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Cascaded adaptive aberration-eliminating multimode fiber imaging

Advanced Photonics 2025年第02期 135-143页

作者： Zhong Wen Qilin Deng Quanzhi Li Yizhou Tan Jingshan Zhong Chiming Zhang Jiahe Zhang Clemens F.Kaminski Ying Gu Xu Liu Qing Yang Zhejiang University College of Optical Science and Engineering State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University International Research Center for Advanced Photonics Zhejiang University ZJU-Hangzhou Global Scientific and Technological Innovation Center Chinese PLA General Hospital The First Medical Center Department of Laser Medicine Chinese PLA General Hospital Hainan HospitalLaser Medicine Center Zhejiang Laboratory Research Center for Intelligent Manufacturing Computing University of Cambridge Department of Chemical Engineering and Biotechnology

In vivo microscopic imaging inside a biological lumen such as the gastrointestinal tract, respiratory airways, or within blood vessels has faced significant technological challenges for decades. A promising candidate technology is the multimode fiber(MMF) endoscope, which enables minimally invasive diagnostics at a resolution reaching the cellular level. However, for in vivo imaging applications deep inside a biological lumen, sample-induced aberrations and the dynamic dispersion in the MMF make the MMF endoscope a chaotic system with many unknowns, where multiple minor fluctuations can couple and compound into intractable problems. We introduce a dynamically encoding, cascaded, optical, and ultrathin polychromatic light-field endoscopy(DECOUPLE) to tackle this challenge. DECOUPLE includes an adaptive aberration correction that can accurately track and control MMF behavior in the spatial-frequency domain to compensate for chaos introduced during complex dynamic imaging processes. We demonstrate the flexibility and practicality of DECOUPLE for noninvasive volumetric imaging in two colors for light passing through various highly aberrating samples including 120-μm-thick onion epidermal slices and 80-μm-thick layers of fat emulsions. To summarize, we represent a significant step toward practical in vivo imaging deep within biological tissue.

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The Distributionally Robust Optimization Model of Sparse Principal Component Analysis

arXiv

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arXiv 2025年

作者： Wang, Lei Liu, Xin Chen, Xiaojun Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

We consider sparse principal component analysis (PCA) under a stochastic setting where the underlying probability distribution of the random parameter is uncertain. This problem is formulated as a distributionally robust optimization (DRO) model based on a constructive approach to capturing uncertainty in the covariance matrix, which constitutes a nonsmooth constrained min-max optimization problem. We further prove that the inner maximization problem admits a closed-form solution, reformulating the original DRO model into an equivalent minimization problem on the Stiefel manifold. This transformation leads to a Riemannian optimization problem with intricate nonsmooth terms, a challenging formulation beyond the reach of existing algorithms. To address this issue, we devise an efficient smoothing manifold proximal gradient algorithm. We prove the Riemannian gradient consistency and global convergence of our algorithm to a stationary point of the nonsmooth minimization problem. Moreover, we establish the iteration complexity of our algorithm. Finally, numerical experiments are conducted to validate the effectiveness and scalability of our algorithm, as well as to highlight the necessity and rationality of adopting the DRO model for sparse PCA. Copyright © 2025, The Authors. All rights reserved.

关键词： Optimization algorithms

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Blockchain-Assisted Privacy-Preserving and Synchronized key Agreement for VDTNs

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Wang, Chenhao Ming, Yang Liu, Hang Feng, Jie Yang, Mengmeng Xiang, Yang Chang'an University School of Information Engineering Xi'an710064 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Commonwealth Scientific and Industrial Research Organization Australia Swinburne University of Technology Department of Computing Technologies VIC3122 Australia

