Teng Pan, Yuchen Yan, Zixuan Wang, Ruiqing Zhang, Guiyang Hou, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

In this paper, a bias-Lyapunov iteration method is proposed to solve the optimal control problem of unknown Markovian jump linear systems. By incorporating a bias parameter into the conventional Lyapunov iteration method, the proposed method eliminates initial admissible control requirements. A model-based theoretical framework is subsequently established, accompanied by a rigorous convergence proof for the modified iteration process. Subsequently, a data-driven version of bias-Lyapunov iteration is developed to learn an optimal control for Markovian jump linear systems with completely unknown dynamics. Simulation examples validate the efficacy and advantage of the proposed bias-Lyapunov iteration method.

Pengzhi Gao, Ruiqing Zhang, Zhongjun He, Hua Wu, Haifeng Wang. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Object detection in optical remote sensing (RS) images is crucial for both military and civilian applications. However, a major challenge in RS object detection lies in the complexity of texture details within the images, which makes it difficult to accurately identify the objects. Currently, many object detection methods based on deep learning focus primarily on network architecture and label assignment design. These methods often employ an end-to-end training approach, where the loss function only directly constraints the final output layer. However, this approach gives each module within the network a significant amount of freedom during the optimization process, which can hinder the network’s ability to effectively focus on the object and limit detection accuracy. To address these limitations, this paper proposes a novel approach called the Attention Feature Guided Network (AFGN). In this approach, a Attention Feature Guided Branch (AFGB) is introduced during the training phase of the CNN-based end-to-end detection network. The AFGB provides additional shallow supervision outside the detector’s output layer, guiding the backbone to effectively focus on the object amidst complex backgrounds. Additionally, a new operation called Background Blur Mask (BBM) is proposed, which is embedded in the AFGB to achieve image-level attention. Experiments conducted on the DIOR dataset demonstrate the effectiveness and efficiency of the proposed method. Our method achieves an mAP (mean average precision) of 0.777, surpassing many state-of-the-art object detection methods.

Contact tooling operations like sanding and polishing have been high in demand for robotics and automation, as manual operations are labour-intensive with inconsistent quality. However, automating these operations remains a challenge since they are highly dependent on prior knowledge about the geometry of the workpiece. While several methods have been developed in existing research to automate the geometry learning process and adjust the contact force, human supervision is heavily required in the calibration of workpieces and the path planning of robot motion in such methods. Furthermore, the stiffness identification of the workpiece is not considered in most of these methods. This paper presents a human-robot collaboration (HRC) framework, which is able to perform surface exploration on an unknown object combining the operator's flexibility with the control precision of the robot. The operator moves the robot along the surface of the target object, and the robot recognizes the surface geometry and surface stiffness while exerting a desired contact force through control. For this purpose, a mesh iterative learning control (MILC) is developed to learn the surface stiffness, plan the exploration path, and adjust contact force through repetitive online correction based on HRC. The proof of learning convergence and the results of the simulation and experiments performed using a 7-DOF Sawyer robot demonstrate the validity of the proposed controller.

End-to-end simultaneous speech-to-text translation aims to directly perform translation from streaming source speech to target text with high translation quality and low latency.A typical simultaneous translation (ST) system consists of a speech translation model and a policy module, which determines when to wait and when to translate.Thus the policy is crucial to balance translation quality and latency.Conventional methods usually adopt fixed policies, e.g.segmenting the source speech with a fixed length and generating translation.However, this method ignores contextual information and suffers from low translation quality.This paper proposes an adaptive segmentation policy for end-toend ST.Inspired by human interpreters, the policy learns to segment the source streaming speech into meaningful units by considering both acoustic features and translation history, maintaining consistency between the segmentation and translation.Experimental results on English-German and Chinese-English show that our method achieves a good accuracylatency trade-off over recently proposed stateof-the-art methods.* Corresponding author. 1 In German, each singular noun is assigned a gender, either masculine, feminine, or neuter, which determines whether the definite article (like "The" in English) preceding the noun is "Der", "Die" or "Das".Therefore, translating "The" hastily without receiving the following noun may cause mistranslation.(b) Word-based policy ist Hund

Zheng Fang, Ruiqing Zhang, Zhongjun He, Hua Wu, Yanan Cao. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Balancing accuracy and latency is a great challenge for simultaneous translation.To achieve high accuracy, the model usually needs to wait for more streaming text before translation, which results in increased latency.However, keeping low latency would probably hurt accuracy.Therefore, it is essential to segment the ASR output into appropriate units for translation.Inspired by human interpreters, we propose a novel adaptive segmentation policy for simultaneous translation.The policy learns to segment the source text by considering possible translations produced by the translation model, maintaining consistency between the segmentation and translation.Experimental results on Chinese-English and German-English translation show that our method achieves a better accuracy-latency trade-off over recently proposed state-of-the-art methods.

Spreadsheets are widely used but subject to various defects. In this paper, we present SGuard to effectively detect spreadsheet defects. SGuard learns spreadsheet features to cluster cells with similar computational semantics, and then refines these clusters to recognize anomalous cells as defects. SGuard well balances the trade-off between the precision (87.8%) and recall rate (71.9%) in the defect detection, and achieves an F-measure of 0.79, exceeding existing spreadsheet defect detection techniques. We introduce the SGuard implementation and its usage by a video presentation (https://youtu.be/gNPmMvQVf5Q), and provide its public download repository (https://github.com/sheetguard/sguard).

Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining road topology. To address the effect attenuation problem, we propose to take account of the traffic of surrounding locations. We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in both temporal and spatial domain. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method.