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Compact and Thermal-Noise-Limited Optical Reference Cavity Operated in Air

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Compact and Thermal-Noise-Limited Optical Reference Cavity O...

Conference on Lasers and Electro-Optics (CLEO)

作者： Yifan Liu Dahyeon Lee Charles A. McLemore Megan Kelleher Takuma Nakamura Naijun Jin Susan Schima Peter Rakich Scott A. Diddams Franklyn Quinlan Department of Physics University of Colorado Boulder Boulder CO USA Time and Frequency Division National Institute of Standards and Technology Boulder CO USA Department of Applied Physics Yale University New Haven CT USA Electrical Computer and Energy Engineering University of Colorado Boulder Boulder CO USA

We present a vacuum-gap, compact Fabry-Pérot ultrastable optical reference cavity that operates without a vacuum enclosure. A laser stabilized to the cavity exhibits low, thermal noise-limited phase noise and $5... 详细信息

ISBN: (纸本)9798350369311

关键词： Phase noise Laser noise Lasers and electrooptics Laser stability Thermal noise Optical noise Thermal stability Electro-optical waveguides

Multi-Instance Task in Swarm Robotics: Sorting Groups of Robots or Objects into Clusters with Minimalist Controllers

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Multi-Instance Task in Swarm Robotics: Sorting Groups of Rob...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Adilson Krischanski Yuri K. Lopes Andre B. Leal Ricardo F. Martins Roberto S.U. Rosso Department of Computer Science Santa Catarina State University (UDESC) Joinville Brazil Graduate Program in Applied Computing (PPGCAP) UDESC Brazil Department of Electrical Engineering Santa Catarina State University (UDESC) Joinville Brazil

Relying only on behaviors that emerge from simple responsive controllers; swarms of robots have been shown capable of autonomously aggregate themselves or objects into clusters without any form of communication. We push these controllers to the limit, requiring robots to sort themselves or objects into different clusters. Based on a responsive controller that maps the current reading of a line-of-sight sensor to a pair of speeds for the robots' differential wheels, we demonstrate how multiple tasks instances can be accomplished by a robotic swarm. Using the dividing rectangles approach and physics simulation, a training step optimizes the parameters of the controller guided by a fitness function. We conducted a series of systematic trials in physics-based simulation and evaluate the performance in terms of dispersion and the ratio of clustered robots/objects. Across 20 trials where 30 robots cluster themselves into 3 groups, an average of 99.83% of them were correctly clustered into their group after 300 s. Across 50 trials where 15 robots cluster 30 objects into 3 groups, an average of 61.20%, 82.87%, and 97.73% of objects were correctly clustered into their group after 600 s, 900 s, and 1800 s, respectively. The object cluster behavior scales well while the aggregation does not, the latter due to the requirement of control tuning based on the number of robots.

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Optimal plant growth through thermo mechatronic analysis 26

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Optimal plant growth through thermo mechatronic analysis

26th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2022

作者： Calderón, J. Alan Tafur, Julio Barriga, Benjamín Alencastre, Jorge Solano, Gonzalo Urbizagástegui, Rodrigo Lozano, John Chancán, Marvin Applied Nanophysics Institute for Physics Technical University of Ilmenau Ilmenau98693 Germany Engineering Department Mechatronic Master Program Pontificia Universidad Católica del Peru Lima15088 Peru Federal University Arkhangelsk163002 Russia Electrical Engineering and Robotics School Queensland University of Technology St LuciaQLD4072 Australia

ISBN: (纸本)9781950492640

This work is described as a proposal to apply modern control techniques and automation tools for optimal plant growth, also it was based on key agricultural strategies that were developed by ancient civilizations such as the Inca Empire. Many of them ancient techniques including the Inca engineering of andenes were forgotten or set aside through time. In this research, however, some of these key techniques are revisited to analyze and evaluate optimal plant growth using sensors and actuators that were not available in ancient civilizations. In addition, predictive and adaptive mathematical models are used for plant growth analysis of thermodynamic parameters such as temperature, humidity and potential of Hydrogen (pH). Furthermore, there were compared performances of sensors (electromechanical sensors) with designed sensors that were based in nanostructures, because of better study of the plant growth techniques. Copyright 2022. © by the International Institute of Informatics and Systemics. All rights reserved.

