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Laser science, LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Olaizola, Aristides Marcano Kingsley, David Kuis, Robinson Johnson, Anthony Division of Physics Engineering Mathematics and Computer Science Delaware State University 1200 North DuPont Highway DoverDE19901 United States Delaware State University 1200 North DuPont Highway DoverDE19901 United States Center for Advanced Studies in Photonic Research University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States

We propose that Raman excitation can generate singlet oxygen in water environments without the intervention of photosensitizers. Preliminary Raman experiments using continuous-wave laser light at 405 nm validate the h... 详细信息

ISBN: (纸本)9781557528209

关键词： Continuous wave lasers

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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Saudi Arabia Smart Grid Conference (SASG)

作者： Yiannis Kamarianakis Yannis Pantazis Evangelia Kalligiannaki Theodoros D. Katsaounis Konstantinos Kotsovos Issam Gereige Marwan Abdullah Aqil Jamal Athanasios Tzavaras Data Science Group Institute of Applied and Computational Mathematics Foundation for Research and Technology-Hellas Greece Dept. of Mathematics and Applied Mathematics University of Crete Greece Carbon Management Division Saudi Aramco R&D Center Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average.

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Hardware-Aware In Situ Learning Based on Stochastic Magnetic Tunnel Junctions

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Physical Review Applied 2022年第1期17卷 014016-014016页

作者： Jan Kaiser William A. Borders Kerem Y. Camsari Shunsuke Fukami Hideo Ohno Supriyo Datta Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47906 USA Laboratory for Nanoelectronics and Spintronics Research Institute of Electrical Communication Tohoku University Sendai Japan Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara California 93106 USA Center for Innovative Integrated Electronic Systems Tohoku University Sendai Japan Center for Spintronics Research Network Tohoku University Sendai Japan Center for Science and Innovation in Spintronics Tohoku University Sendai Japan WPI-Advanced Institute for Materials Research Tohoku University Sendai Japan

One of the big challenges of current electronics is the design and implementation of hardware neural networks that perform fast and energy-efficient machine learning. Spintronics is a promising catalyst for this field with the capabilities of nanosecond operation and compatibility with existing microelectronics. Considering large-scale, viable neuromorphic systems however, variability of device properties is a serious concern. In this paper, we show an autonomously operating circuit that performs hardware-aware machine learning utilizing probabilistic neurons built with stochastic magnetic tunnel junctions. We show that in situ learning of weights and biases in a Boltzmann machine can counter device-to-device variations and learn the probability distribution of meaningful operations such as a full adder. This scalable autonomously operating learning circuit using spintronics-based neurons could be especially of interest for standalone artificial-intelligence devices capable of fast and efficient learning at the edge.

关键词： Spintronics Stochastic inference Synapses Artificial neural networks Magnetic tunnel junctions Stochastic dynamical systems Machine learning

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Approximate Boltzmann distributions in quantum approximate optimization

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Physical Review A 2023年第4期108卷 042411-042411页

作者： Phillip C. Lotshaw George Siopsis James Ostrowski Rebekah Herrman Rizwanul Alam Sarah Powers Travis S. Humble Quantum Computational Science Group Oak Ridge National Laboratory Oak Ridge Tennessee 37381 USA Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996-1200 USA Department of Industrial and Systems Engineering University of Tennessee Knoxville Tennessee 37996-2315 USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge Tennessee 37381 USA Quantum Science Center Oak Ridge National Laboratory Oak Ridge Tennessee 37381 USA

Approaches to compute or estimate the output probability distributions from the quantum approximate optimization algorithm (QAOA) are needed to assess the likelihood it will obtain a quantum computational advantage. We analyze output from QAOA circuits solving 7200 random MaxCut instances, with n=14–23 qubits and depth parameter p≤12, and find that the average basis state probabilities follow approximate Boltzmann distributions: The average probabilities scale exponentially with their energy (cut value), with a peak at the optimal solution. We describe the rate of exponential scaling or effective temperature in terms of a series with a leading-order term T∼Cmin/np, with Cmin the optimal solution energy. Using this scaling, we generate approximate output distributions with up to 38 qubits and find these give accurate accounts of important performance metrics in cases we can simulate exactly.

