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arXiv 2024年

作者： Dorent, Reuben Khajavi, Roya Idris, Tagwa Ziegler, Erik Somarouthu, Bhanusupriya Jacene, Heather LaCasce, Ann Deissler, Jonathan Ehrhardt, Jan Engelson, Sofija Fischer, Stefan M. Gu, Yun Handels, Heinz Kasai, Satoshi Kondo, Satoshi Maier-Hein, Klaus Schnabel, Julia A. Wang, Guotai Wang, Litingyu Wald, Tassilo Yang, Guang-Zhong Zhang, Hanxiao Zhang, Minghui Pieper, Steve Harris, Gordon Kikinis, Ron Kapur, Tina Brigham and Women’s Hospital Harvard Medical School BostonMA United States Massachusetts General Hospital Harvard Medical School BostonMA United States Yunu Inc. CaryNC United States Isomics Inc CambridgeMA United States Dana-Farber Cancer Institute BostonMA United States Technical University Munich Munich Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Munich Germany Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Institute of Medical Informatics University of Lübeck Lübeck Germany German Research Center for Artificial Intelligence Lübeck Germany Niigata University of Health and Welfare Niigata Japan Muroran Institute of Technology Hokkaido Japan Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China University of Electronic Science and Technology of China Chengdu China Heidelberg Germany University of Heidelberg Heidelberg Germany Shanghai AI laboratory Shanghai China

Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated datasets. However, for lymph node segmentation, these datasets are typically small due to the extensive time and expertise required to annotate the numerous lymph nodes in 3D CT scans. Weakly-supervised learning, which leverages incomplete or noisy annotations, has recently gained interest in the medical imaging community as a potential solution. Despite the variety of weakly-supervised techniques proposed, most have been validated only on private datasets or small publicly available datasets. To address this limitation, the Mediastinal Lymph Node Quantification (LNQ) challenge was organized in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). This challenge aimed to advance weakly-supervised segmentation methods by providing a new, partially annotated dataset and a robust evaluation framework. A total of 16 teams from 5 countries submitted predictions to the validation leaderboard, and 6 teams from 3 countries participated in the evaluation phase. The results highlighted both the potential and the current limitations of weakly-supervised approaches. On one hand, weakly-supervised approaches obtained relatively good performance with a median Dice score of 61.0%. On the other hand, top-ranked teams, with a median Dice score exceeding 70%, boosted their performance by leveraging smaller but fully annotated datasets to combine weak supervision and full supervision. This highlights both the promise of weakly-supervised methods and the ongoing need for high-quality, fully annotated data to achieve higher segmentation performance. . . © 2024, CC BY.

关键词： Image segmentation

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Token-specific deep reinforcement learning for energy-efficient capacitated electric vehicle routing problems

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Applied Energy 2025年 396卷

作者： Qingshu Guan Hui Cao Junkai Tan Lixin Jia Dapeng Yan Badong Chen School of Electrical Engineering Xi’an Jiaotong University Xi’an 710049 China State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong University Xi’an 710049 China National Key Laboratory of Human–Machine Hybrid Augmented Intelligence Xi’an Jiaotong University Xi’an 710049 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an 710049 China

Nowadays, the electric vehicle routing problem (EVRP) has garnered considerable attention in sustainable energy and transportation research, driven by the environmental promise of electric vehicles (EVs) to reduce carbon emissions and lower energy consumption. However, existing approaches to EVRPs predominantly aim to minimize travel distance, neglecting real-world factors such as road conditions and driving dynamics, which substantially influence the final energy consumption. To bridge this gap, we introduce the energy-efficient capacitated EVRP (CEVRP) and propose an advanced energy consumption model that incorporates practical considerations involving capacity limits, driving resistance, battery efficiency, road characteristics, and vehicle dynamics to capture the multifaceted challenges of EV routing. Building on this foundation, we frame the CEVRP as a customized Markov decision process (MDP) and develop a token-specific deep reinforcement learning (TS-DRL) approach, structured with an encoder–constructor–decoder framework. The encoder disentangles and synthesizes different groups of features for customers and charging stations, generating highly informative node tokens tailored to their unique roles. To adapt to the dynamic nature of CEVRP, the constructor develops a contextual token that integrates real-time EV state information with the active routing sequence, ensuring a comprehensive and continuously updated representation of the environment. Finally, the decoder leverages a compatibility layer to compute probability distributions for node selection, facilitating efficient and precise routing decisions. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of our proposed TS-DRL across varying problem sizes and node distributions. The results reveal that TS-DRL consistently outperforms a variety of heuristic and DRL-based methods, achieving energy savings of up to 109.86 kWh (i.e., a reduction of 13.79 %) in sce

