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Jarrett Booz, Wei Yu, Guobin Xu, David W. Griffith, Nada T. Golmie
Accurate weather forecast is important to our daily life. Through physical atmospheric models, the weather can be accurately forecasted in a short period time. To provide weather forecast, machines learning techniques can be used for understanding and
Alice Mizrahi, thomas Marsh, Brian D. Hoskins, Mark D. Stiles
Finding the shortest path in a graph has applications to a wide range of optimization problems. However, algorithmic methods scale with the size of the graph in terms of time and energy. We propose a method to solve the shortest path problem using circuits
Sonia Buckley, Adam McCaughan, Jeff Chiles, Richard Mirin, Sae Woo Nam, Jeff Shainline
We have previously proposed a novel hardware platform for neuromorphic computing based on superconducting optoelectronics that presents many of the features necessary for information processing in the brain. Here we discuss the design and training of
Jeffrey M. Shainline, Adam N. McCaughan, Jeffrey T. Chiles, Richard P. Mirin, Sae Woo Nam, Sonia M. Buckley
We present designs of superconducting optoelectronic neurons based on superconducting single- photon detectors, Josephson junctions, semiconductor light sources, and multi-planar dielectric waveguides. The neurons send few-photon signals to synaptic
Yan Lu, Zhuo Yang, Douglas Eddy, Sundar Krishnamurty
The current AM development environment is far from being mature. Both software applications and workflow management tools are very limited due to the lack of knowledge to support engineering decision makings. AM knowledge includes design rules, operation
Boonserm Kulvatunyou, Evan K. Wallace, Dimitris Kiritsis, Barry Smith, Chris Will
The current industrial revolution is said to be driven by the digitization of manu-facturing that exploits connected information across all aspects of manufacturing. Standards have been recognized as an important enabler. Ontology is the next generation
Yan Lu, Douglas Eddy, Sundar Krishnamurty, Ian Grosse
Statistical metamodels can robustly predict manufacturing process and engineering systems design results. Various techniques, such as Kriging, polynomial regression, artificial neural network and others, are each best suited for different scenarios that
Deep neural networks have demonstrated their effectiveness in most machine learning tasks, with intrusion detection included. Unfortunately, recent research found that deep neural networks are vulnerable to adversarial examples in the image classification
Jeffrey M. Shainline, Adam N. McCaughan, Sonia M. Buckley, Christine A. Donnelly, Manuel C. Castellanos Beltran, Michael L. Schneider, Richard P. Mirin, Sae Woo Nam
As a means of dynamically reconfiguring the synaptic weight of a superconducting optoelectronic loop neuron, a superconducting flux storage loop is inductively coupled to the synaptic current bias of the neuron. A standard flux memory cell is used to
Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical
Michael P. Majurski, Petru S. Manescu, Joe Chalfoun, Peter Bajcsy, Mary C. Brady
Sampling and augmentation methods have been widely used to generate sufficiently large numbers of representative training images for supervised segmentation methods, such as convolutional neural networks (CNN). There is a need to understand the impact of