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An EDCC-EMD analysis-based network for DAS VSP data denoising in frequency domain

Petroleum science 2025年

作者： Tang, Huan-Huan Cheng, Shi-Jun Li, Wu-Qun Mao, Wei-Jian Center for Computational & Exploration Geophysics Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Hubei Wuhan430077 China State Key Laboratory of Precision Geodesy Hubei Wuhan430077 China Division of Physical Science and Engineering King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

Distributed acoustic sensing (DAS) has rapidly emerged as a transformative technology in seismic exploration, particularly in vertical seismic profiles (VSP). However, the acquired VSP data suffer from strong coherent DAS coupling noise and random noise. Current deep learning denoising methods, dependent on noise labels derived from conventional denoising techniques, fall short in addressing the unique noise properties inherent in DAS data. To address this challenge, we propose an exponential decay curve-constrained empirical mode decomposition (EDCC-EMD) analysis-based supervised denoising network. Our method begins with extracting the initial noise from the field DAS VSP data through the traditional EMD method. Despite containing some signal leakage, this noise is further processed through EMD to derive intrinsic mode functions (IMFs). We, then, analyze the correlation coefficients between these IMFs and the initial noise, applying an exponential decay curve (EDC) law to isolate pure noise. This refined noise data serves as accurate labels, enhancing the denoising network's precision. Meanwhile, most of the methods usually consider the t-x domain features and ignore the important frequency-domain features. Consequently, we train our network with frequency-domain data instead of time domain data, capitalizing on the more distinct separation of noise and signal characteristics, thereby facilitating more effective noise-signal discrimination. The experimental results demonstrate that our method significantly enhances the denoising performance and successfully recovers weak signals. © 2025 The Authors

关键词： Frequency domain analysis

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Enhancing electron transport for efficiency -recorded HTL-free inverted perovskite solar cells by molecular complementary passivation

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Joule 2025年

作者： Cai, Qingbin Tan, Qin He, Jiacheng Tang, Siyuan Sun, Qiang He, Dong Cheng, Tianle Ma, Guoqiang Huang, Jinfeng Su, Gangsen Chen, Chuanxin Gu, Hao Wang, Bingzhe Fan, Jing Xing, Guichuan He, Zhubing No. 1088 Xueyuan Road Guangdong Shenzhen518055 China Joint Key Laboratory of the Ministry of Education Institute of Applied Physics and Materials Engineering University of Macau Avenida da Universidade Macau Taipa999078 China Center for Computational Science and Engineering Southern University of Science and Technology No. 1088 Xueyuan Road Guangdong Shenzhen518055 China

Inverted perovskite solar cells without pre-depositing a layer of hole-transport materials (HTL-free PSCs) still suffer from the non-irradiative recombination at the perovskite/electron-transport layer (ETL) interface. In this work, we report a molecular complementary passivation (MCP) strategy by employing propylphosphonic acid 3-ammonium bromide (PPAABr) to cooperate with phenethylammonium bromide (PEABr) to mutually passivate surface defects of I and formamidinium (FA) vacancies by multi-coordination. This passivation led to an obvious decrease in interfacial defect-state density and greatly improved exciton and carrier lifetime for the perovskite film. Moreover, MCP surface treatment pushes the perovskite surface Fermi level closer to that of ETL, thereby enhancing interfacial electron extraction. As a result, MCP-based HTL-free PSC achieved a record efficiency of 26.40% (25.92% certified). The encapsulated device retains 94.8% of its initial efficiency after 1,000 h of light soaking. The generality of the MCP strategy also generated a competitive efficiency of 23.66% for 1.68 eV wide-band-gap PSCs. © 2025 Elsevier Inc.

