MARSThe MARS digital repository system captures, stores, indexes, preserves, and distributes digital research material.http://mars.gmu.edu:802019-08-20T08:45:45Z2019-08-20T08:45:45ZSpiking Dynamics Observed in Three NeuronsPerera, Kevinhttp://hdl.handle.net/1920/115662019-08-19T20:14:00Z2019-01-01T00:00:00ZSpiking Dynamics Observed in Three Neurons
Perera, Kevin
A leaky integrate and fire (LIF) model is a basic mathematical model designed to simulate a neuron. The simplicity and relative ease with which network architectures can be constructed using patterns of LIF neurons make it a common choice for modeling. The RC circuit is used as the basis of the model. By way of algebraic manipulation, we can solve I(t) = (u(t)–u(rest)/R + C*du/dt, where u-u(rest) is the voltage across the resistor. The product of C*du/dt is the capacitive current. Through integration and Ohm’s Law, we can derive membrane potential in a linear differential equation[1]. Delgado et al. aims to model the membrane potential of the LIF neuron through a random process known as Ornstein-Uhlenbeck. Through the random variable Tf, we can predict the time between neuron firings. We can find an estimator of the average firing rate of the neuron[2]. The Nengo framework is an open-source neural modeling architecture leveraged across Python and the TensorFlow libraries, which are used for developing artificial intelligence models. Using LIF neurons, it is possible to create a high-level model of the brain[3]. More attention is spent by the modeler to conceptualize neural architecture; however, the drawback to using the software is that we are unable to manipulate dynamics on the neuron-to-neuron scale, making Nengo more appropriate for large scale modeling.
2019-01-01T00:00:00ZNetwork Neighborhood Analysis For Detecting Anomalies in Time Series of GraphsGoswami, Suchismitahttp://hdl.handle.net/1920/115652019-08-14T06:28:01ZNetwork Neighborhood Analysis For Detecting Anomalies in Time Series of Graphs
Goswami, Suchismita
Around terabytes of unstructured electronic data are generated every day from twitter networks, scientific collaborations, organizational emails, telephone calls and websites. Ex- cessive communications in communication networks, particularly in organizational e-mail networks, continue to be a major problem. In some cases, for example, Enron e-mails, frequent contact or excessive activities on interconnected networks lead to fraudulent activ- ities. Analyzing the excessive activity in a social network is thus important to understand the behavior of individuals in subregions of a network. In a social network, anomalies can occur as a result of abrupt changes in the interactions among a group of individuals. There- fore, one needs to develop methodologies to analyze and detect excessive communications in dynamic social networks. The motivation of this research work is to investigate the ex- cessive activities and make inferences in dynamic sub networks. In this dissertation work, I implement new methodologies and techniques to detect excessive communications, topic activities and the associated influential individuals in the dynamic networks obtained from organizational emails using scan statistics, multivariate time series models and probabilistic topic modeling. Three major contributions have been presented here to detect anomalies of dynamic networks obtained from organizational emails.
At first, I develop a different approach by invoking the log-likelihood ratio as a scan statistic with overlapping and variable window sizes to rank the clusters, and devise a two-step scan process to detect the excessive activities in an organizations e-mail network as a case study. The initial step is to determine the structural stability of the e-mail count time series and perform differencing and de-seasonalizing operations to make the time series stationary, and obtain a primary cluster using a Poisson process model. I then extract neighborhood ego subnetworks around the observed primary cluster to obtain more refined cluster by invoking the graph invariant betweenness as the locality statistic using the binomial model. I demonstrate that the two-step scan statistics algorithm is more scalable in detecting excessive activity in large dynamic social networks.
Secondly, I implement for the first time the multivariate time series models to detect a group of influential people and their dynamic relationships that are associated with excessive communications, which cannot be assessed using scan statistics models. For the multivariate modeling, a vector auto regressive (VAR) model has been employed in time series of subgraphs in e-mail networks constructed using the graph edit distance, as the nodes or vertices of the subgraphs are interrelated. Anomalies or excessive communications are assessed using the residual thresholds greater than three times the standard deviations,obtained from the fitted time series models.
Finally, I devise a new method of detecting excessive topic activities from the unstruc-
tured text obtained from e-mail contents by combining the probabilistic topic modeling and scan statistics algorithms. Initially, I investigate the major topics discussed using the probabilistic modeling, such as latent Dirichlet allocation (LDA) modeling, then employ scan statistics to assess the excessive topic activities, which has the largest log likelihood ratio in the neighborhood of primary cluster.
These analyses provide new ways of detecting the excessive communications and topic flow through the influential vertices in a dynamic network, and can be extended in other dynamic social networks to critically investigate excessive activities.
A Gospel of Health and Salvation: Modeling the Religious Culture of Seventh-day Adventism, 1843-1920Wieringa, Jerihttp://hdl.handle.net/1920/115642019-08-05T18:25:11ZA Gospel of Health and Salvation: Modeling the Religious Culture of Seventh-day Adventism, 1843-1920
Wieringa, Jeri
A Gospel of Health and Salvation is a work of digital history — defined as the critical application of computational technologies to the study of the past — focused on the relationship between time and gender in Seventh-day Adventism. In it I explore the puzzle of the denomination’s prophet and religious leader, Ellen White, and her varied and seemingly contradictory writings on the role of women in the denomination. One of a few women religious leaders in nineteenth-century America, White is difficult to place within the history of American religion. Rising to prominence at the end of the Second Great Awakening, White promoted a vision of gender and women’s participation in the work of salvation that fails to fit neatly into either histories of American feminism or histories of domesticity. Discussing White and her place in American religious history requires a different approach.
Using computational text analysis to find broad patterns in the denomination’s periodical record, I highlight three cycles of end-times expectation that shaped the complex vision of gender articulated by White and other denomination leaders during the first seventy years of the denomination. These cycles enable me to bring together two theoretical frameworks often used to analyze the development of religious movements. Rather than a linear trajectory from religious sect to denomination, and concurrently from expansive understandings of gender to restrictive ones, the waves of end-times expectation opened space for alternative and expansive visions of gender at a number of points in the denomination’s early history.
Additionally, I argue for the scholarliness of the computational work that grounds my historical analysis. Rather than neutral, the work of selecting the corpus, preparing the text for analysis, selecting modeling algorithms, visualizing the resulting model, and interpreting the results represents the first phase of interpretation and shapes the possibilities of the overall project. To make this multilayered argument, I created the dissertation as a website, rather than a traditional document. Hosted at http://dissertation.jeriwieringa.com, the web interface interweaves the technical, visual, and narrative aspects of the dissertation arguments. The site brings together a topic model of the denomination’s periodical literature, the code used to create and analyze the model, and four interpretive essays. Together these constitute the body of work that is A Gospel of Health and Salvation.
Cultivating Teacher-Librarians through a Community of PracticeKirker, Maoria J.http://hdl.handle.net/1920/115632019-08-05T18:10:16Z2019-01-01T00:00:00ZCultivating Teacher-Librarians through a Community of Practice
Kirker, Maoria J.
2019-01-01T00:00:00Z