Maryland Shared Open Access Repository

MD-SOAR is a shared digital repository platform for twelve colleges and universities in Maryland. It is currently funded by the University System of Maryland and Affiliated Institutions (USMAI) Library Consortium (usmai.org) and other participating partner institutions. MD-SOAR is jointly governed by all participating libraries, who have agreed to share policies and practices that are necessary and appropriate for the shared platform. Within this broad framework, each library provides customized repository services and collections that meet local institutional needs. Please follow the links below to learn more about each library's repository services and collections.

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Bowie State University

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Morgan State University

Salisbury University

Stevenson University

St. Mary's College of Maryland

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University of Maryland, Baltimore County (UMBC)

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Recent Submissions

Non-Negative Matrix Factorization Using Non-Von Neumann Computers
(2025-11-30) Borle, Ajinkya; Nicholas, Charles; Chukwu, Uchenna; Miri, Mohammad-Ali; Chancellor, Nicholas
Non-negative matrix factorization (NMF) is a matrix decomposition problem with applications in unsupervised learning. The general form of this problem (along with many of its variants) is NP-hard in nature. In our work, we explore how this problem could be solved with an energy-based optimization method suitable for certain machines with non-von Neumann architectures. We used the Dirac-3, a device based on the entropy computing paradigm and made by Quantum Computing Inc., to evaluate our approach. Our formulations consist of (i) a quadratic unconstrained binary optimization model (QUBO, suitable for Ising machines) and a quartic formulation that allows for real-valued and integer variables (suitable for machines like the Dirac-3). Although current devices cannot solve large NMF problems, the results of our preliminary experiments are promising enough to warrant further research. For non-negative real matrices, we observed that a fusion approach of first using Dirac-3 and then feeding its results as the initial factor matrices to Scikit-learn's NMF procedure outperforms Scikit-learn's NMF procedure on its own, with default parameters in terms of the error in the reconstructed matrices. For our experiments on non-negative integer matrices, we compared the Dirac-3 device to Google's CP-SAT solver (inside the Or-Tools package) and found that for serial processing, Dirac-3 outperforms CP-SAT in a majority of the cases. We believe that future work in this area might be able to identify domains and variants of the problem where entropy computing (and other non-von Neumann architectures) could offer a clear advantage.
Gaia’s Crown: a deep space mirage seen from DSCOVR/EPIC during lunar transit
(Frontiers, 2026-01-12) Blank, Karin; Herman, Jay; Dangelo, Sarah; Marshak, Alexander; Tennenbaum, Andrew
The Earth Polychromatic Imaging Camera (EPIC), onboard the Deep Space Climate Observatory (DSCOVR) spacecraft, located at the Earth-Sun Lagrange 1 point, has captured a unique optical effect during lunar occultation named “Gaia’s Crown.” In EPIC images, the phenomenon appears as a small “flange” at the Earth–Moon contact when the Moon is roughly half below Earth’s limb; it is present in the visible and near-infrared channels but absent in the ultraviolet. Using atmospheric data and 3D, voxel-based ray tracing models, this effect was identified as a combination of atmospheric distortion and a complex mirage caused by variations in the Earth’s atmosphere. Additionally, it is shown that while satellites closer to the Earth can see a similar phenomenon, Gaia’s Crown presents unique distortion effects that demonstrate how EPIC’s vantage point at 1.5 million kilometers from Earth provides a different perspective on atmospheric optics.
2026 Maryland General Assembly Starts Jan 14. Preview
(I Hate Politics Podcasts, 2026-01-09) Dasgupta, Sunil; Feldman, Brian; Korman, Marc
Maryland is projecting a $1.4 billion budget shortfall, the state is under federal retrenchment, and elections are this year. Sunil Dasgupta talks with two of the most powerful leaders in the state assembly, Brian Feldman, chair of the Education, Energy, and Environment Committee in the Senate, and Marc Korman, chair of the Environment and Transportation Committee in the House of Delegates, about how they see the session ahead. Music by Kara Levchenko.
Prediction of Drug-Induced Autoimmunity Using X Gradient Boost Machine Learning
(2025-09-17) Sistla, Srikar; Carter, Kylie
Drug-induced autoimmunity (DIA) comprises immunemediated adverse events such as lupus, hepatitis, and uveitis that can arise after extended drug exposure, complicating prospective risk assessment. We built a gradient-boosted tree (XGBoost) classifier using 196 RDKit-derived molecular descriptors for 477 compounds[1] and addressed class imbalance with SMOTE. On a held-out test set, the model achieved ROC-AUC of 0.888 with 66.7% recall and 57.1% precision for the positive class; five-fold cross-validation indicated strong generalization (ROC-AUC 0.974 ± 0.067). Gain-based feature importance highlighted topological complexity, aromaticity, and polarity-related descriptors as salient. The framework enables rapid, cost-effective screening of autoimmune risk during early discovery to prioritize compounds for deeper evaluation.
Smile-shaped electron gradient distributions observed during magnetic reconnection at Earth’s magnetopause
(Springer Nature, 2026-01-10) Shuster, Jason R.; Bessho, Naoki; Dorelli, John C.; Gershman, Daniel J.; Beedle, Jason M. H.; Gurram, Harsha; Ng, Jonathan; Chen, Li-Jen; Torbert, Roy B.; Burch, James L.; Giles, Barbara L.; Denton, Richard E.; Cassak, Paul A.; Barbhuiya, M. Hasan; Schwartz, Steven J.; Liu, Yi-Hsin; Norgren, Cecilia; da Silva, Daniel; Genestreti, Kevin J.; Heuer, Steven V.; Argall, Matthew R.; Karimi, Hanieh; Marshall, Andy T.; Nakamura, Rumi; Liang, Haoming; Uritsky, Vadim M.; Afshari, Arya; Payne, Dominic S.
The electron diffusion region is central to NASA’s Magnetospheric Multiscale (MMS) mission to understand collisionless magnetic reconnection, the plasma physics phenomenon crucial to triggering the explosive energy release of solar flares, powering auroras generated in planetary magnetospheres, and driving sawtooth crashes in laboratory fusion devices. Inside the diffusion region, electron velocity distributions exhibit highly-structured velocity-space signatures critical for elucidating the kinetic mechanisms fueling reconnection. Recent multi-spacecraft analysis techniques enabled observational study of the spatial gradient in the electron velocity distribution, which has been reported in electron-scale current layers to explain the kinetic origins of electron pressure gradients. However, electron gradient distributions have not yet been investigated inside the reconnection diffusion region. In this work, we discover that electron gradient distributions exhibit a smile-shaped velocity-space structure in the electron diffusion region of asymmetric magnetic reconnection at Earth’s magnetopause. Characterizing the nature and prevalence of these smile-shaped electron gradient distributions offers a kinetic perspective into how electrons spatially evolve to provide the net electron pressure divergence that self-consistently supports non-ideal electric fields in the electron diffusion region of magnetopause reconnection. These results are relevant to space, astrophysical, and laboratory plasma communities working to understand the long-standing mystery of collisionless magnetic reconnection.