243 research outputs found
County Finance, House Prices, and Financial Decision-Making
This work is in three chapters. The first chapter investigates the extent to which county-level property tax changes are capitalized into house prices. The literature mainly focuses on specific tax events, but this chapter generalizes the context to large U.S. cities. The main findings suggest that county-level property tax capitalization occurs and varies along the distribution of house prices. The second chapter measures differences in county-aggregated self-assessed valuations of house prices and county- aggregated sale prices to determine whether there are trends in the level of misperceived house value. Factors like age, time of tenure in a house, and house price all matter when analyzing the difference between self-assessed values and sale prices. Further, the empirical work aims to measure differences in salience for tax policies at different governance levels. The third chapter analyzes whether increases in financial-decision making capabilities has an impact on health insurance purchase decisions and other health-related financial decisions. Financial literacy is shown to reduce undesirable outcomes in these dimensions. These chapters focus on decisions that many households face, and housing and health make up large portions of the typical household budget.
Adviser: Sam Allgoo
Undergraduate and Graduate Course Descriptions, 2023 Spring
Wright State University undergraduate and graduate course descriptions from Spring 2023
Robust Network Topology Inference and Processing of Graph Signals
The abundance of large and heterogeneous systems is rendering contemporary
data more pervasive, intricate, and with a non-regular structure. With
classical techniques facing troubles to deal with the irregular (non-Euclidean)
domain where the signals are defined, a popular approach at the heart of graph
signal processing (GSP) is to: (i) represent the underlying support via a graph
and (ii) exploit the topology of this graph to process the signals at hand. In
addition to the irregular structure of the signals, another critical limitation
is that the observed data is prone to the presence of perturbations, which, in
the context of GSP, may affect not only the observed signals but also the
topology of the supporting graph. Ignoring the presence of perturbations, along
with the couplings between the errors in the signal and the errors in their
support, can drastically hinder estimation performance. While many GSP works
have looked at the presence of perturbations in the signals, much fewer have
looked at the presence of perturbations in the graph, and almost none at their
joint effect. While this is not surprising (GSP is a relatively new field), we
expect this to change in the upcoming years. Motivated by the previous
discussion, the goal of this thesis is to advance toward a robust GSP paradigm
where the algorithms are carefully designed to incorporate the influence of
perturbations in the graph signals, the graph support, and both. To do so, we
consider different types of perturbations, evaluate their disruptive impact on
fundamental GSP tasks, and design robust algorithms to address them.Comment: Dissertatio
General Course Catalog [2022/23 academic year]
General Course Catalog, 2022/23 academic yearhttps://repository.stcloudstate.edu/undergencat/1134/thumbnail.jp
Generalized moving least squares vs. radial basis function finite difference methods for approximating surface derivatives
Approximating differential operators defined on two-dimensional surfaces is
an important problem that arises in many areas of science and engineering. Over
the past ten years, localized meshfree methods based on generalized moving
least squares (GMLS) and radial basis function finite differences (RBF-FD) have
been shown to be effective for this task as they can give high orders of
accuracy at low computational cost, and they can be applied to surfaces defined
only by point clouds. However, there have yet to be any studies that perform a
direct comparison of these methods for approximating surface differential
operators (SDOs). The first purpose of this work is to fill that gap. For this
comparison, we focus on an RBF-FD method based on polyharmonic spline kernels
and polynomials (PHS+Poly) since they are most closely related to the GMLS
method. Additionally, we use a relatively new technique for approximating SDOs
with RBF-FD called the tangent plane method since it is simpler than previous
techniques and natural to use with PHS+Poly RBF-FD. The second purpose of this
work is to relate the tangent plane formulation of SDOs to the local coordinate
formulation used in GMLS and to show that they are equivalent when the tangent
space to the surface is known exactly. The final purpose is to use ideas from
the GMLS SDO formulation to derive a new RBF-FD method for approximating the
tangent space for a point cloud surface when it is unknown. For the numerical
comparisons of the methods, we examine their convergence rates for
approximating the surface gradient, divergence, and Laplacian as the point
clouds are refined for various parameter choices. We also compare their
efficiency in terms of accuracy per computational cost, both when including and
excluding setup costs
Edge Cloud Computing for Geospatial Data Processing and Approximate Queries
Architecture for optimizing geospatial data processing pipelines in the cloud by making use of edge nodes deployed on containers in an urban moving taxi scenario (specifically Shenzhen, China). Edge nodes are using Geohash for efficient data preprocessing, including Geohash-based stratified sampling, and neighborhood location of incoming messages.
Apache Kafka was then used to send data to a Spark cluster using a spatially-aware technique for data distribution. In particular, a Kafka topic for each neighborhood of the city considered was created, and each of these topics contained only messages originated in the same neighborhood
LIPIcs, Volume 244, ESA 2022, Complete Volume
LIPIcs, Volume 244, ESA 2022, Complete Volum
Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures
In the past, when elements in sructures were composed of perishable materials, such as wood, the maintenance of houses, bridges, etc., was considered of vital importance for their safe use and to preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary concern. When it was realized that even for structures fabricated with these materials that the useful life has an end and that it was being approached, planning maintenance became an important and non-negligible aspect. Thus, the concept of structural health monitoring (SHM) was introduced, designed, and implemented as a multidisciplinary method. Computational mechanics, static and dynamic analysis of structures, electronics, sensors, and, recently, the Internet of Things (IoT) and artificial intelligence (AI) are required, but it is also important to consider new materials, especially those with intrinsic self-diagnosis characteristics, and to use measurement and survey methods typical of modern geomatics, such as satellite surveys and highly sophisticated laser tools
Undergraduate and Graduate Course Descriptions, 2022 Fall
Wright State University undergraduate and graduate course descriptions from Fall 2022
- …