Relevant Emotion Ranking from Text Constraint with Emotion Relationships

Text might contain or invoke multiple emotions with varying intensities. As such, emotion detection, to predict multiple emotions associated with a given text, can be cast into a multi-label classification problem. We would like to go one step further so that a ranked list of relevant emotions are generated where top ranked emotions are more intensely associated with text compared to lower ranked emotions, whereas the rankings of irrelevant emotions are not important. A novel framework of relevant emotion ranking is proposed to tackle the problem. In the framework, the objective loss function is designed elaborately so that both emotion prediction and rankings of only relevant emotions can be achieved. Moreover, we observe that some emotions cooccur more often while other emotions rarely co-exist. Such information is incorporated into the framework as constraints to improve the accuracy of emotion detection. Experimental results on two real-world corpora show that the proposed framework can effectively deal with emotion detection and performs remarkably better than the state-of-the-art emotion detection approaches and multi-label learning methods

Interpretable relevant emotion ranking with event-driven attention

Multiple emotions with different intensities are often evoked by events described in documents. Oftentimes, such event information is hidden and needs to be discovered from texts. Unveiling the hidden event information can help to understand how the emotions are evoked and provide explainable results. However, existing studies often ignore the latent event information. In this paper, we proposed a novel interpretable relevant emotion ranking model with the event information incorporated into a deep learning architecture using the event-driven attentions. Moreover, corpus-level event embeddings and document-level event distributions are introduced respectively to consider the global events in corpus and the document-specific events simultaneously. Experimental results on three real-world corpora show that the proposed approach performs remarkably better than the state-of-the-art emotion detection approaches and multi-label approaches. Moreover, interpretable results can be obtained to shed light on the events which trigger certain emotions

Graph Condensation via Eigenbasis Matching

The increasing amount of graph data places requirements on the efficiency and scalability of graph neural networks (GNNs), despite their effectiveness in various graph-related applications. Recently, the emerging graph condensation (GC) sheds light on reducing the computational cost of GNNs from a data perspective. It aims to replace the real large graph with a significantly smaller synthetic graph so that GNNs trained on both graphs exhibit comparable performance. However, our empirical investigation reveals that existing GC methods suffer from poor generalization, i.e., different GNNs trained on the same synthetic graph have obvious performance gaps. What factors hinder the generalization of GC and how can we mitigate it? To answer this question, we commence with a detailed analysis and observe that GNNs will inject spectrum bias into the synthetic graph, resulting in a distribution shift. To tackle this issue, we propose eigenbasis matching for spectrum-free graph condensation, named GCEM, which has two key steps: First, GCEM matches the eigenbasis of the real and synthetic graphs, rather than the graph structure, which eliminates the spectrum bias of GNNs. Subsequently, GCEM leverages the spectrum of the real graph and the synthetic eigenbasis to construct the synthetic graph, thereby preserving the essential structural information. We theoretically demonstrate that the synthetic graph generated by GCEM maintains the spectral similarity, i.e., total variation, of the real graph. Extensive experiments conducted on five graph datasets verify that GCEM not only achieves state-of-the-art performance over baselines but also significantly narrows the performance gaps between different GNNs.Comment: Under Revie

The Game Theory: Applications in the Wireless Networks

Recent years have witnessed a lot of applications in the computer science, especially in the area of the wireless networks. The applications can be divided into the following two main categories: applications in the network performance and those in the energy efficiency. The game theory is widely used to regulate the behavior of the users; therefore, the cooperation among the nodes can be achieved and the network performance can be improved when the game theory is utilized. On the other hand, the game theory is also adopted to control the media access control protocol or routing protocol; therefore, the energy exhaust owing to the data collision and long route can be reduced and the energy efficiency can be improved greatly. In this chapter, the applications in the network performance and the energy efficiency are reviewed. The state of the art in the applications of the game theory in wireless networks is pointed out. Finally, the future research direction of the game theory in the energy harvesting wireless sensor network is presented

Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements

Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework. In this approach, we use 3D total variation (TV) to enhance the spatio-temporal continuity of foregrounds, and Tucker decomposition to model the spatio-temporal correlations of video background. Based on this idea, we design a basic tensor RPCA model over the video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize the correlations among the groups of similar 3D patches of video background, we further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint tensor Tucker decompositions of 3D patch groups for modeling the video background. Efficient algorithms using alternating direction method of multipliers (ADMM) are developed to solve the proposed models. Extensive experiments on simulated and real-world videos demonstrate the superiority of the proposed approaches over the existing state-of-the-art approaches.Comment: To appear in IEEE TI

Thermal expansion and impurity effects on lattice thermal conductivity of solid argon

Thermal expansion and impurity effects on the lattice thermal conductivity of solid argon have been investigated with equilibrium molecular dynamics simulation. Thermal conductivity is simulated over the temperature range of 20 – 80 K. Thermal expansion effects, which strongly reduce thermal conductivity, are incorporated into the simulations using experimentally measured lattice constants of solid argon at different temperatures. It is found that the experimentally measured deviations from a T-1 high-temperature dependence in thermal conductivity can be quantitatively attributed to thermal expansion effects. Phonon scattering on defects also contributes to the deviations. Comparison of simulation results on argon lattices with vacancy and impurity defects to those predicted from the theoretical models of Klemens and Ashegi et al. demonstrates that phonon scattering on impurities due to lattice strain is stronger than that due to differences in mass between the defect and the surrounding matrix. In addition, the results indicate the utility of molecular dynamics simulation for determining parameters in theoretical impurity scattering models under a wide range of conditions. It is also confirmed from the simulation results that thermal conductivity is not sensitive to the impurity concentration at high temperatures