Steven Johnston

Richard G. Freedman, Joseph B. Mueller, Jack Ladwig et al.

Robots that interact with humans in a physical space or application need to think about the person's posture, which typically comes from visual sensors like cameras and infra-red. Artificial intelligence and machine learning algorithms use information from these sensors either directly or after some level of symbolic abstraction, and the latter usually partitions the range of observed values to discretize the continuous signal data. Although these representations have been effective in a variety of algorithms with respect to accuracy and task completion, the underlying models are rarely interpretable, which also makes their outputs more difficult to explain to people who request them. Instead of focusing on the possible sensor values that are familiar to a machine, we introduce a qualitative spatial reasoning approach that describes the human posture in terms that are more familiar to people. This paper explores the derivation of our symbolic representation at two levels of detail and its preliminary use as features for interpretable activity recognition.

Devanshu Agrawal, Adrian Del Maestro, Steven Johnston et al.

We introduce the group-equivariant autoencoder (GE-autoencoder) -- a deep neural network (DNN) method that locates phase boundaries by determining which symmetries of the Hamiltonian have spontaneously broken at each temperature. We use group theory to deduce which symmetries of the system remain intact in all phases, and then use this information to constrain the parameters of the GE-autoencoder such that the encoder learns an order parameter invariant to these ``never-broken'' symmetries. This procedure produces a dramatic reduction in the number of free parameters such that the GE-autoencoder size is independent of the system size. We include symmetry regularization terms in the loss function of the GE-autoencoder so that the learned order parameter is also equivariant to the remaining symmetries of the system. By examining the group representation by which the learned order parameter transforms, we are then able to extract information about the associated spontaneous symmetry breaking. We test the GE-autoencoder on the 2D classical ferromagnetic and antiferromagnetic Ising models, finding that the GE-autoencoder (1) accurately determines which symmetries have spontaneously broken at each temperature; (2) estimates the critical temperature in the thermodynamic limit with greater accuracy, robustness, and time-efficiency than a symmetry-agnostic baseline autoencoder; and (3) detects the presence of an external symmetry-breaking magnetic field with greater sensitivity than the baseline method. Finally, we describe various key implementation details, including a new method for extracting the critical temperature estimate from trained autoencoders and calculations of the DNN initialization and learning rate settings required for fair model comparisons.

Abdulelah Abuabat, Steven Johnston, Mohammed Aldosari et al.

Regional Intergovernmental Organizations (RIGOs) are constituted by the local governments within their respective regions and are supported by the active engagement of the regions community and citizens. Metropolitan Statistical Areas (MSAs), on the other hand, are classified by the federal government based on commuting and commerce patterns. They do not adhere to any local government. The Graduate School of Policy and International Affairs Center for Metropolitan Studies (GSPIA) at the University of Pittsburgh have been researching the boundaries of RIGOs and the characteristics defining them. In this paper, we propose, design, and implement an approach to enhance the current visualization by visualizing two categorical data: RIGOs and MSAs and the overlapping between them. We attempted to use a combination of visual attributes that leverage human perception system and do not impose cognitive and mental effort. The overall result of the evaluation shows that our work proved to be more effective than the current visualization.