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Full-Text Articles in Physical Sciences and Mathematics
Decomposing The Hamiltonian Of Quantum Circuits Using Machine Learning, Jordan Burns, Yih Sung, Colby Wight
Decomposing The Hamiltonian Of Quantum Circuits Using Machine Learning, Jordan Burns, Yih Sung, Colby Wight
Physics Capstone Projects
Quantum computing is one of the most promising techniques for simulating physical systems that cannot be simulated on classical computers[1]. A significant drawback of this approach is the inherent difficulty in designing circuits that can represent these systems on quantum computers. Every quantum circuit is built out of small components called quantum gates. Each of these gates manipulate the quantum system in a specific way. When used in combination, a finite subset of these gates, the set of universal gates, can be used to construct any possible quantum circuit[2].
Fast And Effective Techniques For Lwir Radiative Transfer Modeling: A Dimension-Reduction Approach, Nicholas M. Westing [*], Brett J. Borghetti, Kevin C. Gross
Fast And Effective Techniques For Lwir Radiative Transfer Modeling: A Dimension-Reduction Approach, Nicholas M. Westing [*], Brett J. Borghetti, Kevin C. Gross
Faculty Publications
The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. These detailed measurements allow for material classification, with many recent advancements from the fields of machine learning and deep learning. In many scenarios, the hyperspectral image must first be corrected or compensated for atmospheric effects. Radiative Transfer (RT) computations can provide look up tables (LUTs) to support these corrections. This research investigates a dimension-reduction approach using machine learning methods to create an effective sensor-specific long-wave infrared (LWIR) RT model.
Computational Studies Of Thermal Properties And Desalination Performance Of Low-Dimensional Materials, Yang Hong
Department of Chemistry: Dissertations, Theses, and Student Research
During the last 30 years, microelectronic devices have been continuously designed and developed with smaller size and yet more functionalities. Today, hundreds of millions of transistors and complementary metal-oxide-semiconductor cells can be designed and integrated on a single microchip through 3D packaging and chip stacking technology. A large amount of heat will be generated in a limited space during the operation of microchips. Moreover, there is a high possibility of hot spots due to non-uniform integrated circuit design patterns as some core parts of a microchip work harder than other memory parts. This issue becomes acute as stacked microchips get …
Magnetic Borophenes From An Evolutionary Search, Meng-Hong Zhu, Xiao-Ji Weng, Guoying Gao, Shuai Dong, Ling-Fang Lin, Wei-Hua Wang, Qiang Zhu, Artem R. Oganov, Xiao Dong, Yongjun Tian, Xiang-Feng Zhou, Hui-Tian Wang
Magnetic Borophenes From An Evolutionary Search, Meng-Hong Zhu, Xiao-Ji Weng, Guoying Gao, Shuai Dong, Ling-Fang Lin, Wei-Hua Wang, Qiang Zhu, Artem R. Oganov, Xiao Dong, Yongjun Tian, Xiang-Feng Zhou, Hui-Tian Wang
Physics & Astronomy Faculty Research
A computational methodology based on ab initio evolutionary algorithms and spin-polarized density functional theory was developed to predict two-dimensional magnetic materials. Its application to a model system borophene reveals an unexpected rich magnetism and polymorphism. A metastable borophene with nonzero thickness is an antiferromagnetic semiconductor from first-principles calculations, and can be further tuned into a half-metal by finite electron doping. In this borophene, the buckling and coupling among three atomic layers are not only responsible for magnetism, but also result in an out-of-plane negative Poisson's ratio under uniaxial tension, making it the first elemental material possessing auxetic and magnetic properties …
Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre
Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre
Honors Scholar Theses
Abnormal ocular motility is a common manifestation of many underlying pathologies particularly those that are neurological. Dynamics of saccades, when the eye rapidly changes its point of fixation, have been characterized for many neurological disorders including concussions, traumatic brain injuries (TBI), and Parkinson’s disease. However, widespread saccade analysis for diagnostic and research purposes requires the recognition of certain eye movement parameters. Key information such as velocity and duration must be determined from data based on a wide set of patients’ characteristics that may range in eye shapes and iris, hair and skin pigmentation [36]. Previous work on saccade analysis has …