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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yin, Heyuan Yang, Jingxiang Liu, Jia Xiao, Liang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Analysis of Mathematical Theory and Modeling of Complex Systems Ministry of Industry and Information Technology China

ISBN: (纸本)9798350368741

Effectively reconstructing 3D hyperspectral images (HSIs) from 2D measurements presents a significant challenge in Coded Aperture Snapshot Spectral Imaging (CASSI) systems. While recent transformers exhibit potential in HSI reconstruction, they often suffer from inadequate exploration of multi-scale spatial-spectral self-similarity, leading to mean effects and information loss. Additionally, these methods struggle with insufficient modeling of the degradation inherent in the compressive imaging process. To address these issues, we propose a novel Mask-guided Multi-scale Spatial-Spectral Transformer (MMSST). Specifically, we introduce a Degradation Aware Mask Attention (DAMA) module to incorporate degradation information of the compressive imaging process. Furthermore, MMSST leverages Local-Regional SpAtial attention (LRSA) and Global-Regional SpEctral attention (GRSE) to effectively exploit multi-scale self-similarity across spatial and spectral dimensions. Extensive experimental results demonstrate the effectiveness of our MMSST. © 2025 IEEE.

关键词： hyperspectral reconstruction Snapshot Compressive Imaging spatial-spectral transformer

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Cryo-EM structure of a minimal reaction center–light-harvesting complex from the phototrophic bacterium Chloroflexus aurantiacus

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Journal of Integrative Plant Biology 2025年第4期 967-978页

作者： Guoqiang Huang Shishang Dong Lin Ma Lin Li Jinxin Ju Mei-Jiao Wang Jian-Ping Zhang Sen-Fang Sui Xiaochun Qin State Key Laboratory of Membrane Biology Beijing Advanced Innovation Center for Structural Biology School of Life SciencesTsinghua University Multiscale Research Institute of Complex Systems Fudan University School of Biological Science and Technology University of Jinan School of Chemistry and Life Resources Renmin University of China School of Life Sciences Southern University of Science and Technology

Photosynthetic organisms have developed various light-harvesting antenna systems to capture light and transfer energy to reaction centers（RCs）.Simultaneous utilization of the integral membrane light-harvesting antenna（LH complex） and the extrinsic antenna（chlorosomes） makes the phototrophic bacterium Chloroflexus（Cfx.） aurantiacus an ideal model for studying filamentous anoxygenic phototrophs（FAPs）. Here, we determined the structure of a minimal RC–LH photocomplex from Cfx. aurantiacus J-10-fl（CaRC–LH） at 3.05-? resolution. The CaRC–LH binds only to seven LH subunits, which form a crescent-shaped antenna surrounding the movable menaquinone-10（QB）binding site of Ca RC. In this complex with minimal LH units, an extra antenna is required to ensure sufficient light-gathering, providing a clear explanation for the presence of chlorosomes in Cfx. aurantiacus. More importantly, the semicircle of the antenna represents a novel RC–LH assembly pattern. Our structure provides a basis for understanding the existence of chlorosomes in *** and the possible assembly pattern of RC–LH.

关键词： Chloroflexus aurantiacus chlorosomes electron transfer photosynthesis RC–LH

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Large Deviations for a Critical Galton-Watson Branching Process

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Acta Mathematicae Applicatae Sinica 2025年第2期41卷 456-478页

作者： Dou-dou LI Wan-lin SHI Mei ZHANG College of Statistics and Data Science Faculty of ScienceBeijing University of TechnologyBeijing 100124China Department of Mathematics and Physics North China Electric Power UniversityBeijing 102206China Laboratory of Mathematics and Complex Systems(Ministry of Education) School of Mathematical SciencesBeijing Normal UniversityBeijing 100875China

In this paper,a critical Galton-Watson branching process {Z_(n)} is *** deviation rates of SZ_(n):=Σ_(i=1)^_(Z_(n)) Xi are obtained,where {X_(i);i≥1} is a sequence of independent and identically distributed random variables and X_(1) is in the domain of attraction of an-stable law with α∈(0,2).One shall see that the convergence rate is determined by the tail index of X_(1) and the variance of Z_(1).Our results can be compared with those ones of the supercritical case.

关键词： large deviation branching process critical α-stable

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Dynamic-Aware and Static Context Network for large-scale 3D place recognition

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Knowledge-Based systems 2025年 319卷

作者： Liao, Ming Di, Xiaoguang Liu, Maozhen Lv, Teng Zhang, Xiaofei Zhu, Runwen Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150080 China

3D point cloud-based place recognition enables robots to obtain precise global positions without GPS, correct trajectory drift in SLAM, and recover from the kidnapped robot problem. However, in outdoor environments, the presence of moving objects can cause occlusions in point clouds and introduce noise into the data, leading to localization failures. To address this issue, we propose a Dynamic-Aware and Static Context Network (DASC-Net) for large-scale 3D place recognition. Our approach leverages the spatio-temporal consistency of point cloud sequences to accurately segment dynamic objects while incorporating static point cloud context to compensate for feature loss caused by noise interference or occlusions from dynamic objects, thereby enhancing robustness and generalization. Specifically, DASC-Net adopts a two-stage strategy: first, it introduces a coarse-to-fine moving object segmentation method to effectively eliminate dynamic noise;second, it utilizes spatial context association and multi-scale feature aggregation to improve static feature representation and matching. Extensive experimental results demonstrate that DASC-Net outperforms existing place recognition approaches, particularly in dynamic scenes. © 2025

关键词： Image segmentation

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Multi-scale Dilated Attention-based Generative Adversarial Network for Infrared and Visible Image Fusion

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Current Optics and Photonics 2025年第2期9卷 120-129页

作者： Yang, Shunyuan Shi, Xiaoping Huo, Ju Sun, Kangjian Wang, Yu School of Astronautics Harbin Institute of Technology Harbin150001 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150001 China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150001 China Shanghai Space Propulsion Technology Research Institute Shanghai201109 China

In the field of optics, infrared and visible images are often required for use in different situations. In particular, in the field of optical sensors, infrared and visible sensors are mainly used to obtain different band images, which are applied to improve the comprehensive information content of images and to improve the quality and availability of images. In existing infrared and visible image fusion methods, the focus is often placed on retaining the background information from visible images and the salient targets from infrared images. To address this issue, we introduce a multi-scale dilated attention module into the encoder-decoder structure of the generator. By applying dilated convolution and selfattention mechanisms, this module improves the perceptual capability of the model, thereby improving performance without increasing network complexity. This design emphasizes gradient information and detailed features in visible images. Experimental results on the public TNO dataset demonstrate that our method achieves superior visual quality and preserves the most abundant image information. Moreover, experiments on spacecraft images validate the robustness and applicability of our approach. Simultaneously, our method also provides significant technical support for the optical field. © 2025 Current Optics and Photonics.

关键词： Image coding

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Online Machine Learning for Real-Time Cell Culture Process Monitoring 37th

Online Machine Learning for Real-Time Cell Culture Process ...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Khuat, Thanh Tung Bassett, Robert Otte, Ellen Gabrys, Bogdan Complex Adaptive Systems Laboratory The Data Science Institute University of Technology Sydney UltimoNSW2007 Australia CSL Innovation MelbourneVIC3000 Australia

ISBN: (纸本)9789819603503

In the biopharmaceutical industry, the production of monoclonal antibodies (mAbs) relies heavily on the optimization and real-time monitoring of cell culture processes. This study assesses the effectiveness of online machine learning (OML) models for the real-time monitoring of nutrient and metabolite concentrations, such as glucose, lactate, and ammonium, during the cell cultivation process using Raman spectroscopy as input data. The research addresses the challenge of limited data inherent in cell culture processes, particularly when transferring learned information to new bioreactor runs with different cell lines and media compositions. Traditional pretrained machine learning models often fail to generalize their learned information in such context. Our research demonstrates that OML models can dynamically adapt to the evolving conditions of cell culture processes, providing accurate and timely updates on critical nutrient and metabolite concentrations. We conducted a series of experiments comparing the performance of OML models against pretrained models in monitoring cell culture processes. The findings indicate that OML models exhibit superior performance in capturing the dynamic changes of nutrient and metabolite concentrations during cell cultivation. In contrast, pretrained models exhibit substantial performance degradation when applied to a completely new bioreactor run, highlighting the limitations of static learning approaches in handling the complex, dynamic nature of cell cultures. This study emphasizes the potential of OML in enhancing the robustness and efficiency of real-time process monitoring in mAb production, paving the way for more adaptive and resilient biomanufacturing processes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Metabolites

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YFLM: An Improved Levenberg-Marquardt Algorithm for Global Bundle Adjustment 41st

YFLM: An Improved Levenberg-Marquardt Algorithm for Global ...

