Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior

Previous research using evolutionary computation in Multi-Agent Systems indicates that assigning fitness based on team vs.\ individual behavior has a strong impact on the ability of evolved teams of artificial agents to exhibit teamwork in challenging tasks. However, such research only made use of single-objective evolution. In contrast, when a multiobjective evolutionary algorithm is used, populations can be subject to individual-level objectives, team-level objectives, or combinations of the two. This paper explores the performance of cooperatively coevolved teams of agents controlled by artificial neural networks subject to these types of objectives. Specifically, predator agents are evolved to capture scripted prey agents in a torus-shaped grid world. Because of the tension between individual and team behaviors, multiple modes of behavior can be useful, and thus the effect of modular neural networks is also explored. Results demonstrate that fitness rewarding individual behavior is superior to fitness rewarding team behavior, despite being applied to a cooperative task. However, the use of networks with multiple modules allows predators to discover intelligent behavior, regardless of which type of objectives are used

Experiments with ceramic coatings

Report describes the procedures and techniques used in the application of a ceramic coating and the evaluation of test parts through observation of the cracks that occur in this coating due to loading

Assessment of Public Sector Service Quality: Gauging Experiences and Perceptions of Racial Profiling

The absence of a culturally competent public sector workforce has led to increased public scrutiny and heightened levels of distrust. In the field of public safety, this is particularly important due to the sensitive nature of the task performed and the historically strained relationships that exist between racial minorities and law enforcement. Using national survey data to gauge the prevalence of citizen’s experiences and perceptions of racial profiling, this research reveals significant discrepancies amongst minorities and their white counterparts. In response, this research encourages public officials and agencies to eliminate inconsistencies in their interactions with the citizenry as a whole. Furthermore, it appeals to public administration scholars to address these issues in their classrooms as well as in their future research. Combined, these efforts can bring awareness and help mitigate the disparate delivery and administering of public-sector services

Champagne Flutes and Brandy Snifters: Modelling Protostellar Outflow-Cloud Chemical Interfaces

A rich variety of molecular species has now been observed towards hot cores in star forming regions and in the interstellar medium. An increasing body of evidence from millimetre interferometers suggests that many of these form at the interfaces between protostellar outflows and their natal molecular clouds. However, current models have remained unable to explain the origin of the observational bias towards wide-angled "brandy snifter" shaped outflows over narrower "champagne flute" shapes in carbon monoxide imaging. Furthermore, these wide-angled systems exhibit unusually high abundances of the molecular ion HCO$^+$. We present results from a chemo-dynamic model of such regions where a rich chemistry arises naturally as a result of turbulent mixing between cold, dense molecular gas and the hot, ionized outflow material. The injecta drives a rich and rapid ion-neutral chemistry in qualitative and quantitative agreement with the observations. The observational bias towards wide-angled outflows is explained naturally by the geometry-dependent ion injection rate causing rapid dissociation of CO in the younger systems.Comment: Accepted to MNRAS. 12 pages, 8 Figure

High-Elevation Draba (Cruciferae) of the White Mountains of California and Nevada

Accurate identification of the species of Draba at high elevations in the White Mountains of eastern California and adjacent Nevada has been impossible because of frequent misinterpretations in the literature and the lack of a coherent source of information. A preliminary investigation of newly collected material suggested that several species of Draba, endemic or nearly so to these mountains, had not been recognized. Descriptions and relevant comparisons are given for a new combination, D. californica, and two new species, D. monoensis and D. subumbellata. Specimen citations for all taxa of Draba occurring at high elevations, roughly above 3000 m, are provided as a basis for our taxonomic decisions and to aid others in the interpretation of the taxonomy of the genus as it occurs in the White Mountains

Applying Blockchain Solutions to Address Research Reproducibility and Enable Scientometric Analysis

A worldwide reproducibility crisis around published scientific studies has gained attention from academics, journalists, and concerned citizens in recent decades. The inability to reliably reproduce experiments from scholarly research—especially in areas of high- impact science—has far-reaching social and economic implications. Fraud may seem an obvious culprit, but in our data-intensive world, vague methods, unclear standards, and even accidental mismanagement of digital resources can all be contributing factors. Reproducibility is an area of increasing focus within the scientometrics community and looking to emerging technologies to help mitigate reproducibility challenges makes practical sense. In the Web 3.0 era, the promise of distributed computing, the maturation of cloud services, and other novel convergences point toward new ways to enable bibliometric reproducibility. Concurrently, research artifacts beyond the peer-reviewed article are growing in prominence—datasets, algorithms, pre-prints—all serve an expanding role in research dissemination and discovery. In this paper we present an overview of some new approaches—with particular focus on the benefits of blockchain-based software systems—for managing research information and improving scientometric reproducibility

Shifting the Blame in Public Education: Are There Parallels Between Opinion and Policy?

Federal policies have expanded the use of market driven performance reforms. A growing number of these reforms assume that citizens believe educators should be held accountable for outcomes and funding sanctions will cause underperforming school to enhance outcomes. However it is unclear if citizens share these views. This research assesses these assumptions by examining who citizens believe should be most accountable for education outcomes and whether they support policies that removes funds from underperforming schools and rewards them to other education entities. The results reveal that citizens believe either parents or students should be held more accountable for education outcomes, not teachers. Also targeted groups were less supportive of financially sanctioning underperforming schools