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Vortex inverted pin beams: Mitigation of scintillations in strong atmospheric turbulence

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arXiv 2024年

作者： Droulias, Sotiris Loulakis, Michalis Papazoglou, Dimitris G. Tzortzakis, Stelios Chen, Zhigang Efremidis, Nikolaos K. Institute of Applied and Computational Mathematics Foundation for Research and Technology - Hellas Crete Heraklion70013 Greece Institute of Electronic Structure and Laser Foundation for Research and Technology - Hellas Crete Heraklion70013 Greece Department of Materials Science and Technology University of Crete Crete Heraklion70013 Greece MOE Key Laboratory of Weak-Light Nonlinear Photonics TEDA Applied Physics Institute School of Physics Nankai University Tianjin300457 China Department of Mathematics and Applied Mathematics University of Crete Crete Heraklion70013 Greece

We recently introduced a new class of optical beams with a Bessel-like transverse profile and increasing beam width during propagation, akin to an "inverted pin". Owing to their specially engineered distribution, these beams have shown remarkable performance in atmospheric turbulence. Specifically, inverted pin beams were found to have reduced scintillation index as compared to collimated or focused Gaussian beams as well as other types of pin beams especially in moderate to strong turbulence. In this work, we demonstrate that inverted pin beams carrying orbital angular momentum can further suppress intensity scintillations in moderate to strong irradiance fluctuation conditions. Our results can be useful in improving the performance and link availability of free-space optical communication systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Atmospheric turbulence

Causal Adjacency Learning for Spatiotemporal Prediction Over Graphs

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Causal Adjacency Learning for Spatiotemporal Prediction Over...

International Conference on Intelligent Transportation

作者： Zhaobin Mo Qingyuan Liu Baohua Yan Longxiang Zhang Xuan Di Department of Civil Engineering and Engineering Mechanics Columbia University New York NY USA Department of Electrical Engineering Columbia University New York NY USA Department of Applied Physics and Applied Mathematics Columbia University New York NY USA Data Science Institute Columbia University New York NY USA

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Spatiotemporal prediction over graphs (STPG) is crucial for transportation systems. In existing STPG models, an adjacency matrix is an important component that captures the relations among nodes over graphs. However, most studies calculate the adjacency matrix by directly memorizing the data, such as distance- and correlation-based matrices. These adjacency matrices do not consider potential pattern shift for the test data, and may result in suboptimal performance if the test data has a different distribution from the training one. This issue is known as the Out-of-Distribution generalization problem. To address this issue, in this paper we propose a Causal Adjacency Learning (CAL) method to discover causal relations over graphs. The learned causal adjacency matrix is evaluated on a downstream spatiotemporal prediction task using real-world graph data. Results demonstrate that our proposed adjacency matrix can capture the causal relations, and using our learned adjacency matrix can enhance prediction performance on the OOD test data, even though causal learning is not conducted in the downstream task.

关键词： Training Correlation Urban areas Transportation Encoding Spatiotemporal phenomena Geospatial analysis

An asymptotic-preserving IMEX PN method for the gray model of the radiative transfer equation

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arXiv 2024年

作者： Fu, Jinxue Cheng, Juan Li, Weiming Xiong, Tao Wang, Yanli Institute of Applied Physics and Computational Mathematics Beijing100088 China Center for Applied Physics and Technology College of Engineering Peking University Beijing100871 China School of Mathematical Sciences University of Science and Technology of China Anhui Hefei230026 China Beijing Computational Science Research Center Beijing100193 China

An asymptotic-preserving (AP) implicit-explicit PN numerical scheme is proposed for the gray model of the radiative transfer equation, where the first- and second-order numerical schemes are discussed for both the linear and nonlinear models. The AP property of this numerical scheme is proved theoretically and numerically, while the numerical stability of the linear model is verified by Fourier analysis. Several classical benchmark examples are studied to validate the efficiency of this numerical scheme. © 2024, CC BY-NC-ND.

