17,247 research outputs found
Tailoring a coherent control solution landscape by linear transforms of spectral phase basis
Finding an optimal phase pattern in a multidimensional solution landscape becomes easier and faster if local optima are suppressed and contour lines are tailored towards closed convex patterns. Using wideband second harmonic generation as a coherent control test case, we show that a linear combination of spectral phase basis functions can result in such improvements and also in separable phase terms, each of which can be found independently. The improved shapes are attributed to a suppressed nonlinear shear, changing the relative orientation of contour lines. The first order approximation of the process shows a simple relation between input and output phase profiles, useful for pulse shaping at ultraviolet wavelengths
GalPak3D: A Bayesian parametric tool for extracting morpho-kinematics of galaxies from 3D data
We present a method to constrain galaxy parameters directly from
three-dimensional data cubes. The algorithm compares directly the data with a
parametric model mapped in coordinates. It uses the spectral
lines-spread function (LSF) and the spatial point-spread function (PSF) to
generate a three-dimensional kernel whose characteristics are instrument
specific or user generated. The algorithm returns the intrinsic modeled
properties along with both an `intrinsic' model data cube and the modeled
galaxy convolved with the 3D-kernel. The algorithm uses a Markov Chain Monte
Carlo (MCMC) approach with a nontraditional proposal distribution in order to
efficiently probe the parameter space. We demonstrate the robustness of the
algorithm using 1728 mock galaxies and galaxies generated from hydrodynamical
simulations in various seeing conditions from 0.6" to 1.2". We find that the
algorithm can recover the morphological parameters (inclination, position
angle) to within 10% and the kinematic parameters (maximum rotation velocity)
to within 20%, irrespectively of the PSF in seeing (up to 1.2") provided that
the maximum signal-to-noise ratio (SNR) is greater than pixel
and that the ratio of the galaxy half-light radius to seeing radius is greater
than about 1.5. One can use such an algorithm to constrain simultaneously the
kinematics and morphological parameters of (nonmerging) galaxies observed in
nonoptimal seeing conditions. The algorithm can also be used on adaptive-optics
(AO) data or on high-quality, high-SNR data to look for nonaxisymmetric
structures in the residuals.Comment: 16 pages, 10 figures, accepted to publication in AJ, revised version
after proofs corrections. Algorithm available at http://galpak.irap.omp.e
LO-Net: Deep Real-time Lidar Odometry
We present a novel deep convolutional network pipeline, LO-Net, for real-time
lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations
that go through individually designed feature selection, feature matching, and
pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a
new mask-weighted geometric constraint loss, LO-Net can effectively learn
feature representation for LO estimation, and can implicitly exploit the
sequential dependencies and dynamics in the data. We also design a scan-to-map
module, which uses the geometric and semantic information learned in LO-Net, to
improve the estimation accuracy. Experiments on benchmark datasets demonstrate
that LO-Net outperforms existing learning based approaches and has similar
accuracy with the state-of-the-art geometry-based approach, LOAM
ArtDeco: A beam deconvolution code for absolute CMB measurements
We present a method for beam deconvolution for cosmic microwave background
(CMB) anisotropy measurements. The code takes as input the time-ordered data,
along with the corresponding detector pointings and known beam shapes, and
produces as output the harmonic a_Tlm, a_Elm, and a_Blm coefficients of the
observed sky. From these one can further construct temperature and Q and U
polarisation maps. The method is applicable to absolute CMB measurements with
wide sky coverage, and is independent of the scanning strategy. We test the
code with extensive simulations, mimicking the resolution and data volume of
Planck 30GHz and 70GHz channels, but with exaggerated beam asymmetry. We apply
it to multipoles up to l=1700 and examine the results in both pixel space and
harmonic space. We also test the method also in presence of white noise.Comment: 15 page
Millisecond single-molecule localization microscopy combined with convolution analysis and automated image segmentation to determine protein concentrations in complexly structured, functional cells, one cell at a time
We present a single-molecule tool called the CoPro (Concentration of
Proteins) method that uses millisecond imaging with convolution analysis,
automated image segmentation and super-resolution localization microscopy to
generate robust estimates for protein concentration in different compartments
of single living cells, validated using realistic simulations of complex
multiple compartment cell types. We demonstrates its utility experimentally on
model Escherichia coli bacteria and Saccharomyces cerevisiae budding yeast
cells, and use it to address the biological question of how signals are
transduced in cells. Cells in all domains of life dynamically sense their
environment through signal transduction mechanisms, many involving gene
regulation. The glucose sensing mechanism of S. cerevisiae is a model system
for studying gene regulatory signal transduction. It uses the multi-copy
expression inhibitor of the GAL gene family, Mig1, to repress unwanted genes in
the presence of elevated extracellular glucose concentrations. We fluorescently
labelled Mig1 molecules with green fluorescent protein (GFP) via chromosomal
integration at physiological expression levels in living S. cerevisiae cells,
in addition to the RNA polymerase protein Nrd1 with the fluorescent protein
reporter mCherry. Using CoPro we make quantitative estimates of Mig1 and Nrd1
protein concentrations in the cytoplasm and nucleus compartments on a
cell-by-cell basis under physiological conditions. These estimates indicate a
4-fold shift towards higher values in concentration of diffusive Mig1 in the
nucleus if the external glucose concentration is raised, whereas equivalent
levels in the cytoplasm shift to smaller values with a relative change an order
of magnitude smaller. This compares with Nrd1 which is not involved directly in
glucose sensing, which is almost exclusively localized in the nucleus under
high and..
The Impact of Atmospheric Fluctuations on Degree-scale Imaging of the Cosmic Microwave Background
Fluctuations in the brightness of the Earth's atmosphere originating from
water vapor are an important source of noise for ground-based instruments
attempting to measure anisotropy in the Cosmic Microwave Background. This paper
presents a model for the atmospheric fluctuations and derives simple
expressions to predict the contribution of the atmosphere to experimental
measurements. Data from the South Pole and from the Atacama Desert in Chile,
two of the driest places on Earth, are used to assess the level of fluctuations
at each site.Comment: 29 pages, 7 figures, 1 table, appears in The Astrophysical Journa
Variational Downscaling, Fusion and Assimilation of Hydrometeorological States via Regularized Estimation
Improved estimation of hydrometeorological states from down-sampled
observations and background model forecasts in a noisy environment, has been a
subject of growing research in the past decades. Here, we introduce a unified
framework that ties together the problems of downscaling, data fusion and data
assimilation as ill-posed inverse problems. This framework seeks solutions
beyond the classic least squares estimation paradigms by imposing proper
regularization, which are constraints consistent with the degree of smoothness
and probabilistic structure of the underlying state. We review relevant
regularization methods in derivative space and extend classic formulations of
the aforementioned problems with particular emphasis on hydrologic and
atmospheric applications. Informed by the statistical characteristics of the
state variable of interest, the central results of the paper suggest that
proper regularization can lead to a more accurate and stable recovery of the
true state and hence more skillful forecasts. In particular, using the Tikhonov
and Huber regularization in the derivative space, the promise of the proposed
framework is demonstrated in static downscaling and fusion of synthetic
multi-sensor precipitation data, while a data assimilation numerical experiment
is presented using the heat equation in a variational setting
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