35 citations · 48 across the 28 of their papers we have counts for
10 papers · 1 filter
Foundation Model for Lossy Compression of Spatiotemporal Scientific Data
Xiao Li, Jaemoon Lee, Anand Rangarajan +1
We present a foundation model (FM) for lossy scientific data compression, combining a variational autoencoder (VAE) with a hyper-prior structure and a super-resolution (SR) module.…
Dynamic Graph Attention Networks for Travel Time Distribution Prediction in Urban Arterial Roads
Nooshin Yousefzadeh, Rahul Sengupta, Sanjay Ranka
Effective congestion management along signalized corridors is essential for improving productivity and reducing costs, with arterial travel time serving as a key performance metric…
Attention Based Machine Learning Methods for Data Reduction with Guaranteed Error Bounds
Xiao Li, Jaemoon Lee, Anand Rangarajan +1
Scientific applications in fields such as high energy physics, computational fluid dynamics, and climate science generate vast amounts of data at high velocities. This exponential…
Video-based Pedestrian and Vehicle Traffic Analysis During Football Games
Jacques P. Fleischer, Ryan Pallack, Ahan Mishra +7
This paper utilizes video analytics to study pedestrian and vehicle traffic behavior, focusing on analyzing traffic patterns during football gamedays. The University of Florida (UF…
Graph Attention Network for Lane-Wise and Topology-Invariant Intersection Traffic Simulation
Nooshin Yousefzadeh, Rahul Sengupta, Yashaswi Karnati +2
Traffic congestion has significant economic, environmental, and social ramifications. Intersection traffic flow dynamics are influenced by numerous factors. While microscopic traff…
MTDT: A Multi-Task Deep Learning Digital Twin
Nooshin Yousefzadeh, Rahul Sengupta, Yashaswi Karnati +2
Traffic congestion has significant impacts on both the economy and the environment. Measures of Effectiveness (MOEs) have long been the standard for evaluating traffic intersection…
Machine Learning Techniques for Data Reduction of Climate Applications
Xiao Li, Qian Gong, Jaemoon Lee +3
Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals…
Machine Learning Techniques for Data Reduction of CFD Applications
Jaemoon Lee, Ki Sung Jung, Qian Gong +5
We present an approach called guaranteed block autoencoder that leverages Tensor Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid dynamics…
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
Qian Gong, Jieyang Chen, Ben Whitney +13
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional data compression capa…
Spatiotemporally adaptive compression for scientific dataset with feature preservation -- a case study on simulation data with extreme climate events analysis
Qian Gong, Chengzhu Zhang, Xin Liang +10
Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated…