The Licensing Protein ORC4 is Required for Polar Body Extrusion During Murine Meiosis.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices

The rapid development in representation learning techniques such as deep neural networks and the availability of large-scale, well-annotated medical imaging datasets have to a rapid increase in the use of supervised machine learning in the 3D medical image analysis and diagnosis. In particular, deep convolutional neural networks (D-CNNs) have been key players and were adopted by the medical imaging community to assist clinicians and medical experts in disease diagnosis and treatment. However, training and inferencing deep neural networks such as D-CNN on high-resolution 3D volumes of Computed Tomography (CT) scans for diagnostic tasks pose formidable computational challenges. This challenge raises the need of developing deep learning-based approaches that are robust in learning representations in 2D images, instead 3D scans. In this work, we propose for the first time a new strategy to train \emph{slice-level} classifiers on CT scans based on the descriptors of the adjacent slices along the axis. In particular, each of which is extracted through a convolutional neural network (CNN). This method is applicable to CT datasets with per-slice labels such as the RSNA Intracranial Hemorrhage (ICH) dataset, which aims to predict the presence of ICH and classify it into 5 different sub-types. We obtain a single model in the top 4% best-performing solutions of the RSNA ICH challenge, where model ensembles are allowed. Experiments also show that the proposed method significantly outperforms the baseline model on CQ500. The proposed method is general and can be applied to other 3D medical diagnosis tasks such as MRI imaging. To encourage new advances in the field, we will make our codes and pre-trained model available upon acceptance of the paper.Comment: Accepted for presentation at the 22nd IEEE Statistical Signal Processing (SSP) worksho

Modeling Power Systems Dynamics with Symbolic Physics-Informed Neural Networks

In recent years, scientific machine learning, particularly physic-informed neural networks (PINNs), has introduced new innovative methods to understanding the differential equations that describe power system dynamics, providing a more efficient alternative to traditional methods. However, using a single neural network to capture patterns of all variables requires a large enough size of networks, leading to a long time of training and still high computational costs. In this paper, we utilize the interfacing of PINNs with symbolic techniques to construct multiple single-output neural networks by taking the loss function apart and integrating it over the relevant domain. Also, we reweigh the factors of the components in the loss function to improve the performance of the network for instability systems. Our results show that the symbolic PINNs provide higher accuracy with significantly fewer parameters and faster training time. By using the adaptive weight method, the symbolic PINNs can avoid the vanishing gradient problem and numerical instability

Detecting dopant diffusion enhancement at grain boundaries in multicrystalline silicon wafers with microphotoluminescence spectroscopy

Employing microphotoluminescence spectroscopy at low temperatures, we are able to detect dopant diffusion enhancement along various grain boundaries and subgrain boundaries in multicrystalline silicon wafers. We find an enhancement of phosphorus diffusion at all investigated grain boundary types. In addition, the subgrain boundaries are demonstrated to contain a relatively high density of defects and impurities, suggesting that their presence does not significantly hinder the preferential diffusion of dopant atoms along the subgrain boundaries. Finally, we demonstrate that the technique can be applied to different diffused layers for solar cell applications, even at room temperature if an appropriate excitation wavelength is used. The results are validated with secondary electron dopant contrast images, which confirm the higher dopant concentration along the grain boundaries and subgrain boundaries