Nonlocality-controlled interaction of spatial solitons in nematic liquid crystals

We demonstrate experimentally that the interactions between a pair of nonlocal spatial optical solitons in a nematic liquid crystal (NLC) can be controlled by the degree of nonlocality. For a given beam width, the degree of nonlocality can be modulated by varying the pretilt angle of NLC molecules via the change of the bias. When the pretilt angle is smaller than pi/4, the nonlocality is strong enough to guarantee the independence of the interactions on the phase difference of the solitons. As the pretilt angle increases, the degree of nonlocality decreases. When the degree is below its critical value, the two solitons behavior in the way like their local counterpart: the two in-phase solitons attract and the two out-of-phase solitons repulse.Comment: 3 pages, 4 figure

Subgraph Contrastive Link Representation Learning

Graph representation learning (GRL) has emerged as a powerful technique for solving graph analytics tasks. It can effectively convert discrete graph data into a low-dimensional space where the graph structural information and graph properties are maximumly preserved. While there is rich literature on node and whole-graph representation learning, GRL for link is relatively less studied and less understood. One common practice in previous works is to generate link representations by directly aggregating the representations of their incident nodes, which is not capable of capturing effective link features. Moreover, common GRL methods usually rely on full-graph training, suffering from poor scalability and high resource consumption on large-scale graphs. In this paper, we design Subgraph Contrastive Link Representation Learning (SCLRL) -- a self-supervised link embedding framework, which utilizes the strong correlation between central links and their neighborhood subgraphs to characterize links. We extract the "link-centric induced subgraphs" as input, with a subgraph-level contrastive discrimination as pretext task, to learn the intrinsic and structural link features via subgraph mini-batch training. Extensive experiments conducted on five datasets demonstrate that SCLRL has significant performance advantages in link representation learning on benchmark datasets and prominent efficiency advantages in terms of training speed and memory consumption on large-scale graphs, when compared with existing link representation learning methods.Comment: 8 pages, 4 figure

A normally closed in-channel micro check valve

We present here the first surface-micromachined, normally closed, in-channel, Parylene check valve. This device is fabricated monolithically on a silicon substrate using a five-layer Parylene process. The operating structure of the check valve is a circular sealing plate on top of a ring-shaped valve seat. The sealing plate is center-anchored on top of a chamber diaphragm that is vacuum-collapsed to the bottom of the chamber in order to achieve a normally closed position. A thin gold layer on the roughened valve seat surface is used to reduce stiction between the sealing plate and the valve seat. We have achieved an in-channel check valve with a cracking (opening) pressure of 20/spl sim/40 kPa under forward bias and no measurable leakage under reverse bias up to 270 kPa. Using this design, this valve performs well in two-phase microfluidic systems (i.e. microchannel flows containing gas, liquid, or gas/liquid mixture)