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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Vinod Kumar Isha Jain Durga Sritha Dongla Shivangi Rai Vani Rastogi Eva Kaushik Rohit Kaushik School of Engineering & Applied Science The George Washington University Washington D.C USA Computer Science & Engineering Department Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Computer Science & Engineering Department Gandhi Institute of Technology and Management Visakhapatnam Andhra Pradesh Data Scientist DXC Technology Noida India CS and Mathematics Department Data Analytics Program University of Illinois Springfield USA

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions.

关键词： Deep learning Pathology Accuracy Melanoma Computer architecture Benchmark testing Skin Convolutional neural networks Lesions Medical diagnosis

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Machine-learning-based non-Newtonian fluid model with molecular fidelity

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Physical Review E 2020年第4期102卷 043309-043309页

作者： Huan Lei Lei Wu Weinan E Department of Computational Mathematics Science & Engineering and Department of Statistics & Probability Michigan State University East Lansing Michigan 48824 USA Department of Mathematics and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

We introduce a machine-learning-based framework for constructing continuum a non-Newtonian fluid dynamics model directly from a microscale description. Dumbbell polymer solutions are used as examples to demonstrate the essential ideas. To faithfully retain molecular fidelity, we establish a micro-macro correspondence via a set of encoders for the microscale polymer configurations and their macroscale counterparts, a set of nonlinear conformation tensors. The dynamics of these conformation tensors can be derived from the microscale model, and the relevant terms can be parametrized using machine learning. The final model, named the deep non-Newtonian model (DeePN2), takes the form of conventional non-Newtonian fluid dynamics models, with a generalized form of the objective tensor derivative that retains the microscale interpretations. Both the formulation of the dynamic equation and the neural network representation rigorously preserve the rotational invariance, which ensures the admissibility of the constructed model. Numerical results demonstrate the accuracy of DeePN2 where models based on empirical closures show limitations.

关键词： Learning Neuronal networks Complex fluids Non-Newtonian fluids Rheology Viscoelasticity Multiscale modeling

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Erratum: “Poisson stochastic master equation unravelings and the measurement problem: A quantum stochastic calculus perspective” [J. Math. Phys. 61, 032101 (2020)]

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Journal of Mathematical Physics 2022年第10期63卷

作者： Dustin Keys Jan Wehr 1Program in Applied Mathematics University of Arizona Tucson Arizona 85721 USA 2Department of Mathematics and Program in Applied Mathematics University of Arizona Tucson Arizona 85721 USA

The article was published in March of 2020.1 There is an error in the Hudson–Parthasarathy equation, first appearing as Eq. (15). The equation should read

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Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications

arXiv

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arXiv 2024年

作者： Iguchi, Yuga Jasra, Ajay Maama, Mohamed Beskos, Alexandros Department of Statistical Science University College London LondonWC1E 6BT United Kingdom School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Motivated by recent developments, we introduce a new MLMC estimator of expectations, which does not require simulation of intractable Lévy areas but has a weak error of order 2 and achieves the optimal computational complexity. We then show how this approach can be used in the context of the filtering problem associated to partially observed diffusions with discrete time observations. We illustrate with numerical simulations that our new approaches provide efficiency gains for several problems relative to some existing methods. © 2024, CC BY.

关键词： Diffusion

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Magnon squeezing enhanced ground-state cooling in cavity magnomechanics

arXiv

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arXiv 2022年

作者： Asjad, M. Li, Jie Zhu, Shi-Yao You, J.Q. Department of Applied Mathematics and Sciences Khalifa University Abu Dhabi127788 United Arab Emirates Interdisciplinary Center of Quantum Information State Key Laboratory of Modern Optical Instrumentation Zhejiang Province Key Laboratory of Quantum Technology and Device Department of Physics Zhejiang University Hangzhou310027 China

Cavity magnomechanics has recently become a new platform for studying macroscopic quantum phenomena. The magnetostriction induced vibration mode of a large-size ferromagnet or ferrimagnet reaching its ground state represents a genuine macroscopic quantum state. Here we study the ground-state cooling of the mechanical vibration mode in a cavity magnomechanical system, and focus on the role of magnon squeezing in improving the cooling efficiency. The magnon squeezing is obtained by exploiting the magnon self-Kerr nonlinearity. We find that the magnon squeezing can significantly and even completely suppress the magnomechanical Stokes scattering. It thus becomes particularly useful in realizing ground-state cooling in the unresolved-sideband regime, where the conventional sideband cooling protocols become inefficient. We also find that the coupling to the microwave cavity plays only an adverse effect in mechanical cooling. This makes essentially the two-mode magnomechanical system (without involving the microwave cavity) a preferred system for cooling the mechanical motion, in which the magnon mode is established by a uniform bias magnetic field and a microwave drive field. © 2022, CC0.

