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29th International Conference on Neural Information Processing

作者： Zhang, Siqi Zhang, Lu Liu, Zhiyong Feng, Hangtao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

ISBN: (纸本)9783031301100;9783031301117

Domain adaptive object detection (DAOD) aims to adapt the detector from a labelled source domain to an unlabelled target domain. In recent years, DAOD has attracted massive attention since it can alleviate performance degradation due to the large shift of data distributions in the wild. To align distributions between domains, adversarial learning is widely used in existing DAOD methods. However, the decision boundary for the adversarial domain discriminator may be inaccurate, causing the model biased towards the source domain. To alleviate this bias, we propose a novel Frequency-based Image Translation (FIT) framework for DAOD. First, by keeping domain-invariant frequency components and swapping domain-specific ones, we conduct image translation to reduce domain shift at the input level. Second, hierarchical adversarial feature learning is utilized to further mitigate the domain gap at the feature level. Finally, we design a joint loss to train the entire network in an end-to-end manner without extra training to obtain translated images. Extensive experiments on three challenging DAOD benchmarks demonstrate the effectiveness of our method.

关键词： Unsupervised Domain Adaptation Object Detection Frequency Domain Image Translation Adversarial Learning

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management-Oriented Operating systems: Harnessing the Power of DAOs and Foundation Models

Management-Oriented Operating Systems: Harnessing the Power ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Qin, Rui Li, Juanjuan Liang, Xiaolong Wang, Fei-Yue Institute of Automation State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering Macau999078 China Institute of Automation The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9798350337020

This paper presents management-Oriented Operating systems (M2OS) that leverages the power of parallel intelligence theory, Decentralized Autonomous Organizations (DAOs) and foundation models to revolutionize the manner of management in Cyber-Physical-Social systems (CPSS). The parallel architecture on M2OS is proposed, including the parallel interactive actual M2OS and artificial M2OS. Among them, the artificial M2OSs provide digital infrastructures for organizations to operate, collaborate, and make decisions in the virtual space, and conduct computational experiments to evaluate management decisions and predict future states of the actual M2OS. Through parallel execution and closed-loop feedback between the artificial and actual M2OSs, the management and control, experimentation and evaluation, as well as learning and training of the actual M2OS can be realized. Moreover, the functional layers of M2OS, including the infrastructure layer, data layer, scenario layer, modeling layer, decision layer, and application layer, are discussed. These layers work together to support the intelligent, autonomous, collaborative, and adaptive nature of the M2OS, and facilitate data-driven decision-making, optimize business operations, and empower managers with real-time actionable insights. The proposed M2OS paradigm has great potential to transform the management paradigm and opens up new possibilities for intelligent and collaborative decision-making. © 2023 IEEE.

关键词： cyber-physical-social systems Decentralized Autonomous Organizations foundation models management-oriented operating systems parallel intelligence

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In Memory of Wolter J. Fabrycky: A Pioneer of systems Engineering and US-Sino Academic Exchange

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IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 839-840页

作者： Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China

The passing of Professor Wolter “Wolt” Fabrycky, an outstanding member and great leader, is a big loss to our international systems engineering professional community. “Wolt was a legend in the systems engineering community with his teaching, advising, and dissemination of knowledge through the books he authored.”, as stated by Professor Eileen Aken, a former student of Wolt and the head of the Virginia Tech's Grado Department of Industrial and systems Engineeirng where Wolt had served and led for 30 years and retired as John L. Lawrence Professor emeritus.

