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2,741 results on '"Smith, Michael J. A."'

1. The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data

Author

The Multimodal Universe Collaboration, Audenaert, Jeroen, Bowles, Micah, Boyd, Benjamin M., Chemaly, David, Cherinka, Brian, Ciucă, Ioana, Cranmer, Miles, Do, Aaron, Grayling, Matthew, Hayes, Erin E., Hehir, Tom, Ho, Shirley, Huertas-Company, Marc, Iyer, Kartheik G., Jablonska, Maja, Lanusse, Francois, Leung, Henry W., Mandel, Kaisey, Martínez-Galarza, Juan Rafael, Melchior, Peter, Meyer, Lucas, Parker, Liam H., Qu, Helen, Shen, Jeff, Smith, Michael J., Stone, Connor, Walmsley, Mike, and Wu, John F.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics
Abstract

We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MULTIMODAL UNIVERSE contains hundreds of millions of astronomical observations, constituting 100\,TB of multi-channel and hyper-spectral images, spectra, multivariate time series, as well as a wide variety of associated scientific measurements and "metadata". In addition, we include a range of benchmark tasks representative of standard practices for machine learning methods in astrophysics. This massive dataset will enable the development of large multi-modal models specifically targeted towards scientific applications. All codes used to compile the MULTIMODAL UNIVERSE and a description of how to access the data is available at https://github.com/MultimodalUniverse/MultimodalUniverse, Comment: Accepted at NeurIPS Datasets and Benchmarks track

Published
2024

2. Towards more efficient agricultural practices via transformer-based crop type classification

Author

Moya-Sánchez, E. Ulises, Mikail, Yazid S., Nyang'anyi, Daisy, Smith, Michael J., and Smythe, Isabella

Subjects
Physics - Geophysics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Machine learning has great potential to increase crop production and resilience to climate change. Accurate maps of where crops are grown are a key input to a number of downstream policy and research applications. In this proposal, we present preliminary work showing that it is possible to accurately classify crops from time series derived from Sentinel 1 and 2 satellite imagery in Mexico using a pixel-based binary crop/non-crop time series transformer model. We also find preliminary evidence that meta-learning approaches supplemented with data from similar agro-ecological zones may improve model performance. Due to these promising results, we propose further development of this method with the goal of accurate multi-class crop classification in Jalisco, Mexico via meta-learning with a dataset comprising similar agro-ecological zones.

Published
2024

3. pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy

Author

Iyer, Kartheik G., Yunus, Mikaeel, O'Neill, Charles, Ye, Christine, Hyk, Alina, McCormick, Kiera, Ciuca, Ioana, Wu, John F., Accomazzi, Alberto, Astarita, Simone, Chakrabarty, Rishabh, Cranney, Jesse, Field, Anjalie, Ghosal, Tirthankar, Ginolfi, Michele, Huertas-Company, Marc, Jablonska, Maja, Kruk, Sandor, Liu, Huiling, Marchidan, Gabriel, Mistry, Rohit, Naiman, J. P., Peek, J. E. G., Polimera, Mugdha, Rodriguez, Sergio J., Schawinski, Kevin, Sharma, Sanjib, Smith, Michael J., Ting, Yuan-Sen, and Walmsley, Mike

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Digital Libraries, Computer Science - Information Retrieval
Abstract

The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with keywords. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), Pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use keywords or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool's versatility through case studies, showcasing its application in various research scenarios. The system's performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, Pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature., Comment: 25 pages, 9 figures, submitted to AAS jorunals. Comments are welcome, and the tools mentioned are available online at https://pfdr.app

Published
2024

4. AstroPT: Scaling Large Observation Models for Astronomy

Author

Smith, Michael J., Roberts, Ryan J., Angeloudi, Eirini, and Huertas-Company, Marc

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Computer Science - Machine Learning
Abstract

This work presents AstroPT, an autoregressive pretrained transformer developed with astronomical use-cases in mind. The AstroPT models presented here have been pretrained on 8.6 million $512 \times 512$ pixel $grz$-band galaxy postage stamp observations from the DESI Legacy Survey DR8. We train a selection of foundation models of increasing size from 1 million to 2.1 billion parameters, and find that AstroPT follows a similar saturating log-log scaling law to textual models. We also find that the models' performances on downstream tasks as measured by linear probing improves with model size up to the model parameter saturation point. We believe that collaborative community development paves the best route towards realising an open source `Large Observation Model' -- a model trained on data taken from the observational sciences at the scale seen in natural language processing. To this end, we release the source code, weights, and dataset for AstroPT under the MIT license, and invite potential collaborators to join us in collectively building and researching these models., Comment: 12 pages, 4 figures, 1 table. Code available at https://github.com/Smith42/astroPT

Published
2024

5. AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets

Author

Perkowski, Ernest, Pan, Rui, Nguyen, Tuan Dung, Ting, Yuan-Sen, Kruk, Sandor, Zhang, Tong, O'Neill, Charlie, Jablonska, Maja, Sun, Zechang, Smith, Michael J., Liu, Huiling, Schawinski, Kevin, Iyer, Kartheik, and UniverseTBD, Ioana Ciucă for

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics, Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

We explore the potential of enhancing LLM performance in astronomy-focused question-answering through targeted, continual pre-training. By employing a compact 7B-parameter LLaMA-2 model and focusing exclusively on a curated set of astronomy corpora -- comprising abstracts, introductions, and conclusions -- we achieve notable improvements in specialized topic comprehension. While general LLMs like GPT-4 excel in broader question-answering scenarios due to superior reasoning capabilities, our findings suggest that continual pre-training with limited resources can still enhance model performance on specialized topics. Additionally, we present an extension of AstroLLaMA: the fine-tuning of the 7B LLaMA model on a domain-specific conversational dataset, culminating in the release of the chat-enabled AstroLLaMA for community use. Comprehensive quantitative benchmarking is currently in progress and will be detailed in an upcoming full paper. The model, AstroLLaMA-Chat, is now available at https://huggingface.co/universeTBD, providing the first open-source conversational AI tool tailored for the astronomy community., Comment: 4 pages, 1 figure, model is available at https://huggingface.co/universeTBD, published in RNAAS

Published
2024

6. Detangling the role of climate in vegetation productivity with an explainable convolutional neural network

Author

Lourenço, Ricardo Barros, Smith, Michael J., Smullin, Sylvia, Jain, Umangi, Gonsamo, Alemu, and Ouaknine, Arthur

Subjects
Physics - Atmospheric and Oceanic Physics, Physics - Geophysics
Abstract

Forests of the Earth are a vital carbon sink while providing an essential habitat for biodiversity. Vegetation productivity (VP) is a critical indicator of carbon uptake in the atmosphere. The leaf area index is a crucial vegetation index used in VP estimation. This work proposes to predict the leaf area index (LAI) using climate variables to better understand future productivity dynamics; our approach leverages the capacities of the V-Net architecture for spatiotemporal LAI prediction. Preliminary results are well-aligned with established quality standards of LAI products estimated from Earth observation data. We hope that this work serves as a robust foundation for subsequent research endeavours, particularly for the incorporation of prediction attribution methodologies, which hold promise for elucidating the underlying climate change drivers of global vegetation productivity., Comment: 7 pages, 2 figures, submitted to Tackling Climate Change with Machine Learning at NeurIPS 2023

Published
2023

7. EarthPT: a time series foundation model for Earth Observation

Author

Smith, Michael J., Fleming, Luke, and Geach, James E.

