An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss

Affect conveys important implicit information in human communication. Having the capability to correctly express affect during human-machine conversations is one of the major milestones in artificial intelligence. In recent years, extensive research on open-domain neural conversational models has been conducted. However, embedding affect into such models is still under explored. In this paper, we propose an end-to-end affect-rich open-domain neural conversational model that produces responses not only appropriate in syntax and semantics, but also with rich affect. Our model extends the Seq2Seq model and adopts VAD (Valence, Arousal and Dominance) affective notations to embed each word with affects. In addition, our model considers the effect of negators and intensifiers via a novel affective attention mechanism, which biases attention towards affect-rich words in input sentences. Lastly, we train our model with an affect-incorporated objective function to encourage the generation of affect-rich words in the output responses. Evaluations based on both perplexity and human evaluations show that our model outperforms the state-of-the-art baseline model of comparable size in producing natural and affect-rich responses.Comment: AAAI-1

EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels. In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. RGNN considers the biological topology among different brain regions to capture both local and global relations among different EEG channels. Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and sparseness of the adjacency matrix are inspired by neuroscience theories of human brain organization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to better handle cross-subject EEG variations and noisy labels, respectively. Extensive experiments on two public datasets, SEED and SEED-IV, demonstrate the superior performance of our model than state-of-the-art models in most experimental settings. Moreover, ablation studies show that the proposed adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our RGNN model. Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition

Meaning of Justice for Mississippians with Regard to Health Care Pricing

Throughout most of human history, justice has been perceived as an extremely important virtue. The primary objective of this study is to investigate the meaning of justice with a focus on a particular subject—pricing, specifically as it pertains to healthcare. In addition to the primary goal, there are also some secondary objectives: uncovering the procedure of healthcare pricing, revealing the role of government in achieving justice of healthcare pricing, and identifying the influential factors that affect the formation of people’s understanding of justice regarding healthcare pricing. The findings indicate that the equity perspective and the perspective of the need principle have substantial influence on the formation of people’s understanding of justice with regard to healthcare pricing. From the equity perspective, people believe that a just healthcare pricing should be reasonably based on cost. From the need principle perspective, people believe that just healthcare pricing should guarantee the affordability of healthcare, especially basic care. In regard to the role of government, a majority of participants believe that the government-market mixed mechanism is the most just pricing mechanism and government should play the role of a regulator. Government interventions should strive toward assisting the spontaneous forces of the market competition. Finally, findings in this study state that the participants’ general belief of distributive justice exerts a significant effect upon their understanding of justice regarding healthcare pricing. However, there is no one-to-one correspondence between these two. These findings prove that people have the tendency to treat healthcare as a special good and view justice of healthcare pricing as a particular subject to which the general belief of distributive justice may not be applicable. By focusing on justice of healthcare pricing, this study bridges the research gap and contributes to the literature on ethical study of pricing. The identifications of the popularly shared understanding of justice regarding healthcare pricing and the proper role of government provide important reference information to governments and policy makers, enlightening people with new solutions to some pressing healthcare issues

A New Unconditionally Stable Method for Telegraph Equation Based on Associated Hermite Orthogonal Functions

The present paper proposes a new unconditionally stable method to solve telegraph equation by using associated Hermite (AH) orthogonal functions. Unlike other numerical approaches, the time variables in the given equation can be handled analytically by AH basis functions. By using the Galerkin’s method, one can eliminate the time variables from calculations, which results in a series of implicit equations. And the coefficients of results for all orders can then be obtained by the expanded equations and the numerical results can be reconstructed during the computing process. The precision and stability of the proposed method are proved by some examples, which show the numerical solution acquired is acceptable when compared with some existing methods

An identification based network link backup method

in order to solve the problem of network link failure or link congestion, this paper proposes an identifi cation based link backup method, which uses the identification network to carry out collaborative backup of links, formulates the link level through the network identifi cation mechanism, divides the routing characteristics through the link level, and calculates the link level through the link backup protocol between routers. When the high priority link fails or the link congestion occurs, the low priority link can be used for routing; When the transmission rate of a single link decreases, the low priority link can also be enabled. So as to achieve network load balancing and maximize link utilization. Through mini net simulation, the experimental topology is built and verifi ed. The results show that this method can quickly repair the link failure, quickly switch the link, reduce the network interruption delay, when the high priority link failure or congestion, it can quickly establish the route update, and quickly recover, so as to achieve the purpose of network load balancing

Class-Sensitive Principal Components Analysis

Research in a number of fields requires the analysis of complex datasets. Principal Components Analysis (PCA) is a popular exploratory method. However it is driven entirely by variation in the dataset without using any predefined class label information. Linear classifiers make up a family of popular discrimination methods. However, these will face the data piling issue often when the dimension of the dataset gets higher. In this dissertation, we first study the geometric representation of an interesting dataset with strongly auto-regressive errors under the High Dimensional Low Sample Size (HDLSS) setting and understand why the Maximal Data Piling (MDP), proposed by Ahn et al. (2007), is the best in terms of classification compared with several other commonly used linear discrimination methods. Then we introduce the Class-Sensitive Principal Components Analysis (CSPCA), which is a compromise of PCA and MDP, that seeks new direction vectors for better Class-Sensitive visualization. Specifically, this method will be applied to the Thyroid Cancer dataset (see Agrawal et al. (2014)). Additionally, we investigate the asymptotic behavior of the sample and population MDP normal vector and Class-Sensitive Principal Component directions under the HDLSS setting. Moreover, the Multi-class version of CSPCA (MCSP) will be introduced as the last part of this dissertation.Doctor of Philosoph