Loose Party Times: The Political Crisis of the 1850s in Westchester County, New York

On November 7, 1848 William H. Robertson rose early and rushed to the post office in Bedford, a town in Westchester County, New York. The young lawyer was brimming with excitement because two weeks earlier, the Whigs in the county?s northern section had nominated him as their candidate for the New York State Assembly. Only twenty-four years old and a rising legal star, Robertson hoped that holding political office would launch his nascent career. After casting his ballot at the Bedford Post Office, Robertson paid a visit to Sheriff James M. Bates, his political manager, to await the election results. Robertson?s intelligence, collected a week before Election Day, that “news from every part of the district is favorable,” proved accurate. The Whig attorney heard later that evening that he had defeated his Democratic opponent, with 57% of the vote. To celebrate, Robertson and Bates feasted on “chickens, turkeys, oysters, and Champaign” before retiring around midnight at Philer Betts? Hotel. The following afternoon, they boarded the 3:00 PM train from Bedford to the county seat of White Plains, seventeen miles south. There, the two triumphant Whigs gossiped and caught up with their counterparts from Westchester?s usually Democratic southern section. Hearing of their friends? overwhelming victories surprised Robertson, leading him to exclaim, “The Whigs have carried almost everything!” Indeed, the Whigs had swept every elective office in Westchester County. [excerpt

The Codependent Development of Patriotism and Xenophobia in the United States, Particularly in Regard to Arabs and Muslims in America Following September 11, 2001

The United States has always claimed to be endowed with unique values, such as tolerance and justice, and so throughout its history has sought to convey these values with expressions of patriotism. However, is this patriotism simply symbolic, and further, does it even lead itself to xenophobia and racism. This thesis seeks to answer this question by examining the genesis and development of patriotism throughout the country’s history, as well as the way in which its racism and xenophobia have changed. Beginning with a general examination of the usefulness and positivity of patriotism from a scholarly standpoint, the basic points regarding the controversial issue are laid out. The main ideas of this dispute are provided by noted scholars George Kateb and John Kleinig in their works Patriotism and Other Mistakes and The Ethics of Patriotism: A Debate, respectively. Next, using research on history of the United States beginning from the Revolution, and ending with the Vietnam Era, an extensive picture of these issues in America develops. This then provides good comparison to the main discussion of this thesis; the change in patriotism and islamophobia following September 11th, and how they are connected. This will mainly revolve around the changing relationship that America had with its Arab and Muslim citizens, as well its changing relationship with the world. (The former is in many ways a result of the latter). In this more recent era, more primary sources are to be used, such as One America in the 21st Century: The President\u27s Initiative on Race, as well as Newspaper articles. The positions of Patriotism and Islamophobia following soon after 2001 will be the peak of the research and discussion, as further than this is arguably too recent to garner useful research. Throughout this thesis, the various issues with patriotism are explored, as well as its possibility for usefulness. What is meant to be shown throughout is that patriotism can and has been used to uphold the positive values of the country, but only when it is iconoclastic and willing to be admitted as false. When patriotism has been used symbolically and nationalistically, it has been the cause of extreme racism and xenophobia, especially in times of crisis such as during World War Two and after September 11th. In fact, patriotism has been a self fulfilling idea, as it seeks to protect itself by weeding out dissent. What this all shows is that patriotism is a hard term to get a clear definition of, but its form in the first decade of the 21st century was very damaging. It must be made to resemble a purer form of loyalty to the ideal rather than the symbol to ever be practical again

Medical Image Registration Using Deep Neural Networks

Registration is a fundamental problem in medical image analysis wherein images are transformed spatially to align corresponding anatomical structures in each image. Recently, the development of learning-based methods, which exploit deep neural networks and can outperform classical iterative methods, has received considerable interest from the research community. This interest is due in part to the substantially reduced computational requirements that learning-based methods have during inference, which makes them particularly well-suited to real-time registration applications. Despite these successes, learning-based methods can perform poorly when applied to images from different modalities where intensity characteristics can vary greatly, such as in magnetic resonance and ultrasound imaging. Moreover, registration performance is often demonstrated on well-curated datasets, closely matching the distribution of the training data. This makes it difficult to determine whether demonstrated performance accurately represents the generalization and robustness required for clinical use. This thesis presents learning-based methods which address the aforementioned difficulties by utilizing intuitive point-set-based representations, user interaction and meta-learning-based training strategies. Primarily, this is demonstrated with a focus on the non-rigid registration of 3D magnetic resonance imaging to sparse 2D transrectal ultrasound images to assist in the delivery of targeted prostate biopsies. While conventional systematic prostate biopsy methods can require many samples to be taken to confidently produce a diagnosis, tumor-targeted approaches have shown improved patient, diagnostic, and disease management outcomes with fewer samples. However, the available intraoperative transrectal ultrasound imaging alone is insufficient for accurate targeted guidance. As such, this exemplar application is used to illustrate the effectiveness of sparse, interactively-acquired ultrasound imaging for real-time, interventional registration. The presented methods are found to improve registration accuracy, relative to state-of-the-art, with substantially lower computation time and require a fraction of the data at inference. As a result, these methods are particularly attractive given their potential for real-time registration in interventional applications

