906 research outputs found
2023-2024 Catalog
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
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
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
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
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Process Data Applications in Educational Assessment
The widespread adoption of computer-based testing has opened up new possibilities for collecting process data, providing valuable insights into the problem-solving processes that examinees engage in when answering test items. In contrast to final response data, process data offers a more diverse and comprehensive view of test takers, including construct-irrelevant characteristics. However, leveraging the potential of process data poses several challenges, including dealing with serial categorical responses, navigating nonstandard formats, and handling the inherent variability. Despite these challenges, the incorporation of process data in educational assessments holds immense promise as it enriches our understanding of students' cognitive processes and provides additional insights into their interactive behaviors. This thesis focuses on the application of process data in educational assessments across three key aspects.
Chapter 2 explores the accurate assessment of a student's ability by incorporating process data into the assessment. Through a combination of theoretical analysis, simulations, and empirical study, we demonstrate that appropriately integrating process data significantly enhances assessment precision.
Building upon this foundation, Chapter 3 takes a step further by addressing not only the target attribute of interest but also the nuisance attributes present in the process data to mitigate the issue of differential item functioning. We present a novel framework that leverages process data as proxies for nuisance attributes in item response functions, effectively reducing or potentially eliminating differential item functioning. We validate the proposed framework using both simulated data and real data from the PIAAC PSTRE items.
Furthermore, this thesis extends beyond the analysis of existing tests and explores enhanced strategies for item administration. Specifically, in Chapter 4, we investigate the potential of incorporating process data in computerized adaptive testing. Our adaptive item selection algorithm leverages information about individual differences in both measured proficiency and other meaningful traits that can influence item informativeness. A new framework for process-based adaptive testing, encompassing real-time proficiency scoring and item selection is presented and evaluated through a comprehensive simulation study to demonstrate the efficacy
Super Multiset RSK and a Mixed Multiset Partition Algebra
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
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
Lindenwood University Undergraduate Course Catalog.https://digitalcommons.lindenwood.edu/catalogs/1209/thumbnail.jp
Translanguaging for Equal Opportunities : Speaking Romani at School
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
This article continues our study of - and -key polynomials, which are
(non-symmetric) "partial" Schur - and -functions as well as "shifted"
versions of key polynomials. Our main results provide a crystal interpretation
of - and -key polynomials, namely, as the characters of certain connected
subcrystals of normal crystals associated to the queer Lie superalgebra
. In the -key case, the ambient normal crystals are the
-crystals studied by Grantcharov et al., while in the -key
case, these are replaced by the extended -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 - and -key polynomials. We verify
these generalized conjectures in a few special cases. Along the way, we
establish some miscellaneous results about normal -crystals and
Demazure -crystals.Comment: 60 pages, 6 figure
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