Subjects
Computer Science - Machine Learning, Physics - Geophysics
Abstract

We introduce EarthPT -- an Earth Observation (EO) pretrained transformer. EarthPT is a 700 million parameter decoding transformer foundation model trained in an autoregressive self-supervised manner and developed specifically with EO use-cases in mind. We demonstrate that EarthPT is an effective forecaster that can accurately predict future pixel-level surface reflectances across the 400-2300 nm range well into the future. For example, forecasts of the evolution of the Normalised Difference Vegetation Index (NDVI) have a typical error of approximately 0.05 (over a natural range of -1 -> 1) at the pixel level over a five month test set horizon, out-performing simple phase-folded models based on historical averaging. We also demonstrate that embeddings learnt by EarthPT hold semantically meaningful information and could be exploited for downstream tasks such as highly granular, dynamic land use classification. Excitingly, we note that the abundance of EO data provides us with -- in theory -- quadrillions of training tokens. Therefore, if we assume that EarthPT follows neural scaling laws akin to those derived for Large Language Models (LLMs), there is currently no data-imposed limit to scaling EarthPT and other similar `Large Observation Models.', Comment: 7 pages, 4 figures, accepted to NeurIPS CCAI workshop at https://www.climatechange.ai/papers/neurips2023/2 . Code available at https://github.com/aspiaspace/EarthPT

Published
2023

8. Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy

Author

Smith, Michael J. and Geach, James E.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning
Abstract

In this review, we explore the historical development and future prospects of artificial intelligence (AI) and deep learning in astronomy. We trace the evolution of connectionism in astronomy through its three waves, from the early use of multilayer perceptrons, to the rise of convolutional and recurrent neural networks, and finally to the current era of unsupervised and generative deep learning methods. With the exponential growth of astronomical data, deep learning techniques offer an unprecedented opportunity to uncover valuable insights and tackle previously intractable problems. As we enter the anticipated fourth wave of astronomical connectionism, we argue for the adoption of GPT-like foundation models fine-tuned for astronomical applications. Such models could harness the wealth of high-quality, multimodal astronomical data to serve state-of-the-art downstream tasks. To keep pace with advancements driven by Big Tech, we propose a collaborative, open-source approach within the astronomy community to develop and maintain these foundation models, fostering a symbiotic relationship between AI and astronomy that capitalizes on the unique strengths of both fields., Comment: 75 pages, 327 references, 32 figures. Review accepted in Royal Society Open Science

Published
2022
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9. Efficient synthesis for each of the eight stereoisomers of the iminosugars lentiginosine and 1,4-dideoxy-1,4-imino-D-arabinitol (DAB)

Author

Liotta, Louis J., Antoine, Jessica, Brammer Basta, Leighanne A., Campbell, Andrew S., Cole, Gabrielle Y., Demick Brazile, Kristen A., Dogal Gardner, Natalie M., Fitzgerald, Megan E., Francois, Jean E.K., French, Brian M., Garafola, Sara L., Giannetti, Catherine A., Granatosky, Eve A., Harney, Alycen M., Hummel, James T., Joyce, Andrew P., Keylor, Mitchell H., Khubchandani, Jasmine A., Korzeniecki, Claudia, Lieberman, Diana C., Litterio, Joshua M., Maiorano, Madison O., Marshall, Jessica F., McCarthy, Kelly A., Mendes Vieira, Andreia, Miller, Ruby M., Morrison, Emily R., Moura, Steven P., Neumann, Dillon F., Oliveira, Aliza F., Pace, Nicholas J., Plouffe, Jodie X., Pomfret, Meredith N., Reardon, Katelyn N., Sheller-Miller, Samantha M., Smith, Michael J., Sullivan, Jessica L., Sweeney, Samantha W., and Tougas, Kerstin L.

Published
2024
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10. Realistic galaxy image simulation via score-based generative models

Author

Smith, Michael J., Geach, James E., Jackson, Ryan A., Arora, Nikhil, Stone, Connor, and Courteau, Stéphane

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Computer Science - Machine Learning
Abstract

We show that a Denoising Diffusion Probabalistic Model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies. Our method is tested with Dark Energy Spectroscopic Instrument (DESI) grz imaging of galaxies from the Photometry and Rotation curve OBservations from Extragalactic Surveys (PROBES) sample and galaxies selected from the Sloan Digital Sky Survey. Subjectively, the generated galaxies are highly realistic when compared with samples from the real dataset. We quantify the similarity by borrowing from the deep generative learning literature, using the `Fr\'echet Inception Distance' to test for subjective and morphological similarity. We also introduce the `Synthetic Galaxy Distance' metric to compare the emergent physical properties (such as total magnitude, colour and half light radius) of a ground truth parent and synthesised child dataset. We argue that the DDPM approach produces sharper and more realistic images than other generative methods such as Adversarial Networks (with the downside of more costly inference), and could be used to produce large samples of synthetic observations tailored to a specific imaging survey. We demonstrate two potential uses of the DDPM: (1) accurate in-painting of occluded data, such as satellite trails, and (2) domain transfer, where new input images can be processed to mimic the properties of the DDPM training set. Here we `DESI-fy' cartoon images as a proof of concept for domain transfer. Finally, we suggest potential applications for score-based approaches that could motivate further research on this topic within the astronomical community., Comment: 11 pages, 8 figures. Code: https://github.com/smith42/astroddpm . Follow the Twitter bot @ThisIsNotAnApod for DDPM-generated APODs

Published
2021
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11. Comparison of bivalent and monovalent SARS-CoV-2 variant vaccines: the phase 2 randomized open-label COVAIL trial

Author

Branche, Angela R., Rouphael, Nadine G., Diemert, David J., Falsey, Ann R., Losada, Cecilia, Baden, Lindsey R., Frey, Sharon E., Whitaker, Jennifer A., Little, Susan J., Anderson, Evan J., Walter, Emmanuel B., Novak, Richard M., Rupp, Richard, Jackson, Lisa A., Babu, Tara M., Kottkamp, Angelica C., Luetkemeyer, Anne F., Immergluck, Lilly C., Presti, Rachel M., Bäcker, Martín, Winokur, Patricia L., Mahgoub, Siham M., Goepfert, Paul A., Fusco, Dahlene N., Malkin, Elissa, Bethony, Jeffrey M., Walsh, Edward E., Graciaa, Daniel S., Samaha, Hady, Sherman, Amy C., Walsh, Stephen R., Abate, Getahun, Oikonomopoulou, Zacharoula, El Sahly, Hana M., Martin, Thomas C. S., Kamidani, Satoshi, Smith, Michael J., Ladner, Benjamin G., Porterfield, Laura, Dunstan, Maya, Wald, Anna, Davis, Tamia, Atmar, Robert L., Mulligan, Mark J., Lyke, Kirsten E., Posavad, Christine M., Meagher, Megan A., Stephens, David S., Neuzil, Kathleen M., Abebe, Kuleni, Hill, Heather, Albert, Jim, Telu, Kalyani, Mu, Jinjian, Lewis, Teri C., Giebeig, Lisa A., Eaton, Amanda, Netzl, Antonia, Wilks, Samuel H., Türeli, Sina, Makhene, Mamodikoe, Crandon, Sonja, Montefiori, David C., Makowski, Mat, Smith, Derek J., Nayak, Seema U., Roberts, Paul C., and Beigel, John H.

Published
2023
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12. Using deep learning to explore ultra-large scale astronomical datasets

Author

Smith, Michael J.

