Entity alignment (EA) is a fundamental task in knowledge graph (KG) integration, aiming to identify equivalent entities across different KGs for a unified and comprehensive representation. Recent advances have explored pre-trained language models (PLMs) to enhance the semantic understanding of entities, achieving notable improvements. However, existing methods face two major limitations. First, they rely heavily on human-annotated labels for training, leading to high computational costs and poor scalability. Second, some approaches use large language models (LLMs) to predict alignments in a multi-choice question format, but LLM outputs may deviate from expected formats, and predefined options may exclude correct matches, leading to suboptimal performance. To address these issues, we propose LEA, an LLM-augmented entity alignment framework that eliminates the need for labeled data and enhances robustness by mitigating information heterogeneity at both embedding and semantic levels. LEA first introduces an entity textualization module that transforms structural and textual information into a unified format, ensuring consistency and improving entity representations. It then leverages LLMs to enrich entity descriptions, enhancing semantic distinctiveness. Finally, these enriched descriptions are encoded into a shared embedding space, enabling efficient alignment through text retrieval techniques. To balance performance and computational cost, we further propose a selective augmentation strategy that prioritizes the most ambiguous entities for refinement. Experimental results on both homogeneous and heterogeneous KGs demonstrate that LEA outperforms existing models trained on 30 % labeled data, achieving a 30 % absolute improvement in Hit@1 score. As LLMs and text embedding models advance, LEA is expected to further enhance EA performance, providing a scalable and robust paradigm for practical applications. The code and dataset can be found at https://github.com/Longmeix/LEA.

Prior studies on Visual Sentiment Understanding (VSU) primarily rely on the explicit scene information (e.g., facial expression) to judge visual sentiments, which largely ignore implicit scene information (e.g., human action, objection relation and visual background), while such information is critical for precisely discovering visual sentiments. Motivated by this, this paper proposes a new Omni-scene driven visual Sentiment Identifying, Locating and Attributing in videos (Omni-SILA) task, aiming to interactively and precisely identify, locate and attribute visual sentiments through both explicit and implicit scene information. Furthermore, this paper believes that this Omni-SILA task faces two key challenges: modeling scene and highlighting implicit scene beyond explicit. To this end, this paper proposes an Implicit-enhanced Causal MoE (ICM) approach for addressing the Omni-SILA task. Specifically, a Scene-Balanced MoE (SBM) and an Implicit-Enhanced Causal (IEC) blocks are tailored to model scene information and highlight the implicit scene information beyond explicit, respectively. Extensive experimental results on our constructed explicit and implicit Omni-SILA datasets demonstrate the great advantage of the proposed ICM approach over advanced Video-LLMs.

Prior studies on Video Anomaly Detection (VAD) mainly focus on detecting whether each video frame is abnormal or not in the video, which largely ignore the structured video semantic information (i.e., what, when, and where does the abnormal event happen). With this in mind, this paper propose a new chat-paradigm Multi-scene Video Abnormal Event Extraction and Localization (M-VAE) task, aiming to extract the abnormal event quadruples (i.e., subject, event type, object, scene) and localize such event. Further, this paper believes that this new task faces two key challenges, i.e., global-local spatial modeling and global-local spatial balancing. To this end, this paper proposes a Global-local Spatial-sensitive Large Language Model (LLM) named Sherlock, i.e., acting like Sherlock Holmes to track down the criminal events, for this M-VAE task. Specifically, this model designs a Global-local Spatial-enhanced MoE (GSM) module and a Spatial Imbalance Regulator (SIR) to address the two challenges respectively. Extensive experiments on our M-VAE instruction dataset show the significant advantages of Sherlock over several advanced Video-LLMs. This justifies the importance of global-local spatial information for the M-VAE task and the effectiveness of Sherlock in capturing such information.

Power constraints and channel acquisition pose practical challenges in multi-beam satellite communications. Motivated by this, we investigate robust energy-efficient multigroup multicast beamforming in multi-beam satellite communications with full frequency reuse in this paper. Specifically, we consider the problem of minimizing the total power while guaranteeing that the energy efficiency (EE) of each group is above a prescribed threshold. The considered problem is challenging in the sense that the average rates in the definition of the EE generally do not admit an explicit expression and the optimization problem is NP-hard and nonconvex. To tackle this problem, we first adopt a closed-form tight approximation for the average rates. Then the semidefinite relaxation and the concave-convex procedure are utilized to transfer the nonconvex problem into a convex problem. Finally, based on the ranks of the solutions, the eigenvalue decomposition or the Gaussian randomization approach is invoked to generate the final feasible solutions. Numerical results validate the high accuracy of the average rate approximation, and demonstrate that our proposed robust approach significantly outperforms the conventional one, especially for the case with large channel phase error variances.