pixel patch


心が折れそうだ・・・・・・ by きいろからー

Trust No One

A/N: Transcendence AU, inspired by a couple of headcanons and by the fact that with multiple dimensions to play with anything is possible. You could almost consider this a crossover of sorts between currently-canon Gravity Falls (up to S2E11) and TAU itself, in a way…

Also, I’m not sure how to write Stanley Pines beyond “crippling paranoia/trust issues.”

Stanley Pines woke to a throbbing head, the smell of blood and ash and plaster dust, and a demon hovering over him.

He swung before it could as much as blink. The small knife he always kept up his sleeve punched through the creature’s side and sent it spinning away. He didn’t wait for it to recover; in moments he had rolled to a crouch and burst from there into a sprint for cover. He needed time, a more defensible position so he could regroup and figure out where he was, where home was, and what the hell had gone wrong with the portal in the time between shutting it down and waking up here…

He spied a doorway, unblocked by the rubble strewn across half the floor, and veered toward it.

An instant later the demon floated there, knife dangling from gloved fingers. Its golden eyes were half-closed and calculating.

Keep reading


Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and Harvard University have developed a new algorithm that could help astronomers produce the first image of a black hole.

The algorithm would stitch together data collected from radio telescopes scattered around the globe, under the auspices of an international collaboration called the Event Horizon Telescope. The project seeks, essentially, to turn the entire planet into a large radio telescope dish.

“Radio wavelengths come with a lot of advantages,” says Katie Bouman, an MIT graduate student in electrical engineering and computer science, who led the development of the new algorithm. “Just like how radio frequencies will go through walls, they pierce through galactic dust. We would never be able to see into the center of our galaxy in visible wavelengths because there’s too much stuff in between.”

But because of their long wavelengths, radio waves also require large antenna dishes. The largest single radio-telescope dish in the world has a diameter of 1,000 feet, but an image it produced of the moon, for example, would be blurrier than the image seen through an ordinary backyard optical telescope.

“A black hole is very, very far away and very compact,” Bouman says. “It’s equivalent to taking an image of a grapefruit on the moon, but with a radio telescope. To image something this small means that we would need a telescope with a 10,000-kilometer diameter, which is not practical, because the diameter of the Earth is not even 13,000 kilometers.”

The solution adopted by the Event Horizon Telescope project is to coordinate measurements performed by radio telescopes at widely divergent locations. Currently, six observatories have signed up to join the project, with more likely to follow.

But even twice that many telescopes would leave large gaps in the data as they approximate a 10,000-kilometer-wide antenna. Filling in those gaps is the purpose of algorithms like Bouman’s.

Bouman will present her new algorithm — which she calls CHIRP, for Continuous High-resolution Image Reconstruction using Patch priors — at the Computer Vision and Pattern Recognition conference in June. She’s joined on the conference paper by her advisor, professor of electrical engineering and computer science Bill Freeman, and by colleagues at MIT’s Haystack Observatory and the Harvard-Smithsonian Center for Astrophysics, including Sheperd Doeleman, director of the Event Horizon Telescope project.

Hidden delays

The Event Horizon Telescope uses a technique called interferometry, which combines the signals detected by pairs of telescopes, so that the signals interfere with each other. Indeed, CHIRP could be applied to any imaging system that uses radio interferometry.

Usually, an astronomical signal will reach any two telescopes at slightly different times. Accounting for that difference is essential to extracting visual information from the signal, but the Earth’s atmosphere can also slow radio waves down, exaggerating differences in arrival time and throwing off the calculation on which interferometric imaging depends.

Bouman adopted a clever algebraic solution to this problem: If the measurements from three telescopes are multiplied, the extra delays caused by atmospheric noise cancel each other out. This does mean that each new measurement requires data from three telescopes, not just two, but the increase in precision makes up for the loss of information.

Preserving continuity

Even with atmospheric noise filtered out, the measurements from just a handful of telescopes scattered around the globe are pretty sparse; any number of possible images could fit the data equally well. So the next step is to assemble an image that both fits the data and meets certain expectations about what images look like. Bouman and her colleagues made contributions on that front, too.

The algorithm traditionally used to make sense of astronomical interferometric data assumes that an image is a collection of individual points of light, and it tries to find those points whose brightness and location best correspond to the data. Then the algorithm blurs together bright points near each other, to try to restore some continuity to the astronomical image.

To produce a more reliable image, CHIRP uses a model that’s slightly more complex than individual points but is still mathematically tractable. You could think of the model as a rubber sheet covered with regularly spaced cones whose heights vary but whose bases all have the same diameter.

Fitting the model to the interferometric data is a matter of adjusting the heights of the cones, which could be zero for long stretches, corresponding to a flat sheet. Translating the model into a visual image is like draping plastic wrap over it: The plastic will be pulled tight between nearby peaks, but it will slope down the sides of the cones adjacent to flat regions. The altitude of the plastic wrap corresponds to the brightness of the image. Because that altitude varies continuously, the model preserves the natural continuity of the image.

Of course, Bouman’s cones are a mathematical abstraction, and the plastic wrap is a virtual “envelope” whose altitude is determined computationally. And, in fact, mathematical objects called splines, which curve smoothly, like parabolas, turned out to work better than cones in most cases. But the basic idea is the same.


Prior knowledge

Finally, Bouman used a machine-learning algorithm to identify visual patterns that tend to recur in 64-pixel patches of real-world images, and she used those features to further refine her algorithm’s image reconstructions. In separate experiments, she extracted patches from astronomical images and from snapshots of terrestrial scenes, but the choice of training data had little effect on the final reconstructions.

Bouman prepared a large database of synthetic astronomical images and the measurements they would yield at different telescopes, given random fluctuations in atmospheric noise, thermal noise from the telescopes themselves, and other types of noise. Her algorithm was frequently better than its predecessors at reconstructing the original image from the measurements and tended to handle noise better. She’s also made her test data publicly available online for other researchers to use.




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Finding Home || Closed RP @ glitch-generation

ZZAZZ – or Zazzles, as he had started asking people to refer to him as, thinking it sounded more like a normal name – stood by the edge of the pool at Cal’s apartment complex, staring down at the water doubtfully. He felt awkward in his swim trunks and formfitting swim shirt; he’d refused to go outside without something to cover his upper body, and even then, he was showing a lot more skin than he was comfortable with. He hated his skin. It was too white and patched with pixellated blotches of yellow and red, and one look at it was enough to confirm to anyone that he was a glitch, not a person. Not normal.

He was really starting to hate what he was.

The past couple of months had been filled with lessons about how to act normal, and Zazzles had been blown away by how much he didn’t know. The human world was so big, and there was so much stuff in it, and everything looked different and did different things…and it always felt like everyone knew what they were doing except for him. Well, besides Jacred. Jacred helped him feel a little bit less alone, but once he’d seen just how many humans there were in the world, the effect was negligible.

Mister No never could have succeeded in his quest to replace the humans – not when there were this many of them. Not in a million years…

At first, Zazzles had participated in his lessons with gusto, certain that he could get the knack of this human kind of life. But by now, he was starting to realize that no matter how much he learned, he’d always be too far behind. He couldn’t learn to be normal when every fiber of his being was so odd. Odd enough that, even after all this time, Cal’s superiors still hadn’t found anyone willing to take the boys in. Odd enough that he was seriously getting discouraged about it…

But the lessons continued. So there he was, on one of the first days when it was hot enough to swim, staring at too much water and thinking that if he stepped into that pool, it would close right over his head and suffocate him. Which, come to think about it, was how he felt about most things lately.