34 citations · 69 across the 11 of their papers we have counts for
11 papers
From Causal Plausibility to Causal Reliability: Evaluating LLMs as Calibrated Direct Causal-Edge Classifiers
Amit Kumar, Elnur Adl Zarabi, Suranjana Trivedy +4
Large language models (LLMs) are increasingly used to provide prior causal knowledge for structural causal discovery, yet whether their direct-edge judgments and confidence can be…
Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices
Shengkun Wang, YangXiao Bai, Taoran Ji +3
Predicting stock market is vital for investors and policymakers, acting as a barometer of the economic health. We leverage social media data, a potent source of public sentiment, i…
ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction
Shengkun Wang, YangXiao Bai, Kaiqun Fu +3
For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social medi…
LaTeX: Language Pattern-aware Triggering Event Detection for Adverse Experience during Pandemics
Kaiqun Fu, Yangxiao Bai, Weiwei Zhang +1
The COVID-19 pandemic has accentuated socioeconomic disparities across various racial and ethnic groups in the United States. While previous studies have utilized traditional surve…
Social media use among American Indians in South Dakota: Preferences and perceptions
Deepthi Kolady, Amrit Dumre, Weiwei Zhang +3
Social media use data is widely being used in health, psychology, and marketing research to analyze human behavior. However, we have very limited knowledge on social media use amon…
DG-Trans: Dual-level Graph Transformer for Spatiotemporal Incident Impact Prediction on Traffic Networks
Yanshen Sun, Kaiqun Fu, Chang-Tien Lu
The prompt estimation of traffic incident impacts can guide commuters in their trip planning and improve the resilience of transportation agencies' decision-making on resilience. H…
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang +7
Deep learning's performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does…
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang +7
Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing. As an…
TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction
Kaiqun Fu, Taoran Ji, Liang Zhao +1
Critical incident stages identification and reasonable prediction of traffic incident duration are essential in traffic incident management. In this paper, we propose a traffic inc…
Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks
Taoran Ji, Zhiqian Chen, Nathan Self +3
Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional…
SAFEBIKE: A Bike-sharing Route Recommender with Availability Prediction and Safe Routing
Weisheng Zhong, Fanglan Chen, Kaiqun Fu +1
This paper presents SAFEBIKE, a novel route recommendation system for bike-sharing service that utilizes station information to infer the number of available bikes in dock and reco…