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Ultrastrong Spin-Photon Coupling in Gadolinium Gallium Garnet

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Ultrastrong Spin-Photon Coupling in Gadolinium Gallium Garne...

2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Kritzell, T. Elijah Bao, Junzhe Doumani, Jacques Lee, Jae Joon Xu, Hongjing Tay, Fuyang Nojiri, Hiroyuki Bamba, Motoaki Baydin, Andrey Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Institute for Materials Research Tohoku University Sendai980-8577 Japan The Hakubi Center for Advanced Research Kyoto University Kyoto606-8502 Japan Department of Physics I Kyoto University Kyoto606-8502 Japan Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States

We examine ultrastrong coupling of an ensemble of paramagnetic spins in Gd3Ga5O12 with terahertz photons in the presence of a high external magnetic field both in the bulk and thin-film limits. © 2023 The Author(s)

ISBN: (纸本)9781957171258

关键词： Photons

More Optimal Fractional-Order Stochastic Gradient Descent for Non-Convex Optimization Problems

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science Graduate Program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages fractional exponents to capture long-memory effects in optimization. However, its utility is often limited by the difficulty of tuning and stabilizing these exponents. We propose 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), which integrates the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to adapt the fractional exponent in a data-driven manner. By tracking model sensitivity and effective dimensionality, 2SEDFOSGD dynamically modulates the exponent to mitigate oscillations and hasten convergence. Theoretically, for onoconvex optimization problems, this approach preserves the advantages of fractional memory without the sluggish or unstable behavior observed in naïve fractional SGD. Empirical evaluations in Gaussian and α-stable noise scenarios using an autoregressive (AR) model highlight faster convergence and more robust parameter estimates compared to baseline methods, underscoring the potential of dimension-aware fractional techniques for advanced modeling and estimation tasks. © 2025, CC BY.

关键词： Convex optimization

Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

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SSRN

SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety

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arXiv 2023年

作者： Ardabili, Babak Rahimi Pazho, Armin Danesh Noghre, Ghazal Alinezhad Neff, Christopher Bhaskararayuni, Sai Datta Ravindran, Arun Reid, Shannon Tabkhi, Hamed Public Policy Program University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States Electrical Engineering and Computer Systems University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States Criminal Justice University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States

Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of personally identifiable information (PII) commonly pose an increased risk to personal privacy. To address these issues, this paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance. Further, we propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications. Finally, we propose quantitative and qualitative metrics to evaluate intelligent video surveillance systems. The system shows the 17.8 frame-per-second (FPS) processing in extreme video scenes. However, considering privacy in designing such a system results in preferring the pose-based algorithm to the pixel-based one. This choice resulted in dropping accuracy in both action and anomaly detection tasks. The results drop from 97.48 to 73.72 in anomaly detection and 96 to 83.07 in the action detection task. On average, the latency of the end-to-end system is 36.1 seconds. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

Can Personalized Medicine Coexist with Health Equity? Examining the Cost Barrier and Ethical Implications

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arXiv 2024年

作者： Francisco, Kishi Kobe Yee Apuhin, Andrane Estelle Carnicer Tan, Myles Joshua Toledo Byers, Mickael Cavanaugh Maravilla, Nicholle Mae Amor Tan Karim, Hezerul Abdul AlDahoul, Nouar Biology Program College of Arts and Science University of St. La Salle Negros Occidental Bacolod City Philippines Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health & Health Professions College of Medicine University of Florida GainesvilleFL United States Department of Natural Sciences College of Arts and Science University of St. La Salle Negros Occidental Bacolod City Philippines Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City Philippines Yo-Vivo Corporation Negros Occidental Bacolod City Philippines Department of Civil and Coastal Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Faculty of Engineering Multimedia University Selangor Cyberjaya Malaysia Department of Computer Science Division of Science New York University Abu Dhabi Saadiyat Marina District Abu Dhabi United Arab Emirates

Personalized medicine (PM) promises to transform healthcare by providingtreatments tailored to individual genetic, environmental, and lifestylefactors. However, its high costs and infrastructure demands raise concernsabout exacerbating health disparities, especially between high-income countries(HICs) and low- and middle-income countries (LMICs). While HICs benefit fromadvanced PM applications through AI and genomics, LMICs often lack theresources necessary to adopt these innovations, leading to a wideninghealthcare divide. This paper explores the financial and ethical challenges ofPM implementation, with a focus on ensuring equitable access. It proposesstrategies for global collaboration, infrastructure development, and ethicalframeworks to support LMICs in adopting PM, aiming to prevent furtherdisparities in healthcare accessibility and outcomes. © 2024, CC BY.

