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arXiv 2024年

作者： Chung, Shao-Hung Chen, Wei Jen, H.H. Institute of Atomic and Molecular Sciences Academia Sinica Taipei10617 Taiwan Department of Physics Center for Quantum Science and Engineering National Taiwan University Taipei10617 Taiwan Molecular Science and Technology Program Taiwan International Graduate Program Academia Sinica Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan

An atomic array coupled to a photonic crystal waveguide forms a strongly coupled quantum interface, exhibiting various intriguing collective features of quantum dynamics. Here we consider a homogeneous atomic array and theoretically investigate its steady-state distribution when the incident fields drive the atoms from both sides at asymmetric angles. This effectively creates an interface shared by two zones of atoms under different driving angles. This setup introduces a competition between photon-mediated dipole-dipole interactions and the directionality of coupling, while differences of the travelling phases from the incident angles further influence the overall steady-state behavior. Under this asymmetric driving scheme, the presence of strongly confined localization can be identified, where localization can occur either at the interface or at one of edges. Additionally, we examine the size effect on the atomic localization, deriving an empirical formula to predict parameter regimes that favor interfaced localization. We also consider a defect-driving scheme, where a third zone is created by undriven atoms under symmetric travelling phases. This results in strongly confined single-site excitation localization, which can be explained through analytical solutions under the reciprocal coupling. Finally, we propose several methods for precise control of multiple single-site localizations under the defect-driving scheme. Our results provide insights into driven-dissipative quantum systems with nonreciprocal couplings and pave the way for quantum simulation of exotic many-body states relevant to quantum information applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Photonic crystals

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Multimodal Sentiment Analysis using Kernel Based Support Vector Machine 2

Multimodal Sentiment Analysis using Kernel Based Support Vec...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Kumar, V Sunil Pareek, Piyush Kumar De Albuquerque, Victor Hugo Costa Khanna, Ashish Gupta, Deepak Madhumala, R.B. Nitte Meenakshi Institute of Technology Department Artificial Intelligence and Machine Learning Bengaluru India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Federal University of Ceará Department of Teleinformatics Engineering Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Dayananda Sagar Academy of Technology and Management Department of ISE Bangalore India

ISBN: (纸本)9781665493963

There has been a steady expansion in the number of social websites, electronic media outlets, and blogging services, all of which contribute a large number of user messages such as comments, reviews, and opinions from customers. The quantity of these signals, which are used to evaluate the quality of a wide variety of goods and services, is rather substantial. As a result, it is challenging to monitor and identify the feelings of customers. As a result, a method known as Sentiment Analysis (SA), also known as Opinion Mining (OM), was developed. This method gathers data from the internet by using Natural Language Processing (NLP) strategies and then analyses people's thoughts. It does this by extracting subjective information from the words, assessing opinions, and quantifying positive and negative evaluations. The end result is a conclusion about the specific entity, which may be an organisation, the public, or a product. The NLP first gathers the views, and then those opinions are sorted into positive, neutral, and negative categories. Both the marketing promotion and the customer's ability to choose the most appropriate product for their needs may be improved by doing an analysis. The purpose of this study is to evaluate the use of different machine learning and swarm intelligence optimization methods to perform sentiment analysis on data derived from movie reviews. This study helps many businesses increase their profits while simultaneously lowering the number of problems caused by a certain product. Within the scope of this investigation, three distinct models of sentiment analysis were investigated. An effective method for doing sentiment analysis known as a Hybrid Kernel based Support Vector Machine (HKSVM) has been presented. The preprocessing that is described here is meant to cut down on the data inconsistencies and get rid of the data that isn't needed. After that, the Expectation Maximization (EM) technique is used in order to identify an outlier. After t

关键词： Sentiment analysis

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Interactive Ultrasound Prostate Cancer Segmentation using Deep Learning with Principal Curve-based Fine-tuning

Interactive Ultrasound Prostate Cancer Segmentation using De...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Peng, Tao Mao, Hui Wang, Caishan Di, Gongye Hu, Ting Zhang, Lei Soochow University School of Future Science and Engineering Suzhou China Ut Southwestern Medical Center Department of Radiation Oncology DallasTX United States Huangshi Central Hospital Department of Dermatology Hubei China Second Affiliated Hospital of Soochow University Department of Ultrasound Suzhou China Medical University Taizhou People's Hospital Affiliated to Nanjing Department of Ultrasonic Taizhou China The First Affiliated Hospital of Soochow University Department of Obstetrics and Gynaecology Ultrasonography Suzhou China Duke Kunshan University Graduate Program of Medical Physics and Data Science Research Center Kunshan China

