Group Sparse CNNs for Question Classification with Answer Sets

Question classification is an important task with wide applications. However, traditional techniques treat questions as general sentences, ignoring the corresponding answer data. In order to consider answer information into question modeling, we first introduce novel group sparse autoencoders which refine question representation by utilizing group information in the answer set. We then propose novel group sparse CNNs which naturally learn question representation with respect to their answers by implanting group sparse autoencoders into traditional CNNs. The proposed model significantly outperform strong baselines on four datasets.Comment: 6, ACL 201

Dependency-based Convolutional Neural Networks for Sentence Embedding

In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies. To exploit both deep learning and linguistic structures, we propose a tree-based convolutional neural network model which exploit various long-distance relationships between words. Our model improves the sequential baselines on all three sentiment and question classification tasks, and achieves the highest published accuracy on TREC.Comment: this paper has been accepted by ACL 201

Information transfer for characterizing conformation dynamics in network of coupled oscillators

A novel definition of information flow in network dynamical system. The proposed definition of information flow is used for understanding influence structure in network dynamical systems. Information transfer between network states is used to determine relative contributions of various subsystems in the network to the collective or the emergent dynamics of the network. These relative contributions of information flow from individual subsystems to network collective dynamics allows us to determine which subsystem is most influential for the emergent dynamics of the network. Identification of such influential subsystem can be used to take appropriate local control action at the subsystem level for enhancing or suppressing the network collective dynamics. We provide both model-based and data-driven approaches involving operator theoretic methods for the identification of influential subsystem in the complex network. The case studies in the IEEE 39-bus power system and I-80 transportation data, presented the potential of information transfer in predicting global instability phenomenon and identifying the dominant mode of traffic pattern. The future work on the nonlinear system and large scale data could extend the application of information transfer further

Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators

In this paper, we propose linear operator theoretic framework involving Koopman operator for the data-driven identification of power system dynamics. We explicitly account for noise in the time series measurement data and propose robust approach for data-driven approximation of Koopman operator for the identification of nonlinear power system dynamics. The identified model is used for the prediction of state trajectories in the power system. The application of the framework is illustrated using an IEEE nine bus test system.Comment: Accepted for publication in IEEE Power and Energy System General Meeting 201