Subjects
Neural Networks, Astronomy, Deep Learning, Galaxies
Abstract

In every field that deep learning has infiltrated we have seen a reduction in the use of specialist knowledge, to be replaced with knowledge automatically derived from data. We have already seen this process play out in many 'applied deep learning' fields such as computer Go, protein folding, natural language processing, and computer vision. This thesis argues that astronomy is no different to these applied deep learning fields. To this end, this thesis' introduction serves as a historical background on astronomy's 'three waves' of increasingly automated connectionism: initial work on multilayerperceptrons within astronomy required manually selected emergent properties as input; the second wave coincided with the dissemination of convolutional neural networks and recurrent neural networks, models where the multilayer perceptron's manually selected inputs are replaced with raw data ingestion; and in the current third wave we are seeing the removal of human supervision altogether with deep learning methods inferring labels and knowledge directly from the data. §2, §3, and §4 of this thesis explore these waves through application. In §2 I show that a convolutional/recurrent encoder/decoder network is capable of emulating a complicated semi-manual galaxy processing pipeline. I find that this 'Pix2Prof' neural network can satisfactorily carry out this task over 100x faster than the method it emulates. §3 and §4 explore the application of deep generative models to astronomical simulation. §3 uses a generative adversarial network to generate mock deep field surveys, and finds it capable of generating mock images that are statistically indistinguishable from the real thing. Likewise, §4 demonstrates that a Diffusion model is capable of generating galaxy images that are both qualitatively and quantitatively indistinguishable from the training set. The main benefit of these deep learning based simulations is that they do not rely on a possibly flawed (or incomplete) physical knowledge of their subjects and observation processes. Also, once trained, they are capable of rapidly generating a very large amount of mock data. §5 looks to the future and predicts that we will soon enter a fourth wave of astronomical connectionism. If astronomy follows in the footsteps of other applied deep learning fields we will see the removal of expertly crafted deep learning models, to be replaced with finetuned versions of an all-encompassing 'foundation' model. As part of this fourth wave I argue for a symbiosis between astronomy and connectionism. This symbiosis is predicated on astronomy's relative data wealth, and contemporary deep learning's enormous data appetite; many ultra-large datasets in machine learning are proprietary or of poor quality, and so astronomy as a whole could develop and provide a high quality multimodal public dataset. In turn, this dataset could be used to train an astronomical foundation model that can be used for state-of-the-art downstream tasks. Due to the foundation models' hunger for data and compute, a single astronomical research group could not bring about such a model alone. Therefore, I conclude that astronomy as a whole has slim chance of keeping up with a research pace set by the Big Tech goliaths-that is, unless we follow the examples of EleutherAI and HuggingFace and pool our resources in a grassroots open source fashion.

Published
2022
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14. Pix2Prof: fast extraction of sequential information from galaxy imagery via a deep natural language 'captioning' model

Author

Smith, Michael J., Arora, Nikhil, Stone, Connor, Courteau, Stéphane, and Geach, James E.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Computer Science - Machine Learning
Abstract

We present 'Pix2Prof', a deep learning model that can eliminate any manual steps taken when extracting galaxy profiles. We argue that a galaxy profile of any sort is conceptually similar to a natural language image caption. This idea allows us to leverage image captioning methods from the field of natural language processing, and so we design Pix2Prof as a float sequence 'captioning' model suitable for galaxy profile inference. We demonstrate the technique by approximating a galaxy surface brightness (SB) profile fitting method that contains several manual steps. Pix2Prof processes $\sim$1 image per second on an Intel Xeon E5 2650 v3 CPU, improving on the speed of the manual interactive method by more than two orders of magnitude. Crucially, Pix2Prof requires no manual interaction, and since galaxy profile estimation is an embarrassingly parallel problem, we can further increase the throughput by running many Pix2Prof instances simultaneously. In perspective, Pix2Prof would take under an hour to infer profiles for $10^5$ galaxies on a single NVIDIA DGX-2 system. A single human expert would take approximately two years to complete the same task. Automated methodology such as this will accelerate the analysis of the next generation of large area sky surveys expected to yield hundreds of millions of targets. In such instances, all manual approaches -- even those involving a large number of experts -- will be impractical., Comment: Accepted for publication in MNRAS. 10 pages, and 8 figures. Code: https://github.com/Smith42/pix2prof

Published
2020
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15. AstroVaDEr: Astronomical Variational Deep Embedder for Unsupervised Morphological Classification of Galaxies and Synthetic Image Generation

Author

Spindler, Ashley, Geach, James E., and Smith, Michael J.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

We present AstroVaDEr, a variational autoencoder designed to perform unsupervised clustering and synthetic image generation using astronomical imaging catalogues. The model is a convolutional neural network that learns to embed images into a low dimensional latent space, and simultaneously optimises a Gaussian Mixture Model (GMM) on the embedded vectors to cluster the training data. By utilising variational inference, we are able to use the learned GMM as a statistical prior on the latent space to facilitate random sampling and generation of synthetic images. We demonstrate AstroVaDEr's capabilities by training it on gray-scaled \textit{gri} images from the Sloan Digital Sky Survey, using a sample of galaxies that are classified by Galaxy Zoo 2. An unsupervised clustering model is found which separates galaxies based on learned morphological features such as axis ratio, surface brightness profile, orientation and the presence of companions. We use the learned mixture model to generate synthetic images of galaxies based on the morphological profiles of the Gaussian components. AstroVaDEr succeeds in producing a morphological classification scheme from unlabelled data, but unexpectedly places high importance on the presence of companion objects---demonstrating the importance of human interpretation. The network is scalable and flexible, allowing for larger datasets to be classified, or different kinds of imaging data. We also demonstrate the generative properties of the model, which allow for realistic synthetic images of galaxies to be sampled from the learned classification scheme. These can be used to create synthetic image catalogs or to perform image processing tasks such as deblending., Comment: Accepted in MNRAS. 23 Pages, 16 Figures. GitHub: https://github.com/AshleySpindler/AstroVaDEr-Public

Published
2020
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16. Geometrical and Mechanical Characterisation of Hollow Thermoplastic Microspheres for Syntactic Foam Applications

Author

Curd, Matthew E., Morrison, Neil F., Smith, Michael J. A., Gajjar, Parmesh, Yousaf, Zeshan, and Parnell, William

Subjects
Physics - Applied Physics, Condensed Matter - Materials Science
Abstract

Recently, hollow thermoplastic microspheres, such as Expancel made by Nouryon, have emerged as an innovative filler material for use in polymer-matrix composites. The resulting all-polymer syntactic foam takes on excellent damage tolerance properties, strong recoverability under large strains, and favourable energy dissipation characteristics. Despite finding increasing usage in various industries and applications, including in coatings, films, sealants, packaging, composites for microfluidics, medical ultrasonics and cementious composites, there is a near-complete absence of statistical geometrical information for Expancel microspheres. Further, their mechanical properties have not yet been reported. In this work we characterise the geometrical quantities of two classes of Expancel thermoplastic microspheres using X-ray computed tomography, focused ion beam and electron microscopy. We also observe the spatial distribution of microspheres within a polyurethane-matrix syntactic foam. We show that the volume-weighted polydisperse shell diameter in both classes of microsphere follows a normal distribution. Interestingly, polydispersity of the shell wall thickness is not observed and in particular the shell thickness is not correlated to the shell diameter. We employ the measured geometrical information in analytical micromechanical techniques in the small strain regime to determine, for the first time, estimates of the Young's modulus and Poisson's ratio of the microsphere shell material. Our results contribute to potential future improvements in the design and fabrication of syntactic foams that employ thermoplastic microspheres. Given the breadth of fields which utilise thermoplastic microspheres, we anticipate that our results, together with the methods used, will be of use in a much broader context in future materials research., Comment: 19 page, 7 figure, 3 tables

