报告题目：Temporal Order-based First-Take-All Hashing for Fast Attention-Deficit-Hyperactive-Disorder Detection

报告人：齐国君 博士

报告单位：美国中佛罗里达大学

报告时间：2016年12月13日 (星期二) 上午10:45-11:30

报告地点：学术活动中心二楼小报告厅

报告摘要：Attention Deficit Hyperactive Disorder (ADHD) is one of the most common childhood disorders and can continue through adolescence and adulthood. Although the root cause of the problem still remains unknown, recent advancements in brain imaging technology reveal there exists differences between neural activities of Typically Developing Children (TDC) and ADHD subjects. Inspired by this, we propose a novel First-Take-All (FTA) hashing framework to investigate the problem of fast ADHD subjects detection through the fMRI time-series of neuron activities. By hashing time courses from regions of interests (ROIs) in the brain into fixed-size hash codes, FTA can compactly encode the temporal order differences between the neural activity patterns that are key to distinguish TDC and ADHD subjects. Such patterns can be directly learned via minimizing the training loss incurred by the generated FTA codes. By conducting similarity search on the resultant FTA codes, data-driven ADHD detection can be achieved in an efficient fashion. The experiments’ results on real-world ADHD detection bench-marks demonstrate the FTA can outperform the state-of-the-art baselines using only neural activity time series with-out any phenotypic information.

报告人简介：齐国君博士是美国中佛罗里达大学助理教授，他的研究方向包括大数据分析与知识发现、智能决策系统等。齐国君博士在Proceedings of IEEE、TPAMI、TKDE、TIP、SIGKDD、ICML、ACM MM、CVPR、ICDM、ICDE等顶尖国际期刊与会议发表论文超过六十篇，并获得ICDM2014最佳学生论文、ICDE2013最佳论文、ACM MM2007最佳论文。齐国君博士曾获得微软学者奖一次，IBM学者奖两次。齐国君博士是Multimedia Modeling(MMM)2016的大会共同主席，SIGKDD、CIKM、ACM MM的领域主席(Area Chair)，CVPR、ICCV、SIGKDD、IJCAI、ICMR等国际会议程序委员会委员，亦是《IEEE大数据汇刊》、《IEEE多媒体汇刊》等国际顶尖期刊的客座编辑。

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