DOC: Deep Open Classification of Text Documents

Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification presents an important problem. This problem is called open-world classification or open classification. This paper proposes a novel deep learning based approach. It outperforms existing state-of-the-art techniques dramatically.Comment: accepted at EMNLP 201

On the Initial Conditions for Brane Inflation

String theory gives rise to various mechanisms to generate primordial inflation, of which ``brane inflation'' is one of the most widely considered. In this scenario, inflation takes place while two branes are approaching each other, and the modulus field representing the separation between the branes plays the role of the inflaton field. We study the phase space of initial conditions which can lead to a sufficiently long period of cosmological inflation, and find that taking into account the possibility of nonvanishing initial momentum can significantly change the degree of fine tuning of the required initial conditions.Comment: 11 pages, 2 figure