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arXiv 2024年

作者： Jewani, Yordanos Petry, Michael Sanchez-Arias, Rei Amezcua-Correa, Rodrigo Habib, Md Selim The Department of Data Science and Business Analytics Florida Polytechnic University 4700 Research Way LakelandFL33805 United States The Faculty of Electrical Engineering and Information Technology University of Applied Sciences KarlsruheBW-76133 Germany The CREOL University of Central Florida OrlandoFL32816 United States The Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL32901 United States

Hollow-core anti-resonant fibers (HC-ARFs) have proven to be an indispensable platform for various emerging applications due to their unique and extraordinary optical properties. However, accurately estimating the propagation loss of nested HC-ARFs remains a challenging task due to their complex structure and the lack of precise analytical and theoretical models. To address this challenge, we propose a supervised machine-learning framework that presents an effective solution to accurately predict the propagation loss of a 5-tube nested HC-ARF. Multiple supervised learning models, including random forest, logistic regression, quadratic discriminant analysis, tree-based methods, extreme gradient boosting, and K-nearest neighbors are implemented and compared using a simulated dataset. Among these methods, the random forest algorithm is identified as the most effective, delivering accurate predictions. Notably, this study considers the impact of random structural perturbations on fiber geometry, encompassing random variations in tube wall thicknesses and tube gap separations. In particular, these perturbations involve randomly varying outer and nested tube wall thicknesses, tube angle offsets, and randomly distributed non-circular, anisotropic shapes within the cladding structure. It is worth noting that these specific perturbations have not been previously investigated. Each tube exhibits its unique set of random values, leading to longer simulation times for combinations of these values compared to regular random variables in HC-ARFs with similar tube characteristics. The comprehensive consideration of these factors allows for precise predictions, significantly contributing to the advancement of HC-ARFs for many emerging applications. © 2024, CC BY.

关键词： Fibers

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Semi-Implicit Lagrangian Voronoi Approximation for Compressible Viscous Fluid Flows

arXiv

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arXiv 2024年

作者： Kincl, Ondřej Peshkov, Ilya Boscheri, Walter Laboratory of Applied Mathematics DICAM University of Trento Trento38123 Italy Laboratoire de Mathématiques UMR 5127 CNRS Université Savoie Mont Blanc Le Bourget du Lac73376 France Department of Mathematics and Computer Science University of Ferrara Ferrara44121 Italy

This paper contributes to the recent investigations of Lagrangian methods based on Voronoi meshes. The aim is to design a new conservative numerical scheme that can simulate complex flows and multi-phase problems with more accuracy than SPH (Smoothed Particle Hydrodynamics) methods but, unlike diffuse interface models on fixed grid topology, does not suffer from the deteriorating quality of the computational grid. The numerical solution is stored at particles, which move with the fluid velocity and also play the role of the generators of the computational mesh, that is efficiently re-constructed at each time step. The main novelty stems from combining a Lagrangian Voronoi scheme with a semi-implicit integrator for compressible flows. This allows to model low-Mach number flows without the extremely stringent stability constraint on the time step and with the correct scaling of numerical viscosity. The implicit linear system for the unknown pressure is obtained by splitting the reversible from the irreversible (viscous) part of the dynamics, and then using entropy conservation of the reversible sub-system to derive an auxiliary elliptic equation. The method, called SILVA (Semi-Implicit Lagrangian Voronoi Approximation), is validated in a variety of test cases that feature diverse Mach numbers, shocks and multi-phase flows. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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Life Test Plan Optimization for Electrical Insulation During Temperature Load: A Semi-Optimal Approach

Life Test Plan Optimization for Electrical Insulation During...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Rahul Vishnoi Sanjeev Kumar Verma Vishal Sharma K. Yuvaraj S. Gopinath Deepika Dongre Department of ECE TMU Department of Applied Sciences Invertis University Bareilly Uttar Pradesh Film & Television Studies School of Media IIMT University Meerut Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore Vishwakarma Institute of Information Technology Pune

Considering that the Arrhenius equation between stress condition and lifetime is valid and that the proportional lifespan at an excessive pressure reflects some reliable statistical properties. It is essential to demonstrate the highest diagnostic strategy from the perspective of effectiveness under such living scenarios. Knowing the semi-optimal test plan, where effectiveness is similar to that of the maximum one and test conditions are straightforward, is additionally helpful. The goal of minimization is to determine the ideal amount of test subjects with each test stressors, and we assume that there are three sample stress levels. The root-average-squared error for the duration in use circumstance serves as the benchmark for optimization problems. We were using the hyperparameters in an actual trial situation to adjust for realism. If predictability of the Characteristic curve is necessary, we have ended up finding that there is just a subtle distinction between both the optimal results obtained and the mainstream test result when making comparisons it to the data achieved that use the mainstream tester, in which test samples are equitably represented to each experiment anxiety levels. The traditional testing phase is one of the partially ideal test plans, in our opinion. The agreement between the theory and computer findings has been examined.

