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Driver-automation shared steering control considering driver neuromuscular delay characteristics based on stackelberg game

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Green Energy and Intelligent Transportation 2022年第2期1卷 88-96页

作者： Jun Liu Hongyan Guo Wanqing Shi Qikun Dai Jiaming Zhang State Key Laboratory of Automotive Simulation and Control Jilin UniversityRenmin Street 5988Changchun130025China College of Communication Engineering Jilin UniversityRenmin Street 5988Changchun130025China

To promote the intelligent vehicle safety and reduce the driver steering workload,stackelberg game theory is adopted to design the shared steering control strategy that takes the driver neuromuscular delay characteristics into ***,a shared steering control framework with adjustable driving weight is proposed,and a coupling interaction model considering the driver neuromuscular delay characteristics is constructed by using the stackelberg game ***,the driver-automation optimal control strategy is deduced theoretically when the game equilibrium is ***,simulation and virtual driving tests are carried out to verify the superiority of the proposed *** results illustrate that the raised method can enhance the vehicle safety with low driving weight intervention,and it can achieve better auxiliary effect with less control *** addition,the driver-in-the-loop test results show that the proposed strategy can achieve better performance in assisting drivers with low driving skills.

关键词： Shared steering control Game theory Driver-automation interaction Driver neuromuscular delay characteristics

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Segregation behaviors in{101^(-)1}compressive twin boundaries of Mg-RE alloy under deformation at room temperature

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Journal of Magnesium and Alloys 2025年第1期13卷 330-337页

作者： Yujie Cui Lili Guo Yunwei Gui Kenta Aoyagi Haotian Tong Qinqin Wei Fangzhou Liu Yuichiro Hayasaka Akihiko Chiba Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China Key Laboratory for Advanced Materials Processing(MOE) University of Science and Technology BeijingBeijing 100083China Beijing Laboratory of Metallic Materials and Processing for Modern Transportation Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China Institute for Materials Research Tohoku University2-1-1 KatahiraAoba-kuSendaiMiyagi 980-8577Japan Department of Materials Science and Engineering Jilin University5988 Renmin StreetChangchunJilin 130025China School of Materials Science and Engineering Wuhan University of TechnologyWuhan 430070China The Electron Microscopy Center Tohoku UniversitySendai 980-8577Japan

Solute atoms and precipitates significantly influence the mechanical properties of Mg *** studies have mainly focused on the segregation behaviors of Mg alloys after *** this study,we investigated the segregation behaviors of an Mg-RE alloy under *** found that the enrichment of solute atoms occurred in{101^(-)1}compressive twin boundaries under compression at 298 K without any annealing in an Mg-RE alloy by scanning transmission electron microscopy and energy-dispersive X-ray *** segregated solutes and precipitates impeded the twin growth,partially contributing to the formation of small-sized{101^(-)1}compressive *** research indicates the twin boundaries can be strengthened by segregated solutes and precipitates formed under deformation at room temperature.

关键词： Magnesium alloys Compression test Scanning/transmission electron microscopy(STEM) Segregation Twin boundaries

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QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation

QGEval: Benchmarking Multi-dimensional Evaluation for Questi...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Fu, Weiping Wei, Bifan Hu, Jianxiang Cai, Zhongmin Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Continuing Education Xi'an Jiaotong University Xi'an China MOE KLINNS Lab School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798891761643

Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in the field of question generation (QG) and serves as the gold standard for automatic metrics. However, there is a lack of unified human evaluation criteria, which hampers consistent and reliable evaluations of both QG models and automatic metrics. To address this, we propose QGEval, a multi-dimensional Evaluation benchmark for Question Generation, which evaluates both generated questions and existing automatic metrics across 7 dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. We demonstrate the appropriateness of these dimensions by examining their correlations and distinctions. Through consistent evaluations of QG models and automatic metrics with QGEval, we find that 1) most QG models perform unsatisfactorily in terms of answerability and answer consistency, and 2) existing metrics fail to align well with human judgments when evaluating generated questions across the 7 dimensions. We expect this work to foster the development of both QG technologies and their evaluation. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Planning with Logical Graph-Based Language Model for Instruction Generation 27

Planning with Logical Graph-Based Language Model for Instruc...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhang, Fan Jin, Kebing Zhuo, Hankz Hankui School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (纸本)9781643685489

