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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Xie, Zetao Chen, Zeling Li, Hao Yan, Qinghui Chen, Hongsheng Lin, Xiao Kaminer, Ido Miller, Owen D. Yang, Yi Department of Physics HK Institute of Quantum Science and Technology The University of Hong Kong Pokfulam Hong Kong Department of Applied Physics Energy Sciences Institute Yale University New HavenCT06520 United States Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Science and Technology Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Solid State Institute Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa32000 Israel

ISBN: (纸本)9781957171395

Free-electron quantum optics facilitates electron-photon entanglement for quantum information processing. The challenge is finding specialized photonic-structures optimizing electron-photon interactions. We present a first-principle upper limit on the interaction strength, guiding the design of future structures. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Quantum optics

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Concrete Structure Defect Detection Using You Only Look Once Version 5 with AlexNet

Concrete Structure Defect Detection Using You Only Look Once...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： T Devi. Hassan Mohamed Ali Zaid Alsalami S. Senthil kumar K. Sangeetha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Concrete structures are made from cement, water, and admixtures, which are used in buildings, water management systems, and foundations. There are many face pressing issues such as corrosion, cracking, and chemical attacks, requiring innovative solutions for sustainable infrastructure. Various methods have been proposed previously to recognize the defects of concrete structures. To overcome these problems, a Deep Learning (DL) model of You Only Look Once version 5 with Alex-Krizhevsky Network (YOLOv5- AlexNet) is proposed for detecting the defects of concrete structures. Primarily, a Surface Crack Detection (SCD) dataset is considered as an input for recognizing the defects of concrete structures. For preprocessing, image resizing and normalization, data augmentation, contrast enhancement, image filtering, and image binarization are considered for efficient processing, enhancing robustness, improving crack visibility, edge detection, and to simplify crack detection. Then for feature extraction, U-shaped Network (U-Net) is employed to reduce noise sensitivity, and to fine-tune the model. A YOLOv5-AlexNet is considered for classification to improve performance and generalizability, and random search for hyperparameter tuning. The proposed YOLOv5-AlexNet model attained higher performance metrics such as accuracy, precision, recall, and f1 score of 99.57%, 98.23%, 98.65%, and 99.86%, when compared to other existing methods like Convolutional Neural Network (CNN).

关键词： YOLO Accuracy Sensitivity Noise Feature extraction Data augmentation Robustness Image filtering Convolutional neural networks Tuning

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Revolutionizing Cancer Diagnosis with Deep Learning: A Case Study Using U-Net

Revolutionizing Cancer Diagnosis with Deep Learning: A Case ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Himanshu Rai Goyal Mohammed Al-Farouni A. Deepak R. Akhilesh Reddy Kanchan Yadav V Revathi Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana Department of Electrical Engineering GLA University Mathura Department of Applied Sciences New Horizon College of Engineering Bangalore

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The U-Net architecture is a convolutional neural network model that is well-known for its skills in biomedical image segmentation. This study investigates the revolutionary potential that the U-Net architecture presents in the context of cancer diagnosis. We guarantee that the input is of high quality by using sophisticated preprocessing methods, which we do by constructing a robust $\mathbf{U}$-Net-based system that is specifically designed for the segmentation and classification of tumour regions in medical imaging data. An exhaustive examination that makes use of criteria such as the Dice similarity coefficient, precision, recall, and $F 1$ score indicates that our model performs substantially better than conventional techniques when it comes to identifying and distinguishing malignant tissues. Case studies that cover a wide range of cancer types provide further validation of the flexibility and practical application of the system. These studies also highlight the significant influence that deep learning has on increasing diagnosis accuracy, lowering the risk of human error, and overall patient outcomes in the field of cancer treatment.

