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arXiv 2024年

作者： Ding, Chao Wang, Shi Wang, Yaonan Gao, Weibo College of Electrical and Information Engineering Hunan University Changsha410082 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore The Photonics Institute Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore637371 Singapore

Quantum machine learning is considered one of the current research fields with great potential. In recent years, Havlíček et al. [Nature 567, 209-212 (2019)] have proposed a quantum machine learning algorithm with quantum-enhanced feature spaces, which effectively addressed a binary classification problem on a superconducting processor and offered a potential pathway to achieving quantum advantage. However, a straightforward binary classification algorithm falls short in solving multiclass classification problems. In this paper, we propose a quantum algorithm that rigorously demonstrates that quantum kernel methods enhance the efficiency of multiclass classification in real-world applications, providing quantum advantage. To demonstrate quantum advantage, we design six distinct quantum kernels within the quantum algorithm to map input data into quantum state spaces and estimate the corresponding quantum kernel matrices. The results from quantum simulations reveal that the quantum algorithm outperforms its classical counterpart in handling six real-world multiclass classification problems. Furthermore, we leverage a variety of performance metrics to comprehensively evaluate the classification and generalization performance of the quantum algorithm. The results demonstrate that the quantum algorithm achieves superior classification and better generalization performance relative to classical counterparts. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum efficiency

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Capturing Contact Surfaces by a Frustrated Total Internal Reflection System Using a Curved Plate for Comparison of the Beginning of Touching Motions by Humanitude Experts and Novices

Capturing Contact Surfaces by a Frustrated Total Internal Re...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Akishige Yuguchi Mayuki Toyoda Sung-Gwi Cho Atsushi Nakazawa Jun Takamatsu Koichiro Yoshino Tsukasa Ogasawara Faculty of Advanced Engineering Tokyo University of Science Katsushika-ku Tokyo Japan Guardian Robot Project RIKEN Seika-cho Sorakugun Kyoto Japan Nara Institute of Science and Technology Ikoma Nara Japan School of Science and Engineering Tokyo Denki University Hatoyama-machi Hiki-gun Saitama Japan Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University Kita-ku Okayama Japan Applied Robotics Re-search Microsoft Corporation Redmond WA USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Analyzing time-series changes of the contact surface by touching is important to elucidate the touching skills of Humanitude as one of the pervasive multimodal comprehensive care methodologies. For the analysis, there is a frustrated total internal reflection (FTIR) method to capture contact surfaces on a transparent flat plate by a camera. However, this conventional flat plate is far from the actual surfaces of care receivers because the surfaces of humans consist of curved shapes. In this paper, we propose an FTIR sensing system using a transparent curved plate to capture more ideal contact states with a surface shape more similar to the human body. We collect the contact surface data of the beginning of touching motions by Humanitude experts and novices using the FTIR sensing system with the curved and flat plates. Then, we compare the data by the experts and novices in terms of time-series contact areas and the quantitative indices and subjective evaluation and discuss the analysis results. Through these experiments, we confirm that the proposed system has the potential for the novices to perform more correctly the beginning of Humanitude's touching motions.

关键词： Correlation Shape Receivers Cameras Reflection Sensors Cybernetics

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Faster Optimal Coalition Structure Generation via Offline Coalition Selection and Graph-Based Search

arXiv

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arXiv 2024年

作者： Taguelmimt, Redha Aknine, Samir Boukredera, Djamila Changder, Narayan Sandholm, Tuomas Univ Lyon UCBL CNRS INSA Lyon Centrale Lyon Univ Lyon 2 LIRIS UMR5205 Lyon France Laboratory of Applied Mathematics Faculty of Exact Sciences University of Bejaia Bejaia Algeria TCG Centres for Research and Education in Science and Technology Kolkata India Computer Science Department Carnegie Mellon University Pittsburgh United States Strategy Robot Inc. United States Strategic Machine Inc. United States Optimized Markets Inc. United States

