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 of Unstructured High-Density Crowded Scenes for Crowd Monitoring
Alexandre Matov
Analysis of Partially-Calibrated Sparse Subarrays for Direction Finding with Extended Degrees of Freedom
W. S. Leite, R. C. de Lamare
Towards an Analysis of Discourse and Interactional Pragmatic Reasoning Capabilities of Large Language Models
Amelie Robrecht, Judith Sieker, Clara Lachenmaier, Sina Zarieß, Stefan Kopp
Analysis of Argument Structure Constructions in a Deep Recurrent Language Model
Pegah Ramezani, Achim Schilling, Patrick Krauss
A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model
Jingwen Zhou, Qinghua Lu, Jieshan Chen, Liming Zhu, Xiwei Xu, Zhenchang Xing, Stefan Harrer
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