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OpenBU is Boston University’s digital institutional repository for scholarly articles, theses and dissertations, preprints, and grey literature. This repository enables BU researchers to share, disseminate, and preserve their scholarship, and makes their research more accessible
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Recent Submissions
Testing, Testing, 1, 2, 3...Building Consistent and Collaborative Workflows for a University Library Digitization Program
(2025-03-18) Wright, Sami
Building a new digitization program is a long, multifaceted project. Once you get institutional buy-in, ensure the infrastructure is in place, and select the equipment...what happens next? This presentation discusses a real-world approach to testing and establishing efficient imaging workflows for multiple use cases with a focus on sustainable thresholds, consistent application, and cross-institutional collaboration. It also walks through designing an overarching digitization project workflow for working with repository partners to clearly communicate expectations, generate detailed documentation, and provide a consistent, yet flexible foundation for project structures to be built on.
Local step-flow dynamics in thin film growth with desorption
(American Physical Society (APS), 2024) Zhang, Xiaozhi; Ulbrandt, Jeffrey G.; Myint, Peco; Fluerasu, Andrei; Wiegart, Lutz; Zhang, Yugang; Nelson, Christie; Ludwig, Karl F.; Headrick, Randall L.
Properties of relativistic electron precipitation: a comparative analysis of wave-induced and field line curvature scattering processes
(Frontiers Media SA, 2024-11-08) Capannolo, Luisa; Staff, Andrew; Li, Wen; Duderstadt, Katharine; Sivadas, Nithin; Pettit, Joshua; Elliot, Sadie; Qin, Murong; Shen, Xiao-Chen; Ma, Qianli
We analyze the properties of relativistic (>700 keV) electron precipitation (REP) events measured by the low-Earth-orbit (LEO) POES/MetOp constellation of spacecraft from 2012 through 2023. Leveraging the different profiles of REP observed at LEO, we associate each event with its possible driver: waves or field line curvature scattering (FLCS). While waves typically precipitate electrons in a localized radial region within the outer radiation belt, FLCS drives energy-dependent precipitation at the edge of the belt. Wave-driven REP is detected at any MLT sector and L shell, with FLCS-driven REP occurring only over the nightside–a region where field line stretching is frequent. Wave-driven REP is broader in radial extent on the dayside and accompanied by proton precipitation over 03–23 MLT, either isolated or without a clear energy-dependent pattern, possibly implying that electromagnetic ion cyclotron (EMIC) waves are the primary driver. Across midnight, both wave-driven and FLCS-driven REP occur poleward of the proton isotropic boundary. On average, waves precipitate a higher flux of >700 keV electrons than FLCS. Both contribute to energy deposition into the atmosphere, estimated of a few MW. REP is more associated with substorm activity than storms, with FLCS-driven REP and wave-driven REP at low L shells occurring most often during strong activity (SML* < −600 nT). A preliminary analysis of the Solar Wind (SW) properties before the observed REP indicates a more sustained (∼5 h) dayside reconnection for FLCS-driven REP than for wave-driven REP (∼3 h). The magnetosphere appears more compressed during wave-driven REP, while FLCS-driven REP is associated with a faster SW of lower density. These findings are useful not only to quantify the contribution of >700 keV precipitation to the atmosphere but also to shed light on the typical properties of wave-driven vs FLCS-driven precipitation which can be assimilated into physics-based and/or predictive radiation belt models. In addition, the dataset of ∼9,400 REP events is made available to the community to enable future work.
Dear diary: a randomized controlled trial of generative AI coding tools in the workplace
Hadley, Constance; Butler, Jenna; Suh, Jina; Haniyur, Sankeerti
Comments on “comparing the productive vocabularies of grey parrots (Psittacus erithacus) and young children”
(Springer Science and Business Media LLC, 2024-11-25) Pepperberg, Irene M.
Integrating deep convolutional surrogate solvers and particle swarm optimization for efficient inverse design of plasmonic patch nanoantennas
(Walter de Gruyter GmbH, 2024-09) Hemayat, Saeed; Moayed Baharlou, Sina; Sergienko, Alexander; Ndao, Abdoulaye
Plasmonic nanoantennas with suitable far-field characteristics are of huge interest for utilization in optical wireless links, inter-/intrachip communications, LiDARs, and photonic integrated circuits due to their exceptional modal confinement. Despite its success in shaping robust antenna design theories in radio frequency and millimeter-wave regimes, conventional transmission line theory finds its validity diminished in the optical frequencies, leading to a noticeable void in a generalized theory for antenna design in the optical domain. By utilizing neural networks, and through a one-time training of the network, one can transform the plasmonic nanoantennas design into an automated, data-driven task. In this work, we have developed a multi-head deep convolutional neural network serving as an efficient inverse-design framework for plasmonic patch nanoantennas. Our framework is designed with the main goal of determining the optimal geometries of nanoantennas to achieve the desired (inquired by the designer) S 11 and radiation pattern simultaneously. The proposed approach preserves the one-to-many mappings, enabling us to generate diverse designs. In addition, apart from the primary fabrication limitations that were considered while generating the dataset, further design and fabrication constraints can also be applied after the training process. In addition to possessing an exceptionally rapid surrogate solver capable of predicting S 11 and radiation patterns throughout the entire design frequency spectrum, we are introducing what we believe to be the pioneering inverse design network. This network enables the creation of efficient plasmonic antennas while concurrently accommodating customizable queries for both S 11 and radiation patterns, achieving remarkable accuracy within a single network framework. Our framework is capable of designing a wide range of devices, including single band, dual band, and broadband antennas, with directivities and radiation efficiencies reaching 11.07 dBi and 75 %, respectively, for a single patch. The proposed approach has been developed as a transformative shift in the inverse design of photonics components, with its impact extending beyond antenna design, opening a new paradigm toward real-time design of application-specific nanophotonic devices.
Unveiling bias in AI model training data: exploring the impact of intrinsic data variability on lung ultrasound video classification models
(2024-10-21) Bhattacharjee, Saunak; Khan, Umair; Thompson, Russell; Etter, Lauren P.; Camelo, Ingrid; Pieciak, Rachel C.; Castro-Aragon, Ilse; Setty, Bindu; Gill, Christopher C.; Demi, Libertario; Betke, Margrit
Comparing classic to state-of-the-art image features: a clustering approach using local binary patterns and ResNet-18 features for lung ultrasound video classification
(2024-10-21) Bhattacharjee, Saunak; Khan, Umair; Thompson, Russell; Etter, Lauren P.; Camelo, Ingrid; Pieciak, Rachel C.; Castro-Aragon, Ilse; Setty, Bindu; Gill, Christopher C.; Betke, Margrit
Comparative evaluation of computationally efficient and explainable 1D brightness profiles from axial projections for lung ultrasound frame classification
(2024-10-21) Jain, Srishti; Khan, Umair; Thompson, Russell; Etter, Lauren P.; Camelo, Ingrid; Pieciak, Rachel C.; Castro-Aragon, Ilse; Setty, Bindu; Gill, Christopher C.; Betke, Margrit
DebiasPI: inference-time debiasing by prompt iteration of a text-to-image generative model
(2024-09-29) Bonner, Sarah; Huang, Yu-Cheng; Novozhilova, Ekaterina; Paik, Sejin; Shan, Zhengyang; Feng, Michelle Yilin; Gao, Ge; Tayal, Yonish; Kulkarni, Rushil; Yu, Jialin; Divekar, Nupur; Ghadiyaram, Deepti; Wijaya, Derry; Betke, Margrit