Efficient spin-current injection in single-molecule magnet junctions

We study theoretically spin transport through a single-molecule magnet (SMM) in the sequential and cotunneling regimes, where the SMM is weakly coupled to one ferromagnetic and one normalmetallic leads. By a master-equation approach, it is found that the spin polarization injected from the ferromagnetic lead is amplified and highly polarized spin-current can be generated, due to the exchange coupling between the transport electron and the anisotropic spin of the SMM. Moreover, the spin-current polarization can be tuned by the gate or bias voltage, and thus an efficient spin injection device based on the SMM is proposed in molecular spintronics.Comment: 4 figure

On the structure of Accretion Disks with Outflows

In order to study the outflows from accretion disks, we solve the set of hydrodynamic equations for accretion disks in the spherical coordinates ($r\theta\phi$) to obtain the explicit structure along the $\theta$ direction. Using self-similar assumptions in the radial direction, we change the equations to a set of ordinary differential equations (ODEs) about the $\theta$-coordinate, which are then solved with symmetrical boundary conditions in the equatorial plane, and the velocity field is obtained. The $\alpha$ viscosity prescription is applied and an advective factor $f$ is used to simplify the energy equation.The results display thinner, quasi-Keplerian disks for Shakura-Sunyaev Disks (SSDs) and thicker, sub-Keplerian disks for Advection Dominated Accretion Flows (ADAFs) and slim disks, which are consistent with previous popular analytical models. However, an inflow region and an outflow region always exist, except when the viscosity parameter $\alpha$ is too large, which supports the results of some recent numerical simulation works. Our results indicate that the outflows should be common in various accretion disks and may be stronger in slim disks, where both advection and radiation pressure are dominant. We also present the structure dependence on the input parameters and discuss their physical meanings. The caveats of this work and possible improvements in the future are discussed.Comment: 24 pages, 20 figures. Accepted for publication in Ap

NRPA: Neural Recommendation with Personalized Attention

Existing review-based recommendation methods usually use the same model to learn the representations of all users/items from reviews posted by users towards items. However, different users have different preference and different items have different characteristics. Thus, the same word or similar reviews may have different informativeness for different users and items. In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews. We use a review encoder to learn representations of reviews from words, and a user/item encoder to learn representations of users or items from reviews. We propose a personalized attention model, and apply it to both review and user/item encoders to select different important words and reviews for different users/items. Experiments on five datasets validate our approach can effectively improve the performance of neural recommendation.Comment: 4 pages, 4 figure