Results 181 to 190 of about 1,903,201 (339)
Bioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application
Advanced Materials, EarlyView.This review comprehensively summarizes the recent progress in the design and fabrication of sensory‐adaptation‐inspired devices and highlights their valuable applications in electronic skin, wearable electronics, and machine vision. The existing challenges and future directions are addressed in aspects such as device performance optimization ...
Guodong Gong +12 more
wiley +1 more source
MI-HGNN: Morphology-Informed Heterogeneous Graph Neural Network for Legged Robot Contact Perception [PDF]
Daniel Butterfield +3 more
openalex +1 more source
Scalable Neural Network Training over Distributed Graphs [PDF]
, 2023 Aashish Kolluri +3 more
openalex +1 more source
Advanced Materials, EarlyView.AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll +19 more
wiley +1 more source
Jet Discrimination with Quantum Complete Graph Neural Network [PDF]
Yi‐An Chen, K. F. Chen
openalex +1 more source
Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services [PDF]
, 2023 Liekang Zeng +5 more
openalex +1 more source
Engineered Protein‐Based Ionic Conductors for Sustainable Energy Storage Applications
Advanced Materials, EarlyView.Rational incorporation of charged residues into an engineered, self‐assembling protein scaffold yields solid‐state protein films with outstanding ionic conductivity. Salt‐doping further enhances conductivity, an effect amplified in the engineered variants. These properties enable the material integration into an efficient supercapacitor.
Juan David Cortés‐Ossa +14 more
wiley +1 more source
How Graph Neural Networks Learn: Lessons from Training Dynamics [PDF]
, 2023 Chenxiao Yang +4 more
openalex +1 more source
Conformal inductive graph neural networks
Conformal prediction (CP) transforms any model's output into prediction sets guaranteed to include (cover) the true label. CP requires exchangeability, a relaxation of the i.i.d. assumption, to obtain a valid distribution-free coverage guarantee. This makes it directly applicable to transductive node-classification.
Zargarbashi, Soroush H. +1 more
openaire +2 more sources