With the continuous development of digitization evolutions, vehicular digital twin networks (VDTNs) facilitate traffic data and optimization results to be exchanged between the vehicle and digital twin as well as shared among a group of digital twins. However, the data exchange and group sharing processes take place in real-time over public communication channels, which suffer from various security and privacy threats. key agreement technologies are promising to enable secure data communications for entities, but the existing key agreement schemes generally fail to fulfill the requirements of synchronization, privacy, and entity management for VDTNs. Therefore, we propose a blockchain-assisted privacy-preserving and synchronized key agreement scheme for VDTNs. In the proposed scheme, the anonymous vehicle and digital twin can negotiate a secret session key in the case of synchronization to achieve secure data exchange. Meanwhile, digital twins are capable of utilizing synchronized state information to dynamically establish a common group encryption key but hold individual decryption keys, which guarantee the security of group sharing. Additionally, the proposed scheme is able to protect identity privacy and manage vehicles and digital twins with the assistance of blockchain and smart contract. The security analysis demonstrates that the proposed scheme provides security and privacy assurances for VDTNs. The performance evaluation indicates that it has excellent expressions in terms of efficiency, practicality, and smart contract consumption. © 2004-2012 IEEE.

关键词： Differential privacy

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Hierarchical-control-based output synchronization of coexisting attractor networks

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Chinese Physics B 2010年第2期19卷 127-134页

作者：宋运忠唐贻发 Complex Networks Laboratory College of Electrical Engineering&AutomationHenan Polytechnic University State Key Laboratory of Scientific&Engineering Computing Academy of Mathematics&Systems ScienceChinese Academy of Sciences

This paper introduces the concept of hierarchical-control-based output synchronization of coexisting attractor networks. Within the new framework, each dynamic node is made passive at first utilizing intra-control around its own arena. Then each dynamic node is viewed as one agent, and on account of that, the solution of output synchronization of coexisting attractor networks is transformed into a multi-agent consensus problem, which is made possible by virtue of local interaction between individual neighbours; this distributed working way of coordination is coined as inter-control, which is only specified by the topological structure of the network. Provided that the network is connected and balanced, the output synchronization would come true naturally via synergy between intra and inter-control actions, where the rightness is proved theoretically via convex composite Lyapunov functions. For completeness, several illustrative examples are presented to further elucidate the novelty and efficacy of the proposed scheme.

关键词： hierarchical control passive control composite Lyapunov function the Newton--Leipnik equation attractor

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A diffuse-interface lattice Boltzmann method for the dissolution through nonlinear heterogeneous reaction

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Physics of Fluids 2025年第5期37卷

作者： Yang, Xiaofeng Zhan, Chengjie Liu, Xi Chai, Zhenhua School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

In this work, a diffuse-interface lattice Boltzmann method (DI-LBM) is proposed for the dissolution through nonlinear heterogeneous reaction. In this method, the sharp boundary between the fluid and solid phases is replaced by a diffuse interface with a nonzero thickness. To describe the changes of physical variables from the fluid region to the solid region, a smooth function is introduced, and simultaneously, the nonlinear Robin boundary condition imposed on the fluid-solid interface is reformulated as a source term into the modified convection-diffusion equation. We then test the DI-LBM through several benchmark problems, and find that the numerical results are in agreement with the analytical solutions and available data. Finally, the present DI-LBM is adopted to study the dissolution in a two-dimensional porous medium, and the results show that with the decrease in reaction order, the reactant in the central region with a large fluid velocity reacts faster and forms a conical pathway. © 2025 Author(s).

关键词： Kinetic theory of gases

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Global method for monotone variational inequality problems on polyhedral sets

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OPTIMIZATION METHODS & SOFTWARE 1997年第2期7卷 111-122页

作者： Peng, JM [a] State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific Computing Beijing China

In this paper, we consider the optimization method for monotone variational inequality problems on polyhedral sets. First, we consider the mixed complementarity problem based on the original problem. Then, a merit function for the mixed complementarity problem is proposed and some desirable properties of the merit function are obtained. Under certain assumptions: we show that any stationary point of the merit function is a solution of the original problem. A descent method for the optimization problem is proposed and the global convergence of the method is shown.

