Precision voltage regulator

Balanced positive and negative voltage output circuit, in which error voltage for control is developed from difference in absolute value of positive and negative voltages referenced to a common point, regulates voltage for use with inertial reference unit. Fast-acting, temperature-compensated, high-gain operational amplifier circuits maintain common point

Electronic integrator for gyro rate output voltages

Circuit which integrates spacecraft gyro output voltages to provide analog position signals has been developed. Accurate integration is provided by all solid state system which uses no choppers and takes advantage of commercially available flight qualified components

Bayesian anomaly detection methods for social networks

Learning the network structure of a large graph is computationally demanding, and dynamically monitoring the network over time for any changes in structure threatens to be more challenging still. This paper presents a two-stage method for anomaly detection in dynamic graphs: the first stage uses simple, conjugate Bayesian models for discrete time counting processes to track the pairwise links of all nodes in the graph to assess normality of behavior; the second stage applies standard network inference tools on a greatly reduced subset of potentially anomalous nodes. The utility of the method is demonstrated on simulated and real data sets.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS329 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Efficient estimation of AUC in a sliding window

In many applications, monitoring area under the ROC curve (AUC) in a sliding window over a data stream is a natural way of detecting changes in the system. The drawback is that computing AUC in a sliding window is expensive, especially if the window size is large and the data flow is significant. In this paper we propose a scheme for maintaining an approximate AUC in a sliding window of length $k$. More specifically, we propose an algorithm that, given $\epsilon$, estimates AUC within $\epsilon / 2$, and can maintain this estimate in $O((\log k) / \epsilon)$ time, per update, as the window slides. This provides a speed-up over the exact computation of AUC, which requires $O(k)$ time, per update. The speed-up becomes more significant as the size of the window increases. Our estimate is based on grouping the data points together, and using these groups to calculate AUC. The grouping is designed carefully such that ($i$) the groups are small enough, so that the error stays small, ($ii$) the number of groups is small, so that enumerating them is not expensive, and ($iii$) the definition is flexible enough so that we can maintain the groups efficiently. Our experimental evaluation demonstrates that the average approximation error in practice is much smaller than the approximation guarantee $\epsilon / 2$, and that we can achieve significant speed-ups with only a modest sacrifice in accuracy

Noticing for Equity to Sustain Multilingual Literacies

This department explores how teachers can sustain students’ multilingual literacies and reimagine literacy learning across multiple contexts in conversation with researchers, practitioners, and communities

Finding Groups in Gene Expression Data

The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large-scale, high-throughput investigations of gene activity and have thus provided the data analyst with a distinctive, high-dimensional field of study. Many questions in this field relate to finding subgroups of data profiles which are very similar. A popular type of exploratory tool for finding subgroups is cluster analysis, and many different flavors of algorithms have been used and indeed tailored for microarray data. Cluster analysis, however, implies a partitioning of the entire data set, and this does not always match the objective. Sometimes pattern discovery or bump hunting tools are more appropriate. This paper reviews these various tools for finding interesting subgroups

Investigation of the Gravitational Potential Dependence of the Fine-Structure Constant Using Atomic Dysprosium

Radio-frequency E1 transitions between nearly degenerate, opposite parity levels of atomic dysprosium were monitored over an eight month period to search for a variation in the fine-structure constant. During this time period, data were taken at different points in the gravitational potential of the Sun. The data are fitted to the variation in the gravitational potential yielding a value of $(-8.7 \pm 6.6) \times 10^{-6}$ for the fit parameter $k_\alpha$. This value gives the current best laboratory limit. In addition, our value of $k_{\alpha}$ combined with other experimental constraints is used to extract the first limits on k_e and k_q. These coefficients characterize the variation of m_e/m_p and m_q/m_p in a changing gravitational potential, where m_e, m_p, and m_q are electron, proton, and quark masses. The results are $k_e = (4.9 \pm 3.9) \times 10^{-5}$ and $k_q = (6.6 \pm 5.2) \times 10^{-5}$.Comment: 6 pages, 3 figure

On the combination of omics data for prediction of binary outcomes

Enrichment of predictive models with new biomolecular markers is an important task in high-dimensional omic applications. Increasingly, clinical studies include several sets of such omics markers available for each patient, measuring different levels of biological variation. As a result, one of the main challenges in predictive research is the integration of different sources of omic biomarkers for the prediction of health traits. We review several approaches for the combination of omic markers in the context of binary outcome prediction, all based on double cross-validation and regularized regression models. We evaluate their performance in terms of calibration and discrimination and we compare their performance with respect to single-omic source predictions. We illustrate the methods through the analysis of two real datasets. On the one hand, we consider the combination of two fractions of proteomic mass spectrometry for the calibration of a diagnostic rule for the detection of early-stage breast cancer. On the other hand, we consider transcriptomics and metabolomics as predictors of obesity using data from the Dietary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome (DILGOM) study, a population-based cohort, from Finland

Dear Wife : the Civil War letters of Chester K. Leach

Occasional paper (University of Vermont. Center for Research on Vermont) ; no. 20