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LeopoldS

[0812.2633] Ghost imaging with a single detector - 2 views

arxiv.org/0812.2633

PHY NAN

shared by LeopoldS on 20 Sep 11 - No Cached
  • LeopoldS on 20 Sep 11
     
    anything happening on this since 3 years?
  • pacome delva on 28 Sep 11
     
    yes it seems like. most of it seems however directed toward understanding this effect, and not toward applications. But i'm still convinced that we could find many very interesting applications !!! a few references from ADS: 1 2011PhRvA..83f3807B 1.000 06/2011 A E X R C U Brida, G.; Chekhova, M. V.; Fornaro, G. A.; Genovese, M.; Lopaeva, E. D.; Berchera, I. Ruo Systematic analysis of signal-to-noise ratio in bipartite ghost imaging with classical and quantum light 2 2011PhRvA..83e3808L 1.000 05/2011 A E R U Liu, Ying-Chuan; Kuang, Le-Man Theoretical scheme of thermal-light many-ghost imaging by Nth-order intensity correlation 3 2011PhRvA..83e1803D 1.000 05/2011 A E R C U Dixon, P. Ben; Howland, Gregory A.; Chan, Kam Wai Clifford; O'Sullivan-Hale, Colin; Rodenburg, Brandon; Hardy, Nicholas D.; Shapiro, Jeffrey H.; Simon, D. S.; Sergienko, A. V.; Boyd, R. W.; Howell, John C. Quantum ghost imaging through turbulence 4 2011SPIE.7961E.160O 1.000 03/2011 A E T Ohuchi, H.; Kondo, Y. Complete erasing of ghost images caused by deeply trapped electrons on computed radiography plates 5 2011ApPhL..98k1115M 1.000 03/2011 A E R U Meyers, Ronald E.; Deacon, Keith S.; Shih, Yanhua Turbulence-free ghost imaging 6 2011ApPhL..98k1102G 1.000 03/2011 A E R C U Gan, Shu; Zhang, Su-Heng; Zhao, Ting; Xiong, Jun; Zhang, Xiangdong; Wang, Kaige Cloaking of a phase object in ghost imaging 7 2011RScI...82b3110Y 1.000 02/2011 A E R U Yang, Hao; Zhao, Baosheng; Qiu
Ma Ru

I know at least *some* of you will like it... - 13 views

www.nerdtests.com/ft_nq.php

fun inf phy science

shared by Ma Ru on 29 Mar 10 - Cached
LeopoldS liked it
  • Ma Ru on 29 Mar 10
     
  • ...9 more comments...
  • Luzi Bergamin on 29 Mar 10
     
    Shit!! I only got 79, should have lied better...
  • Ma Ru on 29 Mar 10
     
    My score was obtained with *sincere* answers, don't cheat!
  • pacome delva on 30 Mar 10
     
    ouah, 80...! didn't think i was so nerd...!
  • Ma Ru on 30 Mar 10
     
    Dario, Francesco, we're waiting for your scores... are you afraid of the truth??
  • nikolas smyrlakis on 31 Mar 10
     
    hmm "Low Ranking Nerd. Definitely a nerd but low on the totem pole of nerds." , as of a score of 66
  • LeopoldS on 02 Apr 10
  • Dario Izzo on 02 Apr 10
     
    I am disappointed!!!!! Shame on me.......
  • Francesco Biscani on 02 Apr 10
     
    Sigh
  • LeopoldS on 02 Apr 10
     
    wow!
  • Francesco Biscani on 02 Apr 10
     
    My girlfriend... She must be an archaeological nerd...
  • Ma Ru on 03 Apr 10
     
    Great Scott, Leo! Honest answers?? I was kinda expecting Francesco's score, but this...
Alexander Wittig

Picture This: NVIDIA GPUs Sort Through Tens of Millions of Flickr Photos - 2 views

blogs.nvidia.com/...flickr

AI

shared by Alexander Wittig on 21 Jul 15 - No Cached
LeopoldS liked it
  • Alexander Wittig on 21 Jul 15
     
    Strange and exotic cityscapes. Desolate wilderness areas. Dogs that look like wookies. Flickr, one of the world's largest photo sharing services, sees it all. And, now, Flickr's image recognition technology can categorize more than 11 billion photos like these. And it does it automatically. It's called "Magic View." Magical deep learning! Buzzword attack!
  • ...4 more comments...
  • LeopoldS on 21 Jul 15
     
    and here comes my standard question: how can we use this for space? fast detection of natural disasters onboard?
  • Alexander Wittig on 22 Jul 15
     
