Martin Luther King Jr. and Leadership: Building the Beloved Communities within the Academy

Leaders seek to build communities to further the work of universities, but vibrant communities embracing our differences and, at times, animosities remain elusive. However, King’s (Smith & Zepp, 1974) concept of the Beloved Community provides an image about how this might be possible. While abstract, King’s idea offers compelling linkages to servant leadership (Greenleaf, 1977) and how to counter the destructive, rivalistic behaviors (Kirwan, 2005) prevalent in higher education. King (1991) outlines three principles: 1) the sacredness of humans, 2) the need for freedom, and 3) the recognition of interdependence. Each principle is described and applied to the higher education context and then explored through the lenses of mimetic theory (Girard, 1989) and servant leadership

Martin Luther King Jr. and Leadership: Building the Beloved Communities within the Academy

Leaders seek to build communities to further the work of universities, but vibrant communities embracing our differences and, at times, animosities remain elusive. However, King’s (Smith & Zepp, 1974) concept of the Beloved Community provides an image about how this might be possible. While abstract, King’s idea offers compelling linkages to servant leadership (Greenleaf, 1977) and how to counter the destructive, rivalistic behaviors (Kirwan, 2005) prevalent in higher education. King (1991) outlines three principles: 1) the sacredness of humans, 2) the need for freedom, and 3) the recognition of interdependence. Each principle is described and applied to the higher education context and then explored through the lenses of mimetic theory (Girard, 1989) and servant leadership

Content-based image retrieval for brain MRI: An image-searching engine and population-based analysis to utilize past clinical data for future diagnosis

AbstractRadiological diagnosis is based on subjective judgment by radiologists. The reasoning behind this process is difficult to document and share, which is a major obstacle in adopting evidence-based medicine in radiology. We report our attempt to use a comprehensive brain parcellation tool to systematically capture image features and use them to record, search, and evaluate anatomical phenotypes. Anatomical images (T1-weighted MRI) were converted to a standardized index by using a high-dimensional image transformation method followed by atlas-based parcellation of the entire brain. We investigated how the indexed anatomical data captured the anatomical features of healthy controls and a population with Primary Progressive Aphasia (PPA). PPA was chosen because patients have apparent atrophy at different degrees and locations, thus the automated quantitative results can be compared with trained clinicians' qualitative evaluations. We explored and tested the power of individual classifications and of performing a search for images with similar anatomical features in a database using partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA). The agreement between the automated z-score and the averaged visual scores for atrophy (r = 0.8) was virtually the same as the inter-evaluator agreement. The PCA plot distribution correlated with the anatomical phenotypes and the PLS-DA resulted in a model with an accuracy of 88% for distinguishing PPA variants. The quantitative indices captured the main anatomical features. The indexing of image data has a potential to be an effective, comprehensive, and easily translatable tool for clinical practice, providing new opportunities to mine clinical databases for medical decision support

Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy

The proliferation of big data makes it possible to better target city services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction algorithms. Cities could hire consultants, but a cheaper alternative is to crowdsource competence by making data public and offering a reward for the best algorithm. A simple model suggests that open tournaments dominate consulting contracts when cities can tolerate risk and when there is enough labor with low opportunity costs. We also report on an inexpensive Boston-based restaurant tournament, which yielded algorithms that proved reasonably accurate when tested "out-of-sample" on hygiene inspections

Co-Evolutionary Learning for Cognitive Computer Generated Entities

In this paper, an approach is advocated to use a hybrid approach towards learning behaviour for computer generated entities (CGEs) in a serious gaming setting. Hereby, an agent equipped with cognitive model is used but this agent is enhanced with Machine Learning (ML) capabilities. This facilitates the agent to exhibit human like behaviour but avoid an expert having to define all parameters explicitly. More in particular, the ML approach utilizes co-evolution as a learning paradigm. An evaluation in the domain of one-versus-one air combat shows promising results

Space Use and Relative Habitat Selection for Immature Green Turtles Within a Caribbean Marine Protected Area

Background A better understanding of sea turtle spatial ecology is critical for the continued conservation of imperiled sea turtles and their habitats. For resource managers to develop the most effective conservation strategies, it is especially important to examine how turtles use and select for habitats within their developmental foraging grounds. Here, we examine the space use and relative habitat selection of immature green turtles (Chelonia mydas) using acoustic telemetry within the marine protected area, Buck Island Reef National Monument (BIRNM), St. Croix, United States Virgin Islands. Results Space use by turtles was concentrated on the southern side of Buck Island, but also extended to the northeast and northwest areas of the island, as indicated by minimum convex polygons (MCPs) and 99%, 95%, and 50% kernel density estimations (KDEs). On average space use for all categories was \u3c 3 km2 with mean KDE area overlap ranging from 41.9 to 67.7%. Cumulative monthly MCPs and their proportions to full MCPs began to stabilize 3 to 6 detection months after release, respectively. Resource selection functions (RSFs) were implemented using a generalized linear mixed effects model with turtle ID as the random effect. After model selection, the accuracy of the top model was 77.3% and showed relative habitat selection values were highest at shallow depths, for areas in close proximity to seagrass, and in reef zones for both day and night, and within lagoon zones at night. The top model was also extended to predict across BIRNM at both day and night. Conclusion More traditional acoustic telemetry analyses in combination with RSFs provide novel insights into animal space use and relative resource selection. Here, we demonstrated immature green turtles within the BIRNM have small, specific home ranges and core use areas with temporally varying relative selection strengths across habitat types. We conclude the BIRNM marine protected area is providing sufficient protection for immature green turtles, however, habitat protection could be focused in both areas of high space use and in locations where high relative selection values were determined. Ultimately, the methodologies and results presented here may help to design strategies to expand habitat protection for immature green turtles across their greater distribution