Linrui Ma, Chun Hei Lo, Xinyu Wang, Peng Lu, Xihao Yuan, Hanting Chen, Kai Han, Xinghao Chen, Chengjun Zhan, Hanlin xu, Yichun Yin, Lifeng Shang, Feng Wen, Boxing Chen, Yufei Cui. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Aviation electrical connectors are essential components in the aircraft electrical wiring interconnection system (EWIS), responsible for information and energy transmission. Even a minor fault in a connector pin can critically affect the reliability and stability of the EWIS. However, traditional faulty pin detection methods rely heavily on manual visual inspection, which is inefficient and susceptible to missed or false detections due to the inherent limitations of human observation. To address these challenges, this article introduces a novel system-level approach that transforms the defect detection process from the physical domain to a virtual one powered by digital twin (DT) technology. A general framework for DT-based defect detection is proposed and instantiated through the design and implementation of a DT-enabled automated faulty pin detection (DT-AFPD) system. The DT-AFPD system integrates 3-D machine vision into a complete detection pipeline encompassing equipment design, data acquisition, DT model construction, algorithm development, and system deployment. Specifically, a 4-degree-of-freedom (4-DOF) device equipped with a 3-D structured light camera is developed to acquire point cloud data of aviation connectors. Several preprocessing techniques are applied to reduce data volume and enhance point cloud quality. Based on this, a connector structure-aware faulty pin detection algorithm, named CSA-FPD, is designed to detect short and bent pins using limited data. The proposed DT-AFPD system is validated on 13 representative types of aviation electrical connectors, covering over 3600 pins. Experimental results demonstrate that the system achieves an average detection precision of 99.85%, effectively reducing the probability of EWIS reinstallation and enhancing the reliability of faulty pin detection.

We present Oakink2, a dataset of bimanual object manipulation tasks for complex daily activities. In pursuit of constructing the complex tasks into a structured representation, Oakink2 introduces three level of abstraction to organize the manipulation tasks: Affordance, Primitive Task, and Complex Task. OAKINK2 features on an object-centric perspective for decoding the complex tasks, treating them as a sequence of object affordance fulfillment. The first level, Affordance, outlines the functionalities that objects in the scene can afford, the second level, Primitive Task, describes the minimal interaction units that humans interact with the object to achieve its affordance, and the third level, Complex Task, illustrates how Primitive Tasks are composed and interdependent. Oakink2 dataset provides multi-view image streams and precise pose annotations for the human body, hands and various interacting objects. This extensive collection supports applications such as interaction reconstruction and motion synthesis. Based on the 3-level abstraction of Oakink2, we explore a task-oriented framework for Complex Task Completion (CTC). CTC aims to generate a sequence of bimanual manipulation to achieve task objectives. Within the CTC framework, we employ Large Language Models (LLMs) to decompose the complex task objectives into sequences of Primitive Tasks and have developed a Motion Fulfillment Model that generates bimanual hand motion for each Primitive Task. Oakink2 datasets and models are available at https://oakink.net/v2.

Small nucleolar RNAs(snoRNAs) represent an abundant group of noncoding RNAs in the nucleolus of eukaryotes. Recent studies revealed that snoRNAs play a significant role in a wide range of diseases. Identifying snoRNA-disease associations can provide great insights into understanding the disease and treatment and boosting drug discovery development. Traditional methods of using biological experiments are often usually small-scale and time-consuming. Therefore, it is urgent to develop a computational framework to predict snoRNA-disease associations. In this work, we proposed a novel Graph Convolutional Network(GCN) based framework GCNSDA for predicting snoRNA-disease associations. To our best knowledge, GCNSDA is the first framework that uses a graph convolutional network to predict snoRNA-disease associations. GCNSDA is built on the bipartite graph of snoRNAs and diseases; it uses a graph neural network to discover latent factors that cause the association between snoRNAs and diseases and then generate embedded representations of snoRNAs and diseases. Experimental results showed that GCNSDA achieved better performance than the other six state-of-the-art methods. Case study further confirmed the effectiveness of GCNSDA in predicting snoRNA-disease associations.

The research of anticancer drugs has gone through a long process of development, but so far, no drug can cure cancer completely. The drug resistance is one of the main reasons for the failure of cancer treatment. As the relationship between miRNA and cancer is gradually revealed, more and more evidence shows that the sensitivity of cancer cells to anticancer drugs is also affected by miRNA. Research on miRNA-drug sensitivity associations can overcome the challenging clinical situation imposed by drug resistance. However, traditional biological experiments are time-consuming and expensive. Therefore, there is an urgent need to develop a computational method to predict the associations between miRNA and drug sensitivity accurately and efficiently. In this work, we propose a computational method based on simplified GCN to predict the miRNA-drug sensitivity associations, named LGCMDS. We abandon the two common designs in standard GCN-feature transformation and nonlinear activation, only retain the essential component, neighbourhood aggregation, and combine with high-order connectivity in the miRNA-drug graph to effectively integrate the miRNA-drug interactions into the embedding process. The 5-fold cross-validation results show that our proposed method achieves AUC of 0.8872 and AUPR of 0.9026. In comparison with five state-of-the-art models, LGCMDS achieves the best results. In addition, case study for Cisplatin, further proves the effectiveness of LGCMDS in predicting potential miRNA-drug sensitivity associations.