General Analysis
General analysis encompasses a broad range of methodologies applied across diverse scientific domains to extract meaningful insights from data. Current research focuses on developing robust and efficient analytical techniques, including the application of machine learning models like convolutional neural networks, graph neural networks, and transformer architectures, as well as statistical methods for data modeling and hypothesis testing. These advancements are improving the accuracy and efficiency of analyses in fields ranging from medical image processing and materials science to social media analysis and autonomous systems, ultimately leading to more reliable scientific findings and improved decision-making in various applications.
Papers
Analysis and Improvement of Rank-Ordered Mean Algorithm in Single-Photon LiDAR
William C. Yau, Weijian Zhang, Hashan Kavinga Weerasooriya, Stanley H. Chan
BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning
Baoyuan Wu, Hongrui Chen, Mingda Zhang, Zihao Zhu, Shaokui Wei, Danni Yuan, Mingli Zhu, Ruotong Wang, Li Liu, Chao Shen
Analysis of Robotic System Models Through Property Inheritance from Petri Net Meta-models
Maksym Figat, Cezary Zieliński
Modeling and Analysis of Multi-Line Orders in Multi-Tote Storage and Retrieval Autonomous Mobile Robot Systems
Xiaotao Shan, Yichao Jin, Peizheng Li, Koichi Kondo
Depression Detection and Analysis using Large Language Models on Textual and Audio-Visual Modalities
Avinash Anand, Chayan Tank, Sarthak Pol, Vinayak Katoch, Shaina Mehta, Rajiv Ratn Shah