Growth and Stability of Local Government Taxes: An Analysis of the Lexington-Fayette Urban County Government’s Tax Revenue Portfolio

Adequate tax revenues are critical for a government to operate and maintain the delivery of services that its citizens depend. The stability of these revenues is necessary for a government to accurately forecast future revenue growth and to ensure that balanced-budget requirements are met. The Lexington-Fayette Urban County Government (LFUCG) depends upon five primary tax sources for nearly 95% of its tax revenue. These taxes comprise the city’s tax revenue portfolio and include the business net profits tax, employee withholdings tax, franchise tax, insurance premiums tax, and the property tax. Each of tax possesses unique characteristics that dictate their susceptibility to year-to-year fluctuations. These tax sources, on average, maintain a disproportionate share of the total tax revenue portfolio, ranging from 61.4% to 4.8%. This research sought to identify the tax sources which the city depends on most for its tax revenue, their respective degree of volatility, and whether the current tax revenue portfolio is “mix-efficient” with regard to the Markowitz portfolio model. Data for these five tax sources from FY 1993-2007 was used to find the relationship each of tax has on the other taxes within the portfolio. The analysis found that a positive relationship to exist between each possible tax pairing, except for the employee withholding tax-insurance premiums tax combination and the franchise tax-insurance premiums tax combination. A study of variance-covariance of the taxes supported that assumption that the presence of two or more taxes serve to reduce the volatility of the tax revenue portfolio, where the variance of the entire portfolio was significantly less than the variance of any single tax, except for the property tax. This research sought to identify the tax sources which the city depends on most for its tax revenue, their respective degree of volatility, and whether the current tax revenue portfolio is “mix-efficient” with regard to the Markowitz portfolio model. Data for these five tax sources from FY 1993-2007 was used to find the relationship each of tax has on the other taxes within the portfolio. The analysis found that a positive relationship to exist between each possible tax pairing, except for the employee withholding tax-insurance premiums tax combination and the franchise tax-insurance premiums tax combination. A study of variance-covariance of the taxes supported that assumption that the presence of two or more taxes serve to reduce the volatility of the tax revenue portfolio, where the variance of the entire portfolio was significantly less than the variance of any single tax, except for the property tax. The Urban County Government may choose to change the proportions of the tax shares within the revenue portfolio in order to reduce volatility. By placing greater weight on a tax share with lower historical volatility, it can improve the volatility of the overall tax revenue portfolio. However, such an adjustment will reduce the expected the return of the portfolio. Reducing the volatility, a normative concept, through an adjustment of the tax shares has yet to be seen largely due to the theoretical nature of process coupled with the underlying difficulties in undertaking such an activity. Furthermore, academic literature suggests that adjusting the portfolio may not be a necessary requirement for averting risk within a portfolio

Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation

Reinforcement learning (RL) has helped improve decision-making in several applications. However, applying traditional RL is challenging in some applications, such as rehabilitation of people with a spinal cord injury (SCI). Among other factors, using RL in this domain is difficult because there are many possible treatments (i.e., large action space) and few patients (i.e., limited training data). Treatments for SCIs have natural groupings, so we propose two approaches to grouping treatments so that an RL agent can learn effectively from limited data. One relies on domain knowledge of SCI rehabilitation and the other learns similarities among treatments using an embedding technique. We then use Fitted Q Iteration to train an agent that learns optimal treatments. Through a simulation study designed to reflect the properties of SCI rehabilitation, we find that both methods can help improve the treatment decisions of physiotherapists, but the approach based on domain knowledge offers better performance

Decentering the Human in 12th Century Histories and Chronicles: Divine Nonhumans at the Center of the Itinerarium Cambriae and the Historia Anglorum

This thesis primarily investigates nonhumans in the writings of 12th century historians. Where these writings may typically be seen as having little room for nonhuman agency, this thesis investigates how divinity, as an important part of medieval life, creates a space for nonhuman agency that is otherwise overlooked. In chapter one, this thesis discusses the writing of that agency in the form of natura naturans, a medieval idea that brings nature and god together in the writing of the Itinerarium Cambriae. The result is direct political agency of the nonhuman – powerful enough to threaten a king. In chapter two, this thesis discusses how celestial bodies and their motions shape the writing of Henry of Huntingdon’s Historia Anglorum. By influencing and shaping how Henry comprehends and writes time, these celestial events act as a heuristic or a ‘teaching pattern’ which ultimately shape an eschatological form of time in the chronicle

Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation

Reinforcement learning (RL) has helped improve decision-making in several applications. However, applying traditional RL is challenging in some applications, such as rehabilitation of people with a spinal cord injury (SCI). Among other factors, using RL in this domain is difficult because there are many possible treatments (i.e., large action space) and few patients (i.e., limited training data). Treatments for SCIs have natural groupings, so we propose two approaches to grouping treatments so that an RL agent can learn effectively from limited data. One relies on domain knowledge of SCI rehabilitation and the other learns similarities among treatments using an embedding technique. We then use Fitted Q Iteration to train an agent that learns optimal treatments. Through a simulation study designed to reflect the properties of SCI rehabilitation, we find that both methods can help improve the treatment decisions of physiotherapists, but the approach based on domain knowledge offers better performance. Our findings provide a "proof of concept" that RL can be used to help improve the treatment of those with an SCI and indicates that continued efforts to gather data and apply RL to this domain are worthwhile.Comment: 31 pages, 7 figure