关键词： Radiative transfer

Improved Compact Genetic Algorithms with Efficient Caching

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arXiv 2025年

作者： Dutta, Prasanta Mukhopadhyay, Anirban Physics and Applied Mathematics Unit Indian Statistical Institute West Bengal Kolkata700108 India Department of Computer Science and Engineering University of Kalyani West Bengal Kalyani741235 India

Compact Genetic Algorithms (cGAs) are condensed variants of classical Genetic Algorithms (GAs) that use a probability vector representation of the population instead of the complete population. cGAs have been shown to significantly reduce the number of function evaluations required while producing outcomes similar to those of classical GAs. However, cGAs have a tendency to repeatedly generate the same chromosomes as they approach convergence, resulting in unnecessary evaluations of identical chromosomes. This article introduces the concept of caching in cGAs as a means of avoiding redundant evaluations of the same chromosomes. Our proposed approach operates equivalently to cGAs, but enhances the algorithm’s time efficiency by reducing the number of function evaluations. We also present a data structure for efficient cache maintenance to ensure low overhead. The proposed caching approach has an asymptotically constant time complexity on average. The proposed method further generalizes the caching mechanism with higher selection pressure for elitism-based cGAs. We conduct a rigorous analysis based on experiments on benchmark optimization problems using two well-known cache replacement strategies. The results demonstrate that caching significantly reduces the number of function evaluations required while maintaining the same level of performance accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Genetic algorithms

Reconciling S8: Insights from Interacting Dark Sectors

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arXiv 2024年

作者： Shah, Rahul Mukherjee, Purba Pal, Supratik Physics and Applied Mathematics Unit Indian Statistical Institute 203 B.T. Road Kolkata700 108 India Centre for Theoretical Physics Jamia Millia Islamia New Delhi110025 India Technology Innovation Hub on Data Science Big Data Analytics and Data Curation Indian Statistical Institute 203 B.T. Road Kolkata700 108 India

We do a careful investigation of the prospects of dark energy (DE) interacting with cold dark matter in alleviating the S8 clustering tension. To this end, we consider various well-known parametrizations of the DE equation of state (EoS) and consider perturbations in both the dark sectors, along with an interaction term. Moreover, we perform a separate study for the phantom and non-phantom regimes. Using cosmic microwave background (CMB), baryon acoustic oscillations, and Type Ia supernovae data sets, constraints on the model parameters for each case have been obtained and a generic reduction in the H0 − σ8,0 correlation has been observed, both for constant and dynamical DE EoS. This reduction, coupled with a significant negative correlation between the interaction term and σ8,0, contributes to easing the clustering tension by lowering σ8,0 to somewhere in between the early CMB and late-time clustering measurements for the phantom regime, for almost all the models under consideration. Additionally, this is achieved without exacerbating the Hubble tension. In this regard, the interacting Chevallier–Polarski–Linder and Jassal-Bagla-Padmanabhan models perform the best in relaxing the S8 tension to 8,0 tension tends to have worsened, which reassures the merits of phantom DE from latest data. We further investigate the role of redshift space distortion data sets and find an overall reduction in tension, with a σ8,0 value relatively closer to the CMB value. We finally check whether further extensions of this scenario, such as the inclusion of the sound speed of DE and warm dark matter interacting with DE, can have some effects. Copyright © 2024, The Authors. All rights reserved.

关键词： Dark energy

Machine learning and invariant theory

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arXiv 2022年

作者： Blum-Smith, Ben Villar, Soledad Department of Applied Mathematics and Statistics Mathematical Institute for Data Science Johns Hopkins University United States

Inspired by constraints from physical law, equivariant machine learning restricts the learning to a hypothesis class where all the functions are equivariant with respect to some group action. Irreducible representations or invariant theory are typically used to parameterize the space of such functions. In this article, we introduce the topic and explain a couple of methods to explicitly parameterize equivariant functions that are being used in machine learning applications. In particular, we explicate a general procedure, attributed to Malgrange, to express all polynomial maps between linear spaces that are equivariant under the action of a group G, given a characterization of the invariant polynomials on a bigger space. The method also parametrizes smooth equivariant maps in the case that G is a compact Lie group. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

Subspace method based on neural networks for eigenvalue problems

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arXiv 2024年

作者： Dai, Xiaoying Fan, Yunying Sheng, Zhiqiang LSEC Institute of Computational Mathematics and Scientific/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 National Key Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China HEDPS Center for Applied Physics and Technology College of Engineering Peking University Beijing100871 China

With the rapid development of machine learning, numerical discretization methods based on deep neural networks have been widely used in many fields, especially in solving high-dimensional problems where traditional methods face bottlenecks. However, for low-dimensional problems, existing machine learning methods are not as accurate and efficient as traditional methods. In this paper, we propose a subspace method based on neural networks for eigenvalue problems with high accuracy and low cost. Our basic idea is to use neural network based basis functions to span a subspace, then calculate the parameters of the neural network based basis functions through appropriate training, and finally calculate the Galerkin projection of the eigenvalue problem onto the subspace and obtain an approximate solution. In addition, we reduce the dimension of the subspace by applying some dimensionality reduction technique, which can improve the accuracy of the approximate solution in further. Numerical experiments show that we can obtain approximate eigenvalues with accuracy of 10−11 but with less then 400 epochs, which is significantly superior to other existing neural network based *** Codes 65N25, 65L15, 68T07 Copyright © 2024, The Authors. All rights reserved.

