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

Metasurface-mediated anisotropic radiative heat transfer between nanoparticles

Metasurfaces, the two-dimensional (2D) counterpart of metamaterials, have recently attracted a great attention due to their amazing properties such as negative refraction, hyperbolic dispersion, manipulation of the evanescent spectrum. In this work, we propose a theory model for the near field radiative heat transfer (NFRHT) between two nanoparticles in the presence of an anisotropic metasurface. Specifically, we set the metasurface as an array of graphene strips (GS) since it is an ideal platform to implement any metasurface topology, ranging from isotropic to hyperbolic propagation. We show that the NFRHT between two nanoparticles can not only be significantly amplified when they are placed in proximity of the GS, but also be regulated over several orders of magnitude. In this configuration, the anisotropic surface plasmon polaritons (SPPs) supported by the GS are excited and provide a new channel for the near-field energy transport. We analyze how the conductance between two nanoparticles depends on the orientation, the structure parameters and the chemical potential of the GS, on the particle-surface or the particle-surface distances by clearly identifying the characteristics of the anisotropic SPPs such as dispersion relations, propagation length and decay length. Our findings provide a powerful way to regulate the energy transport in the particle systems, meanwhile in turn, open up a way to explore the anisotropic optical properties of the metasurface based on the measured heat transfer properties.Comment: 17 pages, 8figures, Journa