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41st Computer Graphics International Conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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Analysis of the SEIR mean-field model in dynamic networks under intervention

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Infectious Disease Modelling 2025年第3期10卷 850-874页

作者： Li, Jiangmin Jin, Zhen Tang, Ming Complex System Research Center Shanxi University Shanxi Taiyuan 030006 China Key Laboratory of Complex Systems and Data Science of Ministry of Education Shanxi University Shanxi Taiyuan 030006 China Shanxi Key Laboratory for Mathematical Technology in Complex Systems Shanxi University Shanxi Taiyuan 030006 China School of Physics and Electronic Science East China Normal University Shanghai 200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai 200241 China

For emerging respiratory infectious diseases like COVID-19, non-pharmaceutical interventions such as isolation are crucial for controlling the spread. From the perspective of network transmission, non-pharmaceutical interventions like isolation alter the degree distribution and other topological structures of the network, thereby controlling the spread of the infectious disease. In this paper, we establish a SEIR mean-field propagation dynamics model for the synchronous evolution of dynamic networks caused by propagation and tracing isolation. We employ the reducing-dimension method to convert the mean-field model in networks into an equivalent and simpler low-dimension model, and then calculate the exact expression of the final size. In addition, we get the differential equations of the degree distribution over time in dynamic networks under tracing isolation and the relationships between the first and second moment of the dynamic network. While the degree of a node remains constant regardless of its state in many previous studies, this paper takes into account that the degree of each node changes over time whatever its state under the disease spread and intervention measures. © 2025 The Authors

关键词： Degree distribution Dynamic network The final size Tracing isolation

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Fuzzy-Model-Based Fault-Tolerant Control for Stochastic Re-Entrant Manufacturing systems

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3542-3556页

作者： Zhang, Kexin Gao, Qing Ding, Steven X. Lu, Jinhu Qiu, Jianbin Guo, Yige Beihang University School of Cyber Science and Technology Beijing100191 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China Zhongguancun Laboratory Beijing100095 China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg47057 Germany Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China Zhongguancun Laboratory Beijing China

This study addresses the problem of guaranteed cost fault-tolerant fuzzy control for multiline re-entrant manufacturing systems (RMSs) against stochastic disturbances and workstation faults. Initially, a nonlinear hyperbolic impulsive partial differential equation model is employed to describe the complex and hybrid dynamics of RMSs suffering from unexpected faults within the working stations, and then the corresponding approximation T-S fuzzy model is constructed. In what follows, with the aid of the parallel distributed compensation fuzzy control scheme, the main results of stability analysis and controller synthesis for the closed-loop re-entrant manufacturing control system are derived using a timer-dependent Lyapunov functional with spatio-temporal auxiliary variables. It is found that by means of the proposed fault-tolerant control approach, the RMS can be effectively and robustly driven to a desired production mode with steady feeding and production rates while the upper bound of a quadratic cost function is minimized. Finally, the effectiveness of the proposed control approach is validated through numerical simulations. © 2013 IEEE.

关键词： Fuzzy control

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Learning Causal Representations Based on a GAE Embedded Autoencoder

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3472-3484页

作者： Zhou, Kuang Jiang, Ming Gabrys, Bogdan Xu, Yong Northwestern Polytechnical University School of Mathematics and Statistics Xi'an710060 China Northwestern Polytechnical University Moe Key Laboratory for Complexity Science in Aerospace Xi'an710060 China University of Technology Sydney Complex Adaptive Systems Lab UltimoNSW2007 Australia

Traditional machine-learning approaches face limitations when confronted with insufficient data. Transfer learning addresses this by leveraging knowledge from closely related domains. The key in transfer learning is to find a transferable feature representation to enhance cross-domain classification models. However, in some scenarios, some features correlated with samples in the source domain may not be relevant to those in the target. Causal inference enables us to uncover the underlying patterns and mechanisms within the data, mitigating the impact of confounding factors. Nevertheless, most existing causal inference algorithms have limitations when applied to high-dimensional datasets with nonlinear causal relationships. In this work, a new causal representation method based on a Graph autoencoder embedded AutoEncoder, named GeAE, is introduced to learn invariant representations across domains. The proposed approach employs a causal structure learning module, similar to a graph autoencoder, to account for nonlinear causal relationships present in the data. Moreover, the cross-entropy loss as well as the causal structure learning loss and the reconstruction loss are incorporated in the objective function designed in a united autoencoder. This method allows for the handling of high-dimensional data and can provide effective representations for cross-domain classification tasks. Experimental results on generated and real-world datasets demonstrate the effectiveness of GeAE compared with the state-of-the-art methods. © 1989-2012 IEEE.

关键词： Contrastive Learning

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