Entity-relation search: context pattern driven extraction and indexing

Our research focuses on searching relations between entities with context constraints. In particular, we are interested in efficiently searching for the relations among medical entities (e.g. diseases, chemicals, species, genes, or mutations) in a professional medical corpus. Existing relation extraction systems, like OpenIE, are able to extract some relations between entities. However, its results are inseparable in terms of extraction contexts, which prevents it from being able to search for the relations of given contexts. To address this issue, we propose to build an entity-relation search system with an awareness of extraction contexts. In order to achieve this goal, we propose to extract and index contexts for each extracted relation. We evaluate our search model over millions of professional medical abstracts and show that our context indexing is effective to support the task of searching relations into contexts. Note that this rich and novel system is the product of a collaborative team effort: Tianxiao Zhang, Jiarui Xu and Varun Berry, and supervised by Professor Kevin Chang. While we separately document our individual contributions, we intentionally share some parts of our thesis to improve the readability of our overall system design. This thesis mainly focuses on the design of our context extraction and indexing method

Unmanned aerial vehicle-based computer vision for structural vibration measurement and condition assessment: A concise survey

With the rapid advance in camera sensor technology, the acquisition of high-resolution images or videos has become extremely convenient and cost-effective. Computer vision that extracts semantic knowledge directly from digital images or videos, offers a promising solution for non-contact and full-field structural vibration measurement and condition assessment. Unmanned aerial vehicles (UAVs), also known as flying robots or drones, are being actively developed to suit a wide range of applications. Taking advantage of its excellent mobility and flexibility, camera-equipped UAV systems can facilitate the use of computer vision, thus enhancing the capacity of the structural condition assessment. The current article aims to provide a concise survey of the recent progress and applications of UAV-based computer vision in the field of structural dynamics. The different aspects to be discussed include the UAV system design and algorithmic development in computer vision. The main challenges, future trends, and opportunities to advance the technology and close the gap between research and practice will also be stated

Robust Dancer: Long-term 3D Dance Synthesis Using Unpaired Data

How to automatically synthesize natural-looking dance movements based on a piece of music is an incrementally popular yet challenging task. Most existing data-driven approaches require hard-to-get paired training data and fail to generate long sequences of motion due to error accumulation of autoregressive structure. We present a novel 3D dance synthesis system that only needs unpaired data for training and could generate realistic long-term motions at the same time. For the unpaired data training, we explore the disentanglement of beat and style, and propose a Transformer-based model free of reliance upon paired data. For the synthesis of long-term motions, we devise a new long-history attention strategy. It first queries the long-history embedding through an attention computation and then explicitly fuses this embedding into the generation pipeline via multimodal adaptation gate (MAG). Objective and subjective evaluations show that our results are comparable to strong baseline methods, despite not requiring paired training data, and are robust when inferring long-term music. To our best knowledge, we are the first to achieve unpaired data training - an ability that enables to alleviate data limitations effectively. Our code is released on https://github.com/BFeng14/RobustDancerComment: Preliminary video demo: https://youtu.be/gJbxG9QlcU