington might still be a terrible strategist, but he’s learned from his tactical mistakes. No longer seeking glory, he looks more toward victory, and any monarch needs to have victory in mind when it comes to war.

Thanks for the question, Anon.

SomethingLikeALawyer, Hand of the King
Trump’s personal lawyer, Michael Cohen, hires his own lawyer in Russia probe
Cohen’s decision is the latest indication that the Russia probe is intensifying and could end up focusing on a number of Trump associates.

MIAMI — Michael Cohen, who for years has served as President Trump’s personal attorney, has hired a lawyer of his own to help him navigate the expanding Russia investigation.

Cohen confirmed Friday to The Washington Post that he has retained Stephen M. Ryan, a Washington-based lawyer from the law firm McDermott, Will & Emery who has experience prosecuting criminal cases as an assistant U.S. attorney.

Cohen’s hiring of Ryan as his personal lawyer was first reported by Katy Tur of NBC News.

Cohen’s decision is the latest indication that the Russia probe overseen by special counsel Robert S. Mueller III is intensifying and could end up focusing on a number of Trump associates, both inside and outside the White House.

[Trump lashes out at Russia probe; Pence hires a lawyer]

Michael Caputo, a New York-based political operative and radio commentator who served as a senior communications adviser on Trump’s campaign, also has hired a lawyer of his own to navigate the Russia probe.

Caputo has retained Dennis C. Vacco, a former New York state attorney general and a partner at the law firm Lippes Mathias Wexler Friedman.  His hiring also was first reported by NBC’s Tur.

On Thursday, Vice President Pence’s office announced that the vice president had hired outside legal counsel, Richard Cullen, to assist him with inquiries from the Mueller investigation as well as congressional committee probes.

During a Friday morning event here in Miami, The Post asked Pence whether he had any comment about hiring his own lawyer.  The vice president said only: “It’s very routine.  Very routine.”

It is not entirely clear what role Cohen or Caputo might have in the Russia investigation.  Cohen, who worked as a lawyer for the Trump Organization for a decade, made an appearance in the dossier compiled by a former British spy.  The dossier, which was published online in January by BuzzFeed, alleged that he had traveled to Prague to meet with Russians and coordinate their hacking efforts.  Trump has rejected the dossier as “fake news,” and Cohen has vigorously denied its allegations about him, noting he was in California at the time it alleged he had visited Prague.

In January, Cohen was involved in a separate incident that could potentially have drawn the attention of investigators.  He has confirmed that he met with a Ukrainian lawmaker at a New York hotel at the urging of a former Trump business associate named Felix Sater.  At the meeting, Sater gave Cohen a peace plan that the lawmaker had drawn up for his country that would have paved the way for the lifting of sanctions imposed on Russia after its 2014 military incursion in Ukraine.

The New York Times reported that Cohen said he took the plan and left it in the White House office of then national security adviser Michael Flynn, days before Flynn resigned over his contacts with Russia’s ambassador.  Cohen told the Post that he had merely recommended Flynn as the proper recipient for the plan but that he had taken the written proposal home and thrown it away.

[Amid Russia scrutiny, Trump associates received informal Ukraine peace proposal]

Caputo, who briefly worked for the campaign, was an ally to former campaign chairman Paul Manafort.  He lived in Moscow for several years in the 1990s, and briefly held a contract in 2000 with the Russian conglomerate Gazprom Media to improve Russian President Vladimir Putin’s image in the United States.

You know it’s bad when the lawyers start lawyering up.

Scottish missionary, George Patterson, known as ‘Patterson of Tibet’ amputating a Sherpa’s toes. Himalayas, 1960s.

George Patterson arrived in Tibet in 1946, then crossed the Himalayas to India in midwinter three years later to warn the outside world of China’s intention to invade. While he was away the Chinese took over and, with Tibet suddenly sealed off, he was unable to return. Instead, he became a special correspondent for The Daily Telegraph and, for the first 10 years of the Chinese occupation, played a unique role in keeping the world informed of the repression inflicted on Tibetans.



** Synopsis: SLAC and Stanford researchers demonstrate that brain-mimicking ‘neural networks’ can revolutionize the way astrophysicists analyze their most complex data, including extreme distortions in spacetime that are crucial for our understanding of the universe. **

Researchers from the Department of Energy’s SLAC National Accelerator Laboratory and Stanford University have for the first time shown that neural networks – a form of artificial intelligence – can accurately analyze the complex distortions in spacetime known as gravitational lenses 10 million times faster than traditional methods.

“Analyses that typically take weeks to months to complete, that require the input of experts and that are computationally demanding, can be done by neural nets within a fraction of a second, in a fully automated way and, in principle, on a cell phone’s computer chip,” said postdoctoral fellow Laurence Perreault Levasseur, a co-author of a study published today in Nature.

