Results 261 to 270 of about 1,049,471 (309)

Tumbling Magnetic Microrobots for Targeted In Vivo Drug Delivery in the GI Tract

Advanced Robotics Research, EarlyView.
We introduce a microrobot design and integrated system for on‐demand targeted drug release in the gastrointestinal tract. The microrobot has an embedded magnet for actuation with external magnetic fields and is visualized in real time using ultrasound. It has two drug release ports sealed with a thermally sensitive wax. Local heating of the wax using a
Aaron C. Davis, Luis Carlos Sanjuan Acosta, Shubh P. Mehta, Baiyan Qi, Yang Yang, Luis Solorio, Craig J. Goergen, David J. Cappelleri   +7 more
wiley   +1 more source

Auditory–Tactile Congruence for Synthesis of Adaptive Pain Expressions in RoboPatients

Advanced Robotics Research, EarlyView.
In this work, we explore auditory–tactile congruence for synthesizing adaptive vocal pain expressions in robopatients. Using a robopatient platform that integrates vocal pain sounds with palpation forces, we conducted 7680 trials across 20 participants.
Saitarun Nadipineni, Chapa Sirithunge, Yue Xie, Fumiya Iida, Thilina Dulantha Lalitharatne   +4 more
wiley   +1 more source

Backpropagation Through Soft Body: Investigating Information Processing in Brain–Body Coupling Systems

Advanced Robotics Research, EarlyView.
This study explores how information processing is distributed between brains and bodies through a codesign approach. Using the “backpropagation through soft body” framework, brain–body coupling agents are developed and analyzed across several tasks in which output is generated through the agents’ physical dynamics.
Hiroki Tomioka, Katsuma Inoue, Yasuo Kuniyoshi, Kohei Nakajima   +3 more
wiley   +1 more source

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MGAT: Multi-view Graph Attention Networks

Neural Networks, 2020
Multi-view graph embedding is aimed at learning low-dimensional representations of nodes that capture various relationships in a multi-view network, where each view represents a type of relationship among nodes.
Yu Xie, Yuanqiao Zhang, Maoguo Gong, Zedong Tang, Chao Han   +4 more
semanticscholar   +5 more sources

When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks

IEEE International Conference on Data Engineering, 2023
Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to the temporal changes and the dynamic spatial correlations of the traffic data.
Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Liang Zeng, Chenxing Wang   +6 more
semanticscholar   +1 more source

HGAT: Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification

ACM Trans. Inf. Syst., 2021
Short text classification has been widely explored in news tagging to provide more efficient search strategies and more effective search results for information retrieval. However, most existing studies, concentrating on long text classification, deliver
Tianchi Yang, Linmei Hu, C. Shi, Houye Ji, Xiaoli Li, Liqiang Nie   +5 more
semanticscholar   +1 more source

Meta Learning With Graph Attention Networks for Low-Data Drug Discovery

IEEE Transactions on Neural Networks and Learning Systems, 2023
Finding candidate molecules with favorable pharmacological activity, low toxicity, and proper pharmacokinetic properties is an important task in drug discovery.
Qiujie Lv, Guanxing Chen, Ziduo Yang, Weihe Zhong, Calvin Yu‐Chian Chen   +4 more
semanticscholar   +1 more source

Simple and deep graph attention networks

Knowledge-Based Systems
Guangxin Su, Hanchen Wang, Ying Zhang, Wenjie Zhang, Xuemin Lin   +4 more
semanticscholar   +3 more sources

Path reliability-based graph attention networks

Neural Networks, 2023
Self-attention mechanism has been successfully introduced in Graph Neural Networks (GNNs) for graph representation learning and achieved state-of-the-art performances in tasks such as node classification and node attacks. In most existing attention-based GNNs, attention score is only computed between two directly connected nodes with their ...
Yayang, Li, Shuqing, Liang, Yuncheng, Jiang   +2 more
openaire   +2 more sources

TC-GATN: Temporal Causal Graph Attention Networks With Nonlinear Paradigm for Multivariate Time-Series Forecasting in Industrial Processes

IEEE Transactions on Industrial Informatics, 2023
Multivariate time-series (MTS) forecasting plays an important role in industrial process monitoring, control, and optimization. Usually, hierarchical interactive behaviors among industrial MTS have formed complex nonlinear causal characteristics, which ...
Jince Li, Yilin Shi, Hongguang Li, Bo Yang   +3 more
semanticscholar   +1 more source