NeurIPS 2022
Liquid Marbles under Electric Fields: New Capabilities for Non-wetting Droplet Manipulation and Beyond | Langmuir
![PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/5969da915e121e2e2001e119f5962a1c413e44c1/3-Figure2-1.png "PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar")
PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar
Materials | Free Full-Text | Experimental Study on Ultra-Precision Polishing of Ti-6Al-4V by Ultraviolet-Induced Nanoparticle Colloid Jet Machining
High Surface Area Networks from Tetrahedral Monomers: Metal-Catalyzed Coupling, Thermal Polymerization, and “Click” Chemistry | Macromolecules
Intramolecular Cyclization Reactions of 5-Halo- and 5-Nitro-Substituted Furans | Organic Letters
Pathway and network analysis of cancer genomes | Nature Methods
![PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/5969da915e121e2e2001e119f5962a1c413e44c1/6-Figure5-1.png "PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar")
PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar
Effect of Nanowire Number, Diameter, and Doping Density on Nano-FET Biosensor Sensitivity | ACS Nano
NeurIPS 2022
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation | SpringerLink
HSIN-YU CHEN - 美國印第安納大學伯明頓分校- 台灣| LinkedIn
![PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/5969da915e121e2e2001e119f5962a1c413e44c1/6-Table3-1.png "PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar")
PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar
Predicting anticancer hyperfoods with graph convolutional networks | Human Genomics | Full Text
![PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/5969da915e121e2e2001e119f5962a1c413e44c1/4-Figure3-1.png "PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar")
PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar
![PDF] Geometrically Principled Connections in Graph Neural Networks | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/229f331f1e9dc5f17e0f29ec9ae0a4b0346850cc/12-Figure7-1.png "PDF] Geometrically Principled Connections in Graph Neural Networks | Semantic Scholar")
PDF] Geometrically Principled Connections in Graph Neural Networks | Semantic Scholar
![PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/5969da915e121e2e2001e119f5962a1c413e44c1/4-Table1-1.png "PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar")
PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar
![PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/5969da915e121e2e2001e119f5962a1c413e44c1/1-Figure1-1.png "PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar")
PDF] SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator | Semantic Scholar
Frontiers | The Resistance Mechanism of Mycoplasma bovis From Yaks in Tibet to Fluoroquinolones and Aminoglycosides
arXiv:2205.10662v2 [cs.LG] 27 Aug 2022
Frontiers | Polypharmacology-based approach for screening TCM against coinfection of Mycoplasma gallisepticum and Escherichia coli
![PDF] Geometrically Principled Connections in Graph Neural Networks | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/229f331f1e9dc5f17e0f29ec9ae0a4b0346850cc/6-Table1-1.png "PDF] Geometrically Principled Connections in Graph Neural Networks | Semantic Scholar")
PDF] Geometrically Principled Connections in Graph Neural Networks | Semantic Scholar
