Publications (17)
Impact of ion-electron collisions on nonlocal ion heat conduction, viscous stress, and diffusion
Nicholas Mitchell, David Chapman, Grigory Kagan
By applying a first-principles reduced kinetic method, this work demonstrates the impact of ion-electron collisions on ion transport for strongly inhomogeneous plasmas in the nonlo…
Mitigating domain shift in AI-based tuberculosis screening with unsupervised domain adaptation
Nishanjan Ravin, Sourajit Saha, Alan Schweitzer +4
We demonstrate that Domain Invariant Feature Learning (DIFL) can improve the out-of-domain generalizability of a deep learning Tuberculosis screening algorithm. It is well known th…
Nonlocal current-driven heat flow in ideal plasmas
Nicholas Mitchell, David Chapman, Grigory Kagan
Electron heat flux is an important and often dominant mechanism of energy transport in a variety of collisional plasmas in a confined fusion or astrophysical context. While nonloca…
A reduced kinetic method for investigating non-local ion heat transport in ideal multi-species plasmas
Nicholas Mitchell, David Chapman, Christopher McDevitt +2
A reduced kinetic method (RKM) with a first-principle collision operator is introduced in a 1D2V planar geometry and implemented in a computationally inexpensive code to investigat…
Deep Expectation-Maximization for Semi-Supervised Lung Cancer Screening
Sumeet Menon, David Chapman, Phuong Nguyen +3
We present a semi-supervised algorithm for lung cancer screening in which a 3D Convolutional Neural Network (CNN) is trained using the Expectation-Maximization (EM) meta-algorithm.…
Why CNN Features Are not Gaussian: A Statistical Anatomy of Deep Representations
David Chapman, Parniyan Farvardin
Deep convolutional neural networks (CNNs) are commonly analyzed through geometric and linear-algebraic perspectives, yet the statistical distribution of their internal feature acti…
RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget
Sourajit Saha, Shaswati Saha, Md Osman Gani +2
Learning High-Resolution representations is essential for semantic segmentation. Convolutional neural network (CNN)architectures with downstream and upstream propagation flow are p…
Semi-supervised Contrastive Outlier removal for Pseudo Expectation Maximization (SCOPE)
Sumeet Menon, David Chapman
Semi-supervised learning is the problem of training an accurate predictive model by combining a small labeled dataset with a presumably much larger unlabeled dataset. Many methods…
Richtmyer-Meshkov Instability at high Mach Number: Non-Newtonian Effects
Usman Rana, Thomas Abadie, David Chapman +2
The Richtmyer-Meshkov instability (RMI) occurs when a shock wave passes through an interface between fluids of different densities, a phenomenon prevalent in a variety of scenarios…
End-to-end Feature Alignment: A Simple CNN with Intrinsic Class Attribution
Parniyan Farvardin, David Chapman
We present Feature-Align CNN (FA-CNN), a prototype CNN architecture with intrinsic class attribution through end-to-end feature alignment. Our intuition is that the use of unordere…
CCS-GAN: COVID-19 CT-scan classification with very few positive training images
Sumeet Menon, Jayalakshmi Mangalagiri, Josh Galita +7
We present a novel algorithm that is able to classify COVID-19 pneumonia from CT Scan slices using a very small sample of training images exhibiting COVID-19 pneumonia in tandem wi…
FLAIM: A reduced volume ignition model for the compression and thermonuclear burn of spherical fuel capsules
Abd Essamade Saufi, Hannah Bellenbaum, Martin Read +5
We present the "First Light Advanced Ignition Model" (FLAIM), a reduced model for the implosion, adiabatic compression, volume ignition and thermonuclear burn of a spherical DT fue…
Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network
Jayalakshmi Mangalagiri, David Chapman, Aryya Gangopadhyay +7
We present a novel conditional Generative Adversarial Network (cGAN) architecture that is capable of generating 3D Computed Tomography scans in voxels from noisy and/or pixelated a…
A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning
Michael Majurski, Sumeet Menon, Parniyan Farvardin +1
Discriminative deep learning models with a linear+softmax final layer have a problem: the latent space only predicts the conditional probabilities but not the full joint d…
Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer Learning GAN
Sumeet Menon, Joshua Galita, David Chapman +7
COVID-19 is a novel infectious disease responsible for over 800K deaths worldwide as of August 2020. The need for rapid testing is a high priority and alternative testing strategie…
Person Re-Identification with a Locally Aware Transformer
Charu Sharma, Siddhant R. Kapil, David Chapman
Person Re-Identification is an important problem in computer vision-based surveillance applications, in which the same person is attempted to be identified from surveillance photog…
A First-Principles Closure for Nonlocal Magnetized Transport
Nicholas Mitchell, David Chapman, Grigory Kagan
A reduced kinetic method (RKM) for describing nonlocal transport in magnetized plasmas is derived from first principles and considered in a 1D3V geometry. Unlike standard nonlocal…