关键词： Quantum algorithms & computation Quantum benchmarking Quantum simulation

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Port-LLM: A Port Prediction Method for Fluid Antenna based on Large Language Models

arXiv

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arXiv 2025年

作者： Zhang, Yali Yin, Haifan Li, Weidong Björnson, Emil Debbah, Mérouane School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Division of Communication Systems KTH Royal Institute of Technology Stockholm Sweden KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

The objective of this study is to address the mobility challenges faced by User Equipment (UE) through the implementation of fluid antenna (FA) on the UE side. This approach aims to maintain the time-varying channel in a relatively stable state by strategically relocating the FA to an appropriate port. To the best of our knowledge, this paper introduces, for the first time, the application of large language models (LLMs) in the prediction of FA ports, presenting a novel model termed Port-LLM. The proposed method consists of two primary steps for predicting the moving port of the FA: the first involves utilizing the channel tables that encompass historical channel state information from all movable ports of the FA to forecast the channel tables for subsequent time periods;the second step entails selecting the port of the FA for the forthcoming time based on the predicted channel tables and the known reference channels that require alignment. To enhance the learning capabilities of the LLM model in the context of FA port prediction, we incorporate the Low-Rank Adaptation (LoRA) fine-tuning technology. Furthermore, during the model training phase, we implement the warmup-aided cosine learning rate (LR) technique to augment the accuracy of the predictions. The simulation results show that our model exhibits strong generalization ability and robustness under different numbers of base station antennas and medium-to-high mobility speeds of UE. In comparison to existing FA port calculation methods, the performance of the port predicted by our model demonstrates superior efficacy. Additionally, our model exhibits lower prediction costs and faster prediction and reasoning speeds. Copyright © 2025, The Authors. All rights reserved.

关键词： Prediction models

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A Test Suite and An Optimizer for Dietary Nutrition Optimization Problem: From Constrained Many-Objective Perspective

A Test Suite and An Optimizer for Dietary Nutrition Optimiza...

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Congress on Evolutionary Computation

作者： Yaqi Ti Changhe Li Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

With increasing people who suffer from diet-related diseases, providing suggestions for personal daily nutrient-dense intake is highly expected. However, current dietary nutrition models are less precise, and dietary nutrition optimizers usually fail to give satisfactory solutions. Therefore, we construct a constrained many-objective nutrition model with more precise nutrient assessments and a scalable constrained many-objective benchmark set. This test suite has great flexibility in evaluating algorithms' performance on high dimensional search and objective spaces with some feasible region fragments. We also propose a kd-tree based dynamic constrained many-objective evolutionary algorithm to search for customized food combinations according to personal daily consumption and intake preference. Experiments show that our algorithm has better diversity maintenance ability in high dimension space.

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Narrow-area Bragg-edge transmission of iron samples using superconducting neutron sensor 34

Narrow-area Bragg-edge transmission of iron samples using su...

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34th International Symposium on Superconductivity, ISS 2021

作者： Vu, The Dang Shishido, Hiroaki Aizawa, Kazuya Oku, Takayuki Oikawa, Kenichi Harada, Masahide Kojima, Kenji M. Miyajima, Shigeyuki Soyama, Kazuhiko Koyama, Tomio Hidaka, Mutsuo Suzuki, Soh Y. Tanaka, Manobu M. Malins, Alex MacHida, Masahiko Ishida, Takekazu Division of Quantum and Radiation Engineering Osaka Prefecture University Osaka599-8570 Japan Materials and Life Science Division J-PARC Center Japan Atomic Energy Agency Ibaraki319-1195 Japan Department of Physics and Electronics Graduate School of Engineering Osaka Prefecture University Osaka599-8531 Japan NanoSquare Research Institute Osaka Prefecture University Osaka599-8570 Japan Centre for Molecular and Materials Science Triumf 4004 Wesbrook Mall VancouverBC2A3 Canada Advanced Ict Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi-ku Hyogo Kobe651-2492 Japan Advanced Industrial Science and Technology Tsukuba305-8568 Japan Tsukuba305-0801 Japan Tsukuba305-0801 Japan Center for Computational Science & E-Systems Japan Atomic Energy Agency 178-4-4 Wakashiba Chiba Kashiwa277-0871 Japan

This study investigates a current-biased kinetic inductance detector (CB-KID) performance by investigating Bragg-edge spectra from the restricted-area neuron transmission of materials. Iron samples with a size of 5 × 5 ×2 mm3 were used as typical test materials. The ergodic theorem was used to obtain a visible transmission spectrum so that a long-time averaging of a transmission spectrum can alternatively be evaluated using a space average of independently selected area spectra with the same ensemble size. The most visible edges were observed with a limited area sample of 0.43 mm2 using a minimum time bin of 25 μs in a time-of-flight (ToF) spectrum or a wavelength resolution of 0.0007 nm of each neutron pulse at beamline BL10 of the Japan Proton Accelerator research Complex (J-PARC) center. The main Bragg edge of iron as a sum of random 100 ensembles (with an ensemble size of 3.1 × 2.3 μm2) thus obtained has a distinctive signal-to-noise ratio and can be fitted well with the Rietveld Imaging of Transmission Spectra (RITS) program with Miller indices. We consider that our CB-KID system is, in principle, able to analyze the Bragg edge of samples as small as 3.1 × 2.3 μm2. © Published under licence by IOP Publishing Ltd.

