Search Results for author: Xiaowei Jia

Found 45 papers, 5 papers with code

Physics-enhanced Neural Operator for Simulating Turbulent Transport

no code implementations31 May 2024 Shengyu Chen, Peyman Givi, Can Zheng, Xiaowei Jia

The precise simulation of turbulent flows is of immense importance in a variety of scientific and engineering fields, including climate science, freshwater science, and the development of energy-efficient manufacturing processes.

When are Foundation Models Effective? Understanding the Suitability for Pixel-Level Classification Using Multispectral Imagery

no code implementations17 Apr 2024 Yiqun Xie, Zhihao Wang, Weiye Chen, Zhili Li, Xiaowei Jia, Yanhua Li, Ruichen Wang, Kangyang Chai, Ruohan Li, Sergii Skakun

This work aims to enhance the understanding of the status and suitability of foundation models for pixel-level classification using multispectral imagery at moderate resolution, through comparisons with traditional machine learning (ML) and regular-size deep learning models.

Self-Supervised Learning

LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models

1 code implementation1 Apr 2024 Haoran Li, Junqi Liu, Zexian Wang, Shiyuan Luo, Xiaowei Jia, Huaxiu Yao

To address these issues, we propose LITE -- a multimodal large language model for environmental ecosystems modeling.

Decision Making Language Modelling +1

Knowledge-guided Machine Learning: Current Trends and Future Prospects

no code implementations24 Mar 2024 Anuj Karpatne, Xiaowei Jia, Vipin Kumar

We discuss different facets of KGML research in terms of the type of scientific knowledge used, the form of knowledge-ML integration explored, and the method for incorporating scientific knowledge in ML.

Improving Explainable Object-induced Model through Uncertainty for Automated Vehicles

no code implementations23 Feb 2024 Shihong Ling, Yue Wan, Xiaowei Jia, Na Du

The rapid evolution of automated vehicles (AVs) has the potential to provide safer, more efficient, and comfortable travel options.

Decision Making

Referee-Meta-Learning for Fast Adaptation of Locational Fairness

no code implementations20 Feb 2024 Weiye Chen, Yiqun Xie, Xiaowei Jia, Erhu He, Han Bao, Bang An, Xun Zhou

When dealing with data from distinct locations, machine learning algorithms tend to demonstrate an implicit preference of some locations over the others, which constitutes biases that sabotage the spatial fairness of the algorithm.

Decision Making Fairness +1

Nature-Guided Cognitive Evolution for Predicting Dissolved Oxygen Concentrations in North Temperate Lakes

no code implementations15 Feb 2024 Runlong Yu, Robert Ladwig, Xiang Xu, Peijun Zhu, Paul C. Hanson, Yiqun Xie, Xiaowei Jia

Using these simulated labels, we implement a multi-population cognitive evolutionary search, where models, mirroring natural organisms, adaptively evolve to select relevant feature interactions within populations for different lake types and tasks.

Combining Satellite and Weather Data for Crop Type Mapping: An Inverse Modelling Approach

no code implementations29 Jan 2024 Praveen Ravirathinam, Rahul Ghosh, Ankush Khandelwal, Xiaowei Jia, David Mulla, Vipin Kumar

We finally discuss the impact of weather by correlating our results with crop phenology to show that WSTATT is able to capture physical properties of crop growth.

Crop Type Mapping

SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models

no code implementations27 Jan 2024 Zhihao Wang, Yiqun Xie, Zhili Li, Xiaowei Jia, Zhe Jiang, Aolin Jia, Shuo Xu

Fairness-awareness has emerged as an essential building block for the responsible use of artificial intelligence in real applications.


Phenotyping calcification in vascular tissues using artificial intelligence

no code implementations15 Jan 2024 Mehdi Ramezanpour, Anne M. Robertson, Yasutaka Tobe, Xiaowei Jia, Juan R. Cebral

Fundamental studies are needed to determine how risk is influenced by the diverse calcification phenotypes.


FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems

no code implementations17 Nov 2023 Shiyuan Luo, Juntong Ni, Shengyu Chen, Runlong Yu, Yiqun Xie, Licheng Liu, Zhenong Jin, Huaxiu Yao, Xiaowei Jia

This raises a fundamental question in advancing the modeling of environmental ecosystems: how to build a general framework for modeling the complex relationships amongst various environmental data over space and time?

Future prediction

From molecules to scaffolds to functional groups: building context-dependent molecular representation via multi-channel learning

no code implementations5 Nov 2023 Yue Wan, Jialu Wu, Tingjun Hou, Chang-Yu Hsieh, Xiaowei Jia

Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks.

