Engineering Aggregation Operators for Relational In-Memory Database Systems

In this thesis we study the design and implementation of Aggregation operators in the context of relational in-memory database systems. In particular, we identify and address the following challenges: cache-efficiency, CPU-friendliness, parallelism within and across processors, robust handling of skewed data, adaptive processing, processing with constrained memory, and integration with modern database architectures. Our resulting algorithm outperforms the state-of-the-art by up to 3.7x

MAGE: Nearly Zero-Cost Virtual Memory for Secure Computation

Secure Computation (SC) is a family of cryptographic primitives for computing on encrypted data in single-party and multi-party settings. SC is being increasingly adopted by industry for a variety of applications. A significant obstacle to using SC for practical applications is the memory overhead of the underlying cryptography. We develop MAGE, an execution engine for SC that efficiently runs SC computations that do not fit in memory. We observe that, due to their intended security guarantees, SC schemes are inherently oblivious -- their memory access patterns are independent of the input data. Using this property, MAGE calculates the memory access pattern ahead of time and uses it to produce a memory management plan. This formulation of memory management, which we call memory programming, is a generalization of paging that allows MAGE to provide a highly efficient virtual memory abstraction for SC. MAGE outperforms the OS virtual memory system by up to an order of magnitude, and in many cases, runs SC computations that do not fit in memory at nearly the same speed as if the underlying machines had unbounded physical memory to fit the entire computation.Comment: 19 pages; Accepted to OSDI 202

Feasibility study for a numerical aerodynamic simulation facility. Volume 1

A Numerical Aerodynamic Simulation Facility (NASF) was designed for the simulation of fluid flow around three-dimensional bodies, both in wind tunnel environments and in free space. The application of numerical simulation to this field of endeavor promised to yield economies in aerodynamic and aircraft body designs. A model for a NASF/FMP (Flow Model Processor) ensemble using a possible approach to meeting NASF goals is presented. The computer hardware and software are presented, along with the entire design and performance analysis and evaluation

Automatic analysis of electronic drawings using neural network

Neural network technique has been found to be a powerful tool in pattern recognition. It captures associations or discovers regularities with a set of patterns, where the types, number of variables or diversity of the data are very great, the relationships between variables are vaguely understood, or the relationships are difficult to describe adequately with conventional approaches. In this dissertation, which is related to the research and the system design aiming at recognizing the digital gate symbols and characters in electronic drawings, we have proposed: (1) A modified Kohonen neural network with a shift-invariant capability in pattern recognition; (2) An effective approach to optimization of the structure of the back-propagation neural network; (3) Candidate searching and pre-processing techniques to facilitate the automatic analysis of the electronic drawings. An analysis and the system performance reveal that when the shift of an image pattern is not large, and the rotation is only by nx90°, (n = 1, 2, and 3), the modified Kohonen neural network is superior to the conventional Kohonen neural network in terms of shift-invariant and limited rotation-invariant capabilities. As a result, the dimensionality of the Kohonen layer can be reduced significantly compared with the conventional ones for the same performance. Moreover, the size of the subsequent neural network, say, back-propagation feed-forward neural network, can be decreased dramatically. There are no known rules for specifying the number of nodes in the hidden layers of a feed-forward neural network. Increasing the size of the hidden layer usually improves the recognition accuracy, while decreasing the size generally improves generalization capability. We determine the optimal size by simulation to attain a balance between the accuracy and generalization. This optimized back-propagation neural network outperforms the conventional ones designed by experience in general. In order to further reduce the computation complexity and save the calculation time spent in neural networks, pre-processing techniques have been developed to remove long circuit lines in the electronic drawings. This made the candidate searching more effective

A Dynamical Synthesis of Planetary Systems

Over the past three decades, complementary lines of evidence have each provided tantalizing hints about the underlying mechanisms driving the diverse set of planetary system architectures. This dissertation leverages dynamics to synthesize the various components of planetary systems, including stars, planets, and minor planets. My work progresses at the intersection of subfields, drawing evidence from both solar system and exoplanet studies to advance a cohesive picture of planetary system evolution. This dissertation is fundamentally focused on interactions between the components of planetary systems. As a result, it is organized into three segments detailing the relationship between these components. A brief summary is provided as follows. Part I (Chapter 2): The Star-Minor Planet Connection. This chapter explores the use of occultation measurements, in which foreground asteroids briefly block out the light of background stars, as a mechanism to precisely probe the positions of minor planets within the solar system. We demonstrate that this method can be applied to constrain the presence of neighboring masses, including the predicted ``Planet Nine\u27\u27, in the distant solar system. Part II (Chapters 3-4): The Planet-Minor Planet Connection. These two chapters examine how minor planets can inform our understanding of planets more broadly. In Chapter 3, we describe a novel algorithm developed to directly search for distant solar system objects relevant to the Planet Nine hypothesis using data from the Transiting Exoplanet Survey Satellite (TESS). Then, in Chapter 4, we demonstrate that the long-period Neptune-mass exoplanet population suggested by protoplanetary disk images can also efficiently eject neighboring minor planets, accounting for the high rate of observed interstellar objects passing through the solar system. Part III (Chapters 5-7): The Star-Planet Connection. These three chapters investigate the relationship between stars and planets in two distinct ways: through compositional studies and through dynamical analyses. In Chapter 5, we describe the development of a machine learning algorithm that rapidly extracts stellar parameters, including 15 elemental abundances, from input optical stellar spectra. In Chapter 6, we introduce the Stellar Obliquities in Long-period Exoplanet Systems (SOLES) survey to investigate the origins of exoplanet spin-orbit misalignments. Finally, in Chapter 7 we conduct a population study of the stellar obliquity distribution that provides evidence for high-eccentricity migration and tidal damping as the two key mechanisms crafting the dynamical evolution of hot Jupiters