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Numerical evolution of Maxwell’s equations for wavefronts propagating through a spherical gravitational potential

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arXiv 2025年

作者： Shanmugaraj, Annamalai P. Schnetter, Erik Koksbang, Sofie Marie CP3-Origins University of Southern Denmark Campusvej 55 Odense M DK-5230 Denmark Perimeter Institute for Theoretical Physics WaterlooON Canada Department of Physics and Astronomy University of Waterloo WaterlooON Canada Center for Computation & Technology Louisiana State University Baton RougeLA United States

We present a scheme for numerically solving Maxwell’s equations in a weakly perturbed spacetime without introducing the usual geometric optics approximation. Using this scheme, we study light propagation through a spherical perturbation of a stationary metric in the linear limit. Going beyond geometric approximation allows us to explore the wave optics effects that occur when the wavefront propagates through the gravitational potential. We find that the wavefront breaks on the gravitational potential due to the Shapiro time delay, i.e. due to the slowing down of light when propagating in an inhomogeneous spacetime. This again leads to constructive interference, resulting in a persistent frequency dependent amplification of parts of the wave amplitude. When the wave length is shorter than a certain threshold set by the induced gravitational mass, the waves are reflected on the curvature gradient. © 2025, CC BY-NC-ND.

关键词： Wavefronts

Causality-inspired Unsupervised Domain Adaptation with Target Style Imitation for Medical Image Segmentation

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Song, Jincai Chen, Haipeng Lyu, Yingda Nie, Weizhi Liu, An-An Jilin University College of Computer Science and Technology Jilin Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China Jilin University Public Computer Education and Research Center Jilin Changchun130012 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China

Deep learning performance may decrease substantially with unseen heterogeneous data. While most unsupervised domain adaptation (UDA) methods seek to address this through image alignment, they often ignore uncertainty style fluctuations within the target domain. When testing image styles vary in both direction and intensity, such models may fail to adapt. Furthermore, existing UDA methods tend to over-reliance on domain-level entire feature alignment, resulting in potentially over-exploiting semantic content-independent cues (e.g., intensity) as shortcut features. To address these limitations, this paper introduces an innovative and model-agnostic Causality-inspired Representation Learning Based on Target Style Imitation method for UDA. Specifically, we propose a novel Target Style Imitation (TSI) data augmentation approach to diversify the training data and align training and unseen target testing image styles. TSI constructs a Gaussian distribution for the target domain style and simulates unseen testing style variations through random sampling. Additionally, inspired by the stable and generalizable causal mechanism, we propose Causality-inspired Representation Learning (CRL) based on TSI method to enforce feature representations to adhere to causal properties (i.e., Separation and Independence) essential for robust UDA, thus fostering the model to focus on the domain-invariant semantic features. Our method surpasses state-of-the-art methods on two cross-modality medical image segmentation datasets. © 1991-2012 IEEE.

关键词： Semantic Segmentation

ENHANCED GRADIENT RECOVERY-BASED A POSTERIORI ERROR ESTIMATOR AND ADAPTIVE FINITE ELEMENT METHOD FOR ELLIPTIC EQUATIONS

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arXiv 2025年

作者： Liu, Ying Xiao, Jingjing Yi, Nianyu Cao, Huihui Department of Mathematics School of Science Xi’an University of Technology Xi’an710000 China School of Mathematics Shandong University Jinan250100 China School of Mathematics and Computational Science Xiangtan University Hunan Key Laboratory for Computation and Simulation in Science and Engineering Hunan National Center for Applied Mathematics Xiangtan411105 China

Recovery type a posteriori error estimators are popular, particularly in the engineering community, for their computationally inexpensive, easy to implement, and generally asymptotically exactness. Unlike the residual type error estimators, one can not establish upper and lower a posteriori error bounds for the classical recovery type error estimators without the saturation assumption. In this paper, we first present three examples to show the unsatisfactory performance in the practice of standard residual or recovery-type error estimators, then, an improved gradient recovery-based a posteriori error estimator is constructed. The proposed error estimator contains two parts, one is the difference between the direct and post-processed gradient approximations, and the other is the residual of the recovered gradient. The reliability and efficiency of the enhanced estimator are derived. Based on the improved recovery-based error estimator and the newest-vertex bisection refinement method with a tailored mark strategy, an adaptive finite element algorithm is designed. We then prove the convergence of the adaptive method by establishing the contraction of gradient error plus oscillation. Numerical experiments are provided to illustrate the asymptotic exactness of the new recovery-based a posteriori error estimator and the high efficiency of the corresponding adaptive algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Finite element method

Accurate Classification of Time-Varying Microalgae by Stokes Imaging With Multiple Polarization Illumination

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Han, Baohui Li, Jiajin Hu, Zheng Jiang, Feng Yang, Jianxiong Tao, Yi Liao, Ran Ma, Hui Tsinghua University Shenzhen Key Laboratory of Marine IntelliSensing and Computation Institute for Ocean Engineering Shenzhen International Graduate School Shenzhen518055 China Tsinghua University Guangdong Provincial Engineering Research Center for Urban Water Recycling and Environmental Safety Shenzhen International Graduate School Shenzhen518055 China Tsinghua University Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology Shenzhen International Graduate School Shenzhen518055 China

