arXiv subjects

William Moore

Publications and source records attributed to William Moore.

11 recordsLinked to original sources

arXiv2025

WaveDriver: a Laser Guide Star AO System for HWO

HWO's Tier 1 Contrast Stability Technology Gap presents a key challenge for technology development in the coming years, requiring to a >100x more stable system than JWST. WaveDriver is a concept for a laser guide star spacecraft coupled to an adaptive optics (AO) system onboard HWO that would enable HWO to reach its picometer-level wavefront stability requirements while relaxing other HWO subsystem requirements. At LLNL and UCSC we are revisiting the concept initially proposed by Douglas et al. (2019). We present results from our project's first year, including (1) AO control developments, including with Linear Quadratic Gaussian control and machine learning, (2) AO wavefront sensor (WFS) trade study simulations, and (3) simulations, fabrication, and testing of a 133-port photonic lantern WFS/spectrograph. A key finding from our work is that WaveDriver could be needed to enable HWO's primary mirror segment stability and/or low order wavefront stability requirements.

astro-ph.IM↗

arXiv2025

Self-Supervised Joint Reconstruction and Denoising of T2-Weighted PROPELLER MRI of the Lungs at 0.55T

Purpose: This study aims to improve 0.55T T2-weighted PROPELLER lung MRI through a self-supervised joint reconstruction and denoising model. Methods: T2-weighted 0.55T lung MRI dataset including 44 patients with previous covid infection were used. A self-supervised learning framework was developed, where each blade of the PROPELLER acquisition was split along the readout direction into two partitions. One subset trains the unrolled reconstruction network, while the other subset is used for loss calculation, enabling self-supervised training without clean targets and leveraging matched noise statistics for denoising. For comparison, Marchenko-Pastur Principal Component Analysis (MPPCA) was performed along the coil dimension, followed by conventional parallel imaging reconstruction. The quality of the reconstructed lung MRI was assessed visually by two experienced radiologists independently. Results: The proposed self-supervised model improved the clarity and structural integrity of the lung images. For cases with available CT scans, the reconstructed images demonstrated strong alignment with corresponding CT images. Additionally, the proposed model enables further scan time reduction by requiring only half the number of blades. Reader evaluations confirmed that the proposed method outperformed MPPCA-denoised images across all categories (Wilcoxon signed-rank test, p<0.001), with moderate inter-reader agreement (weighted Cohen's kappa=0.55; percentage of exact and within +/-1 point agreement=91%). Conclusion: By leveraging intrinsic structural redundancies between two disjoint splits of k-space subsets, the proposed self-supervised learning model effectively reconstructs the image while suppressing the noise for 0.55T T2-weighted lung MRI with PROPELLER sampling.

eess.IV↗

arXiv2025

Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for Joint MRI Reconstruction and Denoising in Low-Field MRI

Deep learning has demonstrated strong potential for MRI reconstruction. However, conventional supervised learning requires high-quality, high-SNR references for network training, which are often difficult or impossible to obtain in different scenarios, particularly in low-field MRI. Self-supervised learning provides an alternative by removing the need for training references, but its reconstruction performance can degrade when the baseline SNR is low. To address these limitations, we propose hybrid learning, a two-stage training framework that integrates self-supervised and supervised learning for joint MRI reconstruction and denoising when only low-SNR training references are available. Hybrid learning is implemented in two sequential stages. In the first stage, self-supervised learning is applied to fully sampled low-SNR data to generate higher-quality pseudo-references. In the second stage, these pseudo-references are used as targets for supervised learning to reconstruct and denoise undersampled noisy data. The proposed technique was evaluated in multiple experiments involving simulated and real low-field MRI in the lung and brain at different field strengths. Hybrid learning consistently improved image quality over both standard self-supervised learning and supervised learning with noisy training references at different acceleration rates, noise levels, and field strengths, achieving higher SSIM and lower NMSE. The hybrid learning approach is effective for both Cartesian and non-Cartesian acquisitions. Hybrid learning provides an effective solution for training deep MRI reconstruction models in the absence of high-SNR references. By improving image quality in low-SNR settings, particularly for low-field MRI, it holds promise for broader clinical adoption of deep learning-based reconstruction methods.

eess.IV↗

arXiv2022

Electrochemically-driven insulator-metal transition in ionic-liquid-gated antiferromagnetic Mott-insulating NiS$_2$ single crystals