Published
2020

20. Risk of COVID-19 after natural infection or vaccination

Author

Adams, Atoya, Miller, Eric, Rankin, Bruce G., Shinn, Steven, Nash, Marshall, Green, Sinikka L., Jacobsen, Colleen, Krishnankutty, Jayasree, Phungwayo, Sikhongi, Glover, Richard M., II, Slechta, Stacy, Holdeman, Troy, Hartvickson, Robyn, Grant, Amber, Poling, Terry L., Klein, Terry D., Klein, Thomas C., Klein, Tracy R., Smith, William B., Gibson, Richard L., Winbigler, Jennifer, Parker, Elizabeth, Wijewardane, Priyantha N., Bravo, Eric, Thessing, Jeffrey, Maxwell, Michelle, Horn, Amanda, Healy, Catherine Mary, Akamine, Christine, Chu, Laurence, Chouteau, R. Michelle, Cotugno, Michael J., Bauer, George H., Jr., Hachigian, Greg, Oshita, Masaru, Cancilla, Michael, Kiersey, Kristen, Seger, William, Antwi, Mohammed, Green, Allison, Kim, Anthony, Desjardins, Michael, Johnson, Jennifer A., Sherman, Amy, Walsh, Stephen R., Borger, Judith, Saleem, Nafisa, Solis, Joel, Medina, Martha Carmen, Keating, Westly, Garcia, Edgar, Bueno, Cynthia, Segall, Nathan, Denham, Douglas S., Weiss, Thomas, Avworo, Ayoade, Hedges, Parke, Strout, Cynthia Becher, Santiago, Rica, Davis, Yvonne, Howenstine, Patty, Bondell, Alison, Marks, Kristin, Wang, Tina, Wilkin, Timothy, Vogler, Mary, Johnston, Carrie, Andrasik, Michele P., Andriesen, Jessica G., Broder, Gail, Eaton, Niles, Gelderblom, Huub G., McClennen, Rachael, Michael, Nelson, Robb, Merlin, Sopher, Carrie, Miller, Vicki E., Santiago, Fredric, Gomez, Blanca, Valika, Insiya, Starr, Amy, Cantos, Valeria D., Kandiah, Sheetal, Rio, Carlos del, Rouphael, Nadine, Edupuganti, Srilatha, Anderson, Evan J., Camacho-Gonzalez, Andres, Kamidani, Satoshi, Teherani, Meghan, Diemert, David J., Malkin, Elissa, Siegel, Marc, Roberts, Afsoon, Simon, Gary, Balani, Bindu, Stephenson, Carolene, Sperber, Steven, Cicogna, Cristina, Zervos, Marcus J., Kilgore, Paul, Ramesh, Mayur, Herc, Erica, Zenlea, Kate, Burgher, Abram, Milliken, Ann M., Davis, Joseph D., Levy, Brendan, Kelman, Sandra, Doust, Matthew W., Sample, Denise, Erickson, Sandra, Christensen, Shane G., Matich, Christopher, Longe, James, Witbeck, John, Peterson, James T., Clark, Alexander, Kelty, Gerald, Pena-Renteria, Issac, Koren, Michael J., Bartilucci, Darlene, Patel, Alpa, Tran, Carolyn, Kennelly, Christina, Brownlee, Robert, Coleman, Jacob, Webster, Hala, Fierro, Carlos A., Leistner, Natalia, Thompson, Amy, Gonzalez, Celia, Jackson, Lisa A., Suyehira, Janice, Haber, Milton, Regalado, Maria M., Procasky, Veronica, Lutat, Alisha, Griffin, Carl P., Hollister, Ripley R., Brown, Jeremy, Ronk, Melody, Harper, Wayne L., Cohen, Lisa, Eckert, Lynn, Hong, Matthew, Rouhbakhsh, Rambod, Danford, Elizabeth, Johnson, John, Calderone, Richard, Khetan, Shishir K., Olanrewaju, Oyebisi, Zhai, Nan, Nieves, Kimberly, O'Brien, Allison, Bradley, Paul S., Lilienthal, Amanda, Callis, Jim, Brosz, Adam B., Clement, Andrea, West, Whitney, Friesen, Luke, Cramer, Paul, Eder, Frank S., Little, Ryan, Engler, Victoria, Rattenbury-Shaw, Heather, Ensz, David J., Oplinger, Allie, Essink, Brandon J., Meyer, Jay, Raiser, Frederick, III, Mueller, Kimberly, Vrbicky, Keith W., Harper, Charles, Nutsch, Chelsie, Lewis, Wendell, III, Laflan, Cathy, Whatley, Jordan L., Harrell, Nicole, Shannon, Amie, Rowell, Crystal, Dedon, Christopher, Makhene, Mamodikoe, Gottschlich, Gregory M., Harden, Kate, Gottschlich, Melissa, Smith, Mary, Powell, Richard, Kimmel, Murray A., Pinto, Simmy, Vachris, Timothy P., Hutchens, Mark, Daniels, Stephen, Wells, Margaret, Van Der Leden, Mimi, Jackson-Booth, Peta-Gay, Baron, Mira, Kane, Pamela, Seversen, Shannen, Kryvicky, Mara, Lord, Julia, Saleh, Jamshid, Miles, Matthew, Lupercio, Rafael, McGettigan, John W., Jr., Patton, Walter, Brakema, Riemke, Choquette, Karin, McGettigan, Jonlyn, Kirstein, Judith L., Bernard, Marcia, Manning, Mary Beth, Rothenberg, Joan, Briskin, Toby, Roadman, Denise, Tedder-Edwards, Sharita, Schwartz, Howard I., Mederos, Surisday, Swaminathan, Shobha, Nyaku, Amesika, Varughese, Tilly, DallaPiazza, Michelle, Frey, Sharon E., Graham, Irene, Abate, Getahun, Hoft, Daniel, Allen, Leland N., III, Edwards, Leslie A., Davis, William S., Jr., Mena, Jessica M., Kutner, Mark E., Caso, Jorge, Moran, Maria Hernandez, Carvajal, Marianela, Mendez, Janet, Wadsworth, Larkin T., III, Adams, Michael R., Iverson, Leslie, Newberg, Joseph L., Pearlman, Laura, Nugent, Paul J., Reynolds, Michele D., Bashour, Jennifer, Schmidt, Robert, Sheth, Neil P., Steil, Kenneth, Toma, Ramy J., Kirby, William, Folmar, Pink, Williams, Samantha, Pickrell, Paul, Mott, Stefanie, Linebarger, Carol Ann, Malbari, Hussain, Pampe, David, Fragoso, Veronica G., Holloway, Lisa, McKeown-Bragas, Cecilia, Becker, Teresa, Williams, Barton G., Jones, William H., Clark, Jesse L., Shoptaw, Steven, Vertucci, Michele, Hernandez, Will, Spector, Stephen A., Moodley, Amaran, Blumenthal, Jill, Stangl, Lisa, Deutsch, Karen, Mullane, Kathleen M., Pitrak, David, Nuss, Cheryl, Pi, Judy, Fichtenbaum, Carl, Powers-Fletcher, Margaret, Saemann, Michelle, Kohrs, Sharon, Campbell, Thomas B., Lauria, Andrew, Mancilla, Jose C., Dunlevy, Hillary, Novak, Richard M., Wendrow, Andrea, Borgetti, Scott, Ladner, Ben, Chrisley, Lisa, Young, Cheryl, Doblecki-Lewis, Susanne, Alcaide, Maria L., Gonzales-Zamora, Jose, Morris, Stephen, Wohl, David, Eron, Joseph, Jr., Frank, Ian, Dunbar, Debora, Metzger, David, Momplaisir, Florence, Martin, Judith, Hoberman, Alejandro, Shope, Timothy, Muniz, Gysella, Rupp, Richard, Stanford, Amber, Berman, Megan, Porterfield, Laura, Lewis, Michael, Ghadishah, Elham, Yusin, Joseph, Pham, Mai, Creech, Clarence B., II, Walker, Shannon, Rolsma, Stephanie, Samuels, Robert, Thomsen, Isaac, Kalams, Spyros A., Wilson, Greg, Lucksinger, Gregg H., Parks, Kevin, Israelsen, Ryan, Ostovar, Jaleh, Kelly, Kary, Overcash, Jeffrey S., Chu, Hanh, Lee, Kia, De La Cruz, Luis I., Clemons, Steve, Everette, Elizabeth, Studdard, Suzanna, Mohan, Gowdhami, Tyson, Stefanie, Peay, Alyssa-Kay, Johnson, Danyel, Feldman, Gregory J., Suen, May-Yin, Muenzner, Jacqueline, Boscia, Joseph, Siddiqui, Farhan, Sanders, John, Peacock, James, Nasim, Julio, Levin, Michael L., Hussey, Julie, Kulic, Marcy, McKenzie, Mark M., Deese, Teresa, Osmundsen, Erica, Sweet, Christy, Ebuh, Valentine M., Elnagar, Elwaleed, Ebuh, Georgette, Iwuala, Genevieve, Han-Conrad, Laurie J., Simmons, Todd, Tarakjian, Denis, Ackermann, Jeremy, Adams, Mark S., Alemán, José O., Al-Ibrahim, Mohamed S., Andes, David R., Andrews, Jeb, Arduino, Roberto C., Bäcker, Martín, Badillo, Diana, Bainbridge, Emma, Batteiger, Teresa A., Bazan, Jose A., Bedimo, Roger J., Benitez, Jorge A., Bennett, Annette R., Bernstein, David I., Bialobok, Kristin, Boas, Rebecca, Brady, Judith, Brown, Cynthia, Bunce, Catherine A., Call, Robert S., Campbell, Wesley, Carmody, Ellie, Carpenter, Christopher, Carsons, Steven E., Castellon, Marvin, Castro, Mario, Catan, Hannah, Chang, Jennifer, Chebib, Mouna