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Imaginations Generate Images for Multi-modal Machine Translation 1

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13th International Conference on Computer Engineering and Networks (CENet)

作者： Yang, Xiaona Sun, Wenli Wei, Wei Li, Yinlin Shi, Xiayang Zhengzhou Univ Light Ind Zhengzhou Peoples R China Wuhan Second Ship Design & Res Inst Wuhan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (数字)9789819992430

ISBN: (纸本)9789819992454;9789819992430;9789819992423

Multi-modal machine translation (MMT) aims at exploring better translation systems by integrating the visual annotation which presents the content described in the bilingual parallel sentence pair into the conventional only-text neural machine translation (NMT). However, existing methods heavily rely on the manual annotated images data set. The cost of manual image annotation is relatively high at this stage. In this paper, we propose the generative imagination network with transformer to automatically generate visual annotations semantic-equivalent with source and target sentences. The proposed model receives the inputs of source-target bilingual sentences and generates visual annotations for MMT. Experiments analysis demonstrate that our model can generate high-quality annotated images and prompt the performance of MMT. Additionally, we use our model to generate annotated images for a famous English-German IWSLT-2015, the experimental results show the improvement for MMT.

关键词： Multi-modal machine translation Visual annotation

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LC-LLM: Explainable lane-change intention and trajectory predictions with Large Language Models

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COMMUNICATIONS IN TRANSPORTATION RESEARCH 2025年 5卷

作者： Peng, Mingxing Guo, Xusen Chen, Xianda Chen, Kehua Zhu, Meixin Chen, Long Wang, Fei-Yue Hong Kong Univ Sci & Technol Guangzhou Intelligent Transportat INTR Syst Hub Guangzhou 511400 Peoples R China Hong Kong Univ Sci & Technol Acad Interdisciplinary Studies Div Emerging Interdisciplinary Areas EMIA Hong Kong 999077 Peoples R China Guangdong Prov Key Lab Integrated Commun Sensing & Guangzhou 511400 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretability. In this study, we address these challenges by proposing a Lane Change-Large Language Model (LC-LLM), an explainable lane change prediction model that leverages the strong reasoning capabilities and self explanation abilities of Large Language Models (LLMs). Essentially, we reformulate the lane change prediction task as a language modeling problem, processing heterogeneous driving scenario information as natural language prompts for LLMs and employing supervised fine-tuning to tailor LLMs specifically for lane change prediction task. Additionally, we finetune the Chain-of-Thought (CoT) reasoning to improve prediction transparency and reliability, and include explanatory requirements in the prompts during the inference stage. Therefore, our LC-LLM not only predicts lane change intentions and trajectories but also provides CoT reasoning and explanations for its predictions, enhancing its interpretability. Extensive experiments based on the large-scale highD dataset demonstrate the superior performance and interpretability of our LC-LLM in lane change prediction task. To the best of our knowledge, this is the first attempt to utilize LLMs for predicting lane change behavior. Our study shows that LLMs can effectively encode comprehensive interaction information for understanding driving behavior.

关键词： Lane change (LC) Large language models (LLMs) Intention prediction Trajectory prediction Fine-tuning Interpretability Autonomous driving

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Sustainability Opportunities and Ethical Challenges of AI-Enabled Connected Autonomous Vehicles Routing in Urban Areas

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 55-58页

作者： Guo, Rongge Vallati, Mauro Wang, Yutong Zhang, Hui Chen, Yuanyuan Wang, Fei-Yue Univ Huddersfield Sch Comp & Engn Huddersfield HD13DH England Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beihang Univ Sch Transportat Sci & Engn Beijing 100091 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Engn Macau 999078 Peoples R China

The advent of Connected Autonomous Vehicles (CAVs) paves the way to a new era of urban traffic control and management, driven by Artificial Intelligence (AI)-enabled strategies. This advancement promises significant improvements in infrastructure use optimization, traffic delay reduction, and overall sustainability. The autonomous driving capabilities of CAVs, coupled with the communication technology, allow vehicles to play an active role in urban traffic control: they can follow tailored instructions and can act as highly accurate moving sensors for traffic authorities. However, such improved capabilities come at the cost of unprecedented vulnerabilities to cyber exploitation, and with the concrete potential to increase social and economic disparities. As an extension of TIV-DHW (Distributed/Decentralized Hybrid Workshop) on ERS (Ethics, Responsibility, and Sustainability), this letter explores how the AI-enabled routing methodology can enhance urban transportation sustainability while also discussing its ethical implications and challenges.

