arXiv subjects

William Parker

Publications and source records attributed to William Parker.

8 recordsLinked to original sources

arXiv2026

Reactor Antineutrino Oscillations and Geoneutrinos in SNO+

SNO+ is a multipurpose liquid-scintillator neutrino detector located 2 km underground at SNOLAB, Canada. Three large nuclear reactors at baselines of 240-350 km allow a precise measurement of the neutrino oscillation parameter $\Delta m^2_{21}$ and, to a lesser extent, $\theta_{12}$. A spectral analysis is performed, simultaneously fitting $\Delta m^2_{21}$, $\theta_{12}$, the reactor antineutrino flux, background rates, and associated systematics. Using data collected between May 2022 and July 2025, corresponding to a livetime of 685 days, a value of $\Delta m^2_{21} = (7.93^{+0.21}_{-0.24}) \times 10^{-5}$ eV$^2$ is obtained. This result is compatible with other long-baseline reactor antineutrino measurements by KamLAND and JUNO. SNO+ has also made the first measurement of the geoneutrino flux in the Western Hemisphere, measuring $49^{+13}_{-12}$ TNU, in agreement with predictions from geological models.

hep-ex↗

arXiv2024

Uncertainty Propagation and Filtering via the Koopman Operator in Astrodynamics

The Koopman Operator (KO) provides an analytical solution of dynamical systems in terms of orthogonal polynomials. This work exploits this representation to include the propagation of uncertainties, where the polynomials are modified to work with stochastic variables. Thus, a new uncertainty quantification technique is proposed, where the KO solution is expanded to include the prediction of central moments, up to an arbitrary order. The propagation of uncertainties is then expanded to develop a new filtering algorithm, where measurements are considered as additional observables in the KO mathematics. Numerical simulations in astrodynamics assess the accuracy and performance of the new methodologies.

cs.IT↗

arXiv2021

A High Pressure Time Projection Chamber with Optical Readout

Measurements of proton-nucleus scattering and high resolution neutrino-nucleus interaction imaging are key to reduce neutrino oscillation systematic uncertainties in future experiments. A High Pressure Time Projection Chamber (HPTPC) prototype has been constructed and operated at Royal Holloway University of London and CERN as a first step in the development of a HPTPC capable of performing these measurements as part of a future long-baseline neutrino oscillation experiment such as the Deep Underground Neutrino Experiment. In this paper we describe the design and operation of the prototype HPTPC with an argon based gas mixture. We report on the successful hybrid charge and optical readout, using four CCD cameras, of signals from Am-241 sources.

physics.ins-det↗

arXiv2020

Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT

Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations. Materials and Methods: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobewise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April, 2020). Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth. Results: Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (P < .001), 0.97 for PHO(P < .001), 0.91 for LSS (P < .001), 0.90 for LHOS (P < .001). 98 of 100 healthy controls had a predicted PO of less than 1%, 2 had between 1-2%. Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations. Conclusion: A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores.

eess.IV↗

arXiv2020

Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread into a global pandemic. A form of pneumonia, presenting as opacities with in a patient's lungs, is the most common presentation associated with this virus, and great attention has gone into how these changes relate to patient morbidity and mortality. In this work we provide open source models for the segmentation of patterns of pulmonary opacification on chest Computed Tomography (CT) scans which have been correlated with various stages and severities of infection. We have collected 663 chest CT scans of COVID-19 patients from healthcare centers around the world, and created pixel wise segmentation labels for nearly 25,000 slices that segment 6 different patterns of pulmonary opacification. We provide open source implementations and pre-trained weights for multiple segmentation models trained on our dataset. Our best model achieves an opacity Intersection-Over-Union score of 0.76 on our test set, demonstrates successful domain adaptation, and predicts the volume of opacification within 1.7\% of expert radiologists. Additionally, we present an analysis of the inter-observer variability inherent to this task, and propose methods for appropriate probabilistic approaches.

eess.IV↗

arXiv2020

A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images

Coronavirus Disease 2019 (COVID-19) has spread aggressively across the world causing an existential health crisis. Thus, having a system that automatically detects COVID-19 in tomography (CT) images can assist in quantifying the severity of the illness. Unfortunately, labelling chest CT scans requires significant domain expertise, time, and effort. We address these labelling challenges by only requiring point annotations, a single pixel for each infected region on a CT image. This labeling scheme allows annotators to label a pixel in a likely infected region, only taking 1-3 seconds, as opposed to 10-15 seconds to segment a region. Conventionally, segmentation models train on point-level annotations using the cross-entropy loss function on these labels. However, these models often suffer from low precision. Thus, we propose a consistency-based (CB) loss function that encourages the output predictions to be consistent with spatial transformations of the input images. The experiments on 3 open-source COVID-19 datasets show that this loss function yields significant improvement over conventional point-level loss functions and almost matches the performance of models trained with full supervision with much less human effort. Code is available at: \url{https://github.com/IssamLaradji/covid19_weak_supervision}.

eess.IV↗

arXiv2020

A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images

One of the key challenges in the battle against the Coronavirus (COVID-19) pandemic is to detect and quantify the severity of the disease in a timely manner. Computed tomographies (CT) of the lungs are effective for assessing the state of the infection. Unfortunately, labeling CT scans can take a lot of time and effort, with up to 150 minutes per scan. We address this challenge introducing a scalable, fast, and accurate active learning system that accelerates the labeling of CT scan images. Conventionally, active learning methods require the labelers to annotate whole images with full supervision, but that can lead to wasted efforts as many of the annotations could be redundant. Thus, our system presents the annotator with unlabeled regions that promise high information content and low annotation cost. Further, the system allows annotators to label regions using point-level supervision, which is much cheaper to acquire than per-pixel annotations. Our experiments on open-source COVID-19 datasets show that using an entropy-based method to rank unlabeled regions yields to significantly better results than random labeling of these regions. Also, we show that labeling small regions of images is more efficient than labeling whole images. Finally, we show that with only 7\% of the labeling effort required to label the whole training set gives us around 90\% of the performance obtained by training the model on the fully annotated training set. Code is available at: \url{https://github.com/IssamLaradji/covid19_active_learning}.

eess.IV↗

arXiv2015

Giant optical enhancement of strain gradient in ferroelectric thin films and its physics origin

The coupling between strain gradients and polarization, known as flexoelectricity, offers a new mechanism to control the functionality of dielectric materials. However, for the effect to be practically attractive, dynamic control of the strain gradient with magnitudes far exceeding those achievable via mechanical deformation (~10 $m^{-1}$) is needed. Strain-engineered thin films exhibit extraordinary strain gradients of $10^5-10^6 m{-1}$ arising from structural relaxation within a short space range that greatly enhances the steady-state flexoelectric effect. Here we report a giant, optically initiated dynamic enhancement of the strain gradient, also on the order of $10^5 -10^6 m^{-1}$, in ferroelectric BiFeO3 epitaxial thin films via time-dependent coherence analysis of X-ray diffractions. The finding opens the door for dynamic coupling of the flexoelectric effect with light, making optical switching of polarization, and thus application such as direct optical writing of non-volatile ferroelectric memory, possible. A combination of time-resolved X-ray scattering and optical spectroscopy shows that the enhancement of the strain gradient is due to a piezoelectric effect driven by a transient screening electric field, opening the opportunity for new ways of studying flexoelectric effect in strain engineered ferroelectric thin films.

cond-mat.mtrl-sci↗