G., Chen, Corey M., Cheng, Margaret, Chow, Brian D.W., Ciambruschini, Annie, Connor, Joseph P., Conway, James H., Cooney, Maureen, Curlin, Marcel, De La Matta Rodriguez, Claudia, Dedon, Jon F., Degan, Emily, Dickey, Michelle, Dietz, Craig, Dong, Jennifer L., Dorcely, Brenda, Dube, Michael P., Dyer, Carmel B., Eckhardt, Benjamin, Ellerbeck, Edward, Ewers, Evan C., Falk, Amy, Feijoo, Brittany, Felsen, Uriel R., Fiel, Tom, Fitz-Patrick, David, Fogarty, Charles M., Ford, Stacy, Forero, Lina M., Formentini, Elizabeth, Franco-Vitteri, Doris, Frenck, Robert W., Jr., Gharib, Elie, Gharib, Suzanne, Rucker, Rola G., Goldenberg, James N., González, Luis H., Gray, Brett, Greene, Rusty, Grossberg, Robert M., Guanira-Carranza, Juan V., Guerreros Benavides, Alfredo Gilberto, Guillory, Clint C., Gunaratne, Shauna H., Halpert, David, Hamilton, Holli, Hartman, William R., Henderson, Sheryl L., Herati, Ramin, Guarin, Laura Hernandez, Hilder, Robin, Ho, Ken, Hojat, Leila, Hosek, Sybil G., Jacobson, Jeffrey M., Jay, Melanie, Johnson, Diane H., Jones, Kathleen S., Jones-López, Edward C., Justman, Jessica E., Kahney, Scott, Katz, Lois, Katz, Melinda, Kaul, Daniel, Keefer, Michael C., Kennedy, Ashley, Knishinsky, Jennifer, Kogelman, Laura, Koletar, Susan L., Kottkamp, Angelica, Laguio-Vila, Maryrose, Landovitz, Raphael J., Lee, Jessica L., Liu, Albert, Llerena Zegarra, Eneyda Giuvanela, Lok, Anna S., Lovell, James, Lubelchek, Ronald, Lucaj, John, Luckasen, Gary, Luetkemeyer, Annie, Lugogo, Njira Lucia, Maenza, Janine, Malvestutto, Carlos, Mauri, Monica, Maves, Ryan C., Mayer, Kenneth H., McCartney, Michael J., McCort, Margaret E., McElrath, M. Juliana, McNairy, Meredith, Merino, Fernando L., Meyerowitz, Eric A., Mitchell, Carol L., Monaco, Cynthia L., Muhammad, Sauda, Muñoz-Gómez, Sigridh, Munsiff, Sonal, Nee, Paul, Nollen, Nicole L., Noor, Asif, Lagos, Claudio Nuñez, Okulicz, Jason F., Oliver, Patrick A., Ortega, Jessica, Palmer, Steven, Parameswaran, Lalitha, Parikh, Purvi, Parker, Susan, Parungao, Reza, Pavie, Juana R., Madan, Rebecca P., Peralta, Henry, Petts, Jennifer, Pierce, Kristen K., Pretell Alva, E. Javier, Purpura, Lawrence J., Raabe, Vanessa, Recuenco, Sergio E., Richards, Tamara, Riddler, Sharon A., Rizzardi, Barbara, Rokser, Rachel, Rolle, Charlotte-Paige, Rosen, Adam, Rosen, Jeffrey, Freese, Lena R., Santolaya, María E., Schipani, Linda M., Schwartz, Adam, Schwasinger-Schmidt, Tiffany, Scott, Hyman, Sha, Beverly E., Shankaran, Shivanjali, Shapiro, Adrienne E., Sharp, Stephan C., Shopsin, Bo, Sims, Matthew D., Skipper, Stephanie, Smith, Derek M., Smith, Michael J., Sobhanie, M. Mahdee, Sovic, Brit, Sterling, Stephanie, Striker, Robert, Tafur Bances, Karla Beatriz, Talaat, Kawsar R., Tavel, Edward M., Jr., Tieu, Hong V., Tomaszewski, Christian, Tomlinson, Ryan, Torres, Juan P., Torres, Julian A., Treanor, John J., Tukuru, Sade, Ulrich, Robert J., Utz, Gregory C., Viar, Veronica, Viau Colindres, Roberto A., Walsh, Edward E., Walsh, Mary C., Walter, Emmanuel B., Weidler, Jessica L., Wu, Yi H., Yang, Kinara S., Yrivarren Giorza, Juan Luis, Zemanek, Arthur L., Zhang, Kevin, Zingman, Barry S., Gorman, Richard, Paez, Carmen A., Swann, Edith, Takuva, Simbarashe G., Greninger, Alex, Roychoudhury, Pavitra, Coombs, Robert W., Jerome, Keith R., Castellino, Flora, Tong, Xiaomi, Pavetto, Corrina, Gipson, Teletha, Tong, Tina, Lee, Marina, Zhou, James, Fay, Michael, McQuarrie, Kelly, Nnadi, Chimeremma, Sogbetun, Obiageli, Ahmad, Nina, De Proost, Ian, Hoseyni, Cyrus, Coplan, Paul, Khan, Najat, Ronco, Peter, Furey, Dawn, Meck, Jodi, Vingerhoets, Johan, Brandenburg, Boerries, Custers, Jerome, Hendriks, Jenny, Juraszek, Jarek, Marit de Groot, Anne, Van Roey, Griet, Heerwegh, Dirk, Van Dromme, Ilse, Méndez Galván, Jorge F., Carrascal, Monica B., Duran, Adriana Sordo, Sanchez Guerrero, Laura Ruy, Gómora Madrid, Martha Cecilia, Barrat Hernández, Alejandro Quintín, Guizar, Sharzhaad Molina, González Estrada, Denisse Alejandra, Martínez Pérez, Silvano Omar, Zárate Hinojosa, Zindy Yazmín, Ruiz-Palacios, Guillermo Miguel, Cruz-Valdez, Aurelio, Pacheco-Flores, Janeth, Lara, Anyela, Díaz-Miralrio, Secia, Reyes Fentanes, María José, Olmos Vega, Jocelyn Zuleica, Méndez, Daniela Pineda, Martínez, Karina Cano, Alvarez León, Winniberg Stephany, Ruiz Herrera, Vida Veronica, Vázquez Saldaña, Eduardo Gabriel, Camacho Choza, Laura Julia, Vega Orozco, Karen Sofia, Ortega Domínguez, Sandra Janeth, Chacón, Jorge A., Rivera, Juan J., Cutz, Erika A., Ortegón, Maricruz E., Rivera, María I., Browder, David, Burch, Cortney, Moye, Terri, Bondy, Paul, Browder, Lesley, Manning, Rickey D., Hurst, James W., Sturgeon, Rodney E., Wakefield, Paul H., Kirby, John A., Andersen, James, Fearon, Szheckera, Negron, Rosa, Medina, Amy, Hill, John M., Rajasekhar, Vivek, Williams, Hayes, Cade, LaShondra, Fouts, Rhodna, Moya, Connie, Anderson, Corey G., Devine, Naomi, Ramsey, James, Perez, Ashley, Tatelbaum, David, Jacobs, Michael, Menasche, Kathleen, Mirkil, Vincent, Winkle, Peter J., Haggag, Amina Z., Haynes, Michelle, Villegas, Marysol, Raja, Sabina, Riesenberg, Robert, Plavin, Stanford, Lerman, Mark, Woodside, Leana, Johnson, Maria, Healy, C. Mary, Whitaker, Jennifer A., Keitel, Wendy A., Atmar, Robert L., Horwith, Gary, Mason, Robin, Johnson, Lisa, Dora, Tambra, Murray, Deborah, Ledbetter, Logan, Ewing, Beverly, Stephenson, Kathryn E., Tan, Chen S., Zash, Rebecca, Ansel, Jessica L., Jaegle, Kate, Guiney, Caitlin J., Henderson, Jeffrey A., O'Leary, Marcia, Enright, Kendra, Kessler, Jill, Ducheneaux, Pete, Inniss, Asha, Brandon, Donald M., Davis, William B., Lawler, Daniel T., Oppong, Yaa D., Starr, Ryan P., Syndergaard, Scott N., Shelly, Rozeli, Majumder, Mashrur Islam, Sugimoto, Danny, Dugas, Jeffrey, Sr., Rijos, Dolores, Shelton, Sandra, Hong, Stephan, Schwartz, Howard, Sanchez-Crespo, Nelia, Schwartz, Jennifer, Piedra, Terry, Corral, Barbara, Medina, Carmen, Dever, Michael E., Shah, Mitul, Delgado, Michael, Scott, Tameika, Usdan, Lisa S., McGill, Lora J., Arnold, Valerie K., Scatamacchia, Carolyn, Anthony, Codi M., Merchant, Rajan, Yoon, Anelgine C., Hill, Janet, Ng-Price, Lucy, Thompson-Seim, Teri, Ackerman, Ronald, Ackerman, Jamie, Aristy, Florida, Ketter, Nzeera, Finley, Jon, Stull, Mildred, Murray, Monica, Rizvi, Zainab, Guerrero, Sonia, Paliwal, Yogesh K., Paliwal, Amit, Gordon, Sarah, Gordon, Bryan, Montano-Pereira, Cynthia, Galloway, Christopher, Montros, Candice, Aleman, Lily, Shairi, Samira, Van Ever, Wesley, Freeman, George H., Harmon, Esther L., Cross, Marshall A., Sales, Kacie, Gular, Catherine Q., Hepburn, Matthew, Alderson, Nathan, Harshell, Shana, Mahgoub, Siham, Maxwell, Celia, Mellman, Thomas, Thompson, Karl M., Wortman, Glenn, Kingsley, Jeff, Pixler, April, Curry, LaKondria, Afework, Sarah, Swanson, Austin, Jacqmein, Jeffry, Bowers, Maggie, Robison, Dawn, Mosteller, Victoria, Garvey, Janet, Easley, Mary, Kurnat, Rebecca