Collection development of non-Christian religious holiday books in elementary school libraries

While many Christian holiday books for children are widely reviewed and available from mainstream publishers, it is more difficult to find and evaluate books about non-Christian holidays. Elementary library collections may under-represent non-Christian religious books because of a lack of school library media specialists\u27 personal knowledge about non-Christian holidays. This study surveyed school library media specialists through a written questionnaire about the variety and size of their library\u27s current collection of non-fiction books about non-Christian religious holidays, and determined how school library media specialists approach collection development in this area. The sample and population consisted of 124 elementary public school librarians in Atlantic, Ocean, and Cape May Counties. Results showed that the collections of non-fiction religious holiday books were not very diverse, with more than half of the books owned reflecting Christian holidays. A large number of school library media specialists considered treatment of material and reading level as important criteria when selecting non-fiction books about religious holidays. Follett\u27s Titlewave was the most commonly used selection source for nonfiction religious holiday books, with almost 80% of respondents using it

Parallel Adaptive Collapsed Gibbs Sampling

Rao-Blackwellisation is a technique that provably improves the performance of Gibbs sampling by summing-out variables from the PGM. However, collapsing variables is computationally expensive, since it changes the PGM structure introducing factors whose size is dependent upon the Markov blanket of the variable. Therefore, collapsing out several variables jointly is typically intractable in arbitrary PGM structures. This thesis proposes an adaptive approach for Rao-Blackwellisation, where additional parallel Markov chains are defined over different collapsed PGM structures. The collapsed variables are chosen based on their convergence diagnostics. Adding chains requires re-burn-in the chain, thus wasting samples. To address this, new chains are initialized from a mean field approximation for the distribution, that improves over time, thus reducing the burn-in period. The experiments on several UAI benchmarks shows that this approach is more accurate than state-of-the-art inference systems such as Merlin which have previously won the UAI inference challenge

Consumer Preferences for Locally Made Specialty Food Products Across Northern New England

Does willingness to pay a premium for local specialty food products differ between consumers in Maine, New Hampshire, and Vermont? Two food categories are investigated: low-end ($5) and high-end ($20) products. Premia estimates are compared across states and across base prices within states using dichotomous choice contingent valuation methods. Results suggest that the three states of northern New England have many similarities, including comparable price premia for the lower-priced good. However, there is some evidence that the premium for the higher-priced good is greater for the pooled Vermont and Maine treatment than for the New Hampshire treatment. Vermont and New Hampshire residents are willing to pay a higher premium for a $20 than for a$5 food item, while the evidence suggests that Maine residents are not.local specialty foods, willingness to pay, contingent valuation, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety,

TEMPORAL PAYMENT ISSUES IN CONTINGENT VALUATION ANALYSIS

We analyze agent response to disparate payment schedules for protection of critical habitat units for the Seller sea lion in Alaska. The model allows for identification of implicit and explicit discount rates using information from a system of maximum likelihood equations. Testing is done using data for one, five, and fifteen year payment treatments.Research Methods/ Statistical Methods,

NU-AIR -- A Neuromorphic Urban Aerial Dataset for Detection and Localization of Pedestrians and Vehicles

This paper presents an open-source aerial neuromorphic dataset that captures pedestrians and vehicles moving in an urban environment. The dataset, titled NU-AIR, features 70.75 minutes of event footage acquired with a 640 x 480 resolution neuromorphic sensor mounted on a quadrotor operating in an urban environment. Crowds of pedestrians, different types of vehicles, and street scenes featuring busy urban environments are captured at different elevations and illumination conditions. Manual bounding box annotations of vehicles and pedestrians contained in the recordings are provided at a frequency of 30 Hz, yielding 93,204 labels in total. Evaluation of the dataset's fidelity is performed through comprehensive ablation study for three Spiking Neural Networks (SNNs) and training ten Deep Neural Networks (DNNs) to validate the quality and reliability of both the dataset and corresponding annotations. All data and Python code to voxelize the data and subsequently train SNNs/DNNs has been open-sourced.Comment: 20 pages, 5 figure

The Amazing Liaison: Innovative Ideas to Engage Diverse Populations with STEM

Librarians have a unique opportunity to engage our communities in meaningful educational experiences that foster life-long learning as well as support STEM education for under-served populations. By serving as educational centers for the community, libraries can bridge the STEM literacy and achievement gaps by providing access to not only a variety of resources, but also other members of our community that can benefit from educational partnerships and programming. This presentation will include innovative ideas for expanding existing STEM programs and services that can reach library users across multi-generational, socioeconomic, educational, and cultural backgrounds as well as connecting the diverse populations at our home institutions in academia as program liaisons