A Game-Theoretic Approach to Energy-Efficient Resource Allocation in Device-to-Device Underlay Communications

Despite the numerous benefits brought by Device-to-Device (D2D) communications, the introduction of D2D into cellular networks poses many new challenges in the resource allocation design due to the co-channel interference caused by spectrum reuse and limited battery life of User Equipments (UEs). Most of the previous studies mainly focus on how to maximize the Spectral Efficiency (SE) and ignore the energy consumption of UEs. In this paper, we study how to maximize each UE's Energy Efficiency (EE) in an interference-limited environment subject to its specific Quality of Service (QoS) and maximum transmission power constraints. We model the resource allocation problem as a noncooperative game, in which each player is self-interested and wants to maximize its own EE. A distributed interference-aware energy-efficient resource allocation algorithm is proposed by exploiting the properties of the nonlinear fractional programming. We prove that the optimum solution obtained by the proposed algorithm is the Nash equilibrium of the noncooperative game. We also analyze the tradeoff between EE and SE and derive closed-form expressions for EE and SE gaps.Comment: submitted to IET Communications. arXiv admin note: substantial text overlap with arXiv:1405.1963, arXiv:1407.155

CHARACTERIZATION AND COMPARISON ON PHENOTYPIC DIFFERENCES OF VACCINE AND CIRCULATING STRAINS OF INFLUENZA B VIRUSES IN B/VICTORIA AND B/YAMAGATA LINEAGES IN THE 2017-18 SEASON

Influenza in the human population is mainly caused by infection with influenza A viruses (IAVs) and influenza B viruses (IBVs). Although pandemic influenza is only caused by IAV, IBV is detected at increasing rates in seasonal influenza. IBV is divided into two antigenically and genetically distinct lineages, B/Yamagata and B/Victoria based on the hemagglutinin (HA) protein. Annual influenza vaccines include both IBV lineages in the quadrivalent vaccines and induce strain-specific protection. Vaccine production traditionally involved passaging viruses in embryonated eggs. More recent manufacturing techniques use cell culture-based methods which can increase virus production and possibly reduce the risk of antigenic changes by egg adaptation. Both egg and cell adaptation have been extensively studied in IAV, but comparable research in IBV is not abundant. We hypothesized that egg and cell culture adaptation also happen in IBV, and the changes lead to alteration of replication fitness in primary human epithelial cell cultures. To test this hypothesis, I focused on characterizing the phenotypic differences between vaccine and circulating strains of IBV in the B/Victoria and B/Yamagata lineages during the 2017-18 season. The results showed that the cell-derived vaccine strain had lower replication fitness in both lineages, but egg-derived vaccine strains had no observable phenotypic change compared to the circulating strains. Sequencing of the HA segment showed both cell and egg adaptation induced mutations in the HA. Egg adaptation caused a substitution at residue 197 which resulted in loss of a N-linked glycosylation site. Results from neutralization assays showed better recognition of egg-derived vaccine virus in the B/Yamagata lineage by both convalescent and post-vaccination human sera. These results suggested that cell and egg adaptations can cause viral fitness changes in IBV in cell culture and alter the antigenic profile of the virus. The possibility of antigenic changes causing vaccine mismatches needs to be considered during vaccine producing by the traditional egg method

Multi-task learning with mutual learning for joint sentiment classification and topic detection

Recently, advances in neural network approaches have achieved many successes in both sentiment classification and probabilistic topic modelling. On the one hand, latent topics derived from the global context of documents could be helpful in capturing more accurate word semantics and hence could potentially improve the sentiment classification accuracy. On the other hand, the word-level attention vectors obtained during the learning of sentiment classifiers could carry word-level polarity information and can be used to guide the discovery of topics in topic modelling. This paper proposes a multi-task learning framework which jointly learns a sentiment classifier and a topic model by making the word-level latent topic distributions in the topic model to be similar to the word-level attention vectors in the classifier through mutual learning. Experimental results on the Yelp and IMDB datasets verify the superior performance of the proposed framework over strong baselines on both sentiment classification accuracy and topic modelling evaluation results including perplexity and topic coherence measures. The proposed framework also extracts more interpretable topics compared to other conventional topic models and neural topic models

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

Learning representations from heterogeneous network for sentiment classification of product reviews

There have been increasing interests in natural language processing to explore effective methods in learning better representations of text for sentiment classification in product reviews. However, most existing methods do not consider subtle interplays among words appeared in review text, authors of reviews and products the reviews are associated with. In this paper, we make use of a heterogeneous network to model the shared polarity in product reviews and learn representations of users, products they commented on and words they used simultaneously. The basic idea is to first construct a heterogeneous network which links users, products, words appeared in product reviews, as well as the polarities of the words. Based on the constructed network, representations of nodes are learned using a network embedding method, which are subsequently incorporated into a convolutional neural network for sentiment analysis. Evaluations on the product reviews, including IMDB, Yelp 2013 and Yelp 2014 datasets, show that the proposed approach achieves the state-of-the-art performance