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 Classifier-Free Guidance Weight Schedulers
Xi Wang, Nicolas Dufour, Nefeli Andreou, Marie-Paule Cani, Victoria Fernandez Abrevaya, David Picard, Vicky Kalogeiton
An Analysis of Driver-Initiated Takeovers during Assisted Driving and their Effect on Driver Satisfaction
Robin Schwager, Michael Grimm, Xin Liu, Lukas Ewecker, Tim Bruehl, Tin Stribor Sohn, Soeren Hohmann
Cross-cultural Inspiration Detection and Analysis in Real and LLM-generated Social Media Data
Oana Ignat, Gayathri Ganesh Lakshmy, Rada Mihalcea
Toward a Better Understanding of Fourier Neural Operators from a Spectral Perspective
Shaoxiang Qin, Fuyuan Lyu, Wenhui Peng, Dingyang Geng, Ju Wang, Xing Tang, Sylvie Leroyer, Naiping Gao, Xue Liu, Liangzhu Leon Wang
PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System
Steve Rhyner, Haocong Luo, Juan Gómez-Luna, Mohammad Sadrosadati, Jiawei Jiang, Ataberk Olgun, Harshita Gupta, Ce Zhang, Onur Mutlu
Driver Attention Tracking and Analysis
Dat Viet Thanh Nguyen, Anh Tran, Hoai Nam Vu, Cuong Pham, Minh Hoai