Large Margin Neural Language Model

We propose a large margin criterion for training neural language models. Conventionally, neural language models are trained by minimizing perplexity (PPL) on grammatical sentences. However, we demonstrate that PPL may not be the best metric to optimize in some tasks, and further propose a large margin formulation. The proposed method aims to enlarge the margin between the "good" and "bad" sentences in a task-specific sense. It is trained end-to-end and can be widely applied to tasks that involve re-scoring of generated text. Compared with minimum-PPL training, our method gains up to 1.1 WER reduction for speech recognition and 1.0 BLEU increase for machine translation.Comment: 9 pages. Accepted as a long paper in EMNLP201

Chromatographic Fingerprinting Coupled with Chemometrics for Quality Control of Traditional Chinese Medicines

The holistic system of traditional Chinese medicine (TCM) is reflected by the integrity of the ingredients contained in herbal medicines, which creates a challenge in establishing quality control standards for raw materials and the standardization of finished herbal drugs because no single component contributes to the total efficacy. Thus, the chromatographic fingerprinting technique of TCM has proved to be a comprehensive strategy for assessing the intact quality of herbal medicine, since the origin of the herbal medicines could be identified and classified based on so-called phytoequivalence. On the other hand, chromatographic fingerprinting is essentially a high-throughput technique and an integral tool to explore the complexity of herbal medicines. In order to further control the comprehensive quality of TCMs, some strategies are proposed to trace the chemical changes of chromatographic fingerprints both in product processing and/or after their administration by modern chromatographic techniques and chemometrics. Combined with the techniques developed in systems biology, it seems also possible to reveal the working mechanism of TCMs and to further control their intrinsic quality

Is the late near-infrared bump in short-hard GRB 130603B due to the Li-Paczynski kilonova?

Short-hard gamma-ray bursts (GRBs) are widely believed to be produced by the merger of two binary compact objects, specifically by two neutron stars or by a neutron star orbiting a black hole. According to the Li-Paczynski kilonova model, the merger would launch sub-relativistic ejecta and a near-infrared/optical transient would then occur, lasting up to days, which is powered by the radioactive decay of heavy elements synthesized in the ejecta. The detection of a late bump using the {\em Hubble Space Telescope} ({\em HST}) in the near-infrared afterglow light curve of the short-hard GRB 130603B is indeed consistent with such a model. However, as shown in this Letter, the limited {\em HST} near-infrared lightcurve behavior can also be interpreted as the synchrotron radiation of the external shock driven by a wide mildly relativistic outflow. In such a scenario, the radio emission is expected to peak with a flux of $\sim 100 \mu$Jy, which is detectable for current radio arrays. Hence, the radio afterglow data can provide complementary evidence on the nature of the bump in GRB 130603B. It is worth noting that good spectroscopy during the bump phase in short-hard bursts can test validity of either model above, analogous to spectroscopy of broad-lined Type Ic supernova in long-soft GRBs.Comment: 4 pages, 2 figures, published in ApJ Lette