43,471 research outputs found
Contextual normalization applied to aircraft gas turbine engine diagnosis
Diagnosing faults in aircraft gas turbine engines is a complex problem. It involves several tasks,
including rapid and accurate interpretation of patterns in engine sensor data. We have investigated
contextual normalization for the development of a software tool to help engine repair technicians
with interpretation of sensor data. Contextual normalization is a new strategy for employing
machine learning. It handles variation in data that is due to contextual factors, rather than the
health of the engine. It does this by normalizing the data in a context-sensitive manner. This
learning strategy was developed and tested using 242 observations of an aircraft gas turbine
engine in a test cell, where each observation consists of roughly 12,000 numbers, gathered over a
12 second interval. There were eight classes of observations: seven deliberately implanted classes
of faults and a healthy class. We compared two approaches to implementing our learning strategy:
linear regression and instance-based learning. We have three main results. (1) For the given
problem, instance-based learning works better than linear regression. (2) For this problem,
contextual normalization works better than other common forms of normalization. (3) The
algorithms described here can be the basis for a useful software tool for assisting technicians with
the interpretation of sensor data
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
This paper contributes improvements on both the effectiveness and efficiency
of Matrix Factorization (MF) methods for implicit feedback. We highlight two
critical issues of existing works. First, due to the large space of unobserved
feedback, most existing works resort to assign a uniform weight to the missing
data to reduce computational complexity. However, such a uniform assumption is
invalid in real-world settings. Second, most methods are also designed in an
offline setting and fail to keep up with the dynamic nature of online data. We
address the above two issues in learning MF models from implicit feedback. We
first propose to weight the missing data based on item popularity, which is
more effective and flexible than the uniform-weight assumption. However, such a
non-uniform weighting poses efficiency challenge in learning the model. To
address this, we specifically design a new learning algorithm based on the
element-wise Alternating Least Squares (eALS) technique, for efficiently
optimizing a MF model with variably-weighted missing data. We exploit this
efficiency to then seamlessly devise an incremental update strategy that
instantly refreshes a MF model given new feedback. Through comprehensive
experiments on two public datasets in both offline and online protocols, we
show that our eALS method consistently outperforms state-of-the-art implicit MF
methods. Our implementation is available at
https://github.com/hexiangnan/sigir16-eals.Comment: 10 pages, 8 figure
Removal Energies and Final State Interaction in Lepton Nucleus Scattering
We investigate the binding energy parameters that should be used in modeling
electron and neutrino scattering from nucleons bound in a nucleus within the
framework of the impulse approximation. We discuss the relation between binding
energy, missing energy, removal energy (), spectral functions and
shell model energy levels and extract updated removal energy parameters from
eep spectral function data. We address the difference in parameters
for scattering from bound protons and neutrons. We also use inclusive e-A data
to extract an empirical parameter to account
for the interaction of final state nucleons (FSI) with the optical potential of
the nucleus. Similarly we use to account for the Coulomb potential of
the nucleus. With three parameters ,
and we can describe the energy of final state electrons for all
available electron QE scattering data. The use of the updated parameters in
neutrino Monte Carlo generators reduces the systematic uncertainty in the
combined removal energy (with FSI corrections) from 20 MeV to 5
MeV.Comment: 21 pages, 22 Figures, 11 Tables, Accepted for publication in Eur.
