Network Theory 
In computer and network science, network theory is the study of graphs as a representation of either symmetric relations or, more generally, of asymmetric relations between discrete objects. Network theory is a part of graph theory. It has applications in many disciplines including statistical physics, particle physics, computer science, electrical engineering, biology, economics, operations research, and sociology. Applications of network theory include logistical networks, the World Wide Web, Internet, gene regulatory networks, metabolic networks, social networks, epistemological networks, etc; see List of network theory topics for more examples. Euler’s solution of the Seven Bridges of Königsberg problem is considered to be the first true proof in the theory of networks. …
TypeSQL
Interacting with relational databases through natural language helps users of any background easily query and analyze a vast amount of data. This requires a system that understands users’ questions and converts them to SQL queries automatically. In this paper we present a novel approach, TypeSQL, which views this problem as a slot filling task. Additionally, TypeSQL utilizes type information to better understand rare entities and numbers in natural language questions. We test this idea on the WikiSQL dataset and outperform the prior state-of-the-art by 5.5% in much less time. We also show that accessing the content of databases can significantly improve the performance when users’ queries are not well-formed. TypeSQL gets 82.6% accuracy, a 17.5% absolute improvement compared to the previous content-sensitive model. …
Conditional Extreme Value Models
Extreme value theory (EVT) is often used to model environmental, financial and internet traffic data. Multivariate EVT assumes a multivariate domain of attraction condition for the distribution of a random vector necessitating that each component satisfy a marginal domain of attraction condition. Heffernan and Tawn [2004] and Heffernan and Resnick [2007] developed an approximation to the joint distribution of the random vector by conditioning on one of the components being in an extreme value domain. The usual method of analysis using multivariate extreme value theory often is not helpful either because of asymptotic independence or due to one component of the observation vector not being in a domain of attraction. These defects can be addressed by using the conditional extreme value model. …
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