关键词： Energy gap

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Guardian of the Ensembles: Introducing Pairwise Adversarially Robust Loss for Resisting Adversarial Attacks in DNN Ensembles

Guardian of the Ensembles: Introducing Pairwise Adversariall...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Shubhi Shukla Subhadeep Dalui Manaar Alam Shubhajit Datta Arijit Mondal Debdeep Mukhopadhyay Partha Pratim Chakrabarti Centre for Computational and Data Sciences IIT Kharagpur India Computer Science and Engineering Department IIT Kharagpur India Center for Cyber Security New York University Abu Dhabi UAE Department of Artificial Intelligence IIT Kharagpur India Computer Science and Engineering Department IIT Patna India

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Adversarial attacks rely on transferability, where an adversarial example (AE) crafted on a surrogate classifier tends to mislead a target classifier. Recent ensemble methods demonstrate that AEs are less likely to mislead multiple classifiers in an ensemble. This paper proposes a new ensemble training using a Pairwise Adversarially Robust Loss (PARL) that by construction produces an ensemble of classifiers with diverse decision boundaries. PARL utilizes outputs and gradients of each layer with respect to network parameters in every classifier within the ensemble simultaneously. PARL is demonstrated to achieve higher robustness against black-box transfer attacks than previous ensemble methods as well as adversarial training without adversely affecting clean example accuracy. Extensive experiments using standard Resnet20, WideResnet28-10 classifiers demonstrate the robustness of PARL against state-of-the-art adversarial attacks. While maintaining similar clean accuracy and lesser training time, the proposed architecture has a 24.8% increase in robust accuracy (∊= 0.07) from the state-of-the art method. Code is available at: https://***/shubhishukla10/PARL

关键词： Training Computer vision Accuracy Perturbation methods Neural networks Closed box Computer architecture Robustness Ensemble learning Standards

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Conservative mixed Discontinuous Galerkin finite element method with full decoupling strategy for incompressible MHD problems

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Communications in Nonlinear science and Numerical Simulation 2025年 147卷

作者： Huang, Huayi Huang, Yunqing Tang, Qili Yin, Lina Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China National Center for Applied Mathematics in Hunan Xiangtan University Xiangtan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan411105 China Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China

In this research, we propose a mixed Discontinuous Galerkin (DG) finite element method (FEM) for the numerical solution of incompressible magnetohydrodynamics (MHD) problems, ensuring the divergence-free condition for the velocity field. Our proposed methodology is characterized by its energy stability. Subsequently, we construct a novel finite element discretization scheme, for which we establish the theoretical framework of energy stability. Furthermore, we incorporate a preconditioning strategy that facilitates the development of an alternative positive definite, fully decoupled linearization approach. A significant advantage of this scheme is its ability to circumvent the complexities associated with solving saddle point problems at each time step. We demonstrate the uniqueness and convergence of the solutions within this fully decoupled, block-structured linear system. This theoretical validation is complemented by a series of numerical experiments that substantiate the effectiveness and accuracy of our proposed method. © 2025 Elsevier B.V.

关键词： Finite element method

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Joint Multi-Scale Contextual and Noise Suppression for Group Emotion Recognition

Joint Multi-Scale Contextual and Noise Suppression for Group...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wangdong Guo Qing Zhu Qirong Mao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Group Emotion Recognition (GER) seeks to identify emotional states within multi-person groups. The complexity of the environment and the reliance on a single group emotion label often result in noisy individuals with inconsistent emotional expressions, hampering accurate emotion classification. Mainstream GER networks attempt to downweight noisy individuals, but when their numbers are high and over-relying on a single group label, the negative impact of noise on individuals remains significant, further complicating classification and diminishing accuracy. To address these limitations, we propose the Multi-Scale Contextual and Noise Suppression Model (MCon-NSM), a novel framework that enhances GER by capturing fine-grained interaction contexts and collaboratively suppressing noise at both the individual and group label levels. Extensive experiments on the GAF series datasets demonstrate that our method achieves results comparable to state-of-the-art techniques, validating its effectiveness in mitigating noise in GER.

