1,649 research outputs found
Comprehensive quantum Monte Carlo study of the quantum critical points in planar dimerized/quadrumerized Heisenberg models
We study two planar square lattice Heisenberg models with explicit
dimerization or quadrumerization of the couplings in the form of ladder and
plaquette arrangements. We investigate the quantum critical points of those
models by means of (stochastic series expansion) quantum Monte Carlo
simulations as a function of the coupling ratio . The
critical point of the order-disorder quantum phase transition in the ladder
model is determined as improving on previous
studies. For the plaquette model we obtain
establishing a first benchmark for this model from quantum Monte Carlo
simulations. Based on those values we give further convincing evidence that the
models are in the three-dimensional (3D) classical Heisenberg universality
class. The results of this contribution shall be useful as references for
future investigations on planar Heisenberg models such as concerning the
influence of non-magnetic impurities at the quantum critical point.Comment: 10+ pages, 7 figures, 4 table
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
Image segmentation is a fundamental problem in biomedical image analysis.
Recent advances in deep learning have achieved promising results on many
biomedical image segmentation benchmarks. However, due to large variations in
biomedical images (different modalities, image settings, objects, noise, etc),
to utilize deep learning on a new application, it usually needs a new set of
training data. This can incur a great deal of annotation effort and cost,
because only biomedical experts can annotate effectively, and often there are
too many instances in images (e.g., cells) to annotate. In this paper, we aim
to address the following question: With limited effort (e.g., time) for
annotation, what instances should be annotated in order to attain the best
performance? We present a deep active learning framework that combines fully
convolutional network (FCN) and active learning to significantly reduce
annotation effort by making judicious suggestions on the most effective
annotation areas. We utilize uncertainty and similarity information provided by
FCN and formulate a generalized version of the maximum set cover problem to
determine the most representative and uncertain areas for annotation. Extensive
experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node
ultrasound image segmentation dataset show that, using annotation suggestions
by our method, state-of-the-art segmentation performance can be achieved by
using only 50% of training data.Comment: Accepted at MICCAI 201
Research on nonlinear optical materials: an assessment. IV. Photorefractive and liquid crystal materials
This panel considered two separate subject areas: photorefractive materials used for nonlinear optics and liquid crystal materials used in light valves. Two related subjects were not considered due to lack of expertise on the panel: photorefractive materials used in light valves and liquid crystal materials used in nonlinear optics. Although the inclusion of a discussion of light valves by a panel on nonlinear optical materials at first seems odd, it is logical because light valves and photorefractive materials perform common functions
Optical homodyne tomography with polynomial series expansion
We present and demonstrate a method for optical homodyne tomography based on
the inverse Radon transform. Different from the usual filtered back-projection
algorithm, this method uses an appropriate polynomial series to expand the
Wigner function and the marginal distribution and discretize Fourier space. We
show that this technique solves most technical difficulties encountered with
kernel deconvolution based methods and reconstructs overall better and smoother
Wigner functions. We also give estimators of the reconstruction errors for both
methods and show improvement in noise handling properties and resilience to
statistical errors.Comment: v3: 3 typos were corrected in some mathematical expressions. v2: Many
typos corrected. Added a paragraph on distance to target state in Sec. I
Design strategies for optimizing holographic optical tweezers setups
We provide a detailed account of the construction of a system of holographic
optical tweezers. While much information is available on the design, alignment
and calibration of other optical trapping configurations, those based on
holography are relatively poorly described. Inclusion of a spatial light
modulator in the setup gives rise to particular design trade-offs and
constraints, and the system benefits from specific optimization strategies,
which we discuss.Comment: 16 pages, 15 figure
Variability and multi-periodic oscillations in the X-ray light curve of the classical nova V4743 Sgr
The classical nova V4743 Sgr was observed with XMM-Newton for about 10 hours
on April 4 2003, 6.5 months after optical maximum. At this time, this nova had
become the brightest supersoft X-ray source ever observed. We present the
results of a time series analysis performed on the X-ray light curve obtained
in this observation, and in a previous shorter observation done with Chandra 16
days earlier. Intense variability, with amplitude as large as 40% of the total
flux, was observed both times. Similarities can be found between the two
observations in the structure of the variations. Most of the variability is
well represented as a combination of oscillations at a set of discrete
frequencies lower than 1.7 mHz. At least five frequencies are constant over the
16 day time interval between the two observations. We suggest that a periods in
the power spectrum of both light curves at the frequency of 0.75 mHz and its
first harmonic are related to the spin period of the white dwarf in the system,
and that other observed frequencies are signatures of nonradial white dwarf
