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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    Variable bit rate GPU texture decompression
    (Wiley, 2011-07-19) Olano, Marc; Baker, Dan; Griffin, Wesley; Barczak, Joshua
    Variable bit rate compression can achieve better quality and compression rates than fixed bit rate methods. None the less, GPU texturing uses lossy fixed bit rate methods like DXT to allow random access and on-the-fly decompression during rendering. Changes in games and GPUs since DXT was developed make its compression artifacts less acceptable, and texture bandwidth less of an issue, but texture size is a serious and growing problem. Games use a large total volume of texture data, but have a much smaller active set. We present a new paradigm that separates GPU decompression from rendering. Rendering is from uncompressed data, avoiding the need for random access decompression. We demonstrate this paradigm with a new variable bit rate lossy texture compression algorithm that is well suited to the GPU, including a new GPU-friendly formulation of range decoding, and a new texture compression scheme averaging 12.4:1 lossy compression ratio on 471 real game textures with a quality level similar to traditional DXT compression. The total game texture set are stored in the GPU in compressed form, and decompressed for use in a fraction of a second per scene.
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    Edge States Effects in Quantum Work Statistics
    (2026-01-26) Cavalcante, M. F.
    Motivated by the objective of quantifying the energetic cost of accessing boundary phases through local control, we investigate here a simple, analytically tractable quantum impurity model. This model exhibits a rich boundary phase diagram, characterized by phases with different numbers of edge states. By considering a local quench protocol that drives the system out of equilibrium, we calculate exactly the resulting quantum work distribution across these phases. Our results show that the presence of edge states strongly alters this distribution. In particular, we analytically determine key fingerprints of these states both near the low-energy threshold and in the high-energy region.
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    Snowcrete 2026, Potomac Sewage Spill, ICE Warehouses, Closing Chinese Immersion Program
    (I Hate Politics Podcasts, 2026-02-03) Dasgupta, Sunil; Friedson, Andrew
    How do other jurisdictions and past snow events compare to Snowcrete 2026? Montgomery County Councilmember Andrew Friedson weighs in prior to oversight hearings. Leaders from Republican-leaning jurisdictions oppose the 287g ICE agreement ban because they say the arrangement preempts ICE violence. MD Delegate Vaughn Stewart explores legislative ideas to limit ICE presence after the federal agency buys two warehouses in the state to serve as detention centers. In Prince Geroge's County, a Chinese Immersion Program in a Title 1 school in College Park is being cut as part of budget woes. And more. Newly in public domain music by George Gershwin, Paul Whiteman band, and Marian Andersen.
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    Does Reasoning Help LLM Agents Play Dungeons and Dragons? A Prompt Engineering Experiment
    (Wordplay Workshop, 2025-11-09) Delafuente, Patricia J; Honraopatil, Arya; Martin, Lara J.
    This paper explores the application of Large Language Models (LLMs) and reasoning to predict Dungeons & Dragons (DnD) player actions and format them as Avrae Discord bot commands. Using the FIREBALL dataset, we evaluated a reasoning model, DeepSeek-R1-Distill-LLaMA-8B, and an instruct model, LLaMA-3.1-8B-Instruct, for command generation. Our findings highlight the importance of providing specific instructions to models, that even single sentence changes in prompts can greatly affect the output of models, and that instruct models are sufficient for this task compared to reasoning models.
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    Revisiting the Home Literacy Environment in the Digital Age: Insights from a High-SES Context
    (MDPI, 2026-02-04) Krasniqi, Besjanë; Grossman, Julie A.; Sonnenschein, Susan; Stites, Michele
    Decades of research have documented the critical role of parental practices, beliefs, and resources in shaping children’s literacy development. This study examined how home practices shape children’s literacy engagement across both digital and traditional formats. Parents of children aged 4–9 years (N = 357) filled out a Qualtrics survey about the home learning environment, including beliefs, confidence, attitudes, resources, and practices. Results showed that while parents’ beliefs and traditional literacy engagement were linked to encouragement for digital reading, children’s independent digital reading was best predicted by parents’ own digital reading, shared digital reading, and encouragement to read on devices. Device availability and general encouragement to use technology were not significant. Children’s audiobook use was predicted primarily by shared digital reading. These findings extend the home literacy environment literature by showing that while traditional practices remain central, their expression in the digital age depends on parental modeling and encouragement. The results highlight that in the digital era, parental engagement and shared reading experiences, and not access alone, are central to fostering children’s digital literacy development.