Separable Gaussian Neural Networks: Structure, Analysis, and Function Approximations

The Gaussian-radial-basis function neural network (GRBFNN) has been a popular choice for interpolation and classification. However, it is computationally intensive when the dimension of the input vector is high. To address this issue, we propose a new feedforward network - Separable Gaussian Neural Network (SGNN) by taking advantage of the separable property of Gaussian functions, which splits input data into multiple columns and sequentially feeds them into parallel layers formed by uni-variate Gaussian functions. This structure reduces the number of neurons from O(N^d) of GRBFNN to O(dN), which exponentially improves the computational speed of SGNN and makes it scale linearly as the input dimension increases. In addition, SGNN can preserve the dominant subspace of the Hessian matrix of GRBFNN in gradient descent training, leading to a similar level of accuracy to GRBFNN. It is experimentally demonstrated that SGNN can achieve 100 times speedup with a similar level of accuracy over GRBFNN on tri-variate function approximations. The SGNN also has better trainability and is more tuning-friendly than DNNs with RuLU and Sigmoid functions. For approximating functions with complex geometry, SGNN can lead to three orders of magnitude more accurate results than a RuLU-DNN with twice the number of layers and the number of neurons per layer

Herd Booster: Examining the Impacts of Recommendation and Product Type on Herd Behaviors

The information systems literature has mixed findings on herd behavior’s effects on online purchase decisions. This research aims to bridge the gaps in the existing herd behavior literature by examining what leads to herd behavior and under what conditions herd behavior may result in positive/negative outcomes. In particular, we examine how different types of recommendations (i.e., collaborative and social) and products (i.e., experience and search) would interact and trigger herd behaviors in an e-commerce environment. We developed a research model based on the literature on herd behavior. An experiment was conducted with 335 college students to examine the research model. The results suggest that herd behavior is more likely to occur for collaborative recommendations with experience products. In addition, we find herd behavior is more likely to result in user regrets if the recommendation is a search product; while it will lead to dissatisfaction if the recommendation is an experience product. This study has significant research and practical implications

Entanglement dynamics of a superconducting phase qubit coupled to a two-level system

We report the observation and quantitative characterization of driven and spontaneous oscillations of quantum entanglement, as measured by concurrence, in a bipartite system consisting of a macroscopic Josephson phase qubit coupled to a microscopic two-level system. The data clearly show the behavior of entanglement dynamics such as sudden death and revival, and the effect of decoherence and ac driving on entanglement.Comment: 6 pages,4 figure

Synthesis and Biological Study of Adenylyl Cyclase Inhibitors

Adenylyl cyclases (AC) is a critical family of enzymes which modulates the dynamic cellular level of cAMP, cyclic adenosine monophosphate. The study of cAMP showed that it is indispensable for the signal transduction cascades during many physiological processes, such as immune responses and metabolism which highly relate to cancers. Previous studies of AC inhibitors have been limited due to a lack of isoform-selective small molecule modulators. Selectivity of the molecules is imperative to the activation of only the desired AC inhibitor. The design of the described project was to test the structure activity relationship (SAR) by synthesizing a class of AC I inhibitors and then use the results to develop a small molecule with maximum selectivity for therapeutic targeting. Multi-step synthesis featured with epoxide ring-opening reaction followed by the Friedel–Crafts reaction. Compounds were differentiated by changing substituents on the nitrogen atom. The synthetic molecules have been tested via SAR of AC I inhibitor and IC50. Once synthesized, the compounds were tested for their inhibition rate and the results showed that the majority of scaffolds had great SAR rates at 40 µM and two also had impressive rates as low as 4 µM. Further investigation with IC50 studies is on-going. The results suggest that the current synthetic compounds are potentially great AC I inhibitors and further study will continue which will contribute to cancer research

Tunable Quantum Beam Splitters for Coherent Manipulation of a Solid-State Tripartite Qubit System

Coherent control of quantum states is at the heart of implementing solid-state quantum processors and testing quantum mechanics at the macroscopic level. Despite significant progress made in recent years in controlling single- and bi-partite quantum systems, coherent control of quantum wave function in multipartite systems involving artificial solid-state qubits has been hampered due to the relatively short decoherence time and lacking of precise control methods. Here we report the creation and coherent manipulation of quantum states in a tripartite quantum system, which is formed by a superconducting qubit coupled to two microscopic two-level systems (TLSs). The avoided crossings in the system's energy-level spectrum due to the qubit-TLS interaction act as tunable quantum beam splitters of wave functions. Our result shows that the Landau-Zener-St\"{u}ckelberg interference has great potential in the precise control of the quantum states in the tripartite system.Comment: 24 pages, 3 figure

ENABLING EFFICIENT FLEET COMPOSITION SELECTION THROUGH THE DEVELOPMENT OF A RANK HEURISTIC FOR A BRANCH AND BOUND METHOD

In the foreseeable future, autonomous mobile robots (AMRs) will become a key enabler for increasing productivity and flexibility in material handling in warehousing facilities, distribution centers and manufacturing systems. The objective of this research is to develop and validate parametric models of AMRs, develop ranking heuristic using a physics-based algorithm within the framework of the Branch and Bound method, integrate the ranking algorithm into a Fleet Composition Optimization (FCO) tool, and finally conduct simulations under various scenarios to verify the suitability and robustness of the developed tool in a factory equipped with AMRs. Kinematic-based equations are used for computing both energy and time consumption. Multivariate linear regression, a data-driven method, is used for designing the ranking heuristic. The results indicate that the unique physical structures and parameters of each robot are the main factors contributing to differences in energy and time consumption. improvement on reducing computation time was achieved by comparing heuristic-based search and non-heuristic-based search. This research is expected to significantly improve the current nested fleet composition optimization tool by reducing computation time without sacrificing optimality. From a practical perspective, greater efficiency in reducing energy and time costs can be achieved.Ford Motor CompanyNo embargoAcademic Major: Aerospace Engineerin

Optimal Inter-area Oscillation Damping Control: A Transfer Deep Reinforcement Learning Approach with Switching Control Strategy

Wide-area damping control for inter-area oscillation (IAO) is critical to modern power systems. The recent breakthroughs in deep learning and the broad deployment of phasor measurement units (PMU) promote the development of datadriven IAO damping controllers. In this paper, the damping control of IAOs is modeled as a Markov Decision Process (MDP) and solved by the proposed Deep Deterministic Policy Gradient (DDPG) based deep reinforcement learning (DRL) approach. The proposed approach optimizes the eigenvalue distribution of the system, which determines the IAO modes in nature. The eigenvalues are evaluated by the data-driven method called dynamic mode decomposition. For a given power system, only a subset of generators selected by participation factors needs to be controlled, alleviating the control and computing burdens. A Switching Control Strategy (SCS) is introduced to improve the transient response of IAOs. Numerical simulations of the IEEE-39 New England power grid model validate the effectiveness and advanced performance of the proposed approach as well as its robustness against communication delays. In addition, we demonstrate the transfer ability of the DRL model trained on the linearized power grid model to provide effective IAO damping control in the non-linear power grid model environment