Occlusion Aware Unsupervised Learning of Optical Flow

It has been recently shown that a convolutional neural network can learn optical flow estimation with unsupervised learning. However, the performance of the unsupervised methods still has a relatively large gap compared to its supervised counterpart. Occlusion and large motion are some of the major factors that limit the current unsupervised learning of optical flow methods. In this work we introduce a new method which models occlusion explicitly and a new warping way that facilitates the learning of large motion. Our method shows promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets. Especially on KITTI dataset where abundant unlabeled samples exist, our unsupervised method outperforms its counterpart trained with supervised learning.Comment: CVPR 2018 Camera-read

Holographic coherent states from random tensor networks

Random tensor networks provide useful models that incorporate various important features of holographic duality. A tensor network is usually defined for a fixed graph geometry specified by the connection of tensors. In this paper, we generalize the random tensor network approach to allow quantum superposition of different spatial geometries. We set up a framework in which all possible bulk spatial geometries, characterized by weighted adjacent matrices of all possible graphs, are mapped to the boundary Hilbert space and form an overcomplete basis of the boundary. We name such an overcomplete basis as holographic coherent states. A generic boundary state can be expanded on this basis, which describes the state as a superposition of different spatial geometries in the bulk. We discuss how to define distinct classical geometries and small fluctuations around them. We show that small fluctuations around classical geometries define "code subspaces" which are mapped to the boundary Hilbert space isometrically with quantum error correction properties. In addition, we also show that the overlap between different geometries is suppressed exponentially as a function of the geometrical difference between the two geometries. The geometrical difference is measured in an area law fashion, which is a manifestation of the holographic nature of the states considered.Comment: 33 pages, 8 figures. An error corrected on page 14. Reference update

Can water allocation in the Yellow River basin be improved?: Insights from a multi-agent system model

In 1999, the Government of China enforced a cross-provincial, quota-based Water Allocation Agreement that was developed in 1987 and titled Unified Water Flow Regulation (UWFR) to ensure that flow to the Yellow River mouth would not be cut off. This policy was in line with the refocus of the Government, over the last decade, on sustainable water use and keeping the Yellow River healthy. The policy enforcement ended more than two decades of flow-cutoffs, that is, periods when the Yellow River did not reach the Bohai Sea at its mouth, during an increasing number of days every year.Water allocation, river basin management, multi-agent system,