no code implementations • 13 May 2024 • Sahara Ali, Omar Faruque, Jianwu Wang
We then propose our deep learning based potential outcome model for spatiotemporal causal inference.
no code implementations • 10 Apr 2024 • Seraj Al Mahmud Mostafa, Jinbo Wang, Benjamin Holt, Jianwu Wang
Ocean eddies play a significant role both on the sea surface and beneath it, contributing to the sustainability of marine life dependent on oceanic behaviors.
no code implementations • 3 Apr 2024 • Sahara Ali, Uzma Hasan, Xingyan Li, Omar Faruque, Akila Sampath, Yiyi Huang, Md Osman Gani, Jianwu Wang
This survey paper covers the breadth and depth of time-series and spatiotemporal causality methods, and their applications in Earth Science.
no code implementations • 1 Apr 2024 • Omar Faruque, Sahara Ali, Xue Zheng, Jianwu Wang
The growing availability and importance of time series data across various domains, including environmental science, epidemiology, and economics, has led to an increasing need for time-series causal discovery methods that can identify the intricate relationships in the non-stationary, non-linear, and often noisy real world data.
1 code implementation • 29 Jan 2024 • Xingyan Li, Andrew M. Sayer, Ian T. Carroll, Xin Huang, Jianwu Wang
In response, this paper introduces MT-HCCAR, an end-to-end deep learning model employing multi-task learning to simultaneously tackle cloud masking, cloud phase retrieval (classification tasks), and COT prediction (a regression task).
no code implementations • 10 Aug 2023 • Weilong Ding, Tianpu Zhang, Jianwu Wang, Zhuofeng Zhao
In our method, data normalization strategy is used to deal with data imbalance, due to long-tail distribution of traffic flow at network-wide toll stations.
1 code implementation • 8 Aug 2023 • Sahara Ali, Jianwu Wang
Arctic amplification has altered the climate patterns both regionally and globally, resulting in more frequent and more intense extreme weather events in the past few decades.
1 code implementation • 27 Apr 2023 • Omar Faruque, Francis Ndikum Nji, Mostafa Cham, Rohan Mandar Salvi, Xue Zheng, Jianwu Wang
Concentrating on joint deep representation learning of spatial and temporal features, we propose Deep Spatiotemporal Clustering (DSC), a novel algorithm for the temporal clustering of high-dimensional spatiotemporal data using an unsupervised deep learning method.
no code implementations • 22 Feb 2023 • Sahara Ali, Omar Faruque, Yiyi Huang, Md. Osman Gani, Aneesh Subramanian, Nicole-Jienne Shchlegel, Jianwu Wang
Through experiments on synthetic and observational data, we show how our research can substantially improve the ability to quantify leading causes of Arctic sea ice melt, further paving paths for causal inference in observational Earth science.
no code implementations • 16 Feb 2023 • Zichong Wang, Yang Zhou, Meikang Qiu, Israat Haque, Laura Brown, Yi He, Jianwu Wang, David Lo, Wenbin Zhang
The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern.
no code implementations • 10 May 2022 • Xin Wang, Azim Khan, Jianwu Wang, Aryya Gangopadhyay, Carl E. Busart, Jade Freeman
In this paper, we study how to best leverage edge and cloud resources to achieve better accuracy and latency for stream analytics using a type of RNN model called long short-term memory (LSTM).
1 code implementation • 17 Dec 2021 • Xin Wang, Pei Guo, Xingyan Li, Aryya Gangopadhyay, Carl E. Busart, Jade Freeman, Jianwu Wang
To tackle these problems, we leverage serverless computing and containerization techniques for automated scalable execution and reproducibility, and utilize the adapter design pattern to enable application portability and reproducibility across different clouds.
1 code implementation • 27 Jul 2021 • Sahara Ali, Yiyi Huang, Xin Huang, Jianwu Wang
Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play.
no code implementations • 24 Dec 2020 • Pei Guo, Achuna Ofonedu, Jianwu Wang
Causality discovery mines cause-effect relationships among different variables of a system and has been widely used in many disciplines including climatology and neuroscience.
no code implementations • 24 Aug 2018 • Wenbin Zhang, Jianwu Wang, Daeho Jin, Lazaros Oreopoulos, Zhibo Zhang
A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high-dimensional input space of the training samples into a low-dimensional space with the topology relations preserved.