Tables of f/us, ub/ and g/us, ub/ functions for semiconductor surface calculations

Derivation of mathematical functions for calculating changes in semiconductor surfaces due to applied surface charg

Integrating The Executive MBA Curriculum: Tales Of The Cat Herder

Continuous improvement has been a strategic priority for Loyola College in Maryland’s Executive MBA (EMBA) Program since the program’s inception in 1973.  In the summer of 2008, Loyola began an intensive EMBA curriculum review.  The process resulted in a recommendation to make a significant shift in the curriculum’s emphasis. This paper reports on the factors involved in that review process and the leadership lessons learned from the endeavor.  The lessons learned are reported using the metaphor of tales of the cat herder in reference to a widely-held belief among academicians that working with faculty is like herding cats

Joint and individual variation explained (JIVE) for integrated analysis of multiple data types

Research in several fields now requires the analysis of data sets in which multiple high-dimensional types of data are available for a common set of objects. In particular, The Cancer Genome Atlas (TCGA) includes data from several diverse genomic technologies on the same cancerous tumor samples. In this paper we introduce Joint and Individual Variation Explained (JIVE), a general decomposition of variation for the integrated analysis of such data sets. The decomposition consists of three terms: a low-rank approximation capturing joint variation across data types, low-rank approximations for structured variation individual to each data type, and residual noise. JIVE quantifies the amount of joint variation between data types, reduces the dimensionality of the data and provides new directions for the visual exploration of joint and individual structures. The proposed method represents an extension of Principal Component Analysis and has clear advantages over popular two-block methods such as Canonical Correlation Analysis and Partial Least Squares. A JIVE analysis of gene expression and miRNA data on Glioblastoma Multiforme tumor samples reveals gene-miRNA associations and provides better characterization of tumor types. Data and software are available at https://genome.unc.edu/jive/Comment: Published in at http://dx.doi.org/10.1214/12-AOAS597 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

On the politics and ambition of the ‘turn’: unpacking the relations between Future 1 and Future 3

This paper suggests that advocates of the ‘knowledge turn’ have been united in their opposition to Young and Muller’s (2010) Future 2, but that this ‘union’ has masked very different views of the relations between Young and Muller’s Future 1 and Future 3. Whereas some who subscribe to the ‘turn’ see a ‘weak boundary’ between Futures 1 and 3 (and therefore consider them similar), others construe these Futures as very different and strongly bounded. We argue that these positions are often underpinned by irreconcilable political persuasions and conceptions of education, society and the curriculum. In order to illustrate the argument, we discuss the political project of the UK-based Academy of Ideas, many of whose members have been involved in advocating implicitly or explicitly for a weak boundary between Futures 1 and 3. This position is then contrasted with those in the UK who are more strongly committed to exploring a distinctive Future 3, and the situation in South Africa, where the tensions between different educational Futures are acutely visible due to the social, cultural and political context and academic and policy debates around the curriculum. We conclude with some implications of our arguments for the Future 3 principles of disciplinarity and sociality

Simulated LSST Survey of RR Lyrae Stars throughout the Local Group

We report on a study to determine the efficiency of the Large Synoptic Survey Telescope (LSST) to recover the periods, brightnesses, and shapes of RR Lyrae stars' light curves in the volume extending to heliocentric distances of 1.5 Mpc. We place the smoothed light curves of 30 type ab and 10 type c RR Lyrae stars in 1007 fields across the sky, each of which represents a different realization of the LSST sampling cadences, and that sample five particular observing modes. A light curve simulation tool was used to sample the idealized RR Lyrae stars' light curves, returning each as it would have been observed by LSST, including realistic photometric scatter, limiting magnitudes, and telescope downtime. We report here the period, brightness, and light curve shape recovery as a function of apparent magnitude and for survey lengths varying from 1 to 10 years. We find that 10 years of LSST data are sufficient to recover the pulsation periods with a fractional precision of ~10^(–5) for ≥90% of ab stars within ≈360 kpc of the Sun in Universal Cadence fields and out to ≈760 kpc for Deep Drilling fields. The 50% completeness level extends to ≈600 kpc and ≈1.0 Mpc for the same fields, respectively. For virtually all stars that had their periods recovered, their light curve shape parameter φ_31 was recovered with sufficient precision to also recover photometric metallicities to within 0.14 dex (the systematic error in the photometric relations). With RR Lyrae stars' periods and metallicities well measured to these distances, LSST will be able to search for halo streams and dwarf satellite galaxies over half of the Local Group, informing galaxy formation models and providing essential data for mapping the Galactic potential. This study also informs the LSST science operations plan for optimizing observing strategies to achieve particular science goals. We additionally present a new [Fe/H]-φ_31 photometric relation in the r band and a new and generally useful metric for defining period recovery for time domain surveys

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics

Safety Performance of Airborne Separation: Preliminary Baseline Testing

The Safety Performance of Airborne Separation (SPAS) study is a suite of Monte Carlo simulation experiments designed to analyze and quantify safety behavior of airborne separation. This paper presents results of preliminary baseline testing. The preliminary baseline scenario is designed to be very challenging, consisting of randomized routes in generic high-density airspace in which all aircraft are constrained to the same flight level. Sustained traffic density is varied from approximately 3 to 15 aircraft per 10,000 square miles, approximating up to about 5 times today s traffic density in a typical sector. Research at high traffic densities and at multiple flight levels are planned within the next two years. Basic safety metrics for aircraft separation are collected and analyzed. During the progression of experiments, various errors, uncertainties, delays, and other variables potentially impacting system safety will be incrementally introduced to analyze the effect on safety of the individual factors as well as their interaction and collective effect. In this paper we report the results of the first experiment that addresses the preliminary baseline condition tested over a range of traffic densities. Early results at five times the typical traffic density in today s NAS indicate that, under the assumptions of this study, airborne separation can be safely performed. In addition, we report on initial observations from an exploration of four additional factors tested at a single traffic density: broadcast surveillance signal interference, extent of intent sharing, pilot delay, and wind prediction error

Artificial Intelligence and National Security

This report discuses research and development of artificial intelligence (AI) applications for the military, their potential uses, and the programs of competing nations such as China and Russia