Phys. J. C. 2019, all fits to Optical potential redone with respect to
(q3+k)^
Momentum Distribution in Nuclear Matter and Finite Nuclei
A simple method is presented to evaluate the effects of short-range
correlations on the momentum distribution of nucleons in nuclear matter within
the framework of the Green's function approach. The method provides a very
efficient representation of the single-particle Green's function for a
correlated system. The reliability of this method is established by comparing
its results to those obtained in more elaborate calculations. The sensitivity
of the momentum distribution on the nucleon-nucleon interaction and the nuclear
density is studied. The momentum distributions of nucleons in finite nuclei are
derived from those in nuclear matter using a local-density approximation. These
results are compared to those obtained directly for light nuclei like .Comment: 17 pages REVTeX, 10 figures ps files adde
Improving the Efficiency of Genomic Selection
We investigate two approaches to increase the efficiency of phenotypic
prediction from genome-wide markers, which is a key step for genomic selection
(GS) in plant and animal breeding. The first approach is feature selection
based on Markov blankets, which provide a theoretically-sound framework for
identifying non-informative markers. Fitting GS models using only the
informative markers results in simpler models, which may allow cost savings
from reduced genotyping. We show that this is accompanied by no loss, and
possibly a small gain, in predictive power for four GS models: partial least
squares (PLS), ridge regression, LASSO and elastic net. The second approach is
the choice of kinship coefficients for genomic best linear unbiased prediction
(GBLUP). We compare kinships based on different combinations of centring and
scaling of marker genotypes, and a newly proposed kinship measure that adjusts
for linkage disequilibrium (LD).
We illustrate the use of both approaches and examine their performances using
three real-world data sets from plant and animal genetics. We find that elastic
net with feature selection and GBLUP using LD-adjusted kinships performed
similarly well, and were the best-performing methods in our study.Comment: 17 pages, 5 figure
Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning
Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In
these media, dynamic and still elements are juxtaposed to create an artistic
and narrative experience. Creating a high-quality, aesthetically pleasing
cinemagraph requires isolating objects in a semantically meaningful way and
then selecting good start times and looping periods for those objects to
minimize visual artifacts (such a tearing). To achieve this, we present a new
technique that uses object recognition and semantic segmentation as part of an
optimization method to automatically create cinemagraphs from videos that are
both visually appealing and semantically meaningful. Given a scene with
multiple objects, there are many cinemagraphs one could create. Our method
evaluates these multiple candidates and presents the best one, as determined by
a model trained to predict human preferences in a collaborative way. We
demonstrate the effectiveness of our approach with multiple results and a user
study.Comment: To appear in ICCV 2017. Total 17 pages including the supplementary
materia
Selected Topics in High Energy Semi-Exclusive Electro-Nuclear Reactions
We review the present status of the theory of high energy reactions with
semi-exclusive nucleon electro-production from nuclear targets. We demonstrate
how the increase of transferred energies in these reactions opens a complete
new window in studying the microscopic nuclear structure at small distances.
The simplifications in theoretical descriptions associated with the increase of
the energies are discussed. The theoretical framework for calculation of high
energy nuclear reactions based on the effective Feynman diagram rules is
described in details. The result of this approach is the generalized eikonal
approximation (GEA), which is reduced to Glauber approximation when nucleon
recoil is neglected. The method of GEA is demonstrated in the calculation of
high energy electro-disintegration of the deuteron and A=3 targets.
Subsequently we generalize the obtained formulae for A>3 nuclei. The relation
of GEA to the Glauber theory is analyzed. Then based on the GEA framework we
discuss some of the phenomena which can be studied in exclusive reactions,
these are: nuclear transparency and short-range correlations in nuclei. We
illustrate how light-cone dynamics of high-energy scattering emerge naturally
in high energy electro-nuclear reactions.Comment: LaTex file with 51 pages and 23 eps figure
Stochastic Optimal Prediction with Application to Averaged Euler Equations
Optimal prediction (OP) methods compensate for a lack of resolution in the
numerical solution of complex problems through the use of an invariant measure
as a prior measure in the Bayesian sense. In first-order OP, unresolved
information is approximated by its conditional expectation with respect to the
invariant measure. In higher-order OP, unresolved information is approximated
by a stochastic estimator, leading to a system of random or stochastic
differential equations.
We explain the ideas through a simple example, and then apply them to the
solution of Averaged Euler equations in two space dimensions.Comment: 13 pages, 2 figure
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