Drug Discovery Molecular Property Prediction +3

Koopman Invertible Autoencoder: Leveraging Forward and Backward Dynamics for Temporal Modeling

no code implementations19 Sep 2023 Kshitij Tayal, Arvind Renganathan, Rahul Ghosh, Xiaowei Jia, Vipin Kumar

Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes.

Decision Making

HOSSnet: an Efficient Physics-Guided Neural Network for Simulating Crack Propagation

no code implementations14 Jun 2023 Shengyu Chen, Shihang Feng, Yao Huang, Zhou Lei, Xiaowei Jia, Youzuo Lin, Estaben Rougier

Hybrid Optimization Software Suite (HOSS), which is a combined finite-discrete element method (FDEM), is one of the advanced approaches to simulating high-fidelity fracture and fragmentation processes but the application of pure HOSS simulation is computationally expensive.

Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement

no code implementations24 Apr 2023 Shengyu Chen, Tianshu Bao, Peyman Givi, Can Zheng, Xiaowei Jia

The results on two different types of turbulent flow data confirm the superiority of the proposed method in reconstructing the high-resolution DNS data and preserving the physical characteristics of flow transport.


Entity Aware Modelling: A Survey

no code implementations16 Feb 2023 Rahul Ghosh, HaoYu Yang, Ankush Khandelwal, Erhu He, Arvind Renganathan, Somya Sharma, Xiaowei Jia, Vipin Kumar

However, these entity characteristics are not readily available in many real-world scenarios, and different ML methods have been proposed to infer these characteristics from the data.

Fairness Uncertainty Quantification

STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19

no code implementations20 Jan 2023 Han Bao, Xun Zhou, Yiqun Xie, Yanhua Li, Xiaowei Jia

While deep learning approaches outperform conventional estimation techniques on tasks with abundant training data, the continuously evolving pandemic poses a significant challenge to solving this problem due to data nonstationarity, limited observations, and complex social contexts.

Generative Adversarial Network

Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin

no code implementations1 Jan 2023 Leikun Yin, Rahul Ghosh, Chenxi Lin, David Hale, Christoph Weigl, James Obarowski, Junxiong Zhou, Jessica Till, Xiaowei Jia, Troy Mao, Vipin Kumar, Zhenong Jin

In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season.

Clustering Decision Making

Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability

1 code implementation10 Dec 2022 Zhexiong Liu, Licheng Liu, Yiqun Xie, Zhenong Jin, Xiaowei Jia

One major advantage of our proposed method is that it improves the model adaptation to a large number of heterogeneous tasks.


Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applications

1 code implementation15 Oct 2022 Shaoming Xu, Ankush Khandelwal, Xiang Li, Xiaowei Jia, Licheng Liu, Jared Willard, Rahul Ghosh, Kelly Cutler, Michael Steinbach, Christopher Duffy, John Nieber, Vipin Kumar

To address this issue, we further propose a new strategy which augments a training segment with an initial value of the target variable from the timestep right before the starting of the training segment.

Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes

no code implementations19 Aug 2022 Can Zheng, Yanshan Wang, Xiaowei Jia

Semantic textual similarity (STS) in the clinical domain helps improve diagnostic efficiency and produce concise texts for downstream data mining tasks.

Language Modelling Semantic Textual Similarity +1

Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water Temperature

no code implementations11 Feb 2022 Xiaowei Jia, Shengyu Chen, Yiqun Xie, HaoYu Yang, Alison Appling, Samantha Oliver, Zhe Jiang

However, the information of released water flow is often not available for many reservoirs, which makes it difficult for data-driven models to capture the impact to downstream river segments.

Physical Simulations

Heterogeneous Stream-reservoir Graph Networks with Data Assimilation

no code implementations11 Oct 2021 Shengyu Chen, Alison Appling, Samantha Oliver, Hayley Corson-Dosch, Jordan Read, Jeffrey Sadler, Jacob Zwart, Xiaowei Jia

In this paper, we propose a heterogeneous recurrent graph model to represent these interacting processes that underlie stream-reservoir networks and improve the prediction of water temperature in all river segments within a network.

Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology

no code implementations14 Sep 2021 Rahul Ghosh, Arvind Renganathan, Kshitij Tayal, Xiang Li, Ankush Khandelwal, Xiaowei Jia, Chris Duffy, John Neiber, Vipin Kumar

Furthermore, we show that KGSSL is relatively more robust to distortion than baseline methods, and outperforms the baseline model by 35\% when plugging in KGSSL inferred characteristics.

Self-Supervised Learning

Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks

no code implementations6 Sep 2021 Shengyu Chen, Shervin Sammak, Peyman Givi, Joseph P. Yurko1, Xiaowei Jia

Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers.

