To Be or Not to Be…Greek: A Study of Theory of Mind, Moral Reasoning, and Moral Development in Affiliated and Non-Affiliated Students

Since college is an important time in emotional and moral development, understanding factors that affect emotional intelligence and morality during these years (i.e., participation in Greek organizations) is critical. While past studies have investigated theory of mind and moral development in Greek and non-Greek college students, the research is limited. Thus, in this study, I explored theory of mind (ToM), moral development (MD), and moral reasoning (MR) in Greek members (n = 54) and their non-affiliated peers (n = 50) across their college years. Results indicated that Greek and non-Greek students differed in theory of mind and moral reasoning, but not in moral development. Greek men and women demonstrated equivalent theory of mind abilities across class years, whereas non-Greek students’ theory of mind abilities differed depending upon their gender and class year. Specifically, non-Greek men showed a pattern of decreased theory of mind across the college years, whereas non-Greek women’s theory of mind improved from sophomores to juniors to seniors. Additionally, non-Greek students tended to consider the feelings of others more than themselves when reaching moral decisions, whereas Greek students’ moral reasoning focused more on following rules and social norms. Taken together, these results suggest that involvement in Greek life during college may impact both emotional intelligence and moral reasoning without directly affecting the levels of moral development reached by students

Crater population and resurfacing of the Martian north polar layered deposits

Present-day accumulation in the north polar layered deposits (NPLD) is thought to occur via deposition on the north polar residual cap. Understanding current mass balance in relation to current climate would provide insight into the climatic record of the NPLD. To constrain processes and rates of NPLD resurfacing, a search for craters was conducted using images from the Mars Reconnaissance Orbiter Context Camera. One hundred thirty craters have been identified on the NPLD, 95 of which are located within a region defined to represent recent accumulation. High Resolution Imaging Science Experiment images of craters in this region reveal a morphological sequence of crater degradation that provides a qualitative understanding of processes involved in crater removal. A classification system for these craters was developed based on the amount of apparent degradation and infilling and where possible depth/diameter ratios were determined. The temporal and spatial distribution of crater degradation is interpreted to be close to uniform. Through comparison of the size-frequency distribution of these craters with the expected production function, the craters are interpreted to be an equilibrium population with a crater of diameter D meters having a lifetime of ~30.75D^(1.14) years. Accumulation rates within these craters are estimated at 7.2D^(−0.14) mm/yr, which corresponds to values of ~3–4 mm/yr and are much higher than rates thought to apply to the surrounding flat terrain. The current crater population is estimated to have accumulated in the last ~20 kyr or less

Development of a Higher-Order Ice Sheet Model Using a Rescaled Coordinate System

The Intergovernmental Panel on Climate Change (IPCC) has estimated between 9 and 88 cm of sea level rise over the next hundred years. Of this, only negative 19 to 11 cm is attributed to the largest ice masses on the planet, the Antarctic and Greenland ice sheets. Over the last decade, dramatic activity in the outlet glaciers of Greenland and the Antarctic Peninsula raise the possibility that these large ice sheets will have a much greater contribution to sea level rise over the next century than was predicted by the IPCC. Recent studies have shown these areas are exhibiting decadal scale changes in response to climate forcings, whereas IPCC models show that ice is not responsive to climate change over such short periods of time. Many believe the IPCC type models fail to show short term climate responses due to the simplifications they make to ice sheet mechanics. Here, we develop a higher-order model -- a new ice sheet model which contains all relevant flow physics. In order to gauge our progress, we perform a verification of our model around a structured set of experiments. The analysis reveals our model is performing well over a range of different scenarios

Doctor of Philosophy

dissertationStatistical analysis of time dependent imaging data is crucial for understanding normal anatomical development as well as disease progression. The most promising studies are of longitudinal design, where repeated observations are obtained from the same subjects. Analysis in this case is challenging due to the difficulty in modeling longitudinal changes, such as growth, and comparing changes across different populations. In any case, the study of anatomical change over time has the potential to further our understanding of many dynamic processes. What is needed are accurate computational models to capture, describe, and quantify anatomical change over time. Anatomical shape is encoded in a variety of representations, such as medical imaging data and derived geometric information extracted as points, curves, and/or surfaces. By considering various shape representations embedded into the same ambient space as a shape complex, either in 2D or 3D, we obtain a more comprehensive description of the anatomy than provided by an single isolated shape. In this dissertation, we develop spatiotemporal models of anatomical change designed to leverage multiple shape representations simultaneously. Rather than study directly the geometric changes to a shape itself, we instead consider how the ambient space deforms, which allows all embedded shapes to be included simultaneously in model estimation. Around this idea, we develop two complementary spatiotemporal models: a flexible nonparametric model designed to capture complex anatomical trajectories, and a generative model designed as a compact statistical representation of anatomical change. We present several ways spatiotemporal models can support the statistical analysis of scalar measurements, such as volume, extracted from shape. Finally, we cover the statistical analysis of higher dimensional shape features to take better advantage of the rich morphometric information provided by shape, as well as the trajectory of change captured by spatiotemporal models

