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Adam Anderson

Publications and source records attributed to Adam Anderson.

14 recordsLinked to original sources
arXiv2024

Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 -- Ex vivo imaging: data processing, comparisons with microscopy, and tractography

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging.

physics.med-ph↗
arXiv2024

A first demonstration of active feedback control and multi-frequency imaging techniques for kinetic inductance detectors

RF-ICE is a signal processing platform for the readout of large arrays of superconducting resonators. Designed for flexibility, the system's low digital latency and ability to independently and dynamically set the frequency and amplitude of probe tones in real time has enabled previously-inaccessible views of resonator behaviour, and opened the door to novel resonator control schemes. We introduce a multi-frequency imaging technique, developed with RF-ICE, which allows simultaneous observation of the entire resonance bandwidth. We demonstrate the use of this technique in the examination of the response of superconducting resonators to variations in applied readout current and thermal loading. We observe that, used in conjunction with a conventional frequency sweep at sufficiently large amplitude to induce resonance bifurcation, the multi-frequency imaging technique reveals a resonator response which is not captured by the frequency sweep measurement alone. We demonstrate that equivalent resonant frequency shifts can be achieved using either thermal, optical, or readout loading, and use this equivalence to counteract a change in thermal loading by digitally modulating the readout current through a resonator. We develop and implement a proof-of-concept closed-loop negative electro-quasiparticle feedback algorithm which first sets and then maintains the resonant frequency of a lumped element kinetic inductance detector while the loading on it is varied. Although this simple implementation is not yet suitable to deploy at scale, it demonstrates the utility of this feedback technique to improve linearity while addressing amplifier distortion, resonator response non-uniformity, and crosstalk. It can be applied to kinetic inductors in non-bolometric operation, and sets the stage for future developments.

astro-ph.IM↗
arXiv2022

Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 2 -- Ex vivo imaging: added value and acquisition

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts. Another major advantage of ex vivo dMRI is the direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a 3-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We give general considerations and foundational knowledge that must be considered when designing experiments. We describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

physics.med-ph↗
arXiv2022

Considerations and Recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

physics.med-ph↗
arXiv2022

Snowmass 2021 CMB-S4 White Paper

This Snowmass 2021 White Paper describes the Cosmic Microwave Background Stage 4 project CMB-S4, which is designed to cross critical thresholds in our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. We provide an overview of the science case, the technical design, and project plan.

astro-ph.CO↗
arXiv2021

Development of MKIDs for measurement of the Cosmic Microwave Background with the South Pole Telescope

We present details of the design, simulation, and initial test results of prototype detectors for the fourth-generation receiver of the South Pole Telescope (SPT). Optimized for the detection of key secondary anisotropies of the cosmic microwave background (CMB), SPT-3G+ will measure the temperature and polarization of the mm/sub-mm sky at 220, 285, and 345 GHz, beyond the peak of the CMB blackbody spectrum. The SPT-3G+ focal plane will be populated with microwave kinetic inductance detectors (MKIDs), allowing for significantly increased detector density with reduced cryogenic complexity. We present simulation-backed designs for single-color dual-polarization MKID pixels at each SPT-3G+ observation frequency. We further describe design choices made to promote resonator quality and uniformity, enabling us to maximize the available readout bandwidth. We also discuss aspects of the fabrication process that enable rapid production of these devices and present an initial dark characterization of a series of prototype devices.

astro-ph.IM↗
arXiv2021

Rewrite Rule Inference Using Equality Saturation

Many compilers, synthesizers, and theorem provers rely on rewrite rules to simplify expressions or prove equivalences. Developing rewrite rules can be difficult: rules may be subtly incorrect, profitable rules are easy to miss, and rulesets must be rechecked or extended whenever semantics are tweaked. Large rulesets can also be challenging to apply: redundant rules slow down rule-based search and frustrate debugging. This paper explores how equality saturation, a promising technique that uses e-graphs to apply rewrite rules, can also be used to infer rewrite rules. E-graphs can compactly represent the exponentially large sets of enumerated terms and potential rewrite rules. We show that equality saturation efficiently shrinks both sets, leading to faster synthesis of smaller, more general rulesets. We prototyped these strategies in a tool dubbed ruler. Compared to a similar tool built on CVC4, ruler synthesizes 5.8X smaller rulesets 25X faster without compromising on proving power. In an end-to-end case study, we show ruler-synthesized rules which perform as well as those crafted by domain experts, and addressed a longstanding issue in a popular open source tool.

cs.PL↗
arXiv2020

Phase-Only Beam Broadening of Contiguous Uniform Subarrayed Arrays Utilizing Three Metaheuristic Global Optimization Techniques