Despite the superior performance of large language models to generate natural language texts, it is hard to generate texts with correct logic according to a given task, due to the difficulties for neural models to capture strict logic from free-form texts. In this paper, we propose a novel graph-based language model, Logical-GLM, to extract strict logic from free-form texts and then infuse into language models. Specifically, we first capture information from natural language instructions and construct logical probability graphs that generally describe domains. Next, we generate logical skeletons to guide language model training, infusing domain knowledge into language models. At last, we alternately optimize the searching policy of graphs and language models until convergence. The experimental results show that Logical-GLM is both effective and efficient compared with traditional language models, despite using smaller-scale training data and fewer parameters. Our approach can generate instructional texts with more correct logic owing to the internalized domain knowledge. Moreover, the search of logical graphs reflects the inner mechanism of the language models, which improves the interpretability of black-box models. © 2024 The Authors.

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Personalized Federated Learning with Feature Alignment via knowledge Distillation 21st

Personalized Federated Learning with Feature Alignment via...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Qi, Guangfei Qu, Zhihao Lyu, Shen-Huan Jia, Ninghui Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9789819601189

Personalized Federated Learning (PFL) has gained significant attention for its ability to handle heterogeneous data effectively. Parameter decoupling is a typical approach to PFL. It decouples the model into a feature extractor and a classifier head, where the feature extractor is trained collaboratively to learn a common representation and the classifier head is personalized for local data. Since local training only learns personalized feature information and ignores global information, the generalization ability of the feature extractor is limited. To improve the performance of the local model, a feasible approach is to make the local feature extractor more generalized. However, prior work requires the transmission of additional feature data beyond the transmission of model parameters, which leads to privacy leakage and higher communication overhead. To address these shortcomings, we propose a PFL algorithm with feature alignment via knowledge distillation, named PFAKD. PFAKD enhances the training of local feature extractors by explicitly aligning each sample’s local features with global features, providing more detailed guidance. Meanwhile, it avoids additional communication overhead and the risk of privacy leakage. We conduct extensive experiments in heterogeneous data scenarios. PFAKD outperforms other state-of-the-art methods by up to 4.35% in terms of model accuracy. Our code is available at https://***/fei0829/PFAKD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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LaCo: Large Language Model Pruning via Layer Collapse

LaCo: Large Language Model Pruning via Layer Collapse

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yang, Yifei Cao, Zouying Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

ISBN: (纸本)9798891761681

Large language models (LLMs) based on transformer are witnessing a notable trend of size expansion, which brings considerable costs to both model training and inference. However, existing methods such as model quantization, knowledge distillation, and model pruning are constrained by various issues, including hardware support limitations, the need for extensive training, and alterations to the model internal structure. In this paper, we propose a concise layer-wise structured pruner called Layer Collapse (LaCo), in which rear model layers collapse into a prior layer, enabling a rapid reduction in model size while preserving the model structure. Comprehensive experiments show that our method maintains an average task performance of over 80% at pruning ratios of 25-30%, significantly outperforming existing state-of-the-art structured pruning methods. We also conduct post-training experiments to confirm that the LaCo effectively inherits the parameters of the original model. Additionally, we perform ablation studies on various settings of LaCo. Finally, we discuss our motivation from the perspective of layer-wise similarity and evaluate the performance of the pruned LLMs across various pruning ratios. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Short Text Topic Modeling Techniques, Applications, and Performance: A Survey

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IEEE Transactions on knowledge and data engineering 2022年第3期34卷 1427-1445页

作者： Qiang, Jipeng Qian, Zhenyu Li, Yun Yuan, Yunhao Wu, Xindong Department of Computer Science Yangzhou University Jiangsu225009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230026 China Mininglamp Academy of Sciences Minininglamp Beijing China

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm. © 2020 IEEE.

关键词： Surveys

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Characteristics Analysis and Risk Assessment of Extreme Water Levels Based on 60-Year Observation data in Xiamen, china

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Journal of Ocean university of china 2022年第2期21卷 315-322页

作者： MIAO Qingsheng YUE Xinyang YANG Jinkun WANG Zhifeng XU Shanshan YANG Yang CHU Siqi National Marine Data and Information Service Tianjin 300171China Shandong Province Key Laboratory of Ocean Engineering Ocean University of ChinaQingdao 266100China