Gravitational Wave Measurement in the Mid-Band with Atom Interferometers

Gravitational Waves (GWs) have been detected in the $\sim$100 Hz and nHz bands, but most of the gravitational spectrum remains unobserved. A variety of detector concepts have been proposed to expand the range of observable frequencies. In this work, we study the capability of GW detectors in the ``mid-band'', the $\sim$30 mHz -- 10 Hz range between LISA and LIGO, to measure the signals from and constrain the properties of ${\sim}$1 -- 100 $M_\odot$ compact binaries. We focus on atom-interferometer-based detectors. We describe a Fisher matrix code, AIMforGW, which we created to evaluate their capabilities, and present numerical results for two benchmarks: terrestrial km-scale detectors, and satellite-borne detectors in medium Earth orbit. Mid-band GW detectors are particularly well-suited to pinpointing the location of GW sources on the sky. We demonstrate that a satellite-borne detector could achieve sub-degree sky localization for any detectable source with chirp mass $\mathcal{M}_c \lesssim 50 M_\odot$. We also compare different detector configurations, including different locations of terrestrial detectors and various choices of the orbit of a satellite-borne detector. As we show, a network of only two terrestrial single-baseline detectors or one single-baseline satellite-borne detector would each provide close-to-uniform sky-coverage, with signal-to-noise ratios varying by less than a factor of two across the entire sky. We hope that this work contributes to the efforts of the GW community to assess the merits of different detector proposals.Comment: 45+15 pages, many figures. Code available at github.com/sbaum90/AIMforGW. v2: updated to match the published versio

Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration

Neural networks have been proposed for medical image registration by learning, with a substantial amount of training data, the optimal transformations between image pairs. These trained networks can further be optimized on a single pair of test images - known as test-time optimization. This work formulates image registration as a meta-learning algorithm. Such networks can be trained by aligning the training image pairs while simultaneously improving test-time optimization efficacy; tasks which were previously considered two independent training and optimization processes. The proposed meta-registration is hypothesized to maximize the efficiency and effectiveness of the test-time optimization in the "outer" meta-optimization of the networks. For image guidance applications that often are time-critical yet limited in training data, the potentially gained speed and accuracy are compared with classical registration algorithms, registration networks without meta-learning, and single-pair optimization without test-time optimization data. Experiments are presented in this paper using clinical transrectal ultrasound image data from 108 prostate cancer patients. These experiments demonstrate the effectiveness of a meta-registration protocol, which yields significantly improved performance relative to existing learning-based methods. Furthermore, the meta-registration achieves comparable results to classical iterative methods in a fraction of the time, owing to its rapid test-time optimization process.Comment: Accepted to ASMUS 2022 Workshop at MICCA

Meta-Learning Initializations for Interactive Medical Image Registration

We present a meta-learning framework for interactive medical image registration. Our proposed framework comprises three components: a learning-based medical image registration algorithm, a form of user interaction that refines registration at inference, and a meta-learning protocol that learns a rapidly adaptable network initialization. This paper describes a specific algorithm that implements the registration, interaction and meta-learning protocol for our exemplar clinical application: registration of magnetic resonance (MR) imaging to interactively acquired, sparsely-sampled transrectal ultrasound (TRUS) images. Our approach obtains comparable registration error (4.26 mm) to the best-performing non-interactive learning-based 3D-to-3D method (3.97 mm) while requiring only a fraction of the data, and occurring in real-time during acquisition. Applying sparsely sampled data to non-interactive methods yields higher registration errors (6.26 mm), demonstrating the effectiveness of interactive MR-TRUS registration, which may be applied intraoperatively given the real-time nature of the adaptation process.Comment: 11 pages, 10 figures. Paper accepted to IEEE Transactions on Medical Imaging (October 26 2022

Multimodality Biomedical Image Registration using Free Point Transformer Networks

