Effect of incommensurate disorder on the resonant tunneling through Majorana bound states on the topological superconductor chains

We study the transport through the Kitaev's chain with incommensurate potentials coupled to two normal leads by the numerical operator method. We find a quantized linear conductance of $e^2/h$, which is independent to the disorder strength and the gate voltage in a wide range, signaling the Majorana bound states. While the incommensurate disorder suppresses the current at finite voltage bias, and then narrows the linear response regime of the $I-V$ curve which exhibits two plateaus corresponding to the superconducting gap and the band edge respectively. The linear conductance abruptly drops to zero as the disorder strength reaches the critical value $2+2\Delta$ with $\Delta$ the p-wave pairing amplitude, corresponding to the transition from the topological superconducting phase to the Anderson localized phase. Changing the gate voltage will also cause an abrupt drop of the linear conductance by driving the chain into the topologically trivial superconducting phase, whose $I-V$ curve exhibits an exponential shape.Comment: 9 pages, 7 figure

Promoting information spreading by using contact memory

Promoting information spreading is a booming research topic in network science community. However, the exiting studies about promoting information spreading seldom took into account the human memory, which plays an important role in the spreading dynamics. In this paper we propose a non-Markovian information spreading model on complex networks, in which every informed node contacts a neighbor by using the memory of neighbor's accumulated contact numbers in the past. We systematically study the information spreading dynamics on uncorrelated configuration networks and a group of $22$ real-world networks, and find an effective contact strategy of promoting information spreading, i.e., the informed nodes preferentially contact neighbors with small number of accumulated contacts. According to the effective contact strategy, the high degree nodes are more likely to be chosen as the contacted neighbors in the early stage of the spreading, while in the late stage of the dynamics, the nodes with small degrees are preferentially contacted. We also propose a mean-field theory to describe our model, which qualitatively agrees well with the stochastic simulations on both artificial and real-world networks.Comment: 6 pages, 6 figure

Shallow Water Depth Inversion Based on Data Mining Models

This thesis focuses on applying machine-learning algorithms on water depth inversion from remote sensing images, with a case study in Michigan lake area. The goal is to assess the use of the public available Landsat images on shallow water depth inversion. Firstly, ICESAT elevation data were used to determine the absolute water surface elevation. Airborne bathymetry Lidar data provide systematic measure of water bottom elevation. Subtracting water bottom elevation from water surface elevation will result in water depth. Water depth is associated with reflectance recorded as DN value in Landsat images. Water depth inversion was tested on ANN models, SVM models with four different kernel functions and regression tree model that exploit the correlation between water depth and image band ratios. The result showed that the RMSE (root-mean-square error) of all models are smaller than 1.5 meters and the R2 of them are greater than 0.81. The conclusion is Landsat images can be used to measure water depth in shallow area of the lakes. Potentially, water volume change of the Great Lakes can be monitored by using the procedure explored in this research

Dust in the Local Group

How dust absorbs and scatters starlight as a function of wavelength (known as the interstellar extinction curve) is crucial for correcting for the effects of dust extinction in inferring the true luminosity and colors of reddened astrophysical objects. Together with the extinction spectral features, the extinction curve contains important information about the dust size distribution and composition. This review summarizes our current knowledge of the dust extinction of the Milky Way, three Local Group galaxies (i.e., the Small and Large Magellanic Clouds, and M31), and galaxies beyond the Local Group.Comment: 21 pages, 11 figures; invited review article published in "LESSONS FROM THE LOCAL GROUP -- A Conference in Honour of David Block and Bruce Elmegreen" eds. Freeman, K.C., Elmegreen, B.G., Block, D.L. & Woolway, M. (SPRINGER: NEW YORK), pp. 85-10

Learning Social Affordance Grammar from Videos: Transferring Human Interactions to Human-Robot Interactions

In this paper, we present a general framework for learning social affordance grammar as a spatiotemporal AND-OR graph (ST-AOG) from RGB-D videos of human interactions, and transfer the grammar to humanoids to enable a real-time motion inference for human-robot interaction (HRI). Based on Gibbs sampling, our weakly supervised grammar learning can automatically construct a hierarchical representation of an interaction with long-term joint sub-tasks of both agents and short term atomic actions of individual agents. Based on a new RGB-D video dataset with rich instances of human interactions, our experiments of Baxter simulation, human evaluation, and real Baxter test demonstrate that the model learned from limited training data successfully generates human-like behaviors in unseen scenarios and outperforms both baselines.Comment: The 2017 IEEE International Conference on Robotics and Automation (ICRA