Hot hole transport and noise phenomena in silicon at cryogenic temperatures from first principles

The transport properties of hot holes in silicon at cryogenic temperatures exhibit several anomalous features, including the emergence of two distinct saturated drift velocity regimes and a non-monotonic trend of the current noise versus electric field at microwave frequencies. Despite prior investigations, these features lack generally accepted explanations. Here, we examine the microscopic origin of these phenomena by extending a recently developed ab-initio theory of high-field transport and noise in semiconductors. We find that the drift velocity anomaly may be attributed to scattering dominated by acoustic phonon emission, leading to an additional regime of drift velocity saturation at temperatures $\sim 40$ K for which the acoustic phonon occupation is negligible; while the non-monotonic trend in the current noise arises due to the decrease in momentum relaxation time with electric field. The former conclusion is consistent with the findings of prior work, but the latter distinctly differs from previous explanations. This work highlights the use of high-field transport and noise phenomena as sensitive probes of microscopic charge transport phenomena in semiconductors.Comment: 19 pages, 4 figure

Using Molecular-Level Simulations to Determine Diffusivities in the Classroom

We present work describing the practical use of molecular-level simulations to determine diffusivities in a course targeted at the general audience of first-year chemical engineering graduate students. We show how the simulation techniques can be used to directly complement traditional methods for obtaining diffusivities. Our philosophy is to provide a utilitarian tool that can be used in a manner analogous to existing techniques to obtain diffusion coefficients. The advantage of the simulation approach is that it will work in the absence of experimental data and can be easily applied to multicomponent mixtures with an arbitrary number of species. In the implementation of this work, we remain keenly aware of constraints due to time, computational resources, money, and target-audience qualifications, so that the implementation is feasible. We demonstrate that these simulations require only a few minutes to run on a contemporary (AMD Athlon 850 MHz) processor. In our approach we outline the basic steps necessary to obtain a transport diffusivity via molecular-level simulations. We also provide an example problem, where we compare the results of the simulation to the predictions from corresponding states and kinetic theory

Quantum computing with nearest neighbor interactions and error rates over 1%

Large-scale quantum computation will only be achieved if experimentally implementable quantum error correction procedures are devised that can tolerate experimentally achievable error rates. We describe a quantum error correction procedure that requires only a 2-D square lattice of qubits that can interact with their nearest neighbors, yet can tolerate quantum gate error rates over 1%. The precise maximum tolerable error rate depends on the error model, and we calculate values in the range 1.1--1.4% for various physically reasonable models. Even the lowest value represents the highest threshold error rate calculated to date in a geometrically constrained setting, and a 50% improvement over the previous record.Comment: 4 pages, 8 figure

Scientific Computing Meets Big Data Technology: An Astronomy Use Case

Scientific analyses commonly compose multiple single-process programs into a dataflow. An end-to-end dataflow of single-process programs is known as a many-task application. Typically, tools from the HPC software stack are used to parallelize these analyses. In this work, we investigate an alternate approach that uses Apache Spark -- a modern big data platform -- to parallelize many-task applications. We present Kira, a flexible and distributed astronomy image processing toolkit using Apache Spark. We then use the Kira toolkit to implement a Source Extractor application for astronomy images, called Kira SE. With Kira SE as the use case, we study the programming flexibility, dataflow richness, scheduling capacity and performance of Apache Spark running on the EC2 cloud. By exploiting data locality, Kira SE achieves a 2.5x speedup over an equivalent C program when analyzing a 1TB dataset using 512 cores on the Amazon EC2 cloud. Furthermore, we show that by leveraging software originally designed for big data infrastructure, Kira SE achieves competitive performance to the C implementation running on the NERSC Edison supercomputer. Our experience with Kira indicates that emerging Big Data platforms such as Apache Spark are a performant alternative for many-task scientific applications

Hydraulic Resistance of Grass Media on Shallow Overland Flow

Simulated dense vegetation with random blade arrangements and different blade flexibilities were used to determine the hydraulic properties of flow of small, non-submerging depths. With the water flowing among the randomly patterned vegetation blades, drag resistance becomes the dominant force that retards the flow. An equation of flow was established based on the momentum balance in the system. Experimental results were used to determine the coefficient of blade resistance, RD, and plotted in terms of blade width and flow depth Reynolds number respectively

Optical Polarization Analogs in Inelastic Free Electron Scattering

Advances in the ability to manipulate free electron phase profiles within the electron microscope have spurred development of quantum-mechanical descriptions of electron energy loss (EEL) processes involving transitions between phase-shaped transverse states. Here, we elucidate an underlying connection between two ostensibly distinct optical polarization analogs identified in EEL experiments as manifestations of the same conserved scattering flux. Our work introduces a procedure for probing general tensorial target characteristics including global mode symmetries and local polarization

Identifying Structural Variation in Haploid Microbial Genomes from Short-Read Resequencing Data Using Breseq

Mutations that alter chromosomal structure play critical roles in evolution and disease, including in the origin of new lifestyles and pathogenic traits in microbes. Large-scale rearrangements in genomes are often mediated by recombination events involving new or existing copies of mobile genetic elements, recently duplicated genes, or other repetitive sequences. Most current software programs for predicting structural variation from short-read DNA resequencing data are intended primarily for use on human genomes. They typically disregard information in reads mapping to repeat sequences, and significant post-processing and manual examination of their output is often required to rule out false-positive predictions and precisely describe mutational events. Results: We have implemented an algorithm for identifying structural variation from DNA resequencing data as part of the breseq computational pipeline for predicting mutations in haploid microbial genomes. Our method evaluates the support for new sequence junctions present in a clonal sample from split-read alignments to a reference genome, including matches to repeat sequences. Then, it uses a statistical model of read coverage evenness to accept or reject these predictions. Finally, breseq combines predictions of new junctions and deleted chromosomal regions to output biologically relevant descriptions of mutations and their effects on genes. We demonstrate the performance of breseq on simulated Escherichia coli genomes with deletions generating unique breakpoint sequences, new insertions of mobile genetic elements, and deletions mediated by mobile elements. Then, we reanalyze data from an E. coli K-12 mutation accumulation evolution experiment in which structural variation was not previously identified. Transposon insertions and large-scale chromosomal changes detected by breseq account for similar to 25% of spontaneous mutations in this strain. In all cases, we find that breseq is able to reliably predict structural variation with modest read-depth coverage of the reference genome (>40-fold). Conclusions: Using breseq to predict structural variation should be useful for studies of microbial epidemiology, experimental evolution, synthetic biology, and genetics when a reference genome for a closely related strain is available. In these cases, breseq can discover mutations that may be responsible for important or unintended changes in genomes that might otherwise go undetected.U.S. National Institutes of Health R00-GM087550U.S. National Science Foundation (NSF) DEB-0515729NSF BEACON Center for the Study of Evolution in Action DBI-0939454Cancer Prevention & Research Institute of Texas (CPRIT) RP130124University of Texas at Austin startup fundsUniversity of Texas at AustinCPRIT Cancer Research TraineeshipMolecular Bioscience

FLEA: Fresnel-limited extraction algorithm applied to spectral phase interferometry for direct field reconstruction (SPIDER)

We present a novel extraction algorithm for spectral phase interferometry for direct field reconstruction (SPIDER) for the so-called X-SPIDER configuration. Our approach largely extends the measurable time windows of pulses without requiring any modification to the experimental X-SPIDER set-up.Comment: 24 pages 26 references 8 figure