    Even on ground. You could for example teach it what nuclear reactors or missiles or other weapons you don't want look like on satellite pictures and automatically scan the world for them (basically replacing intelligence analysts).
  • Alexander Wittig on 23 Jul 15
     
    In fact, I think this could make a nice ACT project: counting seals from satellite imagery is an actual (and quite recent) thing: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0092613 In this publication they did it manually from a GeoEye 1 b/w image, which sounds quite tedious. Maybe one can train one of those image recognition algorithms to do it automatically. Or maybe it's a bit easier to count larger things, like elephants (also a thing).
  • jcunha on 23 Jul 15
     
    In HiPEAC (High Performance, embedded architecture and computation) conference I attended in the beginning of this year there was a big trend of CUDA GPU vs FPGA for hardware accelerated image processing. Most of it orbitting around discussing who was faster and cheaper with people from NVIDIA in one side and people from Xilinx and Intel in the other. I remember of talking with an IBM scientist working on hardware accelerated data processing working together with the Radio telescope institute in Netherlands about the solution where they working on (GPU CUDA). I gathered that NVIDIA GPU suits best in applications that somehow do not rely in hardware, having the advantage of being programmed in a 'easy' way accessible to a scientist. FPGA's are highly reliable components with the advantage of being available in radhard versions, but requiring specific knowledge of physical circuit design and tailored 'harsh' programming languages. I don't know what is the level of rad hardness in NVIDIA's GPUs... Therefore FPGAs are indeed the standard choice for image processing in space missions (a talk with the microelectronics department guys could expand on this), whereas GPUs are currently used in some ground based (radio astronomy or other types of telescopes). I think that on for a specific purpose as the one you mentioned, this FPGA vs GPU should be assessed first before going further.
  • Paul N on 29 Jul 15
     
    You're forgetting power usage. GPUs need 1000 hamster wheels worth of power while FPGAs can run on a potato. Since space applications are highly power limited, putting any kind of GPU monster in orbit or on a rover is failed idea from the start. Also in FPGAs if a gate burns out from radiation you can just reprogram around it. Looking for seals offline in high res images is indeed definitely a GPU task.... for now.
  • jcunha on 31 Jul 15
     
    The discussion of how to make FPGA hardware acceleration solutions easier to use for the 'layman' is starting btw http://reconfigurablecomputing4themasses.net/.
alekenolte

Research Blog: Inceptionism: Going Deeper into Neural Networks - 0 views

googleresearch.blogspot.de/...-going-deeper-into-neural.html

FUN AI

shared by alekenolte on 18 Jun 15 - No Cached
  • alekenolte on 18 Jun 15
     
    Deep neural networks "dreaming" psychedelic images
  • ...3 more comments...
  • Paul N on 25 Jun 15
     
    Although that's not technically correct. The networks don't actually generate the images, rather the features that get triggered in the network already get amplified through some heuristic. Still fun tho`
  • Paul N on 25 Jun 15
     
    Now in real time: http://www.twitch.tv/317070
  • alekenolte on 26 Jun 15
     
    Yes, true for the later images, but for the first images they start with random noise and a 'natural image' prior, no? But I guess calling it "hallucinating" might have been more accurate ;)
  • Luís F. Simões on 02 Jul 15
  • Paul N on 08 Jul 15
     
    Funny how representation errors in NNs suddenly become art. God.... neo-post-modernism.
Nina Nadine Ridder

Watching an exoplanet in motion around a distant star - 5 views

www.sciencedaily.com/...150916112424.htm

science exoplanets planet_formation imaging optics

shared by Nina Nadine Ridder on 18 Sep 15 - No Cached
  • Nina Nadine Ridder on 18 Sep 15
     
    Imaging of gas giant orbiting its central star (related to Jai's YGT proposal): With GPI, astronomers image the actual planet--a remarkable feat given that an orbiting world typically appears a million times fainter than its parent star. This is possible because GPI's adaptive optics sharpen the image of the target star by cancelling out the distortion caused by the Earth's atmosphere; it then blocks the bright image of the star with a device called a coronagraph, revealing the exoplanet.
  • Thijs Versloot on 21 Sep 15
     
    such a simple image, such an awesome feat of science and engineering!
Dario Izzo

File Compression: New Tool for Life Detection? - 4 views

www.astrobio.net/...on-new-tool-for-life-detection

life AI image compression

shared by Dario Izzo on 21 Sep 10 - No Cached
  • Dario Izzo on 21 Sep 10
     
    As mentioned today during coffee .... we could think to link this to source localization
  • ...3 more comments...
  • Ma Ru on 22 Sep 10
     