Motivated by the existence of superconductivity in pyrite-structure CuS$_2$, we explore the possibility of ionic-liquid-gating-induced superconductivity in the proximal antiferromagnetic Mott insulator NiS$_2$. A clear gating-induced transition from a two-dimensional insulating state to a three-dimensional metallic state is observed at positive gate bias on single crystal surfaces. No evidence for superconductivity is observed down to the lowest measured temperature of 0.45 K, however. Based on transport, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, atomic force microscopy, and other techniques, we deduce an electrochemical gating mechanism involving a substantial decrease in the S:Ni ratio (over hundreds of nm), which is both non-volatile and irreversible. This is in striking contrast to the reversible, volatile, surface-limited, electrostatic gate effect in pyrite FeS$_2$. We attribute this stark difference in electrochemical vs. electrostatic gating response in NiS$_2$ and FeS$_2$ to the much larger S diffusion coefficient in NiS$_2$, analogous to the different behaviors observed among electrolyte-gated oxides with differing O-vacancy diffusivities. The gating irreversibility, on the other hand, is associated with the lack of atmospheric S; this is in contrast to the better understood oxide case, where electrolysis of atmospheric H$_2$O provides an O reservoir. This study of NiS$_2$ thus provides new insight into electrolyte gating mechanisms in functional materials, in a previously unexplored limit.

cond-mat.mtrl-sci↗

arXiv2021

COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction

The rapid spread of COVID-19 cases in recent months has strained hospital resources, making rapid and accurate triage of patients presenting to emergency departments a necessity. Machine learning techniques using clinical data such as chest X-rays have been used to predict which patients are most at risk of deterioration. We consider the task of predicting two types of patient deterioration based on chest X-rays: adverse event deterioration (i.e., transfer to the intensive care unit, intubation, or mortality) and increased oxygen requirements beyond 6 L per day. Due to the relative scarcity of COVID-19 patient data, existing solutions leverage supervised pretraining on related non-COVID images, but this is limited by the differences between the pretraining data and the target COVID-19 patient data. In this paper, we use self-supervised learning based on the momentum contrast (MoCo) method in the pretraining phase to learn more general image representations to use for downstream tasks. We present three results. The first is deterioration prediction from a single image, where our model achieves an area under receiver operating characteristic curve (AUC) of 0.742 for predicting an adverse event within 96 hours (compared to 0.703 with supervised pretraining) and an AUC of 0.765 for predicting oxygen requirements greater than 6 L a day at 24 hours (compared to 0.749 with supervised pretraining). We then propose a new transformer-based architecture that can process sequences of multiple images for prediction and show that this model can achieve an improved AUC of 0.786 for predicting an adverse event at 96 hours and an AUC of 0.848 for predicting mortalities at 96 hours. A small pilot clinical study suggested that the prediction accuracy of our model is comparable to that of experienced radiologists analyzing the same information.

cs.CV↗

arXiv2020

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

cs.LG↗

arXiv2019

Assessing the Intrinsic Uncertainty and Structural Stability of Planetary Models: 1) Parameterized Thermal-Tectonic History Models

Thermal history models, that have been used to understand the geological history of Earth, are now being coupled to climate models to map conditions that allow planets to maintain surface water over geologic time - a criteria considered crucial for life. However, the lack of intrinsic uncertainty assessment has blurred guidelines for how thermal history models can be used toward this end. A model, as a representation of something real, is not expected to be complete. Unmodeled effects are assumed to be small enough that the model maintains utility for the issue(s) it was designed to address. The degree to which this holds depends on how unmodeled factors affect the certainty of model predictions. We quantify this intrinsic uncertainty for several parameterized thermal history models (a widely used subclass of planetary models). Single perturbation analysis is used to determine the reactance time of different models. This provides a metric for how long it takes low amplitude, unmodeled effects to decay or grow. Reactance time is shown to scale inversely with the strength of the dominant feedback (negative or positive) within a model. A perturbed physics analysis is then used to determine uncertainty shadows for model outputs. This provides probability distributions for model predictions and tests the structural stability of a model. That is, do model predictions remain qualitatively similar, and within assumed model limits, in the face of intrinsic uncertainty. Once intrinsic uncertainty is accounted for, model outputs/predictions and comparisons to observational data should be treated in a probabilistic way.

astro-ph.EP↗

arXiv2017

Exploring Other Worlds: Science Questions for Future Direct Imaging Missions (EXOPAG SAG15 Report)

