Lucas Polson, Percussion

Frivolity / George Hamilton Green; arr. William Cahn; Reflections on the Nature of Water / Jacob Druckman; She Who Sleeps with a Small Blanket / Kevin Volans; A Stillness that Better Suits this Machine / Casey Cangelos

PyTomography: A Python Library for Quantitative Medical Image Reconstruction

Background: There is a scarcity of open-source libraries in medical imaging dedicated to both (i) the development and deployment of novel reconstruction algorithms and (ii) support for clinical data. Purpose: To create and evaluate a GPU-accelerated, open-source, and user-friendly image reconstruction library, designed to serve as a central platform for the development, validation, and deployment of novel tomographic reconstruction algorithms. Methods: PyTomography was developed using Python and inherits the GPU-accelerated functionality of PyTorch for fast computations. The software uses a modular design that decouples the system matrix from reconstruction algorithms, simplifying the process of integrating new imaging modalities or developing novel reconstruction techniques. As example developments, SPECT reconstruction in PyTomography is validated against both vendor-specific software and alternative open-source libraries. Bayesian reconstruction algorithms are implemented and validated. Results: PyTomography is consistent with both vendor-software and alternative open source libraries for standard SPECT clinical reconstruction, while providing significant computational advantages. As example applications, Bayesian reconstruction algorithms incorporating anatomical information are shown to outperform the traditional ordered subset expectation maximum (OSEM) algorithm in quantitative image analysis. PSF modeling in PET imaging is shown to reduce blurring artifacts. Conclusions: We have developed and publicly shared PyTomography, a highly optimized and user-friendly software for quantitative image reconstruction of medical images, with a class hierarchy that fosters the development of novel imaging applications.Comment: 26 pages, 7 figure

Lucas Polson, Percussion

Frivolity / George Hamilton Green; arr. William Cahn; Reflections on the Nature of Water / Jacob Druckman; She Who Sleeps with a Small Blanket / Kevin Volans; A Stillness that Better Suits this Machine / Casey Cangelos

Application of machine learning for energy reconstruction in the ATLAS liquid argon calorimeter

The beam intensity of the Large Hadron Collider will be significantly increased during the Phase-II long shut down of 2024-2026. Signal processing techniques that are used to extract the energy of detected particles in ATLAS will suffer a significant loss in performance under these conditions. This study compares the presently used optimal filter technique to alternative machine learning algorithms for signal processing. The machine learning algorithms are shown to outperform the optimal filter in many relevant metrics for energy extraction. This thesis also explores the implementation of machine learning algorithms on ATLAS hardware.Graduat

Beam Analysis for the Large Hadron Collider

Wire scanners are used in the Large Hadron Collider (LHC) to obtain measurements of the beam profile during low intensity calibration runs. These measurements are used to calibrate Beam Syncrotron Radiation Telescopes (BSRTs) which can be used to obtain beam profiles during high intensity data runs. This paper examines emittance, brightness, and intensity measurements obtained through wire scanner aquisition for calibration fills 6699 and 6913. Wire scanners can also be used to collect beam profile information during the injection phase of normal runs. This paper demonstrates that the data collected by the wire scanner during the normal runs is faulty

Application of Machine Learning for Energy Reconstruction in the ATLAS Liquid Argon Calorimeter

The beam intensity of the Large Hadron Collider will be significantly increased during the Phase-II long shut down of 2024-2026. Signal processing techniques that are used to extract the energy of detected particles in ATLAS will suffer a significant loss in performance under these conditions. This study compares the presently used optimal filter technique to alternative machine learning algorithms for signal processing. The machine learning algorithms are shown to outperform the optimal filter in many relevant metrics for energy extraction. This thesis also explores the implementation of machine learning algorithms on ATLAS hardware

The War on Drugs ::A History /

A revealing look at the history and legacy of the "War on Drugs"Fifty years after President Richard Nixon declared a "War on Drugs," the United States government has spent over a trillion dollars fighting a losing battle. In recent years, about 1.5 million people have been arrested annually on drug charges-most of them involving cannabis-and nearly 500,000 Americans are currently incarcerated for drug offenses. Today, as a response to the dire human and financial costs, Americans are fast losing their faith that a War on Drugs is fair, moral, or effective.In a rare multi-faceted overview of the underground drug market, featuring historical and ethnographic accounts of illegal drug production, distribution, and sales, The War on Drugs: A History examines how drug war policies contributed to the making of the carceral state, racial injustice, regulatory disasters, and a massive underground economy. At the same time, the collection explores how aggressive anti-drug policies produced a "deviant" form of globalization that offered economically marginalized people an economic life-line as players in a remunerative transnational supply and distribution network of illicit drugs. While several essays demonstrate how government enforcement of drug laws disproportionately punished marginalized suppliers and users, other essays assess how anti-drug warriors denigrated science and medical expertise by encouraging moral panics that contributed to the blanket criminalization of certain drugs. By analyzing the key issues, debates, events, and actors surrounding the War on Drugs, this timely and impressive volume provides a deeper understanding of the role these policies have played in making our current political landscape and how we can find the way forward to a more just and humane drug policy regime