NASA Pathways: Intern Employment Program Work Report Summer 2014

This report documents the work experience and project involvement of Kyle Davidson during his tenure at Kennedy Space Center for the summer of 2014. Projects include the Nitrogen Oxygen Recharge System (NORS), Restore satellite servicing program, and mechanical handling operations for the SAGE III and Rapidscat payloads

A Data Transformation System for Biological Data Sources

Scientific data of importance to biologists in the Human Genome Project resides not only in conventional databases, but in structured files maintained in a number of different formats (e.g. ASN.1 and ACE) as well a.s sequence analysis packages (e.g. BLAST and FASTA). These formats and packages contain a number of data types not found in conventional databases, such as lists and variants, and may be deeply nested. We present in this paper techniques for querying and transforming such data, and illustrate their use in a prototype system developed in conjunction with the Human Genome Center for Chromosome 22. We also describe optimizations performed by the system, a crucial issue for bulk data

A Methodology for Assessing the Feasibility of Pumped Hydroelectric Storage within Existing USACE Facilities

Variable, renewable energy (VRE) generation such as solar power has seen a rapid increase in usage over the past decades. These power generation sources offer benefits due to their low marginal costs and reduced emissions. However, VRE assets are not dispatchable, which can result in a mismatch of the electric supply and demand curves. Pumped-storage hydropower (PSH) seeks to solve this by pumping water uphill during times of excess energy production and releasing the water back downhill through turbines during energy shortages, thus serving as a rechargeable battery. Creating new PSH systems, however, requires a large amount of capital and suitable locations. The United States Army Corps. of Engineers (USACE) is the largest producer of hydroelectric power within the United States, and as such, may have favorable sites for the addition of PSH. This study seeks to develop a method for evaluating these existing hydroelectric facilities using techno-economic methods to assess the potential for adding PSH. Each USACE facility was evaluated based on site specific characteristics from previously unpublished data to estimate the power generation and energy storage potential. The temporal nature of local wholesale electricity prices was accounted for to help estimate the financial feasibility of varying locations. Sensitivity analysis was performed to highlight how the method would identify the viability of facilities with different operational conditions. The methodologies detailed in this study will inform decision-making processes, and help enable a sustainable electric grid

Influence of complex terrain on a flow above a forest with clearing

Large Eddy simulations is compared to LiDAR measurements for the Lemnhult wind farm\ua0in Sweden. Two scans are performed, the south to represent a forest flow and the west to\ua0represent a flow over a clearing. A forest model is implemented in STAR-CCM+ and the local\ua0forest height at the site is used. The terrain height is included in the simulations to investigate\ua0its effect. The complex terrain simulations show the best agreement with the measurement.\ua0The uncertainty of the stratification of the measurements can be the reason for some of the\ua0difference. The vertical wind speed for the clearing scan change a lot when the complex terrain\ua0is included, but its magnitudes are however still low compared to the horizontal velocity. Thisshows the importance of including complex terrain for simulations with large change in terrain\ua0height. The normalized turbulent kinetic energy for the vertical profiles is showing a decrease\ua0in the clearing scan compared to the forest scan which should be benecial for wind turbines

A hybrid neural network and genetic programming approach to the automatic construction of computer vision systems

Both genetic programming and neural networks are machine learning techniques that have had a wide range of success in the world of computer vision. Recently, neural networks have been able to achieve excellent results on problems that even just ten years ago would have been considered intractable, especially in the area of image classification. Additionally, genetic programming has been shown capable of evolving computer vision programs that are capable of classifying objects in images using conventional computer vision operators. While genetic algorithms have been used to evolve neural network structures and tune the hyperparameters of said networks, this thesis explores an alternative combination of these two techniques. The author asks if integrating trained neural networks with genetic programming, by framing said networks as components for a computer vision system evolver, would increase the final classification accuracy of the evolved classifier. The author also asks that if so, can such a system learn to assemble multiple simple neural networks to solve a complex problem. No claims are made to having discovered a new state of the art method for classification. Instead, the main focus of this research was to learn if it is possible to combine these two techniques in this manner. The results presented from this research indicate that such a combination does improve accuracy compared to a vision system evolved without the use of these networks

Application of the dual-kinetic-balance sets in the relativistic many-body problem of atomic structure

