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A GPU-Based Parallel Implementation of the GWO Algorithm: Application to the Solution of Large-Scale Nonlinear Equation Systems 11

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A GPU-Based Parallel Implementation of the GWO Algorithm: Ap...

11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Silva, Bruno Lopes, Luiz Guerreiro University of Madeira Sre Regional Government of Madeira PhD Program in Computer Engineering Funchal Madeira Portugal University of Madeira Faculty of Exact Sciences and Engineering Department of Mathematics Funchal Madeira9020-105 Portugal

ISBN: (纸本)9798350306941

Population-based computational intelligence algorithms are natural candidates for parallelization and have been utilized to solve a variety of difficult and complex real-world problems. The gray wolf optimizer (GWO) is one of these algorithms. It is a metaheuristic that simulates the leadership hierarchy and hunting mechanism of gray wolves in the wild and has been used successfully to solve several hard optimization problems. However, the study of its applicability to the solution of nonlinear equation systems, which is arguably the most difficult class of numerical problems, is still quite incipient and needs to be better accessed and verified, particularly in the case of large-scale systems of nonlinear equations, which have not been considered until now and whose resolution difficulty increases with the number of equations they contain. This paper presents a new and efficient GPU-based massively parallel implementation of the gray wolf optimizer algorithm for solving large-scale optimization problems. The proposed parallelization of the GWO algorithm is illustrated by its application to solving large systems of nonlinear equations, a class of problems that appear and have great importance in different fields of pure and applied sciences and engineering. The GPU-accelerated version of GWO was implemented in Julia and tested on a GeForce RTX 3090 GPU with 24 GB GDDR6X VRAM and 10 496 CUDA cores using a set of hard, scalable systems of nonlinear equations with dimensions ranging from 500 to 2000. The obtained results, with average speedups between 69.91× and 241.54× , show the efficiency of the proposed GPU-based acceleration of the GWO algorithm. © 2023 IEEE.

关键词： Graphics processing unit

Pairwise Learning for Autism Spectrum Disorder Imbalanced Classification 6

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Pairwise Learning for Autism Spectrum Disorder Imbalanced Cl...

6th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Liu, Shu Wu, Qiang Yang, Xin Computational and Data Science PhD Program Middle Tennessee State University MurfreesboroTN United States University of Tennessee Department of Mathematics KnoxvilleTN United States Middle Tennessee State University Department of Computer Science MurfreesboroTN United States

ISBN: (纸本)9798350396133

This paper performs a classification task on data obtained from the Autism Brain Imaging Data Exchange (ABIDE) repository. In real-world case analysis, the number of autism spectrum disorder (ASD) patients is much smaller than typically developed people. To address this issue, this paper proposes the utilization of pairwise robust support vector machine (PRSVM) algorithms to classify autism spectrum disorder (ASD) patients. In this project's experiments, the correlation matrix derived from functional magnetic resonance imaging (fMRI) data was employed as a classification feature. A comprehensive evaluation was conducted to compare the classification performance of PRSVM with various machine learning methods. The comparative analysis encompassed various aspects, including different data dimensions, imbalanced ratios, and sample sizes, providing valuable insights into the relative performance of the algorithms under different experimental conditions. The experimental results demonstrate that PRSVM can detect autistic patients more accurately when the data is imbalanced. Moreover, the results indicate that PRSVM outperforms or achieves comparable performance to other conventional classification methods in a variety of situations. Furthermore, our approach can be further improved by augmenting the training set with either exclusively normal person samples or by incorporating patient samples and normal people samples in a proportionate manner. This augmentation strategy holds promising application value, as it contributes to improving the performance and robustness of our method. © 2024 IEEE.

关键词： Diseases

About well-posedness and lack of exponential stability of Shear beam models

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Annali dell'Universita di Ferrara 2022年第1期68卷 129-136页

作者： Ramos, A.J.A. Almeida Júnior, D.S. Freitas, M.M. Veras, L.S. Faculty of Mathematics Federal University of Pará Raimundo Santana Street s/n PA Salinópolis 68721-000 Brazil PhD Program in Mathematics Federal University of Pará Augusto Corrêa Street 01 PA Belém 66075-110 Brazil

In this paper, we consider the Shear beam model (no rotary inertia) and we stablished a decay result of the total energy of solutions by taking a feedback law acting on angle rotation. Unlike the dissipative Shear beam model with damping effect acting on vertical displacement, where the exponential decay holds irrespective any relationship between coefficients of the system, here we prove that system is non-exponential stability by using semigroup techniques. Also, the well-posedness is achieved by using semigroup theory. © 2022, The Author(s) under exclusive license to Università degli Studi di Ferrara.

关键词： Lack of exponential stability Shear beam model Well-posedness

Adaptive Behavior with Stable Synapses

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arXiv 2024年

作者： Capone, Cristiano Falorsi, Luca Natl. Center for Radiation Protection and Computational Physics Istituto Superiore di Sanità Rome00161 Italy PhD Program in Mathematics Dept. of Mathematics "Sapienza" University of Rome Rome00185 Italy