ISBN: (纸本)9798350337488

Segmentation of prostate cancer on ultrasound (US) images is challenging because of the low-intensity contrast around the region of interest (ROI) contour caused by the intestinal gas. To the best of our knowledge, our work is one of the extremely rare groups to segment prostate cancer on US images, which designs a coarse-to-refinement segmentation framework with four merits: 1) it fuses the advantages of the deep learning- and principal curve-based algorithms to locate the ROI and fit the data center automatically, respectively;2) a principal curve-based polygon searching method is designed by newly adding some constraints so that the performance of our method could be enhanced;3) the quantum characteristics are added into evolution network, while newly adding numerous- operator scheme and global optimum search schemes;4) an interpretable mathematical mapping model is developed to denote the ROI contour. Segmentation results show that our network outperforms other medical image segmentation models. © 2023 IEEE.

关键词： Automatic segmentation explainability-guided mathematical expression polygon searching quantum evolution network ultrasound prostate cancer image

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Interactive Facial Expression Editing with Non-linear Blendshape Interpolation

Interactive Facial Expression Editing with Non-linear Blends...

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43rd Annual Conference on European Association for Computer Graphics, EUROGRAPHICS 2022

作者： Roh, Ji Hyun Kim, Seong Uk Jang, Hanyoung Seol, Yeongho Kim, Jongmin Department of Computer Science and Engineering Kangwon National University Korea Republic of Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Korea Republic of Game AI Lab NC Korea Republic of NVIDIA Korea Republic of

ISBN: (纸本)9783038681694

The ability to manipulate facial animations interactively is vital for enhancing the productivity and quality of character animation. In this paper, we present a novel interactive facial animation editing system that can express the naturalness of non-linear facial movements in real-time. The proposed system is based on a fully automatic algorithm that maintains all positional constraints while deforming the facial mesh as realistic as possible. Our method is based on direct manipulation with non-linear blendshape interpolation. We formulate the facial animation editing as a two-step quadratic minimization and solve it efficiently. From our results, the proposed method produces the desired and realistic facial animation better compared to existing mesh deformation methods, which are mainly based on linear combination and optimization. © 2022 The Author(s).

关键词： Interpolation

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Diving into COVID-19: Visualizing SARS-CoV-2 Patient Proteins using Liquid-Electron Microscopy

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 662-664页

作者： Berry, Samantha DiCecco, Liza-Anastasia Gray, Jennifer Boylan, Jack Solares, Maria Kelly, Deborah F. Department of Biomedical Engineering Pennsylvania State University University Park PA United States Department of Materials Science and Engineering McMaster University Hamilton ON Canada Materials Research Institute Pennsylvania State University University Park PA United States Molecular Cellular Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park PA United States Huck Institutes of the Life Sciences Pennsylvania State University University Park PA United States

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When data disappear: public health pays as US policy strays

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The Lancet Digital Health 2025年 100874页

作者： McAndrew, Thomas Lover, Andrew A Hoyt, Garrik Majumder, Maimuna S Department of Biostatistics and Health Data Science College of Health Lehigh University BethlehemPA United States Department of Biostatistics and Epidemiology School of Public Health and Health Sciences University of Massachusetts Amherst AmherstMA United States Department of Computer Science and Engineering PC Rossin College of Engineering and Applied Sciences Lehigh University BethlehemPA United States Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children's Hospital BostonMA United States

Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources;and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good. © 2025 The Author(s)

关键词： Health insurance

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Prediction of Internet Broadband Connectivity Classification in West Java Indonesia using Machine Learning

Prediction of Internet Broadband Connectivity Classification...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Wahyu Haris Kusuma Atmaja Yaya Heryadi Ilvico Sonata Lukas Wayan Suparta Computer Science Department Binus Graduate Program - Computer Science Bina Nusantara University West Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Engineering Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect Internet connectivity in clusters of West Java, Indonesia. It also aims to evaluate the results of models that forecast how these internal factors will affect internet connectivity. According to the research findings, the FTTH Cluster has eight significant Internet predictor connectivity out of 18 elements. Activation delay and Administration Documentation of Activation with Connectivity were the two major parameters modeled out of the 18 components. Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree (DT) models were used to predict Internet Broadband Connectivity Classification based on the two most important variables. According to the study, ANN is the best model, with 99% accuracy and 99.6% precision when compared to SVM and DT. Furthermore, it exceeds previous studies that used the ANN model and achieved 97.92% accuracy.