关键词： Nanostructures

Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

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Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

Telecommunications Conference (ITC-Egypt), International

作者： Rana M. NourEldeen Rahma A. Elshshtawy Omnia A. Elgohary Fatma Alzahraa A. Wahba Marco K. Attia Zeinab B. Abo elazm Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %.

关键词： 6G mobile communication Deep learning Multiple sclerosis Accuracy Internet of Medical Things Telecommunications Convolutional neural networks

Motif distribution and function of sparse deep neural networks

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arXiv 2024年

作者： Zahn, Olivia Daniel, Thomas Kutz, J. Nathan Department of Physics University of Washington SeattleWA United States Department of Biology University of Washington SeattleWA United States Department of Applied Mathematics University of Washington SeattleWA United States Department of Electrical and Computer Engineering University of Washington SeattleWA United States

We characterize the connectivity structure of feed-forward, deep neural networks (DNNs) using network motif theory. To address whether a particular motif distribution is characteristic of the training task, or function of the DNN, we compare the connectivity structure of 350 DNNs trained to simulate a bio-mechanical flight control system with different randomly initialized parameters. We develop and implement algorithms for counting 2nd- and 3rd-order motifs and calculate their significance using their Z-score. The DNNs are trained to solve the inverse problem of the flight dynamics model in [1] (i.e., predict the controls necessary for controlled flight from the initial and final state-space inputs) and are sparsified through an iterative pruning and retraining algorithm [2]. We show that, despite random initialization of network parameters, enforced sparsity causes DNNs to converge to similar connectivity patterns as characterized by their motif distributions. The results suggest how neural network function can be encoded in motif distributions, suggesting a variety of experiments for informing function and control. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

Emergence of Giant Magnetic Chirality during Dimensionality Crossover of Magnetic Materials

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arXiv 2025年

作者： Kim, Dae-Yun Nam, Yun-Seok Kim, Younghak Kim, Kyoung-Whan Go, Gyungchoon Lee, Seong-Hyub Moon, Joon Chang, Jun-Young Lee, Ah-Yeon Park, Seung-Young Min, Byoung-Chul Lee, Kyung-Jin Yang, Hyunsoo Kim, Duck-Ho Choe, Sug-Bong Seoul02792 Korea Republic of Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Physics Institute of Applied Physics Seoul National University Seoul08826 Korea Republic of Gyeongbuk Pohang37673 Korea Republic of Department of Physics Yonsei University Seoul03722 Korea Republic of Daejeon34141 Korea Republic of Daejeon34133 Korea Republic of

Chirality, an intrinsic preference for a specific handedness, is a fundamental characteristic observed in nature. In magnetism, magnetic chirality arises from the antisymmetric Dzyaloshinskii-Moriya interaction in competition with the symmetric Heisenberg exchange interaction. Traditionally, the anti-symmetric interaction has been considered minor relative to the symmetric interaction. In this study, we demonstrate an observation of giant magnetic chirality during the dimensionality crossover of magnetic materials from three-dimensional to two-dimensional. The ratio between the antisymmetric and symmetric interactions exhibits a reversal in their dominance over this crossover, overturning the traditional consideration. This observation is validated theoretically using a non-local interaction model and tight-binding calculation with distinct pairing schemes for each exchange interaction throughout the crossover. Additional experiments investigating the asphericity of orbital moments corroborate the robustness of our findings. Our findings highlight the critical role of dimensionality in shaping magnetic chirality and offer strategies for engineering chiral magnet states with unprecedented strength, desired for the design of spintronic materials. © 2025, CC BY.