关键词： Emotion recognition Analytical models Accuracy Noise Noise reduction Speech recognition Complexity theory Noise measurement Speech processing Context modeling

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Patch Triplet Similarity Purification for Guided Real-World Low-Dose CT Image Denoising

arXiv

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arXiv 2025年

作者： Long, Junhao Yang, Fengwei Yan, Juncheng Zhang, Baoping Jin, Chao Yang, Jian Zou, Changliang Xu, Jun School of Statistics and Data Science Nankai University Tianjin300071 China Department of Radiology The First Affiliated Hospital of Xi’an Jiaotong University Xi’an China Shanxi Engineering Research Center of Computational Imaging and Medical Intelligence Xi’an China Xi’an Key Laboratory of Medical Computational Imaging Xi’an China

Image denoising of low-dose computed tomography (LDCT) is an important problem for clinical diagnosis with reduced radiation exposure. Previous methods are mostly trained with pairs of synthetic or misaligned LDCT and normal-dose CT (NDCT) images. However, trained with synthetic noise or misaligned LDCT/NDCT image pairs, the denoising networks would suffer from blurry structure or motion artifacts. Since non-contrast CT (NCCT) images share the content characteristics to the corresponding NDCT images in a three-phase scan, they can potentially provide useful information for real-world LDCT image denoising. To exploit this aspect, in this paper, we propose to incorporate clean NCCT images as useful guidance for the learning of real-world LDCT image denoising networks. To alleviate the issue of spatial misalignment in training data, we design a new Patch Triplet Similarity Purification (PTSP) strategy to select highly similar patch (instead of image) triplets of LDCT, NDCT, and NCCT images for network training. Furthermore, we modify two image denoising transformers of SwinIR and HAT to accommodate the NCCT image guidance, by replacing vanilla self-attention with cross-attention. On our collected clinical dataset, the modified transformers trained with the data selected by our PTSP strategy show better performance than 15 comparison methods on real-world LDCT image denoising. Ablation studies validate the effectiveness of our NCCT image guidance and PTSP strategy. We will publicly release our data and code. Copyright © 2025, The Authors. All rights reserved.

关键词： Image denoising

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Study of the atmospheric neutron radiation effects using compact laser-driven spallation neutron source

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Physical Review Research 2025年第1期7卷 013071-013071页

作者： Z. B. Wu Y. L. Yao X. F. Shen K. Li A. D. Liu X. T. He B. Qiao Center for Applied Physics and Technology HEDPS and SKLNPT School of Physics Peking University Beijing 100871 China Research Center of Laser Fusion China Academy of Engineering Physics (CAEP) Mianyang 621900 China Institute of System Engineering China Academy of Engineering Physics (CAEP) Mianyang 621900 China Institute of Applied Physics and Computational Mathematics Beijing 100094 China Frontiers Science Center for Nano-optoelectronic Peking University Beijing 100094 China

We propose a laboratory accelerated test method for study of the atmospheric neutron radiation effects by using a compact laser-driven spallation neutron source (LDSNS). The LDSNS is obtained by injecting a GeV quasimonoenergetic proton beam accelerated by light-sail (LS) radiation pressure of intense lasers into a lead target. According to the LS scaling law, proton energy spectra can be regulated by adjusting the laser and foil target parameters, so that the neutron spectra at various atmospheric altitudes are well reproduced. Integrated PIC and MC simulations have verified the scheme (for that at altitude 12 km, laser intensity of 1022W/cm2 is required) and shown that the neutron fluence per laser shot is as high as 1.73×108n/cm2, equivalent to 3.7 year flux accumulation in atmosphere. Using the G4SEE toolkit, we estimate that the HM628128 SRAM may undergo an average of ten single event upsets per shot for the proposed laboratory test.

关键词： Spalling

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Exploring data driven soliton and rogue waves in symmetric and spatio-temporal potentials using PINN and SC-PINN methods

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The European Physical Journal Plus 2025年第3期140卷 1-27页

作者： Anand, R. Manikandan, K. Serikbayev, N. Centre for Computational Modeling Chennai Institute of Technology Chennai India Center for Nonlinear and Complex Networks SRM TRP Engineering College Tiruchirappalli India Center for Research SRM Institute of Science and Technology - Tiruchirappalli Tiruchirappalli India Department of General and Theoretical Physics L. N. Gumilyov Eurasian National University Astana Kazakhstan

