906 research outputs found

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    Limit Theory under Network Dependence and Nonstationarity

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    These lecture notes represent supplementary material for a short course on time series econometrics and network econometrics. We give emphasis on limit theory for time series regression models as well as the use of the local-to-unity parametrization when modeling time series nonstationarity. Moreover, we present various non-asymptotic theory results for moderate deviation principles when considering the eigenvalues of covariance matrices as well as asymptotics for unit root moderate deviations in nonstationary autoregressive processes. Although not all applications from the literature are covered we also discuss some open problems in the time series and network econometrics literature.Comment: arXiv admin note: text overlap with arXiv:1705.08413 by other author

    SARS-CoV-2 Seroprevalence and Vaccine Correlate of Protection Standardization

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    In the COVID-19 pandemic, there was great interest in population seroprevalence estimationof individuals with antibodies against SARS-CoV-2 and in evaluation of antibodies as surrogatemarkers for vaccine efficacy. In the first paper, methods for estimation of seroprevalencefrom surveys which can have selection bias and serologic tests which can have measurementerror are presented. These challenges are addressed with the leveraging of auxiliary datafrom target populations, e.g., population census data, and of validation laboratory studies offalse positive and false negative rates. Direct standardization is used for the development ofnonparametric and parametric seroprevalence estimators. The estimators are proven consistentand asymptotically normal. Simulation studies demonstrate performance across a variety ofselection bias and misclassification error scenarios. The proposed methods are applied toSARS-CoV-2 seroprevalence studies in New York City, Belgium, and North Carolina. Drawing a simple comparison of COVID-19 vaccine trial efficacy estimates is problematicwithout considering factors affecting the trial context and design, including characteristics ofa study’s population (Rapaka et al., 2022). A meta-analytic paradigm for surrogate endpointevaluation entails estimating an association between the treatment effects on the surrogateand clinical endpoints, respectively, using data from multiple clinical trials. This approachcan be used to estimate the association between vaccine induced anti-SARS-CoV-2 antibodiesand vaccine efficacy against symptomatic COVID-19 illnesss. In the second paper, multiplevaccine trials are standardized to a common target population. Meta-analytic causal associationparameters, estimators, and the asymptotic distributions of the estimators are considered. A hypothesis test of an implication of a conditional exchangeability assumption is proposed.Simulation studies demonstrate the methods in scenarios motivated by data from several U.S.government Phase 3 SARS-CoV-2 vaccine trials. When data are fused across data sets, often the random variables are assumed to be independentbut not identically distributed, as in the preceding chapters. However, standard estimatingequation theory assumes an independent and identically distributed set up. In the third paper,the consistency and asymptotic normality of estimating equation estimators when data areindependent but not identically distributed is considered. Regularity conditions for consistencyand asymptotic normality in the non-iid setting are presented and examples for application ofthe estimating equation theory to data fusion estimators are provided.Doctor of Philosoph

    Applications of Deep Learning to Differential Equation Models in Oncology

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    The integration of quantitative tools in biology and medicine has led to many groundbreaking advances in recent history, with many more promising discoveries on the horizon. Conventional mathematical models, particularly differential equation-based models, have had great success in various biological applications, including modelling bacterial growth, disease propagation, and tumour spread. However, these approaches can be somewhat limited due to their reliance on known parameter values, initial conditions, and boundary conditions, which can dull their applicability. Furthermore, their forms are directly tied to mechanistic phenomena, making these models highly explainable, but also requiring a comprehensive understanding of the underlying dynamics before modelling the system. On the other hand, machine learning models typically require less prior knowledge of the system but require a significant amount of data for training. Although machine learning models can be more flexible, they tend to be black boxes, making them difficult to interpret. Hybrid models, which combine conventional and machine learning approaches, have the potential to achieve the best of both worlds. These models can provide explainable outcomes while relying on minimal assumptions or data. An example of this is physics-informed neural networks, a novel deep learning approach that incorporates information from partial differential equations into the optimization of a neural network. This hybrid approach offers significant potential in various contexts where differential equation models are known, but data is scarce or challenging to work with. Precision oncology is one such field. This thesis employs hybrid conventional/machine learning models to address problems in cancer medicine, specifically aiming to advance personalized medicine approaches. It contains three projects. In the first, a hybrid approach is used to make patient-specific characterizations of brain tumours using medical imaging data. In the second project, a hybrid approach is employed to create subject-specific projections of drug-carrying cancer nanoparticle accumulation and intratumoral interstitial fluid pressure. In the final project, a hybrid approach is utilized to optimize radiation therapy scheduling for tumours with heterogeneous cell populations and cancer stem cells. Overall, this thesis showcases several examples of how quantitative tools, particularly those involving both conventional and machine learning approaches, can be employed to tackle challenges in oncology. It further supports the notion that the continued integration of quantitative tools in medicine is a key strategy in addressing problems and open questions in healthcare

