2,107 research outputs found
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)
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