    Not sure by what you mean by source localisation, but this using gzip to discern "biological" from "non-biological" images seems to me *very* tricky... I mean, there's a lot of other factors that may affect compressibility of an image than just mere "regularity" of the pattern, and if they haven't controlled for these, this is just bullsh1t... (For instance did they use the same imaging device to take those images? What about lighting conditions and exposure? etc). The apostle of sometimes surprising uses of compression is prof. Shmidhuber from IDSIA...
  • Dario Izzo on 22 Sep 10
     
    I completely agree with you..... still if you have one instrument on board the spacecraft and your picture compressibility is a noisy indicator of some interesting source .... we could try to perform some probabilistic reasoning
  • Juxi Leitner on 24 Sep 10
     
    I think they (IDSIA-Schmidhuber) are planning on putting something about that also inside the Acta Futura paper...
  • Ma Ru on 24 Sep 10
     
    Really, you think they'd target such a low impact factor publication? ;-P
  • Dario Izzo on 26 Sep 10
     
    you will all soon be begging to publish in Acta Futura! We will be bigger than Nature.
Dario Izzo

Stacked Approximated Regression Machine: A Simple Deep Learning Approach - 5 views

arxiv.org/1608.04062

AI deeplearning

shared by Dario Izzo on 05 Sep 16 - No Cached
  • Dario Izzo on 05 Sep 16
     
    from one of the reddit threads discussing this: "bit fishy, crazy if real". "Incredible claims: - Train only using about 10% of imagenet-12, i.e. around 120k images (i.e. they use 6k images per arm) - get to the same or better accuracy as the equivalent VGG net - Training is not via backprop but more simpler PCA + Sparsity regime (see section 4.1), shouldn't take more than 10 hours just on CPU probably "
  • Paul N on 13 Sep 16
     
    clicking the link says the manuscript was withdrawn :))
  • johannessimon81 on 15 Sep 16
     
    This "one-shot learning" paper by Googe Deepmind also claims to be able to learn from very few training data. Thought it might be interesting for you guys: https://arxiv.org/pdf/1605.06065v1.pdf
Luís F. Simões

Image evolution - 5 views

alteredqualia.com/...evolve

image evolution AI fun

shared by Luís F. Simões on 09 Sep 10 - Cached
LeopoldS liked it
  • Luís F. Simões on 09 Sep 10
     
    A very simple, but very creative application of evolution! Try it with your images. You can read about the first implementation of this kind here: http://developers.slashdot.org/developers/08/12/09/0238252.shtml
Luís F. Simões

Picbreeder: Collaborative Interactive Art Evolution (Genetic Art) - 1 views

picbreeder.org

evolution art crowdsourcing collaboration

shared by Luís F. Simões on 28 Sep 10 - Cached
  • Luís F. Simões on 28 Sep 10
     
    Following up on our coffee-time discussion, here's an Evolutionary Algorithm where you are the fitness function, and evolution is guided by your subjective artistic sense. Start from scratch, or pick an existing image in the database, and start evolving. At every generation, you are presented with the individuals/images in the population. Pick the ones you like. Those will be the parents from which the next generation will be bred. Repeat, repeat... where do you get to? If you want to learn more about the science behind this, check the tutorial below by Kenneth Stanley, who is also this site's supervisor: http://dx.doi.org/10.1145/1830761.1830920
ESA ACT

Google Image resultaat voor http://www.sciencedaily.com/images/2004/07/040729092403.jpg - 0 views

images.google.nl/imgres

BIO

shared by ESA ACT on 24 Apr 09 - No Cached
  • ESA ACT on 24 Apr 09
     
    IR sensor from the fire beetle
ESA ACT

Three-dimensional left-handed material lens - 0 views

scitation.aip.org/...GetabsServlet

PHY Photonics optics

shared by ESA ACT on 24 Apr 09 - No Cached
  • ESA ACT on 24 Apr 09
     
    A model is provided for the image formation by a three-dimensional lens with negative index of refraction (n) and compared with results for an array of split-ring resonators (SRRs) and wires. For n=−1, a linear decrease in image distance r is expected w
ESA ACT

NASA Images - 0 views

www.nasaimages.org

nasa images repository

shared by ESA ACT on 24 Apr 09 - Cached
  • ESA ACT on 24 Apr 09
     
    new NASA image repository website
anonymous

Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable ... - 4 views

arxiv.org/1412.1897

AI neural networks images deep learning

shared by anonymous on 10 Dec 14 - No Cached
  • anonymous on 10 Dec 14
     
    Other possible study: get a textbook example of an image of a pen, evolve it just enough so NN can't recognize it anymore, while minimizing the distance between the original and evolved images. EDIT: Its been done already: http://cs.nyu.edu/~zaremba/docs/understanding.pdf
  • Paul N on 11 Dec 14
     
