Faculty and Staff

Two classes of scaling behaviours, namely the super-linear scaling of links or activities, and the sub-linear scaling of area, diversity, or time elapsed with respect to size have been found to prevail in the growth of complex networked systems. Despite some pioneering modelling approaches proposed for specific systems, whether there exists some general mechanisms that account for the origins of such scaling behaviours in different contexts, especially in socioeconomic systems, remains an open question. We address this problem by introducing a geometric network model without free parameter, finding that both super-linear and sub-linear scaling behaviours can be simultaneously reproduced and that the scaling exponents are exclusively determined by the dimension of the Euclidean space in which the network is embedded. We implement some realistic extensions to the basic model to offer more accurate predictions for cities of various scaling behaviours and the Zipf distribution reported in the literature and observed in our empirical studies. All of the empirical results can be precisely recovered by our model with analytical predictions of all major properties. By virtue of these general findings concerning scaling behaviour, our models with simple mechanisms gain new insights into the evolution and development of complex networked systems.

While PhD dissertations are typically accessible many other terminal degree projects remain invisible and inaccessible to a greater audience. Over the past year and a half, librarians at Arizona State University collaborated with faculty and departmental administrators across a variety of fields to develop and create institutional repository collections that highlight and authoritatively share this type of student scholarship with schools, researchers, and future employers. This poster will present the benefits, challenges, and considerations required to successfully implement and manage these collections of applied final projects or capstone projects. Specifically, issues/challenges related to metadata consistency, faculty buy-in, and developing an ingest process, as well as benefits related to increased visibility and improved educational and employment opportunities will be discussed. This interactive presentation will also discuss lessons learned from the presenter’s experiences in context of how they can easily apply to benefit their respective institutions.

Digital technology has enabled us to record and share our memories and histories faster and in greater numbers than previously imagined. However digital files rely on hardware, software, and descriptive information to be used. As formats change and equipment to read them goes out of use we are all challenged to connect our present to our future. How long do you want your digital files to last? Decades or even a few years from now will you still be able to access and enjoy those pictures, documents and other digital items you create today?

Libraries, museums and archives spend countless hours and resources preserving physical items from the past and present, but may be forfeiting the longevity of our digital work and connecting to future generations through unintended neglect. Using practical examples and employing best practices of research institutions, participants will learn important first steps to digital preservation including the importance of metadata to personal history, recommended file formats, and approaches they can immediately use to ensure the work they create today will still be enjoyed tomorrow. Help yourself, your organization, and your patrons continue to connect their digital heritage to the generations yet to come.

Given a complex geospatial network with nodes distributed in a two-dimensional region of physical space, can the locations of the nodes be determined and their connection patterns be uncovered based solely on data? We consider the realistic situation where time series/signals can be collected from a single location. A key challenge is that the signals collected are necessarily time delayed, due to the varying physical distances from the nodes to the data collection centre. To meet this challenge, we develop a compressive-sensing-based approach enabling reconstruction of the full topology of the underlying geospatial network and more importantly, accurate estimate of the time delays. A standard triangularization algorithm can then be employed to find the physical locations of the nodes in the network. We further demonstrate successful detection of a hidden node (or a hidden source or threat), from which no signal can be obtained, through accurate detection of all its neighbouring nodes. As a geospatial network has the feature that a node tends to connect with geophysically nearby nodes, the localized region that contains the hidden node can be identified.