The calculation of optical absorption spectra using linear-scaling density-functional theory

The goal of the work presented in this thesis was to develop and implement a method for calculating optical absorption spectra for large electronic systems within a linear-scaling density-functional theory (LS-DFT) formalism. The key feature of this method was the development of a scheme for optimizing a set of localized orbitals to accurately represent unoccupied Kohn-Sham states, which are not well represented by the localized orbital basis sets used for ground state LS-DFT calculations. Three different schemes were compared for the calculation of unoccupied states using a one-dimensional “toy model” and the most promising of these, based on the use of a projection operator, was implemented in a fully-functional LS-DFT code. Using the toy model, two methods for the calculation of band structures within a localized basis set were investigated and some of the features required by localized basis sets in order to produce accurate band structures were identified. The method was tested by the application to both molecular and extended systems, with calculations of densities of states, band structures and optical absorption spectra. The results for the smaller systems were validated by comparison with a cubic-scaling plane-wave density-functional theory code, with which excellent agreement was achieved. Additionally, the method was shown to be linear-scaling for a conjugated polymer for system sizes up to 1000 atoms. The use of the projection method was shown to be crucial for calculating the above results, as was the implementation of a momentum operator based formalism for the calculation of spectra. Finally, it was shown that the method can be used to identify the transitions responsible for particular peaks in the spectra and is sensitive enough to distinguish between spectra for systems with very similar structures, demonstrating the capabilities of the method to aid the interpretation of experimental results

ISIS in America: A Sociohistorical Analysis

During the summer of 2014, the terrorist organization Islamic State (commonly referred to as Islamic State in Iraq and Syria, or ISIS) garnered international attention after its unprecedented territorial acquisitions and violence in the Middle East. Today, ISIS vies with al-Qaeda for leadership of the global Islamic Extremist movement and has extended its violence all over the world, including the United States. U.S. based supporters generally choose to engage with the ideology in one of three categories: as a foreign fighter, domestic plotter, or domestic non-plotter. Despite this threat, there is very little quantitative research concerning U.S. ISIS supporters and the incidents they plan. Utilizing data from the American Terrorism Study (ATS), the current study compares ISIS perpetrators across the three support type categories, as well as ISIS and al-Qaeda and Associated Movements (AQAM) affiliated persons and incidents in the United States. I conducted Chi Square and Conjunctive Analysis of Case Configurations to determine significant differences. The analysis indicated significant difference across ISIS support types with regard to gender and age of the individuals, and suggested common patterns in the types of individuals who choose to leave the U.S. or stay and engage in violence. Additional analysis indicated significant differences in the residency status and race between ISIS and AQAM perpetrators. Finally, results showed that, although ISIS and AQAM incidents have different configurations concerning targets, weapons, and group size, their success rates are relatively the same. In conclusion, there are important differences between ISIS and AQAM affiliated persons and incidents that may merit considering them as separate entities rather than together under the umbrella of Islamic Extremist

Spacecraft software training needs assessment research

The problems were identified, along with their causes and potential solutions, that the management analysts were encountering in performing their jobs. It was concluded that sophisticated training applications would provide the most effective solution to a substantial portion of the analysts' problems. The remainder could be alleviated through the introduction of tools that could help make retrieval of the needed information from the vast and complex information resources feasible

Spacecraft software training needs assessment research, appendices

The appendices to the previously reported study are presented: statistical data from task rating worksheets; SSD references; survey forms; fourth generation language, a powerful, long-term solution to maintenance cost; task list; methodology; SwRI's instructional systems development model; relevant research; and references

Searching for invariants using genetic programming and mutation testing

Invariants are concise and useful descriptions of a program's behaviour. As most programs are not annotated with invariants, previous research has attempted to automatically generate them from source code. In this paper, we propose a new approach to invariant generation using search. We reuse the trace generation front-end of existing tool Daikon and integrate it with genetic programming and a mutation testing tool. We demonstrate that our system can find the same invariants through search that Daikon produces via template instantiation, and we also find useful invariants that Daikon does not. We then present a method of ranking invariants such that we can identify those that are most interesting, through a novel application of program mutation

Test Result of Time-Of-Propagation Cherenkov Counter

A new concept concerning Cherenkov detector for particle identification by means of measuring both the Time-of-Propagation (TOP) and horizontal emission angle ($\Phi$) of Cherenkov photons is described here. Some R&D works are also reported.Comment: 5 pages, 7 Figures, LaTe

Time-varying boundaries for diffusion models of decision making and response time

Diffusion models are widely-used and successful accounts of the time course of two-choice decision making. Most diffusion models assume constant boundaries, which are the threshold levels of evidence that must be sampled from a stimulus to reach a decision. We summarize theoretical results from statistics that relate distributions of decisions and response times to diffusion models with time-varying boundaries. We then develop a computational method for finding time-varying boundaries from empirical data, and apply our new method to two problems. The first problem involves finding the time-varying boundaries that make diffusion models equivalent to the alternative sequential sampling class of accumulator models. The second problem involves finding the time-varying boundaries, at the individual level, that best fit empirical data for perceptual stimuli that provide equal evidence for both decision alternatives. We discuss the theoretical and modeling implications of using time-varying boundaries in diffusion models, as well as the limitations and potential of our approach to their inference

Geometry shapes evolution of early multicellularity

Organisms have increased in complexity through a series of major evolutionary transitions, in which formerly autonomous entities become parts of a novel higher-level entity. One intriguing feature of the higher-level entity after some major transitions is a division of reproductive labor among its lower-level units. Although it can have clear benefits once established, it is unknown how such reproductive division of labor originates. We consider a recent evolution experiment on the yeast Saccharomyces cerevisiae as a unique platform to address the issue of reproductive differentiation during an evolutionary transition in individuality. In the experiment, independent yeast lineages evolved a multicellular "snowflake-like'' cluster form in response to gravity selection. Shortly after the evolution of clusters, the yeast evolved higher rates of cell death. While cell death enables clusters to split apart and form new groups, it also reduces their performance in the face of gravity selection. To understand the selective value of increased cell death, we create a mathematical model of the cellular arrangement within snowflake yeast clusters. The model reveals that the mechanism of cell death and the geometry of the snowflake interact in complex, evolutionarily important ways. We find that the organization of snowflake yeast imposes powerful limitations on the available space for new cell growth. By dying more frequently, cells in clusters avoid encountering space limitations, and, paradoxically, reach higher numbers. In addition, selection for particular group sizes can explain the increased rate of apoptosis both in terms of total cell number and total numbers of collectives. Thus, by considering the geometry of a primitive multicellular organism we can gain insight into the initial emergence of reproductive division of labor during an evolutionary transition in individuality.Comment: 7 figure