Extreme water levels are related to astronomical tides and storm *** typhoon systems,which have caused extreme water level rises,were selected based on 60-yr water level data from the Xiamen tide gauge *** these 11 typhoon systems,the astronomical tide component accounts for 71%-95%of the total water *** Gumbel distribution of extreme water level rise was estimated,and the impact of typhoon surges on water levels during the return period was *** ex-treme tide levels caused by typhoons Herb(1996)and Dujuan(2015)are much higher than those of other typhoons and correspond to the return period of 76 yr and 71 yr,*** differences of sea levels in the presence and absence of these two typhoons in the 10-100 yr return period are 5.8-11.1 *** the 100-yr return period,the total risks within 10,25,50,and 100 yr increase by 94.3%,85.4%,72.9%,and 54.4%,respectively,if the Herb and Dujuan are not *** that typhoon Herb(1996)occurred during the highest astronomical tide,it will produce a water level higher than that of the 1000-yr return *** level rise has an important influence on the water level return period,and the contribution of nonlinear sea level rise in the next 100 yr is estimated to be 10.34%.

关键词： storm surge astronomical tide extreme water level hazard risk

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The GECAM Real-time Burst Alert System

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Research in Astronomy and Astrophysics 2024年第10期24卷 36-47页

作者： Yue Huang Dongli Shi Xiaolu Zhang Xiang Ma Peng Zhang Shijie Zheng Liming Song Xiaoyun Zhao Wei Chen Rui Qiao Xinying Song Jin Wang Ce Cai Shuo Xiao Yanqiu Zhang Shaolin Xiong Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China Southwest Jiaotong University Chengdu 611756China Qufu Normal University Qufu 273165China College of Electronic and Information Engineering Tongji UniversityShanghai 201804China College of Physics and Hebei Key Laboratory of Photophysics Research and Application Hebei Normal UniversityShijiazhuang 050024China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang 550001China University of Chinese Academy of Sciences Chinese Academy of SciencesBeijing 100049China

Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor(GECAM),consisting of two microsatellites,is designed to detect gamma-ray bursts associated with gravitational-wave ***,we introduce the real-time burst alert system of GECAM,with the adoption of the BeiDou-3 short message communication *** present the post-trigger operations,the detailed ground-based analysis,and the performance of the *** the first year of the in-flight operation,GECAM was triggered by 42 gamma-ray *** GECAM real-time burst alert system has the ability to distribute the alert within~1 minute after being triggered,which enables timely follow-up observations.

关键词： (stars:)gamma-ray burst:general gravitational waves methods:data analysis

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Three-dimensional diabetic macular edema thickness maps based on fluid segmentation and fovea detection using deep learning

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International Journal of Ophthalmology(English edition) 2022年第3期15卷 495-501页

作者： Jing-Jing Xu Yang Zhou Qi-Jie Wei Kang Li Zhen-Ping Li Tian Yu Jian-Chun Zhao Da-Yong Ding Xi-Rong Li Guang-Zhi Wang Hong Dai School of Medicine Tsinghua UniversityBeijing 100084China Visionary Intelligence Company Limited Beijing 100872China Department of Ophthalmology Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical ScienciesBeijing 100730China Key Lab of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijing 100872China

AIM: To explore a more accurate quantifying diagnosis method of diabetic macular edema(DME) by displaying detailed 3D morphometry beyond the gold-standard quantification indicator-central retinal thickness(CRT) and apply it in follow-up of DME ***: Optical coherence tomography(OCT) scans of 229 eyes from 160 patients were *** manually annotated cystoid macular edema(CME), subretinal fluid(SRF) and fovea as ground *** convolution neural networks(DCNNs) were constructed including U-Net, sASPP, HRNetV2-W48, and HRNetV2-W48+Object-Contextual Representation(OCR) for fluid(CME+SRF) segmentation and fovea detection respectively, based on which the thickness maps of CME, SRF and retina were generated and divided by Early Treatment Diabetic Retinopathy Study(ETDRS) ***: In fluid segmentation, with the best DCNN constructed and loss function, the dice similarity coefficients(DSC) of segmentation reached 0.78(CME), 0.82(SRF), and 0.95(retina).In fovea detection, the average deviation between the predicted fovea and the ground truth reached 145.7±117.8 μ*** generated macular edema thickness maps are able to discover center-involved DME by intuitive morphometry and fluid volume, which is ignored by the traditional definition of CRT>250 μ*** maps could also help to discover fluid above or below the fovea center ignored or underestimated by a single OCT ***: Compared to the traditional unidimensional indicator-CRT, 3D macular edema thickness maps are able to display more intuitive morphometry and detailed statistics of DME, supporting more accurate diagnoses and follow-up of DME patients.

关键词： diabetic macular edema fluid segmentation fovea detection 3D macular edema thickness maps deep learning

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