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Experimental and Theoretical Study of the Pyrolysis of Β-O-4 Type Lignin Model Compounds: The Catalytic Effects of Potassium and Calcium Additives

SSRN

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SSRN 2022年

作者： Guo, Yilin Fang, Yang Gong, Xun Liu, Shuai Tian, Hong Chen, Jingwei Jiaqiang, E. Leng, Erwei College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Innovation Institute of Industrial Design and Machine Intelligence Quanzhou-Hunan University Quanzhou362006 China State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan430074 China Hunan province Key Laboratory of Efficient & Clean Thermal Power Generation Technologies State Grid Hunan Electric Power Corporation Research Institute Changsha410007 China School of Energy and Power Engineering Key Laboratory of Renewable Energy Electric Technology of Hunan Province Changsha University of Science & Technology Hunan Changsha410114 China

In this work, to investigate the catalytic effects of potassium and calcium additives on lignin pyrolysis, two β-O-4 type model compounds for lignin containing different oxygen-containing functional groups (a carbonyl group or a hydroxyl group) on C α were prepared, and their catalytic fast pyrolysis experiments were carried out on Py-GC/MS. Furthermore, the possible catalytic mechanism of potassium and calcium additives was further investigated using density functional theory (DFT) calculations. The experimental results show that the addition of potassium acetate and calcium acetate is beneficial to the formation of phenol, while the promotion effect of potassium addition is stronger than that of calcium addition. Moreover, considering the contributions of temperature and activation entropy ΔS ‡ , the activation Gibbs free energy ΔG ‡ was required as the basis to explain the pyrolysis mechanism of lignin. It was determined that the C β -O homolytic reaction performs the most favorable in the initial depolymerization of both dimers. By comparing the ΔG ‡ , it was confirmed that the addition of K + and Ca 2+ are beneficial to the C β -O homolytic reaction and further promoted the conversion of the intermediate to phenol. Furthermore, the different effects of K + and Ca 2+ additives on the side chains were compared cleavage through homolytic reaction and concerted decomposition. The calculation results showed that K + is more conducive to the simple homolytic reaction, while Ca 2+ is more conducive to the concerted decomposition. © 2022, The Authors. All rights reserved.

关键词： Density functional theory

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Point Cloud-based Grasping for Soft Hand Exoskeleton

arXiv

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arXiv 2025年

作者： Hu, Chen Tricomi, Enrica Rho, Eojin Kim, Daekyum Masia, Lorenzo Luo, Shan Gionfrida, Letizia King’s College London LondonWC2R 2LS United Kingdom Heidelberg University Heidelberg69120 Germany School of Computing KAIST Daejeon34141 Korea Republic of School of Mechanical Engineering School of Smart Mobility Korea University Seoul02841 Korea Republic of Munich Institute for Robotics and Machine Intelligence Technical University of Munich Munich80333 Germany

Grasping is a fundamental skill for interacting with and manipulating objects in the environment. However, this ability can be challenging for individuals with hand impairments. Soft hand exoskeletons designed to assist grasping can enhance or restore essential hand functions, yet controlling these soft exoskeletons to support effectively users remains difficult due to the complexity of understanding the environment. This study presents a vision-based predictive control framework that leverages contextual awareness from depth perception to predict the grasping target and determine the next control state for activation. Unlike data-driven approaches that require extensive labelled datasets and struggle with generalizability, our method is grounded in geometric modelling, enabling robust adaptation across diverse grasping scenarios. The Grasping Ability Score (GAS) was used to evaluate performance, with our system achieving a state-of-the-art GAS of 91 ± 2% across 15 objects and healthy participants, demonstrating its effectiveness across different object types. The proposed approach maintained reconstruction success for unseen objects, underscoring its enhanced generalizability compared to learning-based models. Copyright © 2025, The Authors. All rights reserved.

