Meeting Students Where They Are: Adapting Natural Resource Education to Emerging Digital Landscapes

For students, connecting through text, email, chat, and social media has become an integral part of their daily lives. The near endless supply of digital media outlets and information sources has created a new landscape at the interface of the student-professor relationship; incorporating these emergent platforms into hybrid educational environments can enhance student engagement with course material while improving communication between and amongst classmates, including the instructor. This new hyper-connected reality, however, presents both challenges and opportunities in educating tomorrow’s natural resource leaders and professors must “meet students where they are” by communicating and teaching in relatable ways (i.e. through emergent digital platforms). Students are increasingly being called upon to provide the “digital voice” for employers as new professionals, acquiring these requisite skills is now an essential component to a satisfactory education in the field of natural resources that must be met through adapting traditional classroom approaches. This presentation outlines the creation and use of a hybrid digital learning environment and provides a road map to start integrating various platforms into the classroom. Integrating digital media into traditional classroom dynamics can be difficult, confusing, and scary but also rewarding and beneficial. As such, the discussion answers two pertinent questions: how can natural resource educators utilize digital outlets to enhance the learning process? And, how can natural resource educators modify course requirements and expectations to better develop contemporary skill requirements? Considerations of privacy, content, digital lifespan, and communication are explicitly touched upon along with a review of current trends in the use of social media and other digital outlets amongst students and, increasingly, a wide variety of natural resource stakeholders (i.e. managers, environmental organizations, locals)

Images, Silences, and the Archival Record: An Interview with Michelle Caswell

Dr. Michelle Caswell is an Associate Professor of Archival Studies in the Department of Information Studies at the University of California, Los Angeles, where she is also an affiliated faculty member with the Department of Asian American Studies and the Center for Southeast Asian Studies. Her book, Archiving the Unspeakable: Silence, Memory, and the Photographic Record in Cambodia (2014), which explores the role of archives and records in the construction of memory about the Khmer Rouge in Cambodia through a collection of mug shots taken at Tuol Sleng prison, won the 2015 Waldo Grifford Leland award for Best Publication from the Society of American Archivists. Caswell is also the co-founder of the South Asian American Digital Archive, an online repository which documents and provides access to the diverse stories of South Asian Americans

Project Shearwater Ground Effect UAV

The Shearwater unmanned aerial vehicle is a maritime fixed-wing drone that is designed to use ground effect force generated between the aircraft and a body of water to efficiently propel itself near the surface of a body of water. Shearwater features a virtual reality pilot interface and will act as a hybrid underwater vehicle that will eventually be able to operate both above and beneath the ocean’s surface. The Shearwater team developed existing design work to produce major subsystems that culminated in a flyable functioning prototype. An existing airframe was updated with working control surfaces tested in simulation and in practice, an electrical control system, and a working virtual reality (VR) pilot view. The Shearwater team tested a practical prototype and developed an optimized virtual reality command and control system

Radar target recognition using bispectrum correlation

Ship commanders and pilots make life or death decisions based on the information they have at their disposal at the instant a decision is made. One component of that information is whether a radar contact is an enemy or a friend. Various systems exist which try to answer that question based on the characteristics of signals emitted or scattered from the contact. The goal is to maximize the accuracy of identification in order to build trust that when the system tells the operator the contact is an incoming friendly, he knows that it is. This thesis examines the technique of using the bispectrum of backscattered radar energy to identify a contact. Bispectra allow the examination of multiple scattering contributions to the return. This technique is compared to one using radar range profiles. A library of sample radar signatures is built using computational radar cross section estimation tools and 3-D model aircraft. This library is the basis of a series of simulations with aircraft at multiple aspects and configurations to determine whether using the bispectrum enhances the performance of identification systems using range profiles. It is determined that a bispectrum method meets or exceeds the identification accuracy of a range profile method especially with high-bandwidth systems.http://archive.org/details/radartargetrecog109453386US Navy (USN) author.Approved for public release; distribution is unlimited

Numerals in early Greek New Testament manuscripts: text-critical, scribal and theological studies

