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1. Building Volumetric Appearance Models of Fabric using Micro CT Imaging (CACM Research Highlights)

  • Published: 2014-11-08T19:51:34+00:00
  • Duration: 427
  • By Shuang Zhao
Building Volumetric Appearance Models of Fabric using Micro CT Imaging (CACM Research Highlights)

Cloth is essential to our everyday lives; consequently, visualizing and rendering cloth has been an important area of research in graphics for decades. One important aspect contributing to the rich appearance of cloth is its complex 3D structure. Volumetric algorithms that model this 3D structure can correctly simulate the interaction of light with cloth to produce highly realistic images of cloth. But creating volumetric models of cloth is difficult: writing specialized procedures for each type of material is onerous, and requires significant programmer effort and intuition. Further, the resulting models look unrealistically “perfect” because they lack visually important features like naturally occurring irregularities. This paper proposes a new approach to acquiring volume models, based on density data from X-ray computed tomography (CT) scans and appearance data from photographs under uncontrolled illumination. To model a material, a CT scan is made, yielding a scalar density volume. This 3D data has micron resolution details about the structure of cloth but lacks all optical information. So we combine this density data with a reference photograph of the cloth sample to infer its optical properties. We show that this approach can easily produce volume appearance models with extreme detail, and at larger scales the distinctive textures and highlights of a range of very different fabrics such as satin and velvet emerge automatically—all based simply on having accurate mesoscale geometry.


2. Creating Connection with Autonomous Facial Animation

  • Published: 2016-11-04T17:15:28+00:00
  • Duration: 296
  • By CACM
Creating Connection with Autonomous Facial Animation

Mark Sagar discusses "Creating Connection with Autonomous Facial Animation" (http://cacm.acm.org/magazines/2016/12/210362), a Contributed Article in the December 2016 Communications of the ACM.


3. Lessons Learned from 30 Years of MINIX

  • Published: 2016-02-04T18:12:26+00:00
  • Duration: 260
  • By CACM
Lessons Learned from 30 Years of MINIX

Andrew S. Tanenbaum discusses "Lessons Learned from 30 Years of MINIX" (http://cacm.acm.org/magazines/2016/3/198874), his Contributed Article in the March 2016 CACM.


4. Mars Code

  • Published: 2014-01-24T21:47:04+00:00
  • Duration: 3744
  • By CACM
Mars Code

Gerard Holzmann, senior research scientist at NASA’s Jet Propulsion Laboratory, describes creating the software for the Curiosity rover in this video accompaniment to “Mars Rover,” his article in the February 2014 Communications of the ACM (http://cacm.acm.org/magazines/2014/2/171689).


5. Attack of the Killer Microseconds

  • Published: 2017-03-03T15:13:48+00:00
  • Duration: 337
  • By CACM
Attack of the Killer Microseconds

Luis Barroso discusses "Attack of the Killer Microseconds" (http://cacm.acm.org/magazines/2017/4/215032), a Contributed Article in the April 2017 CACM.


6. Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence

  • Published: 2015-07-30T16:54:35+00:00
  • Duration: 291
  • By CACM
Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence

Ernest Davis and Gary Marcus discuss the shortcomings of AI systems and "Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence" (http://cacm.acm.org/magazines/2015/9/191169), their Review Article in the September 2015 Communications of the ACM.


7. Leslie Lamport, 2013 ACM A.M. Turing Award Recipient

  • Published: 2014-05-13T17:48:46+00:00
  • Duration: 358
  • By CACM
Leslie Lamport, 2013 ACM A.M. Turing Award Recipient

This video reviews the achievements of Leslie Lamport, recipient of the 2013 ACM A.M. Turing Award, subject of "General Agreement" by Neil Savage (http://cacm.acm.org/news/175166), and featured in "Q&S: Divide and Conquer," by Leah Hoffman (http://cacm.acm.org/magazines/2014/6/175174), in the June 2014 Communications of the ACM.


