Restrictive Tier Induction

This dissertation proposes the Restrictive Tier Learner, which automatically induces only the tiers that are absolutely necessary in capturing phonological long-distance dependencies. The core of my learner is the addition of an extra evaluation step to the existing Inductive Projection Learner (Gouskova and Gallagher 2020), where the necessity and accuracy of the candidate tiers are determined. An important building block of my learner is a typological observation, namely the dichotomy between trigram-bound and unbounded patterns. The fact that this dichotomy is attested in both consonant interactions and vowel interactions allows for a unified approach to be used. Another important piece of information is that only unboundedness implies trigram-boundedness, and not vice versa. These typological observations together shed light on the critical role of trigrams in phonological learning. The premise that there is no other distance at which a restriction holds than these two lets us safely assume that searching only up to trigrams might actually be a near-exhaustive search for local interactions. On top of that, the fact that interaction beyond a trigram window, which we need tiers for, always implies interaction within a trigram window guarantees that all necessary tiers can be discovered by looking at trigram constraints. Hence, a learner can confidently search up to trigrams for local interactions and expand its search for non-local ones from the discovered trigrams. I present several case studies to test the abilities of the Restrictive Tier Learner in capturing various long-distance dependencies that are attested in natural languages. The current version of the learner maintains all the strengths of the previous learning algorithms while showing improved performance in critical cases

Understanding Hydroclimatic Controls on Stream Network Dynamics using LiDAR Data

This dissertation investigates the hydroclimatic controls on drainage network dynamics and characterizes the variation of drainage density in various climate regions. The methods were developed to extract the valley and wet channel networks based on Light Detection and Ranging (LiDAR) data including the elevation and intensity of laser returns. The study watersheds were selected based on the availability of streamflow observations and LiDAR data. Climate aridity index was used as a quantitative indicator for climate. The climate controls on drainage density were re-visited using watersheds with minimal anthropogenic interferences and compared with the U-shape relationship reported in the previous studies. A curvature-based method was developed to extract a valley network from 1-m LiDAR-based Digital Elevation Models. The relationship between drainage density and climate aridity index showed a monotonic increasing trend and the discrepancy was explained by human interventions and underestimated drainage density due to the coarse spatial resolution (30-meter) of the topographic maps used in previous research. Observations of wet channel networks are limited, especially in headwater catchments in comparison with the importance of stream network expansion and contraction. A systematic method was developed to extract wet channel networks based on the signal intensities of LiDAR ground returns, which are lower on water surfaces than on dry surfaces. The frequency distributions of intensities associated with wet surface and dry surface returns were constructed. With the aid of LiDAR-based ground elevations, signal intensity thresholds were identified for extracting wet channels. The developed method was applied to Lake Tahoe area during recession periods in five watersheds. A power-law relationship between streamflow and wet channel length was obtained and the scaling exponent was consistent with the reported findings from field work in other regions. Perennial streams flow for the most of the time during normal years and are usually defined based on a flow duration threshold. The streamflow characteristics of perennial streams in this research were assessed using the relationship between streamflow exceedance probability and wet channel ratio based on wet channel networks extracted from LiDAR data. Non-dimensional analysis based on the relationship between streamflow exceedance probability and wet channel ratio showed that results were consistent with previous research about perennial stream definition, and provided the possibility to use wet channel ratio to define perennial streams. Wetlands are important natural resources and need to be monitored regularly in order to understand their inundation dynamics, function and health. Wetland mapping is a key part of monitoring programs. A framework for detecting wetland was developed based on LiDAR elevation and intensity information. After masking out densely vegetated areas, wet areas were identified based on signal intensity of ground returns for barrier islands in East-Central Florida. The intensity threshold of wet surface was identified by decomposing composite probability distribution functions using a Gamma mixture model and the Expectation-Maximization algorithm. This method showed good potential for wetland mapping. The methodology developed in this dissertation demonstrated that incorporating LiDAR data into the drainage networks, stream network dynamics and wetlands results in enhanced understanding of hydroclimatic controls on stream network dynamics. LiDAR data provide a rich information source including elevation and intensity, and are of great benefit to hydrologic research community

Patterns of Bisphosphonates Utilization in Patients under Age 45 in a Large Cohort of Commercial Insurance Beneficiaries in the United States