关键词： Support vector machines Java Accuracy Web and internet services Artificial neural networks Predictive models Internet Broadband communication Optical fiber subscriber loops Robots

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Learning from 2D: machine learning of 3D effective properties of heterogeneous materials based on 2D microstructure sections

arXiv

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arXiv 2023年

作者： Hu, Guangyu Latypov, Marat I. Department of Materials Science and Engineering University of Arizona TucsonAZ85721 United States Graduate Interdisciplinary Program in Applied Mathematics University of Arizona TucsonAZ85721 United States

Microstructure–property relationships are key to effective design of structural materials for advanced applications. Advances in computational methods enabled modeling microstructure-sensitive properties using 3D models (e.g., finite elements) based on microstructure representative volumes. 3D microstructure data required as input to these models are typically obtained from either 3D characterization experiments or digital reconstruction based on statistics from 2D microstructure images. In this work, we present machine learning (ML) approaches to modeling effective properties of heterogeneous materials directly from 2D microstructure sections. To this end, we consider statistical learning models based on spatial correlations and convolutional neural networks as two distinct ML strategies. In both strategies, models are trained on a dataset of synthetically generated 3D microstructures and their properties obtained from micromechanical 3D simulations. Upon training, the models predict properties from 2D microstructure sections. The advantage of the presented models is that they only need 2D sections, whose experimental acquisition is more accessible compared to 3D characterization. Furthermore, the present models do not require digital reconstruction of 3D microstructures. © 2023, CC BY.

关键词： Microstructure

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Two-phase cost-sensitive-learning-based framework on customer-side quality inspection for TFT-LCD industry

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Journal of Intelligent Manufacturing 2024年

作者： Shih, Ming-Sung Chen, James C. Chen, Tzu-Li Hsu, Ching-Lan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu30013 Taiwan Graduate Institute of Intelligent Manufacturing Technology National Taiwan University of Science and Technology Taipei106335 Taiwan

The Covid-19 outbreak in 2020 boosted the stay-at-home economy, causing a surge in electronics industry demand, especially benefiting the LCD panel sector. However, as the pandemic situation improved, countries revised policies, leading to the gradual discontinuation of remote work arrangements in various industries. This resulted in declining dividends for the stay-at-home economy. The decreased demand created intense competition within the TFT-LCD industry, urging panel companies to prioritize product quality enhancement to meet customer expectations. Panel quality inspection heavily relied on manual labor, causing varying inspection levels due to subjective judgments. Understanding and aligning with customer expectations regarding product quality inspections became imperative. Identifying defective products during inspection led to additional costs for the companies. Balancing customer product quality requirements and re-inspection costs became crucial for optimal benefits. This study addresses the binary classification problem of customer-side quality inspection through cost-sensitive learning. The predictive model considers panel process yield, production history, customer feedback, inspection capacity constraints, and cost minimization to predict panel quality as accepted or defective. To tackle the highly imbalanced data, a two-phase cost-sensitive-learning-based framework is proposed, combining data preprocessing methods and models, while considering re-inspection capacity constraints and costs to enhance accuracy. The model’s evaluation uses key performance indicators like AUC and G-mean. Actual inspection cost and defective parts per million (DPPM) are calculated based on the company’s practical assessment. Two products are used for experimentation to validate the proposed model, demonstrating over 50% reduction in inspection cost and over 10% improvement in DPPM. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Spr

关键词： Inspection

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Large effective magnetic fields from chiral phonons in rare-earth halides

arXiv

引用

arXiv 2023年

作者： Luo, Jiaming Lin, Tong Zhang, Junjie Chen, Xiaotong Blackert, Elizabeth R. Xu, Rui Yakobson, Boris I. Zhu, Hanyu Department of Materials Science and Nano Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Rice University HoustonTX77005 United States

Time-reversal symmetry (TRS) is pivotal for materials’ optical, magnetic, topological, and transport properties. Chiral phonons, characterized by atoms rotating unidirectionally around their equilibrium positions, generate dynamic lattice structures that break TRS. Here we report that coherent chiral phonons, driven by circularly polarized terahertz light pulses, can polarize the paramagnetic spins in CeF3 like a quasi-static magnetic field on the order of 1 Tesla. Through time-resolved Faraday rotation and Kerr ellipticity, we found the transient magnetization is only excited by pulses resonant with phonons, proportional to the angular momentum of the phonons, and growing with magnetic susceptibility at cryogenic temperatures, as expected from the spin-phonon coupling model. The time-dependent effective magnetic field quantitatively agrees with that calculated from phonon dynamics. Our results may open a new route to directly investigate mode-specific spin-phonon interaction in ultrafast magnetism, energy-efficient spintronics, and non-equilibrium phases of matter with broken TRS. Copyright © 2023, The Authors. All rights reserved.

关键词： Phonons

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