3,325,392 research outputs found

    The interaction of polymer dispersed liquid crystal sensors with ultrasound

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    Polymer dispersed liquid crystals (PDLCs) have been shown to be sensitive to ultrasound through the acousto-optic effect. The acousto-optic response of PDLCs was studied over a broad frequency range (0.3–10 MHz). We demonstrate that the displacements required to produce acousto-optic clearing of PDLC films can be as low as a few nanometers, which is at least 103 times smaller than the PDLC droplet size, is 105 times smaller than the PDLC layer thickness, and of the order of the molecular size of the liquid crystal constituents. This suggests that the acousto-optic effect in PDLCs is due to the microscopic effects of the LC reorientation under torques or flows rather than the LC reorientation through macroscopic droplet deformation. The displacement required for clearing is related to the frequency of operation via an exponential decay. We attribute the observed frequency response to a freezing out of the rotational motion around the short axis of the liquid crystal. The reported frequency dependence and displacements required indicate that the effects and materials described here could be used for ultrasound visualization in a non-destructive testing context

    Interaction-mediated surface state instability in disordered three-dimensional topological superconductors with spin SU(2) symmetry

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    We show that arbitrarily weak interparticle interactions destabilize the surface states of 3D topological superconductors with spin SU(2) invariance (symmetry class CI), in the presence of non-magnetic disorder. The conduit for the instability is disorder-induced wavefunction multifractality. We argue that time-reversal symmetry breaks spontaneously at the surface, so that topologically-protected states do not exist for this class. The interaction-stabilized surface phase is expected to exhibit ferromagnetic order, or to reside in an insulating plateau of the spin quantum Hall effect.Comment: v2: 5+3 pages, 1 figure; expanded introduction, added background on topological superconductors and multifractality, technical details relegated to sup info (published version

    Interaction Embeddings for Prediction and Explanation in Knowledge Graphs

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    Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-directional effects between entities and relations --- help select related information when predicting a new triple, but haven't been formally discussed before. In this paper, we propose CrossE, a novel knowledge graph embedding which explicitly simulates crossover interactions. It not only learns one general embedding for each entity and relation as most previous methods do, but also generates multiple triple specific embeddings for both of them, named interaction embeddings. We evaluate embeddings on typical link prediction tasks and find that CrossE achieves state-of-the-art results on complex and more challenging datasets. Furthermore, we evaluate embeddings from a new perspective --- giving explanations for predicted triples, which is important for real applications. In this work, an explanation for a triple is regarded as a reliable closed-path between the head and the tail entity. Compared to other baselines, we show experimentally that CrossE, benefiting from interaction embeddings, is more capable of generating reliable explanations to support its predictions.Comment: This paper is accepted by WSDM201