Lightning Fast Complex Analysis

The team at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC), a joint institute of SLAC and Stanford, used neural networks to analyze images of strong gravitational lensing, where the image of a faraway galaxy is multiplied and distorted into rings and arcs by the gravity of a massive object, such as a galaxy cluster, that’s closer to us. The distortions provide important clues about how mass is distributed in space and how that distribution changes over time – properties linked to invisible dark matter that makes up 85 percent of all matter in the universe and to dark energy that’s accelerating the expansion of the universe.

Until now this type of analysis has been a tedious process that involves comparing actual images of lenses with a large number of computer simulations of mathematical lensing models. This can take weeks to months for a single lens.

But with the neural networks, the researchers were able to do the same analysis in a few seconds, which they demonstrated using real images from NASA’s Hubble Space Telescope and simulated ones.

To train the neural networks in what to look for, the researchers showed them about half a million simulated images of gravitational lenses for about a day. Once trained, the networks were able to analyze new lenses almost instantaneously with a precision that was comparable to traditional analysis methods. In a separate paper, submitted to The Astrophysical Journal Letters, the team reports how these networks can also determine the uncertainties of their analyses.

Prepared for Data Floods of the Future

“The neural networks we tested – three publicly available neural nets and one that we developed ourselves – were able to determine the properties of each lens, including how its mass was distributed and how much it magnified the image of the background galaxy,” said the study’s lead author Yashar Hezaveh, a NASA Hubble postdoctoral fellow at KIPAC.

This goes far beyond recent applications of neural networks in astrophysics, which were limited to solving classification problems, such as determining whether an image shows a gravitational lens or not.

The ability to sift through large amounts of data and perform complex analyses very quickly and in a fully automated fashion could transform astrophysics in a way that is much needed for future sky surveys that will look deeper into the universe – and produce more data – than ever before.

The Large Synoptic Survey Telescope (LSST), for example, whose 3.2-gigapixel camera is currently under construction at SLAC, will provide unparalleled views of the universe and is expected to increase the number of known strong gravitational lenses from a few hundred today to tens of thousands.

“We won’t have enough people to analyze all these data in a timely manner with the traditional methods,” Perreault Levasseur said. “Neural networks will help us identify interesting objects and analyze them quickly. This will give us more time to ask the right questions about the universe.”

A Revolutionary Approach

Neural networks are inspired by the architecture of the human brain, in which a dense network of neurons quickly processes and analyzes information.

In the artificial version, the “neurons” are single computational units that are associated with the pixels of the image being analyzed. The neurons are organized into layers, up to hundreds of layers deep. Each layer searches for features in the image. Once the first layer has found a certain feature, it transmits the information to the next layer, which then searches for another feature within that feature, and so on.

“The amazing thing is that neural networks learn by themselves what features to look for,” said KIPAC staff scientist Phil Marshall, a co-author of the paper. “This is comparable to the way small children learn to recognize objects. You don’t tell them exactly what a dog is; you just show them pictures of dogs.”

But in this case, Hezaveh said, “It’s as if they not only picked photos of dogs from a pile of photos, but also returned information about the dogs’ weight, height and age.”

Although the KIPAC scientists ran their tests on the Sherlock high-performance computing cluster at the Stanford Research Computing Center, they could have done their computations on a laptop or even on a cell phone, they said. In fact, one of the neural networks they tested was designed to work on iPhones.

“Neural nets have been applied to astrophysical problems in the past with mixed outcomes,” said KIPAC faculty member Roger Blandford, who was not a co-author on the paper. “But new algorithms combined with modern graphics processing units, or GPUs, can produce extremely fast and reliable results, as the gravitational lens problem tackled in this paper dramatically demonstrates. There is considerable optimism that this will become the approach of choice for many more data processing and analysis problems in astrophysics and other fields.”

TOP IMAGES….KIPAC researchers used images of strongly lensed galaxies taken with the Hubble Space Telescope to test the performance of neural networks, which promise to speed up complex astrophysical analyses tremendously. (Yashar Hezaveh/Laurence Perreault Levasseur/Phil Marshall/Stanford/SLAC National Accelerator Laboratory; NASA/ESA)

LOWER IMAGE….Scheme of an artificial neural network, with individual computational units organized into hundreds of layers. Each layer searches for certain features in the input image (at left). The last layer provides the result of the analysis. The researchers used particular kinds of neural networks, called convolutional neural networks, in which individual computational units (neurons, gray spheres) of each layer are also organized into 2-D slabs that bundle information about the original image into larger computational units. (Greg Stewart/SLAC National Accelerator Laboratory)


The idol survival show THE UNIT has released new casual profile images for THE UNI+G, the girls unit.

(top row - left to right)
TAE.E (of S.E.T)
HAN AREUM (formerly of T-ara)

(middle row - left to right)
HAENA (of Matilda)

(bottom row - left to right)
HEEJIN (of Good Day)
HAN SEOIN (formerly of The Seeya)