52,792 research outputs found
Searching for Signatures of Cosmic Superstrings in the CMB
Because cosmic superstrings generically form junctions and gauge theoretic
strings typically do not, junctions may provide a signature to distinguish
between cosmic superstrings and gauge theoretic cosmic strings. In cosmic
microwave background anisotropy maps, cosmic strings lead to distinctive line
discontinuities. String junctions lead to junctions in these line
discontinuities. In turn, edge detection algorithms such as the Canny algorithm
can be used to search for signatures of strings in anisotropy maps. We apply
the Canny algorithm to simulated maps which contain the effects of cosmic
strings with and without string junctions. The Canny algorithm produces edge
maps. To distinguish between edge maps from string simulations with and without
junctions, we examine the density distribution of edges and pixels crossed by
edges. We find that in string simulations without Gaussian noise (such as
produced by the dominant inflationary fluctuations) our analysis of the output
data from the Canny algorithm can clearly distinguish between simulations with
and without string junctions. In the presence of Gaussian noise at the level
expected from the current bounds on the contribution of cosmic strings to the
total power spectrum of density fluctuations, the distinction between models
with and without junctions is more difficult. However, by carefully analyzing
the data the models can still be differentiated.Comment: 15 page
CASENet: Deep Category-Aware Semantic Edge Detection
Boundary and edge cues are highly beneficial in improving a wide variety of
vision tasks such as semantic segmentation, object recognition, stereo, and
object proposal generation. Recently, the problem of edge detection has been
revisited and significant progress has been made with deep learning. While
classical edge detection is a challenging binary problem in itself, the
category-aware semantic edge detection by nature is an even more challenging
multi-label problem. We model the problem such that each edge pixel can be
associated with more than one class as they appear in contours or junctions
belonging to two or more semantic classes. To this end, we propose a novel
end-to-end deep semantic edge learning architecture based on ResNet and a new
skip-layer architecture where category-wise edge activations at the top
convolution layer share and are fused with the same set of bottom layer
features. We then propose a multi-label loss function to supervise the fused
activations. We show that our proposed architecture benefits this problem with
better performance, and we outperform the current state-of-the-art semantic
edge detection methods by a large margin on standard data sets such as SBD and
Cityscapes.Comment: Accepted to CVPR 201
Detecting and localizing edges composed of steps, peaks and roofs
It is well known that the projection of depth or orientation
discontinuities in a physical scene results in image
intensity edges which are not ideal step edges but
are more typically a combination of steps, peak and
roof profiles. However most edge detection schemes
ignore the composite nature of these edges, resulting
in systematic errors in detection and localization. We
address the problem of detecting and localizing these
edges, while at the same time also solving the problem
of false responses in smoothly shaded regions with
constant gradient of the image brightness. We show
that a class of nonlinear filters, known as quadratic
filters, are appropriate for this task, while linear filters
are not. A series of performance criteria are derived
for characterizing the SNR, localization and multiple
responses of these filters in a manner analogous to
Canny's criteria for linear filters. A two-dimensional
version of the approach is developed which has the
property of being able to represent multiple edges at the
same location and determine the orientation of each
to any desired precision. This permits junctions to be
localized without rounding. Experimental results are
presented
Microwave studies of the fractional Josephson effect in HgTe-based Josephson junctions
The rise of topological phases of matter is strongly connected to their
potential to host Majorana bound states, a powerful ingredient in the search
for a robust, topologically protected, quantum information processing. In order
to produce such states, a method of choice is to induce superconductivity in
topological insulators. The engineering of the interplay between
superconductivity and the electronic properties of a topological insulator is a
challenging task and it is consequently very important to understand the
physics of simple superconducting devices such as Josephson junctions, in which
new topological properties are expected to emerge. In this article, we review
recent experiments investigating topological superconductivity in topological
insulators, using microwave excitation and detection techniques. More
precisely, we have fabricated and studied topological Josephson junctions made
of HgTe weak links in contact with two Al or Nb contacts. In such devices, we
have observed two signatures of the fractional Josephson effect, which is
expected to emerge from topologically-protected gapless Andreev bound states.
We first recall the theoretical background on topological Josephson junctions,
then move to the experimental observations. Then, we assess the topological
origin of the observed features and conclude with an outlook towards more
advanced microwave spectroscopy experiments, currently under development.Comment: Lectures given at the San Sebastian Topological Matter School 2017,
published in "Topological Matter. Springer Series in Solid-State Sciences,
vol 190. Springer
Fabrication and analogue applications of nanoSQUIDs using Dayem bridge junctions
We report here recent work at the U.K. National Physical Laboratory on developing nanoscale SQUIDs using Dayem bridge Josephson junctions. The advantages are simplicity of fabrication, exceptional low-noise performance, toward the quantum limit, and a range of novel applications. Focused ion beam patterned Nb SQUID, possessing exceptionally low noise (∼200 nΦ0/Hz1/2 above 1 kHz), and operating above 4.2 K can be applied to measurement of nanoscale magnetic objects or coupled to nanoelectromechanical resonators, as well as single particle detection of photons, protons, and ions. The limited operating temperature range may be extended by exposing the Dayem bridges to carefully controlled ion beam implantation, leading to nonreversible changes in junction transition temperature.The work reported here was supported in part by the EMRP projects ‘MetNEMS’ NEW-08 and ‘BioQUART’SIB-06. The EMRP is jointly funded by the EMRP participating countries within EURAMET and the European Union
- …