Super-Resolution Vocal Bursts Intensity Prediction

Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping

no code implementations16 Aug 2021 Rahul Ghosh, Xiaowei Jia, Chenxi Lin, Zhenong Jin, Vipin Kumar

Common techniques of addressing this issue, based on the underlying idea of pre-training the Deep Neural Networks (DNN) on freely available large datasets, cannot be used for Remote Sensing due to the unavailability of such large-scale labeled datasets and the heterogeneity of data sources caused by the varying spatial and spectral resolution of different sensors.

Clustering Representation Learning +1

Weakly Supervised Classification Using Group-Level Labels

no code implementations16 Aug 2021 Guruprasad Nayak, Rahul Ghosh, Xiaowei Jia, Vipin Kumar

In many applications, finding adequate labeled data to train predictive models is a major challenge.

Binary Classification Classification +1

CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels

no code implementations26 Jul 2021 Rahul Ghosh, Praveen Ravirathinam, Xiaowei Jia, Ankush Khandelwal, David Mulla, Vipin Kumar

Mapping and monitoring crops is a key step towards sustainable intensification of agriculture and addressing global food security.

Semantic Segmentation

Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping

no code implementations2 May 2021 Rahul Ghosh, Praveen Ravirathinam, Xiaowei Jia, Chenxi Lin, Zhenong Jin, Vipin Kumar

The availability of massive earth observing satellite data provide huge opportunities for land use and land cover mapping.

Land Cover Mapping in Limited Labels Scenario: A Survey

no code implementations3 Mar 2021 Rahul Ghosh, Xiaowei Jia, Vipin Kumar

Land cover mapping is essential for monitoring global environmental change and managing natural resources.

BIG-bench Machine Learning

Physics Guided Machine Learning Methods for Hydrology

no code implementations2 Dec 2020 Ankush Khandelwal, Shaoming Xu, Xiang Li, Xiaowei Jia, Michael Stienbach, Christopher Duffy, John Nieber, Vipin Kumar

The goal of this work is to incorporate our understanding of physical processes and constraints in hydrology into machine learning algorithms, and thus bridge the performance gap while reducing the need for large amounts of data compared to traditional data-driven approaches.

BIG-bench Machine Learning

Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning

1 code implementation10 Nov 2020 Jared D. Willard, Jordan S. Read, Alison P. Appling, Samantha K. Oliver, Xiaowei Jia, Vipin Kumar

This method, Meta Transfer Learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance.

Meta-Learning Transfer Learning

Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks

no code implementations27 Oct 2020 Xiaowei Jia, Beiyu Lin, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, Jordan Read

In this paper, we develop a real-time active learning method that uses the spatial and temporal contextual information to select representative query samples in a reinforcement learning framework.

Active Learning Time Series +1

Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks

no code implementations26 Sep 2020 Xiaowei Jia, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, Steven Markstrom, Jared Willard, Shaoming Xu, Michael Steinbach, Jordan Read, Vipin Kumar

This paper proposes a physics-guided machine learning approach that combines advanced machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks.

BIG-bench Machine Learning

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

no code implementations10 Mar 2020 Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, Vipin Kumar

There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML) techniques.

BIG-bench Machine Learning

Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles

no code implementations28 Jan 2020 Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan S. Read, Jacob A Zwart, Michael Steinbach, Vipin Kumar

Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models, while generating outputs consistent with physical laws.

BIG-bench Machine Learning

Automated Monitoring Cropland Using Remote Sensing Data: Challenges and Opportunities for Machine Learning

no code implementations8 Apr 2019 Xiaowei Jia, Ankush Khandelwal, Vipin Kumar

This paper provides an overview of how recent advances in machine learning and the availability of data from earth observing satellites can dramatically improve our ability to automatically map croplands over long period and over large regions.

BIG-bench Machine Learning

Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles

no code implementations31 Oct 2018 Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Zwart, Michael Steinbach, Vipin Kumar

This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improve the modeling of physical processes.

Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes

no code implementations5 Oct 2018 Xiaowei Jia, Anuj Karpatne, Jared Willard, Michael Steinbach, Jordan Read, Paul C Hanson, Hilary A Dugan, Vipin Kumar

In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many dynamical systems.

Discovery of Shifting Patterns in Sequence Classification

no code implementations19 Dec 2017 Xiaowei Jia, Ankush Khandelwal, Anuj Karpatne, Vipin Kumar

The experiments demonstrate the superiority of our proposed method in sequence classification performance and in detecting discriminative shifting patterns.

Classification General Classification

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