17,522 research outputs found
Supersolid phase in spin dimer XXZ systems under magnetic field
Using quantum Monte Carlo method, we study, under external magnetic fields,
the ground state phase diagram of the two-dimensional spin =1/2 dimer model
with an anisotropic intra-plane antiferromagnetic coupling. With the anisotropy
, a supersolid phase characterized by a non-uniform
bose condensate density that breaks translational symmetry is found. The rich
phase diagram also contains a checkerboard solid and two different types of
superfluid phase formed by and spin triplets, with finite
staggered magnetization in z-axis and in-plane direction, respectively. As we
show, the model can be realized as a consequence of including the next nearest
neighbor coupling among dimers and our results suggest that spin dimer systems
may be an ideal model system to study the supersolid phase.Comment: 4 pages, 5 figure
Conductor-backed coplanar waveguide resonators of Y-Ba-Cu-O and Tl-Ba-Ca-Cu-O on LaAlO3
Conductor-backed coplanar waveguide (CBCPW) resonators operating at 10.8 GHz have been fabricated from Tl-Ba-Ca-O (TBCCO) and Y-Ba-Cu-O (YBCO) thin films on LaAlO3. The resonators consist of a coplanar waveguide (CPW) patterned on the superconducting film side of the LaAlO3 substrate with a gold ground plane coated on the opposite side. These resonators were tested in the temperature range from 14 to 106 K. At 77 K, the best of our TBCCO and YBCO resonators have an unloaded quality factor (Qo) 7 and 4 times, respectively, larger than that of a similar all-gold resonator. In this study, the Qo's of the TBCCO resonators were larger than those of their YBCO counterparts throughout the aforementioned temperature range
Active Semi-Supervised Learning Using Sampling Theory for Graph Signals
We consider the problem of offline, pool-based active semi-supervised
learning on graphs. This problem is important when the labeled data is scarce
and expensive whereas unlabeled data is easily available. The data points are
represented by the vertices of an undirected graph with the similarity between
them captured by the edge weights. Given a target number of nodes to label, the
goal is to choose those nodes that are most informative and then predict the
unknown labels. We propose a novel framework for this problem based on our
recent results on sampling theory for graph signals. A graph signal is a
real-valued function defined on each node of the graph. A notion of frequency
for such signals can be defined using the spectrum of the graph Laplacian
matrix. The sampling theory for graph signals aims to extend the traditional
Nyquist-Shannon sampling theory by allowing us to identify the class of graph
signals that can be reconstructed from their values on a subset of vertices.
This approach allows us to define a criterion for active learning based on
sampling set selection which aims at maximizing the frequency of the signals
that can be reconstructed from their samples on the set. Experiments show the
effectiveness of our method.Comment: 10 pages, 6 figures, To appear in KDD'1
Radiative corrections to the lightest KK states in the T^2/(Z_2\times Z_2') orbifold
We study radiative corrections localized in the fixed points of the orbifold
for the field theory in six dimensions with two dimensions compactified on the
orbifold in a specific realistic model for low energy
physics that solves the proton decay and neutrino mass problem. We calculate
corrections to the masses of the lightest stable KK modes, which could be the
candidates for the dark matter.Comment: 14 pages, 2 figure
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