Results 161 to 170 of about 110,849 (310)

Deep Lagrangian Propagation in Graph Neural Networks [PDF]

, 2020
Graph Neural Networks (Scarselli et al., 2009) exploit an iterative diffusion procedure to compute the node states as the fixed point of the trainable state transition function. In this paper, we show how to cast this scheme as a constrained optimization
Marco Maggini, Stefano Melacci, Matteo Tiezzi, Giuseppe Marra   +3 more
core  

Graph Neural Networks for Bipartite Graphs

Bipartite graphs are a special type of graph data structure where vertices can be divided into two disjoint and independent sets, and each edge connects a vertex from one set to a vertex in the other set. They can be used to model many real-world applications such as user-item interaction networks, authorship networks, and product-customer networks ...
openaire   +2 more sources

Memorization in Graph Neural Networks

CoRR
Deep neural networks (DNNs) have been shown to memorize their training data, yet similar analyses for graph neural networks (GNNs) remain largely under-explored. We introduce NCMemo (Node Classification Memorization), the first framework to quantify label memorization in semi-supervised node classification.
Jamadandi, Adarsh, Xu, Jing, Dziedzic, Adam, Boenisch, Franziska   +3 more
openaire   +3 more sources

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

Advanced Materials Technologies, EarlyView.
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu, Mengyao Xiao, Zhouli Sui, Yinpeng Wang, Daoye Zheng, Yuxin Liu, Luwei Wang, Huajun Liu, Chengkuo Lee   +8 more
wiley   +1 more source

Utilizing correlation in space and time: Anomaly detection for Industrial Internet of Things (IIoT) via spatiotemporal gated graph attention network

Alexandria Engineering Journal
The Industrial Internet of Things (IIoT) infrastructure is inherently complex, often involving a multitude of sensors and devices. Ensuring the secure operation and maintenance of these systems is increasingly critical, making anomaly detection a vital ...
Yuxin Fan, Tingting Fu, Nikolai Izmailovich Listopad, Peng Liu, Sahil Garg, Mohammad Mehedi Hassan   +5 more
doaj   +1 more source

Recent Advances of Slip Sensors for Smart Robotics

Advanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

Graph neural networks and MSO

CoRR
We give an alternative proof for the existing result that recurrent graph neural networks working with reals have the same expressive power in restriction to monadic second-order logic MSO as the graded modal substitution calculus. The proof is based on constructing distributed automata that capture all MSO-definable node properties over trees. We also
Veeti Ahvonen, Damian Heiman, Antti Kuusisto   +2 more
openaire   +2 more sources

Vision‐Augmented Wearable Interfaces: Bioinspired Approaches for Realistic AI‐Human‐Machine Interaction

Advanced Materials Technologies, EarlyView.
This review presents recent progress in vision‐augmented wearable interfaces that combine artificial vision, soft wearable sensors, and exoskeletal robots. Inspired by biological visual systems, these technologies enable multimodal perception and intelligent human–machine interaction.
Jihun Lee, Gwuidong Ryu, Kisoo Kim, Feng Xu, Won Gu Lee   +4 more
wiley   +1 more source

Non-Direct Encoding Method Based on Cellular Automata to Design Neural Network Architectures [PDF]

, 2005
Architecture design is a fundamental step in the successful application of Feed forward Neural Networks. In most cases a large number of neural networks architectures suitable to solve a problem exist and the architecture design is, unfortunately, still ...
Gutiérrez Sánchez, Germán, Molina López, José Manuel, Galván, Inés M., Sanchis de Miguel, María Araceli, Isasi, Pedro   +4 more
core  

At Home Detection of Ovarian Health Biomarker in Menstruation Blood

Advanced Materials Technologies, EarlyView.
A lateral flow assay enables the detection of anti‐Müllerian hormone directly in unprocessed menstrual blood using silica‐gold nanoshells and smartphone‐assisted machine learning analysis. The platform supports decentralized, user‐operated testing in wearable and dipstick formats, highlighting the potential of menstrual blood as a non‐invasive matrix ...
Lucas Dosnon, Thomas Rduch, Salma Sherif Azer, Inge K. Herrmann   +3 more
wiley   +1 more source