关键词： Routing Artificial intelligence Traffic control Sustainable development Urban areas Safety Ethics Connected autonomous vehicles artificial intelligence traffic routing sustainability ethics

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CSIR: Cascaded Sliding CVAEs With Iterative Socially-Aware Rethinking for Trajectory Prediction

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第12期24卷 14957-14969页

作者： Zhou, Hao Yang, Xu Ren, Dongchun Huang, Hai Fan, Mingyu Harbin Engn Univ Natl Key Lab Sci & Technol Underwater Vehicle Harbin 150001 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Meituan Res Ctr Autonomous Vehicles Beijing 100102 Peoples R China Donghua Univ Inst Artificial Intelligence Shanghai 200051 Peoples R China

Pedestrian trajectory prediction is a hot research topic in many applications, such as video surveillance and autonomous driving. Although many efforts have been done on this topic, there are still many challenges, including accumulated prediction errors, insufficient training data usage, and future-past incompatibility. To overcome these challenges, we propose a novel trajectory prediction method, called CSIR, which consists of a cascaded sliding conditional variational autoencoder (CS-CVAE) module and an iterative future-past social compatible rethinking (I-SCR) module. The CS-CVAE module reduces the accumulated prediction errors by using cascaded prediction models for the early future time steps. In this way, the training losses of the early time steps are separately considered and minimized from the later losses. For the following time steps in CS-CVAE, a sliding prediction model with a longer observation time span is used and additional data from the future time span can be collected for training. On the other hand, the I-SCR module generates offsets to improve the predictions iteratively by checking the interaction compatibility between the predicted trajectories and the past trajectories, which resembles with the human rethinking mechanism in motion planning. Experiments results on two widely explored pedestrian trajectory prediction datasets, Stanford Drone Dataset (SDD) and ETH/UCY, show that the proposed method surpasses previous state-of-the-art methods by notable margins.

关键词： Cascaded prediction sliding sequence prediction iterative social-aware rethinking trajectory prediction

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Advanced Scenario Generation for Calibration and Verification of Autonomous Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第5期8卷 3211-3216页

作者： Li, Xuan Teng, Siyu Liu, Bingzi Dai, Xingyuan Na, Xiaoxiang Wang, Fei-Yue Peng Cheng Lab Shenzhen 518000 Peoples R China Hong Kong Baptist Univ Hong Kong 999077 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Cambridge Dept Engn Cambridge CB2 1PZ England

As driving scenarios and autonomous vehicles (AVs) become increasingly intricating, there is an increasing need for innovative frameworks that can enhance and test AV capabilities across diverse scenarios. At present, the design and validation for AVs predominantly rely on simulation software or real-road testing methodologies. However, these approaches possess inherent limitations, leading to inaccuracy, reduced efficiency, and potential hazards during the development process. This letter reports our first DHW (decentralized and hybrid workshop) on Scenarios Engineering (SE), that aims to calibrate and validate AV modules through advanced scenarios. Specifically, the DHW discusses combining SE with existing simulation software and real-road testing strategies, enabling developers to maximize the advantages of each approach while ensuring the safety and efficacy in the development of autonomous vehicles. Our findings demonstrate the benefits of enhancing realism of simulation scenarios in the aspects of both content and appearance. Furthermore, the use of scenarios engineering is explored in order to enhance the diversity and safety of real-road testing by means of integrating virtual and physical components.

关键词： Testing Safety Autonomous vehicles Roads ISO Standards Calibration Standards Scenarios engineering autonomous vehicles parallel intelligence simulation scenarios real-road testing

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Magnetic-optical dual-modality imaging monitoring chemotherapy efficacy of pancreatic ductal adenocarcinoma with a low-dose fibronectin-targeting Gd-based contrast agent

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EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 2024年第7期51卷 1841-1855页