Background: The effectiveness and safety of bisphosphonates treatment used in the young population have not been well studied. Despite insufficient data on effectiveness and safety of bisphosphonates in young patients, bisphosphonates are still considered in younger patients at high risk for osteoporosis or fracture. The objectives of this study were to identify bisphosphonate initiators aged 10–45 years and describe their clinical characteristics and to assess time trends of bisphosphonate use over the past decade in a large U.S. population-based cohort. Methods: Using the medical and pharmacy claims data from a U.S. commercial insurance (2003–2012), patients aged 10–45 years without malignancy who initiated an oral or intravenous bisphosphonate after at least 1 year of insurance enrollment were selected. Baseline demographics, comorbidities, medications and health care utilization were assessed in the year prior to initiating a bisphosphonate. The trend of bisphosphonate use over time was examined. Results: There were 9,082 bisphosphonate initiators (0.02% of the same age group in the population). The mean age was 38.1 years and 79.6% female. Osteoporosis was the most common diagnosis (41.2%). At baseline, 10.8% had a diagnosis of fracture and 29.0% had a bone mineral density measured. Of those who used glucocorticoids (39%) at baseline, the mean 1-year cumulative prednisone-equivalent dose was 2,669 milligrams. The use of bisphosphonates in the young population significantly decreased over the past decade (p<0.001). Conclusions: Among young patients aged 10–45, the use of bisphosphonates was uncommon and significantly decreased over the past decade in the U.S. While most patients initiating bisphosphonates had a diagnosis of osteoporosis and fracture in the preceding year, some had no recorded claims with a diagnosis of fracture, osteoporosis, or long-term glucocorticoids use at baseline. Future research is needed to examine the effectiveness and safety of bisphosphonates in young patients at risk for osteoporosis

Understanding Users' Dissatisfaction with ChatGPT Responses: Types, Resolving Tactics, and the Effect of Knowledge Level

Large language models (LLMs) with chat-based capabilities, such as ChatGPT, are widely used in various workflows. However, due to a limited understanding of these large-scale models, users struggle to use this technology and experience different kinds of dissatisfaction. Researchers have introduced several methods such as prompt engineering to improve model responses. However, they focus on crafting one prompt, and little has been investigated on how to deal with the dissatisfaction the user encountered during the conversation. Therefore, with ChatGPT as the case study, we examine end users' dissatisfaction along with their strategies to address the dissatisfaction. After organizing users' dissatisfaction with LLM into seven categories based on a literature review, we collected 511 instances of dissatisfactory ChatGPT responses from 107 users and their detailed recollections of dissatisfied experiences, which we release as a publicly accessible dataset. Our analysis reveals that users most frequently experience dissatisfaction when ChatGPT fails to grasp their intentions, while they rate the severity of dissatisfaction the highest with dissatisfaction related to accuracy. We also identified four tactics users employ to address their dissatisfaction and their effectiveness. We found that users often do not use any tactics to address their dissatisfaction, and even when using tactics, 72% of dissatisfaction remained unresolved. Moreover, we found that users with low knowledge regarding LLMs tend to face more dissatisfaction on accuracy while they often put minimal effort in addressing dissatisfaction. Based on these findings, we propose design implications for minimizing user dissatisfaction and enhancing the usability of chat-based LLM services

Phonological Trends in Seoul Korean Compound Tensification

This study investigates Seoul Korean compound tensification. Based on the data collected through a survey, it shows that various phonological factors contribute to the overall tensification probability. The tendencies caused by these factors are follows: (i) tensification is more likely with high frequency items; (ii) tensification is more likely when WA ends with an obstruent, followed by a nasal, liquid and a vowel in order; (iii) tensification is less likely when WB contains a laryngeally marked consonant; (iv) tensification is more likely when WB ends with a liquid and WA also begins with a coronal consonant. This study also provides a formal analysis of this phenomenon, using OT constraints. The specific weights of each constraint are evaluated through Maxent Grammar Tool. This weighted set of the constraints was highly successful in predicting the applicability of compound tensification. Thus, the grammar suggested for Seoul Korean compound tensification can be learned from the actual native speakers' pronunciation data, and also that this learned grammar can capture the various tendencies observed in this phenomenon.