Results 111 to 120 of about 3,278,052 (298)
Low Frequency Ultrasonic Voice Activity Detection using Convolutional Neural Networks [PDF]
Low frequency ultrasonic mouth state detection uses reflected audio chirps from the face in the region of the mouth to determine lip state, whether open, closed or partially open.
Song, Yan, McLoughlin, Ian Vince
core
Advanced Science, EarlyView.This study developed an efficacy assessment platform that integrates patient‐derived gastric cancer organoids, atomic force microscopy (AFM)‐based nanomechanical vibration detection, deep learning analysis, and organoid mechanical modeling. It detects picomolar drug effects within 0.1 s signal, achieves 97% classification accuracy, and offers non ...
Ting Zhang +10 more
wiley +1 more source
Groundwater Rise Sustains the World's Largest Alpine Water System Under Global Warming
Advanced Science, EarlyView.Shallow groundwater depth (SGWD) across the non‐permafrost plains of the Qinghai–Xizang Plateau decreased at averagely 0.02 m year−1, adding 31.44 Gt of freshwater storage from 2000 to 2020 and sustaining ∼53 500 km2 of alpine ecosystems. A vadose‐zone capacity of 426.6 Gt reveals these aquifers as promising reservoirs, highlighting groundwater's ...
Jianqing Du +15 more
wiley +1 more source
Ensemble Strategies in Graph Convolutional Networks
IEEE AccessGraph Convolutional Networks (GCNs) are widely used for node classification because they combine node features and graph topology effectively. However, their performance can be limited by structural noise, over smoothing, and sensitivity to graph ...
Rini Widiastuti +3 more
doaj +1 more source
Multi-dimensional Graph Convolutional Networks [PDF]
, 2019 Convolutional neural networks (CNNs) leverage the great power in representation learning on regular grid data such as image and video. Recently, increasing attention has been paid on generalizing CNNs to graph or network data which is highly irregular.
Yao Ma 0001 +4 more
openaire +3 more sources
On Filter Size in Graph Convolutional Networks
, 2018 Recently, many researchers have been focusing on the definition of neural networks for graphs. The basic component for many of these approaches remains the graph convolution idea proposed almost a decade ago. In this paper, we extend this basic component,
Tran, DV, Navarin, N, Sperduti, A
core
Feature-Dependent Graph Convolutional Autoencoders with Adversarial Training Methods
, 2019 Graphs are ubiquitous for describing and modeling complicated data structures, and graph embedding is an effective solution to learn a mapping from a graph to a low-dimensional vector space while preserving relevant graph characteristics.
Michael Blumenstein +11 more
core +1 more source
Advanced Science, EarlyView.Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi +7 more
wiley +1 more source
Advanced Science, EarlyView.A large‐area, noise‐resilient multiplexed triboelectric sensing garment based on a multilayer shielded architecture and SnS2 nanoflower‐engineered hybrid materials enables interference‐suppressed, high‐fidelity full‐body motion tracking, achieving 0.13% signal misrecognition, 57 dB electromagnetic noise attenuation, and >1.5 N threshold force, while ...
Beibei Shao +14 more
wiley +1 more source
GAS-GCN: Gated Action-Specific Graph Convolutional Networks for Skeleton-Based Action Recognition
, 2020 Skeleton-based action recognition has achieved great advances with the development of graph convolutional networks (GCNs). Many existing GCNs-based models only use the fixed hand-crafted adjacency matrix to describe the connections between human body ...
Wensong Chan, Yang Wu, Zhiqiang Tian
core +1 more source