Spin polarization amplification within nonmagnetic semiconductors at room temperature

We demonstrate theoretically that the spin polarization of current can be electrically amplified within nonmagnetic semiconductors by exploiting the fact the spin current, compared to the charge current, is weakly perturbed by electric driving forces. As a specific example, we consider a T-shaped current branching geometry made entirely of a nonmagnetic semiconductor, where the current is injected into one of the branches (input branch) and splits into the other two branches (output branches). We show that when the input current has a moderate spin polarization, the spin polarization in one of the output branches can be higher than the spin polarization in the input branch and may reach 100% when the relative magnitudes of current-driving electric fields in the two output branches are properly tuned. The proposed amplification scheme does not use ferromagnets or magnetic fields, and does not require low temperature operation, providing an efficient way to generate a highly spin polarized current in nonmagnetic semiconductors at room temperature.Comment: 11 pages, 2 figures, to appear in Phys. Rev.

Click-aware purchase prediction with push at the top

Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user-item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.Comment: For the final published journal version, see https://doi.org/10.1016/j.ins.2020.02.06

The economic opportunities and constraints of green growth

노트 : Asie.Visions is an electronic publication dedicated to Asia. With contributions by French and international experts, Asie.Visions deals with economic, strategic, and political issues. The collection aims to contribute to the global debate and to a better understanding of the regional issues at stake. It is published in French and/or in English and upholds Ifri’s standards of quality (editing and anonymous peerreview)

Exploring soft constraints on effective actions

We study effective actions for simultaneous breaking of space-time and internal symmetries. Novel features arise due to the mixing of Goldstone modes under the broken symmetries which, in contrast to the usual Adler's zero, leads to non-vanishing soft limits. Such scenarios are common for spontaneously broken SCFT's. We explicitly test these soft theorems for $\mathcal{N}=4$ sYM in the Coulomb branch both perturbatively and non-perturbatively. We explore the soft constraints systematically utilizing recursion relations. In the pure dilaton sector of a general CFT, we show that all amplitudes up to order $s^{n} \sim \partial^{2n}$ are completely determined in terms of the $k$-point amplitudes at order $s^k$ with $k \leq n$. Terms with at most one derivative acting on each dilaton insertion are completely fixed and coincide with those appearing in the conformal DBI, i.e. DBI in AdS. With maximal supersymmetry, the effective actions are further constrained, leading to new non-renormalization theorems. In particular, the effective action is fixed up to eight derivatives in terms of just one unknown four-point coefficient and one more coefficient for ten-derivative terms. Finally, we also study the interplay between scale and conformal invariance in this context.Comment: 20+4 pages, 1 figure; v2: references added, typos corrected; v3: typos corrected, JHEP versio

Body Information Analysis based Personal Exercise Management System

Recently, people's interest in health is deepening. So health-related systems are being developed. Existing exercise management systems provided users with exercise related information using PC or smart phone. However, there is a problem that the accuracy of the algorithm for analyzing the user's body information and providing information is low.In this paper, we analyze users' body mass index (BMI) and basal metabolic rate (BMR) and we propose a system that provides the user with necessary information through recommendation algorithm. It informs the user of exercise intensity and momentum, and graphs the exercise history of the user. It also allows the user to refer to the fitness history of other users in the same BMI group. This allows the user to receive more personalized services than the existing exercise management system, thereby enabling efficient exercise