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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Revathi, V. Kavin, Balasubramanian Prabhu Thirumalraj, Arunadevi Gangadevi, E. Balusamy, Balamurugan Gite, Shilpa New Horizon College of Engineering Department of Applied Sciences - Physics Ring Road Bellandur Post Bengaluru India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Tamil Nadu Kattankulathur603 203 India K.Ramakrishnan College of Technology Department of Computer Science Engineering Tamil Nadu Trichy India Loyola College Department of Computer Science Chennai600034 India Delhi India Symbiosis Institute of Technology Pune Aiml Department Pune412115 India

ISBN: (纸本)9798350383522

One of the fastest-growing subfields of computer vision and picture categorization is fruit identification. The fruit industry could start using a standardised categorization system so that shoppers can easily distinguish between different types and quality levels of fruit. From a health perspective, fruit quality is essential. The task of classifying fruits is crucial in many commercial contexts. A supermarket cashier may use a fruit categorization system to better recognise the varieties of fruit and their associated costs. It might also be used to determine whether or not a given kind of fruit is suitable for a person's diet. The rising use of intelligent imaging equipment may be directly attributed to the proliferation and development of AI software. Deep learning methods, such as CNN, are now being used by researchers for picture categorization. CNN's superior performance may be achieved without using any manually-crafted characteristics, in contrast to the older methods. For automated categorization, it employs a plethora of filters that strip relevant visual information. Classifying fruits accurately is a challenge in the horticulture sector and calls for the knowledge of an expert. For accurately and quantitatively analysing fruits, the currently mentioned categorization techniques fall short. Therefore, it is beneficial to review the latest suggestions for categorizing fruits. To solve this problem, we need a computerised system that can automatically sort fruits into their respective categories. Deep learning models, such as the Restricted Boltzmann Machine (RBM) and the Adam based Deep Belief Network (DBN), are used in this assignment to categorise fruits according to the attributes that have been determined to be optimum and generated. The proposed approach has been shown to have effective accuracy and quantitative analysis outcomes, and this is just the beginning of this realisation. In addition, the suggested scheme's relatively strong computational mo

关键词： Deep Belief Network Fruits Classification Fruits Grading Handcrafted features Restricted Boltzmann Machine

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Thermolectricity in irradiated bilayer graphene flakes

arXiv

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arXiv 2024年

作者： Osuala, Cynthia Ihuoma Choudhary, Tanu Biswas, Raju K. Ganguly, Sudin Qu, Chunlei Maiti, Santanu K. Department of Physics Stevens Institute of Technology HobokenNJ07030 United States Department of Physics Faculty of Natural Sciences M. S. Ramaiah University of Applied Sciences Bengaluru560058 India Department of Physics School of Applied Sciences University of Science and Technology Meghalaya Ri-Bhoi793101 India Center for Quantum Science and Engineering Stevens Institute of Technology HobokenNJ07030 United States Physics and Applied Mathematics Unit Indian Statistical Institute 203 Barrackpore Trunk Road Kolkata700108 India

We present a comprehensive study on enhancing the thermoelectric (TE) performance of bilayer graphene (BLG) through irradiation with arbitrarily polarized light, focusing on AA- and AB-stacked configurations with zigzag edges. Utilizing a combination of tight-binding theory and density functional theory (DFT), we systematically analyze the impact of light irradiation on electronic and phononic transport properties. Light irradiation alters the electronic hopping parameters, creating an asymmetric transmission function, which significantly increases the Seebeck coefficient, thereby boosting the overall figure of merit (FOM). For the phononic contribution, DFT calculations reveal that AB-stacked BLG exhibits lower lattice thermal conductivity compared to AA-stacked, attributed to enhanced anharmonic scattering and phonon group velocity. The combined analysis shows that FOM exceeds unity in both stacking types, with notable improvements near the irradiation-induced gap. Additionally, we explore the dependence of FOM on the system dimensions and temperature, demonstrating that light-irradiated BLG holds great promise for efficient thermoelectric energy conversion and waste heat recovery. Our results show favorable responses over a wide range of irradiation parameters. These findings provide crucial insights into optimizing BLG for advanced TE applications through light-induced modifications. © 2024, CC BY.

