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 $S$=1/2 dimer model with an anisotropic intra-plane antiferromagnetic coupling. With the anisotropy $4 \gtrsim \Delta \gtrsim 3$, 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 $S_z=+1$ and $S_z=0$ 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 $T_2/(Z_2\times Z_2')$ 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