Cages are fundamental structures in computer graphics, serving as versatile proxies for a wide range of applications. A high-quality cage must balance two competing objectives: minimizing the face count to ensure simplicity, and maximizing tightness to maintain high geometric fidelity to the input mesh. In this paper, we propose PR-Cage, a nested optimization framework for automated cage generation. For the outer control layer, we introduce a thickness parameter τ that defines a feasibility region; the evolving cage is guided by the τ -offset surface. We observe that an optimal balance between simplicity and tightness is achievable by progressively relaxing the parameter τ via a staircase schedule. For the inner iterations, we extend the traditional Quadric Error Metric (QEM) framework by incorporating rigorous linear inequality constraints to suppress triangle degeneration and prevent normal flips. Our algorithm relies exclusively on the atomic operations of edge collapses and edge flips, resulting in high computational efficiency and robustness. Comparative experiments on public datasets demonstrate that PR-Cage consistently outperforms existing methods, achieving extreme simplification while maintaining high adherence to the underlying geometry; see the teaser figure. Due to these favorable properties, we demonstrate the utility of our method in several downstream applications, such as contact simulation and deformation, where PR-Cage exhibits significant advantages in both quality and performance.

Emotion Cause Pair Extraction (ECPE) aims to extract emotions and their causes from a document. Powerful emotion and cause extraction abilities have proven essential in achieving accurate ECPE. However, most existing methods employ shared feature learning of emotion extraction and cause extraction, which can harm the abilities of both tasks as they focus on different information (i.e., task-specific features). Moreover, shared feature learning of the two tasks also leads to the label imbalance problem. To address these issues, this paper proposes a multi-task learning framework named Hierarchical Shared Encoder with Task-specific Transformer Layer Selection (HSE-TTLS). The model achieves ECPE via two subtasks: Emotion Extraction (EE) and Emotion Cause Extraction (ECE). The design of two subtasks for ECPE corresponds to the fact that cause clauses are emotion-dependent and significantly alleviates the label imbalance problem. To effectively extract task-specific features for EE and ECE, we employ BERT as the token-level encoder and select task-specific optimal layers for the two subtasks. Focal loss is used as the objective function for EE to further alleviate the label imbalance problem. Extensive experiments on benchmark ECPE corpus demonstrate the effectiveness of HSE-TTLS, which outperforms state-of-the-art baseline methods by at least 1.56% on the F1 score.

Emotion Cause Extraction Field (ECEF) focuses on the cause that triggers an emotion in a document and mainly includes Emotion Cause Extraction (ECE) and Emotion Cause Pair Extraction (ECPE). Traditional ECE aims to extract the cause based on a given emotion while ECPE aims to extract both the emotion and its corresponding cause. Recently, ECEF has attracted a lot of attention and most of the advances have benefited from significant developments in deep learning techniques, especially machine reading comprehension and neural-network-based information retrieval. The large pre-trained language model of BERT has also shown effectiveness in this field. Following the proposal of ECPE, the development of ECEF has accelerated. However, a comprehensive review of existing approaches and recent trends in the field is lacking. To address this issue, this survey presents a thorough review to summarise existing methods and recent key advances, illustrate the general technical architecture of traditional ECE, introduce several important variants, in particular ECPE, and provide a detailed comparison of several public datasets. Finally, the limitations of existing work and the prospects for further technological advances in ECEF are discussed.

Discourse structure analysis has shown to be useful for many artificial intelligence (AI) tasks such as text sum-marization and text categorization. However, for the Chinese news domain, the discourse structure analysis system is still immature due to the limitation of the lack of expert-annotated datasets. In this paper, we present CNA, a Chinese news corpus containing 1155 news articles annotated by human experts, which covers four domains and four news media sources. Next, we implement several text classification methods as baselines. Experimental results demonstrate that document-level method can achieve a better performance, and we further propose a document-level neural network model with multiple sentence features which achieves the state-of-the-art performance. In the end, we analyze the content type distribution of each sentence in CNA and the prediction errors of our model that occurred on the test set. The codes and dataset will be open-sourced at https://github.com/gzl98/Chinese_Discourse_Profiling.