Microalgae are crucial to the global aquatic ecosystem, and their in situ accurate recognition is essential for early warning of harmful algae blooms. Based on the existing Stokes and Mueller matrix imaging methods of polarization microscope, this article introduces a method for classifying microalgae using Stokes imaging with multiple polarization illumination (SiMPI), effectively addressing the challenges of similar species (which troubles Stokes imaging) and time-varying states (which troubles Mueller matrix imaging), further enhancing the feasibility of polarization microscopy for in situ detection. The SiMPI method achieves 91.6% accuracy in classifying 29 representative harmful microalgae species. Specifically, increasing the polarization illumination number improves the classification accuracy of the eight lowest performing species to 86.3%. Field data from coastal waters show recognition accuracy exceeding 95%. The study highlights that high imaging resolution, optimal polarization illumination combinations, and balanced databases enhance the SiMPI method’s performance. After quantifying the time variation of microalgal cells, it is convincingly demonstrated that the SiMPI method outperforms Mueller matrix imaging. These findings underscore the SiMPI method’s potential as a feasible and robust tool for future aquatic environment monitoring. © 2025 IEEE. All rights reserved.

关键词： Microalgae

Imaginary Hamiltonian variational Ansatz for combinatorial optimization problems

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Physical Review A 2025年第3期111卷 032612-032612页

作者： Xiaoyang Wang Yahui Chai Xu Feng Yibin Guo Karl Jansen Cenk Tüysüz School of Physics Peking University Beijing 100871 China Collaborative Innovation Center of Quantum Matter Beijing 100871 China Center for High Energy Physics Peking University Beijing 100871 China Interdisciplinary Theoretical and Mathematical Sciences Program RIKEN Wako 351-0198 Japan Deutsches Elektronen-Synchrotron DESY Platanenallee 6 15738 Zeuthen Germany Computation-Based Science and Technology Research Center Cyprus Institute 20 Kavafi Street 2121 Nicosia Cyprus Institut für Physik Humboldt-Universität zu Berlin Newtonstraße 15 12489 Berlin Germany

Obtaining exact solutions to combinatorial optimization problems using classical computing is computationally expensive. The current tenet in the field is that quantum computers can address these problems more efficiently. While promising algorithms require fault-tolerant quantum hardware, variational algorithms have emerged as viable candidates for near-term devices. The success of these algorithms hinges on multiple factors, with the design of the Ansatz being of the utmost importance. It is known that popular approaches such as the quantum approximate optimization algorithm (QAOA) and quantum annealing suffer from adiabatic bottlenecks, which lead to either larger circuit depth or evolution time. On the other hand, the evolution time of imaginary-time evolution is bounded by the inverse energy gap of the Hamiltonian, which is constant for most noncritical physical systems. In this work we propose an imaginary Hamiltonian variational Ansatz (iHVA) inspired by quantum imaginary-time evolution to solve the MaxCut problem. We introduce a tree arrangement of the parametrized quantum gates, enabling the exact solution of arbitrary tree graphs using the one-round iHVA. For randomly generated D-regular graphs, we numerically demonstrate that the iHVA solves the MaxCut problem with a small constant number of rounds and sublinear depth, outperforming the QAOA, which requires rounds increasing with the graph size. Furthermore, our Ansatz solves the MaxCut problem exactly for graphs with up to 24 nodes and D≤5, whereas only approximate solutions can be derived by the classical near-optimal Goemans-Williamson algorithm. We validate our simulated results with hardware demonstrations on a graph with 67 nodes.

关键词： Combinatorial optimization

High-dimensional indexing technologies for large scale content-based image retrieval: a review

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Journal of Zhejiang University-Science C(Computers and Electronics) 2013年第7期14卷 505-520页

作者： Lie-fu AI Jun-qing YU Yun-feng HE Tao GUAN School of Computer Science and Technology Huazhong University of Science and Technology Center of Network and Computation Huazhong University of Science and Technology

The boom of Internet and multimedia technology leads to the explosion of multimedia information, especially image, which has created an urgent need of quickly retrieving similar and interested images from huge image collections. The content-based high-dimensional indexing mechanism holds the key to achieving this goal by efficiently organizing the content of images and storing them in computer memory. In the past decades, many important developments in high-dimensional image indexing technologies have occurred to cope with the 'curse of dimensionality'. The high-dimensional indexing mechanisms can mainly be divided into three categories: tree-based index, hashing-based index, and visual words based inverted index. In this paper we review the technologies with respect to these three categories of mechanisms, and make several recommendations for future research issues.