J., Cornelison, Raymond, Gower, Shanda, Schnitz, William, Heinzig-Cartwright, Destiny S., Lewis, Derek, Newton, Fred E., Duhart, Aeiress, Watkins, Breanz, Ball, Brandy, York, Jill, Pickle, Shelby, Musante, David B., Silver, William P., Belhorn, Linda R., Viens, Nicholas A., Dellaero, David, Patel, Priti, Lisec, Kendra, Safirstein, Beth, Zapata, Luz, Gonzalez, Lazaro, Quevedo, Evelyn, Irani, Farah, Grillo, Joseph, Potts, Amy, White, Julie, Flume, Patrick, Headden, Gary, Taylor, Brandie, Warden, Ashley, Chamberlain, Amy, Jeanfreau, Robert, Jeanfreau, Susan, Matherne, Paul G., Caldwell, Amy, Stahl, Jessica, Vowell, Mandy, Newhouse, Lauren, Berthaud, Vladimir, Takizala, Zudi-Mwak, Beninati, Genevieve, Snell, Kimberly, Baker, Sherrie, Walker, James, Harrison, Tavane, Miller, Meagan, Otto, Janet, Gray, Roni, Wilson, Christine, Nemecek, Tiffany, Harrington, Hannah, Eppenbach, Sally, Lewis, Wendell, Bourgeois, Tana, Folsom, Lyndsea, Holt, Gregory, Mirsaeidi, Mehdi, Calderon, Rafael, Lichtenberger, Paola, Quintero, Jalima, Martinez, Becky, Immergluck, Lilly, Johnson, Erica, Chan, Austin, Fas, Norberto, Thomas-Seaton, LaTeshia, Khizer, Saadia, Staben, Jonathan, Beresnev, Tatiana, Jahromi, Maryam, Marovich, Mary A., Hutter, Julia, Nason, Martha, Ledgerwood, Julie, Mascola, John, Leibowitz, Mark, Morales, Fernanda, Delgado, Mike, Sanchez, Rosario, Vega, Norma, Áñez, Germán, Albert, Gary, Coston, Erin, Desai, Chinar, Dunbar, Haoua, Eickhoff, Mark, Garcia, Jenina, Kautz, Margaret, Lee, Angela, Lewis, Maggie, McGarry, Alice, McKnight, Irene, Nelson, Joy, Newingham, Patrick, Price-Abbott, Patty, Reed, Patty, Vegas, Diana, Wilkinson, Bethanie, Smith, Katherine, Woo, Wayne, Cho, Iksung, Glenn, Gregory M., Dubovsky, Filip, Fried, David L., Haughey, Lynne A., Stanton, Ariana C., Rameaka, Lisa Stevens, Rosenberg, David, Tomatsu, Lee, Gonzalez, Viviana, Manalo, Millie, Grunstra, Bernard, Quinn, Donald, Claybrook, Phillip, Olds, Shelby, Dye, Amy, Cannon, Kevin D., Chadwick, Mesha M., Jordan, Bailey, Hussey, Morgan, Nevarez, Hannah, Kelley, Colleen F., Chung, Michael, Moran, Caitlin, Rebolledo, Paulina, Bacher, Christina, Barranco-Santana, Elizabeth, Rodriguez, Jessica, Mendoza, Rafael, Ruperto, Karen, Olivieri, Odette, Ocaña, Enrique, Wylie, Paul E., Henderson, Renea, Jenson, Natasa, Yang, Fan, Kelley, Amy, Finkelstein, Kenneth, Beckmann, David, Hutchins, Tanya, Escallon, Sebastian Garcia, Johnson, Kristen, Sligh, Teresa S., Desai, Parul, Huynh, Vincent, Lopez, Carlos, Mendoza, Erika, Adelglass, Jeffrey, Naifeh, Jerome G., Kucera, Kristine J., Chughtai, Waseem, Jaffer, Shireen H., Davis, Matthew G., Foley, Jennifer, Burgett, Michelle L., Shlotzhauer, Tammi L., Ingalsbe-Geno, Sarah M., Duncanson, Daniel, Kush, Kelly, Nesbitt, Lori, Sonnier, Cora, McCarter, Jennifer, Butcher, Michael B., Fry, James, Percy, Donna, Freudemann, Karen, Gebhardt, Bruce C., Mangu, Padma N., Schroeck, Debra B., Davit, Rajesh K., Hennekes, Gayle D., Luft, Benjamin J., Carr, Melissa, Nachman, Sharon, Pellecchia, Alison, Smith, Candace, Valenti, Bruno, Bermudez, Maria I., Peraita, Noris, Delgado, Ernesto, Arrazcaeta, Alicia, Ramirez, Natalie, Amador, Carmen, Marafioti, Horacio, Dang, Lyly, Clement, Lauren, Berry, Jennifer, Allaw, Mohammed, Geuss, Georgettea, Miles, Chelsea, Bittner, Zachary, Werne, Melody, Calinescu, Cornell, Rodman, Shannon, Rindt, Joshua, Cooksey, Erin, Harrison, Kristina, Cooper, Deanna, Horton, Manisha, Philyaw, Amanda, Jennings, William, Alvarado, Hilario, Baka, Michele, Regalado, Malina, Murray, Linda, Naguib, Sherif, Singletary, Justin, Richmond, Sha-Wanda, Omodele, Sarah, Oppenheim, Emily, Martinez, Reuben, Andriulis, Victoria, Singer, Leonard, Blevins, Jeanne, Thomas, Meagan, Hull, Christine, Pereira, Isabel, Rivero, Gina, Okonya, Tracy, Downing, Frances, Miller, Paulina, Rhee, Margaret, Stapleton, Katherine, Klein, Jeffrey, Hong, Rosamond, Swan, Suzanne, Wahlin, Tami, Bennett, Elizabeth, Salzl, Amy, Phan, Sharine, White, Jewel J., Occhino, Amanda, Paiano, Ruth, McLaughlin, Morgan, Swieboda, Elisa, Garcia-Fragoso, Veronica, Becerra, Maria G., White, Toni, Turley, Christine B., McWilliams, Andrew, Esinhart, Tiffany, Montoya, Natasha, Huskey, Shamika, Paul, Leena, Tashima, Karen, Johnson, Jennie, Neill, Marguerite, Sanchez, Martha, Rybak, Natasha, Mileno, Maria, Cohen, Stuart H., Ruiz, Monica, Boswell, Dean M., Robison, Elizabeth E., Reynolds, Trina L., Neumeister, Sonja, Zorrilla, Carmen D., Rivera, Juana, Ibarra, Jessica, García, Iris, Sierra, Dianca, Ramon, Wanda, Fiorillo, Suzanne, Pitotti, Rebecca, Anderson, Victoria R., Mancilla, Jose Castillo, Le, Nga, Winokur, Patricia L., Ince, Dilek, Hegmann, Theresa, Meier, Jeffrey, Stapleton, Jack, Stulken, Laura, McArthur, Monica, Berry, Andrea, Tapia, Milagritos, Hammershaimb, Elizabeth, Robinson, Toni, MacBryde, Rosa, Kline, Susan, Billings, Joanne L., Cavert, Winston, Forgosh, Les B., Schacker, Timothy W., Bold, Tyler D., Dandachi, Dima, Nelson, Taylor, Bran, Andres, Geiger, Grant, Naqvi, S. Hasan, Florescu, Diana F., Starlin, Richard, Kline, David, Zimmer, Andrea, Abbas, Anum, Wilson, Natasha, Eron, Joseph J., Sciaudone, Michael, Rosengren, A. Lina, Kizer, John S., Rutstein, Sarah E., Bruce, Elizabeth, Espinosa, Claudia, Sanders, Lisa J., Kim, Kami, Casey, Denise, Taylor, Barbara S., Patterson, Thomas, Pinilla, Ruth S., Bullock, Delia, Ponce, Philip, Patterson, Jan, McClelland, R. Scott, Lane, Dakotah C., Wald, Anna, James, Frank, Duke, Elizabeth, Hauge, Kirsten, Heimonen, Jessica, Goecker, Erin A., Huang, Yunda, Fong, Youyi, Kauffman, Carol, Linder, Kathleen, Nofz, Kimberly, McConnell, Andrew, Buynak, Robert J., Webb, Angella, Petty, Taryn, Andree, Stephanie, Sanchez, Erica, Mackey, Nolan, Baudelaire, Clarisse, Dzigiel, Sarah, Marquez, Adrienna, Quillin, Kim, King, Michelle, Abad, Vanessa, Knowles, Jennifer, Waters, Michael, Zepeda, Karla, Coslet, Jordan, Tovar, Dalia, Shaw, Marian E., Turner, Mark A., Huffine, Cory J., Huffine, Esther S., Ake, Julie A., Secord, Elizabeth, McGrath, Eric, Levy, Phillip, Stewart, Brittany, Cromer, Charnell, Walters, Ayanna, Ellsworth, Grant, Greene, Caroline, Galloway, Sarah, Kapadia, Shashi, DeHaan, Elliot, Wilson, Clint, Milligan, Jason, Raley, Danielle, Bocchini, Joseph, McClenathan, Bruce, Hussain, Mary, Lomasney, Evelyn, Hall, Evelyn, Lamberth, Sherry, Schmeck, Christy, Leathers, Vickie, Theodore, Deborah A., Branche, Angela R., Graciaa, Daniel S., Hatlen, Timothy J., Miller, Jacqueline, Sadoff, Jerald, Falsey, Ann R., Sobieszczyk, Magdalena E., Rick, Anne-Marie, Laurens, Matthew B., Huang, Ying, Yu, Chenchen, Martin, Thomas C.S., Rodriguez, Carina A., Rostad, Christina A., Maboa, Rebone M., Baden, Lindsey R., El Sahly, Hana M., Grinsztejn, Beatriz, Gray, Glenda E., Gay, Cynthia L., Gilbert, Peter B., Janes, Holly E., Kublin, James G., Leav, Brett, Hirsch, Ian, Struyf, Frank, Dunkle, Lisa M., Neuzil, Kathleen M., Corey, Lawrence, Goepfert, Paul A., Follmann, Dean, and Kotloff, Karen L.

