2,212 research outputs found
Collaborative Graph Neural Networks for Attributed Network Embedding
Graph neural networks (GNNs) have shown prominent performance on attributed
network embedding. However, existing efforts mainly focus on exploiting network
structures, while the exploitation of node attributes is rather limited as they
only serve as node features at the initial layer. This simple strategy impedes
the potential of node attributes in augmenting node connections, leading to
limited receptive field for inactive nodes with few or even no neighbors.
Furthermore, the training objectives (i.e., reconstructing network structures)
of most GNNs also do not include node attributes, although studies have shown
that reconstructing node attributes is beneficial. Thus, it is encouraging to
deeply involve node attributes in the key components of GNNs, including graph
convolution operations and training objectives. However, this is a nontrivial
task since an appropriate way of integration is required to maintain the merits
of GNNs. To bridge the gap, in this paper, we propose COllaborative graph
Neural Networks--CONN, a tailored GNN architecture for attribute network
embedding. It improves model capacity by 1) selectively diffusing messages from
neighboring nodes and involved attribute categories, and 2) jointly
reconstructing node-to-node and node-to-attribute-category interactions via
cross-correlation. Experiments on real-world networks demonstrate that CONN
excels state-of-the-art embedding algorithms with a great margin
The Role of AM Symbiosis in Plant Adaptation to Drought Stress
Symposium paper Part 1: Function and management of soil microorganisms in agro-ecosystems with special reference to arbuscular mycorrhizal fung
Fast generation of arbitrary optical focus array
We report a novel method to generate arbitrary optical focus arrays (OFAs).
Our approach rapidly produces computer-generated holograms (CGHs) to precisely
control the positions and the intensities of the foci. This is achieved by
replacing the fast Fourier transform (FFT) operation in the conventional
iterative Fourier-transform algorithm (IFTA) with a linear algebra one,
identifying/removing zero elements from the matrices, and employing a
generalized weighting strategy. On the premise of accelerating the calculation
speed by >70 times, we demonstrate OFA with 99% intensity precision in the
experiment. Our method proves effective and is applicable for the systems in
which real-time OFA generation is essential
GW25-e3095 The association between cardiovascular disease and erectile dysfunction among middle-aged and elderly men in south china
Optical orbital-angular-momentum-multiplexed data transmission under high scattering
Multiplexing multiple orbital angular momentum (OAM) channels enables high-capacity optical communication. However, optical scattering from ambient microparticles in the atmosphere or mode coupling in optical fibers significantly decreases the orthogonality between OAM channels for demultiplexing and eventually increases crosstalk in communication. Here, we propose a novel scattering-matrix-assisted retrieval technique (SMART) to demultiplex OAM channels from highly scattered optical fields and achieve an experimental crosstalk of –13.8 dB in the parallel sorting of 24 OAM channels after passing through a scattering medium. The SMART is implemented in a self-built data transmission system that employs a digital micromirror device to encode OAM channels and realize reference-free calibration simultaneously, thereby enabling a high tolerance to misalignment. We successfully demonstrate high-fidelity transmission of both gray and color images under scattering conditions at an error rate of <0.08%. This technique might open the door to high-performance optical communication in turbulent environments
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