It\u27s A Long Way To Berlin, But We\u27ll Get There

Soldiers marching in formation; Photograph of Will J. Wardhttps://scholarsjunction.msstate.edu/cht-sheet-music/11456/thumbnail.jp

On the Accuracy of Hyper-local Geotagging of Social Media Content

Social media users share billions of items per year, only a small fraction of which is geotagged. We present a data- driven approach for identifying non-geotagged content items that can be associated with a hyper-local geographic area by modeling the location distributions of hyper-local n-grams that appear in the text. We explore the trade-off between accuracy, precision and coverage of this method. Further, we explore differences across content received from multiple platforms and devices, and show, for example, that content shared via different sources and applications produces significantly different geographic distributions, and that it is best to model and predict location for items according to their source. Our findings show the potential and the bounds of a data-driven approach to geotag short social media texts, and offer implications for all applications that use data-driven approaches to locate content.Comment: 10 page

Up And Down Town Every Night

Man and woman being driven by chauffeur in automobile; Men and women riding trolley; Illustration of buildings, traffic post, moon, and clouds in backgroundhttps://scholarsjunction.msstate.edu/cht-sheet-music/11331/thumbnail.jp

Spanish Dancer From Madrid

https://digitalcommons.library.umaine.edu/mmb-vp/5920/thumbnail.jp

It\u27s a long way to Berlin, but we\u27ll get there!

https://digitalcommons.library.umaine.edu/mmb-vp/1929/thumbnail.jp

Space Programming for College of Education Building

This report is intended as a working tool for those involved in planning and is at this stage yet unfinished and still being refined. It will stay in this form, somewhat, until the building is occupied. It is reproduced at this time to show the direction of our efforts and to encourage constructive criticism and suggestions on everything except spelling, grammar and form.https://digitalrepository.unm.edu/archives_documents/1000/thumbnail.jp

Feature-based Vector Field Representation and Comparison

In recent years, simulations have steadily replaced real world experiments in science and industry. Instead of performing numerous arduous experiments in order to develop new products or test a hypothesis, the system to be examinded is described by a set of equations which are subsequently solved within the simulation. The produced vector fields describe the system's behavior under the conditions of the experiment. While simulations steadily increase in terms of complexity and precision, processing and analysis are still approached by the same long-standing visual techniques. However, these are limited by the capability of the human visual system and its abilities to depict large, multi-dimensional data sets. In this thesis, we replace the visual processing of data in the traditional workflow with an automated, statistical method. Cluster algorithms are able to process large, multi-dimensional data sets efficiently and therefore resolve the limitations we faced so far. For their application to vector fields we define a special feature vector that describes the data comprehensively. After choosing an appropriate clustering method, the vector field is split into its features. Based on these features the novel flow graph is constructed. It serves as an abstract representation of the vector field and gives a detailed description of its parts as well as their relations. This new representation enables a quantitative analysis and describes the input data. Additionally, the flow graphs are comparable to each other through a uniform description, since techniques of graph theory may be applied. In the traditional workflow, visualization is the bottleneck, because it is built manually by the user for a specific data set. In consequence the output is diminished and the results are likely to be biased by the user. Both issues are solved by our approach, because both the feature extraction and the construction of the flow graph are executed in an un-supervised manner. We will compare our newly developed workflow with visualization techniques based on different data sets and discuss the results. The concluding chapter on the similarity and comparison of graphs applies techniques of graph theory and demonstrates the advantages of the developed representation and its use for the analysis of vector fields using flow graphs