Heat kernel transform for nilmanifolds associated to the Heisenberg group

We study the heat kernel transform on a nilmanifold $M$ of the Heisenberg group. We show that the image of $L^2(M)$ under this transform is a direct sum of weighted Bergman spaces which are related to twisted Bergman and Hermite-Bergman spaces.Comment: Revised version; to appear in Revista Mathematica Iberoamericana, 28

Discontinuous resistance change and domain wall scattering in patterned NiFe wires with a nanoconstriction

A nonlinear current-voltage (I-V) characteristic was observed in patterned NiFe wires with a central "bow-tie" point contact constriction. By passing a dc current through the wire, a sharp resistance drop was obtained for current densities in the range of 1.1-1.4 x 10(7) A/cm(2). This is attributed to current-induced domain wall drag, resulting in displacement of a domain wall away from the constriction. A maximum current-induced resistance change of 0.079% was obtained for a 100-nm constriction, which is comparable with the magnetoresistance due to domain wall scattering in NiFe

Annealing-induced Fe oxide nanostructures on GaAs

We report the evolution of Fe oxide nanostructures on GaAs(100) upon pre- and post-growth annealing conditions. GaAs nanoscale pyramids were formed on the GaAs surface due to wet etching and thermal annealing. An 8.0-nm epitaxial Fe film was grown, oxidized, and annealed using a gradient temperature method. During the process the nanostripes were formed, and the evolution has been demonstrated using transmission and reflection high energy electron diffraction, and scanning electron microscopy. These nanostripes; exhibited uniaxial magnetic anisotropy. The formation of these nanostructures is attributed to surface anisotropy, which in addition could explain the observed uniaxial magnetic anisotropy

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

Effects of Ru Substitution on Dimensionality and Electron Correlations in Ba(Fe_{1-x}Ru_x)_2As_2

We report a systematic angle-resolved photoemission spectroscopy study on Ba(Fe$_{1-x}$Ru$_x$)$_2$As$_2$ for a wide range of Ru concentrations (0.15 $\leq$ \emph{x} $\leq$ 0.74). We observed a crossover from two-dimension to three-dimension for some of the hole-like Fermi surfaces with Ru substitution and a large reduction in the mass renormalization close to optimal doping. These results suggest that isovalent Ru substitution has remarkable effects on the low-energy electron excitations, which are important for the evolution of superconductivity and antiferromagnetism in this system.Comment: 4 pages, 4 figure