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RAFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition

arXiv

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arXiv 2024年

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333031 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Skoda Auto University Na Karmeli 1457 Mlada Boleslav293 01 Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jadavpur University Kolkata32 India

Deep Convolutional Neural Networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing of region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pair aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channel-wise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed Region Attention network for Food items and Agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracies of RAFA-Net are 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net’s generalization capability. © 2024, CC BY.

关键词： Plant diseases

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A Novel U-Net Based CNN Algorithm for High-Accuracy Weather Forecasting

A Novel U-Net Based CNN Algorithm for High-Accuracy Weather ...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Thirumurugan Shanmugam Chirag Chandrashekar Arun Kumar Sivaraman Janakiraman Nithiyanantham Priya Ravindran S T Sarvesh Narayanan Department of Computing and Information Sciences University of Technology and Applied Sciences Suhar Oman School of CSE Vellore Institute of Technology Chennai India Digital Engineering Solution Centre Photon Interactive Inc. Ontario Canada Department of ECE K.L.N. College of Engineering Tamilnadu India School of Computer Science Dalhousie University Halifax Canada

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Accurate weather forecasting is essential for sectors like agriculture, aviation, and disaster management. However, deep learning algorithms face challenges in prediction accuracy due to issues like vanishing gradients, overfitting, and high computational demands. This research proposes a novel U-Net based architecture utilizing a Convolutional Neural Network (CNN) bottleneck layer to improve weather forecasting. Key features include a skip-connection mechanism, modified weight update rules, Gaussian-mutation operations, and the Adam optimizer for enhanced feature extraction and faster, more accurate predictions. The model was tested using precipitation data from Doppler Weather Radar (DWR) Chennai Radar and weather parameters from European Centre for Medium-Range Weather Forecasts (ECMWF). A dedicated GeoServer facilitates realtime data processing. Experimental results show the proposed algorithm achieves 97.5% accuracy, outperforming CNN and long short-term memory (LSTM) models by 5.84% and 2.41%, respectively.

关键词： Accuracy Computational modeling Atmospheric modeling Weather forecasting Predictive models Feature extraction Prediction algorithms Convolutional neural networks Long short term memory Meteorology

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Dual-VQE: A quantum algorithm to lower bound the ground-state energy

arXiv

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arXiv 2023年

作者： Westerheim, Hanna Chen, Jingxuan Holmes, Zoë Luo, Ivy Nuradha, Theshani Patel, Dhrumil Rethinasamy, Soorya Wang, Kathie Wilde, Mark M. School of Applied and Engineering Physics Cornell University IthacaNY14850 United States School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States Department of Computer Science Cornell University IthacaNY14850 United States LausanneCH-1015 Switzerland

The variational quantum eigensolver (VQE) is a hybrid quantum–classical variational algorithm that produces an upper-bound estimate of the ground-state energy of a Hamiltonian. As quantum computers become more powerful and go beyond the reach of classical brute-force simulation, it is important to assess the quality of solutions produced by them. Here we propose a dual variational quantum eigensolver (dual-VQE) that produces a lower-bound estimate of the ground-state energy. As such, VQE and dual-VQE can serve as quality checks on their solutions;in the ideal case, the VQE upper bound and the dual-VQE lower bound form an interval containing the true optimal value of the ground-state energy. The idea behind dual-VQE is to employ semi-definite programming duality to rewrite the ground-state optimization problem as a constrained maximization problem, which itself can be bounded from below by an unconstrained optimization problem to be solved by a variational quantum algorithm. When using a convex combination ansatz in conjunction with a classical generative model, the quantum computational resources needed to evaluate the objective function of dual-VQE are no greater than those needed for that of VQE. We simulated the performance of dual-VQE on the transverse-field Ising model, and found that, for the example considered, while dual-VQE training is slower and noisier than VQE, it approaches the true value with error of order 10−2 Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum computers

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Training Differentially Private Models with Secure Multiparty Computation

arXiv

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arXiv 2022年

作者： Pentyala, Sikha Railsback, Davis Maia, Ricardo J.M. Melanson, David Dowsley, Rafael Nascimento, Anderson De Cock, Martine School of Engineering and Technology University of Washington Tacoma United States Department of Computer Science University of Brasilia Brazil Department of Software Systems and Cybersecurity Monash University Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Mila - Quebec Ai Institute Canada

We address the problem of learning a machine learning model from training data that originates at multiple data owners, while providing formal privacy guarantees regarding the protection of each owner's data. Existing solutions based on Differential Privacy (DP) achieve this at the cost of a drop in accuracy. Solutions based on Secure Multiparty Computation (MPC) do not incur such accuracy loss but leak information when the trained model is made publicly available. We propose an MPC solution for training DP models. Our solution relies on an MPC protocol for model training, and an MPC protocol for perturbing the trained model coefficients with Laplace noise in a privacy-preserving manner. The resulting MPC+DP approach achieves higher accuracy than a pure DP approach, while providing the same formal privacy guarantees. Our work obtained first place in the iDASH2021 Track III competition on confidential computing for secure genome analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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PARTITIONS AND ELEMENTARY SYMMETRIC POLYNOMIALS - AN EXPERIMENTAL APPROACH

arXiv

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arXiv 2024年

作者： Ballantine, Cristina Beck, George Merca, Mircea Department of Mathematics and Computer Science College of the Holy Cross WorcesterMA01610 United States Department of Mathematics and Statistics Dalhousie University HalifaxNSB3H 4R2 Canada Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center University Politehnica of Bucharest BucharestRO-060042 Romania

Given a partition λ, we write ej(λ) for the jth elementary symmetric polynomial ej evaluated at the parts of λ and ejpA(n) for the sum of ej(λ) as λ ranges over the set of partitions of n with parts in A. For ejpA(n), we prove analogs of the classical formula for the partition function, (formula presented), where σ1 is the sum of divisors function. We prove several congruences for e2p4(n), the sum of e2 over the set of partitions of n into four parts. Define the function prej(λ) to be the multiset of monomials in ej(λ), which is itself a partition. If A is a set of partitions, we define prej(A) to be the set of partitions prej(λ) as λ ranges over A. If P(n) is the set of all partitions of n, we conjecture that the number of odd partitions in pre2(P(n)) is at least the number of distinct partitions. We prove some results about pre2(B(n)), where B(n) is the set of binary partitions of n. We conclude with conjectures on the log-concavity of functions related to ejp(n), the sum of ej(λ) for all λ ∈ P(n).MSC Codes 05E05, 11P81 © 2024, CC BY.

关键词： Polynomials

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Weighted dynamic computer generated holography for 3D image display with a commercial video projector

Weighted dynamic computer generated holography for 3D image ...

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Digital Holography and Three-Dimensional Imaging, DH 2021 - Part of OSA Imaging and applied Optics Congress 2021

作者： Xu, Jiayi Curtis, Vincent R. Caira, Nicholas W. Pégard, Nicolas C. Department of Computer Science University of North Carolina Chapel HillNC United States Department of Applied Physical Sciences University of North Carolina Chapel HillNC United States Department of Biomedical Engineering University of North Carolina Chapel HillNC United States

We have developed a new algorithm to generate 3D holograms with a MEMSbased display device and a laser light source. Our technique rapidly superimposes mutually optimized coherent frames by encoding them into 8-bit RG... 详细信息

ISBN: (纸本)9781557528209

关键词： computer generated holography

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Selective-Area Growth of Gan and Algan Nanowires on N-Polar Gan Templates with 4° Miscut by Plasma-Assisted Molecular Beam Epitaxy

SSRN

引用

SSRN 2022年

作者： Khan, Kamruzzaman Jian, Zhe Li, Jonathan Sun, Kai Ahmadi, Elaheh Department of Materials Science and Engineering University of Michigan Ann Arbor United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Applied Physics Program University of Michigan Ann Arbor United States

In this study, selective area growth (SAG)s of AlGaN/GaN nanowires (NWs) on miscut N-polar GaN templates were studied and compared with that grown on Ga-polar templates using plasma-assisted molecular beam epitaxy (MBE). The SAG of N-polar AlGaN/GaN NWs demonstrated higher growth rate and more growth selectivity than that of Ga-polar AlGaN/GaN NWs. Additionally, lateral growth rate of N-polar NWs was significantly lower than Ga-polar NWs. Compared to the pyramidal shaped top surfaces of Ga-polar NWs, the N-polar GaN NWs had flat head top surfaces. The combination of higher growth selectivity, larger growth window, negligible lateral growth, and flat head, makes N-polar (Al,Ga)N NWs attractive for a variety of applications including UV LEDs and detectors as well as quantum sensing. © 2022, The Authors. All rights reserved.

关键词： Aluminum gallium nitride

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