关键词： Magnetic materials

Multimodal Foundation Models for Material Property Prediction and Discovery

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arXiv 2023年

作者： Moro, Viggo Loh, Charlotte Dangovski, Rumen Ghorashi, Ali Ma, Andrew Chen, Zhuo Kim, Samuel Lu, Peter Y. Christensen, Thomas Soljačić, Marin Department of Physics Massachusetts Institute of Technology United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Research and Exploratory Development John Hopkins University Applied Physics Laboratory United States Data Science Institute University of Chicago United States Department of Electrical and Photonics Engineering Technical University of Denmark Denmark

Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials. Recently, publicly available material data repositories have grown rapidly. This growth encompasses not only more materials but also a greater variety and quantity of their associated properties. Existing machine learning efforts in materials science focus primarily on single-modality tasks, i.e., relationships between materials and a single physical property, thus not taking advantage of the rich and multimodal set of material properties. Here, we introduce Multimodal Learning for Materials (MultiMat), which enables self-supervised multi-modality training of foundation models for materials. We demonstrate our framework’s potential using data from the Materials Project database on multiple axes: (i) MultiMat achieves state-of-the-art performance for challenging material property prediction tasks;(ii) MultiMat enables novel and accurate material discovery via latent space similarity, enabling screening for stable materials with desired properties;and (iii) MultiMat encodes interpretable emergent features that may provide novel scientific insights. Copyright © 2023, The Authors. All rights reserved.

关键词： Materials properties

Linear-depth quantum circuits for loading Fourier approximations of arbitrary functions

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arXiv 2023年

作者： Moosa, Mudassir Watts, Thomas W. Chen, Yiyou Sarma, Abhijat McMahon, Peter L. Department of Physics Cornell University IthacaNY14853 United States Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

The ability to efficiently load functions on quantum computers with high fidelity is essential for many quantum algorithms, including those for solving partial differential equations and Monte Carlo estimation. In this work, we introduce the Fourier Series Loader (FSL) method for preparing quantum states that exactly encode multi-dimensional Fourier series using linear-depth quantum circuits. Specifically, the FSL method prepares a (Dn)-qubit state encoding the 2Dn-point uniform discretization of a D-dimensional function specified by a D-dimensional Fourier series. A free parameter, m, which must be less than n, determines the number of Fourier coefficients, (Equation presented), used to represent the function. The FSL method uses a quantum circuit of depth at most (Equation presented), which is linear in the number of Fourier coefficients, and linear in the number of qubits (Dn) despite the fact that the loaded function's discretization is over exponentially many (Equation presented) points. The FSL circuit consists of at most (Equation presented) single-qubit and (Equation presented) two-qubit gates;we present a classical compilation algorithm with runtime (Equation presented) to determine the FSL circuit for a given Fourier series. The FSL method allows for the highly accurate loading of complex-valued functions that are well-approximated by a Fourier series with finitely many terms. We report results from noiseless quantum circuit simulations, illustrating the capability of the FSL method to load various continuous 1D functions, and a discontinuous 1D function, on 20 qubits with infidelities of less than 10−6 and 10−3, respectively. We also demonstrate the practicality of the FSL method for near-term quantum computers by presenting experiments performed on the Quantinuum H1-1 and H1-2 trapped-ion quantum computers: we loaded a complex-valued function on 3 qubits with a fidelity of over 95%, as well as various 1D real-valued functions on up to 6 qubits with class

关键词： Fourier series

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

Telecommunications Conference (ITC-Egypt), International

作者： Mohamed M. El-Sheikh Aya Bayoumy Nada M. Ahmed Amany Mohamed Eldeeb Fatma I. Rabea Amira A. Nawar Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively.

关键词： Deep learning Accuracy Magnetic resonance imaging Soft sensors Medical services Feature extraction Regulation