“Made For Me”: Stories of Discovery Among Autistic Students Traveling Abroad

This qualitative, narrative analysis shares the stories of triumph and challenges recalled by autistic college students who participated in customized, short-term, faculty-led, study abroad trips, and their recommendations for future inclusive trips. This study included eight undergraduate, autistic participants ranging in ages from 19-24. Participants completed an in-person or virtual 60-minute structured interview, that focused on recalling stories of triumph and challenges they had experienced when they were studying abroad. Utilizing Critical Disability Theory, these interviews provided the exploration of stories that revealed themes of discovering me, “Made for Me,” and recommendations for future inclusive trips. Key findings included the discovery of self through autonomy, challenges, and successes. In addition, participants shared gratitude for the trips by sharing why they said yes, finding comfort with their people, and embracing the support for their health and well-being. Lastly, participants shared recommendations for future inclusive study abroad trips that included training for faculty and staff, providing options/flexibility of agenda, and supplying ample sensory aids. The findings of this study provide valuable insights into the key components that made study abroad enjoyable and challenging for autistic students, as well as their recommendations for study abroad instructors, providers, and higher education administrators to devise more inclusive trips for the future

Detailed stratigraphy and bed thickness of the Mars north and south polar layered deposits

The Mars polar layered deposits (PLD) likely hold an extensive record of recent climate during a period of high-amplitude orbit and obliquity cycles. Previous work has detected limited evidence for orbital signatures within PLD stratigraphy, but data from the High Resolution Imaging Science Experiment (HiRISE) permit renewed analysis of PLD stratigraphy at sub-meter scale. Topography derived from HiRISE images using stereogrammetry resolves beds previously detectable only as alternating light and dark bands in visible images. We utilize these data to measure the thickness of individual beds within the PLD, corrected for non-horizontal bed orientation. Stratigraphic columns and bed thickness profiles are presented for two sites within the NPLD, and show several sets of finely bedded units 1–2 m thick; isolated marker beds 3–4 m thick; and undifferentiated sections. Bed thickness measurements for three sites within the SPLD exhibit only one bed type based on albedo and morphology, and bed thicknesses have a larger mean and variance compared to measurements for the NPLD. Power spectra of brightness and slope derived along the measured stratigraphic sections confirm the regularity of NPLD fine bed thickness, and the lack of a dominant SPLD bed thickness. The regularity of fine bed thickness of the NPLD is consistent with quasiperiodic bed formation, albeit with unknown temporal period; the SPLD thickness measurements show no such regularity

Water ice in the dark dune spots of Richardson crater on Mars

In this study we assess the presence, nature and properties of ices - in particular water ice - that occur within these spots using HIRISE and CRISM observations, as well as the LMD Global Climate Model. Our studies focus on Richardson crater (72{\deg}S, 179{\deg}E) and cover southern spring and summer (LS 175{\deg} - 17 341{\deg}). Three units have been identified of these spots: dark core, gray ring and bright halo. Each unit show characteristic changes as the season progress. In winter, the whole area is covered by CO2 ice with H2O ice contamination. Dark spots form during late winter and early spring. During spring, the dark spots are located in a 10 cm thick depression compared to the surrounding bright ice-rich layer. They are spectrally characterized by weak CO2 ice signatures that probably result from spatial mixing of CO2 ice rich and ice free regions within pixels, and from mixing of surface signatures due to aerosols scattering. The bright halo shaped by winds shows stronger CO2 absorptions than the average ice covered terrain, which is consistent with a formation process involving CO2 re-condensation. According to spectral, morphological and modeling considerations, the gray ring is composed of a thin layer of a few tens of {\mu}m of water ice. Two sources/processes could participate to the enrichment of water ice in the gray ring unit: (i) water ice condensation at the surface in early fall (prior to the condensation of a CO2 rich winter layer) or during winter time (due to cold trapping of the CO2 layer); (ii) ejection of dust grains surrounded by water ice by the geyser activity responsible for the dark spot. In any case, water ice remains longer in the gray ring unit after the complete sublimation of the CO2. Finally, we also looked for liquid water in the near-IR CRISM spectra using linear unmixing modeling but found no conclusive evidence for it

Geodesic image regression with a sparse parameterization of diffeomorphisms

pre-printImage regression allows for time-discrete imaging data to be modeled continuously, and is a crucial tool for conducting statistical analysis on longitudinal images. Geodesic models are particularly well suited for statistical analysis, as image evolution is fully characterized by a baseline image and initial momenta. However, existing geodesic image regression models are parameterized by a large number of initial momenta, equal to the number of image voxels. In this paper, we present a sparse geodesic image regression framework which greatly reduces the number of model parameters. We combine a control point formulation of deformations with a L1 penalty to select the most relevant subset of momenta. This way, the number of model parameters reflects the complexity of anatomical changes in time rather than the sampling of the image. We apply our method to both synthetic and real data and show that we can decrease the number of model parameters (from the number of voxels down to hundreds) with only minimal decrease in model accuracy. The reduction in model parameters has the potential to improve the power of ensuing statistical analysis, which faces the challenging problem of high dimensionality

Analysis of longitudinal shape variability via subject specific growth modeling

pre-printStatistical analysis of longitudinal imaging data is crucial for understanding normal anatomical development as well as disease progression. This fundamental task is challenging due to the difficulty in modeling longitudinal changes, such as growth, and comparing changes across different populations. We propose a new approach for analyzing shape variability over time, and for quantifying spatiotemporal population differences. Our approach estimates 4D anatomical growth models for a reference population (an average model) and for individuals in different groups. We define a reference 4D space for our analysis as the average population model and measure shape variability through diffeomorphisms that map the reference to the individuals. Conducting our analysis on this 4D space enables straightforward statistical analysis of deformations as they are parameterized by momenta vectors that are located at homologous locations in space and time. We evaluate our method on a synthetic shape database and clinical data from a study that seeks to quantify brain growth differences in infants at risk for autism

Continuous Trajectories from Discrete Anatomical Shapes

poste