Intensity correlation of ionizing background at high redshifts

Intensity correlation of ionizing background at high redshifts is discussed. The intensity correlation function xi(sub j) and the absorption line equivalent width correlation xi(sub 1/W) are discussed

3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks

Human activity understanding with 3D/depth sensors has received increasing attention in multimedia processing and interactions. This work targets on developing a novel deep model for automatic activity recognition from RGB-D videos. We represent each human activity as an ensemble of cubic-like video segments, and learn to discover the temporal structures for a category of activities, i.e. how the activities to be decomposed in terms of classification. Our model can be regarded as a structured deep architecture, as it extends the convolutional neural networks (CNNs) by incorporating structure alternatives. Specifically, we build the network consisting of 3D convolutions and max-pooling operators over the video segments, and introduce the latent variables in each convolutional layer manipulating the activation of neurons. Our model thus advances existing approaches in two aspects: (i) it acts directly on the raw inputs (grayscale-depth data) to conduct recognition instead of relying on hand-crafted features, and (ii) the model structure can be dynamically adjusted accounting for the temporal variations of human activities, i.e. the network configuration is allowed to be partially activated during inference. For model training, we propose an EM-type optimization method that iteratively (i) discovers the latent structure by determining the decomposed actions for each training example, and (ii) learns the network parameters by using the back-propagation algorithm. Our approach is validated in challenging scenarios, and outperforms state-of-the-art methods. A large human activity database of RGB-D videos is presented in addition.Comment: This manuscript has 10 pages with 9 figures, and a preliminary version was published in ACM MM'14 conferenc

Induced Growth of Asymmetric Nanocantilever Arrays on Polar Surfaces

©2003 The American Physical Society. The electronic version of this article is the complete one and can be found online at: http://link.aps.org/doi/10.1103/PhysRevLett.91.185502DOI: 10.1103/PhysRevLett.91.185502We report that the Zn-terminated ZnO (0001) polar surface is chemically active and the oxygenterminated (0001) polar surface is inert in the growth of nanocantilever arrays. Longer and wider "comblike" nanocantilever arrays are grown from the (0001)-Zn surface, which is suggested to be a self-catalyzed process due to the enrichment of Zn at the growth front. The chemically inactive (0001)-O surface typically does not initiate any growth, but controlling experimental conditions could lead to the growth of shorter and narrower nanocantilevers from the intersections between (0001)-O with (0110) surfaces