A Comparative Study on Spin-Orbit Torque Efficiencies from W/ferromagnetic and W/ferrimagnetic Heterostructures

It has been shown that W in its resistive form possesses the largest spin-Hall ratio among all heavy transition metals, which makes it a good candidate for generating efficient dampinglike spin-orbit torque (DL-SOT) acting upon adjacent ferromagnetic or ferrimagnetic (FM) layer. Here we provide a systematic study on the spin transport properties of W/FM magnetic heterostructures with the FM layer being ferromagnetic Co$_{20}$Fe$_{60}$B$_{20}$ or ferrimagnetic Co$_{63}$Tb$_{37}$ with perpendicular magnetic anisotropy. The DL-SOT efficiency $|\xi_{DL}|$, which is characterized by a current-induced hysteresis loop shift method, is found to be correlated to the microstructure of W buffer layer in both W/Co$_{20}$Fe$_{60}$B$_{20}$ and W/Co$_{63}$Tb$_{37}$ systems. Maximum values of $|\xi_{DL}|\approx 0.144$ and $|\xi_{DL}|\approx 0.116$ are achieved when the W layer is partially amorphous in the W/Co$_{20}$Fe$_{60}$B$_{20}$ and W/Co$_{63}$Tb$_{37}$ heterostructures, respectively. Our results suggest that the spin Hall effect from resistive phase of W can be utilized to effectively control both ferromagnetic and ferrimagnetic layers through a DL-SOT mechanism

DYNAMICAL EFFECTS OF SPRINT START ON DIFFERENT STARTING BLOCKS

The purpose of this study was to examine the dynamical variables of sprint start in two different starting blocks setups. The ReacTime Personal Systems was used to record the Reaction Time (RT) and the Power of 20 teenaged sprinters (15 males and 5 females) in the sprint start. In addition, the Newtest Powertimer photocells were used to collect subjects’ 0 to 10 metre (T10) performance after the sprint start. The variables were tested by the repeated measures one-way ANOVA by SPSS 19.0 statistical software at a .05 significant level. The results showed that there were better effects on the short starting block (SB) in power generation performance than the long starting block (LB). The athletes can apply short starting block and make adjustments and modifications based on their training conditions

The Impact on the Brazilian Economy of the Olympic Games in Rio De Janeiro in 2016

Purpose: This study aims to examine the influence of channel power and influence strategy, in terms of non-coercive strategies, on sanitary equipment manufacturers' relationships with channel members and channel performance.   Theoretical framework: The study is based on the literature on channel relationships, which suggests that using an influence strategy can contribute to managing the relationship with the channel members and benefit organization performance.   Design/Methodology/Approach:  In this study, we sampled from a sanitary equipment manufacturer's channel strategy. We used survey data to examine the effect of channel management strategies from sanitary equipment manufacturers on distributors.   Findings: The finding indicates that a supplier using economic power tends to adopt non-coercive strategies. In addition, economic power and non-coercive strategies positively affect the continuity of the relationship with distributors. Relationship continuity between manufacturers and distributors positively impacts whole channel performance.   Originality/Value: This study sampled the distributors in the sanitary equipment industry, a market in which the consumers are not knowledgeable about the products. Most consumers base their purchase decisions heavily on the channel member’s recommendations. Therefore, how to manage the relationship with the channel members is critical to understand.   Research, Practical & Social implications: Distributors are regarded as an extension of the company's sales capabilities. Channels have always held an essential position in the industry. Maintaining relationships between distributors and improving channel performance is a critical question in distribution management

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training