390 citations · 836 across the 16 of their papers we have counts for
16 papers
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions
Joseph B. Choi, Phong C. H. Nguyen, Oishik Sen +2
Artificial intelligence (AI) is rapidly emerging as an enabling tool for solving various complex materials design problems. This paper aims to review recent advances in AI-driven m…
A physics-aware deep learning model for energy localization in multiscale shock-to-detonation simulations of heterogeneous energetic materials
Phong C. H. Nguyen, Yen-Thi Nguyen, Pradeep K. Seshadri +3
Predictive simulations of the shock-to-detonation transition (SDT) in heterogeneous energetic materials (EM) are vital to the design and control of their energy release and sensiti…
Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization
Yankun Huang, Qihang Lin, Nick Street +1
We propose a federated learning method with weighted nodes in which the weights can be modified to optimize the model's performance on a separate validation set. The problem is for…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
PARC: Physics-Aware Recurrent Convolutional Neural Networks to Assimilate Meso-scale Reactive Mechanics of Energetic Materials
Phong C. H. Nguyen, Yen-Thi Nguyen, Joseph B. Choi +3
The thermo-mechanical response of shock-initiated energetic materials (EM) is highly influenced by their microstructures, presenting an opportunity to engineer EM microstructure in…
On the role of depth predictions for 3D human pose estimation
Alec Diaz-Arias, Mitchell Messmore, Dmitriy Shin +1
Following the successful application of deep convolutional neural networks to 2d human pose estimation, the next logical problem to solve is 3d human pose estimation from monocular…
Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials
Sehyun Chun, Sidhartha Roy, Yen Thi Nguyen +3
The sensitivity of heterogeneous energetic (HE) materials (propellants, explosives, and pyrotechnics) is critically dependent on their microstructure. Initiation of chemical reacti…
Incremental ELMVIS for unsupervised learning
Anton Akusok, Emil Eirola, Yoan Miche +5
An incremental version of the ELMVIS+ method is proposed in this paper. It iteratively selects a few best fitting data samples from a large pool, and adds them to the model. The me…
NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networks
Sehyun Chun, Nima Hamidi Ghalehjegh, Joseph B. Choi +4
A new convolutional neural network (CNN) architecture for 2D driver/passenger pose estimation and seat belt detection is proposed in this paper. The new architecture is more nimble…
Shape Matters: Evidence from Machine Learning on Body Shape-Income Relationship
Suyong Song, Stephen S. Baek
We study the association between physical appearance and family income using a novel data which has 3-dimensional body scans to mitigate the issue of reporting errors and measureme…
Multi-scale Embedded CNN for Music Tagging (MsE-CNN)
Nima Hamidi, Mohsen Vahidzadeh, Stephen Baek
Convolutional neural networks (CNN) recently gained notable attraction in a variety of machine learning tasks: including music classification and style tagging. In this work, we pr…
Deep segmentation networks predict survival of non-small cell lung cancer
Stephen Baek, Yusen He, Bryan G. Allen +13
Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that…
ZerNet: Convolutional Neural Networks on Arbitrary Surfaces via Zernike Local Tangent Space Estimation
Zhiyu Sun, Ethan Rooke, Jerome Charton +3
In this paper, we propose a novel formulation to extend CNNs to two-dimensional (2D) manifolds using orthogonal basis functions, called Zernike polynomials. In many areas, geometri…
Wall Stress Estimation of Cerebral Aneurysm based on Zernike Convolutional Neural Networks
Zhiyu Sun, Jia Lu, Stephen Baek
Convolutional neural networks (ConvNets) have demonstrated an exceptional capacity to discern visual patterns from digital images and signals. Unfortunately, such powerful ConvNets…
A Deep Learning Framework for Short-term Power Load Forecasting
Tinghui Ouyang, Yusen He, Huajin Li +2
The scheduling and operation of power system becomes prominently complex and uncertain, especially with the penetration of distributed power. Load forecasting matters to the effect…
Embedded Spectral Descriptors: Learning the point-wise correspondence metric via Siamese neural networks
Zhiyu Sun, Yusen He, Andrey Gritsenko +2
A robust and informative local shape descriptor plays an important role in mesh registration. In this regard, spectral descriptors that are based on the spectrum of the Laplace-Bel…