    Of course, you can't really use them to extrapolate. The unknown unknown is always the trickiest :P They should just make another class "random bullshit", really and dump all of this stuff in there. I think there's a potential paper right there
Thijs Versloot

Spotting East African Mammals in Open Savannah from Space - 1 views

journals.plos.org/...article

space EO technology

shared by Thijs Versloot on 20 Jan 15 - No Cached
  • Thijs Versloot on 20 Jan 15
     
    A hybrid image classification method was employed for this specific purpose by incorporating the advantages of both pixel-based and object-based image classification approaches. This was performed in two steps: firstly, a pixel-based image classification method, i.e., artificial neural network was applied to classify potential targets with similar spectral reflectance at pixel level; and then an object-based image classification method was used to further differentiate animal targets from the surrounding landscapes through the applications of expert knowledge. As a result, the large animals in two pilot study areas were successfully detected with an average count error of 8.2%, omission error of 6.6% and commission error of 13.7%. The results of the study show for the first time that it is feasible to perform automated detection and counting of large wild animals in open savannahs from space
  • Thijs Versloot on 20 Jan 15
     
    And Paul, it includes neural networks!
  • Paul N on 20 Jan 15
     
    I kept telling you guys but you just laughed and laughed :))
santecarloni

BBC News - Atomic bond types discernible in single-molecule images - 0 views

www.bbc.co.uk/...science-environment-19584301

science physics

shared by santecarloni on 25 Sep 12 - No Cached
  • santecarloni on 25 Sep 12
     
    A pioneering team from IBM in Zurich has published single-molecule images so detailed that the type of atomic bonds between their atoms can be discerned.
Alexander Wittig

Deepest X-ray Image Ever Reveals Black Hole Treasure Trove - 1 views

chandra.harvard.edu/...press_010517cdfs.html

science space

shared by Alexander Wittig on 09 Jan 17 - No Cached
LeopoldS liked it
  • Alexander Wittig on 09 Jan 17
     
    Deepest X-ray Image Ever Reveals Black Hole Treasure Trove An unparalleled image from NASA's Chandra X-ray Observatory gives astronomers the best look yet at the growth of black holes over billions of years beginning soon after the Big Bang.
santecarloni

Our favourite pictures of 2011 - physicsworld.com - 0 views

physicsworld.com/...48140

science

shared by santecarloni on 22 Dec 11 - No Cached
  • Here are 12 of our favourite images of 2011, in no particular order. These range from the beautiful and historical to pictures that show how science affects the world we live in
  • santecarloni on 22 Dec 11
     
    Here are 12 of our favourite images of 2011, in no particular order. These range from the beautiful and historical to pictures that show how science affects the world we live in
Joris _

'Space yacht' IKAROS takes images of its solar sail :: Brahmand.com - 2 views

www.brahmand.com/...13.html

shared by Joris _ on 16 Jun 10 - Cached
LeopoldS liked it
  • LeopoldS on 16 Jun 10
     
    very nice!! could not find the dimensions yet ...
  • Joris _ on 16 Jun 10
  • LeopoldS on 17 Jun 10
     
    that is honestly a little bit less impressive than I had hoped for ... this "just" 200 m2 ... our Furoshiki net had already 130m2 and we deployed it within 1 min under much worse conditions ...
Francesco Biscani

NASA Will Crowdsource Its Photos of Mars | Motherboard - 4 views

www.motherboard.tv/...crowdsource-its-photos-of-mars

ai

shared by Francesco Biscani on 20 Jan 10 - Cached
LeopoldS liked it
  • Researchers hope that crowdsourcing imaging targets will increase the camera’s already bountiful science return.
  • Francesco Biscani on 20 Jan 10
     
    Here we go, material for curiosity cloning, life detection via image compression, etc. etc.
  • Christos Ampatzis on 21 Jan 10
     
    tar cvfz compressed.tgz MarsImages/ Love it!
Francesco Biscani

BBC NEWS | Science & Environment | Herschel yields new galaxy image - 0 views

news.bbc.co.uk/...8127798.stm

astronomy astrophysics herschel

shared by Francesco Biscani on 01 Jul 09 - Cached
  • Francesco Biscani on 01 Jul 09
     
    More Herschel images coming.
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