Published
2023
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22. Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning

Author

Smith, Michael J. and Geach, James E.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

Generative Adversarial Networks (GANs) are a class of artificial neural network that can produce realistic, but artificial, images that resemble those in a training set. In typical GAN architectures these images are small, but a variant known as Spatial-GANs (SGANs) can generate arbitrarily large images, provided training images exhibit some level of periodicity. Deep extragalactic imaging surveys meet this criteria due to the cosmological tenet of isotropy. Here we train an SGAN to generate images resembling the iconic Hubble Space Telescope eXtreme Deep Field (XDF). We show that the properties of 'galaxies' in generated images have a high level of fidelity with galaxies in the real XDF in terms of abundance, morphology, magnitude distributions and colours. As a demonstration we have generated a 7.6-billion pixel 'generative deep field' spanning 1.45 degrees. The technique can be generalised to any appropriate imaging training set, offering a new purely data-driven approach for producing realistic mock surveys and synthetic data at scale, in astrophysics and beyond., Comment: Submitted to MNRAS. Comments welcome. Code available at https://github.com/Smith42/XDF-GAN and 7.6-billion pixel GDF viewable at http://star.herts.ac.uk/~jgeach/gdf

Published
2019
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24. NumBAT: The integrated, open source Numerical Brillouin Analysis Tool

Author

Sturmberg, Björn C. P., Dossou, Kokou B., Smith, Michael J. A., Morrison, Blair, Poulton, Christopher G., and Steel, Michael J.

