skip to main content

Search for: All records

Creators/Authors contains: "Zhu, Chen"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available February 1, 2023
  2. Free, publicly-accessible full text available August 12, 2022
  3. null (Ed.)
  4. Microsphere Photolithography (MPL) is a nanopatterning technique that utilizes a self-assembled monolayer of microspheres as an optical element to focus incident radiation inside a layer of photoresist. The microspheres produces a sub-diffraction limited photonic-jet on the opposite side of each microsphere from the illumination. When combined with pattern transfer techniques such as etching/lift-off, MPL provides a versatile, low-cost fabrication method for producing hexagonal close-packed metasurfaces. This article investigates the MPL process for creating refractive index (RI) sensors on the cleaved tips of optical fiber. The resonant wavelength of metal elements on the surface is dependent on the local dielectric environmentmore »and allows the refractive index of an analyte to be resolved spectrally. A numerical study of hole arrays defined in metal films shows that the waveguide mode provides good sensitivity to the analyte refractive index. This can be readily tuned by adjusting the MPL exposure and the simulation results guide the fabrication of a defect tolerant refractive index sensor on the tip of a fiber tip with a sensitivity of 613 nm/RIU. The conformal nature of the microsphere monolayer simplifies the fabrication process and provides a viable alternative to direct-write techniques such as Focused Ion Beam (FIB) milling« less
  5. Most state-of-the-art Graph Neural Networks (GNNs) can be defined as a form of graph convolution which can be realized by message passing between direct neighbors or beyond. To scale such GNNs to large graphs, various neighbor-, layer-, or subgraph-sampling techniques are proposed to alleviate the "neighbor explosion" problem by considering only a small subset of messages passed to the nodes in a mini-batch. However, sampling-based methods are difficult to apply to GNNs that utilize many-hops-away or global context each layer, show unstable performance for different tasks and datasets, and do not speed up model inference. We propose a principled andmore »fundamentally different approach, VQ-GNN, a universal framework to scale up any convolution-based GNNs using Vector Quantization (VQ) without compromising the performance. In contrast to sampling-based techniques, our approach can effectively preserve all the messages passed to a mini-batch of nodes by learning and updating a small number of quantized reference vectors of global node representations, using VQ within each GNN layer. Our framework avoids the "neighbor explosion" problem of GNNs using quantized representations combined with a low-rank version of the graph convolution matrix. We show that such a compact low-rank version of the gigantic convolution matrix is sufficient both theoretically and experimentally. In company with VQ, we design a novel approximated message passing algorithm and a nontrivial back-propagation rule for our framework. Experiments on various types of GNN backbones demonstrate the scalability and competitive performance of our framework on large-graph node classification and link prediction benchmarks.« less