Elyssa Hofgard

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

arXiv (Cornell University), Jul 17, 2023

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sci... more Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science.

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Exploration of different parameter optimization algorithms within the context of ACTS software framework

Particle track reconstruction, in which the trajectories of charged particles are determined, is ... more Particle track reconstruction, in which the trajectories of charged particles are determined, is a critical and time consuming component of the full event reconstruction chain. The underlying software is complex and consists of a number of mathematically intense algorithms, each dealing with a particular tracking sub-process. These algorithms have many input parameters that need to be supplied in advance. However, it is difficult to determine the configuration of these parameters that produces the best performance. Currently, the input parameter values are decided on the basis of prior experience or by the use of brute force techniques. A parameter optimization approach that is able to automatically tune these parameters for high performance is greatly desirable. In the current work, we explore various machine learning based optimization methods to devise a suitable technique to optimize parameters in the complex track reconstruction environment. These methods are evaluated on the b...

Exploration of different parameter optimization algorithms within the context of ACTS software framework

arXiv (Cornell University), Nov 1, 2022

Particle track reconstruction, in which the trajectories of charged particles are determined, is ... more Particle track reconstruction, in which the trajectories of charged particles are determined, is a critical and time consuming component of the full event reconstruction chain. The underlying software is complex and consists of a number of mathematically intense algorithms, each dealing with a particular tracking sub-process. These algorithms have many input parameters that need to be supplied in advance. However, it is difficult to determine the configuration of these parameters that produces the best performance. Currently, the input parameter values are decided on the basis of prior experience or by the use of brute force techniques. A parameter optimization approach that is able to automatically tune these parameters for high performance is greatly desirable. In the current work, we explore various machine learning based optimization methods to devise a suitable technique to optimize parameters in the complex track reconstruction environment. These methods are evaluated on the basis of a metric that targets high efficiency while keeping the duplicate and fake rates small. We focus on derivative free optimization approaches that can be applied to problems involving non-differentiable loss functions. For our studies, we consider the tracking algorithms defined within A Common Tracking Software (ACTS) framework. We test our methods using simulated data from ACTS software corresponding to the ACTS Generic detector and the ATLAS ITk detector geometries.

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Wind Proofing LIGO

Bulletin of the American Physical Society, 2018

Simultaneous Multi-Isotope PET: A Computational Framework for Line of Response (LOR) Identification

2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2020

PET imaging using non-pure positron emitters as radiotracers can be used to simultaneously image ... more PET imaging using non-pure positron emitters as radiotracers can be used to simultaneously image distributions of different molecules. We study the use of radionuclides that emit a high energy gamma in conjunction with positrons, producing triple coincidences. A given triple coincidence could be a true coincidence corresponding to two annihilation photons and a high energy gamma emitted from the same source, or it could be a random triple coincidence arising from different sources. We develop an algorithm to identify true triple coincidences and the correct line of response (LOR) within these coincidences. We employ a probabilistic approach to the identification problem through synthesizing geometric, spatial, energy, and temporal information. These criteria are used to create a joint MLE representing the probability that a given set of particles is a true triple coincidence and contains a valid LOR. This approach allows for more accurate identification of true triple coincidences as opposed to only using a high energy threshold. For proof of concept image reconstruction, we use GATE Monte Carlo simulations. With a simple phantom composed of three spheres, we correctly identified 99% of true triple coincidences and 97% of true double coincidences for image reconstruction. Further correction factors should be implemented to obtain more accurate images.

GW190521: A Binary Black Hole Merger with a Total Mass of 150 M⊙

Physical Review Letters, 2020

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A rarefaction approach for measuring population differences in rare and common variation

GENETICS

In studying allele-frequency variation across populations, it is often convenient to classify an ... more In studying allele-frequency variation across populations, it is often convenient to classify an allelic type as “rare,” with nonzero frequency less than or equal to a specified threshold, “common,” with a frequency above the threshold, or entirely unobserved in a population. When sample sizes differ across populations, however, especially if the threshold separating “rare” and “common” corresponds to a small number of observed copies of an allelic type, discreteness effects can lead a sample from one population to possess substantially more rare allelic types than a sample from another population, even if the two populations have extremely similar underlying allele-frequency distributions across loci. We introduce a rarefaction-based sample-size correction for use in comparing rare and common variation across multiple populations whose sample sizes potentially differ. We use our approach to examine rare and common variation in worldwide human populations, finding that the sample-si...

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