关键词： Exoskeleton (robotics)

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Abnormal Data Cleaning for Wind Turbines by Image Segmentation Based on Active Shape Model and Class Uncertainty

SSRN

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SSRN 2022年

作者： Liang, Guoyuan Su, Yahao Wu, Xinyu Ma, Jiajun Long, Huan Song, Zhe Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China School of Electrical Engineering Southeast University Jiang Su Province Nanjing China School of Business Nanjing University Jiang Su Province Nanjing China

Wind power curve describes the relationship between wind speed and output power of wind turbine, which may be contaminated due to various unexpected factors. Following the idea of image segmentation in our previous work, in this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The Active Shape Model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. To measure the cost of each candidate, the Class Uncertainty map based on dual feature images is built. And eventually the optimal segmentation boundary is determined by the optimization in the energy space. A series of comparative experiments conducted over the real-world datasets of 37 wind turbines have validated the superior performance of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Wind power

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Localization of near-field sources based on linear prediction and oblique projection operator

Localization of near-field sources based on linear predictio...

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作者： Zuo, Weiliang Xin, Jingmin Liu, Wenyi Zheng, Nanning Ohmori, Hiromitsu Sano, Akira Institute of Artificial Intelligence and Robotics National Engineering Laboratory for Visual Information Processing and Applications School of Artificial Intelligence Xi'An Jiaotong University Xi'an710049 China Department of System Design Engineering Keio University Yokohama223-8522 Japan

This paper investigates the localization of multiple near-field narrowband sources with a symmetric uniform linear array, and a new linear prediction approach based on the truncated singular value decomposition (LPATS) is proposed by taking an advantage of the anti-diagonal elements of the noiseless array covariance matrix. However, when the number of array snapshots is not sufficiently large enough, the 'saturation behavior' is usually encountered in most of the existing localization methods for the near-field sources, where the estimation errors of the estimated directions-of-arrival (DOAs) and ranges cannot decrease with the signal-to-noise ratio. In this paper, an oblique projection based alternating iterative scheme is presented to improve the accuracy of the estimated location parameters. Furthermore, the statistical analysis of the proposed LPATS is studied, and the asymptotic mean-square-error expressions of the estimation errors are derived for the DOAs and ranges. The effectiveness and the theoretical analysis of the proposed LPATS are verified through numerical examples, and the simulation results show that the LPATS provides good estimation performance for both the DOAs and ranges compared to some existing methods. © 1991-2012 IEEE.

关键词： Mean square error

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Correction:Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 182-182页

作者： Yufei Li Yufei Xin Xinni Li Yinrui Zhang Cheng Liu Zhengwen Cao Shaoyi Du Lin Wang School of Information Science and Technology Northwest UniversityXi’an 710127Shaanxi ProvinceChina Department of Ultrasound the Second Affiliated Hospital of Xi’an Jiaotong UniversityXi’anShaanxi Province 710004China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applicationsand Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityXi’anShaanxi Province 710004China

Following publication of the original article[1],the authors reported a typesetting error in the authors’*** to a typesetting error,the affiliations of authors Shaoyi Du and Lin Wang were mistakenly assigned.

关键词： coordinate network assigned

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Programmable cell aggregation by a synthetic biosilicification approach

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iScience 2025年第6期28卷

作者： Wang, Qing Sun, Jing Zang, Lei Yao, Huaxiong Wang, Lin Zhu, Runtao Li, Jie Zeng, Simin Tang, Hongting Wang, Teng Liu, Ji Wang, Bo Li, Bo Liu, Zhiyuan Dai, Zhuojun State Key Laboratory of Quantitative Synthetic Biology Shenzhen Institute of Synthetic Biology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Chemistry and Chemical Engineering Harbin Institute of Technology Harbin 150001 China Center of Neural Engineering CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen 518055 China Department of Civil Engineering University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 China

