On the Images of Braid Group Representations Coming from Braided Fusion Categories

Braided fusion categories are algebraic structures with strong ties to the representation theory of finite groups, Hopf algebras, and quantum groups. These structures also have strong connections with braid groups and low-dimensional topology. Recently, braid group representations coming from braided fusion categories have become a topic of interest in areas of condensed matter physics and topological quantum computation. Particularly interesting are the properties of the images of these representations. Calculations to determine the finiteness of these images have been performed for a few cases. A class of braided fusion categories coming from finite groups (group-theoretical) has been shown to yield finite images. We show that the images of braid group representations coming from the larger class of weakly group-theoretical braided fusion categories are also finite. We then compute the images of the pure braid groups for some specific representations

Multi-source ontology-based maize phenotype search engine

Title from PDF of title page (University of Missouri--Columbia, viewed on September 9, 2010).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. Chi-Ren Shyu.M.S. University of Missouri--Columbia 2009.In the midst of this genomics era, major plant genome databases are collecting massive amounts of heterogeneous information, including sequence data, gene product information, as well as images and descriptions of mutant phenotypes. While basic browsing and search capabilities are available to allow researchers to query and peruse the names and attributes of stored data, advanced search mechanisms that can take advantage of textual descriptions of various types of stored data are nonexistent. Furthermore, though much time and effort have been afforded to the development of plant-related ontologies, the knowledge embedded in these ontologies remains largely unused in available plant search mechanisms. Addressing both of these issues, we have developed a unique search engine for the phenotypes in MaizeGDB. This advanced search mechanism exploits the content and structure of available domain ontologies for the purposes of query enrichment, with currently both the Plant Ontology and Gene Ontology being utilized. The search engine also has the flexibility to integrate various text description sources to aid the user in retrieving desired phenotype information. This framework can be generalized to any domain with a domain-specific ontology or to sets of text sources that are heterogeneous and interconnected.Includes bibliographical references

Letter from the Editors

[Excerpt] The Editorial Team is proud to release this 2016 14th Annual Volume of the Cornell Real Estate Review. This year’s issue explores a wide range of topics, including the deployment of new technologies in multifamily properties, the effects of autonomous vehicles on real estate, and the continued ramifications of the housing crisis through the legal tactics of certain mortgage lenders. Also included, a recent repositioning project– the unique turnaround of a former casino hotel property in Reno, Nevada. Furthermore, this release includes a discussion of value-added multifamily investment strategy, an analysis of the impact of rapid transit on the residential market in Hudson County, New Jersey, and a summary of federal affordable housing incentive programs in the United States. This year’s Pathways features an interview with Toll Brothers Division President Karl Mistry (Baker ’04), and the Baker Viewpoint piece explores the concept of curtailment mortgages

High-throughput analysis and advanced search for visually-observed phenotypes

Title from PDF of title page (University of Missouri--Columbia, viewed on May 13, 2013).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Chi-Ren ShyuIncludes bibliographical references.Vita.Ph. D. University of Missouri--Columbia 2012."May 2012"The trend in many scientific disciplines today, especially in biology and genetics, is towards larger scale experiments in which a tremendous amount of data is generated. As imaging of data becomes increasingly more popular in experiments related to phenotypes, the ability to perform high-throughput big data analyses and to efficiently locate specific information within these data based on increasingly complicated and varying search criteria is of great importance to researchers. This research develops several methods for high-throughput phenotype analysis. This notably includes a registration algorithm called variable object pattern matching for mapping multiple indistinct and dynamic objects across images and detecting the presence of missing, extra, and merging objects. Research accomplishments resulted in a number of unique advanced search mechanisms including a retrieval engine that integrates multiple phenotype text sources and domain ontologies and a search method that retrieves objects based on temporal semantics and behavior. These search mechanisms represent the first of their kind in the phenotype community. While this computational framework is developed primarily for the plant community, it has potential applications in other domains including the medical field.Includes bibliographical references