It is shown theoretically, that an antiferromagnetic dielectric with bi-axial anisotropy, such as NiO, can be used for the rectification of linearly-polarized AC spin current. The AC spin current excites two evanescent modes in the antiferromagnet, which, in turn, create DC spin current flowing back through the antiferromagnetic surface. Spin diode based on this effect can be used in future spintronic devices as direct detector of spin current in the millimeter- and submillimeter-wave bands. The sensitivity of such a spin diode is comparable to the sensitivity of modern electric Schottky diodes and lies in the range 102-103 V/W for 30×30 nm2 structure.
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Hybrid Modular Multilevel Rectifier: A New High-Efficient High-Performance Rectifier Topology for HVDC Power Delivery
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Various natural language processing tasks are structured prediction problems where outputs are constructed with multiple interdependent decisions. Past work has shown that domain knowledge, framed as constraints over the out-put space, can help improve predictive accuracy. However, designing good constraints of-ten relies on domain expertise. In this pa-per, we study the problem of learning such constraints. We frame the problem as that of training a two-layer rectifier network to identify valid structures or substructures, and show a construction for converting a trained net-work into a system of linear constraints over the inference variables. Our experiments on several NLP tasks show that the learned constraints can improve the prediction accuracy,especially when the number of training examples is small.more » « less
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