Wetting of Laser Textured Cu Surface by Ethylene Glycol and Sn

The effect of microcosmic morphologies of textured Cu surface by nanosecond laser on the inert wetting and reactive wetting, i.e., ethylene glycol/copper and tin/copper wetting systems, was studied by using modified sessile drop methods. To create different surface roughness, the microcosmic morphologies with different spacing of grooves were constructed by nanosecond laser. The results showed that the inert wetting (ethylene glycol/copper) was consistent with Wenzel model, while the reactive wetting results deviated from the model. In Sn/Cu reactive wetting system, the interfacial evolution in the early stage and the pinning of triple line by the precipitated h-Cu6Sn5 caused the rougher surface and the worse final wettability. When the scale of artificial roughness exceeded the roughness that was caused by interfacial reaction after reaching the quasi-equilibrium state at interface, the final wettability could be improved

Deflection and gravitational lensing with finite distance effect in the strong deflection limit in stationary and axisymmetric spacetimes

We study the deflection and gravitational lensing (GL) of both timelike and null signals in the equatorial plane of arbitrary stationary and axisymmetric spacetimes in the strong deflection limit. Our approach employs a perturbative method to show that both the deflection angle and the total travel time take quasi-series forms $\displaystyle \sum_{n=0}\left[ C_n\ln (1-b_c/b)+D_n\right] (1-b_c/b)^n$, with the coefficients $C_n$ and $D_n$ incorporating the signal velocity and finite distance effect of the source and detector. This new deflection angle allows us to establish an accurate GL equation from which the apparent angles of the relativistic images and their time delays are found. These results are applied to the Kerr and the rotating Kalb-Ramond (KR) spacetimes to investigate the effect of the spacetime spin in both spacetimes, and the effective charge parameter and a transition parameter in the rotating KR spacetime on various observables. Moreover, using our approach, the effect of the signal velocity and the source angular position on these variables is also studied.Comment: 15 pages, 10 figures; updated to publish versio

M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems

Combining graph representation learning with multi-view data (side information) for recommendation is a trend in industry. Most existing methods can be categorized as \emph{multi-view representation fusion}; they first build one graph and then integrate multi-view data into a single compact representation for each node in the graph. However, these methods are raising concerns in both engineering and algorithm aspects: 1) multi-view data are abundant and informative in industry and may exceed the capacity of one single vector, and 2) inductive bias may be introduced as multi-view data are often from different distributions. In this paper, we use a \emph{multi-view representation alignment} approach to address this issue. Particularly, we propose a multi-task multi-view graph representation learning framework (M2GRL) to learn node representations from multi-view graphs for web-scale recommender systems. M2GRL constructs one graph for each single-view data, learns multiple separate representations from multiple graphs, and performs alignment to model cross-view relations. M2GRL chooses a multi-task learning paradigm to learn intra-view representations and cross-view relations jointly. Besides, M2GRL applies homoscedastic uncertainty to adaptively tune the loss weights of tasks during training. We deploy M2GRL at Taobao and train it on 57 billion examples. According to offline metrics and online A/B tests, M2GRL significantly outperforms other state-of-the-art algorithms. Further exploration on diversity recommendation in Taobao shows the effectiveness of utilizing multiple representations produced by \method{}, which we argue is a promising direction for various industrial recommendation tasks of different focus.Comment: Accepted by KDD 2020 ads track as an oral paper. Code address:https://github.com/99731/M2GR

Lesson Design Of Geometric Sequences Based On The 6-Question Cognitive Theory

The effective teaching design of mathematics should be designed carefully by teachers according to the situation of students' learning and the analysis of teaching materials. Its teaching design should run through the origin and development of knowledge generation, and reasonably ensure the continuity and integrity of teaching. Based on the theory of "6-Question Cognitive Theory", This research using development method with steps to make comparisons between the this paper attempts to make a comparison between the "equal ratio sequence" and the "equal ratio sequence". Results in this study show that The teaching design of "6-Question Cognitive Theory" knowledge embodies the coherence, completeness and operability of the teaching design from six aspects: where the mathematical knowledge comes from, the essence of the knowledge, the connection and difference between the new knowledge and the old knowledge, the transformation of the knowledge, how to apply the knowledge and the process of reflecting the knowledge generation, so as to provide a theoretical and practical reference for the teaching design of high school mathematics Tes