    Super Multiset RSK and a Mixed Multiset Partition Algebra

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    Through dualities on representations on tensor powers and symmetric powers respectively, the partition algebra and multiset partition algebra have been used to study long-standing questions in the representation theory of the symmetric group. In this paper we extend this story to exterior powers, introducing the mixed multiset partition algebra as well as a generalization of the Robinson-Schensted-Knuth algorithm to two-row arrays of multisets with elements from two alphabets. From this algorithm, we obtain enumerative results which reflect representation-theoretic decompositions of this algebra. Furthermore, we use the generalized RSK algorithm to describe the decomposition of a polynomial ring in sets of commuting and anti-commuting variables as a module over both the general linear group and the symmetric group.Comment: 28 pages, 12 figure

    Travels along the hype cycle: a set of blockchain applications and the economic processes they impact

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    Some commentators refer to blockchain as a potential General Purpose Technology. Yet despite a plethora of cryptoassets and projects, it has struggled to gain traction beyond payments and price discovery. This thesis explores how the technology is being applied to better understand the potential and risks of deploying blockchain. It examines four different use cases with econometric and case study methods: (1) Bitcoin mining as the token incentivized processing of records, (2) Initial Coin Offering tokens as a form of venture financing, (3) Uniswap the decentralized exchange and (4) Kompany improving the data integrity of compliance records via notarization to a public blockchain. It finds that blockchain enables capabilities that did not exist before, but that these capabilities are bounded by trade offs and developer priorities. Ultimately this research expands the literature on blockchain applications and argues that blockchain does not build better systems, but different systems that can achieve different objectives. It provides evidence that firms and society are gradually traversing the hype cycle, deploying blockchain, solving real world economic problems and creating value

    2023-2024 Lindenwood University Undergraduate Course Catalog

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    Lindenwood University Undergraduate Course Catalog.https://digitalcommons.lindenwood.edu/catalogs/1209/thumbnail.jp

    Translanguaging for Equal Opportunities : Speaking Romani at School

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    This multi-authored monograph, located in the intersection of translanguaging research and Romani studies, offers a state-of-the-art analysis of the ways in which translanguaging supports bilingual Roma students’ learning in monolingual school systems. Complete with a video repository of translanguaging classroom moments, this comprehensive study is based on long-term participatory ethnographic research and a pedagogical implementation project undertaken in Hungary and Slovakia by a group of primary teachers, bilingual Roma participants, and researchers. Co-written by academic and non-academic participants, the book is an essential reading for researchers, pre- and in-service teachers of Romani-speaking students, and experts working with collaborators (learners, informants, activists) whose home languages are excluded from mainstream education and school curricula

    Crystals for shifted key polynomials

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    This article continues our study of PP- and QQ-key polynomials, which are (non-symmetric) "partial" Schur PP- and QQ-functions as well as "shifted" versions of key polynomials. Our main results provide a crystal interpretation of PP- and QQ-key polynomials, namely, as the characters of certain connected subcrystals of normal crystals associated to the queer Lie superalgebra qn\mathfrak{q}_n. In the PP-key case, the ambient normal crystals are the qn\mathfrak{q}_n-crystals studied by Grantcharov et al., while in the QQ-key case, these are replaced by the extended qn\mathfrak{q}_n-crystals recently introduced by the first author and Tong. Using these constructions, we propose a crystal-theoretic lift of several conjectures about the decomposition of involution Schubert polynomials into PP- and QQ-key polynomials. We verify these generalized conjectures in a few special cases. Along the way, we establish some miscellaneous results about normal qn\mathfrak{q}_n-crystals and Demazure gln\mathfrak{gl}_n-crystals.Comment: 60 pages, 6 figure
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