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
Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox
Yijun Liu, Yuan Meng, Fang Wu, Shenhao Peng, Hang Yao, Chaoyu Guan, Chen Tang, Xinzhu Ma, Zhi Wang, Wenwu Zhu
Memory Faults in Activation-sparse Quantized Deep Neural Networks: Analysis and Mitigation using Sharpness-aware Training
Akul Malhotra, Sumeet Kumar Gupta
How structured are the representations in transformer-based vision encoders? An analysis of multi-object representations in vision-language models
Tarun Khajuria, Braian Olmiro Dias, Jaan Aru
Word Order in English-Japanese Simultaneous Interpretation: Analyses and Evaluation using Chunk-wise Monotonic Translation
Kosuke Doi, Yuka Ko, Mana Makinae, Katsuhito Sudoh, Satoshi Nakamura
Unraveling Code-Mixing Patterns in Migration Discourse: Automated Detection and Analysis of Online Conversations on Reddit
Fedor Vitiugin, Sunok Lee, Henna Paakki, Anastasiia Chizhikova, Nitin Sawhney
Entropy-statistical approach to phase-locking detection of pulse oscillations: application for the analysis of biosignal synchronization
Petr Boriskov, Vadim Putrolaynen, Andrei Velichko, Kristina Peltonen