关键词： variational inequality problem mixed complementarity problem optimization method

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Predictive ISAC Beamforming Using LoRA-Transformer LSTM Integrated with GNN in Ultra-Dense D2D mmWave Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Haochen Bi, Jia Li, Bohan Xiang, Luping Yang, Xin Zhang, Zhaolin Wang, Ling Northwestern Polytechnical University School of Electronics and Information Xi'an710019 China Rutherford Appleton Laboratory Scientific Computing Department STFC DidcotOX11 0QX United Kingdom Ocean University of China Faculty of Information Science and Engineering Qingdao266100 China Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Suzhou China

Effective beamforming is crucial in ultra-dense device-to-device (D2D) mmWave networks to reduce interference and optimize communication. However, current methods often struggle to balance high data rates with minimal signaling overhead, especially with imperfect channel state information (CSI). This paper introduces a machine learning-assisted beamforming design tailored for integrated sensing and communication (ISAC), combining an efficient Low-Rank Adaptation Blocked LSTM Transformer with Message Passing Graph Neural Network that is called as LoRA_BLT-MPGNN. The LoRA_BLT component provides a key advantage by efficiently capturing both temporal and spatial patterns through fixed-weight updates during training and low-rank approximation for fine-tuning, significantly reducing computational complexity without sacrificing accuracy. Using historical channel data, the method implicitly captures channel characteristics and directly predicts the beamforming matrix for the subsequent time slot, minimizing signaling overhead while maximizing the achievable sum rate. Our research and simulation results show that the proposed predictive beamforming design maintains robust sensing performance and achieves a sum rate closely approaching the upper bound achievable with perfect CSI. Additionally, the incorporation of MPGNNs enables efficient modeling and learning of complex inter-node relationships, making the approach adaptable to networks of varying sizes and densities. Simulations further demonstrate that the LoRA_BLT-MPGNN model can be generalized across different network densities and coverage areas, reducing computational complexity by approximately 32× in terms of Tensor Floating Point Operations (TFLOPs) compared to conventional methods while maintaining high performance. © 2025 IEEE.

关键词： Beamforming

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Global method for monotone variational inequality problems with inequality constraints

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 1997年第2期95卷 419-430页

作者： Peng, JM State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academia Sinica Beijing China

We consider optimization methods for monotone variational inequality problems with nonlinear inequality constraints. First, we study the mixed complementarity problem based on the original problem. Then, a merit function for the mixed complementarity problem is proposed, and some desirable properties of the merit function are obtained. Through the merit function, the original variational inequality problem is reformulated as simple bounded minimization. Under certain assumptions, we show that any stationary point of the optimization problem is a solution of the problem considered. Finally, we propose a descent method for the variational inequality problem and prove its global convergence.

关键词： variational inequality problems mixed complementarity problems optimization methods

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Complexity of Tensor Product Functions in Representing Antisymmetry

arXiv

引用

arXiv 2025年

作者： Wang, Yuyang Hu, Yukuan Liu, Xin State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China CERMICS Ecole des Ponts IP Paris Marne-la-Vallée77455 France

Tensor product function (TPF) approximations have been widely adopted in solving high-dimensional problems, such as partial differential equations and eigenvalue problems, achieving desirable accuracy with computational overhead that scales linearly with problem dimensions. However, recent studies have underscored the extraordinarily high computational cost of TPFs on quantum many-body problems, even for systems with as few as three particles. A key distinction in these problems is the antisymmetry requirement on the unknown functions. In the present work, we rigorously establish that the minimum number of involved terms for a class of TPFs to be exactly antisymmetric increases exponentially fast with the problem dimension. This class encompasses both traditionally discretized TPFs and the recent ones parameterized by neural networks. Our proof exploits the link between the antisymmetric TPFs in this class and the corresponding antisymmetric tensors and focuses on the Canonical Polyadic rank of the latter. As a result, our findings uncover a fundamental incompatibility between antisymmetry and low-rank TPFs in high-dimensional contexts and offer new insights for further developments. Copyright © 2025, The Authors. All rights reserved.

关键词： Tensors

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From quasi-Newton methods to non-quasi-Newton methods

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PROGRESS IN NATURAL SCIENCE-MATERIALS INTERNATIONAL 1997年第1期7卷 14-23页

作者： Yuan, YX State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Chinese Academy of Sciences Beijing 100080 China

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