13,931 research outputs found
A wind tunnel investigation of the shape of uncharged raindrops in the presence of an external, electric field
Results of a wind tunnel experiment in which electrically uncharged water drops of 500 to 3000 microns equivalent radius are freely suspended in the vertical air stream of the UCLA cloud tunnel are presented. During this suspension the drops were exposed to external vertical electric fields of 500 to 8,000 volts/cm. The change in drop shape with drop size and electric field strength was noted and is discussed in the light of theoretical work cited in the literature which unfortunately does not take into account the effects of air flow past the drop. The wind tunnel study is documented by stills from a 16 mm film record that demonstrates the shape of water drops in response to both hydrodynamic and electric forces
Research study of some RAM antennas Final report, 18 Nov. 1964 - 18 Jun. 1965
Input impedance and radiation pattern determinations for cylindrical gap, waveguide excited and circular waveguide slot antenna array
Time-varying Learning and Content Analytics via Sparse Factor Analysis
We propose SPARFA-Trace, a new machine learning-based framework for
time-varying learning and content analytics for education applications. We
develop a novel message passing-based, blind, approximate Kalman filter for
sparse factor analysis (SPARFA), that jointly (i) traces learner concept
knowledge over time, (ii) analyzes learner concept knowledge state transitions
(induced by interacting with learning resources, such as textbook sections,
lecture videos, etc, or the forgetting effect), and (iii) estimates the content
organization and intrinsic difficulty of the assessment questions. These
quantities are estimated solely from binary-valued (correct/incorrect) graded
learner response data and a summary of the specific actions each learner
performs (e.g., answering a question or studying a learning resource) at each
time instance. Experimental results on two online course datasets demonstrate
that SPARFA-Trace is capable of tracing each learner's concept knowledge
evolution over time, as well as analyzing the quality and content organization
of learning resources, the question-concept associations, and the question
intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable
or better performance in predicting unobserved learner responses than existing
collaborative filtering and knowledge tracing approaches for personalized
education
Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
We introduce a class of interatomic potential models that can be
automatically generated from data consisting of the energies and forces
experienced by atoms, derived from quantum mechanical calculations. The
resulting model does not have a fixed functional form and hence is capable of
modeling complex potential energy landscapes. It is systematically improvable
with more data. We apply the method to bulk carbon, silicon and germanium and
test it by calculating properties of the crystals at high temperatures. Using
the interatomic potential to generate the long molecular dynamics trajectories
required for such calculations saves orders of magnitude in computational cost.Comment: v3-4: added new material and reference
Reliable estimation of prediction uncertainty for physico-chemical property models
The predictions of parameteric property models and their uncertainties are
sensitive to systematic errors such as inconsistent reference data, parametric
model assumptions, or inadequate computational methods. Here, we discuss the
calibration of property models in the light of bootstrapping, a sampling method
akin to Bayesian inference that can be employed for identifying systematic
errors and for reliable estimation of the prediction uncertainty. We apply
bootstrapping to assess a linear property model linking the 57Fe Moessbauer
isomer shift to the contact electron density at the iron nucleus for a diverse
set of 44 molecular iron compounds. The contact electron density is calculated
with twelve density functionals across Jacob's ladder (PWLDA, BP86, BLYP, PW91,
PBE, M06-L, TPSS, B3LYP, B3PW91, PBE0, M06, TPSSh). We provide systematic-error
diagnostics and reliable, locally resolved uncertainties for isomer-shift
predictions. Pure and hybrid density functionals yield average prediction
uncertainties of 0.06-0.08 mm/s and 0.04-0.05 mm/s, respectively, the latter
being close to the average experimental uncertainty of 0.02 mm/s. Furthermore,
we show that both model parameters and prediction uncertainty depend
significantly on the composition and number of reference data points.
Accordingly, we suggest that rankings of density functionals based on
performance measures (e.g., the coefficient of correlation, r2, or the
root-mean-square error, RMSE) should not be inferred from a single data set.
This study presents the first statistically rigorous calibration analysis for
theoretical Moessbauer spectroscopy, which is of general applicability for
physico-chemical property models and not restricted to isomer-shift
predictions. We provide the statistically meaningful reference data set MIS39
and a new calibration of the isomer shift based on the PBE0 functional.Comment: 49 pages, 9 figures, 7 table
Superconductor-Nanowire Devices from Tunneling to the Multichannel Regime: Zero-Bias Oscillations and Magnetoconductance Crossover
We present transport measurements in superconductor-nanowire devices with a
gated constriction forming a quantum point contact. Zero-bias features in
tunneling spectroscopy appear at finite magnetic fields, and oscillate in
amplitude and split away from zero bias as a function of magnetic field and
gate voltage. A crossover in magnetoconductance is observed: Magnetic fields
above ~ 0.5 T enhance conductance in the low-conductance (tunneling) regime but
suppress conductance in the high-conductance (multichannel) regime. We consider
these results in the context of Majorana zero modes as well as alternatives,
including Kondo effect and analogs of 0.7 structure in a disordered nanowire.Comment: Supplemental Material here:
https://dl.dropbox.com/u/1742676/Churchill_Supplemental.pd
Khovanov homology is an unknot-detector
We prove that a knot is the unknot if and only if its reduced Khovanov
cohomology has rank 1. The proof has two steps. We show first that there is a
spectral sequence beginning with the reduced Khovanov cohomology and abutting
to a knot homology defined using singular instantons. We then show that the
latter homology is isomorphic to the instanton Floer homology of the sutured
knot complement: an invariant that is already known to detect the unknot.Comment: 124 pages, 13 figure
Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes
Functional data are defined as realizations of random functions (mostly
smooth functions) varying over a continuum, which are usually collected with
measurement errors on discretized grids. In order to accurately smooth noisy
functional observations and deal with the issue of high-dimensional observation
grids, we propose a novel Bayesian method based on the Bayesian hierarchical
model with a Gaussian-Wishart process prior and basis function representations.
We first derive an induced model for the basis-function coefficients of the
functional data, and then use this model to conduct posterior inference through
Markov chain Monte Carlo. Compared to the standard Bayesian inference that
suffers serious computational burden and unstableness for analyzing
high-dimensional functional data, our method greatly improves the computational
scalability and stability, while inheriting the advantage of simultaneously
smoothing raw observations and estimating the mean-covariance functions in a
nonparametric way. In addition, our method can naturally handle functional data
observed on random or uncommon grids. Simulation and real studies demonstrate
that our method produces similar results as the standard Bayesian inference
with low-dimensional common grids, while efficiently smoothing and estimating
functional data with random and high-dimensional observation grids where the
standard Bayesian inference fails. In conclusion, our method can efficiently
smooth and estimate high-dimensional functional data, providing one way to
resolve the curse of dimensionality for Bayesian functional data analysis with
Gaussian-Wishart processes.Comment: Under revie
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