Optimal Inference in Crowdsourced Classification via Belief Propagation

Crowdsourcing systems are popular for solving large-scale labelling tasks with low-paid workers. We study the problem of recovering the true labels from the possibly erroneous crowdsourced labels under the popular Dawid-Skene model. To address this inference problem, several algorithms have recently been proposed, but the best known guarantee is still significantly larger than the fundamental limit. We close this gap by introducing a tighter lower bound on the fundamental limit and proving that Belief Propagation (BP) exactly matches this lower bound. The guaranteed optimality of BP is the strongest in the sense that it is information-theoretically impossible for any other algorithm to correctly label a larger fraction of the tasks. Experimental results suggest that BP is close to optimal for all regimes considered and improves upon competing state-of-the-art algorithms.Comment: This article is partially based on preliminary results published in the proceeding of the 33rd International Conference on Machine Learning (ICML 2016

Probing nucleon strangeness in phi electroproduction

We investigate $\phi$ meson electroproduction to probe the hidden strangeness content of the nucleon. We found that even a small amount of the $s\bar{s}$ admixture in the nucleon wavefunction can lead to a significant change in several double polarization asymmetries in $\phi$ electroproduction, which can be tested experimentally at current electron facilities.Comment: 3 pages, 1 figure (2 eps files), LaTeX2e with espcrc1.sty, Talk at the XVI International Conference on Few-Body Problems in Physics (FB16), Taipei, Taiwan, March 6-10, 200