作者： Zhang, Wenjia Liang, Xiaolong Zhang, Xinyu Tong, Wei Shi, Guangyuan Guo, Haozhuo Jin, Zhengyu Tian, Jie Du, Yang Xue, Huadan Chinese Acad Med Sci & Peking Union Med Coll Peking Union Med Coll Hosp Dept Radiol State Key Lab Complex Severe & Rare Dis 1 Shuaifuyuan Beijing 100730 Peoples R China Peking Univ Peoples Hosp Dept Radiol Beijing 100032 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing Key Lab Mol Imaging CAS Key Lab Mol ImagingInst Automat 95 Zhongguancun East Rd Beijing 100190 Peoples R China Peking Univ Third Hosp Dept Ultrasound Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Med Beijing 100191 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Purpose Pancreatic ductal adenocarcinoma (PDAC) is a lethal hypovascular tumor surrounded by dense fibrosis. Albumin-bound paclitaxel and gemcitabine (AG) chemotherapy is the mainstay of PDAC treatment through depleting peritumoral fibrosis and killing tumor cells;however, it remains challenging due to the lack of a noninvasive imaging method evaluating fibrotic changes during AG chemotherapy. In this study, we developed a dual-modality imaging platform that enables noninvasive, dynamic, and quantitative assessment of chemotherapy-induced fibrotic changes through near-infrared fluorescence molecular imaging (FMI) and magnetic resonance imaging (MRI) using an extradomain B fibronectin (EDB-FN)-targeted imaging probe (ZD2-Gd-DOTA-Cy7). Methods The ZD2-Gd-DOTA-Cy7 probe was constructed by conjugating a peptide (Cys-TVRTSAD) to Gd-DOTA and the near-infrared dye Cy7. PDAC murine xenograft models were intravenously injected with ZD2-Gd-DOTA-Cy7 at a Gd concentration of 0.05 mmol/kg or free Cy7 and Gd-DOTA as control. The normalized tumor background ratio (TBR) on FMI and the T1 reduction ratio on MRI were quantitatively analyzed. For models receiving AG chemotherapy or saline, MRI/FMI was performed before and after treatment. Histological analyses were performed for validation. Results The ZD2-Gd-DOTA-Cy7 concentration showed a linear correlation with the fluorescence intensity and T1 relaxation time in vitro. The optimal imaging time was 30 min after injection of the ZD2-Gd-DOTA-Cy7 (0.05 mmol/kg), only half of the clinic dosage of gadolinium. Additionally, ZD2-Gd-DOTA-Cy7 generated a 1.44-fold and 1.90-fold robust contrast enhancement compared with Cy7 (P < 0.05) and Gd-DOTA (P < 0.05), respectively. For AG chemotherapy monitoring, the T1 reduction ratio and normalized TBR in the fibrotic tumor areas were significantly increased by 1.99-fold (P < 0.05) and 1.78-fold (P < 0.05), respectively, in the control group compared with those in the AG group. Conclusion MRI/FMI wi

关键词： Molecular imaging Magnetic resonance imaging Fluorescence molecular imaging Pancreatic cancer Fibronectin

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Valuing equity-linked guaranteed minimum death benefits with European-style Asian payoffs under a regime switching jump-diffusion model

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2024年 128卷

作者： Wang, Yayun Liu, Shengda Chongqing Univ Posts & Telecommun Key Lab Intelligent Anal & Decis Complex Syst Chongqing 400065 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper presents a novel and efficient approach to pricing equity-linked guaranteed minimum death benefits (GMDB) with European-style geometric Asian and arithmetic Asian payoffs. Our method assumes that the underlying asset process follows a regime-switching Levy model, which captures the key features of market dynamics in the continuous transition of the economy. To derive the approximate value of GMDB products, we employ the complex Fourier series (CFS) expansion method. Our error analysis demonstrates that this approach exhibits an exponential convergence rate. In our numerical experiments, we compare the CFS approach to other Fourier transform methods and Monte Carlo simulation. The results show that our method outperforms the other approaches in terms of both efficiency and accuracy. This paper contributes to the literature on pricing equity-linked GMDB products by proposing a novel and efficient approach based on regime-switching Levy models and complex Fourier series expansion. The implications of our results may have significant practical implications for the insurance industry and financial markets.

关键词： Regime-switching Levy model complex Fourier series method European-style Asian option payoffs GMDB

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