Human-AI interactions through a Gricean lens

Grice’s Cooperative Principle (1975), which describes the implicit maxims that guide effective conversation, has long been applied to conversations between humans. However, as humans begin to interact with non-human dialogue systems more frequently and in a broader scope, an important question emerges: what principles govern those interactions? In the present study, this question is addressed, as human-AI interactions are categorized using Grice’s four maxims. In doing so, it demonstrates the advantages and shortcomings of such an approach, ultimately demonstrating that humans do, indeed, apply these maxims to interactions with AI, even making explicit references to the AI’s performance through a Gricean lens. Twenty-three participants interacted with an American English-speaking Alexa and rated and discussed their experience with an in-lab researcher. Researchers then reviewed each exchange, identifying those that might relate to Grice’s maxims: Quantity, Quality, Manner, and Relevance. Many instances of explicit user frustration stemmed from violations of Grice’s maxims. Quantity violations were noted for too little but not too much information, while Quality violations were rare, indicating high trust in Alexa’s responses. Manner violations focused on speed and humanness. Relevance violations were the most frequent of all violations, and they appear to be the most frustrating. While the maxims help describe many of the issues participants encountered with Alexa’s responses, other issues do not fit neatly into Grice’s framework. For example, participants were particularly averse to Alexa initiating exchanges or making unsolicited suggestions. To address this gap, we propose the addition of human Priority to describe human-AI interaction. Humans and AIs are not (yet?) conversational equals, and human initiative takes priority. Moreover, we find that Relevance is of particular importance in human-AI interactions and suggest that the application of Grice’s Cooperative Principles to human-AI interactions is beneficial both from an AI development perspective as well as a tool for describing an emerging form of interaction

Translating HbA1c measurements into estimated average glucose values in pregnant women with diabetes

Aims/hypothesis This study aimed to examine the relationship between average glucose levels, assessed by continuous glucose monitoring (CGM), and HbA1c levels in pregnant women with diabetes to determine whether calculations of standard estimated average glucose (eAG) levels from HbA1c measurements are applicable to pregnant women with diabetes. Methods CGM data from 117 pregnant women (89 women with type 1 diabetes; 28 women with type 2 diabetes) were analysed. Average glucose levels were calculated from 5–7 day CGM profiles (mean 1275 glucose values per profile) and paired with a corresponding (±1 week) HbA1c measure. In total, 688 average glucose–HbA1c pairs were obtained across pregnancy (mean six pairs per participant). Average glucose level was used as the dependent variable in a regression model. Covariates were gestational week, study centre and HbA1c. Results There was a strong association between HbA1c and average glucose values in pregnancy (coefficient 0.67 [95% CI 0.57, 0.78]), i.e. a 1% (11 mmol/mol) difference in HbA1c corresponded to a 0.67 mmol/l difference in average glucose. The random effects model that included gestational week as a curvilinear (quadratic) covariate fitted best, allowing calculation of a pregnancy-specific eAG (PeAG). This showed that an HbA1c of 8.0% (64 mmol/mol) gave a PeAG of 7.4–7.7 mmol/l (depending on gestational week), compared with a standard eAG of 10.2 mmol/l. The PeAG associated with maintaining an HbA1c level of 6.0% (42 mmol/mol) during pregnancy was between 6.4 and 6.7 mmol/l, depending on gestational week. Conclusions/interpretation The HbA1c–average glucose relationship is altered by pregnancy. Routinely generated standard eAG values do not account for this difference between pregnant and non-pregnant individuals and, thus, should not be used during pregnancy. Instead, the PeAG values deduced in the current study are recommended for antenatal clinical care

Symbiotic Legume Nodules Employ Both Rhizobial Exo- and Endo-Hydrogenases to Recycle Hydrogen Produced by Nitrogen Fixation

BACKGROUND: In symbiotic legume nodules, endosymbiotic rhizobia (bacteroids) fix atmospheric N(2), an ATP-dependent catalytic process yielding stoichiometric ammonium and hydrogen gas (H(2)). While in most legume nodules this H(2) is quantitatively evolved, which loss drains metabolic energy, certain bacteroid strains employ uptake hydrogenase activity and thus evolve little or no H(2). Rather, endogenous H(2) is efficiently respired at the expense of O(2), driving oxidative phosphorylation, recouping ATP used for H(2) production, and increasing the efficiency of symbiotic nodule N(2) fixation. In many ensuing investigations since its discovery as a physiological process, bacteroid uptake hydrogenase activity has been presumed a single entity. METHODOLOGY/PRINCIPAL FINDINGS: Azorhizobium caulinodans, the nodule endosymbiont of Sesbania rostrata stems and roots, possesses both orthodox respiratory (exo-)hydrogenase and novel (endo-)hydrogenase activities. These two respiratory hydrogenases are structurally quite distinct and encoded by disparate, unlinked gene-sets. As shown here, in S. rostrata symbiotic nodules, haploid A. caulinodans bacteroids carrying single knockout alleles in either exo- or-endo-hydrogenase structural genes, like the wild-type parent, evolve no detectable H(2) and thus are fully competent for endogenous H(2) recycling. Whereas, nodules formed with A. caulinodans exo-, endo-hydrogenase double-mutants evolve endogenous H(2) quantitatively and thus suffer complete loss of H(2) recycling capability. More generally, from bioinformatic analyses, diazotrophic microaerophiles, including rhizobia, which respire H(2) may carry both exo- and endo-hydrogenase gene-sets. CONCLUSIONS/SIGNIFICANCE: In symbiotic S. rostrata nodules, A. caulinodans bacteroids can use either respiratory hydrogenase to recycle endogenous H(2) produced by N(2) fixation. Thus, H(2) recycling by symbiotic legume nodules may involve multiple respiratory hydrogenases