I’ve been playing around with D3.js the past few weeks and just completed a first-cut visualization of Singapore’s ethnic demographic has changed from 2000 to 2010. Feedback is welcome, particularly on what works and what doesn’t (visually). Some context: the idea for this explorative tool came up after a few conversations with locals about the extent of ethnic integration. Several voiced opinions left me with questions about the extent of racial discrimination in Singaporean society. Although the visualization doesn’t address this question, it was a step towards a better understanding Singapore’s ethnic demography. Feel free to explore the visualization, and you can learn more at Singstats.
It’s been a great December, ending the year quite nicely! I attended NIPS, and bumped into my PhD supervisor Yiannis. We had a enjoyable time at the conference and exploring Montreal (a beautiful city). I also presented a poster at the NIPS Workshop on Networks about how to link node features to eigenvector centrality via a probabilistic model; for example, mapping a person’s attributes to how influential he or she is in a social network:
Abstract: Among the variety of complex network metrics proposed, node importance or centrality has potentially found the most widespread application—from the identification of gene-disease associations to finding relevant pages in web search. In this workshop paper, we present a method that learns mappings from node attributes to latent centralities. We first construct an eigenvector-based Bayesian centrality model, which casts the problem of computing network centrality as one of probabilistic (latent variable) inference. Then, we develop the sparse variational Bayesian centrality Gaussian process (VBC-GP) which simultaneously infers the centralities and learns the mapping. The VBC-GP possesses inherent benefits: it (i) allows a potentially large number of nodes to be represented by the sparse mapping and (ii) permits prediction of centralities on previously unseen nodes. Experiments show that the VBC-GP learns high-quality mappings and compares favorably to a two-step method, i.e., a full-GP trained on the node attributes and network centralities. Finally, we present a case-study using the VBC-GP to distribute a limited number of vaccines to decrease the severity of a viral outbreak.
Well, my first working day at SMART is almost over. 4 minutes and 35 seconds to the stipulated end-of-work-day. But who’s counting? So far, it’s been interesting — met the friendly folks here and saw the cool toys (autonomous vehicles). I’m one of the “early birds” and managed to land a desk with a great view of the NUS campus. That said, I might move down to “The Garage” where all the robots/machines are. Hopefully, I’ll sort out all my administration stuff soon and get on to
playing working with the vehicles and some new learning methods I have in mind.
I did the unthinkable and upgraded my OS (in my final year of my PhD!). And surprise-surprise, some of my code wouldn’t compile anymore. I figured I needed to rebuild my macports-installed *nix software but ran into problems with gcc45 and libstdcxx. The issue is a ld64 bug, that was fixed using user adrian’s solution (replicated here):
sudo port uninstall ld64 sudo port -v install ld64 sudo port clean libstdcxx sudo port -d build libstdcxx build.jobs=1 sudo port install libstdcxx
A little exhausted after a couple of paper submissions to IROS. Looking forward to a break for a few days while I re-organise and re-think. And some opportunity for reading (Kantz and Schreiber’s Nonlinear Time Series Analysis that I bought ages ago but haven’t finished, and Chaitin’s Meta Math that I have read but have largely forgotten).
Plus, my desk is clean again!
Just testing some . Some of the world’s most favourite equations: