As an important task in emotion analysis, Multimodal Emotion-Cause Pair Extraction in conversations (MECPE) aims to extract all the emotion-cause utterance pairs from a conversation. However, there are two shortcomings in the MECPE task: 1) it ignores emotion utterances whose causes cannot be located in the conversation but require contextualized inference; 2) it fails to locate the exact causes that occur in vision or audio modalities beyond text. To address these issues, in this paper, we introduce a new task named Multimodal Emotion-Cause Pair Generation in Conversations (MECPG), which aims to identify the emotion utterances with their emotion categories and generate their corresponding causes in a conversation. To tackle the MECPG task, we construct a dataset based on a benchmark corpus for MECPE. We further propose a generative framework named MONICA, which jointly performs emotion recognition and emotion cause generation with a sequence-to-sequence model. Experiments on our annotated dataset show the superiority of MONICA over several competitive systems. Our dataset and source codes will be publicly released.

Most existing work on aspect-based sentiment analysis (ABSA) focuses on the sentence level, while research at the document level has not received enough attention.Compared to sentence-level ABSA, the document-level ABSA is not only more practical but also requires holistic document-level understanding capabilities such as coreference resolution.To investigate the impact of coreference information on document-level ABSA, we conduct a three-stage research for the document-level target sentiment analysis (DTSA) task: 1) exploring the effectiveness of coreference information for the DTSA task; 2) reducing the reliance on manually annotated coreference information; 3) alleviating the evaluation bias caused by missing the coreference information of opinion targets.Specifically, we first manually annotate the coreferential opinion targets and propose a multi-task learning framework to model the DTSA task and the coreference resolution task jointly.Then we annotate the coreference information with ChatGPT for joint training.Finally, to address the issue of missing coreference targets, we modify the metric from strict matching to a loose matching method based on the clusters of targets.The experimental results demonstrate our framework's effectiveness and reflect the feasibility of using ChatGPT-annotated coreferential entities and the applicability of the modified metric.Our source code is publicly released at https://github.com/NUSTM/DTSA- Coref.

Emotion cause analysis has attracted increasing attention in recent years. However, the integration of multimodal information with emotion causes remains underexplored. Existing studies merely extract utterances from conversations as cause evidence, which is too coarse-grained to locate the exact causes from other modalities, especially those that may be reflected only in a specific video frame of an utterance. To address these limitations, we introduce a new task named Multimodal Emotion Cause Generation in Conversations (MECGC), which aims to generate an abstractive summary clearly and intuitively describing the causes that trigger the given emotion based on the multimodal context of conversations. We accordingly construct a dataset named ECGF that contains 1,374 conversations and 7,690 emotion instances from TV series. We further develop a generative framework that first generates emotion-cause aware video captions (Observe) and then facilitates the generation of emotion causes (Generate). The captioning model is trained with examples synthesized by a Multimodal Large Language Model (MLLM). Experimental results demonstrate the effectiveness of our framework and the significance of multimodal information for emotion cause analysis. Our dataset and source codes are available at https://github.com/NUSTM/MECGC.

Detecting the Thermal Infrared Anomalies (TIA) prior to earthquakes has always been an important issue in seismic studies. Several approaches have been proposed to recognize weak abnormal signals under strong backgrounds noise. The Z-Score (ZS), Robust Satellite Technique (RST), and Eddy Field (EF) methods were considered here to detect Brightness Temperature (BT) anomalies before the L'Aquila earthquake jolted central Italy on April 6, 2009. The results illustrated that the EF method is not suitable to extract BT anomalies before this occurrence. In addition, it was found that the RST method is not sensitive enough to determine the epicenter of forthcoming earthquakes. The ZS method has been observed to reflect the negative correlation between the spatial distance from the epicenter and abnormal values before the earthquake. Moreover, sudden changes in the BT warming approximately 10 days before the shock were recognized using this method. Therefore, the ZS method is considered to be suitable to extract the BT anomalies associated with the L'Aquila earthquake.

The study of vertical crustal movement in the northern segment of the North-South Tectonic Belt is of great significance in understanding regional crustal deformations and earthquake risk prediction. Global navigation satellite system (GNSS) technology has been widely used in the study of horizontal crustal movement, but its application in the study of vertical crustal movement is quite limited. In this paper, the northern segment of the North-South Tectonic Belt is taken as the research area. Based on continuous and discontinuous GNSS station data recorded since 1999, the vertical velocity field in the research area is obtained after fine non-tectonic corrections, and the vertical movement characteristics in the northern segment of the North-South Tectonic Belt are analyzed. The results show that the non-tectonic deformation correction method, combining non-tectonic load and sinusoidal function fitting, is effective for continuous GNSS stations, and a sinusoidal fitting method with polynomials is suitable for discontinuous GNSS. Vertical movements in the research area are quite weak. The southern margin of the Alashan block and the Qilian block are found to be relatively uplifted and the center of subsidence for the Yinchuan, Weihe, and Qaidam basins is found to be located near NXYC, XIAA, and GSLZ stations, respectively. There is still a significant vertical differential movement on both sides of the Helan mountain fault.