5 accepted papers
Fine-grained traffic prediction is critically important for mitigating traffic congestion in key urban areas and for providing lane-change guidance in autonomous vehicles and navigation systems. However, task-specific models are not efficient enough, city-scale pre-trained models often overlook fine…
System-incremental log analysis, involves the ongoing training of a model using logs from diverse systems to enable effective resolution of log analysis tasks across an expanding array of systems. Existing continual learning methods, which are based on prompt tuning, have shown challenges in insuffi…
Variation of Arctic sea ice has significant impacts on polar ecosystems, transporting routes, coastal communities, and global climate. Tracing the change of sea ice at a finer scale is paramount for both operational applications and scientific studies. Recent pan-Arctic sea ice forecasting methods t…
Automated log analysis plays a vital role in software operations, with deep learning methods demonstrating effectiveness for analyzing logs from individual systems. However, existing methods face limitations in efficiency, adaptability, and knowledge preservation in system-incremental log analysis.…
Learning unsigned distance fields (UDF) directly from raw point clouds as the implicit representation for surface reconstruction is a promising learning-based method for reconstructing open surfaces and supervision-free attributes. In most UDF methods, Chamfer Distance (CD), the commonly used metric…