Yec Fi Matching System
Yec Fi Matching System
through this grant, supported by the college of agriculture and life sciences at the university of north carolina at chapel hill and the southern arts foundation, we are able to offer a limited number of matching grants for visual artists to create, develop, and promote arts projects that encourage community engagement, beautify our public spaces, and provide arts learning experiences for youth. the 21st annual matching grant competition will be the last in the program’s history. read on for more details on how to apply and the process.
in addition to the priorities and criteria above, applicants must have made a commitment to provide a 75% match of the project cost with funds raised by the applicant themselves. the matching grant funds must be used for the specific project(s) listed in the application and to be completed by the project’s start date. matching grant funds are not to be used for administrative costs, other expenses, or for purposes other than the project(s) listed in the application.
the matching grant is not intended to be used to replace existing grants, or to fund current projects. matching grant funds may not be used to fund scholarships, loans, or grants to individuals. the matching grant may be used for the following:
the matching grant cannot be used to fund the purchase of property. the matching grant funds cannot be used to pay for insurance, maintenance costs, or for any other operating costs of the property. matching grant funds may be used for the following:
applicants must submit a current non-profit tax form, along with a completed application form. applicants must complete the application form, which includes matching requirements, as outlined in the application instructions. program application instructions and forms are available on the southern arts foundation website. applications are available for download online and can be submitted through the southern arts foundation website.
In another approach to propensity score matching called matched-trees, each candidate treated subject is matched with all untreated subjects with the closest propensity score within a specified distance. As a result, multiple untreated subjects may be matched to each treated subject. The authors evaluated the performance of matched-trees using Monte Carlo simulations. The mean squared error of the estimated treatment effect decreased with increasing numbers of unmatched subjects. The authors conclude that matched-trees can improve the performance of propensity-score matching.
Algorithms and data structures for the efficient matching of sampled data are of increasing importance. This survey article uses the R programming language to describe a class of covering algorithms for mixed clustering and data sampling problems that is suitable for large-scale spatial and social network data. The authors conclude that many of the theory and data-analysis techniques used in traditional matching can be adapted to data-sampling problems. They suggest using the data sampling paradigm to extend and adapt methods for constructing cluster and network samples. They provide an implementation of a novel data-sampling algorithm, called quasi-random covering, that can randomly cover a set of data points with a (1 – epsilon) probability while achieving an expectation of (1+epsilon) covering. This algorithm is applicable to problems involving clustering, network sampling, and coverage of geographic areas. Finally, they provide a suite of functions for implementation and testing of quasi-random covering algorithms.