关键词： Waste heat utilization

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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

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Computer Modeling in Engineering & sciences 2023年第7期136卷 241-255页

作者： Sanjiban Sekhar Roy Ching-Hsien Hsu Akash Samaran Ranjan Goyal Arindam Pande Valentina E.Balas School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung406Taiwan Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big DataFoshan UniversityFoshan528000China Medica Superspecialty Hospital Kolkata700099India Automation and Applied Informatics Aurel Vlaicu University of AradArad310130Romania

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor *** is a need to develop new technologies andmethods to deal with this *** researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a *** different types of angiograms are adopted for CAD *** paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art *** have collected the 2D X-ray images from the hospital,and the proposed model has been applied to *** augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s ***,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high *** the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.

关键词： Angiogram convolution neural network coronary artery disease diagnosis of CAD image segmentation

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Post-inflationary Leptogenesis and Dark Matter production: Metric versus Palatini formalism

arXiv

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arXiv 2024年

作者： Ghoshal, Anish Lalak, Zygmunt Pal, Supratik Porey, Shiladitya Institute of Theoretical Physics Faculty of Physics University of Warsaw ul. Pasteura 5 Warsaw02-093 Poland Physics and Applied Mathematics Unit Indian Statistical Institute 203 B.T. Road Kolkata700108 India Technology Innovation Hub on Data Science Big Data Analytics and Data Curation Indian Statistical Institute 203 B.T. Road Kolkata700108 India Department of Physics Novosibirsk State University Pirogova 2 Novosibirsk630090 Russia

We investigate production of non-thermal dark matter particles and heavy sterile neutrinos from inflaton during the reheating era, which is preceded by a slow-roll inflationary epoch with a quartic potential and non-minimal coupling (ξ) between inflaton and gravity. We compare our analysis between metric and Palatini formalism. For the latter, the tensor-to-scalar ratio, r, decreases with ξ. We find that for ξ = 0.5 and number of e-folds ∼ 60, r can be as small as ∼ O10−3 which may be validated at future reaches of upcoming CMB observation such as CMB-S4 etc. We identify the permissible range of Yukawa coupling yχ between inflaton and fermionic DM χ, to be O10−3.5 yχ O10−20 for metric formalism and O10−4 yχ O10−11 for Palatini formalism which is consistent with current PLANCK data and also within the reach of future CMB experiments. For the scenario of leptogenesis via the decay of sterile neutrinos produced from inflaton decay, we also investigate the parameter space involving heavy neutrino mass MN1 and Yukawa coupling yN1 of sterile neutrino with inflaton, which are consistent with current CMB data and successful generation of the observed baryon asymmetry of the universe via leptogenesis. In contrast to metric formalism, in the case of Palatini formalism, for successful leptogenesis to occur, we find that yN1 has a very narrow allowable range and is severely constrained from the consistency with CMB predictions. Copyright © 2024, The Authors. All rights reserved.

关键词： Galaxies

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(f, Γ)-Divergences: interpolating between f-divergences and integral probability metrics

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2022年第1期23卷 1816-1885页

作者： Jeremiah Birrell Paul Dupuis Markos A. Katsoulakis Yannis Pantazis Luc Rey-Bellet TRIPODS Institute for Theoretical Foundations of Data Science University of Massachusetts Amherst Amherst MA Division of Applied Mathematics Brown University Providence RI Department of Mathematics and Statistics University of Massachusetts Amherst Amherst MA Institute of Applied and Computational Mathematics Foundation for Research and Technology - Hellas Heraklion Greece

We develop a rigorous and general framework for constructing information-theoretic divergences that subsume both f-divergences and integral probability metrics (IPMs), such as the 1-Wasserstein distance. We prove under which assumptions these divergences, hereafter referred to as (f, Γ)-divergences, provide a notion of 'distance' between probability measures and show that they can be expressed as a two-stage mass-redistribution/mass-transport process. The (f, Γ)-divergences inherit features from IPMs, such as the ability to compare distributions which are not absolutely continuous, as well as from f-divergences, namely the strict concavity of their variational representations and the ability to control heavy-tailed distributions for particular choices of f. When combined, these features establish a divergence with improved properties for estimation, statistical learning, and uncertainty quantification applications. Using statistical learning as an example, we demonstrate their advantage in training generative adversarial networks (GANs) for heavy-tailed, not-absolutely continuous sample distributions. We also show improved performance and stability over gradient-penalized Wasserstein GAN in image generation.

