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In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way. Whereas diagrams contain richer information compared with individual image-based or language-based data, proper solutions for automatically understanding them have not been proposed due to their innate characteristics of multi-modality and arbitrariness of layouts. To tackle this problem, we propose a unified diagram-parsing network for generating knowledge from diagrams based on an object detector and a recurrent neural network designed for a graphical structure. Specifically, we propose a dynamic graph-generation network that is based on dynamic memory and graph theory. We explore the dynamics of information in a diagram with activation of gates in gated recurrent unit (GRU) cells. On publicly available diagram datasets, our model demonstrates a state-of-the-art result that outperforms other baselines. Moreover, further experiments on question answering shows potentials of the proposed method for various applications.

Despite their significance for historical demographic research, a major limitation of Chinese genealogies is the relative lack of information on daughters, their husbands, and their descendants, which prevents an examination of how the boundary of family was extended through the marriage network. To fill this "hole" in Chinese genealogies, we use the genealogy of the Andong Kwôn clan, published in 1476, the oldest existing genealogy in Korea. Interestingly, more than 90 percent of those recorded in this Korean genealogy belong to the son-in-law line, revealing the importance of marriage networks for family formation until the late fifteenth century. By looking at the specific families that Andong Kwôn's daughters married into and the occupational titles of their husbands, we explore specific ways in which the family boundary was expanded to include the son-in-law lines.

This paper presents the technical approaches used and experimental results obtained by Team SNU (Seoul National University) at the 2015 DARPA Robotics Challenge (DRC) Finals. Team SNU is one of the newly qualified teams, unlike 12 teams who previously participated in the December 2013 DRC Trials. The hardware platform THORMANG, which we used, has been developed by ROBOTIS. THORMANG is one of the smallest robots at the DRC Finals. Based on this platform, we focused on developing software architecture and controllers in order to perform complex tasks in disaster response situations and modifying hardware modules to maximize manipulability. Ensuring stability and modularization are two main keywords in the technical approaches of the architecture. We designed our interface and controllers to achieve a higher robustness level against disaster situations. Moreover, we concentrated on developing our software architecture by integrating a number of modules to eliminate software system complexity and programming errors. With these efforts on the hardware and software, we successfully finished the competition without falling, and we ranked 12th out of 23 teams. This paper is concluded with a number of lessons learned by analyzing the 2015 DRC Finals.

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