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I think mostly it is for political related contents. Especially things like this: https://en.wikipedia.org/wiki/Tiananmen_Square_protests_of_1... and this: https://en.wikipedia.org/wiki/Liu_Xiaobo

As a bachelor thesis, this is very nicely done. I'm particularly interested in what's been claimed in the future work section: the ability to repartition the graph according to topology change. Wonder after 6 months, is there any progress from the author along that direction. Haven't checked the Spinner paper though, maybe it is a solved problem there.

Actually I have a question regarding spotify's payment model: I heard that every subscriber's payment are put in a big pool, and artists are paid according to their global click/listening rank. If this were true, then when I pay 10 dollar's per month and all I listen to is Carsick Cars (my favorite Chinese indie band), still the majority of 10 dollars will go to Justin Bieber and Adele. Is that right? Another model is to pay the artist according to each subscriber's stats. For example in my previous example, all my payment (except those for Spotify) will go to Carsick Cars. Additionally, I'm wondering which of the above payment model is fair to both the audience and the artists?

We are building a graph processing library on GPUs: http://gunrock.github.io/ Our publication explains why our library runs faster, and in one word: load-balancing, since fundamentally, graph problems are irregular problems. If you look at the road map section, we are moving towards dynamic graphs and also planning to try out asynchronous method by using MIS/coloring. Please try it out, any kind of contribution is welcome.

Very amazing results and fantastic work! I noticed that all the examples are all front faces with at most 20 to 30 degree angel, but no profiles. I haven't read the paper, but does this mean it only reconstructs a 2D texture but not the 3D structure?

We are trying to enable graph query into our library too. Currently working on subgraph matching, but might expand to more general graph query. I think in general, graph with rich attribute is quite difficult to deal with on the GPU. Thanks for your interest.

Shameless self-promotion: Gunrock, a high-performance GPU graph processing library: http://gunrock.github.io/gunrock/

I'm doing GPU graph processing: http://gunrock.github.io I think another critical aspect of getting high performance graph processing is load balancing. Also, there are tons of papers talking about how to reorganize data to get coalesced memory access. Combining the above strategies can outperform pre-processing such as sorting. At least on GPU this holds.

This may seem irrelevant, but as a GPU computing researcher, I'm still disappointed that GPU still hasn't made its way to be the first citizen at data centers. I know world of HPC looks much different, but I wonder when GPU can be vastly used in data centers, and will NVIDIA's Pascal architecture make a huge impact on this?

I'm collaborating with folks from Continuum, they are awesome! Congrats!

This is great. It helps a lot!

I have tried some graph analysis using my own graph analytics framework: Gunrock (http://gunrock.github.io/gunrock/). The PageRank is pretty similar (the first three are DoctorWho, StarWars, and EVA.) My colleague and I are interested in doing more analysis on this site. We are trying to build a crawler and maybe using bipartite graph to build some kind of recommendation system. All inspired by your project here. Stay tuned :-)

One application of Google glass is lifelog which is described in Ted Chiang's story: The Truth of Fact, the Truth of Feeling https://subterraneanpress.com/magazine/fall_2013/the_truth_o...

Thanks a lot for doing this man!

This is awesome work! Is there anywhere we can get the generated graph data? Whether in the form of adjacency list or matrix. I want to do some analysis on this graph too :-)

You guys should check out our high performance GPU graph processing library, Gunrock: http://gunrock.github.io/gunrock/ We are working on multi-GPU distributed version now.

https://logo.clearbit.com/armadilloaerospace.com Where is the armadillo? I want my armadillo.

I really love lolcat. It makes my day. I'm making alias and adding lolcat -f allover my bashrc files now :-)

What do you mean by old neural networks?

Thanks! I've been following the course and so far it's been awesome!

So I read this post afterwards: http://cs.stackexchange.com/questions/9795/is-the-open-quest... Then I know why this proof is important. Hope it can get through some peer-reviewed journal/conference later.

This is awesome! Just curious, do they find images according to instagram tags like #A #B ... within #36daysoftype? Or do they use some kind of visual recognition method to retrieve pictures in real-time?

Since this is relevant today. Here I want to recommend this awesome game to you guys. It's two of my favorite games in one (portal+smb): http://stabyourself.net/mari0/

Who else noticed the flag of China? It should be called ROC or Taiwan if you use that flag.

Thanks.

There is a whole session on machine intelligence in this issue. Just curious, is there more CS-related research on Nature now than before?

Thanks for the explanation!

Great project! May I ask how do you guys choose which libraries to track?

shameless self-promotion of my graph processing library on GPUs, Gunrock: Gunrock paper: http://arxiv.org/abs/1501.05387 paper on multi-GPU graph analytics: http://arxiv.org/abs/1504.04804 github repo: http://gunrock.github.io/

Boring is a very subjective word. Maybe coincidentally the people I met were boring to me. My point is, being good at oral English does not necessarily make one a thoughtful and interesting person. Several of the materials they performed were not that good in terms of content itself. And whenever the results are related to college entrance exam, I cannot help but questioning the purpose of some people who took the competition. That said, I agree there are also true and excellent speakers and thinkers among them. If this competition can serve as a catalyst for ordinary people in China to learn English, especially people from small cities, then it's great.