关键词： Tree-based index Hashing-based index Bag-of-features (BOF) Descriptor encoding Inverted index

Nucleon axial, tensor, and scalar charges and σ-terms in lattice QCD

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arXiv 2025年

作者： Alexandrou, Constantia Bacchio, Simone Finkenrath, Jacob Iona, Christos Koutsou, Giannis Li, Yan Spanoudes, Gregoris Department of Physics University of Cyprus P.O. Box 20537 Nicosia1678 Cyprus Computation-based Science and Technology Research Center The Cyprus Institute 20 Kavafi Str. Nicosia2121 Cyprus Department of Theoretical Physics European Organization for Nuclear Research CERN Geneva 23CH-1211 Switzerland

We determine the nucleon axial, scalar and tensor charges at the continuum limit by analyzing three Nf = 2+1+1 twisted mass fermion ensembles with all quark masses tuned to approximately their physical values. We include all contributions from valence and sea quarks. We use the Akaike Information Criterion to evaluate systematic errors due to excited states and the continuum extrapolation. For the nucleon isovector axial charge we find guA−d = 1.250(24), in agreement with the experimental value. We compute the axial, tensor and scalar charges for each quark flavor. The axial charge provides crucial information on the intrinsic spin carried by quark in the nucleon and the the latter two provide input for experimental searches of physics beyond the standard model. Moreover, we extract the nucleon σ-terms and find σπN = 41.9(8.1) MeV, for the strange σs = 30(17) MeV and for the charm σc = 82(29) MeV. We also present preliminary results on the isovector quantities using a fourth ensemble at smaller lattice spacing. © 2025, CC BY.

关键词： Tensors

GPU-accelerated Subcycling Time Integration with the Einstein Toolkit

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arXiv 2025年

作者： Ji, Liwei Haas, Roland Zlochower, Yosef Brandt, Steven R. Schnetter, Erik Wen, Allen Center for Computational Relativity and Gravitation School of Mathematical Sciences Rochester Institute of Technology 85 Lomb Memorial Drive RochesterNY14623 United States National Center for Supercomputing applications University of Illinois 1205 W Clark St UrbanaIL United States Department of Physics University of Illinois 1110 West Green St UrbanaIL United States Center for Computation & Technology Louisiana State University Baton RougeLA United States Perimeter Institute for Theoretical Physics WaterlooON Canada Department of Physics and Astronomy University of Waterloo WaterlooON Canada

Adaptive Mesh Refinement (AMR) with subcycling in time enables different grid levels to advance using their own time steps, ensuring finer grids employ smaller steps for accuracy while coarser grids take larger steps to improve computational efficiency. We present the development, validation, and performance analysis of a subcycling in time algorithm implemented within the CarpetX driver in the Einstein Toolkit framework. This new approach significantly improves upon the previous subcycling implementation in the Carpet driver by achieving higher-order convergence—fourth order in time instead of second order—and enhanced scaling performance. The key innovation lies in optimizing the exchange of ghost points at refinement boundaries, limiting it to the same number as those at inter-process boundaries using dense output from coarser levels, thereby reducing computational and communication overhead compared to the implementation in Carpet, which required a larger number of buffer zones. To validate the algorithm, we first demonstrate its fourth-order convergence using a scalar wave test. We then apply the algorithm to binary black hole (BBH) simulations, confirming its robustness and accuracy in a realistic astrophysical scenario. The results show excellent agreement with the well-established LazEv code. Scaling tests on CPU (Frontera) and GPU (Vista) clusters reveal significant performance gains, with the new implementation achieving improved speed and scalability compared to the Carpet-based version. Copyright © 2025, The Authors. All rights reserved.

关键词： computational efficiency

Causal-Inspired Multitask Learning for Video-Based Human Pose Estimation

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arXiv 2025年

作者： Chen, Haipeng Wu, Sifan Wang, Zhigang Yin, Yifang Jiao, Yingying Lyu, Yingda Liu, Zhenguang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Zhejiang Gongshang University China A*STAR Singapore Public Computer Education and Research Center Jilin University China The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Video-based human pose estimation has long been a fundamental yet challenging problem in computer vision. Previous studies focus on spatio-temporal modeling through the enhancement of architecture design and optimization strategies. However, they overlook the causal relationships in the joints, leading to models that may be overly tailored and thus estimate poorly to challenging scenes. Therefore, adequate causal reasoning capability, coupled with good interpretability of model, are both indispensable and prerequisite for achieving reliable results. In this paper, we pioneer a causal perspective on pose estimation and introduce a causal-inspired multitask learning framework, consisting of two stages. In the first stage, we try to endow the model with causal spatio-temporal modeling ability by introducing two self-supervision auxiliary tasks. Specifically, these auxiliary tasks enable the network to infer challenging keypoints based on observed keypoint information, thereby imbuing causal reasoning capabilities into the model and making it robust to challenging scenes. In the second stage, we argue that not all feature tokens contribute equally to pose estimation. Prioritizing causal (keypoint-relevant) tokens is crucial to achieve reliable results, which could improve the interpretability of the model. To this end, we propose a Token Causal Importance Selection module to identify the causal tokens and non-causal tokens (e.g., background and objects). Additionally, non-causal tokens could provide potentially beneficial cues but may be redundant. We further introduce a non-causal tokens clustering module to merge the similar non-causal tokens. Extensive experiments show that our method outperforms state-of-the-art methods on three large-scale benchmark datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning