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International Symposium on Autonomous systems (ISAS)

作者： Yu Huang Huiyan Zhang Zixian Chen Jiangyan Zou School of Mechanical Engineering Chongqing Technology and Business University Chongqing China National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing China Chongqing Innovation Center of Industrial Big-Data Co. Ltd. Chongqing China School of Artificial Intelligence Chongqing Technology and Business University Chongqing China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

Under different communication topologies, this paper investigates the consensus problem for multi-agent systems with general linear dynamics under leader following. A distributed dynamic event-triggered control strategy with output feedback adjustment is proposed by using adaptive control technology of auxiliary variables, and it is applied to different follower communication topologies. The simulation results show that the leader-following systems under different communication modes of the followers can solve the completely distributed consensus problem. Moreover, the Zeno behavior of each multi-agent can be excluded. Finally, the effectiveness of the proposed control strategy is verified by taking multi-vehicle cooperative tracking control as an example.

关键词： Trajectory tracking Simulation Consensus control Control systems Topology Vehicle dynamics Adaptive control

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Replace2Self: Self-Supervised Denoising based on Voxel Replacing and Image Mixing for Diffusion MRI

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wu, Linhai Wang, Lihui Deng, Zeyu Zhu, Yuemin Wei, Hongjiang Guizhou University Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology 550025 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 LyonF-69621 France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China

Low signal to noise ratio (SNR) remains one of the limitations of diffusion weighted (DW) imaging. How to suppress the influence of noise on the subsequent analysis about the tissue microstructure is still challenging. This work proposed a novel self-supervised learning model, Replace2Self, to effectively reduce spatial correlated noise in DW images. Specifically, a voxel replacement strategy based on similar block matching in Q-space was proposed to destroy the correlations of noise in DW image along one diffusion gradient direction. To alleviate the signal gap caused by the voxel replacement, an image mixing strategy based on complementary mask was designed to generate two different noisy DW images. After that, these two noisy DW images were taken as input, and the non-correlated noisy DWimage after voxel replacement was taken as learning target, a denoising network was trained for denoising. To promote the denoising performance, a complementary mask mixing consistency loss and an inverse replacement regularization loss were also proposed. Through the comparisons against several existing DW image denoising methods on extensive simulation data with different noise distributions, noise levels and b-values, as well as the acquisition datasets and the ablation experiments, we verified the effectiveness of the proposed method. Regardless of the noise distribution and noise level, the proposed method achieved the highest PSNR, which was at least 1.9% higher than the suboptimal method when the noise level reaches 10%. Furthermore, our method has superior generalization ability due to the use of the proposed strategies. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

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Mathematical model for predicting fungal growth and decomposition rates based on improved Logistic equations

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International Journal of Agricultural and Biological engineering 2023年第1期16卷 60-65页

作者： Jiayong Pan Wenxin Le Zi’ao Wang Jian Chen College of Engineering China Agricultural UniversityBeijing 100083China Shenzhen Key Laboratory of Intelligent Microsatellite Constellation Shenzhen 518107GuangdongChina Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 200063China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518000GuangdongChina Jiangsu Province and Education Ministry Co-sponsored Synergistic Innovation Center of Modern Agricultural Equipment Jiangsu UniversityZhenjiang 212013China Key Laboratory of Smart Agricultural Technology in Tropical South China Ministry of Agriculture and Rural AffairsGuangzhou 510642China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China State Key Laboratory of Clean Energy Utilization Zhejiang UniversityHangzhou 310013China

As the function of the decomposition of fungi has been clearly researched in the global carbon cycle,it is obviously of value to explore the decomposition rate of fungal *** study analyzed the relationship between environmental factors and biodiversity step by *** order to explore the interaction between the fungi and the relationship between the decomposition rate of fungi with time,the model based on the Logistic model was built and the Lotka-Volterra model was employed in the condition of two kinds of fungi existing in an environment with limited *** changing trend of population number and decomposition rate of several fungi under different environmental conditions can be predicted through the *** illustrate the applicability of the model,Laetiporus conifericola and Hyphoderma setigerum were applied as *** results showed that the higher the degree of population diversity,the greater the decomposition rate,and the higher the decomposition efficiency of the *** rich species diversity is conducive to accelerating the decomposition of litter,lignocellulose,and the circulation of the entire *** on the above model and using the data from measuring the mycelial elongation rate of each isolate at 10℃,16℃,and 22℃ under standardized laboratory conditions,the growth patterns of the five fungi combinations were *** results revealed a general increase in growth rate with increasing temperature,which verifies the accuracy of the ***,it also revealed that the total decomposition rate after fungal incorporation was negatively correlated with the decomposition rate of a fungal single *** on the above model,predictions can be made for fungal growth in different environments,and suitable environments for fungal growth can be *** the future,the model can be further optimized,and lignin and cellulose decomposition factors can be added to fit the decomposition of *** application

关键词： Logistic equations fungal decomposition mathematical model

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Artificial intelligence-assisted niacin skin flush screening in early psychosis identification and prediction

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General Psychiatry 2022年第2期35卷 76-78页

作者： Tao Chen Haichun Liu Renfang Tian Ranpiao Gan Wenzuo Xu Tianhong Zhang Jijun Wang Department of Economics University of WaterlooWaterlooOntarioCanada Depatment of Statistics and Actuarial Science(Cross Appointed) University of Waterloo WaterlooOntarioCanada Big Data Research Lab Universty of WaterlooWaterlooOntarioCanada Shanghai Niacin Tech Co. LtdShanghal China Labor and Worklife Program Harvard UniversityCambridgeMassachusettsUSA Department of Automation Shanghai Jao Tong UniversityShanghaiChina Ningbo Artificial Intelligent Institute Shanghai Jiao Tong UniversityNingboChina School of Management Economicsand MathematicsKing's University CollegeLondon0ntarioCanada Shanghai Intelligent Psychological Evaluaton and Intervention Engineering Technology Research Center Shanghai Key Laboratory of Psychotic DisordersShanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders(Ministry of Education)ShanghaiChina CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghaiChina Institute of Psychology and Behavioral Science Shanghai Jiao Tong UniversiyShanghaiChina

Schizophrenia is a devastating mental disorder affecting 20 million people *** diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the early screening of the *** on the observation of diminished flush responses to niacin in patients with schizophrenia Horrobin proposed anoninvasive niacin skin flush screening for schizophrenia.

关键词： diagnosis prognosis prediction

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ARTEMIS: Autoregressive End-to-End Trajectory Planning with Mixture of Experts for Autonomous Driving

arXiv

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arXiv 2025年

作者： Feng, Renju Xi, Ning Chu, Duanfeng Wang, Rukang Deng, Zejian Wang, Anzheng Lu, Liping Wang, Jinxiang Huang, Yanjun Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China Department of Data and Systems Engineering The University of Hong Kong 999077 Hong Kong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China School of Mechanical Engineering Southeast University Nanjing211189 China School of Automotive Studies Tongji University Shanghai201804 China

—This paper presents ARTEMIS, an end-to-end autonomous driving framework that combines autoregressive trajectory planning with Mixture-of-Experts (MoE). Traditional modular methods suffer from error propagation, while existing end-to-end models typically employ static one-shot inference paradigms that inadequately capture the dynamic changes of the environment. ARTEMIS takes a different method by generating trajectory waypoints sequentially, preserves critical temporal dependencies while dynamically routing scene-specific queries to specialized expert networks. It effectively relieves trajectory quality degradation issues encountered when guidance information is ambiguous, and overcomes the inherent representational limitations of singular network architectures when processing diverse driving scenarios. Additionally, we use a lightweight batch reallocation strategy that significantly improves the training speed of the Mixture-of-Experts model. Through experiments on the NAVSIM dataset, ARTEMIS exhibits superior competitive performance, achieving 87.0 PDMS and 83.1 EPDMS with ResNet-34 backbone, demonstrates state-of-the-art performance on multiple metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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ODFormer: Semantic Fundus Image Segmentation Using Transformer for Optic Nerve Head Detection

arXiv

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arXiv 2024年

作者： Wang, Jiayi Mao, Yi-An Ma, Xiaoyu Guo, Sicen Shao, Yuting Lv, Xiao Han, Wenting Christopher, Mark Zangwill, Linda M. Bi, Yanlong Fan, Rui Group The College of Electronics & Information Engineering Tongji University Shanghai201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201210 China School of Life Science Shanghai University 99 Shangda Avenue Baoshan District Shanghai200444 China Department of Ophthalmology Tongji Hospital School of Medicine Tongji University Shanghai200071 China Hamilton Glaucoma Center Division of Ophthalmology Informatics and Data Science Shiley Eye Institute Viterbi Family Department of Ophthalmology University of California San Diego San DiegoCA92037 United States Tongji Eye Institute School of Medicine Tongji University Shanghai200071 China

Optic nerve head (ONH) detection has been a crucial area of study in ophthalmology for years. However, the significant discrepancy between fundus image datasets, each generated using a single type of fundus camera, poses challenges to the generalizability of ONH detection approaches developed based on semantic segmentation networks. Despite the numerous recent advancements in general-purpose semantic segmentation methods using convolutional neural networks (CNNs) and Transformers, there is currently a lack of benchmarks for these state-of-the-art (SoTA) networks specifically trained for ONH detection. Therefore, in this article, we make contributions from three key aspects: network design, the publication of a dataset, and the establishment of a comprehensive benchmark. Our newly developed ONH detection network, referred to as ODFormer, is based upon the Swin Transformer architecture and incorporates two novel components: a multi-scale context aggregator and a lightweight bidirectional feature recalibrator. Our published large-scale dataset, known as TongjiU-DROD, provides multi-resolution fundus images for each participant, captured using two distinct types of cameras. Our established benchmark involves three datasets: DRIONS-DB, DRISHTI-GS1, and TongjiU-DROD, created by researchers from different countries and containing fundus images captured from participants of diverse races and ages. Extensive experimental results demonstrate that our proposed ODFormer outperforms other state-of-the-art (SoTA) networks in terms of performance and generalizability. Our dataset and source code are publicly available at ***/ODFormer. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Deformable registration framework for glioma images with absent correspondence based on auxiliary-image-aided intensity-consistency constraint

Deformable registration framework for glioma images with abs...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kun Tang Lihui Wang Menglong Yang Jingwen Xu Xinyu Cheng Jian Zhang Yuemin Zhu Hongjiang Wei Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China INSA Lyon CNRS Inserm Univ Lyon Lyon France School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Considering the tumor aggressive nature and the significant changes in anatomical structure, aligning the preoperative and follow up scans of glioma patients remains a challenge due to the presence of regions with absent correspondence. To address this challenge, this work proposed a novel bidirectional unsupervised deformable image registration framework for image pairs with missing correspondence based on an auxiliary-image-aided intensity-consistency constraint (ICC) strategy. Specifically, for any fixed and moving image pairs, we introduced an auxiliary image and warped it directly to fixed/moving image or warped it twice through a transition of moving/fixed image. By comparing the difference between these warped images, the weighting maps to identify and exclude regions with absent correspondence between fixed and moving image pairs can be generated. To verify the effectiveness of the proposed framework, we combined it with several deep learning-based registration models and tested it on BraTS-Reg challenge dataset, the results demonstrated that the proposed ICC strategy can improve the registration performance for all the models, with the improvement of average target registration error (TRE) and success rate (SR) being up to 44.9% and 66.7%, respectively. Comparing against the best existing forward-backward consistency strategy for dealing with missing correspondence registration, our auxiliary-image-aided ICC strategy can also decrease average TRE by 2.9%, demonstrating the superiority of the proposed framework. The present work is not limited to the glioma images, it can be used to address the registration problems for any image pairs with absent correspondence or inconsistent intensity.

关键词： Image registration Pathology Deformation Magnetic resonance imaging Anatomical structure Bioinformatics Tumors Biomedical imaging

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Application of Diffusion Artificial Hummingbird Algorithm for Multimodal Transportation Logistics Distribution Routing Problem

Application of Diffusion Artificial Hummingbird Algorithm fo...

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Chinese Automation Congress (CAC)

作者： Fan Yang Lei Yue Xu Zhang YTO Express Co. Ltd. Shanghai China Post Industry Technology Research and Development Center of State Posts Bureau Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Anhui Yougu Express Intelligent Technology Co. Ltd Wuhu China

With the rapid development of e-commerce and the logistics industry, logistics transportation networks based on different modes of transport have gradually become increasingly complex. Currently, the multimodal transportation logistics distribution routing problem, which is considered a complex vehicle routing problem, has gained significant attention. Therefore, the selection of appropriate routes plays a crucial role in enhancing the economic efficiency and rationality of logistics planning. In order to obtain optimal routes, this paper proposes an improved diffusion artificial hummingbird algorithm based on the hummingbird swarm diffusion flight strategy. This algorithm improves the population initialization method to cover the solution space as comprehensively as possible. In addition, a migration diffusion strategy is proposed, by spreading the worst solution to the outer area of the population to find the optimal solution, thereby improving the global search capability of the algorithm. In order to verify the convergence performance of the algorithm, this paper conducts comparative tests with multiple algorithms, effectively verifying the high accuracy and fast convergence performance of the DAHA algorithm. Applying it to the multimodal logistics distribution path problem can effectively avoid excessively slow convergence speed and The disadvantage of low convergence accuracy. Finally, the feasibility and effectiveness of the algorithm are verified through experiments.

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KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation

arXiv

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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

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Interpretable text-to-sql generation with joint optimization 1

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17th International Conference on Web Information systems and Applications, WISA 2020

作者： Zhu, Mingdong Wang, Xianfang Zhang, Yang School of Computer Science and Technology Henan Institute of Technology Xinxiang China Intelligent Industrial Big Data Application Engineering Technology Research Center of Xinxiang Xinxiang China

ISBN: (数字)9783030600297

ISBN: (纸本)9783030600280

The purpose of Text-to-SQL is to obtain the correct answer for a textual question from the database, which can take advantage of advanced database system to provide reliable and efficient response. Existing Text-to-SQL methods generally focus on accuracy by designing complex deep neural network models, and hardly consider interpretability, which is very important for serious applications. To address this, in this paper we propose a novel framework for Interpretable Text-to-SQL Generation (ITSG) with joint optimization, which achieves state-of-the-art accuracy and possesses two-level interpretability at the same time. The framework mainly consists of three layers: a sequence encoder which encodes questions, table headers and significant table contents, an attention-based LSTM layer which generates SQL queries and a reinforcement learning layer which boosts the execution accuracy. Comparing with state-of-the-art methods on benchmark datasets, the experimental results show the effectiveness and interpretability of our ITSG framework. © Springer Nature Switzerland AG 2020.

关键词： Reinforcement learning

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