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
Needle in a Haystack: An Analysis of High-Agreement Workers on MTurk for Summarization
Lining Zhang, Simon Mille, Yufang Hou, Daniel Deutsch, Elizabeth Clark, Yixin Liu, Saad Mahamood, Sebastian Gehrmann, Miruna Clinciu, Khyathi Chandu, João Sedoc
Texture Representation via Analysis and Synthesis with Generative Adversarial Networks
Jue Lin, Gaurav Sharma, Thrasyvoulos N. Pappas
Analysis of Drug repurposing Knowledge graphs for Covid-19
Ajay Kumar Gogineni
Analysis and Utilization of Entrainment on Acoustic and Emotion Features in User-agent Dialogue
Daxin Tan, Nikos Kargas, David McHardy, Constantinos Papayiannis, Antonio Bonafonte, Marek Strelec, Jonas Rohnke, Agis Oikonomou Filandras, Trevor Wood
Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods
Nesryne Mejri, Laura Lopez-Fuentes, Kankana Roy, Pavel Chernakov, Enjie Ghorbel, Djamila Aouada
Controlled Text Generation using T5 based Encoder-Decoder Soft Prompt Tuning and Analysis of the Utility of Generated Text in AI
Damith Chamalke Senadeera, Julia Ive
Hierarchical Decomposition and Analysis for Generalized Planning
Siddharth Srivastava