关键词： f-divergences integral probability metrics Wasserstein metric variational representations GANs

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Oblique Instability of Quasi-Parallel Whistler Waves in the Presence of Cold and Warm Electron Populations

arXiv

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arXiv 2024年

作者： Roytershteyn, Vadim Delzanno, Gian Luca Holmes, Justin C. Space Science Institute BoulderCO United States T-5 Applied Mathematics and Plasma Physics Group Los Alamos National Laboratory Los AlamosNM United States

Whistler waves propagating nearly parallel to the ambient magnetic field experience a nonlinear instability due to transverse currents when the background plasma has a population of sufficiently low energy electrons. Intriguingly, this nonlinear process may generate oblique electrostatic waves, including whistlers near the resonance cone with properties resembling oblique chorus waves in the Earth's magnetosphere. Focusing on the generation of oblique whistlers, earlier analysis of the instability is extended here to the case where low-energy background plasma consists of both a"cold" population with energy of a few eV and a"warm" electron component with energy of the order of 100 eV. This is motivated by spacecraft observations in the Earth's magnetosphere where oblique chorus waves were shown to interact resonantly with the warm electrons. The main new results are: i) the instability producing oblique electrostatic waves is sensitive to the shape of the electron distribution at low energies. In the whistler range of frequencies, two distinct peaks in the growth rate are typically present for the model considered: a peak associated with the warm electron population at relatively low wavenumbers and a peak associated with the cold electron population at relatively high wavenumbers;ii) overall, the instability producing oblique whistler waves near the resonance cone persists (with a reduced growth rate) even in the cases where the temperature of the cold population is relatively high, including cases where cold population is absent and only the warm population is included;iii) particle-in-cell simulations show that the instability leads to heating of the background plasma and formation of characteristic plateau and beam features in the parallel electron distribution function in the range of energies resonant with the instability. The plateau/beam features have been previously detected in spacecraft observations of oblique chorus waves. However, they have been attribu

关键词： Distribution functions

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Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization

arXiv

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arXiv 2025年

作者： Wang, Shiyu Avagyan, Mariam Shen, Yihan Lamy, Arnaud Wang, Tingran Márka, Szabolcs Márka, Zsuzsa Wright, John Department of Electrical Engineering Columbia University New York United States Data Science Institute Columbia University New York United States Department of Computer Science Columbia University New York United States Department of Physics Columbia University New York United States Columbia Astrophysics Laboratory Columbia University New York United States Department of Applied Physics and Applied Mathematics Columbia University New York United States

Learned denoisers play a fundamental role in various signal generation (e.g., diffusion models) and reconstruction (e.g., compressed sensing) architectures, whose success derives from their ability to leverage low-dimensional structure in data. Existing denoising methods, however, either rely on local approximations that require a linear scan of the entire dataset or treat denoising as generic function approximation problems, often sacrificing efficiency and interpretability. We consider the problem of efficiently denoising a new noisy data point sampled from an unknown ddimensional manifold M ∈ D, using only noisy samples. This work proposes a framework for test-time efficient manifold denoising, by framing the concept of "learning-to-denoise" as "learning-to-optimize". We have two technical innovations: (i) online learning methods which learn to optimize over the manifold of clean signals using only noisy data, effectively "growing" an optimizer one sample at a time. (ii) mixed-order methods which guarantee that the learned optimizers achieve global optimality, ensuring both efficiency and near-optimal denoising performance. We corroborate these claims with theoretical analyses of both the complexity and denoising performance of mixed-order traversal. Our experiments on scientific manifolds demonstrate significantly improved complexity-performance tradeoffs compared to nearest neighbor search, which underpins existing provable denoising approaches based on exhaustive search. Copyright © 2025, The Authors. All rights reserved.

关键词： Nearest neighbor search

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TPOPT: EFFICIENT TRAINABLE TEMPLATE OPTIMIZATION ON LOW-DIMENSIONAL MANIFOLDS

arXiv

引用

arXiv 2023年

作者： Yan, Jingkai Wang, Shiyu Wei, Xinyu Rain Wang, Jimmy Márka, Zsuzsanna Márka, Szabolcs Wright, John Department of Electrical Engineering Columbia University United States Department of Computer Science Columbia University United States Department of Physics Columbia University United States Data Science Institute Columbia University United States Columbia Astrophysics Laboratory Columbia University United States Department of Applied Physics and Applied Mathematics Columbia University United States Department of Mathematics Columbia University United States

In scientific and engineering scenarios, a recurring task is the detection of low-dimensional families of signals or patterns. A classic family of approaches, exemplified by template matching, aims to cover the search space with a dense template bank. While simple and highly interpretable, it suffers from poor computational efficiency due to unfavorable scaling in the signal space dimensionality. In this work, we study TpopT (TemPlate OPTimization) as an alternative scalable framework for detecting low-dimensional families of signals which maintains high interpretability. We provide a theoretical analysis of the convergence of Riemannian gradient descent for TpopT, and prove that it has a superior dimension scaling to covering. We also propose a practical TpopT framework for nonparametric signal sets, which incorporates techniques of embedding and kernel interpolation, and is further configurable into a trainable network architecture by unrolled optimization. The proposed trainable TpopT exhibits significantly improved efficiency-accuracy tradeoffs for gravitational wave detection, where matched filtering is currently a method of choice. We further illustrate the general applicability of this approach with experiments on handwritten digit data. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

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Description of elastic scattering induced by the unstable nuclei^(9,10,11,13,14)C

引用

Chinese physics C 2021年第11期45卷 122-127页

作者： Yong-Li Xu Yin-Lu Han Xin-Wu Su Xiao-Jun Sun Hai-Ying Liang Hai-Rui Guo Chong-Hai Cai College of Physics and Electronic Science Shanxi Datong UniversityDatong 037009China Key Laboratory of Nuclear Data China Institute of Atomic EnergyP.O.BOX(275-41)Beijing 102413China College of Physics Guangxi Normal UniversityGuilin 541004China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China Institute of Applied Physics and Computational Mathematics Beijing 100094China Department of Physics Nankai UniversityTianjin 300071China

The elastic-scattering angular distributions and total reaction cross sections of ^(9,10,11,13,14)C projectiles were predicted using the obtained ^(12)C and ^(9)Be global optical model potentials,*** predictions were analyzed in detail by comparison with the available experimental *** results indicate that the ^(12)C and ^(9)Be global optical model potentials provide a satisfactory description of the elastic scattering data for the reactions induced by the ^(9,10,11,13)*** the neutron-rich carbon isotope ^(14)C,the elastic scattering can be well described by changing the real part radius parameter of the ^(12)C global optical model *** physical explanations for the observed differences are further discussed.

关键词： optical model potential elastic-scattering angular distribution reaction cross section

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ANALYSIS ON A NUMERICAL SCHEME WITH SECOND-ORDER TIME ACCURACY FOR NONLINEAR DIFFUSION EQUATIONS

引用

Journal of Computational mathematics 2021年第5期39卷 777-800页

作者： Xia Cui Guangwei Yuan Fei Zhao Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsP.O.Box 8009-26Beijing 100088China College of Science North China University of TechnologyBeijing 1OO144China Graduate School of China Academy of Engineering Physics Beijing 100088China

A nonlinear fully implicit finite difference scheme with second-order time evolution for nonlinear diffusion problem is *** scheme is constructed with two-layer coupled discretization(TLCD)at each time *** does not stir numerical oscillation,while permits large time step length,and produces more accurate numerical solutions than the other two well-known second-order time evolution nonlinear schemes,the Crank-Nicolson(CN)scheme and the backward difference formula second-order(BDF2)*** developing a new reasoning technique,we overcome the difficulties caused by the coupled nonlinear discrete diffusion operators at different time layers,and prove rigorously the TLCD scheme is uniquely solvable,unconditionally stable,and has second-order convergence in both s-pace and *** tests verify the theoretical results,and illustrate its superiority over the CN and BDF2 schemes.

关键词： Nonlinear diffusion problem Nonlinear two-layer coupled discrete scheme Second-order time accuracy Property analysis Unique existence Convergence

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