211 citations · 969 across the 37 of their papers we have counts for
39 papers
Extracting Protein-Protein Interactions (PPIs) from Biomedical Literature using Attention-based Relational Context Information
Gilchan Park, Sean McCorkle, Carlos Soto +2
Because protein-protein interactions (PPIs) are crucial to understand living systems, harvesting these data is essential to probe disease development and discern gene/protein funct…
Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats
Wei Chen, Yihui Ren, Ai Kagawa +9
Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for develo…
Emulating Expert Insight: A Robust Strategy for Optimal Experimental Design
Matthew R. Carbone, Hyeong Jin Kim, Chandima Fernando +7
The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in v…
Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
Alberto Di Meglio, Karl Jansen, Ivano Tavernelli +43
Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage…
Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge
Gilchan Park, Byung-Jun Yoon, Xihaier Luo +3
Background: Identification of the interactions and regulatory relations between biomolecules play pivotal roles in understanding complex biological systems and the mechanisms under…
SwiFT: Swin 4D fMRI Transformer
Peter Yongho Kim, Junbeom Kwon, Sunghwan Joo +6
Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches f…
Accurate, uncertainty-aware classification of molecular chemical motifs from multi-modal X-ray absorption spectroscopy
Matthew R. Carbone, Phillip M. Maffettone, Xiaohui Qu +2
Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry and biology. In this work, we present neu…
Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing
Xihaier Luo, Ahsan Kareem, Shinjae Yoo
Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subs…
UVCGAN v2: An Improved Cycle-Consistent GAN for Unpaired Image-to-Image Translation
Dmitrii Torbunov, Yi Huang, Huan-Hsin Tseng +6
An unpaired image-to-image (I2I) translation technique seeks to find a mapping between two domains of data in a fully unsupervised manner. While initial solutions to the I2I proble…
Comprehensive analysis of gene expression profiles to radiation exposure reveals molecular signatures of low-dose radiation response
Xihaier Luo, Sean McCorkle, Gilchan Park +6
There are various sources of ionizing radiation exposure, where medical exposure for radiation therapy or diagnosis is the most common human-made source. Understanding how gene exp…
Neural Network Based Point Spread Function Deconvolution For Astronomical Applications
Hong Wang, Sreevarsha Sreejith, Yuewei Lin +3
Optical astronomical images are strongly affected by the point spread function (PSF) of the optical system and the atmosphere (seeing) which blurs the observed image. The amount of…
Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles
Animesh Ghose, Mikhail Segal, Fanchen Meng +7
As machine learning (ML) methods continue to be applied to a broad scope of problems in the physical sciences, uncertainty quantification is becoming correspondingly more important…
DUNE Software and High Performance Computing
Bonnie Fleming, Kyle Knoepfel, Meifeng Lin +6
DUNE, like other HEP experiments, faces a challenge related to matching execution patterns of our production simulation and data processing software to the limitations imposed by m…
UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
Dmitrii Torbunov, Yi Huang, Haiwang Yu +5
Unpaired image-to-image translation has broad applications in art, design, and scientific simulations. One early breakthrough was CycleGAN that emphasizes one-to-one mappings betwe…
A Deep Finite Difference Emulator for the Fast Simulation of Coupled Viscous Burgers' Equation
Xihaier Luo, Yihui Ren, Wei Xu +3
This work proposes a deep learning-based emulator for the efficient computation of the coupled viscous Burgers' equation with random initial conditions. In a departure from traditi…
A Bayesian Deep Learning Approach to Near-Term Climate Prediction
Xihaier Luo, Balasubramanya T. Nadiga, Yihui Ren +3
Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a comp…
A Machine Learning-based Characterization Framework for Parametric Representation of Nonlinear Sloshing
Xihaier Luo, Ahsan Kareem, Liting Yu +1
The growing interest in creating a parametric representation of liquid sloshing inside a container stems from its practical applications in modern engineering systems. The resonant…
Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder
Yi Huang, Yihui Ren, Shinjae Yoo +1
Real-time data collection and analysis in large experimental facilities present a great challenge across multiple domains, including high energy physics, nuclear physics, and cosmo…
Galaxy Deblending using Residual Dense Neural networks
Hong Wang, Sreevarsha Sreejith, Anže Slosar +2
We present a new neural network approach for deblending galaxy images in astronomical data using Residual Dense Neural network (RDN) architecture. We train the network on synthetic…
Feature Importance in a Deep Learning Climate Emulator
Wei Xu, Xihaier Luo, Yihui Ren +3
We present a study using a class of post-hoc local explanation methods i.e., feature importance methods for "understanding" a deep learning (DL) emulator of climate. Specifically,…
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning
Hong Wang, Yuefan Deng, Shinjae Yoo +2
While deep neural networks have shown impressive performance in many tasks, they are fragile to carefully designed adversarial attacks. We propose a novel adversarial training-base…
Stochastic Projective Splitting: Solving Saddle-Point Problems with Multiple Regularizers
Patrick R. Johnstone, Jonathan Eckstein, Thomas Flynn +1
We present a new, stochastic variant of the projective splitting (PS) family of algorithms for monotone inclusion problems. It can solve min-max and noncooperative game formulation…
Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
Sau Lan Wu, Shaojun Sun, Wen Guan +20
Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups. In…
Federated Quantum Machine Learning
Samuel Yen-Chi Chen, Shinjae Yoo
Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially impr…