Predicting the educational achievement of preschool and kindergarten children from the cognitive subtests of early screening profiles.

The purpose of the study was to collect predictive validity data on the cognitive subtests and composite of Early Screening Profiles, a screening instrument that will be published in 1990. Data collection involved 135 children, ages 3-6 through 6-11. The scores on Early Screening Profiles were compared to scores on the Achievement Scale of the Kaufman Assessment Battery for Children (K-ABC), the Peabody Picture Vocabulary Test-Revised (PPVT-R), and, for the 85 children in kindergarten or grade one at the time of follow-up testing, a teacher rating scale, Teacher Rating of Academic Performance (TRAP). Time between testing ranged from 5 $1\over2$ to 8 months. For the population studied, statistically significant, strong correlations of.75,.73, and.70 were found between the composite of Early Screening Profiles and K-ABC Achievement, PPVT-R, and TRAP (p $\u3c$.01). Strong or moderate correlations, all significant at the.01 level, resulted when Early Screening Profiles cognitive subtests were compared to criterion subtests. High agreement rates were found for standard scores of one standard deviation above the mean (82%) and one standard deviation below the mean (84%). Comparison of the Early Screening Profiles cognitive composite score with the total scores of all three criterion measures yielded average specificity and sensitivity rates of.80 and.74, respectively, for scores of 115 or higher. For scores of 85 or lower, the average specificity was high (.97) and the average sensitivity rate was modest (.32). No significant differences emerged based on sex. The older group of children scored higher than the younger on the K-ABC Achievement Scale. Research results indicate that the cognitive subtests and composite of Early Screening Profiles show promise of becoming useful and valid additions to the field of early childhood screening

The Number of Seymour Vertices in Random Tournaments and Digraphs

Seymour's distance two conjecture states that in any digraph there exists a vertex (a "Seymour vertex") that has at least as many neighbors at distance two as it does at distance one. We explore the validity of probabilistic statements along lines suggested by Seymour's conjecture, proving that almost surely there are a "large" number of Seymour vertices in random tournaments and "even more" in general random digraphs.Comment: 14 page

Situating Urban Agriculture: What, Where, and Why in New York City

Urban agriculture has the potential to address multiple concerns simultaneously in dense urban spaces. Where and how urban agricultural interventions are sited within cities are critical questions to ask as governments, municipalities, and urban planners address the need for healthy and resilient food systems as well as environmental resiliency. This thesis explores the potential for planners to utilize digital mapping methodologies and multi-criteria decision making analysis (MCDA) in a way in which socio-economically vulnerable neighborhoods and neighborhoods facing environmental vulnerability can be addressed simultaneously. This research demonstrates this process by utilizing a geospatial mapping model that incorporates multiple layers of information on the current state of food access, rates of health, economic need, and water and heat risk that New York City currently exhibits. The results of this model, run multiple times, are applied to each of the tax lots in New York City, thus identifying exactly where the greatest socio-economic need and environmental vulnerability exists. The methodology used in this thesis includes the collection, classification, and rasterization of a series of decision layers that feed into five larger components of analysis. These components are combined to generate an overall map that displays socio-economic need and another that displays environmental vulnerability as the combination of water and heat vulnerability. When analyzed together different sets of core targeted areas are identified and evaluated for potential available and appropriate land and rooftop areas that can be conducive to three different types of urban agriculture — ground level farms, rooftop open-air farms and rooftop greenhouses. This methodology builds on previous methodologies developed by the Urban Design Lab at Columbia University / The Earth Institute that evaluate the potential for urban agriculture in New York City (published in 2011 and 2013). This thesis advocates for the development of a comprehensive city-wide plan for the application of urban agriculture as a networked system of open spaces and productive greenhouses that have the potential to offer co-benefits through proximity, clustering, and strategic siting within the core targeted areas. This plan would ideally be supported by the development of open space zoning and ecological corridor zoning districts. While the data used here supports lot-level and high resolution decision making, it ultimately identifies areas of opportunity which can be starting points for areas of participatory processes and a set of community engagement practices that may be able to address issues such as private owner development constraints in the potential siting of urban agriculture. Mapping and data collection is one part of the decision making process in planning but it is not the end goal. How findings of this type of mapping study are actualized on the ground or made actionable should be done with community involvement. In this regard, utilizing GIS and MCDA with public participation can be seen as a community empowerment strategy whereby (a) communities that can benefit from an intervention are first identified and incorporated into the overall process and (b) the maps generated can be used to advocate for specific types of development that will offer co-benefits. Regardless of the issue being analyzed, this thesis concludes that there are immense benefits to using digital mapping methodologies in making large city-wide decisions and in incorporating the public and non-expert voices into the conversation