Coalition formation is a key capability in multi-agent systems. An important problem in coalition formation is coalition structure generation: partitioning agents into coalitions to optimize the social welfare. This is a challenging problem that has been the subject of active research for the past three decades. In this paper, we present a novel algorithm, SMART, for the problem based on a hybridization of three innovative techniques. Two of these techniques are based on dynamic programming, where we show a powerful connection between the coalitions selected for evaluation and the performance of the algorithms. These algorithms use offline phases to optimize the choice of coalitions to evaluate. The third one uses branch-and-bound and integer partition graph search to explore the solution space. Our techniques bring a new way of approaching the problem and a new level of precision to the field. In experiments over several common value distributions, we show that the hybridization of these techniques in SMART is faster than the fastest prior algorithms (ODP-IP, BOSS) in generating optimal solutions across all the value distributions. Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamic programming

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LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping

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Geoderma 2025年 459卷

作者： Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Department of Agromechatronics Potsdam Germany University of São Paulo (USP) Center of Nuclear Energy in Agriculture (CENA) Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany University of Bonn Institute of Crop Science and Resource Conservation (INRES)—Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Federal University of Santa Maria (UFSM) Academic Coordination Santa Maria Brazil Federal University of Santa Maria (UFSM) Soil Department Santa Maria Brazil Norwegian Institute of Bioeconomy Research (NIBIO) Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence ‘Machine Learning for Science’ Germany Bern Universi

Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil Datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number (<20) of features. These benchmarking results illustrate that the performance of statistical methods can be highly context-dependent. LimeSoDa therefore provides an important resource for improving the development and evaluation of statistical methods in DSM. © 2025 The Author(s)

关键词： Benchmarking Dataset collection Digital soil mapping Machine learning Open data Pedometrics

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EEG-based neurofeedback training with shoulder exoskeleton robot assistance triggered by the contralesional primary motor cortex activity in poststroke patients with severe chronic hemiplegia

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Annals of Physical and Rehabilitation Medicine 2018年 61卷 e94-e95页

作者： Takasaki, K. Liu, F. Ogura, M. Okuyama, K. Kawakami, M. Mizuno, K. Kasuga, S. Noda, T. Morimoto, J. Liu, M. Ushiba, J. Keio University Graduate School of Science and Technology Kanagawa Japan Keio University Department of Rehabilitation Medicine Tokyo Japan Keio University/Keio Institute of Pure and Applied Science KiPAS Faculty of Science and Technology Kanagawa Japan Advanced Telecommunications Research Institute International Department of Brain Robot Interface- Computational Neuroscience Laboratories Kyoto Japan

Introduction/Background Recent clinical studies have shown that electroencephalogram- (EEG) based neurofeedback training promotes functional recovery of finger movements in poststroke patients (Pichiorri F et al., Ann Neurol. 2015). However, impairment of shoulder elevation in poststroke severe chronic hemiplegia is a debilitating condition with no evidence-based, accessible treatment. To verify motor recovery evidence of neurofeedback training with our shoulder exoskeleton robot, this study reports upper extremity section of Fugl-Meyer assessment (FMA-UE) associated with the evaluation of up-conditioning of contralesional corticospinal pathways. Material and method Eight poststroke patients with severe hemiplegia in the chronic stage were recruited (FMA-UE score = 16.8 ± 4.4). Patients engaged in 1 hour daily training for 7 consecutive days ( Fig. 1 ), and clinical and neurophysiological measurements were performed 1 day before and after the intervention. FMA-UE was used for the primary outcome measure. A 128-channel whole-head EEG was used to assess the hemispheric lateralization of event-related desynchronization (ERD). Single-pulse transcranial magnetic stimulation (TMS) was applied to the contralesional primary motor cortex to assess the corticospinal excitability in 6 tested patients. Results All participants finished the intervention without adverse events. FMA-UE was significantly improved after the intervention (paired t -test, P < 0.01). All patients demonstrated improved FMA-UE (mean change = 6.6 ± 3.2) and 6 of 8 patients exceeded the minimal clinically important difference that is set to 5. From the group analysis of hemispheric lateralization of ERD, the contralesional ERD became stronger after interventions (Wilcoxon signed-rank test, P < 0.01). Motor evoked potentials became more apparent after interventions in 3 of the 6 tested patients. Conclusion The results from this study demonstrated targeted up-conditioning of contralesional corticospinal path

