Tingsong Xiao
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View article: Temporally Detailed Hypergraph Neural ODEs for Type 2 Diabetes Progression Modeling
Temporally Detailed Hypergraph Neural ODEs for Type 2 Diabetes Progression Modeling Open
Disease progression modeling aims to characterize and predict how a patient's disease complications worsen over time based on longitudinal electronic health records (EHRs). Accurate modeling of disease progression, such as type 2 diabetes,…
View article: Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities
Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities Open
The Smart Grid (SG) is a critical energy infrastructure that collects real-time electricity usage data to forecast future energy demands using information and communication technologies (ICT). Due to growing concerns about data security an…
View article: DecoyDB: A Dataset for Graph Contrastive Learning in Protein-Ligand Binding Affinity Prediction
DecoyDB: A Dataset for Graph Contrastive Learning in Protein-Ligand Binding Affinity Prediction Open
Predicting the binding affinity of protein-ligand complexes plays a vital role in drug discovery. Unfortunately, progress has been hindered by the lack of large-scale and high-quality binding affinity labels. The widely used PDBbind datase…
View article: XTSFormer: Cross-Temporal-Scale Transformer for Irregular-Time Event Prediction in Clinical Applications
XTSFormer: Cross-Temporal-Scale Transformer for Irregular-Time Event Prediction in Clinical Applications Open
Adverse clinical events related to unsafe care are among the top ten causes of death in the U.S. Accurate modeling and prediction of clinical events from electronic health records (EHRs) play a crucial role in patient safety enhancement. A…
View article: Prompt Inversion Attack against Collaborative Inference of Large Language Models
Prompt Inversion Attack against Collaborative Inference of Large Language Models Open
Large language models (LLMs) have been widely applied for their remarkable capability of content generation. However, the practical use of open-source LLMs is hindered by high resource requirements, making deployment expensive and limiting…
View article: Accelerate Coastal Ocean Circulation Model with AI Surrogate
Accelerate Coastal Ocean Circulation Model with AI Surrogate Open
Nearly 900 million people live in low-lying coastal zones around the world and bear the brunt of impacts from more frequent and severe hurricanes and storm surges. Oceanographers simulate ocean current circulation along the coasts to devel…
View article: Spatio-Temporal Partial Sensing Forecast for Long-term Traffic
Spatio-Temporal Partial Sensing Forecast for Long-term Traffic Open
Traffic forecasting uses recent measurements by sensors installed at chosen locations to forecast the future road traffic. Existing work either assumes all locations are equipped with sensors or focuses on short-term forecast. This paper s…
View article: Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals
Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals Open
Large Language Models (LLMs) have emerged as potent tools for advancing the United Nations' Sustainable Development Goals (SDGs). However, the attitudinal disparities between LLMs and humans towards these goals can pose significant challen…
View article: Spatial-Logic-Aware Weakly Supervised Learning for Flood Mapping on Earth Imagery
Spatial-Logic-Aware Weakly Supervised Learning for Flood Mapping on Earth Imagery Open
Flood mapping on Earth imagery is crucial for disaster management, but its efficacy is hampered by the lack of high-quality training labels. Given high-resolution Earth imagery with coarse and noisy training labels, a base deep neural netw…
View article: XTSFormer: Cross-Temporal-Scale Transformer for Irregular-Time Event Prediction in Clinical Applications
XTSFormer: Cross-Temporal-Scale Transformer for Irregular-Time Event Prediction in Clinical Applications Open
Adverse clinical events related to unsafe care are among the top ten causes of death in the U.S. Accurate modeling and prediction of clinical events from electronic health records (EHRs) play a crucial role in patient safety enhancement. A…
View article: Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery
Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery Open
Deep learning for Earth imagery plays an increasingly important role in geoscience applications such as agriculture, ecology, and natural disaster management. Still, progress is often hindered by the limited training labels. Given Earth im…
View article: Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery
Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery Open
Deep learning for Earth imagery plays an increasingly important role in geoscience applications such as agriculture, ecology, and natural disaster management. Still, progress is often hindered by the limited training labels. Given Earth im…
View article: A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space
A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space Open
Transformers are widely used deep learning architectures. Existing transformers are mostly designed for sequences (texts or time series), images or videos, and graphs. This paper proposes a novel transformer model for massive (up to a mill…