8. The Rise of Social Bots

  • Published: 2016-05-13T16:30:31+00:00
  • Duration: 228
  • By CACM
The Rise of Social Bots

Emilio Ferrara discusses "The Rise of Social Bots" (http://cacm.acm.org/magazines/2016/7/204021), a Review Article in the July 2016 Communications of the ACM.


9. Hopes, Fears, and Software Obfuscation

  • Published: 2016-02-05T17:46:50+00:00
  • Duration: 242
  • By CACM
Hopes, Fears, and Software Obfuscation

Boaz Barak discusses "Hopes, Fears, and Software Obfuscation" (http://cacm.acm.org/magazines/2016/3/198855), his Review Article in the March 2016 CACM.


10. Personalizing Maps

  • Published: 2015-10-30T15:23:25+00:00
  • Duration: 293
  • By CACM
Personalizing Maps

Co-author Andrea Ballatore discusses "Personalizing Maps" (http://cacm.acm.org/magazines/2015/12/194625), a Contributed Article in the December 2015 CACM.


11. Responsible Research and Innovation in the Digital Age

  • Published: 2017-03-31T17:53:36+00:00
  • Duration: 180
  • By CACM
Responsible Research and Innovation in the Digital Age

Video accompaniment for "Responsible Research and Innovation in the Digital Age" (https://cacm.acm.org/magazines/2017/5/216330), a Contributed Article in the May 2017 CACM.


12. Computational Thinking for Teacher Education

  • Published: 2017-03-03T15:08:48+00:00
  • Duration: 238
  • By CACM
Computational Thinking for Teacher Education

Co-author Aman Yadav discusses "Computational Thinking for Teacher Education" (http://cacm.acm.org/magazines/2017/4/215031), a Contributed Article in the April 2017 CACM.


13. Debugging High-Performance Computing Applications at Massive Scales

  • Published: 2015-08-24T13:48:06+00:00
  • Duration: 368
  • By CACM
Debugging High-Performance Computing Applications at Massive Scales

Lawrence Livermore National Laboratory scientists discuss approaches to debugging large-scale applications and their September 2015 CACM Contributed Article, "Debugging High-Performance Computing Applications at Massive Scales" (http://cacm.acm.org/magazines/2015/9/191185)


14. Unifying Logic and Probability

  • Published: 2015-06-19T16:11:08+00:00
  • Duration: 328
  • By CACM
Unifying Logic and Probability

Stuart Russell discusses the BLOG (Bayesian logic) language and open-universe probability models, the subject of "Unifying Logic and Probability" (http://cacm.acm.org/magazines/2015/7/188745), his Review Article in the July 2015 Communications of the ACM.


15. What Makes Paris Look Like Paris?

  • Published: 2015-11-12T03:38:38+00:00
  • Duration: 240
  • By CACM
What Makes Paris Look Like Paris?