关键词： Cooling

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Approximation of Koopman Operators: Irregular Domains and Positive Orbits

Approximation of Koopman Operators: Irregular Domains and Po...

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IEEE Conference on Decision and Control

作者： Nathan Powell Roger Liu Jia Guo Sai Tej Paruchuri John Burns Boone Estes Andrew J. Kurdila Department of Mechanical Engineering Virginia Tech Blacksburg VA USA School of Electrical and Computer Engineering Georgia Tech Atlanta GA USA Department of Mechanical Engineering and Mechanics Lehigh University Bethlehem PA USA Interdisciplinary Center for Applied Mathematics (ICAM) Virginia Tech Blacksburg VA USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper, we derive rates of convergence of a class of approximations of the Koopman operator that are obtained using the extended domain decomposition (EDMD) method. The paper extends the previous approaches that consider the case in which samples become dense in or approach a smooth Riemannian manifold or a regular subset of ℝ d . Here, we extend these results to the case where samples become dense in some compact subset of the state space without requiring the common regularity properties. Rates of convergence of approximations of the Koopman operator are described in terms of approximation spaces, spectral approximation spaces, and in terms of the power function of the kernel of a reproducing kernel Hilbert space $\mathcal{H}$. A numerical example is presented to demonstrate the results.

关键词： Space vehicles Manifolds Orbits Hilbert space Computational efficiency Kernel Convergence

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Generating large disordered stealthy hyperuniform systems with ultrahigh accuracy to determine their physical properties

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Physical Review Research 2023年第3期5卷 033190-033190页

作者： Peter K. Morse Jaeuk Kim Paul J. Steinhardt Salvatore Torquato Department of Chemistry Princeton University Princeton New Jersey 08544 USA Department of Physics Princeton University Princeton New Jersey 08544 USA Princeton Institute of Materials Princeton University Princeton New Jersey 08544 USA Princeton Center for Theoretical Science Princeton University Princeton New Jersey 08544 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

Hyperuniform many-particle systems are characterized by a structure factor S(k) that is precisely zero as |k|→0; and stealthy hyperuniform systems have S(k)=0 for the finite range 0<|k|≤K, called the “exclusion region.” Through a process of collective-coordinate optimization, energy-minimizing disordered stealthy hyperuniform systems of moderate size have been made to high accuracy, and their novel physical properties have shown great promise. However, minimizing S(k) in the exclusion region is computationally intensive as the system size becomes large. In this paper, we present an improved methodology to generate such states using double-double precision calculations on graphical processing units (GPUs) that reduces the deviations from zero within the exclusion region by a factor of approximately 1030 for system sizes more than an order of magnitude larger. We further show that this ultrahigh accuracy is required to draw conclusions about their corresponding characteristics, such as the nature of the associated energy landscape and the presence or absence of Anderson localization, which might be masked, even when deviations are relatively small.

关键词： Exotic phases of matter

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Utilizing probabilistic entanglement between sensors in quantum networks

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Physical Review applied 2024年第6期22卷 064085-064085页

作者： Emily A. Van Milligen Christos N. Gagatsos Eneet Kaur Don Towsley Saikat Guha Department of Physics The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Department of Electrical and Computer Engineering The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Wyant College of Optical Sciences The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Program in Applied Mathematics The University of Arizona Tucson Arizona 85721 USA Cisco Quantum Lab Los Angeles USA College of Computer Science University of Massachusetts Amherst Massachusetts USA Department of Electrical and Computer Engineering The University of Maryland College Park Maryland 20742 USA

One of the most promising applications of quantum networks is entanglement-assisted sensing. The field of quantum metrology exploits quantum correlations to improve the precision bound for applications such as precision timekeeping, field sensing, and biological imaging. When measuring multiple spatially distributed parameters, current literature focuses on quantum entanglement in the discrete-variable case and quantum squeezing in the continuous-variable case, distributed amongst all of the sensors in a given network. However, it can be difficult to ensure that all sensors preshare entanglement of sufficiently high fidelity. This work probes the space between fully entangled and fully classical sensing networks by modeling a star network with probabilistic entanglement generation that is attempting to estimate the average of local parameters. The quantum Fisher information is used to determine which protocols best utilize entanglement as a resource for different network conditions. It is shown that without entanglement distillation there is a threshold fidelity below which classical sensing is preferable. For a network with a given number of sensors and links characterized by a certain initial fidelity and probability of success, this work outlines when and how to use entanglement, when to store it, and when it needs to be distilled.

