no code implementations • 10 Mar 2024 • Arun Sharma, Shashi Shekhar
The problem is challenging due to the difficulty of bounding the possible locations of the moving object during a trajectory gap, and the very high computational cost of detecting gaps in such a large volume of location data.
no code implementations • 22 Feb 2024 • Majid Farhadloo, Arun Sharma, Jayant Gupta, Alexey Leontovich, Svetomir N. Markovic, Shashi Shekhar
Given multi-category point sets from different place-types, our goal is to develop a spatially-lucid classifier that can distinguish between two classes based on the arrangements of their points.
no code implementations • 19 Oct 2023 • Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian
Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty.
no code implementations • 26 Apr 2023 • Hyeonjung, Jung, Jayant Gupta, Bharat Jayaprakash, Matthew Eagon, Harish Panneer Selvam, Carl Molnar, William Northrop, Shashi Shekhar
Ordinary and partial differential equations (DE) are used extensively in scientific and mathematical domains to model physical systems.
1 code implementation • 13 Jan 2023 • Yan Li, Mingzhou Yang, Matthew Eagon, Majid Farhadloo, Yiqun Xie, William F. Northrop, Shashi Shekhar
The eco-toll estimation problem quantifies the expected environmental cost (e. g., energy consumption, exhaust emissions) for a vehicle to travel along a path.
no code implementations • 26 Jun 2022 • Arun Sharma, Zhe Jiang, Shashi Shekhar
Furthermore, it has a detailed survey of parallel formulations of spatiotemporal data mining.
no code implementations • 22 Dec 2021 • Majid Farhadloo, Carl Molnar, Gaoxiang Luo, Yan Li, Shashi Shekhar, Rachel L. Maus, Svetomir N. Markovic, Raymond Moore, Alexey Leontovich
This problem is challenging due to an exponential number of category subsets which may vary in the strength of spatial interactions.
no code implementations • 29 Oct 2021 • Jayant Gupta, Carl Molnar, Gaoxiang Luo, Joe Knight, Shashi Shekhar
The proposed physical interpretation improves the transparency of SVANN models and the analytical results highlight the trade-off between model transparency and model performance (e. g., F1-score).
no code implementations • 2 May 2021 • Harish Panneer Selvam, Yan Li, Pengyue Wang, William F. Northrop, Shashi Shekhar
Given an on-board diagnostics (OBD) dataset and a physics-based emissions prediction model, this paper aims to develop an accurate and computational-efficient AI (Artificial Intelligence) method that predicts vehicle emissions.
2 code implementations • 22 Mar 2021 • Yiqun Xie, Shashi Shekhar, Yan Li
Mapping of spatial hotspots, i. e., regions with significantly higher rates of generating cases of certain events (e. g., disease or crime cases), is an important task in diverse societal domains, including public health, public safety, transportation, agriculture, environmental science, etc.
no code implementations • 5 Jan 2021 • Daniel Lopresti, Shashi Shekhar
Strategic, sustained Federal investments in intelligent infrastructure will increase safety and resilience, improve efficiencies and civic services, and broaden employment opportunities and job growth nationwide.
Computers and Society
no code implementations • 17 Nov 2020 • Jayant Gupta, Yiqun Xie, Shashi Shekhar
Spatial variability has been observed in many geo-phenomena including climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e. g., forest, grasslands, wetlands, and deserts).
no code implementations • 1 Jun 2020 • Pengyue Wang, Yan Li, Shashi Shekhar, William F. Northrop
A physics model-guided online Bayesian framework is described and validated on large number of in-use driving samples of EREVs used for last-mile package delivery.
no code implementations • 1 Jun 2020 • Pengyue Wang, Yan Li, Shashi Shekhar, William F. Northrop
Adversarial examples are firstly investigated in the area of computer vision: by adding some carefully designed ''noise'' to the original input image, the perturbed image that cannot be distinguished from the original one by human, can fool a well-trained classifier easily.
no code implementations • 27 Dec 2016 • Anuj Karpatne, Gowtham Atluri, James Faghmous, Michael Steinbach, Arindam Banerjee, Auroop Ganguly, Shashi Shekhar, Nagiza Samatova, Vipin Kumar
Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery.