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2024 IEEE International Conference on computing, ICOCO 2024

作者： Chan, Vei Siang Mohamed, Farhan Bin Salleh, Faezah Binti Mohd Iglesias, Andres Jamaludin, Najwa Ayuni Binti Mustafa Albakri, Ikmal Faiq Albakri Media and Game Innovation Center of Excellence Institute of Human Centered Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Biosciences Faculty of Science Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Applied Mathematics and Computational Sciences Universidad de Cantabria Santander Spain Pervasive Computing & Educational Technology Department of Media Interactive Fakulti Teknologi Maklumat dan Komunikasi Universiti Teknikal Malaysia Melaka Melaka Malaysia

ISBN: (纸本)9798331530303

Understanding the needs of genomic data visualization techniques is important to help bioinformaticians to perform their comparative analytical tasks effectively. DNA sequencing datasets consist of a long sequential string of nucleotide base pairs combination and are often involved in multiple sequences alignment for comparative analysis. Therefore, existing challenges include designing suitable visualization layout and the need of user-centered approach to ensure optimal user experience. Existing works also featured heavily on the qualitative evaluation of comparative visualization tools, such as use cases and case studies, which are difficult to quantify and conduct follow up evaluation for tool improvement. Hence, this paper aims to understand the needs of visualization tools in comparative genomic analysis to identify visualization tasks and visual design requirements. The methodology includes the creation of semi-structured interview based on personas template as guiding questions and the synthesis of thematic analysis results. These results presented the requirements of the visualization tasks for the biodiversity domain based on comparative targets. For future work, this research will design, develop, and validate the comparative visualization tool based on the discovered requirements. ©2024 IEEE.

关键词： Genome

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On the hyper-singular boundary integral equation methods for dynamic poroelasticity: three dimensional case

arXiv

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arXiv 2022年

作者： Zhang, Lu Xu, Liwei Yin, Tao School of Mathematical Sciences University of Electronic Science and Technology of China Sichuan Chengdu611731 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

In our previous work [SIAM J. Sci. Comput. 43(3) (2021) B784-B810], an accurate hyper-singular boundary integral equation method for dynamic poroelasticity in two dimensions has been developed. This work is devoted to studying the more complex and difficult three-dimensional problems with Neumann boundary condition and both the direct and indirect methods are adopted to construct combined boundary integral equations. The strongly-singular and hyper-singular integral operators are reformulated into compositions of weakly-singular integral operators and tangential-derivative operators, which allow us to prove the jump relations associated with the poroelastic layer potentials and boundary integral operators in a simple manner. Relying on both the investigated spectral properties of the strongly-singular operators, which indicate that the corresponding eigenvalues accumulate at three points whose values are only dependent on two Lamé constants, and the spectral properties of the Calderón relations of the poroelasticity, we propose low-GMRES-iteration regularized integral equations. Numerical examples are presented to demonstrate the accuracy and efficiency of the proposed methodology by means of a Chebyshev-based rectangular-polar solver. Copyright © 2022, The Authors. All rights reserved.

关键词： Boundary integral equations

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Cross-Platform Multimodal Frogeye Leaf Spot Recognition and Classification: Advancing with Cutting-Edge CNN Technologies and Transfer Learning in Diverse Plant Species 7

Cross-Platform Multimodal Frogeye Leaf Spot Recognition and ...

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Mohan, G. Usharani, R. Tomar, Vatsala Arthi, A. Kuriyal, Suniti Kumar Yuvaraj, K.P. K.S.Rangasamy College of Technology Department of Mathematics Tamil Nadu India SRM Institute of Science and Technology School of Computing Department of Computational Intelligence Faculty of Engineering and Technology Chennai India IIMT University School of Life Science & Technology Department of Botany UP Meerut India Karpagam College of Engineering Department of Information Technology Coimbatore India Pt.L.M.S.Sridev Suman Uttarakhand University Campus Department of Botany Rishikesh India Sri Krishna College of Engineering and Technology Department of Mechanical Engineering Coimbatore India

ISBN: (纸本)9798350372816

Frogeye Leaf Spot, a common agricultural disease, poses a significant threat to crop yields. Using multimodal data from different platforms successfully has been a hurdle for existing research in leaf spot detection. Using cutting-edge convolutional neural network (CNN) and transfer learning techniques, this research presents a Cross-Platform Multimodal Recognition system to solve this issue. With a recall of 0.88, F1-score of 0.90, accuracy of 0.91, and precision of 0.92, the suggested method demonstrates exceptional performance. Our accuracy is 6% higher, F1-score is 8% higher, and recall is 9% higher than the preceding state-of-the-art, according to comparisons with previous studies. Improved Frogeye Leaf Spot detection on many platforms is a direct result of our method's success in combining picture and sensor data. This study improves farmers' ability to identify diseases and provides a solid answer for precision agriculture. Our Cross-Platform Multimodal Recognition system has shown to be effective in enhancing agricultural disease recognition, and the shown advances highlight its potential influence on crop management tactics. © 2024 IEEE.

关键词： Convolutional neural networks

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Spectral Galerkin Method for Solving Elastic Wave Scattering Problems with Multiple Open Arcs

SSRN

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SSRN 2022年

作者： Jerez-Hanckes, Carlos Pinto, José Yin, Tao Faculty of Engineering and Sciences Universidad Adolfo Ibáñez Diagonal Las Torres 2700 Peñalolén Santiago Chile Institute of Computational Mathematics and Scientific Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

We study the elastic time-harmonic wave scattering problems on unbounded domains with boundaries composed of finite collections of disjoints finite open arcs (or cracks) in two dimensions. Specifically, we present a fast spectral Galerkin method for solving the associated weakly- and hyper-singular boundary integral equations (BIEs) arising from Dirichlet and Neumann boundary conditions, respectively. Discretization bases of the resulting BIEs employ weighted Chebyshev polynomials that capture the solutions’ edge behavior. We show that these bases guarantee exponential convergence in the polynomial degree when assuming analyticity of sources and arcs geometries. Numerical examples demonstrate the accuracy and robustness of the proposed method with respect to number of arcs and wavenumber. © 2022, The Authors. All rights reserved.

关键词： Boundary integral equations

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TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors

arXiv

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arXiv 2023年

作者： Chen, Xin Ouyang, Yucheng Chen, Xin Chen, Zhenchuan Lin, Rongfen Gao, Xingyu Wang, Lifang Li, Fang Liu, Yin Shang, Honghui Song, Haifeng Institute of Applied Physics and Computational Mathematics Beijing China SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extreme conditions. Ab initio accuracy is now achievable with machine learning based interatomic potentials. With recent advancements in high-performance computing, highly accurate and large-scale simulations become feasible. This study introduces TensorMD, a new machine learning interatomic potential (MLIP) model that integrates physical principles and tensor diagrams. The tensor formalism provides a more efficient computation and greater flexibility for use with other scientific codes. Additionally, we proposed several portable optimization strategies and developed a highly optimized version for the new Sunway supercomputer. Our optimized TensorMD can achieve unprecedented performance on the new Sunway, enabling simulations of up to 52 billion atoms with a time-to-solution of 31 ps/step/atom, setting new records for HPC + AI + MD. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Interactive and Markerless Visual Recognition of Brazilian Sign Language Alphabet

Interactive and Markerless Visual Recognition of Brazilian S...

引用

IEEE Instrumentation and Measurement Technology Conference

作者： Silas Luiz Furtado Jauvane C. De Oliveira Shervin Shirmohammadi Computer Engineering Department Military Institute of Engineering Rio de Janeiro Brazil Mathematical and Computational Methods Department National Laboratory for Scientific Computing Petrópolis Brazil Discover Lab School of EECS University of Ottawa Ottawa Canada

The automatic recognition of sign languages will increase the inclusion of non-verbal persons in society by allowing them to communicate with people who are not familiar with sign language. To this end, recently some systems have been proposed to automatically recognize sign language. Among them, those with external aids such as gloves, markers, clothing/background control, or radars usually have high accuracy, but are not practical in daily life situations. On the other hand, those that use only a camera, such as prevalent smartphone cameras, are practical but have lower accuracy. In this work, we present a system that can recognize the alphabet in the Brazilian sign language, Língua Brasileira de Sinais (LIBRAS), using only a camera yet achieving high accuracy. Like most existing works, our system captures and processes the image of hand gesture and uses a classifier to recognize the sign. However, unlike existing works, the classifier is the Inception-v3 neural network trained with transfer learning on our custom-collected LIBRAS alphabet dataset. Performance evaluations show the system recognizes LIBRAS alphabet with 97% accuracy. We also developed an interactive app, demonstrating that it can run in real time.

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QUASI-LINEAR FRACTIONAL-ORDER OPERATORS IN LIPSCHITZ DOMAINS

arXiv

引用

arXiv 2023年

作者： Borthagaray, Juan Pablo Li, Wenbo Nochetto, Ricardo H. Instituto de Matemática y Estadística "Rafael Laguardia" Facultad de Ingeniería Universidad de la República Montevideo Uruguay Institute of Computational Mathematics and Scientific/Engineering Computing The Chinese Academy of Sciences Beijing100190 China Department of Mathematics Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States

We prove Besov boundary regularity for solutions of the homogeneous Dirichlet problem for fractional-order quasi-linear operators with variable coefficients on Lipschitz domains Ω of Rd. Our estimates are consistent with the boundary behavior of solutions on smooth domains and apply to fractional p-Laplacians and operators with finite horizon. The proof exploits the underlying variational structure and uses a new and flexible local translation operator. We further apply these regularity estimates to derive novel error estimates for finite element approximations of fractional p-Laplacians and present several simulations that reveal the boundary behavior of solutions. © 2023, CC BY.

关键词： Mathematical operators

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Using a Fast Adaptive Function Approximator to calculate Protein-Filament Binding Kinetics

arXiv

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arXiv 2023年

作者： Zhang, Zihan Lamson, Adam Blackwell, Robert Department of Applied Mathematics University of Washington SeattleWA98195 United States Center for Computational Biology Flatiron Institute New YorkNY10010 United States Scientific Computing Core Flatiron Institute New YorkNY10010 United States

The cytoskeleton, consisting of biopolymer filaments, molecular motors, and passive crosslinking proteins, provides the internal structure of cells that facilitate movement, growth, and cell division. Understanding the microscopic motor-filament kinetics and dynamics is essential for comprehending macroscopic behaviors of reconstituted cytoskeletal assemblies, such as self-organized flow and active stress. In this study, we employ an adaptive fast Chebyshev approximator based on tree search and parallel computing to accurately recover the equilibrium distribution of crosslinking proteins. Therefore, it satisfies detailed balance in binding through kinetic Monte Carlo sampling while maintaining cost-effectiveness. Additionally, we offer expandable features, including segregating the simulation process via pre-building and allowing the free-loading of different closed-form formulations of the motor's potential energy. Overall, this research contributes to computational advancement in function approximation and has the potential to better describe the evolution of cytoskeletal active matter. © 2023, CC BY.

关键词： Cost effectiveness

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Group Projected Subspace Pursuit for Block Sparse Signal Reconstruction: Convergence Analysis and Applications

arXiv

引用

arXiv 2024年

作者： He, Roy Y. Liu, Haixia Liu, Hao Department of Mathematics City University of Hong Kong Kowloon Tong Hong Kong School of Mathematics and Statistics Institute of Interdisciplinary Research for Mathematics and Applied Science Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Hubei Wuhan China Department of Mathematics Hong Kong Baptist University Kowloon Tong Hong Kong

In this paper, we present a convergence analysis of the Group Projected Subspace Pursuit (GPSP) algorithm proposed by He et al. [HKL+23] (Group Projected subspace pursuit for IDENTification of variable coefficient differential equations (GP-IDENT), Journal of computational Physics, 494, 112526) and extend its application to general tasks of block sparse signal recovery. We prove that when the sampling matrix satisfies the Block Restricted Isometry Property (BRIP) with a sufficiently small Block Restricted Isometry Constant (BRIC), GPSP exactly recovers the true block sparse signals. When the observations are noisy, this convergence property of GPSP remains valid if the magnitude of the true signal is sufficiently large. GPSP selects the features by subspace projection criterion (SPC) for candidate inclusion and response magnitude criterion (RMC) for candidate exclusion. We compare these criteria with counterparts of other state-of-the-art greedy algorithms. Our theoretical analysis and numerical ablation studies reveal that SPC is critical to the superior performances of GPSP, and that RMC can enhance the robustness of feature identification when observations contain noises. We test and compare GPSP with other methods in diverse settings, including heterogeneous random block matrices, inexact observations, face recognition, and PDE identification. We find that GPSP outperforms the other algorithms in most cases for various levels of block sparsity and block sizes, justifying its effectiveness for general applications. © 2024, CC BY.

关键词： Face recognition

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Blood Cancer Detection and Classification using Auto Encoder and Regularized Extreme Learning Machine 8

Blood Cancer Detection and Classification using Auto Encoder...

引用

8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Kanimozhi, N. Nayak, Swikruti Keshav Kumar, K. Manjramkar, Vinda Kumar, Ranjeet Suganthi, D. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chennai Tamilnadu Kattankulathur India Jecrc University Department of Computer Science and Engineering Rajasthan Jaipur India Department of Humanities and Mathematics Shaikpet Telangana Hyderabad India Zoology Maharashtra Thane India University College of Engineering & Technology Vinoba Bhave University Department of Electronics & Communication Jharkhand Hazaribagh India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Science Thandalam Chennai India

ISBN: (纸本)9798350396638

Leukaemia is considered as the most common type of blood cancer. The length of time one requires to recover from this disease is determined by how quickly it is recognized and treated. Leukaemia develops when the bone marrow generates an abnormally large amount of white blood cells. Since images of human blood samples are studied under the microscope, haematologists employ techniques such as microscopic colour imaging, image segmentation, grouping, and classification to diagnose patients as fast as possible. Microscopy may identify blood cancer in visible and immature white blood cells in a variety of methods. Early and accurate diagnosis of leukaemia is critical for clinicians to properly treat patients. After the input image is provided, the gaussian filter is used to perform image enhancement, and the Wiener filter is used in image Deblurring, Adaptive K-Means clustering technique, Colour feature, Texture feature, GLCM and Hausdroff are also used, and lastly the Model is trained using AE and RELM. The proposed system outperforms conventional models such as RELM and ELM, which generates an accuracy of 98.45%. © 2023 IEEE.

关键词： Image enhancement

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