关键词： Iron

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GraphReg: Dynamical Point Cloud Registration with Geometry-aware Graph Signal Processing

arXiv

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arXiv 2023年

作者： Zhao, Mingyang Ma, Lei Jia, Xiaohong Yan, Dong-Ming Huang, Tiejun Institute of Automation Cas Beijing China The Baai and Institute for Artificial Intelligence Peking University Beijing China National Biomedical Imaging Center Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China The Academy of Mathematics and Systems Science Cas Ucas Beijing China The Nlpr Institute of Automation Cas School of Ai Ucas Beijing China

This study presents a high-accuracy, efficient, and physically induced method for 3D point cloud registration, which is the core of many important 3D vision problems. In contrast to existing physics-based methods that merely consider spatial point information and ignore surface geometry, we explore geometry aware rigid-body dynamics to regulate the particle (point) motion, which results in more precise and robust registration. Our proposed method consists of four major modules. First, we leverage the graph signal processing (GSP) framework to define a new signature, i.e., point response intensity for each point, by which we succeed in describing the local surface variation, resampling keypoints, and distinguishing different particles. Then, to address the shortcomings of current physics-based approaches that are sensitive to outliers, we accommodate the defined point response intensity to median absolute deviation (MAD) in robust statistics and adopt the X84 principle for adaptive outlier depression, ensuring a robust and stable registration. Subsequently, we propose a novel geometric invariant under rigid transformations to incorporate higher-order features of point clouds, which is further embedded for force modeling to guide the correspondence between pairwise scans credibly. Finally, we introduce an adaptive simulated annealing (ASA) method to search for the global optimum and substantially accelerate the registration process. We perform comprehensive experiments to evaluate the proposed method on various datasets captured from range scanners to LiDAR. Results demonstrate that our proposed method outperforms representative state-of-the-art approaches in terms of accuracy and is more suitable for registering large-scale point clouds. Furthermore, it is considerably faster and more robust than most competitors. Our implementation is publicly available at https://***/zikai1/GraphReg. Copyright © 2023, The Authors. All rights reserved.

关键词： Simulated annealing

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Prediction Method of Truck Travel Time in Open Pit Mines Based on LSTM Model

Prediction Method of Truck Travel Time in Open Pit Mines Bas...

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Chinese Control Conference (CCC)

作者： Mengting Ao Changhe Li Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver's behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds.

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Towards Mutual Trust-Based Matching For Federated Learning Client Selection

Towards Mutual Trust-Based Matching For Federated Learning C...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Osama Wehbi Omar Abdel Wahab Azzam Mourad Hadi Otrok Hoda Alkhzaimi Mohsen Guizani Mohammad Bin Zayed University of Artificial Intelligence Abu Dhabi UAE Department of CSM Cyber Security Systems and Applied AI Research Center Lebanese American University Lebanon Department of Computer and Software Engineering Polytechnique Montréal Montreal Quebec Canada Division of Science New York University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE Division of Engineering New York University Abu Dhabi UAE

Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a machine-learning model using their own data locally. Smart cities are areas that can generate high volume and critical data, which has the potential to revolutionize federated learning. Nevertheless, it is highly challenging to select a trustworthy group of clients to collaborate in model training. The utilization of a random selection technique would pose many threats due to malicious clients’ targeted and untargeted attacks. Such vulnerability may cause attacks and poisoning in the produced model. To address this problem, we present a mutual trust client-server selection approach based on matching game theory and bootstrapping mechanisms for federated learning in smart cities. Our solution entails the creation of: (1) preference functions for federated servers and smart devices (i.e., IoT/IoV) that enables them to sort each other based on trust score, (2) light feedback-base technique that leverages the cooperation of multiple client devices to assign trust value to the newly connected federated server, and (3) intelligent matching algorithms consider trust preferences of both parties in their design. According to our simulation results, our technique outperforms the baseline selection approach VanillaFL in terms of increasing the trust level and hence the global accuracy of the federated learning model and optimizing the number of untrusted selected clients.

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