The dual-kinetic-balance (DKB) finite basis set method for solving the Dirac equation for hydrogen-like ions [V. M. Shabaev et al., Phys. Rev. Lett. 93, 130405 (2004)] is extended to problems with a non-local spherically-symmetric Dirac-Hartree-Fock potential. We implement the DKB method using B-spline basis sets and compare its performance with the widely-employed approach of Notre Dame (ND) group [W.R. Johnson and J. Sapirstein, Phys. Rev. Lett. 57, 1126 (1986)]. We compare the performance of the ND and DKB methods by computing various properties of Cs atom: energies, hyperfine integrals, the parity-non-conserving amplitude of the $6s_{1/2}-7s_{1/2}$ transition, and the second-order many-body correction to the removal energy of the valence electrons. We find that for a comparable size of the basis set the accuracy of both methods is similar for matrix elements accumulated far from the nuclear region. However, for atomic properties determined by small distances, the DKB method outperforms the ND approach. In addition, we present a strategy for optimizing the size of the basis sets by choosing progressively smaller number of basis functions for increasingly higher partial waves. This strategy exploits suppression of contributions of high partial waves to typical many-body correlation corrections.Comment: 10 page

The CRISPR/Cas Adaptive Immune System of Pseudomonas aeruginosa Mediates Resistance to Naturally Occurring and Engineered Phages

Here we report the isolation of 6 temperate bacteriophages (phages) that are prevented from replicating within the laboratory strain Pseudomonas aeruginosa PA14 by the endogenous CRISPR/Cas system of this microbe. These phages are only the second identified group of naturally occurring phages demonstrated to be blocked for replication by a nonengineered CRISPR/Cas system, and our results provide the first evidence that the P. aeruginosa type I-F CRISPR/Cas system can function in phage resistance. Previous studies have highlighted the importance of the protospacer adjacent motif (PAM) and a proximal 8-nucleotide seed sequence in mediating CRISPR/Cas-based immunity. Through engineering of a protospacer region of phage DMS3 to make it a target of resistance by the CRISPR/Cas system and screening for mutants that escape CRISPR/Cas-mediated resistance, we show that nucleotides within the PAM and seed sequence and across the non-seed-sequence regions are critical for the functioning of this CRISPR/Cas system. We also demonstrate that P. aeruginosa can acquire spacer content in response to lytic phage challenge, illustrating the adaptive nature of this CRISPR/Cas system. Finally, we demonstrate that the P. aeruginosa CRISPR/Cas system mediates a gradient of resistance to a phage based on the level of complementarity between CRISPR spacer RNA and phage protospacer target. This work introduces a new in vivo system to study CRISPR/Cas-mediated resistance and an additional set of tools for the elucidation of CRISPR/Cas function

Modulating human memory for complex scenes with artificially generated images

Visual memory schemas (VMS) are two-dimensional memorability maps that capture the most memorable regions of a given scene, predicting with a high degree of consistency human observer’s memory for the same images. These maps are hypothesized to correlate with a mental framework of knowledge employed by humans to encode visual memories. In this study, we develop a generative model we term ‘MEMGAN’ constrained by extracted visual memory schemas that generates completely new complex scene images that vary based on their degree of predicted memorability. The generated populations of high and low memorability images are then evaluated for their memorability using a human observer experiment. We gather VMS maps for these generated images from participants in the memory experiment and compare these with the intended target VMS maps. Following the evaluation of observers’ memory performance through both VMS-defined memorability and hit rate, we find significantly superior memory performance by human observers for the highly memorable generated images compared to poorly memorable. Implementing and testing a construct from cognitive science allows us to generate images whose memorability we can manipulate at will, as well as providing a tool for further study of mental schemas in humans

Predicting Human Perception of Scene Complexity

It is apparent that humans are intrinsically capable of determining the degree of complexity present in an image; but it is unclear which regions in that image lead humans towards evaluating an image as complex or simple. Here, we develop a novel deep learning model for predicting human perception of the complexity of natural scene images in order to address these problems. For a given image, our approach, ComplexityNet, can generate both single-score complexity ratings and two-dimensional per-pixel complexity maps. These complexity maps indicate the regions of scenes that humans find to be complex, or simple. Drawing on work in the cognitive sciences we integrate metrics for scene clutter and scene symmetry , and conclude that the proposed metrics do indeed boost neural network performance when predicting complexity

Predicting Visual Memory Schemas with Variational Autoencoders

Visual memory schema (VMS) maps show which regions of an image cause that image to be remembered or falsely remembered. Previous work has succeeded in generating low resolution VMS maps using convolutional neural networks. We instead approach this problem as an image-to-image translation task making use of a variational autoencoder. This approach allows us to generate higher resolution dual channel images that represent visual memory schemas, allowing us to evaluate predicted true memorability and false memorability separately. We also evaluate the relationship between VMS maps, predicted VMS maps, ground truth memorability scores, and predicted memorability scores