The NASA Exoplanet Exploration Program's SAG15 group has solicited, collected, and organized community input on high-level science questions that could be addressed with future direct imaging exoplanet missions and the type and quality of data answering these questions will require. Input was solicited through a variety of forums and the report draft was shared with the exoplanet community continuously during the period of the report development (Nov 2015 -- May 2017). The report benefitted from the input of over 50 exoplanet scientists and from multiple open-forum discussions at exoplanet and astrobiology meetings. The SAG15 team has identified three group of high-level questions, those that focus on the properties of planetary systems (Questions A1--A2), those that focus on the properties of individual planets (Questions B1--B4), and questions that relate to planetary processes (Questions C1--C4). The questions in categories A, B, and C require different target samples and often different observational approaches. For each questions we summarize the current body of knowledge, the available and future observational approaches that can directly or indirectly contribute to answering the question, and provide examples and general considerations for the target sample required.

astro-ph.EP↗

arXiv2017

Exoplanet Biosignatures: Future Directions

Exoplanet science promises a continued rapid accumulation of new observations in the near future, energizing a drive to understand and interpret the forthcoming wealth of data to identify signs of life beyond our Solar System. The large statistics of exoplanet samples, combined with the ambiguity of our understanding of universal properties of life and its signatures, necessitate a quantitative framework for biosignature assessment Here, we introduce a Bayesian framework for guiding future directions in life detection, which permits the possibility of generalizing our search strategy beyond biosignatures of known life. The Bayesian methodology provides a language to define quantitatively the conditional probabilities and confidence levels of future life detection and, importantly, may constrain the prior probability of life with or without positive detection. We describe empirical and theoretical work necessary to place constraints on the relevant likelihoods, including those emerging from stellar and planetary context, the contingencies of evolutionary history and the universalities of physics and chemistry. We discuss how the Bayesian framework can guide our search strategies, including determining observational wavelengths or deciding between targeted searches or larger, lower resolution surveys. Our goal is to provide a quantitative framework not entrained to specific definitions of life or its signatures, which integrates the diverse disciplinary perspectives necessary to confidently detect alien life.

astro-ph.EP↗

arXiv2017

Characterization of the previous normal-dose CT scan induced nonlocal means regularization method for low-dose CT image reconstruction

Repeated computed tomography (CT) scans are required in some clinical applications such as image-guided radiotherapy and follow-up observations over a time period. To optimize the radiation dose utility, a normal-dose (or full-dose) CT scan is often first performed to set up reference, followed by a series of low-dose scans. Using the previous normal-dose scan to improve follow-up low-dose scans reconstruction has drawn great interests recently, such as the previous normal-dose induced nonlocal means (ndiNLM) regularization method. However, one major concern with this method is that whether it would introduce false structures or miss true structures when the previous normal-dose image and current low-dose image have different structures (e.g., a tumor could be present, grow, shrink or absent in either image). This study aims to investigate the performance of the ndiNLM regularization method in the above mentioned situations. A patient with lung nodule for biopsy was recruited to this study. A normal-dose scan was acquired to set up biopsy operation, followed by a few low-dose scans during needle intervention toward the nodule. We used different slices to mimic different possible cases wherein the previous normal-dose image and current low-dose image have different structures. The experimental results characterize performance of our ndiNLM regularization method.

physics.med-ph↗

arXiv2014

Bi-orthogonal Polynomial Sequences and the Asymmetric Simple Exclusion Process

We reformulate the Corteel-Williams equations for the stationary state of the two parameter Asymmetric Simple Exclusion Process (TASEP) as a linear map $\mathcal{L}(\,\cdot\,)$, acting on a tensor algebra built from a rank two free module with basis $\{e_1,e_2\}$. From this formulation we construct a pair of sequences, $\{P_n(e_1)\}$ and $\{Q_m(e_2)\}$, of bi-orthogonal polynomials (BiOPS), that is, they satisfy $\mathcal{L}(P_n(e_1)\otimes Q_m(e_2))=\Lambda_n\delta_{n,m}$. The existence of the sequences arises from the determinant of a Pascal triangle like matrix of polynomials. The polynomials satisfy first order (uncoupled) recurrence relations. We show that the two first moments $\mathcal{L}(P_n\, e_1\, Q_m)$ and $\mathcal{L}(P_n\, e_2\, Q_m)$ give rise to a matrix representation of the ASEP diffusion algebra and hence provide an understanding of the origin of the matrix product Ansatz. The second moment $\mathcal{L}(P_n\, e_1 e_2\,Q_m )$ defines a tridiagonal matrix which makes the connection with Chebyshev-like orthogonal polynomials.

math-ph↗