In this study, we present a deep learning (DL) framework for solving the nonlinear Schrödinger equation with $$\mathcal{P}\mathcal{T}$$ -symmetric potentials using strongly constrained physics-informed neural networks (SC-PINNs). We focus on three types of physically compelling potentials, namely $$\mathcal{P}\mathcal{T}$$ -symmetric rational, Jacobian-periodic, and spatio-temporal dependent. SC-PINNs extend the standard physics-informed neural networks (PINNs) by incorporating compound derivative information into the soft constraints, along with an adaptive weight mechanism to accelerate loss function convergence. This enhancement significantly improves the training efficiency compared to standard PINNs. We employ SC-PINNs to approximate soliton and rogue wave solutions of the system under investigation. Additionally, we uncover the impact of various factors on the neural network’s performance, including five different nonlinear activation functions: ReLU, sigmoid, sech, tanh, and sine. Our results reveal that the SC-PINNs method achieves faster convergence and lower errors compared to traditional PINNs. Notably, when using the sine activation function for the three distinct potentials mentioned above, SC-PINNs reduced errors to the order of $$10^{-7}$$ , $$10^{-6}$$ , and $$10^{-4}$$ , effectively capturing complex physical features for highly accurate predictions. Furthermore, we analyze the effect of $$\mathcal{P}\mathcal{T}$$ -symmetric potential parameters on the obtained approximated solutions. The results demonstrate that our DL model successfully approximates soliton and rogue wave solutions of the considered system with high accuracy, outperforming traditional DL algorithms.

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Building a Software Stack for Quantum-HPC Integration

arXiv

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arXiv 2025年

作者： Shehata, Amir Groszkowski, Peter Naughton, Thomas Meena, Murali Gopalakrishnan Wong, Elaine Claudino, Daniel da Silva, Rafael Ferreira Beck, Thomas National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN United States Computer Science and Mathematics Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences and Engineering Oak Ridge National Laboratory Oak RidgeTN United States

This paper presents a comprehensive software stack architecture for integrating quantum computing (QC) capabilities with High-Performance Computing (HPC) environments. While quantum computers show promise as specialized accelerators for scientific computing, their effective integration with classical HPC systems presents significant technical challenges. We propose a hardware-agnostic software framework that supports both current noisy intermediate-scale quantum devices and future fault-tolerant quantum computers, while maintaining compatibility with existing HPC workflows. The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms to handle both simultaneous and interleaved quantum-classical workloads. Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, (3) A Quantum Platform Manager API that simplifies the integration of various quantum hardware systems, and (4) a comprehensive tool chain for quantum circuit optimization and execution. We demonstrate our architecture through implementation of quantum-classical algorithms, including the variational quantum linear solver, showcasing the framework’s ability to handle complex hybrid workflows while maximizing resource utilization. This work provides a foundational blueprint for integrating QC capabilities into existing HPC infrastructures, addressing critical challenges in resource management, job scheduling, and efficient data movement between classical and quantum resources. © 2025, CC BY.

关键词： Quantum computers

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HIGH ACCURACY TECHNIQUES BASED ADAPTIVE FINITE ELEMENT METHODS FOR ELLIPTIC PDES

arXiv

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arXiv 2025年

作者： Xiao, Jingjing Liu, Ying Yi, Nianyu School of Mathematics Shandong University Jinan250100 China School of Science Xi’an University of Technology Xi’an710048 China School of Mathematics and Computational Science Xiangtan University Hunan National Center for Applied Mathematics Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan411105 China

This paper aims to develop an efficient adaptive finite element method for the second-order elliptic problem. Although the theory for adaptive finite element methods based on residual-type a posteriori error estimator and bisection refinement has been well established, in practical computations, the use of non-asymptotic exact of error estimator and the excessive number of adaptive iteration steps often lead to inefficiency of the adaptive algorithm. We propose an efficient adaptive finite element method based on high-accuracy techniques including the superconvergence recovery technique and high-quality mesh optimization. The centroidal Voronoi Delaunay triangulation mesh optimization is embedded in the mesh adaption to provide high-quality mesh, and then assure that the superconvergence property of the recovered gradient and the asymptotical exactness of the error estimator. A tailored adaptive strategy, which could generate high-quality meshes with a target number of vertices, is developed to ensure the adaptive computation process terminated within 7 steps. The effectiveness and robustness of the adaptive algorithm is numerically demonstrated. Copyright © 2025, The Authors. All rights reserved.

关键词： Triangulation

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