Xiaoqiang Zheng

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TensorFlow [1] is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds(More)
This paper addresses several issues related to topological analysis of 3D second order symmetric tensor fields. First, we show that the degenerate features in such data sets form stable topological lines rather than points, as previously thought. Second, the paper presents two different methods for extracting these features by identifying the individual(More)
Tensor topology is useful in providing a simplified and yet detailed representation of a tensor field. Recently the field of 3D tensor topology is advanced by the discovery that degenerate tensors usually form lines in their most basic configurations. These lines form the backbone for further topological analysis. A number of ways for extracting and tracing(More)
Topological analysis of 3D tensor fields starts with the identification of degeneracies in the tensor field. In this paper, we present a new, intuitive and numerically stable method for finding degenerate tensors in symmetric second order 3D tensors. This method is formulated based on a description of a tensor having an isotropic spherical component and a(More)
We introduce the underlying theory behind degenerate points in 2D tensor fields to study the local flow characteristics in the vicinity of linear and non-linear singularities. The structural stability of these features and their corresponding separatrices are also analyzed. From here, we highlight the main techniques for visualizing and simplifying the(More)