Programmable cell aggregation offers valuable insights into the natural development of synthetic multicellular systems and enables precise control over spatial organization and material structuring. Previous efforts have focused on modifying cells with designed organic-based adhesive modules and assembling cells into defined patterns. Here, we present a different approach to guide cell assembly by tuning the organic-inorganic interactions. Our method involves engineering cells to express a silicifying peptide on their surfaces, which promotes silica deposition on the cell walls. This peptide simultaneously binds to the silica synthesized on adjacent cells, triggering cell clustering. The engineered cells exhibit rapid aggregation, with approximately 95% of cells assembling within 15 min. We further show that this capability can facilitate materials assembly and chemical production. Our biosilicification-based approach offers novel insights into natural multicellularity mechanisms and holds potential for applications in biomanufacturing and materials engineering. © 2025 The Author(s)

关键词： Bioengineering Biomaterials Synthetic biology

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Incentive mechanism design for distributed coded machine learning

arXiv

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arXiv 2020年

作者： Ding, Ningning Fang, Zhixuan Duan, Lingjie Huang, Jianwei Department of Information Engineering Chinese University of Hong Kong Hong Kong Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China Shanghai Qi Zhi Institute Shanghai China Engineering Systems and Design Pillar Singapore University of Technology and Design Singapore School of Science and Engineering The Chinese University of Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

A distributed machine learning platform needs to recruit many heterogeneous worker nodes to finish computation simultaneously. As a result, the overall performance may be degraded due to straggling workers. By introducing redundancy into computation, coded machine learning can effectively improve the runtime performance by recovering the final computation result through the first k (out of the total n) workers who finish computation. While existing studies focus on designing efficient coding schemes, the issue of designing proper incentives to encourage worker participation is still under-explored. This paper studies the platform’s optimal incentive mechanism for motivating proper workers’ participation in coded machine learning, despite the incomplete information about heterogeneous workers’ computation performances and costs. A key contribution of this work is to summarize workers’ multi-dimensional heterogeneity as a one-dimensional metric, which guides the platform’s efficient selection of workers under incomplete information with a linear computation complexity. Moreover, we prove that the optimal recovery threshold k is linearly proportional to the participator number n if we use the widely adopted MDS (Maximum Distance Separable) codes for data encoding. We also show that the platform’s increased cost due to incomplete information disappears when worker number is sufficiently large, but it does not monotonically decrease in worker number. Copyright © 2020, The Authors. All rights reserved.

关键词： machine learning

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Detection of Deepfake videos using long distance attention

arXiv

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arXiv 2021年

作者： Lu, Wei Liu, Lingyi Luo, Junwei Zhao, Xianfeng Zhou, Yicong Huang, Jiwu School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100195 China School of Cyber Security University of Chinese Academy of Sciences Beijing100195 China Department of Computer and Information Science University of Macau 999078 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

With the rapid progress of deepfake techniques in recent years, facial video forgery can generate highly deceptive video contents and bring severe security threats. And detection of such forgery videos is much more urgent and challenging. Most existing detection methods treat the problem as a vanilla binary classification problem. In this paper, the problem is treated as a special fine-grained classification problem since the differences between fake and real faces are very subtle. It is observed that most existing face forgery methods left some common artifacts in the spatial domain and time domain, including generative defects in the spatial domain and inter-frame inconsistencies in the time domain. And a spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces in global perspective respectively. The two components are designed using a novel long distance attention mechanism. The one component of the spatial domain is used to capture artifacts in a single frame, and the other component of the time domain is used to capture artifacts in consecutive frames. They generate attention maps in the form of patches. The attention method has a broader vision which contributes to better assembling global information and extracting local statistic information. Finally, the attention maps are used to guide the network to focus on pivotal parts of the face, just like other fine-grained classification methods. The experimental results on different public datasets demonstrate that the proposed method achieves the state-of-the-art performance, and the proposed long distance attention method can effectively capture pivotal parts for face forgery. Copyright © 2021, The Authors. All rights reserved.

关键词： Time domain analysis

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