关键词： Deep learning Image segmentation Accuracy Liver computer architecture Brain modeling Convolutional neural networks Cancer Tumors Biomedical imaging

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Artificial Intelligence Methods to Support People Management in Organizations

Artificial Intelligence Methods to Support People Management...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： Prashant Ashok Sonawane Krishna Nand Mishra P. Raman Kishor Kumar Nilesh G. Jawarkar Sunanda Ratna. Kandavalli Department of Commerce Gondwana University Gadchiroli Department of Computer Science Radha Govind University Ramgarh Jharkhand India Department of Management Studies Panimalar Engineering College Poonamallee Chennai Tamil Nadu India Department of Applied Mathematics Delhi Technological University Delhi India Department of Mechanical Engineering JSPM' Rajarshi Shahu College of Engineering Pune Department of Electronics & Communication Engineering Sri Padmavati Mahila Visva Vidyalayam Tirupati Andhra Pradesh India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

The research paper showcases in-depth research on AI technology and its direct impact on organizations and people. Along with this research paper also discussed how AI technology can support organizations in streamlining their business processes, improving their decision-making skills, enhancing staff engagement, and raising the rate of production with the help of machine learning and natural language processing. Besides this, it is examined in this paper that AI technology can also be applied to automate the entire screening method, shorten the timetable, and manage the available resources needed for new staff recruitment.

关键词： Training Surveys Technological innovation Sentiment analysis Machine learning algorithms Organizations Machine learning Regulation Artificial intelligence Recruitment

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Revisiting two well studied galaxy samples using alternative gravity 12

Revisiting two well studied galaxy samples using alternative...

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12th Biennial Conference on Classical and Quantum Relativistic Dynamics of Particles and Fields, IARD 2020

作者： O'Brien, James G. Chiarelli, Thomas L. Kerin, William M. Belanger, Jon Department of Mathematics Physics and Computer Science Springfield College SpringfieldMA01109 United States Department of Electromechanical Engineering Wentworth Institute of Technology BostonMA02115 United States Department of Applied Mathematics Wentworth Institute of Technology BostonMA02115 United States

In the last few years, alternative gravity theories have seen increased interest due to the lack of observational evidence of dark matter. Further, new empirical patterns found in rotation curve data such as the Radial Acceleration Rule (RAR) have given new testable features for gravitational theories. In this paper, we revisit two popular surveys of galaxies (Randriamampandry et al 2013 and Bottema et al 2015) which when published were shown to be problematic for alternative gravity. Here, we apply the most recent observational parameters to the surveys and provide fits of Conformal Gravity, MOND as well as the RAR rotation curve formalism and show how these theories can apply to the new findings. We also provide the fits to the RAR and Tully-Fisher relation for each theory and discuss how the RAR may allow for some confining of parameters in the fitting procedure. © Published under licence by IOP Publishing Ltd.

关键词： Gravitation

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Neural network developed for obstacle avoidance of the four wheeled electric vehicle

Neural network developed for obstacle avoidance of the four ...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Grazia Lo Sciuto Paweł Kowol Paweł Nowak Wacław Banás Salvo Coco Giacomo Capizzi Department of Mechatronics Silesian University of Technology Gliwice Poland Department of Electrical Electronics and Informatics Engineering University of Catania Catania Italy Department of Automatic Control and Robotic Silesian University of Technology Gliwice Poland Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice Poland

Several solutions have been proposed for vehicle navigation in unknown environments to ensure safer driving by avoiding obstacles and collisions. In this work, an electric vehicle equipped with Mecanum 4 omnidirectional holonomic wheels, analog joystick to control driving movement, LiDAR sensors and Wi-Fi able to detect obstacles is investigated based on neural network approach. By experimental tests conducted to avoid collisions by detecting the presence of obstacles, the relation between the resistance distance and speed is described by the artificial neural network.

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Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback 41

Position: Social Choice Should Guide AI Alignment in Dealing...

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41st International Conference on Machine Learning, ICML 2024

作者： Conitzer, Vincent Freedman, Rachel Heitzig, Jobst Holliday, Wesley H. Jacobs, Bob M. Lambert, Nathan Mossé, Milan Pacuit, Eric Russell, Stuart Schoelkopf, Hailey Tewolde, Emanuel Zwicker, William S. Foundations of Cooperative AI Lab Computer Science Department Carnegie Mellon University Pittsburgh United States Institute for Ethics in AI University of Oxford Oxford United Kingdom Center for Human-Compatible AI Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Potsdam Institute for Climate Impact Research Brandenburg Potsdam Germany Department of Philosophy University of California Berkeley United States Department of Philosophy and Moral Sciences Ghent University Ghent Belgium Allen Institute for AI BerkeleyCA United States Department of Philosophy University of Maryland College Park United States EleutherAI United States Department of Mathematics Union College Schenectady United States Murat Sertel Center for Advanced Economic Studies Istanbul Bilgi University Istanbul Turkey

Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans' expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How can we aggregate the input into consistent data about "collective" preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December 2023. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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Giant Deep-Ultraviolet Circular Dichroism in Synthetic Architectures of Ordered Carbon Nanotubes

Giant Deep-Ultraviolet Circular Dichroism in Synthetic Archi...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Doumani, Jacques Lou, Minhan Dewey, Oliver Hong, Nina Fan, Jichao Baydin, Andrey Pasquali, Matteo Yomogida, Yohei Yanagi, Kazuhiro Saito, Riichiro Kono, Junichiro Gao, Weilu Department of Electrical and Computer Engineering Rice University HoustonTX United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Carbon Hub Rice University HoustonTX United States Department of Chemical and Biomolecular Engineering Rice University HoustonTX United States J.A. Woollam Co. LincolnNE United States The Smalley-Curl Institute Rice University HoustonTX United States Department of Chemistry Rice University HoustonTX United States Department of Materials Science and NanoEngineering Rice University HoustonTX United States Department of Physics and Astronomy Tokyo Metropolitan University Tokyo Japan Department Physics Tohoku University Sendai Japan Department Physics and Astronomy Rice University HoustonTX United States

We demonstrate tunable, giant, and structure-induced deep-ultraviolet circular dichroism in macroscopically chiral assemblies of racemic carbon nanotubes prepared using two approaches: mechanical-rotation-assisted vac... 详细信息

ISBN: (纸本)9781957171258

关键词： Dichroism

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Enabling All-to-Circular Polarization Up-Conversion by Nonlinear Chiral Metasurfaces with Rotational Symmetry

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Physical Review Letters 2025年第2期134卷 023804-023804页

作者： Dmitrii Gromyko Jun Siang Loh Jiangang Feng Cheng-Wei Qiu Lin Wu Science Mathematics and Technology (SMT) Singapore University of Technology and Design (SUTD) 8 Somapah Road 487372 Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 117583 Singapore Singapore Institute of High Performance Computing ASTAR (Agency for Science Technology and Research) 1 Fusionopolis Way No. 16-16 Connexis 138632 Singapore Singapore

We introduce a stacking strategy to design nonlinear chiral metasurfaces with high rotational symmetry, enabling degenerate quasi-bound-in-the-continuum resonances of absolute chirality. This symmetry allows for converting a circularly polarized pump into a circularly polarized nonlinear signal. Consequently, our rotation-symmetric bilayered metasurface design, tailored to respond solely to one specific circular polarization, can up-convert a linear or arbitrary polarized pump into circularly polarized nonlinear signals. The intensity ratios of these signals scale as the fourth (second) power of the chiral resonance amplitude for the second (third) harmonic.

关键词： Circular polarization

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Enhancing Quality Control in the Age of Industry 4.0 through Real-Time Automation and Big Data Analysis

Enhancing Quality Control in the Age of Industry 4.0 through...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Nilesh Bhosale Shrishail B Sollapur Monali Ravindra Borade V.J. Vijayalakshmi Damanjeet Aulakh Pratik Kodmalwar Department of Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering Nagpur India Department of Aerospace Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of AI & Ds Vishwakarma Institute of Technology Pune India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Department of MBA Datta Meghe Institute of Management Studies India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Industry 4.0 has transformed industries, with intelligent and automated systems introduced into the manufacturing process on a larger scale. But this shift introduces problems of scale that still need to be solved using traditional quality control measures - manual inspections may take time and leave space for human error. Summary: This paper introduces a new solution for improving quality control in the era of Industry 4.0 using real-time automation and extensive data analysis as an abstract. This eliminates human error and ensures that all inspections are performed in the same way to produce consistent and accurate results. Moreover, the information from these automated activities can be examined in genuine time for repetition and real-time anomaly identification, helping to maintain adequate quality control action. To sum up, using real-time automation and big data analysis can significantly improve quality control by ensuring that products are sent off on time in top working conditions under Industry 4.0 methodological processes. This benefit is a better quality product and more productivity and cost-effectiveness for industries from manufacturing the products.

关键词： Industries Manufacturing processes Data analysis Quality control Big Data Inspection Real-time systems Time measurement Fourth Industrial Revolution Standards

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