BPS D-branes from an Unstable D-brane

We search for exact tachyon kink solutions of DBI type effective action describing an unstable D-brane with worldvolume gauge field turned in both the flat and a curved background. There are various kinds of solutions in the presence of electromagnetic fields in the flat space, such as periodic arrays, topological tachyon kinks, half kinks, and bounces. We identify a BPS object, D($p$-1)F1 bound state, which describes a thick brane with string flux density. The curved background of interest is the ten-dimensional lift of the Salam-Sezgin vacuum and, in the asymptotic limit, it approaches ${\rm R}^{1,4}\times {\rm T}^2\times {\rm S}^3$. The solutions in the curved background are identified as composites of lower-dimensional D-branes and fundamental strings, and, in the BPS limit, they become a D4D2F1 composite wrapped on ${\rm R}^{1,2}\times {\rm T}^2$ where ${\rm T}^2$ is inside ${\rm S}^3$.Comment: 4 pages, to appear in the proceeding of PASCOS 2005, Gyeongju, Korea, May 30-June 4, 200

A Suspended Nanogap Formed by Field-Induced Atomically Sharp Tips

A sub-nanometer scale suspended gap (nanogap) defined by electric field-induced atomically sharp metallic tips is presented. A strong local electric field (\u3e109 V=m) across micro/nanomachined tips facing each other causes the metal ion migration in the form of dendrite-like growth at the cathode. The nanogap is fully isolated from the substrate eliminating growth mechanisms that involve substrate interactions. The proposed mechanism of ion transportation is verified using real-time imaging of the metal ion transportation using an in situ biasing in transmission electron microscope (TEM). The configuration of the micro/nanomachined suspended tips allows nanostructure growth of a wide variety of materials including metals, metal-oxides, and polymers. VC 2012 American Institute of Physics

Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure