Novel Adaptive Sampling Algorithm for POD-Based Non-Intrusive Reduced Order Model

The proper orthogonal decomposition (POD) based reduced-order model (ROM) has been an effective tool for flow field prediction in the engineering industry. The sample selection in the design space for POD basis construction affects the ROM performance sensitively. Adaptive sampling can significantly reduce the number of samples to achieve the required model accuracy. In this work, we propose a novel adaptive sampling algorithm, called conjunction sampling strategy, which is based on proven strategies. The conjunction sampling strategy is demonstrated on airfoil flow field prediction within the transonic regime. We demonstrate the performance of the proposed strategy by running 10 trials for each strategy for the robustness tests. Results show that the conjunction sampling strategy consistently achieves higher predictive accuracy compared with Latin hypercube sampling (LHS) and existing strategies. Specifically, under the same computational budget (40 training samples in total), the conjunction strategy reduced the L2 error by 56.7% compared with LHS. In addition, the conjunction strategy reduced the standard deviation of L2 errors by 62.1% with a 2.6% increase on the mean error compared with the best existing strategy

A convolutional attentional neural network for sentiment classification

Neural network models with attention mechanism have shown their efficiencies on various tasks. However, there is little research work on attention mechanism for text classification and existing attention model for text classification lacks of cognitive intuition and mathematical explanation. In this paper, we propose a new architecture of neural network based on the attention model for text classification. In particular, we show that the convolutional neural network (CNN) is a reasonable model for extracting attentions from text sequences in mathematics. We then propose a novel attention model base on CNN and introduce a new network architecture which combines recurrent neural network with our CNN-based attention model. Experimental results on five datasets show that our proposed models can accurately capture the salient parts of sentences to improve the performance of text classification

Commonsense knowledge enhanced memory network for stance classification

Stance classification aims at identifying, in the text, the attitude toward the given targets as favorable, negative, or unrelated. In existing models for stance classification, only textual representation is leveraged, while commonsense knowledge is ignored. In order to better incorporate commonsense knowledge into stance classification, we propose a novel model named commonsense knowledge enhanced memory network, which jointly represents textual and commonsense knowledge representation of given target and text. The textual memory module in our model treats the textual representation as memory vectors, and uses attention mechanism to embody the important parts. For commonsense knowledge memory module, we jointly leverage the entity and relation embeddings learned by TransE model to take full advantage of constraints of the knowledge graph. Experimental results on the SemEval dataset show that the combination of the commonsense knowledge memory and textual memory can improve stance classification

Convolution-based neural attention with applications to sentiment classification

Neural attention mechanism has achieved many successes in various tasks in natural language processing. However, existing neural attention models based on a densely connected network are loosely related to the attention mechanism found in psychology and neuroscience. Motivated by the finding in neuroscience that human possesses the template-searching attention mechanism, we propose to use convolution operation to simulate attentions and give a mathematical explanation of our neural attention model. We then introduce a new network architecture, which combines a recurrent neural network with our convolution-based attention model and further stacks an attention-based neural model to build a hierarchical sentiment classification model. The experimental results show that our proposed models can capture salient parts of the text to improve the performance of sentiment classification at both the sentence level and the document level

Empathetic Response Generation with State Management

A good empathetic dialogue system should first track and understand a user's emotion and then reply with an appropriate emotion. However, current approaches to this task either focus on improving the understanding of users' emotion or on proposing better responding strategies, and very few works consider both at the same time. Our work attempts to fill this vacancy. Inspired by task-oriented dialogue systems, we propose a novel empathetic response generation model with emotion-aware dialogue management. The emotion-aware dialogue management contains two parts: (1) Emotion state tracking maintains the current emotion state of the user and (2) Empathetic dialogue policy selection predicts a target emotion and a user's intent based on the results of the emotion state tracking. The predicted information is then used to guide the generation of responses. Experimental results show that dynamically managing different information can help the model generate more empathetic responses compared with several baselines under both automatic and human evaluations

Transition-based directed graph construction for emotion-cause pair extraction

Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text. Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation. Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction. The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently. Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71% (p<0.01) in F1 measure

A knowledge regularized hierarchical approach for emotion cause analysis

Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperforming a number of competitive baselines by at least 2.08% in F-measure

Polarization-based probabilistic discriminative model for quantitative characterization of cancer cells

We propose a polarization-based probabilistic discriminative model for deriving a set of new sigmoid-transformed polarimetry feature parameters, which not only enables accurate and quantitative characterization of cancer cells at pixel level, but also accomplish the task with a simple and stable model. By taking advantages of polarization imaging techniques, these parameters enable a low-magnification and wide-field imaging system to separate the types of cells into more specific categories that previously were distinctive under high magnification. Instead of blindly choosing the model, the L0 regularization method is used to obtain the simplified and stable polarimetry feature parameter. We demonstrate the model viability by using the pathological tissues of breast cancer and liver cancer, in each of which there are two derived parameters that can characterize the cells and cancer cells respectively with satisfactory accuracy and sensitivity. The stability of the final model opens the possibility for physical interpretation and analysis. This technique may bypass the typically labor-intensive and subjective tumor evaluating system, and could be used as a blueprint for an objective and automated procedure for cancer cell screening