4,203 research outputs found
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 . More specifically, we propose an algorithm that,
given , estimates AUC within , and can maintain this
estimate in time, per update, as the window slides.
This provides a speed-up over the exact computation of AUC, which requires
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 () the groups are small enough, so that the error stays
small, () the number of groups is small, so that enumerating them is not
expensive, and () 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 ,
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 studentsā 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 for the fit parameter . This
value gives the current best laboratory limit. In addition, our value of
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 and .Comment: 6 pages, 3 figure
Measuring classifier performance: aĀ coherent alternative to the area under the ROC curve
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
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