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Found 6 papers, 1 papers with code
Spatial-variant causal Bayesian inference for rapid seismic ground failures and impacts estimation
Rapid and accurate estimation of post-earthquake ground failures and building damage is critical for effective post-disaster responses.
Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language Models
Traditional systems for estimating human loss in disasters often depend on manually collected early casualty reports from global media, a process that's labor-intensive and slow with notable time delays.
Normalizing flow-based deep variational Bayesian network for seismic multi-hazards and impacts estimation from InSAR imagery
Onsite disasters like earthquakes can trigger cascading hazards and impacts, such as landslides and infrastructure damage, leading to catastrophic losses; thus, rapid and accurate estimates are crucial for timely and effective post-disaster responses.
Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery
We gathered and annotated building damage ground truth data in Lee County, Florida, and compared the introduced method's estimation results with the ground truth and benchmarked it against state-of-the-art models to assess the effectiveness of our proposed method.
Black-box Prompt Learning for Pre-trained Language Models
Particularly, instead of fine-tuning the model in the cloud, we adapt PLMs by prompt learning, which efficiently optimizes only a few parameters of the discrete prompts.
Explainable Sentence-Level Sentiment Analysis for Amazon Product Reviews
For the study of interpretability, we consider the attention weights distribution of single sentence and the attention weights of main aspect terms.
