Data-Driven Database Education: A Quantitative Study of SQL Learning in an Introductory Database Course

The Structured Query Language (SQL) is widely used and challenging to master. Within the context of lab exercises in an introductory database course, this thesis analyzes the student learning process and seeks to answer the question: ``Which SQL concepts, or concept combinations, trouble students the most?\u27\u27 We provide comprehensive taxonomies of SQL concepts and errors, identify common areas of student misunderstanding, and investigate the student problem-solving process. We present an interactive web application used by students to complete SQL lab exercises. In addition, we analyze data collected by this application and we offer suggestions for improvement to database lab activities

Quantum-enhanced reinforcement learning for finite-episode games with discrete state spaces

Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum annealing machines produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization and sampling tasks. Here, we present a way to partially embed both Monte Carlo policy iteration for finding an optimal policy on random observations, as well as how to embed (n) sub-optimal state-value functions for approximating an improved state-value function given a policy for finite horizon games with discrete state spaces on a D-Wave 2000Q quantum processing unit (QPU). We explain how both problems can be expressed as a quadratic unconstrained binary optimization (QUBO) problem, and show that quantum-enhanced Monte Carlo policy evaluation allows for finding equivalent or better state-value functions for a given policy with the same number episodes compared to a purely classical Monte Carlo algorithm. Additionally, we describe a quantum-classical policy learning algorithm. Our first and foremost aim is to explain how to represent and solve parts of these problems with the help of the QPU, and not to prove supremacy over every existing classical policy evaluation algorithm.Comment: 17 pages, 7 figure

Microearthquake activity adjacent to the Rocklin pluton near Auburn, California

The occurrence of the Oroville earthquake (M_L = 5.7) of August 1, 1975 heightened interest in the seismotectonics of the Sierra foothills region and particularly the Foothills fault system. Clark (1960) recognized the Foothills fault system as being a major structural feature of the western Sierra Nevada. An 11-week reconnalssance microearthquake study was conducted by Woodward-Clyde Consultants (WCC) for the U.S. Bureau of Reclamation (USBR) to assess the seismic activity of the Sierra foothills 75 km S-SE of Oroville near Auburn, California. Microearthquake activity was detected in the Rocklin-Auburn region with eight events recorded during July 1 to July 24 and seven events during August 15 to October 8, 1976 (see Figure 1). The mobile seismic array consisted of eight Sprengnether MEQ-800 portable seismographs with Mark Products L-4C vertical seismometers. Data recordings were normally 48 hours in length, with a record speed of 1 mm/sec

Quantum-assisted finite-element design optimization

Quantum annealing devices such as the ones produced by D-Wave systems are typically used for solving optimization and sampling tasks, and in both academia and industry the characterization of their usefulness is subject to active research. Any problem that can naturally be described as a weighted, undirected graph may be a particularly interesting candidate, since such a problem may be formulated a as quadratic unconstrained binary optimization (QUBO) instance, which is solvable on D-Wave's Chimera graph architecture. In this paper, we introduce a quantum-assisted finite-element method for design optimization. We show that we can minimize a shape-specific quantity, in our case a ray approximation of sound pressure at a specific position around an object, by manipulating the shape of this object. Our algorithm belongs to the class of quantum-assisted algorithms, as the optimization task runs iteratively on a D-Wave 2000Q quantum processing unit (QPU), whereby the evaluation and interpretation of the results happens classically. Our first and foremost aim is to explain how to represent and solve parts of these problems with the help of a QPU, and not to prove supremacy over existing classical finite-element algorithms for design optimization.Comment: 17 pages, 5 figure

Identification of antiviral roles for the exon-junction complex and nonsense-mediated decay in flaviviral infection.

West Nile virus (WNV) is an emerging mosquito-borne flavivirus, related to dengue virus and Zika virus. To gain insight into host pathways involved in WNV infection, we performed a systematic affinity-tag purification mass spectrometry (APMS) study to identify 259 WNV-interacting human proteins. RNA interference screening revealed 26 genes that both interact with WNV proteins and influence WNV infection. We found that WNV, dengue and Zika virus capsids interact with a conserved subset of proteins that impact infection. These include the exon-junction complex (EJC) recycling factor PYM1, which is antiviral against all three viruses. The EJC has roles in nonsense-mediated decay (NMD), and we found that both the EJC and NMD are antiviral and the EJC protein RBM8A directly binds WNV RNA. To counteract this, flavivirus infection inhibits NMD and the capsid-PYM1 interaction interferes with EJC protein function and localization. Depletion of PYM1 attenuates RBM8A binding to viral RNA, suggesting that WNV sequesters PYM1 to protect viral RNA from decay. Together, these data suggest a complex interplay between the virus and host in regulating NMD and the EJC

Quantum-assisted finite-element design optimization

Quantum annealing devices such as the ones produced by D-Wave systems are typically used for solving optimization and sampling tasks, and in both academia and industry the characterization of their usefulness is subject to active research. Any problem that can naturally be described as a weighted, undirected graph may be a particularly interesting candidate, since such a problem may be formulated a as quadratic unconstrained binary optimization (QUBO) instance, which is solvable on D-Wave's Chimera graph architecture. In this paper, we introduce a quantum-assisted finite-element method for design optimization. We show that we can minimize a shape-specific quantity, in our case a ray approximation of sound pressure at a specific position around an object, by manipulating the shape of this object. Our algorithm belongs to the class of quantum-assisted algorithms, as the optimization task runs iteratively on a D-Wave 2000Q quantum processing unit (QPU), whereby the evaluation and interpretation of the results happens classically. Our first and foremost aim is to explain how to represent and solve parts of these problems with the help of a QPU, and not to prove supremacy over existing classical finite-element algorithms for design optimization.Algorithms and the Foundations of Software technolog