关键词： Quantum entanglement

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Business confidence in the shift to renewable energy: A country-specific assessment in major Asian economies

Journal of Economy and Technology

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Journal of Economy and Technology 2025年 3卷 44-68页

作者： Irsan Hardi Ghalieb Mutig Idroes Yoshihiro Hamaguchi Muhlis Can Teuku Rizky Noviandy Rinaldi Idroes Department of Economics Faculty of Economics and Business Universitas Syiah Kuala Banda Aceh Indonesia Economic Modeling and Data Analytics Unit Graha Primera Saintifika Aceh Besar Indonesia Energy and Green Economics Unit Graha Primera Saintifika Aceh Besar Indonesia Department of Economics Faculty of Economics and Business Administration Kyoto University of Advanced Science Kyoto Japan Social Science Research Lab BETA Akademi Istanbul Turkey Interdisciplinary Innovation Research Unit Graha Primera Saintifika Aceh Besar 23371 Indonesia School of Mathematics and Applied Sciences Universitas Syiah Kuala Banda Aceh 23111 Indonesia

The growing awareness of the importance of transitioning to sustainable energy sources emphasizes the necessity of fostering business optimism toward renewable energy, as businesses wield significant influence in driving innovation and scaling up renewable energy deployment. Therefore, this study investigates the impact of business confidence on long-term renewable energy generation in selected major Asian economies: China, Japan, South Korea, Indonesia, and Turkey. Through country-specific assessments, we utilized three methods capable of yielding long-term empirical results: Fully-Modified OLS (FMOLS), Dynamic OLS (DOLS), and Canonical Cointegrating Regressions (CCR). The study also conducted a robustness check by utilizing the Robust Least Squares (RLS) method, preceded by multiple preliminary tests, to ensure the validity and reliability of the results. The findings show that businesses in all selected countries exhibit confidence toward long-term renewable energy. However, there are variations in the confidence level, with businesses in Japan, South Korea, and Turkey demonstrating high confidence while those in China and Indonesia show low confidence. The study also found a trade-off between business confidence levels and energy consumption. In Japan, South Korea, and Turkey, high business confidence correlates with a negative impact of energy consumption, while in Indonesia, low business confidence is aligned with a positive effect of energy consumption on renewable energy. This suggests that business confidence and energy consumption dynamics influence renewable energy development. Policy recommendations tailored to each of the selected countries are provided to address these findings, aiming to enhance business trust and optimism in renewable energy within major Asian economies.

关键词： Renewable Energy Business Confidence China Japan South Korea Indonesia Turkey

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Principal Curvatures Estimation with Applications to Single Cell Data

Principal Curvatures Estimation with Applications to Single ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yanlei Zhang Lydia Mezrag Xingzhi Sun Charles Xu Kincaid Macdonald Dhananjay Bhaskar Smita Krishnaswamy Guy Wolf Bastian Rieck Dept. of Math. & Stat. Université de Montréal Mila – Quebec AI Institute Montréal CA Dept. of Comp. Sci. Yale University Dept. of Genetics Applied Mathematics Program New Haven CT USA Helmholtz Zentrum München Institute of AI for Health Munich BY DE Department of Informatics Université de Fribourg Fribourg CH

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datasets. A common method in manifold learning consists in hypothesizing that datasets lie on a lower dimensional manifold. This allows to study the geometry of point clouds by extracting meaningful descriptors like curvature. In this work, we will present Adaptive Local PCA (AdaL-PCA), a data-driven method for accurately estimating various notions of intrinsic curvature on data manifolds, in particular principal curvatures for surfaces. The model relies on local PCA to estimate the tangent spaces. The evaluation of AdaL-PCA on sampled surfaces shows state-of-the-art results. Combined with a PHATE embedding, the model applied to single-cell RNA sequencing data allows us to identify key variations in the cellular differentiation.

关键词： Manifolds Sequential analysis Adaptation models Data analysis RNA Transcriptomics Curve fitting Speech processing Tuning Principal component analysis

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