Sir Thomas Roe at the Mughal Court: Seventeenth Century English Cultural Assumptions

This paper seeks to explore how, although in reality the inferior partner in trade, the English in India thought of themselves as superior. To do this, I will analyze the journal of Sir Thomas Roe, the first official English ambassador to the Mughal Court, in light of English and Mughal history, English cultural constructions, and the works of contemporaries such as Edward Terry and the Emperor Jahangir. This paper will show that Roe judges the people of the Mughal court by English cultural standards, and uses these assump- tions to claim English superiority. Furthermore, the paper will show that, while not intentionally written with imperialist aims, Roe\u27s journal does demonstrate that the basic attitudes of the later imperialist

Texts, Bodies, and the Memory of Bloody Sunday

We examine here recent arguments that embodied experience is an important site of collective memory, and related challenges to the standard emphasis on discourse and symbols in collective memory research. We argue that although theories of embodied memory offer new insights, they are limited by (1) an overdrawn distinction between embodied memory and textual memory that neglects the complex relations between the two, (2) an overemphasis on ritual performance at the expense of collective conversation, (3) an oversimplified view of performativity, and (4) an underestimation of the ambiguity in embodied performance. Theories of embodied collective memory should be narrowed and specified with focused comparisons examining the influence of embodied experience in the formation of collective identities, in conflicts over collective memories, and in the persistence and malleability of memories across generations. We illustrate our argument throughout with examples drawn from the collective memory of Bloody Sunday in Northern Ireland in 1972

Improving police officer and justice personnel attitudes and de-escalation skills: A pilot study of Policing the Teen Brain

This pilot study assessed whether police officers and juvenile justice personnel reported improved attitudes toward youth and knowledge about de-escalation skills after attending Policing the Teen Brain, a training created to prevent arrests by improving officer-youth interactions. Pre- and post-intervention surveys asked about participant attitudes toward adolescents, adolescence as a stressful stage, and punishing youth in the justice system. Among the 232 participants, paired sample t-tests indicated significant differences between mean pre- and post-survey responses on nearly all survey subscales. A hierarchical regression model significantly predicted improvement in knowledge, with educated, female participants most likely to improve knowledge of de-escalation skills

Guidelines and recommendations for record keeping and data use for feline trap-neuter-return programs

Feral and free roaming domestic cat (Felis catus) populations are a growing concern for animal welfare, wildlife mortality, and human and domestic animal health. This means that the issue is also a growing topic for discussion for animal welfare proponents as well as wildlife and environmental scientists and managers, as it should be. Trap-neuter-return (TNR) programs are increasingly being used and pushed as an effective way to limit and even reduce population growth of feral cat colonies. However, there is criticism from much of the scientific community on whether this method is actually effective in controlling or reducing feral cat populations, as well as the effect they have on wildlife. A program called Operation Catnip Stillwater is a TNR program that has been ongoing since 2012 in Stillwater, Oklahoma. This program holds seven clinics a year to sterilize and vaccinate feral cats caught in traps by volunteers. The main objective of my project was to examine the effects of the TNR (trap-neuter-release) program, Operation Catnip, on the feral cat population in Stillwater, Oklahoma. Specifically, I wanted to know whether there has been a noticeable decline or growth of cat colonies, using intake data from several source. Unfortunately, the data I available to me was inconsistent and limited. Therefore, I could not complete my original objective. Instead, I realized the need for a standardized set of data recommendations for TNR operations and those organizations that work closely with them. I formed a list of record-keeping recommendations and why I chose them

Motivating compliance: Juvenile probation officer strategies and skills

Juvenile probation officers aim to improve youth compliance with probation conditions, but questions remain about how officers motivate youth. The study’s purpose was to determine which officer-reported probation strategies (client-centered vs. confrontational) were associated with their use of evidence-based motivational interviewing skills. Officers (N = 221) from 18 Indiana counties demonstrated motivational interviewing skills by responding to scenarios depicting issues common to youth probationers. Results of a hierarchical multiple regression analysis indicated that, while officer endorsement of client-centered strategies was not associated with differential use of motivational interviewing skills, officers endorsing confrontational strategies were less likely to demonstrate motivational interviewing skills

Machine Learning Technologies and Their Applications for Science and Engineering Domains Workshop -- Summary Report

The fields of machine learning and big data analytics have made significant advances in recent years, which has created an environment where cross-fertilization of methods and collaborations can achieve previously unattainable outcomes. The Comprehensive Digital Transformation (CDT) Machine Learning and Big Data Analytics team planned a workshop at NASA Langley in August 2016 to unite leading experts the field of machine learning and NASA scientists and engineers. The primary goal for this workshop was to assess the state-of-the-art in this field, introduce these leading experts to the aerospace and science subject matter experts, and develop opportunities for collaboration. The workshop was held over a three day-period with lectures from 15 leading experts followed by significant interactive discussions. This report provides an overview of the 15 invited lectures and a summary of the key discussion topics that arose during both formal and informal discussion sections. Four key workshop themes were identified after the closure of the workshop and are also highlighted in the report. Furthermore, several workshop attendees provided their feedback on how they are already utilizing machine learning algorithms to advance their research, new methods they learned about during the workshop, and collaboration opportunities they identified during the workshop

Parameter Inference from Event Ensembles and the Top-Quark Mass

One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the effects of varying different parameters are highly correlated, a large ensemble of data may be needed to resolve parameter-space degeneracies. An important example is measuring the top-quark mass, where other physical and unphysical parameters in the simulation must be marginalized over when fitting the top-quark mass parameter. We compare three different methodologies for top-quark mass measurement: a classical histogram fitting procedure, similar to one commonly used in experiment optionally augmented with soft-drop jet grooming; a machine-learning method called DCTR; and a linear regression approach, either using a least-squares fit or with a dense linearly-activated neural network. Despite the fact that individual events are totally uncorrelated, we find that the linear regression methods work most effectively when we input an ensemble of events sorted by mass, rather than training them on individual events. Although all methods provide robust extraction of the top-quark mass parameter, the linear network does marginally best and is remarkably simple. For the top study, we conclude that the Monte-Carlo-based uncertainty on current extractions of the top-quark mass from LHC data can be reduced significantly (by perhaps a factor of 2) using networks trained on sorted event ensembles. More generally, machine learning from ensembles for parameter estimation has broad potential for collider physics measurements.Comment: v1: 27 + 5 pages, 14 + 3 figures. v2: Matches version accepted to JHE