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Flexural bending of southern Tibet in a retro foreland setting

Description

The highest elevation of the Tibetan Plateau, lying 5,700 m above sea level, occurs within the part of the Lhasa block immediately north of the India-Tibet suture zone (Yarlung Zangbo

The highest elevation of the Tibetan Plateau, lying 5,700 m above sea level, occurs within the part of the Lhasa block immediately north of the India-Tibet suture zone (Yarlung Zangbo suture zone, YZSZ), being 700 m higher than the maximum elevation of more northern parts of the plateau. Various mechanisms have been proposed to explain this differentially higher topography and the rock uplift that led to it, invoking crustal compression or extension. Here we present the results of structural investigations along the length of the high elevation belt and suture zone, which rather indicate flexural bending of the southern margin of the Lhasa block (Gangdese magmatic belt) and occurrence of an adjacent foreland basin (Kailas Basin), both elements resulting from supra-crustal loading of the Lhasa block by the Zangbo Complex (Indian plate rocks) via the Great Counter Thrust. Hence we interpret the differential elevation of the southern margin of the plateau as due originally to uplift of a forebulge in a retro foreland setting modified by subsequent processes. Identification of this flexural deformation has implications for early evolution of the India-Tibet continental collision zone, implying an initial (Late Oligocene) symmetrical architecture that subsequently transitioned into the present asymmetrical wedge architecture.

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Date Created
  • 2015-07-15

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Towards robust semantic attribute learning in visual computing

Description

The rapid growth of social media in recent years provides a large amount of user-generated visual objects, e.g., images and videos. Advanced semantic understanding approaches on such visual objects are

The rapid growth of social media in recent years provides a large amount of user-generated visual objects, e.g., images and videos. Advanced semantic understanding approaches on such visual objects are desired to better serve applications such as human-machine interaction, image retrieval, etc. Semantic visual attributes have been proposed and utilized in multiple visual computing tasks to bridge the so-called "semantic gap" between extractable low-level feature representations and high-level semantic understanding of the visual objects.

Despite years of research, there are still some unsolved problems on semantic attribute learning. First, real-world applications usually involve hundreds of attributes which requires great effort to acquire sufficient amount of labeled data for model learning. Second, existing attribute learning work for visual objects focuses primarily on images, with semantic analysis on videos left largely unexplored.

In this dissertation I conduct innovative research and propose novel approaches to tackling the aforementioned problems. In particular, I propose robust and accurate learning frameworks on both attribute ranking and prediction by exploring the correlation among multiple attributes and utilizing various types of label information. Furthermore, I propose a video-based skill coaching framework by extending attribute learning to the video domain for robust motion skill analysis. Experiments on various types of applications and datasets and comparisons with multiple state-of-the-art baseline approaches confirm that my proposed approaches can achieve significant performance improvements for the general attribute learning problem.

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Created

Date Created
  • 2016