1,829 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Learning World Models with Identifiable Factorization

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    Extracting a stable and compact representation of the environment is crucial for efficient reinforcement learning in high-dimensional, noisy, and non-stationary environments. Different categories of information coexist in such environments -- how to effectively extract and disentangle these information remains a challenging problem. In this paper, we propose IFactor, a general framework to model four distinct categories of latent state variables that capture various aspects of information within the RL system, based on their interactions with actions and rewards. Our analysis establishes block-wise identifiability of these latent variables, which not only provides a stable and compact representation but also discloses that all reward-relevant factors are significant for policy learning. We further present a practical approach to learning the world model with identifiable blocks, ensuring the removal of redundants but retaining minimal and sufficient information for policy optimization. Experiments in synthetic worlds demonstrate that our method accurately identifies the ground-truth latent variables, substantiating our theoretical findings. Moreover, experiments in variants of the DeepMind Control Suite and RoboDesk showcase the superior performance of our approach over baselines

    2023 SDSU Data Science Symposium Presentation Abstracts

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    This document contains abstracts for presentations and posters 2023 SDSU Data Science Symposium

    Undergraduate Catalog of Studies, 2022-2023

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    Data Science for Hospital Antibiotic Stewardship

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    Antibiotics are widely used to treat bacterial infections, but their misuse leads to antibiotic resistance. Antibiotic resistance is one of the biggest threats to global health, food security, and development today. Antibiotic resistance leads to higher medical costs, prolonged hospital stays, and increased mortality. Antimicrobial stewardship is an approach to measure and improve the appropriate use of antibiotics in healthcare settings. Data science has the potential to support these programs by providing insights into antibiotic prescribing patterns, identifying areas for improvement, and predicting patient outcomes. We explored the role of data science in hospital antibiotic stewardship programs, including statistical methods and data visualization techniques. We conducted statistical analysis to identify trends and seasonality in antibiotic usage using autoregressive integrated moving average (ARIMA) models and generalized additive models (GAMs). We developed a pilot interactive dashboard for hospital inpatient antibiotic stewardship using Python. The dashboard visualizes trends in the antibiotic stewardship metric days of therapy (DOT) by various categories, such as indication, therapeutic class, and period. The use of digital dashboards in healthcare is becoming increasingly popular, and our work demonstrates the potential of data visualization tools in hospital antibiotic stewardship

    Metapopulation Genomics of American Goshawks in the Intermountain West

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    Understanding a species dispersal ecology and population dynamics is essential to effectively manage and conserve a species. As advancing technology improves our knowledge of species movements, it is becoming clear that many species form metapopulations to some extent. A metapopulation is a network of interconnected subpopulations that exchange reproductive individuals with subpopulations occupying nearby patches. Metapopulations have been observed in a variety of species, ranging from plants to vertebrates, and can vary greatly in their dynamics (level of connectivity and gene flow) based on the species behavior and life history strategy. Forming a metapopulation can add much resilience to the subpopulation. A steady inflow of new individuals can protect the subpopulation from inbreeding depression and adds more standing variation for natural selection to work on. However, as the metapopulation breaks down and patches become isolated, that resilience is quickly lost. Habitat fragmentation due to anthropogenic changes poses a significant danger to metapopulations. Understanding these metapopulation dynamics is of key importance to formulating effective and efficient conservation and management plans. Information such as where, when, and how these species are dispersing can tell us how to best preserve these paths and maintain the metapopulation structure. One species that forms a highly extensive metapopulation, is the American goshawk (Accipiter atricapillus). In this study we evaluated the metapopulation genomics of American goshawks in the Intermountain West by investigating the genetic diversity and differentiation, as well as gene flow and connectivity, of four subpopulations. The goshawk metapopulation is connected mainly through the natal dispersal of juveniles. Natal dispersal has been and continues to be very hard to track due the technological limitations. Here we show that genomics can offer an alternative when species cannot be easily tracked. While specific dispersal routes cannot be elucidated, we were able to discover the level and direction of gene flow between subpopulations, giving a rough idea of where and how far individuals were dispersing. We found little to no differentiation and very high gene flow between these subpopulations despite the hundreds of kilometers between them. There was no geographic structuring shown both by an isolation by distance test and a correlation test between geographic distances and the estimated number of migrants exchanged. This information is vital to understanding the species movements and ecology in order to create an effective management plan
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