645 citations · 784 across the 26 of their papers we have counts for
27 papers
An Empirical Study on the Robustness of the Segment Anything Model (SAM)
Yuqing Wang, Yun Zhao, Linda Petzold
The Segment Anything Model (SAM) is a foundation model for general image segmentation. Although it exhibits impressive performance predominantly on natural images, understanding it…
DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text
Xianjun Yang, Wei Cheng, Yue Wu +3
Large language models (LLMs) have notably enhanced the fluency and diversity of machine-generated text. However, this progress also presents a significant challenge in detecting th…
Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting
Xinlu Zhang, Shiyang Li, Xianjun Yang +3
Large language models (LLMs) demonstrate remarkable medical expertise, but data privacy concerns impede their direct use in healthcare environments. Although offering improved data…
Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding
Yuqing Wang, Yun Zhao, Linda Petzold
Large language models (LLMs) have made significant progress in various domains, including healthcare. However, the specialized nature of clinical language understanding tasks prese…
Dynamic Prompting: A Unified Framework for Prompt Tuning
Xianjun Yang, Wei Cheng, Xujiang Zhao +3
It has been demonstrated that the art of prompt tuning is highly effective in efficiently extracting knowledge from pretrained foundation models, encompassing pretrained language m…
MatKB: Semantic Search for Polycrystalline Materials Synthesis Procedures
Xianjun Yang, Stephen Wilson, Linda Petzold
In this paper, we present a novel approach to knowledge extraction and retrieval using Natural Language Processing (NLP) techniques for material science. Our goal is to automatical…
OASum: Large-Scale Open Domain Aspect-based Summarization
Xianjun Yang, Kaiqiang Song, Sangwoo Cho +4
Aspect or query-based summarization has recently caught more attention, as it can generate differentiated summaries based on users' interests. However, the current dataset for aspe…
PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text
Xianjun Yang, Ya Zhuo, Julia Zuo +3
Scientific action graphs extraction from materials synthesis procedures is important for reproducible research, machine automation, and material prediction. But the lack of annotat…
Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling
Xinlu Zhang, Shiyang Li, Zhiyu Chen +2
Health conditions among patients in intensive care units (ICUs) are monitored via electronic health records (EHRs), composed of numerical time series and lengthy clinical note sequ…
VREN: Volleyball Rally Dataset with Expression Notation Language
Haotian Xia, Rhys Tracy, Yun Zhao +3
This research is intended to accomplish two goals: The first goal is to curate a large and information rich dataset that contains crucial and succinct summaries on the players' act…
Few-Shot Document-Level Event Argument Extraction
Xianjun Yang, Yujie Lu, Linda Petzold
Event argument extraction (EAE) has been well studied at the sentence level but under-explored at the document level. In this paper, we study to capture event arguments that actual…
Interpretable Polynomial Neural Ordinary Differential Equations
Colby Fronk, Linda Petzold
Neural networks have the ability to serve as universal function approximators, but they are not interpretable and don't generalize well outside of their training region. Both of th…
Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning
Yuqing Wang, Yun Zhao, Linda Petzold
As critically ill patients frequently develop anemia or coagulopathy, transfusion of blood products is a frequent intervention in the Intensive Care Units (ICU). However, inappropr…
Enhancing Transformer Efficiency for Multivariate Time Series Classification
Yuqing Wang, Yun Zhao, Linda Petzold
Most current multivariate time series (MTS) classification algorithms focus on improving the predictive accuracy. However, for large-scale (either high-dimensional or long-sequenti…
Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis
Yuqing Wang, Yun Zhao, Rachael Callcut +1
Sepsis is a leading cause of death in the Intensive Care Units (ICU). Early detection of sepsis is critical for patient survival. In this paper, we propose a multimodal Transformer…
Empirical Quantitative Analysis of COVID-19 Forecasting Models
Yun Zhao, Yuqing Wang, Junfeng Liu +5
COVID-19 has been a public health emergency of international concern since early 2020. Reliable forecasting is critical to diminish the impact of this disease. To date, a large num…
Multiple Organ Failure Prediction with Classifier-Guided Generative Adversarial Imputation Networks
Xinlu Zhang, Yun Zhao, Rachael Callcut +1
Multiple organ failure (MOF) is a severe syndrome with a high mortality rate among Intensive Care Unit (ICU) patients. Early and precise detection is critical for clinicians to mak…
Empirical Analysis of Machine Learning Configurations for Prediction of Multiple Organ Failure in Trauma Patients
Yuqing Wang, Yun Zhao, Rachael Callcut +1
Multiple organ failure (MOF) is a life-threatening condition. Due to its urgency and high mortality rate, early detection is critical for clinicians to provide appropriate treatmen…
BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients
Yun Zhao, Qinghang Hong, Xinlu Zhang +3
Survival analysis is a technique to predict the times of specific outcomes, and is widely used in predicting the outcomes for intensive care unit (ICU) trauma patients. Recently, d…
Model Optimization for Deep Space Exploration via Simulators and Deep Learning
James Bird, Kellan Colburn, Linda Petzold +1
Machine learning, and eventually true artificial intelligence techniques, are extremely important advancements in astrophysics and astronomy. We explore the application of deep lea…
How Much Does It Hurt: A Deep Learning Framework for Chronic Pain Score Assessment
Yun Zhao, Franklin Ly, Qinghang Hong +5
Chronic pain is defined as pain that lasts or recurs for more than 3 to 6 months, often long after the injury or illness that initially caused the pain has healed. The "gold standa…
Scalable Bayesian Functional Connectivity Inference for Multi-Electrode Array Recordings
Yun Zhao, Richard Jiang, Zhenni Xu +3
Multi-electrode arrays (MEAs) can record extracellular action potentials (also known as 'spikes') from hundreds or thousands of neurons simultaneously. Inference of a functional ne…
Advances in Deep Space Exploration via Simulators & Deep Learning
James Bird, Linda Petzold, Philip Lubin +1
The StarLight program conceptualizes fast interstellar travel via small wafer satellites (wafersats) that are propelled by directed energy. This process is wildly different from tr…
Multiscale modeling meets machine learning: What can we learn?
Grace C. Y. Peng, Mark Alber, Adrian Buganza Tepole +9
Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incred…