Quanta Magazine

Networks Hold the Key to a Decades-Old Problem About Waves

Mathematicians are still trying to understand fundamental properties of the Fourier transform, one of their most ubiquitous ...
IEEE

Revisiting Deformable Convolution on Graphs: Large-range Modeling and Robustness

Abstract: Graph Convolution Networks (GCNs) have achieved remarkable success in representation of structured graph data. As we know that traditional GCNs are generally defined on the fixed first-order ...
Scientific Research Publishing

Geo-Refined Point Transformer: Coordinate-Aware Excitation and Positional Upsampling for 3D Scene Segmentation ()

The proposed Coordinate-Aware Feature Excitation (CAFE) module and Position-Aware Upsampling (Pos-Up) module both adhere to ...
Investopedia

Bell Curve: Definition, How It Works, and Example

Andrew Bloomenthal has 20+ years of editorial experience as a financial journalist and as a financial services marketing writer. Samantha (Sam) Silberstein, CFP®, CSLP®, EA, is an experienced ...
unr.edu

University of Nevada, Reno

At the University of Nevada, Reno, we believe in the power of purpose and the strength of the Pack. Whether you're engineering clean energy, studying literature’s role in social change, or designing ...
IEEE

GDNET: A Graph Convolution And Hybrid Dilated Convolution Network For Road Extraction From High-Resolution Satellite Images

Road extraction from high-resolution remote sensing images has become a critical task in various applications. Accurate road extraction is essential for generating up-to-date maps,supporting ...
GitHub

ImpSGNv2: Improved Semantic-Guided Networkwith Attention-based Graph Convolution (GCNs)For Skeleton-based Action Recognition

A critical challenge in skeleton-based action recognition lies in effectively extracting meaningful features from skeleton joints. However, existing state-of-the-art networks for this task are often ...