Subjects
Physics - Optics
Abstract

We describe NumBAT, an open-source software tool for modelling stimulated Brillouin scattering in waveguides of arbitrary cross-section. It provides rapid calculation of optical and elastic dispersion relations, field profiles and gain with an easy-to-use Python front end. Additionally, we provide an open and extensible set of standard problems and reference materials to facilitate the bench-marking of NumBAT against subsequent tools. Such a resource is needed to help settle discrepancies between existing formulations and implementations, and to facilitate comparison between results in the literature. The resulting standardised testing framework will allow the community to gain confidence in new algorithms and will provide a common tool for the comparison of experimental designs of opto-acoustic waveguides., Comment: 17 pages, 18 figures

Published
2018
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25. Modelling hollow thermoplastic syntactic foams under high-strain compressive loading

Author

Smith, Michael J. A., Yousaf, Zeshan, Potluri, Prasad, and Parnell, William J.

Subjects
Condensed Matter - Soft Condensed Matter
Abstract

The mechanical response of syntactic foams comprising hollow thermoplastic microspheres (HTMs) embedded in a polyurethane matrix were experimentally examined under uniaxial compressive strain. Phenomenological strain energy models were subsequently developed to capture both the axial stress-strain and transverse strain response of the foams. HTM syntactic foams were found to exhibit increased small-strain stiffness with reduced density, revealing a highly tuneable and extremely lightweight syntactic foam blend for applications. The foams were also found to become strongly compressible at large strains and possess a high threshold for plastic deformation, making them a robust alternative to hollow glass microsphere syntactic foams. The non-standard transverse strain relationship exhibited by HTM syntactic foams at high filling fractions was captured by Ogden-type strain energy models. The thermal characteristics of these syntactic foams were also explored with Differential Scanning Calorimetry testing which showed that HTMs have a negligible impact on the thermal characteristics of the matrix., Comment: 31 pages, 14 figures, 3 tables

Published
2018

28. Preface

Author

Smith, Michael J., primary and Klimczuk-Massion, Stephen, additional

Published
2023
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32. Notes

Author

Smith, Michael J., primary and Klimczuk-Massion, Stephen, additional

Published
2023
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44. Reflection from a multi-species material and its transmitted effective wavenumber

Author

Gower, Artur L., Smith, Michael J. A., Parnell, William J., and Abrahams, Ian David

Subjects
Physics - Classical Physics, Physics - Applied Physics, 78-02, 82D02
Abstract

We formally deduce closed-form expressions for the transmitted effective wavenumber of a material comprising multiple types of inclusions or particles (multi-species), dispersed in a uniform background medium. The expressions, derived here for the first time, are valid for moderate volume fractions and without restriction on the frequency. We show that the multi-species effective wavenumber is not a straightforward extension of expressions for a single species. Comparisons are drawn with state-of-the-art models in acoustics by presenting numerical results for a concrete and a water-oil emulsion in two dimensions. The limit of when one species is much smaller than the other is also discussed and we determine the background medium felt by the larger species in this limit. Surprisingly, we show that the answer is not the intuitive result predicted by self-consistent multiple scattering theories. The derivation presented here applies to the scalar wave equation with cylindrical or spherical inclusions, with any distribution of sizes, densities, and wave speeds. The reflection coefficient associated with a half-space of multi-species cylindrical inclusions is also formally derived., Comment: The supplementary material has simpler self-contained formulas, it is an ancillary file and can be downloaded from a link on the right. The code to reproduce all graphs is provided in https://github.com/arturgower/EffectiveWaves.jl

Published
2017
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47. Evaluation and regularization of generalized Eisenstein series and application to 2D cylindrical harmonic sums

Author

Chen, Parry Y., Smith, Michael J. A., and McPhedran, Ross C.

Subjects
Mathematical Physics
Abstract

In the study of periodic media, conditionally convergent series are frequently encountered and their regularization is crucial for applications. We derive an identity that regularizes two-dimensional generalized Eisenstein series for all Bravais lattices, yielding physically meaningful values. We also obtain explicit forms for the generalized series in terms of conventional Eisenstein series, enabling their closed-form evaluation for important high symmetry lattices. Results are then used to obtain representations for the related cylindrical harmonic sums, which are also given for all Bravais lattices. Finally, we treat displaced lattices of high symmetry, expressing them in terms of origin-centered lattices via geometric multi-set identities. These identities apply to all classes of two-dimensional sums, allowing sums to be evaluated over each constituent of a unit cell that possesses multiple inclusions., Comment: 25 pages, 3 figures

Published
2016

49. Patterns of inpatient antibiotic utilization by race and ethnicity at US children’s hospitals

Author

Wattles, Bethany A., Campbell, Jeffrey I., Kluthe, Theresa, Feygin, Yana B., Jawad, Kahir, Stevenson, Michelle D., Davis, Deborah Winders, Porter, Jennifer, Jones, V. Faye, Hall, Matt, and Smith, Michael J.

Abstract

AbstractBackground and Objectives:Racial and ethnic variations in antibiotic utilization are well-reported in outpatient settings but little is known about inpatient settings. Our objective was to describe national inpatient antibiotic utilization among children by race and ethnicity.Methods:This study included hospital visit data from the Pediatric Health Information System between 01/01/2022 and 12/31/2022 for patients <20 years. Primary outcomes were the percentage of hospitalization encounters that received an antibiotic and antibiotic days of therapy (DOT) per 1000 patient days. Mixed-effect regression models were used to determine the association of race-ethnicity with outcomes, adjusting for covariates.Results:There were 846,530 hospitalizations. 45.2% of children were Non-Hispanic (NH) White, 27.1% were Hispanic, 19.2% were NH Black, 4.5% were NH Other, 3.5% were NH Asian, 0.3% were NH Native Hawaiian/Other Pacific Islander (NHPI) and 0.2% were NH American Indian. Adjusting for covariates, NH Black children had lower odds of receiving antibiotics compared to NH White children (aOR 0.96, 95%CI 0.94–0.97), while NH NHPI had higher odds of receiving antibiotics (aOR 1.16, 95%CI 1.05–1.29). Children who were Hispanic, NH Asian, NH American Indian, and children who were NH Other received antibiotic DOT compared to NH White children, while NH NHPI children received more antibiotic DOT.Conclusions:Antibiotic utilization in children’s hospitals differs by race and ethnicity. Hospitals should assess policies and practices that may contribute to disparities in treatment; antibiotic stewardship programs may play an important role in promoting inpatient pharmacoequity. Additional research is needed to examine individual diagnoses, clinical outcomes, and drivers of variation.

Published
2025
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50. Student Skill Development with the Real World: Analyzing 'tert'-Butyl Alcohol Content in Gasoline Samples

Author

Gonçalves, Bruna F., Botelho, Gabriela, Medeiros, Maria J., and Smith, Michael J.

Abstract

In the activity described in this communication, the authors present a simple experiment that can be implemented with moderate operational costs and that allows students to acquire the manipulative skills necessary for chromatographic analysis of a familiar fuel that still plays a fundamental role in providing energy for transport of passengers and goods. The use of gasoline formulations in a laboratory activity presents students with a motivating subject of study and an opportunity to apply analytical procedures to the characterization of controlled substances used in vehicle fuel formulations.

Published
2019
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