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Very large-scale classification taxonomies typically have hundreds of thousands of categories, deep hierarchies, and skewed category distribution over documents. However, it is still an open question whether the state-of-the-art technologies in automated text categorization can scale to (and perform well on) such large taxonomies. In this paper, we report the first evaluation of Support Vector Machines (SVMs) in web-page classification over the full taxonomy of the Yahoo! categories. Our accomplishments include: 1) a data analysis on the Yahoo! taxonomy; 2) the development of a scalable system for large-scale text categorization; 3) theoretical analysis and experimental evaluation of SVMs in hierarchical and non-hierarchical settings for classification; 4) an investigation of threshold tuning algorithms with respect to time complexity and their effect on the classification accuracy of SVMs. We found that, in terms of scalability, the hierarchical use of SVMs is efficient enough for very large-scale classification; however, in terms of effectiveness, the performance of SVMs over the Yahoo! Directory is still far from satisfactory, which indicates that more substantial investigation is needed.

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The oxide interface between LaAlO3 and KTaO3(111) can harbor a superconducting state. We report that by applying a gate voltage (VG) across KTaO3, the interface can be continuously tuned from superconducting into insulating states, yielding a dome-shaped Tc-VG dependence, where Tc is the transition temperature. The electric gating has only a minor effect on carrier density but a strong one on mobility. We interpret the tuning of mobility in terms of change in the spatial profile of the carriers in the interface and hence, effective disorder. As the temperature is decreased, the resistance saturates at the lowest temperature on both superconducting and insulating sides, suggesting the emergence of a quantum metallic state associated with a failed superconductor and/or fragile insulator.

No abstract