Graph regularized nonnegative matrix factorization (GNMF) has been widely used in data representation due to its excellent dimensionality reduction. When it comes to clustering polluted data, GNMF inevitably learns inaccurate representations, leading to models that are unusually sensitive to outliers in the data. For example, in a face dataset, obscured by items such as a mask or glasses, there is a high probability that the graph regularization term incorrectly describes the association relationship for that sample, resulting in an incorrect elicitation in the matrix factorization process. In this article, a novel self-initiated unsupervised subspace learning method named robust nonnegative matrix factorization with self-initiated multigraph contrastive fusion (RNMF-SMGF) is proposed. RNMF-SMGF is capable of creating samples with different angles and learning different graph structures based on these different angles in a self-initiated method without changing the original data. In the process of subspace learning guided by graph regularization, these different graph structures are fused into a more accurate graph structure, along with entropy regularization, $L_{2,1/2}$ -norm constraints to facilitate the robust learning of the proposed model and the formation of different clusters in the low-dimensional space. To demonstrate the effectiveness of the proposed model in robust clustering, we have conducted extensive experiments on several benchmark datasets and demonstrated the effectiveness of the proposed method. The source code is available at: https://github.com/LstinWh/RNMF-SMGF/.

The emotion-cause pair extraction (ECPE) task is to simultaneously extract emotions and causes as pairs (EC-pairs) from documents, which is important for natural language processing. Previous research tackled this task via a two-step approach, which first predicts separately the emotion and cause clauses, and then pairs them up by using a binary classifier. However, such a two-step approach may suffer from the possible propagation of errors, and it neglects the interaction between emotions and causes. In this article, an end-to-end double-graph method with relational enhancement (DGRE) is proposed to stimulate two relationship modes among clauses, i.e., semantic dependence and logical dependence. First, two united graph encoders are established to embed the semantic dependence into the representation of clauses and pairs. The first encoder is built on graph attention networks (GATs) for clause-level representation, the result of which is used by a relational graph convolutional network (RGCN) for the refinement of pair-level representation. Aiming to enhance the fitting ability of logical dependence, the emotion-type classification task is introduced into the multitask learning framework of GATs, which can effectively distinguish the logical relations between clauses according to their emotion types. Moreover, seven types of dependence relations have been designed for the node connections in RGCN, which emphasize the contextual interaction and clustering among neighboring nodes. Experiments on a benchmark Chinese corpus demonstrate that the proposed DGRE approach could effectively establish the communication mechanism between clauses and pairs from multiple perspectives, and comparisons with state-of-the-art (SOTA) models well validate its effectiveness.

Abstract Recently, 3D object detection technology based on point clouds has developed rapidly. However, too few points of distant and occluded objects are scanned by the sensor, and thus these objects suffer from too insufficient features to be detected. This case damages the detection accuracy. Therefore, we constitute a novel 3D object detection with Context-aware and dimensional Interaction Attention Network (CIANet) to explore vital geometric cues for enriching the feature representation of the object, thus boosting the overall detection performance. Specifically, in the first stage, we employ the 3D sparse convolution to extract voxel features, and then construct a Channel-Spatial Hybrid Attention (CSHA) module and a Contextual Self-Attention (CSA) module to enhance voxel features for generating proposals. The CSHA module aims to enhance the key information of the channel and spatial domains of 2D Bird’s Eye View (BEV) features, and the CSA module is applied to supplement contextual information to the enhanced BEV features, thus generating accurate proposals. In the second stage, we construct a Dimensional Interaction Attention (DIA) module to refine Region of Interest (RoI) features within the proposals. It enhances the interactions among the channel and spatial dimensions of RoI features to learn accurate boundaries of objects for proposal refinement. Extensive experiments on the KITTI and Waymo benchmarks show the superior detection performance of CIANet compared to recent methods, especially for objects such as pedestrians and cyclists.

Financial sentiment analysis is a fine-grained sentiment analysis task that needs to predict the sentiment value toward a given target entity. Recently, dependency-based graph neural networks have been introduced for target-based sentiment analysis. However, financial sentiment analysis with implicit sentiment expression is more challenging than target-based explicit sentiment analysis, requiring a deep understanding of the complex association between the sentiment clue in context and the target entity. In previous work related to financial sentiment analysis, most methods focused on learning the simple word-to-word relations between the contextual words and the target entity based on the dependency tree of the sentence, ignoring the exploitation of span-boundary information and phrase-level syntactic knowledge with regard to the target entity. In this paper, we perform financial implicit sentiment analysis by taking phrases as basic semantic units and proposing a graph attention network ($PhraseGAT$) based on the constituent tree to leverage the phrase syntactic knowledge. To enhance the information flow between the nodes in the graph, we construct a heterogeneous graph based on the constituent tree and encode higher-order neighbor information. In addition, we introduce a multi-edge-type graph attention network ($MET$-$GAT$) to take full consideration of syntax and semantic interactions for the final prediction on the sentiment value of the target entity. Our proposed approach achieves 85.56% and 84.37% cosine similarity on public benchmark HEADLINE and MICROBLOG datasets, outperforms several strong baselines and achieves new state-of-the-art performance, verifying its effectiveness.

The task of event extraction contains subtasks including detections for entity mentions, event triggers and argument roles. Traditional methods solve them as a pipeline, which does not make use of task correlation for their mutual benefits. There have been recent efforts towards building a joint model for all tasks. However, due to technical challenges, there has not been work predicting the joint output structure as a single task. We build a first model to this end using a neural transition-based framework, incrementally predicting complex joint structures in a state-transition process. Results on standard benchmarks show the benefits of the joint model, which gives the best result in the literature.

In network virtualization, how to efficiently embed virtual networks with both node and link demands into a shared physical network, namely virtual network embedding, has attracted significant attention. Most of prior studies on this problem have the following two limitations: i) they assumed that the virtual network demands are constant values, which does not hold in real-world network since such demands may vary a lot over time; ii) their primary goal was to generate more revenues for the physical network, with no consideration of the energy cost, which has become a critical issue for the physical network. In this paper, we bridge the gap and study the energy aware virtual network embedding with dynamic demands. Specifically, we first model the dynamics of virtual network demands as a combination of following Gaussian distribution and exhibiting daily diurnal pattern. We then design an efficient heuristic algorithm by leveraging the dynamic characteristic of virtual network demands to minimize the energy consumption while keeping high revenue for the physical network. We implemented our algorithm in C++ and performed side-by-side comparison with prior algorithm. Extensive simulations show that our algorithm can significantly reduce the energy cost by up to 16% over the state-of-the-art algorithm, while maintaining near the same revenue.

The social network is a theoretical construct useful in the social sciences to study relationships between individuals, groups, organizations, or even entire societies. To deal with social networks, various approaches have been proposed to analyze them. What has not been done is to properly and effectively represent, manage and use various social networks. In this paper, we propose a semantic model that can naturally represent various academic social networks, especially various complex semantic relationships among social actors. This model can be used as the foundation for managing, manipulating and querying academic social networks. We also introduce a concise language to represent and query academic social networks.