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Prikvačeni tweet
My new paper on photonic quantum programmable gate arrays is now on arXiv!


We describe an architecture for a nanophotonic integrated circuit which can be reprogrammed to perform any quantum computation. https://arxiv.org/pdf/1910.10141.pdf …pic.twitter.com/XsFmLip2c8Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Ben Bartlett proslijedio/la je Tweet
I used to wonder why the interfaces on Star Trek are so clunky given that it's centuries in the future, but I guess that's just Enterprise software for you
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Ben Bartlett proslijedio/la je TweetHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Ben Bartlett proslijedio/la je TweetHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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and the last two years should just be playing with a charged slinky while moving at .99c
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the first two years of undergrad physics should just be playing with a slinky 5 hr a day
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The animation in the first tweet was likely generated by taking a starting input vector and uniformly rotating it around in a high dimensional circle, which smoothly varies the images output by the generator network and loops the animation back to the original image. [5/5]pic.twitter.com/SX0H1dPmBy
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You train both networks and if one doesn't overpower the other (which can be tricky in practice), then eventually the discriminator becomes very good at identifying real images and the generator gets better at fooling the discriminator with very realistic images. [4/5]pic.twitter.com/rIxxQ5xVCe
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The generator tries to transform a random high-dimensional input vector into a vector that represents a sample from a distribution, such as pictures of landscapes. The discriminator takes in both real and generated images and assigns probabilities that an image is fake. [3/5]pic.twitter.com/ygmJE5pIHV
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GANs were first proposed by
@goodfellow_ian et al in this 2014 paper: http://arxiv.org/pdf/1406.2661.pdf … A GAN is a system of two competing neural networks - a "generator" and "discriminator" - which are trained against each other (hence "adversarial" learning). [2/5]pic.twitter.com/PvH1x5ApFP
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This type of trippy video is generated by stepping through the latent space of a machine learning model called a generative adversarial network (GAN). Here's a short thread on how they work: [1/5] (Video source: http://bit.ly/3137Uhn )pic.twitter.com/0oNglyxnZr
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and if you buy the newest version of Pokemon then you can evolve your Σ into whatever Generation VIII bullshit this is:pic.twitter.com/Z48AM5jQ8Q
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if you walk your Σ a along a closed path ∂S then you can evolve it into a:pic.twitter.com/43uQDszOAM
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other obscure evolutions: if you give your Σ a 𝘃𝗼𝗹𝘂𝗺𝗲 𝘀𝘁𝗼𝗻𝗲 it will evolve into:pic.twitter.com/zfSg3cF02A
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+, Σ, and ∫ are just different evolutions of the same Pokémon
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The Leidenfrost effect creates a layer of vapor below the water and makes its motion nearly frictionlesspic.twitter.com/cWYgci92EB
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Ben Bartlett proslijedio/la je Tweet
Visualizing the projection of a rotating tesseract is less confusing if you compare it with the projection of a rotating cubepic.twitter.com/SHvhXYQ1bH
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Ben Bartlett proslijedio/la je Tweet
comprehensive diagram of the salient layers of modern computer architecturepic.twitter.com/zsM6GLQcId
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I am become decoherence, the destroyer of computations
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When you burn steel wool, it gets heavier! While burning, the steel wool oxidizes to form iron oxide: 2Fe + 3O₂ → 2Fe₂O₃pic.twitter.com/wfaD4pBxu3
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cop: do you know why I pulled you over? me: I swear that light was green cop: you were doing 450,000,000 in a 40
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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and quantum computers 