Radar beam broadening provides continuous coverage of a wider angular extent. While many methods have been published that address beam broadening of traditional (nonsubarrayed) arrays, there is a knowledge gap in the published literature with respect to efficient and effective beam broadening of contiguous uniform subarrayed arrays. This paper presents efficient and effective methods for beam broadening of contiguous uniform subarrayed arrays where elements of the array are grouped together to have the same element excitations. Particularly, this paper focuses on phase-only optimization to preserve maximum power output. The high dimensionality of the solution space of possible phase settings causes brute force techniques to be infeasible for exhaustively evaluating the entire space. This paper presents three metaheuristic global optimization techniques that efficiently and effectively search for optimal phase values in this large solution space that satisfy the desired broadened pattern. The techniques presented in this paper are simulated annealing, genetic algorithm with elitism, and particle swarm optimization. These techniques are evaluated on idealized 40x40 and 80x80 element rectangular arrays with 5x5 element subarrays. The results of this study show that as configured in this paper the simulated annealing and particle swarm techniques outshine the genetic algorithm technique for 40x40 and 80x80 rectangular arrays grouped into contiguous uniform 5x5 element subarrays.

eess.SP↗
arXiv2020

CMB-S4: Forecasting Constraints on Primordial Gravitational Waves

CMB-S4---the next-generation ground-based cosmic microwave background (CMB) experiment---is set to significantly advance the sensitivity of CMB measurements and enhance our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. Among the science cases pursued with CMB-S4, the quest for detecting primordial gravitational waves is a central driver of the experimental design. This work details the development of a forecasting framework that includes a power-spectrum-based semi-analytic projection tool, targeted explicitly towards optimizing constraints on the tensor-to-scalar ratio, $r$, in the presence of Galactic foregrounds and gravitational lensing of the CMB. This framework is unique in its direct use of information from the achieved performance of current Stage 2--3 CMB experiments to robustly forecast the science reach of upcoming CMB-polarization endeavors. The methodology allows for rapid iteration over experimental configurations and offers a flexible way to optimize the design of future experiments given a desired scientific goal. To form a closed-loop process, we couple this semi-analytic tool with map-based validation studies, which allow for the injection of additional complexity and verification of our forecasts with several independent analysis methods. We document multiple rounds of forecasts for CMB-S4 using this process and the resulting establishment of the current reference design of the primordial gravitational-wave component of the Stage-4 experiment, optimized to achieve our science goals of detecting primordial gravitational waves for $r > 0.003$ at greater than $5\sigma$, or, in the absence of a detection, of reaching an upper limit of $r < 0.001$ at $95\%$ CL.

astro-ph.CO↗
arXiv2019

Synthesizing Structured CAD Models with Equality Saturation and Inverse Transformations

Recent program synthesis techniques help users customize CAD models(e.g., for 3D printing) by decompiling low-level triangle meshes to Constructive Solid Geometry (CSG) expressions. Without loops or functions, editing CSG can require many coordinated changes, and existing mesh decompilers use heuristics that can obfuscate high-level structure. This paper proposes a second decompilation stage to robustly "shrink" unstructured CSG expressions into more editable programs with map and fold operators. We present Szalinski, a tool that uses Equality Saturation with semantics-preserving CAD rewrites to efficiently search for smaller equivalent programs. Szalinski relies on inverse transformations, a novel way for solvers to speculatively add equivalences to an E-graph. We qualitatively evaluate Szalinski in case studies, show how it composes with an existing mesh decompiler, and demonstrate that Szalinski can shrink large models in seconds.

cs.PL↗
arXiv2019

Deep Modulation (Deepmod): A Self-Taught PHY Layer for Resilient Digital Communications

Traditional physical (PHY) layer protocols contain chains of signal processing blocks that have been mathematically optimized to transmit information bits efficiently over noisy channels. Unfortunately, this same optimality encourages ubiquity in wireless communication technology and enhances the potential for catastrophic cyber or physical attacks due to prolific knowledge of underlying physical layers. Additionally, optimal signal processing for one channel medium may not work for another without significant changes in the software protocol. Any truly resilient communications protocol must be capable of immediate redeployment to meet quality of service (QoS) demands in a wide variety of possible channel media. Contrary to many traditional approaches which use immutable man-made signal processing blocks, this work proposes generating real-time blocks {\it ad hoc} through a machine learning framework, so-called deepmod, that is only relevant to the particular channel medium being used. With this approach, traditional signal processing blocks are replaced with machine learning graphs which are trained, used, and discarded as needed. Our experiments show that deepmod, using the same machine intelligence, converges to viable communication links over vastly different channels including: radio frequency (RF), powerline communications (PLC), and acoustic channels.

eess.SP↗
arXiv2019

CMB-S4 Decadal Survey APC White Paper

We provide an overview of the science case, instrument configuration and project plan for the next-generation ground-based cosmic microwave background experiment CMB-S4, for consideration by the 2020 Decadal Survey.

astro-ph.IM↗
arXiv2013

Inferring Social Rank in an Old Assyrian Trade Network

We present work in jointly inferring the unique individuals as well as their social rank within a collection of letters from an Old Assyrian trade colony in K\"ultepe, Turkey, settled by merchants from the ancient city of Assur for approximately 200 years between 1950-1750 BCE, the height of the Middle Bronze Age. Using a probabilistic latent-variable model, we leverage pairwise social differences between names in cuneiform tablets to infer a single underlying social order that best explains the data we observe. Evaluating our output with published judgments by domain experts suggests that our method may be used for building informed hypotheses that are driven by data, and that may offer promising avenues for directed research by Assyriologists.

cs.CY↗