关键词： Personalized medicine

Sequential Markov Chain Monte Carlo for Lagrangian Data Assimilation with Applications to Unknown Data Locations

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arXiv 2023年

作者： Ruzayqat, Hamza Beskos, Alexandros Crisan, Dan Jasra, Ajay Kantas, Nikolas 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 Department of Statistical Science University College London LondonWC1E 6BT United Kingdom Department of Mathematics Imperial College London LondonSW7 2AZ United Kingdom

We consider a class of high-dimensional spatial filtering problems, where the spatial locations of observations are unknown and driven by the partially observed hidden signal. This problem is exceptionally challenging as not only is high-dimensional, but the model for the signal yields longer-range time dependencies through the observation locations. Motivated by this model we revisit a lesser-known and provably convergent computational methodology from [4, 11, 28] that uses sequential Markov Chain Monte Carlo (MCMC) chains. We extend this methodology for data filtering problems with unknown observation locations. We benchmark our algorithms on Linear Gaussian state space models against competing ensemble methods and demonstrate a significant improvement in both execution speed and accuracy. Finally, we implement a realistic case study on a high-dimensional rotating shallow water model (of about 104 - 105 dimensions) with real and synthetic data. The data is provided by the National Oceanic and Atmospheric Administration (NOAA) and contains observations from ocean drifters in a domain of the Atlantic Ocean restricted to the longitude and latitude intervals [-51◦,-41◦], [17◦,27◦] *** Codes 62M20, 60G35, 60J20, 94A12, 93E11, 65C40 © 2023, CC BY.

关键词： Location

Meta-optic Accelerators for Object Classifiers

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arXiv 2022年

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision making when computation resources are limited. Here, we demonstrate a meta-optic based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems resulting in a robust classifier that achieves 95% accurate classification of handwriting digits and 94% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine-vision and artificial intelligence. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy utilization

Scientometric Analysis of Artificial Intelligence Research in Agriculture

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Scientometric Analysis of Artificial Intelligence Research i...

International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Fairuz Iqbal Maulana Andi Pramono Miftahul Hamim Sukeipah Yuli Prihatin Rahman Arifuddin Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Entrepreneurship Business Creation BINUS Business School Bina Nusantara University Jakarta Indonesia Informatics Study Program Politeknik Kota Malang Malang Indonesia Department of Electrical Engineering University of Merdeka Malang Malang Indonesia

ISBN: (纸本)9781665473286

Artificial Intelligence Research in Agriculture has grown so common, and the potential they provide is so revolutionary that it is considered essential for competitive growth. Until we conducted this research, only 1064 academic documents on Trends of Artificial Intelligence Research in Agriculture that we found from 2012 to 2021 were obtained by searching the Scopus database. This study uses bibliometric analysis methods to map scientific publications worldwide. We use an article selection process, the year limitation, then the database is exported to RIS and CSV format files. Researchers in the United States had the most published articles and were indexed by Scopus among the most prolific authors ( $\mathrm{N}=287$ ), followed by the United Kingdom at second ( $\mathrm{N}=139$ ) and Italy with 66 academic publications. Data analysis reveals an increasing trend in the number of Trends of Artificial Intelligence Research in Agriculture publications worldwide, as measured by the Scopus index.

关键词： Data analysis Databases Multimedia systems Bibliometrics Market research Agriculture Indexes

Frequency-dependent dielectric constant prediction of polymers using machine learning

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npj Computational Materials 2020年第1期6卷 1147-1155页

作者： Lihua Chen Chiho Kim Rohit Batra Jordan P.Lightstone Chao Wu Zongze Li Ajinkya A.Deshmukh Yifei Wang Huan D.Tran Priya Vashishta Gregory A.Sotzing Yang Cao Rampi Ramprasad School of Materials Science and Engineering Georgia Institute of Technology771 Ferst Drive NWAtlantaGA 30332USA Electrical and Computer Engineering University of Connecticut371 Fairfield WayStorrsCT 06269USA Polymer Program Institute of Material ScienceUniversity of Connecticut97 North Eagleville RoadStorrsCT 06269USA Collaboratory for Advanced Computing and Simulations University of Southern CaliforniaLos AngelesCA 90089-0242USA

The dielectric constant(ϵ)is a critical parameter utilized in the design of polymeric dielectrics for energy storage capacitors,microelectronic devices,and high-voltage ***,agile discovery of polymer dielectrics with desirableϵremains a challenge,especially for high-energy,high-temperature *** aid accelerated polymer dielectrics discovery,we have developed a machine-learning(ML)-based model to instantly and accurately predict the frequency-dependentϵof polymers with the frequency range spanning 15 orders of *** model is trained using a dataset of 1210 experimentally measuredϵvalues at different frequencies,an advanced polymer fingerprinting scheme and the Gaussian process regression algorithm.

关键词： dielectric constant polymer