Experimental study of energy-minimizing point configurations on spheres

In this paper we report on massive computer experiments aimed at finding spherical point configurations that minimize potential energy. We present experimental evidence for two new universal optima (consisting of 40 points in 10 dimensions and 64 points in 14 dimensions), as well as evidence that there are no others with at most 64 points. We also describe several other new polytopes, and we present new geometrical descriptions of some of the known universal optima.Comment: 41 pages, 12 figures, to appear in Experimental Mathematic

Overview: Computer vision and machine learning for microstructural characterization and analysis

The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.Comment: submitted to Materials and Metallurgical Transactions

The architecture of convention hotels in the United States, 1940-1976

The convention hotel emerged as a distinct building type in the years of the Second World War and its aftermath. The earliest examples of convention hotels were distinguished from their pre-war counterparts by the design of their meeting facilities and the layout of public areas. In these projects, new techniques in architectural design were used only where they were critical to hotel operation. As the number of hotels increased in the fifties, competition for business required new approaches to design. For some hotel companies, the policy was to improve a hotel's capability for handling groups in order to attract sizable conventions to the property. In resort cities, hotel operators found that innovations in style and decor enhanced popular appeal, thereby increasing business. In the late fifties and early sixties, the participation of developers and corporations outside the hotel industry in building new properties brought about an increasing diversity. In the projects, design was based on potential profitability regardless of traditional hotel principles. At the same time, the inclusion of convention hotels in large-scale urban developments called for innovations in site planning and expansion of public amenities. While these hotels and their predecessors of the fifties rarely displayed architectural excellence, their contribution to guidelines for modern hotel design was critical to later, more spectacular developments of the building type. One project of the late sixties, the Hyatt Regency Atlanta, dramatically explored the potential of new approaches to hotel architecture. The astounding design of the public spaces, the integration of the hotel with surrounding development, and the hotel's subsequent popularity have served to transform this commercial building type into significant public architecture. The success of the. Atlanta Hyatt has led to a repetition of the concept by the hotel company, while inspiring new experiments by the architect. In the early seventies, a series of hotels of remarkable design opened in the United States. Their public appeal confirmed the value of good architecture to the successful operation of a hotel. Hotel professionals were forced to reconsider the necessary elements of hotel design, while architects were encouraged to re-examine the possibilities inherent in this commercial building type

Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data

We propose instance segmentation as a useful tool for image analysis in materials science. Instance segmentation is an advanced technique in computer vision which generates individual segmentation masks for every object of interest that is recognized in an image. Using an out-of-the-box implementation of Mask R-CNN, instance segmentation is applied to images of metal powder particles produced through gas atomization. Leveraging transfer learning allows for the analysis to be conducted with a very small training set of labeled images. As well as providing another method for measuring the particle size distribution, we demonstrate the first direct measurements of the satellite content in powder samples. After analyzing the results for the labeled data dataset, the trained model was used to generate measurements for a much larger set of unlabeled images. The resulting particle size measurements showed reasonable agreement with laser scattering measurements. The satellite measurements were self-consistent and showed good agreement with the expected trends for different samples. Finally, we provide a small case study showing how instance segmentation can be used to measure spheroidite content in the UltraHigh Carbon Steel Database, demonstrating the flexibility of the technique.Comment: 16 pages, 12 figure