Loss of Consortium and Loss of Services Actions: A Legacy of Separate Spheres

Loss of Consortium and Loss of Services Actions: A Legacy of Separate Sphere

Evaluation Of Retesting in Kentucky\u27s Driver License Process

The objectives of this research were to first evaluate the existing practices regarding driver license renewal, driver retesting, and medical review board procedures and then identify and recommend methods that would improve these processes. The analysis of the Medical Review Board process indicated that, while it operates at an acceptable level in major urban areas, it is almost non-existent in most areas of the state. A brochure describing the process was developed for distribution to physicians. There is a universal agreement among researchers that vision has a significant role in driving performance and that visual abilities deteriorate with age. It is apparent that some type of vision screening should be implemented during the renewal process since it could identify individuals with potential deficiencies. Such screening could be achieved either with a test during the license renewal or with an eye exam prior to license renewal. In addition to the testing, a policy that identifies potential at-risk drivers should be considered. The combination of convictions (points) and crashes was considered as an appropriate means to distinguish such drivers. Special consideration should be given for older drivers at driver license renewal. In addition to the vision screening, a written test could be administered at license renewal along with a set of medical questions to determine their physical and mental status

REVIEWING THE ROOTS OF RESPONSE TO INTERVENTION:IS THERE ENOUGH RESEARCH TO SUPPORT THE PROMISE?

In the United States, Response to Intervention (RtI) is used to promote the use of evidence-based instruction in educational institutions, with the goal of supporting general and specialized educators and enabling these professionals to work together in a comprehensive, integrated manner. In doing so, RtI provides a protocol for identifying students with specific academic deficits and who demonstrate the need for individualized forms of instruction. Specifically, professional educators utilize quantitative data accumulated from common student assessment scores, which is thought to reflect a student’s response to instruction in the general classroom, in addition to his or her response to more targeted forms of intervention. This article presents a conceptual overview of RtI and discusses key dimensions most salient to its development and implementation within the United States, while carefully reviewing the research supporting the effectiveness of this multi-tiered framework. As RtI gains prominence in other countries, this article serves to educate others on what may well become a more universal response to intervention

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the errors (or, misclassifications) over the given historical data. However, it is quite possible that the optimally trained classifier makes decisions for people belonging to different social groups with different misclassification rates (e.g., misclassification rates for females are higher than for males), thereby placing these groups at an unfair disadvantage. To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates. We then propose intuitive measures of disparate mistreatment for decision boundary-based classifiers, which can be easily incorporated into their formulation as convex-concave constraints. Experiments on synthetic as well as real world datasets show that our methodology is effective at avoiding disparate mistreatment, often at a small cost in terms of accuracy.Comment: To appear in Proceedings of the 26th International World Wide Web Conference (WWW), 2017. Code available at: https://github.com/mbilalzafar/fair-classificatio

HST Observations of the Host Galaxies of BL Lacertae Objects

Six BL Lac objects from the complete 1 Jy radio-selected sample of 34 objects were observed in Cycle 5 with the HST WFPC2 camera to an equivalent limiting flux of mu_I~26 mag/arcsec^2. Here we report results for the second half of this sample, as well as new results for the first three objects, discussed previously by Falomo et al. (1997). In addition, we have analyzed in the same way HST images of three X-ray-selected BL Lacs observed by Jannuzi et al. (1997). The ensemble of 9 BL Lac objects spans the redshift range from z=0.19 to ~1. Host galaxies are clearly detected in seven cases, while the other two, at z~0.258 (redshift highly uncertain) and z=0.997, are not resolved. The HST images constitute a homogeneous data set with unprecedented morphological information between a few tenths of an arcsecond and several arcseconds from the nucleus, allowing us in 6 of the 7 detected host galaxies to rule out definitively a pure disk light profile. The host galaxies are luminous ellipticals with an average absolute magnitude of M_I~-24.6 mag (with dispersion 0.7 mag), more than a magnitude brighter than L* and comparable to brightest cluster galaxies. The morphologies are generally smooth and have small ellipticities (epsilon<0.2). Given such roundness, there is no obvious alignment with the more linear radio structures. In the six cases for which we have HST WFPC2 images in two filters, the derived color profiles show no strong spatial gradients and are as expected for K-corrected passively evolving elliptical galaxies. The host galaxies of the radio-selected and X-ray-selected BL Lacs for this very limited sample are comparable in both morphology and luminosity.Comment: 23 pages, including 6 postscript figures and 3 tables (embedded). Latex requires aaspp4.sty and psfig.sty (not included). Accepted for publication in the Astrophysical Journa

Numerical schemes for a model for nonlinear dispersive waves

A description is given of a number of numerical schemes to solve an evolution equation that arises when modelling the propagation of water waves in a channel. The discussion also includes the results of numerical experiments made with each of the schemes. It is suggested, on the basis of these experiments, that one of the schemes may have (discrete) solitary-wave solutions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25569/1/0000111.pd

Segregate or cooperate- a study of the interaction between two species of Dictyostelium

<p>Abstract</p> <p>Background</p> <p>A major challenge for evolutionary biology is explaining altruism, particularly when it involves death of one party and occurs across species. Chimeric fruiting bodies of <it>Dictyostelium discoideum </it>and <it>Dictyostelium purpureum </it>develop from formerly independent amoebae, and some die to help others. Here we examine co-aggregation between <it>D. discoideum </it>and <it>D. purpureum</it>, determine its frequency and which party benefits, and the extent of fair play in contribution to the altruistic caste.</p> <p>Results</p> <p>We mixed cells from both species in equal proportions, and then we analyzed 198 individual fruiting bodies, which always had either a <it>D. discoideum </it>or <it>D. purpureum </it>phenotype (<it>D. discoideum</it>- 98, <it>D. purpureum</it>- 100). Fifty percent of the fruiting bodies that looked like <it>D. discoideum </it>and 22% of the fruiting bodies that looked like <it>D. purpureum </it>were chimeric, though the majority of spores in any given fruiting body belonged to one species (<it>D. discoideum </it>fruiting bodies- 0.85 ± 0.03, <it>D. purpureum </it>fruiting bodies- 0.94 ± 0.02). Clearly, there is species level recognition occurring that keeps the cells mostly separate. The number of fruiting bodies produced with the <it>D. discoideum </it>phenotype increased from 225 ± 32 fruiting bodies when <it>D. discoideum </it>was alone to 486 ± 61 in the mix treatments. However, the number of <it>D. discoideum </it>spores decreased, although not significantly, from 2.75e⁷± 1.29e⁷spores in the controls to 2.06e⁷± 8.33e⁶spores in the mix treatments. <it>D. purpureum </it>fruiting body and spore production decreased from 719 ± 111 fruiting bodies and 5.81e⁷± 1.26e⁷spores in the controls to 394 ± 111 fruiting bodies and 9.75e⁶± 2.25e⁶spores in the mix treatments.</p> <p>Conclusion</p> <p>Both species appear to favor clonality but can cooperate with each other to produce fruiting bodies. Cooperating amoebae are able to make larger fruiting bodies, which are advantageous for migration and dispersal, but both species here suffer a cost in producing fewer spores per fruiting body.</p

Comparing ultra-high spatial resolution remote-sensing methods in mapping peatland vegetation

Peer reviewe

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors