Context-aware Path Ranking for Knowledge Base Completion

Knowledge base (KB) completion aims to infer missing facts from existing ones in a KB. Among various approaches, path ranking (PR) algorithms have received increasing attention in recent years. PR algorithms enumerate paths between entity pairs in a KB and use those paths as features to train a model for missing fact prediction. Due to their good performances and high model interpretability, several methods have been proposed. However, most existing methods suffer from scalability (high RAM consumption) and feature explosion (trains on an exponentially large number of features) problems. This paper proposes a Context-aware Path Ranking (C-PR) algorithm to solve these problems by introducing a selective path exploration strategy. C-PR learns global semantics of entities in the KB using word embedding and leverages the knowledge of entity semantics to enumerate contextually relevant paths using bidirectional random walk. Experimental results on three large KBs show that the path features (fewer in number) discovered by C-PR not only improve predictive performance but also are more interpretable than existing baselines

New Ωc0\Omega_c^0 baryons discovered by LHCb as the members of 1P1P and 2S2S states

Inspired by the newly observed $\Omega_c^0$ states at LHCb, we decode their properties by performing an analysis of mass spectrum and decay behavior. Our studies show that the five narrow states, i.e., $\Omega_c(3000)^0$, $\Omega_c(3050)^0$, $\Omega_c(3066)^0$, $\Omega_c(3090)^0$, and $\Omega_c(3119)^0$, could be grouped into the $1P$ states with negative parity. Among them, the $\Omega_c(3000)^0$ and $\Omega_c(3090)^0$ states could be the $J^P=1/2^-$ candidates, while $\Omega_c(3050)^0$ and $\Omega_c(3119)^0$ are suggested as the $J^P=3/2^-$ states. $\Omega_c(3066)^0$ could be regarded as a $J^P=5/2^-$ state. Since the the spin-parity, the electromagnetic transitions, and the possible hadronic decay channels $\Omega_c^{(\ast)}\pi$ have not been measured yet, other explanations are also probable for these narrow $\Omega_c^0$ states. Additionally, we discuss the possibility of the broad structure $\Omega_c(3188)^0$ as a $2S$ state with $J^P=1/2^+$ or $J^P=3/2^+$. In our scheme, $\Omega_c(3119)^0$ cannot be a $2S$ candidate.Comment: 10 pages, 3 figures, 5 tables, typos corrected. Published in Phys. Rev.

Testing for pure-jump processes for high-frequency data

Pure-jump processes have been increasingly popular in modeling high-frequency financial data, partially due to their versatility and flexibility. In the meantime, several statistical tests have been proposed in the literature to check the validity of using pure-jump models. However, these tests suffer from several drawbacks, such as requiring rather stringent conditions and having slow rates of convergence. In this paper, we propose a different test to check whether the underlying process of high-frequency data can be modeled by a pure-jump process. The new test is based on the realized characteristic function, and enjoys a much faster convergence rate of order $O(n^{1/2})$ (where $n$ is the sample size) versus the usual $o(n^{1/4})$ available for existing tests; it is applicable much more generally than previous tests; for example, it is robust to jumps of infinite variation and flexible modeling of the diffusion component. Simulation studies justify our findings and the test is also applied to some real high-frequency financial data.Comment: Published at http://dx.doi.org/10.1214/14-AOS1298 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog

We present a novel end-to-end trainable neural network model for task-oriented dialog systems. The model is able to track dialog state, issue API calls to knowledge base (KB), and incorporate structured KB query results into system responses to successfully complete task-oriented dialogs. The proposed model produces well-structured system responses by jointly learning belief tracking and KB result processing conditioning on the dialog history. We evaluate the model in a restaurant search domain using a dataset that is converted from the second Dialog State Tracking Challenge (DSTC2) corpus. Experiment results show that the proposed model can robustly track dialog state given the dialog history. Moreover, our model demonstrates promising results in producing appropriate system responses, outperforming prior end-to-end trainable neural network models using per-response accuracy evaluation metrics.Comment: Published at Interspeech 201