Co-author Carl Doersch discusses "What Makes Paris Look Like Paris?" (http://cacm.acm.org/magazines/2015/12/194622), a Research Highlights article in the December 2015 CACM. --- TRANSCRIPT 00:00 Ah, Paris. The restaurants, the architecture, the street life. That's not Paris, you say? You're right! But how did you know that? 00:15 We feel a city's distinctive qualities, often without knowing why. Somehow, the way that buildings, vehicles, signs, and other objects interact tells us that we're in Paris. Or London. Or Prague. 00:35 Join us as a computer vision researcher tells us how he found out What Makes Paris Look Like Paris. 00:45 [Intro graphics/music] 00:54 Carl Doersch is a doctoral student in the Machine Learning Department at Pittsburgh's Carnegie Mellon University. A trip to Paris with his advisor opened his eyes to architectural features he'd never noticed before. 01:12 CARL DOERSCH: And it just struck me a lot that you can go to different boulevards in Paris and see the same kinds of balconies and railings on the windows, and all of these features that are just repeated everywhere. 01:27 So Doersch and advisor Alexei Efros joined three other researchers to teach computers how to unlock the city's visual secrets. 01:36 They started with thousands of Google Street View images from Paris and eleven other cities for comparison. Next, they let their computers divide each image into about 25,000 patches. 01:49 DOERSCH: So if you start with a patch that's too small, you're going to find lots of matches because you have very few features in the patch. You'll be able to find lots of things that are similar to that. But you're probably not going to have anything interesting in that patch. 02:04 After dividing the images into patches, the algorithm selects some at random and looks for elements that occur frequently, but more often in Paris than in not-Paris. 02:15 DOERSCH: So for example: The Eiffel Tower is very informative. You see that, you know immediately that you're looking at Paris. But the problem is there's only one of them. … On the other hand, if you think about something like ordinary streets, like just the pavement, that's very frequent, but it's not discriminative. 02:33 After selecting patches, the real magic begins, as the algorithm looks for similar ones, with the knowledge of whether the source image is from Paris or another location. 02:44 CARL DOERSCH [4867/0127] If the street sign is actually distinctive, then it's only going to match to other street signs in Paris. And the pavement is going to match to pavement that occurs everywhere. 02:53 The algorithm then uses the best-matching patches as training data, and the winnowing process repeats. The result is a system that can go far beyond street scenes. 03:05 DOERSCH: You could also apply this to images of products and, for example, try to figure out what differentiates Apple products from everybody else's products. 03:14 But even if the algorithm is used solely on cities, what the paper calls "computational geocultural modeling" could reveal knowledge about who we are, and where we came from. 03:27 DOERSCH: We can get a map which shows us where the architectural influence crossed borders, and it can tell us how cultures interacted in the past. 03:37 Find out more in in the Research Highlights article, "What Makes Paris Look like Paris?", in the December 2015 issue of Communications of the ACM, . 03:49 [Outro and credits]


16. Ur/Web: A Simple Model for Programming the Web

  • Published: 2016-07-05T16:32:28+00:00
  • Duration: 175
  • By CACM
Ur/Web: A Simple Model for Programming the Web

Adam Chlipala discusses "Ur/Web: A Simple Model for Programming the Web" (http://cacm.acm.org/magazines/2016/8/205041), his Research Highlights article in the August 2016 Communications of the ACM.


17. Exponential Laws of Computing Growth

  • Published: 2016-12-05T14:58:34+00:00
  • Duration: 211
  • By CACM
Exponential Laws of Computing Growth

Authors Peter J. Denning and Ted G. Lewis discuss "Exponential Laws of Computing Growth" (http://cacm.acm.org/magazines/2017/1/211094), a Contributed Article in the January 2017 CACM.


18. IllumiRoom: Immersive Experiences Beyond the TV Screen

  • Published: 2015-05-12T17:40:35+00:00
  • Duration: 324
  • By CACM
IllumiRoom: Immersive Experiences Beyond the TV Screen

Hrvoje Benko and Brett R. Jones discuss "IllumiRoom: Experiences Beyond the TV Screen," their Research Highlights article in the June 2015 CACM (http://cacm.acm.org/magazines/2015/6/187312).


19. Sir Tim Berners-Lee, 2016 ACM A.M. Turing Award Recipient

  • Published: 2017-05-01T17:26:04+00:00
  • Duration: 388
  • By CACM
Sir Tim Berners-Lee, 2016 ACM A.M. Turing Award Recipient

ACM A.M. Turing Award Recipient Sir Tim Berners-Lee on fixing social networks, live tweeting the 2012 London Olympics, and what he'd say to Alan Turing. See also https://cacm.acm.org/magazines/2017/6/217732.


20. Theory of Algorithmic Self-Assembly

  • Published: 2012-11-25T05:01:47+00:00
  • Duration: 441
  • By David Doty
Theory of Algorithmic Self-Assembly

This video accompanies a review article, "Theory of Algorithmic Self-Assembly", by David Doty, in the December 2012 issue of Communications of the ACM. It explains the experimental background and theoretical model that underlies the ideas and results discussed in the paper. Watching it before reading the article would likely make the article easier to understand. A preprint of the article is available here: http://www.dna.caltech.edu/~ddoty/papers/tasa.pdf The official version of the paper is here: http://cacm.acm.org/magazines/2012/12/157881-theory-of-algorithmic-self-assembly/