This thesis examines the phenomenon of numerals as they were written by early New Testament scribes. Chapter 1 briefly introduces the two basic ways that early scribes wrote numerals, either as longhand words or in alphabetic shorthand (e.g., δύο or β̅), and summarizes the fundamental research question: how did early Christian scribes write numerals and why? The need for such a study is described in chapter 2, which reviews past discussions of the phenomenon of scribal number-writing in New Testament manuscripts. While scholars are aware of the feature and have been eager to draw it into a variety of important discussions, this has been done without any systematic or thorough study of the phenomenon itself. After these introductory chapters, the thesis proceeds in two basic parts: the first isolates the relevant data in question and the second aims to examine those data more fully and from several different angles. Part one is a systematic examination of all numerals, both cardinal and ordinal, that are extant in New Testament manuscripts dated up through the fifth century CE (II–V/VI). The principal concern is when and where numerical shorthand occurs in these manuscripts. Can we discern a Christian style of number-writing that can be distinguished from contemporary scribal customs, and, if so, what is the nature of that style? One aim is to discern the function of number-writing within individual codices, and so its relation to other codicological and scribal features is also considered. Chapter 3 examines numerals in papyrus witnesses and chapter 4 examines them in majuscules written on parchment. Part two then comprises a more thorough investigation of some important issues that arose in part one. Chapter 5 approaches the feature of number-writing from the angle of textual genealogy. Did scribes ever mimic the particular numberforms as they were written in their exemplars or did they choose between them at their own leisure? In either case, what implications does this have for our understanding of textual relationships? Chapter 6 takes a brief detour to evaluate a commonly repeated axiom: that, in Greek copies of the Old Testament scriptures, Jewish scribes consistently used longhand numerals and avoided numerical shorthand. I argue that this idea is invalid and has distorted our understanding of the provenance of some early manuscripts. Chapter 7 then considers whether theological reflection ever influenced a scribe’s decision to employ numerical shorthand. In the same way that devotional practice seems to lie at the origin of the nomina sacra, the group of scribal contractions for divine names and titles, can we detect similar patterns of number-writing that relate to theologically significant concepts and/or referents? I argue that, aside from a handful of isolated yet intriguing examples, no coherent system similar to the nomina sacra can be detected—a conclusion that nonetheless sheds a great deal of light on devotional practices among early Christians. In chapter 8, I describe a hypothesis that seeks to make sense of much of the data observed in part one. In our examination of the numerals in the early manuscripts, four curious features are identified that distinguish Christian scribal practice from that found in other corpora, all relating to numerals (or kinds of numerals) that Christian scribes, as a rule, wrote longhand rather than in shorthand. I argue that this unique adaptation of numerical abbreviation in New Testament manuscripts reflects an awareness and intentional policy to avoid forms that were potentially ambiguous in the reading of those texts, and especially in their public reading. The final portion, chapter 9, then summarizes the thesis, draws out some implications of the study, and suggests areas in which more research would be potentially fruitful

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante 1 hypotheses . The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, a meta-analytic approach that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors possibly related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discusse

Convolutional neural networks can decode eye movement data: A black box approach to predicting task from eye movements

Previous attempts to classify task from eye movement data have relied on model architectures designed to emulate theoretically defined cognitive processes and/or data that have been processed into aggregate (e.g., fixations, saccades) or statistical (e.g., fixation density) features. Black box convolutional neural networks (CNNs) are capable of identifying relevant features in raw and minimally processed data and images, but difficulty interpreting these model architectures has contributed to challenges in generalizing lab-trained CNNs to applied contexts. In the current study, a CNN classifier was used to classify task from two eye movement datasets (Exploratory and Confirmatory) in which participants searched, memorized, or rated indoor and outdoor scene images. The Exploratory dataset was used to tune the hyperparameters of the model, and the resulting model architecture was retrained, validated, and tested on the Confirmatory dataset. The data were formatted into timelines (i.e., x-coordinate, y-coordinate, pupil size) and minimally processed images. To further understand the informational value of each component of the eye movement data, the timeline and image datasets were broken down into subsets with one or more components systematically removed. Classification of the timeline data consistently outperformed the image data. The Memorize condition was most often confused with Search and Rate. Pupil size was the least uniquely informative component when compared with the x- and y-coordinates. The general pattern of results for the Exploratory dataset was replicated in the Confirmatory dataset. Overall, the present study provides a practical and reliable black box solution to classifying task from eye movement data