Real Time
Real-time processing focuses on developing systems capable of analyzing and responding to data instantaneously, crucial for applications demanding immediate feedback. Current research emphasizes efficient algorithms and model architectures, such as those based on deep learning, to reduce computational latency in diverse domains including robotics, healthcare, and AI-assisted tutoring. This field's advancements are driving progress in areas like autonomous navigation, personalized healthcare monitoring, and human-computer interaction, enabling more responsive and effective systems.
Papers
Real-time Adapting Routing (RAR): Improving Efficiency Through Continuous Learning in Software Powered by Layered Foundation Models
Kirill Vasilevski, Dayi Lin, Ahmed Hassan
NeuralDEM - Real-time Simulation of Industrial Particulate Flows
Benedikt Alkin, Tobias Kronlachner, Samuele Papa, Stefan Pirker, Thomas Lichtenegger, Johannes Brandstetter
Real-time Deformation-aware Control for Autonomous Robotic Subretinal Injection under iOCT Guidance
Demir Arikan, Peiyao Zhang, Michael Sommersperger, Shervin Dehghani, Mojtaba Esfandiari, Russel H. Taylor, M. Ali Nasseri, Peter Gehlbach, Nassir Navab, Iulian Iordachita
Psycho Gundam: Electroencephalography based real-time robotic control system with deep learning
Chi-Sheng Chen, Wei-Sheng Wang
Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play
Sha Li, Revanth Gangi Reddy, Khanh Duy Nguyen, Qingyun Wang, May Fung, Chi Han, Jiawei Han, Kartik Natarajan, Clare R. Voss, Heng Ji
A Causal Graph-Enhanced Gaussian Process Regression for Modeling Engine-out NOx
Shrenik Zinage, Ilias Bilionis, Peter Meckl
Real-time 3D-aware Portrait Video Relighting
Ziqi Cai, Kaiwen Jiang, Shu-Yu Chen, Yu-Kun Lai, Hongbo Fu, Boxin Shi, Lin Gao