Search Results for author: Shashi Shekhar

Found 15 papers, 2 papers with code

Physics-Guided Abnormal Trajectory Gap Detection

no code implementations10 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.

Towards Spatially-Lucid AI Classification in Non-Euclidean Space: An Application for MxIF Oncology Data

no code implementations22 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.

Domain Adaptation

Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach

no code implementations19 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.

regression

A Survey on Solving and Discovering Differential Equations Using Deep Neural Networks

no code implementations26 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.

Navigate

Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation

1 code implementation13 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.

Spatiotemporal Data Mining: A Survey

no code implementations26 Jun 2022 Arun Sharma, Zhe Jiang, Shashi Shekhar

Furthermore, it has a detailed survey of parallel formulations of spatiotemporal data mining.

Epidemiology

Towards Comparative Physical Interpretation of Spatial Variability Aware Neural Networks: A Summary of Results

no code implementations29 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).

Vehicle Emissions Prediction with Physics-Aware AI Models: Preliminary Results

no code implementations2 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.

Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey

2 code implementations22 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.

Clustering

A National Research Agenda for Intelligent Infrastructure: 2021 Update

no code implementations5 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

Towards Spatial Variability Aware Deep Neural Networks (SVANN): A Summary of Results

no code implementations17 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).

A Physics Model-Guided Online Bayesian Framework for Energy Management of Extended Range Electric Delivery Vehicles

no code implementations1 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.

energy management Management

Adversarial Attacks on Reinforcement Learning based Energy Management Systems of Extended Range Electric Delivery Vehicles

no code implementations1 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.

energy management Management

Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data

no code implementations27 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.

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