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Depression Diagnosis: Bioinformatics Uncovers Cellular Abnormalities in Female-Specific Genes With Ai-Driven Blood Biomarker-Based Prediction

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Depression Diagnosis: Bioinformatics Uncovers Cellular Abnor...

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Neda Firoz Nivedita Yadav Olga Grigorievna Beresteneva Sergey Aksyonov Vladimirovich Institute of Applied Mathematics and Computer Science Tomsk State Univeristy Tomsk Russia Indian biological data center Regional Centre for Biotechnology Faridabad India Tomsk Polytechnic Univeristy Tomsk Russia

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

With an estimated 264 million people affected by MDD globally, there is an urgent need for novel approaches to diagnosis and treatment. This study explored the complex gene expression profiles linked to Major Depressive Disorder (MDD) using state-of-the-art Next-Generation Sequencing (NGS), namely RNA-Seq. There is a need for accurate, non-invasive diagnostics, particularly through gender-specific biomarker research in female RNA-Seq data. To address this disparity, RNA-Seq data was analyzed using Machine Learning (ML) techniques to categorize female-specific MDD, identifying 10 critical biomarkers and utilizing Artificial Intelligence (AI) and bioinformatics to extract transcriptome data from publicly available datasets, while employing Generative Adversarial Networks (GANs) for data augmentation due to the limited availability of large RNA-Seq datasets. Gene set enrichment analysis (GSEA) and other bioinformatics analyses further demonstrated that genes differentially expressed in MDD patients were enriched in pathways related to the cell cycle, neutrophil degranulation, resolution of sister chromatid cohesion, and the formation of mitotic spindles by EML4 and NUDC. Ten important biomarkers associated with MDD were identified by an integrative bioinformatics investigation: IGHV3-64D, IGHV5-10-1, IFI27, LILRB5, PBK, SIGLEC1, IFI44L, CDCA5, and SLC4A1. Machine learning models based on these genes achieved $95 \%$ accuracy, highlighting potential of RNA-Seq and ML to enhance MDD diagnosis and reveal its molecular mechanisms.

关键词： Sequential analysis Accuracy Biological system modeling RNA Medical treatment Depression Bioinformatics Blood Next generation networking Overfitting

AUTOAMG(θ): AN AUTO-TUNED AMG METHOD BASED ON DEEP LEARNING FOR STRONG THRESHOLD

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arXiv 2023年

作者： Zou, Haifeng Xu, Xiaowen Zhang, Chen-Song Mo, Zeyao Graduate School of China Academy of Engineering Physics China Academy of Engineering Physics China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics China Academy of Mathematics and Systems Science Chinese Academy of Sciences China

Algebraic Multigrid (AMG) is one of the most used iterative algorithms for solving large sparse linear equations Ax = b. In AMG, the coarse grid is a key component that affects the efficiency of the algorithm, the construction of which relies on the strong threshold parameter θ. This parameter is generally chosen empirically, with a default value in many current AMG solvers of 0.25 for 2D problems and 0.5 for 3D problems. However, for many practical problems, the quality of the coarse grid and the efficiency of the AMG algorithm are sensitive to θ;the default value is rarely optimal, and sometimes is far from it. Therefore, how to choose a better θ is an important question. In this paper, we propose a deep learning based auto-tuning method, AutoAMG(θ) for multiscale sparse linear equations, which are widely used in practical problems. The method uses Graph Neural Networks (GNNs) to extract matrix features, and a Multilayer Perceptron (MLP) to build the mapping between matrix features and the optimal θ, which can adaptively output θ values for different matrices. Numerical experiments show that AutoAMG(θ) can achieve significant speedup compared to the default θ value. Copyright © 2023, The Authors. All rights reserved.

关键词： Linear equations