We describe a point-set registration algorithm based on a novel free point transformer (FPT) network, designed for points extracted from multimodal biomedical images for registration tasks, such as those frequently encountered in ultrasound-guided interventional procedures. FPT is constructed with a global feature extractor which accepts unordered source and target point-sets of variable size. The extracted features are conditioned by a shared multilayer perceptron point transformer module to predict a displacement vector for each source point, transforming it into the target space. The point transformer module assumes no vicinity or smoothness in predicting spatial transformation and, together with the global feature extractor, is trained in a data-driven fashion with an unsupervised loss function. In a multimodal registration task using prostate MR and sparsely acquired ultrasound images, FPT yields comparable or improved results over other rigid and non-rigid registration methods. This demonstrates the versatility of FPT to learn registration directly from real, clinical training data and to generalize to a challenging task, such as the interventional application presented.Comment: 10 pages, 4 figures. Accepted for publication at International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) workshop on Advances in Simplifying Medical UltraSound (ASMUS) 202

Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data

Free Point Transformer (FPT) has been proposed as a data-driven, non-rigid point set registration approach using deep neural networks. As FPT does not assume constraints based on point vicinity or correspondence, it may be trained simply and in a flexible manner by minimizing an unsupervised loss based on the Chamfer Distance. This makes FPT amenable to real-world medical imaging applications where ground-truth deformations may be infeasible to obtain, or in scenarios where only a varying degree of completeness in the point sets to be aligned is available. To test the limit of the correspondence finding ability of FPT and its dependency on training data sets, this work explores the generalizability of the FPT from well-curated non-medical data sets to medical imaging data sets. First, we train FPT on the ModelNet40 dataset to demonstrate its effectiveness and the superior registration performance of FPT over iterative and learning-based point set registration methods. Second, we demonstrate superior performance in rigid and non-rigid registration and robustness to missing data. Last, we highlight the interesting generalizability of the ModelNet-trained FPT by registering reconstructed freehand ultrasound scans of the spine and generic spine models without additional training, whereby the average difference to the ground truth curvatures is 1.3 degrees, across 13 patients.Comment: Accepted to ASMUS 2022 Workshop at MICCA

Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images

We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training. We envision the segmentation as a boundary detection problem, rather than a pixel-level classification as in previous works. This outlook on segmentation may allow for boundary delineation under challenging scenarios such as where noise artefacts may be present within the region-of-interest (ROI) boundaries, where traditional pixel-level classification-based weakly supervised methods may not be able to effectively segment the ROI. Particularly of interest, ultrasound images, where intensity values represent acoustic impedance differences between boundaries, may also benefit from the boundary delineation approach. Our method uses reinforcement learning to train a controller function to localise boundaries of ROIs using a reward derived from a pre-trained boundary-presence classifier. The classifier indicates when an object boundary is encountered within a patch, as the controller modifies the patch location in a sequential Markov decision process. The classifier itself is trained using only binary patch-level labels of object presence, which are the only labels used during training of the entire boundary delineation framework, and serves as a weak signal to inform the boundary delineation. The use of a controller function ensures that a sliding window over the entire image is not necessary. It also prevents possible false-positive or -negative cases by minimising number of patches passed to the boundary-presence classifier. We evaluate our proposed approach for a clinically relevant task of prostate gland segmentation on trans-rectal ultrasound images. We show improved performance compared to other tested weakly supervised methods, using the same labels e.g., multiple instance learning.Comment: Accepted to MICCAI Workshop MLMI 2023 (14th International Conference on Machine Learning in Medical Imaging

Rapid Lung Ultrasound COVID-19 Severity Scoring with Resource-Efficient Deep Feature Extraction

Artificial intelligence-based analysis of lung ultrasound imaging has been demonstrated as an effective technique for rapid diagnostic decision support throughout the COVID-19 pandemic. However, such techniques can require days- or weeks-long training processes and hyper-parameter tuning to develop intelligent deep learning image analysis models. This work focuses on leveraging 'off-the-shelf' pre-trained models as deep feature extractors for scoring disease severity with minimal training time. We propose using pre-trained initializations of existing methods ahead of simple and compact neural networks to reduce reliance on computational capacity. This reduction of computational capacity is of critical importance in time-limited or resource-constrained circumstances, such as the early stages of a pandemic. On a dataset of 49 patients, comprising over 20,000 images, we demonstrate that the use of existing methods as feature extractors results in the effective classification of COVID-19-related pneumonia severity while requiring only minutes of training time. Our methods can achieve an accuracy of over 0.93 on a 4-level severity score scale and provides comparable per-patient region and global scores compared to expert annotated ground truths. These results demonstrate the capability for rapid deployment and use of such minimally-adapted methods for progress monitoring, patient stratification and management in clinical practice for COVID-19 patients, and potentially in other respiratory diseases.Comment: Accepted to ASMUS 2022 Workshop at MICCA