pulsations. A possible signal with a 24000 sec period is also found in the
XMM-Newton light curve: a cycle and a half are clearly identified. This period
is consistent with the 24278 s periodicity discovered in the optical light
curve of the source and thought to be the orbital period of the nova binary
system.Comment: In press in Monthly Notices of the Royal Astronomical Societ
A manually reconfigurable reflective spatial sound modulator for ultrasonic waves in air
Precise control of ultrasonic acoustic waves with frequencies f ≳ 20 kHz is useful in a range of applications from ultrasonic scanners to nondestructive testing and consumer haptic devices. A spatial sound modulator (SSM) is the acoustic analogy to the spatial light modulator (SLM) in optics and is highly sought after by acoustics researchers. A spatial sound modulator is constrained by very distinct practical conditions. Namely, it must be a reconfigurable device which modulates sound arbitrarily from a decoupled source. Here a reflective phase modulating device is realized, whose local units can be tuned to imprint a phase signature to an incoming wave. It is manually reconfigurable and consists of 1024 rigidly ended square waveguides with sliding bottom surfaces to provide variable phase delays. Experiments demonstrate the ability of this device to focus ultrasonic waves in air at different points in space, generate accurate pressure landscapes, and perform multiplane holography. Moreover, thanks to the subwavelength nature of the unit cells, this device outperforms state‐of‐the‐art phased‐array transducers of the same size in the quality and energy distribution of generated acoustic holographic images. These results pave the way for the construction of electronically controlled reflective SSM
Critical Exponents of the Classical 3D Heisenberg Model: A Single-Cluster Monte Carlo Study
We have simulated the three-dimensional Heisenberg model on simple cubic
lattices, using the single-cluster Monte Carlo update algorithm. The expected
pronounced reduction of critical slowing down at the phase transition is
verified. This allows simulations on significantly larger lattices than in
previous studies and consequently a better control over systematic errors. In
one set of simulations we employ the usual finite-size scaling methods to
compute the critical exponents from a few
measurements in the vicinity of the critical point, making extensive use of
histogram reweighting and optimization techniques. In another set of
simulations we report measurements of improved estimators for the spatial
correlation length and the susceptibility in the high-temperature phase,
obtained on lattices with up to spins. This enables us to compute
independent estimates of and from power-law fits of their
critical divergencies.Comment: 33 pages, 12 figures (not included, available on request). Preprint
FUB-HEP 19/92, HLRZ 77/92, September 199
A Path Algorithm for Constrained Estimation
Many least squares problems involve affine equality and inequality
constraints. Although there are variety of methods for solving such problems,
most statisticians find constrained estimation challenging. The current paper
proposes a new path following algorithm for quadratic programming based on
exact penalization. Similar penalties arise in regularization in model
selection. Classical penalty methods solve a sequence of unconstrained problems
that put greater and greater stress on meeting the constraints. In the limit as
the penalty constant tends to , one recovers the constrained solution.
In the exact penalty method, squared penalties are replaced by absolute value
penalties, and the solution is recovered for a finite value of the penalty
constant. The exact path following method starts at the unconstrained solution
and follows the solution path as the penalty constant increases. In the
process, the solution path hits, slides along, and exits from the various
constraints. Path following in lasso penalized regression, in contrast, starts
with a large value of the penalty constant and works its way downward. In both
settings, inspection of the entire solution path is revealing. Just as with the
lasso and generalized lasso, it is possible to plot the effective degrees of
freedom along the solution path. For a strictly convex quadratic program, the
exact penalty algorithm can be framed entirely in terms of the sweep operator
of regression analysis. A few well chosen examples illustrate the mechanics and
potential of path following.Comment: 26 pages, 5 figure
Monte Carlo Study of Cluster-Diameter Distribution: A New Observable to Estimate Correlation Lengths
We report numerical simulations of two-dimensional -state Potts models
with emphasis on a new quantity for the computation of spatial correlation
lengths. This quantity is the cluster-diameter distribution function
, which measures the distribution of the diameter of
stochastically defined cluster. Theoretically it is predicted to fall off
exponentially for large diameter , , where
is the correlation length as usually defined through the large-distance
behavior of two-point correlation functions. The results of our extensive Monte
Carlo study in the disordered phase of the models with , 15, and on
large square lattices of size , , and , respectively, clearly confirm the theoretically predicted behavior.
Moreover, using this observable we are able to verify an exact formula for the
correlation length in the disordered phase at the first-order
transition point with an accuracy of about for all considered
values of . This is a considerable improvement over estimates derived from
the large-distance behavior of standard (projected) two-point correlation
functions, which are also discussed for comparison.Comment: 20 pages, LaTeX + 13 postscript figures. See also
http://www.cond-mat.physik.uni-mainz.de/~janke/doc/home_janke.htm
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