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

A Currency Union in East Asia

This paper investigates prospects of a currency union in East Asia, focusing on trade and financial integration occurring in the region. We find, based on a dynamic factor model, regional common shocks have been quantitatively important for output variations in the Asian economies. We expect that continuing trade integration in the region will lead to further synchronization of business cycles, thereby encouraging East Asian countries to create a currency union in the region. In contrast to trade, however, financial liberalization in East Asia tends to lead to more global integration, rather than regional integration, of the financial systems, and thereby is not likely to develop favorable conditions for forming a regional currency union among East Asian countries.

Embedding of Virtual Network Requests over Static Wireless Multihop Networks

Network virtualization is a technology of running multiple heterogeneous network architecture on a shared substrate network. One of the crucial components in network virtualization is virtual network embedding, which provides a way to allocate physical network resources (CPU and link bandwidth) to virtual network requests. Despite significant research efforts on virtual network embedding in wired and cellular networks, little attention has been paid to that in wireless multi-hop networks, which is becoming more important due to its rapid growth and the need to share these networks among different business sectors and users. In this paper, we first study the root causes of new challenges of virtual network embedding in wireless multi-hop networks, and propose a new embedding algorithm that efficiently uses the resources of the physical substrate network. We examine our algorithm's performance through extensive simulations under various scenarios. Due to lack of competitive algorithms, we compare the proposed algorithm to five other algorithms, mainly borrowed from wired embedding or artificially made by us, partially with or without the key algorithmic ideas to assess their impacts.Comment: 22 page

Iterative Bayesian Learning for Crowdsourced Regression

Crowdsourcing platforms emerged as popular venues for purchasing human intelligence at low cost for large volume of tasks. As many low-paid workers are prone to give noisy answers, a common practice is to add redundancy by assigning multiple workers to each task and then simply average out these answers. However, to fully harness the wisdom of the crowd, one needs to learn the heterogeneous quality of each worker. We resolve this fundamental challenge in crowdsourced regression tasks, i.e., the answer takes continuous labels, where identifying good or bad workers becomes much more non-trivial compared to a classification setting of discrete labels. In particular, we introduce a Bayesian iterative scheme and show that it provably achieves the optimal mean squared error. Our evaluations on synthetic and real-world datasets support our theoretical results and show the superiority of the proposed scheme

Additive Manufacturing of Ti6Al4V Alloy: A Review

In this paper, the recent progress on Ti6Al4V fabricated by three mostly developed additive manufacturing (AM) techniques-directed energy deposition (DED), selective laser melting (SLM) and electron beammelting (EBM)-is thoroughly investigated and compared. Fundamental knowledge is provided for the creation of links between processing parameters, resultant microstructures and associated mechanical properties. Room temperature tensile and fatigue properties are also reviewed and compared to traditionally manufactured Ti6Al4V parts. The presence of defects in as-builtAMTi6Al4V components and the influences of these defects on mechanical performances are also critically discussed