Behavioral changes in animals and humans, as a consequence of an error or a verbal instruction, can be extremely rapid. Improvement in behavioral performances are usually associated learning theories to synaptic plasticity. However, such rapid changes are not coherent with the timescales of biological synaptic plasticity, suggesting that the mechanism responsible for that could be a dynamical reconfiguration of the network involved, without changing its weights. In the last few years, similar capabilities have been observed in transformers, foundational architecture in the field of machine learning that are widely used in applications such as natural language and image processing. Transformers are capable of in-context learning, the ability to adapt and acquire new information dynamically within the context of the task or environment they are currently engaged in, without the need for significant changes to their underlying parameters. Building upon the notion of something unique within transformers enabling the emergence of this property, we claim that it could be supported by gain-modulation, feature extensively observed in biological networks, such as in pyramidal neurons thanks to input segregation and dendritic amplification. We propose a constructive approach to induce in-context learning in an architecture composed of recurrent networks with gain modulation, demonstrating abilities inaccessible to standard networks. In particular, we show that, such architecture can dynamically implement standard gradient-based by encoding weight changes in the activity of another network. We argue that, while these algorithms are traditionally associated with synaptic plasticity, their reliance on non-local terms suggests that, in the brain, they can be more naturally realized at the level of neural circuits. We demonstrate that we can extend our approach to non-linear and temporal tasks and to reinforcement learning. Our framework contributes to understanding the principles

关键词： Reinforcement learning

QUADRATIC DIFFERENTIALS AND FUNCTION THEORY ON RIEMANN SURFACES

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arXiv 2024年

作者： Šarić, Dragomir PhD Program in Mathematics The Graduate Center CUNY 365 Fifth Ave. N.Y. NY10016 United States Department of Mathematics Queens College CUNY 65–30 Kissena Blvd. FlushingNY11367 United States

A finite-area holomorphic quadratic differentials on an arbitrary Riemann surface X = H/Γ is uniquely determined by its horizontal measured foliation. By extending our prior result for Γ of the first kind to arbitrary Fuchsian group Γ, we obtain that a measured foliation F is realized by the horizontal foliation of a finite-area holomorphic quadratic differential on X if and only if F has finite Dirichlet integral. We determine the image of this correspondence when the infinite Riemann surface has bounded geometry- an extension of the realization result of Hubbard and Masur for compact surfaces. A corollary is that a planar surface X with bounded pants decomposition and with (at most) countably many ends is parabolic, i.e., does not support Green’s function, in notation X ∈ OG where G is Green’s function. The class of harmonic functions with finite Dirichlet integral is denoted by HD. We give a geometric proof that the class OHD of the Riemann surfaces (that do not support non-constant HD-functions) is invariant under quasiconformal maps. Lyons proved that the OHB class (surfaces that do not support non-constant bounded harmonic functions) is not invariant under quasiconformal maps, and it is well-known that the OG class is invariant. Therefore, the noninvariant class OHB is between two invariant classes: OG ⊂ OHB ⊂ OHD. Copyright © 2024, The Authors. All rights reserved.

关键词： Harmonic functions

BOUNDED IDEAL TRIANGULATIONS OF INFINITE RIEMANN SURFACES

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arXiv 2025年

作者： Whitney, Casey Šarić, Dragomir Mathematics PhD. Program Graduate Center of the City University of New York 365 Fifth Avenue New YorkNY10016-4309 United States Department of Mathematics Queens College of the City University of New York 65–30 Kissena Blvd. FlushingNY11367 United States

We introduce a notion of a bounded ideal triangulation of an infinite Riemann surface and parametrize Teichmüller spaces of infinite surfaces which allow bounded triangulations. We prove that our parametrization is real-analytic. Riemann surfaces with bounded geometry and countably many punctures belong to the class of surfaces with bounded ideal triangulations. In comparison, the Fenchel-Nielsen parametrization for surfaces with bounded geometry is not known, while the Fenchel-Nielsen parametrization for surfaces with bounded pants decompositions is known as a homeomorphism but it is not known whether it is real-analytic Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

Pairwise Learning for Autism Spectrum Disorder Imbalanced Classification

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Pairwise Learning for Autism Spectrum Disorder Imbalanced Cl...

IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Shu Liu Qiang Wu Xin Yang Computational and Data Science PhD Program Middle Tennessee State University Murfreesboro TN USA Department of Mathematics University of Tennessee Knoxville TN USA Department of Computer Science Middle Tennessee State University Murfreesboro TN USA

ISBN: (数字)9798350396133

ISBN: (纸本)9798350396140

关键词： Training Support vector machines Neuroimaging Autism Machine learning algorithms Functional magnetic resonance imaging Vectors Robustness Classification algorithms Sustainable development

Counting cusp excursions of reciprocal geodesics

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arXiv 2024年

作者： Basmajian, Ara Valli, Robert Suzzi PhD Program in Mathematics The Graduate Center CUNY 365 Fifth ave. New YorkNY10016 United States Department of Mathematics and Statistics Hunter College CUNY 695 Park ave. New YorkNY10065 United States Department of Mathematics Manhattan College 4513 Manhattan College Pkwy. RiverdaleNY10471 United States

For a fixed cusp neighborhood (determined by depth D) of the modular surface, we investigate the class of reciprocal geodesics that enter this neighborhood (called a cusp excursion) a fixed number of times. Since reciprocal geodesics traverse their image an even number of times such a geodesic only has an even number of cusp excursions, say 2n. Denoting this class of geodesics as (formula presented), we derive several growth rate results for (formula presented), where |γ| is the word length of γ in Z2 ∗ Z3 with respect to the generators of the factors. © 2024, CC BY.

关键词： Geodesy

A GPU-Based Parallel Implementation of the GWO Algorithm: Application to the Solution of Large-Scale Nonlinear Equation Systems

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A GPU-Based Parallel Implementation of the GWO Algorithm: Ap...

International Symposium on Computing and Networking Workshops (CANDARW)

作者： Bruno Silva Luiz Guerreiro Lopes PhD Program in Computer Engineering University of Madeira SRE Regional Government of Madeira Funchal Madeira Is. Portugal Department of Mathematics Faculty of Exact Sciences and Engineering University of Madeira Funchal Madeira Is. Portugal

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