George Mason University

About: George Mason University is a education organization based out in Fairfax, Virginia, United States. It is known for research contribution in the topics: Population & Politics. The organization has 12490 authors who have published 39989 publications receiving 1301688 citations. The organization is also known as: Mason & George Mason.

Lattice-based access control models

Abstract: Lattice-based access control models were developed in the early 1970s to deal with the confidentiality of military information. In the late 1970s and early 1980s, researchers applied these models to certain integrity concerns. Later, application of the models to the Chinese Wall policy, a confidentiality policy unique to the commercial sector, was demonstrated. A balanced perspective on lattice-based access control models is provided. Information flow policies, the military lattice, access control models, the Bell-LaPadula model, the Biba model and duality, and the Chinese Wall lattice are reviewed. The limitations of the models are identified. >

Causal Explanation, Qualitative Research, and Scientific Inquiry in Education

Abstract: A National Research Council report, Scientific Research in Education, has elicited considerable criticism from the education research community, but this criticism has not focused on a key assumption of the report—its Humean, regularity conception of causality. It is argued that this conception, which also underlies other arguments for “scientifically-based research,” is narrow and philosophically outdated, and leads to a misrepresentation of the nature and value of qualitative research for causal explanation. An alternative, realist approach to causality is presented that supports the scientific legitimacy of using qualitative research for causal investigation, reframes the arguments for experimental methods in educational research, and can support a more productive collaboration between qualitative and quantitative researchers.

Deep learning for short-term traffic flow prediction

Abstract: We develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that combines a linear model that is fitted using l 1 regularization and a sequence of tanh layers. The challenge of predicting traffic flows are the sharp nonlinearities due to transitions between free flow, breakdown, recovery and congestion. We show that deep learning architectures can capture these nonlinear spatio-temporal effects. The first layer identifies spatio-temporal relations among predictors and other layers model nonlinear relations. We illustrate our methodology on road sensor data from Interstate I-55 and predict traffic flows during two special events; a Chicago Bears football game and an extreme snowstorm event. Both cases have sharp traffic flow regime changes, occurring very suddenly, and we show how deep learning provides precise short term traffic flow predictions.

On the temporal relationship between coronal mass ejections and flares

Abstract: The temporal relationship between coronal mass ejections (CMEs) and associated solar flares is of great importance to understanding the origin of CMEs, but it has been difficult to study owing to the nature of CME detection. In this paper, we investigate this issue using the Large Angle and Spectrometric Coronagraph and the EUV Imaging Telescope observations combined with GOES soft X-ray observations. We present four well-observed events whose source regions are close to the limb such that we are able to directly measure the CMEs' initial evolution in the low corona (~ 1-3 R☉) without any extrapolation; this height range was not available in previous space-based coronagraph observations. The velocity-time profiles show that kinematic evolution of three of the four CMEs can be described in a three-phase scenario: the initiation phase, impulsive acceleration phase, and propagation phase. The initiation phase is characterized by a slow ascension with a speed less than 80 km s-1 for a period of tens of minutes. The initiation phase always occurs before the onset of the associated flare. Following the initiation phase, the CMEs display an impulsive acceleration phase that coincides very well with the flares' rise phase lasting for a few to tens of minutes. The acceleration of CMEs ceases near the peak time of the soft X-ray flares. The CMEs then undergo a propagation phase, which is characterized by a constant speed or slowly decreasing in speed. The acceleration rates in the impulsive acceleration phase are in the range of 100-500 m s-2. One CME (on 1997 November 6, associated with an X9.4 flare) does not show an initiation phase. It has an extremely large acceleration rate of 7300 m s-2. The possible causes of CME initiation and acceleration in connection with flares are explored.