关键词： Brain machine interface Neurorehabilitation Phase 2 clinical study

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Real-time imaging of accelerated solid-liquid-gas reactions with nanobubbles

Research Square

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Research Square 2020年

作者： Wang, Wen Xu, Tao Chen, Jige Shangguan, Junyi Dong, Hui Ma, Huishu Zhang, Qiubo Bai, Tingting Guo, Zhirui Fang, Haiping Zheng, Haimei Sun, Litao SEU-FEI Nano-Pico Center Key Laboratory of MEMS of Ministry of Education Collaborative Innovation Center for Micro/Nano Fabrication Device and System Southeast University Nanjing210096 China Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Shanghai Synchrotron Radiation Facility Zhangjiang Laboratory Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201204 China Department of Physics East China University of Science and Technology Shanghai200237 China Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai201800 China Key Laboratory of Welding Robot and Application Technology of Hunan Province Engineering Research Center of Complex Tracks Processing Technology Equipment of Ministry of Education Xiangtan University Xiangtan411105 China The Second Affiliated Hospital Key Laboratory for Aging & Disease Nanjing Medical University Nanjing210011 China

Solid-liquid-gas reactions are ubiquitous. An understanding of how gases influence the reactions at the nanoscale is significant for achieving the enhanced triple-phase reactions. Here, we report a real-time observation of the accelerated etching of gold nanorods with oxygen nanobubbles in aqueous hydrobromic acid using liquid cell transmission electron microscopy (TEM). Our observation reveals that when an oxygen nanobubble is close to a nanorod below the critical distance (~1nm), the local etching rate is significantly enhanced with over an order of magnitude faster. Molecular dynamics simulations results show that the strong attractive van der Waals interaction between the gold nanorod and oxygen molecules facilitates the transport of oxygen through the thin liquid layer to the gold surface and thus plays a crucial role in increasing the etching rate. This result sheds light on the rational design of solid-liquid-gas reactions for enhanced activities. © 2020, CC BY.

关键词： Molecular dynamics

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NONLINEAR NETWORK STRUCTURES FOR FEEDBACK CONTROL

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Asian Journal of Control 2008年第4期1卷

作者： F.L. Lewis Dept. of Electical Engineering The University of Texas at Arlington U.S.A. F. L. Lewis was born in Wärzburg. Germany subsequently studyning in Chile and Goruonstoun School in Scotland. He obtained the Bachelor's Degree in Physics/Electrical Engineering and the Master's of Electrical Engineering Degree at Rice University in 1971. He spent six years in the U.S. navy serving as Navigator aboard the frigate USS Trippe (FF-1075) and Executive Officer and Acting Commanding Officer aboard USS Salinan (ATF-161). In 1977 he received the Master's of Science in Aeronautical Engineering from the University of West Florida. In 1981 he obtained the Ph.D. degree at The Georgia Institute of Technology in Atlanta where he was employed as a professor from 1981 to 1990 and is currently an Adjunct Professor. He is a Professor of Electrical Engineering at The University of Texas at Arlington where he was awarded the Moncrief-O'Donnell Endowed Chair in 1990 at the Automation and Robotics Research Institute. Dr. Lewis has studied the geometric analytic and structural properties of dynamical systems and feedback control automation. His current interests include robotics intelligent control neural and fuzzy systes nonlinear systems and manufacturing process control. He is the author/co-author of 2 U.S. patents 124 journal papers 20 chapters and encyclopedia articles 210 refereed conference papers seven books: Optimal Control Optimal Estimation Applied Optimal Control and Estimation Aircraft Control and Simulation Control of Robot Manipulators Neural Network Control High-Level Feedback Control with Neural Networks and the IEEE reprint volume Robot Control. Dr. Lewis is a registered Professional Engineer in the State of Texas and was selected to the Editorial Boards of International Journal of Control Neural Computing and Applications and Int. J. Intelligent Control Systems. He is the recipient of an NSF Research Initiation Grant and has been continuously funded by NSF since 1982. Since 1991 he has received $1.8 m

A framework is given for controller design using Nonlinear Network Structures, which include both neural networks and fuzzy logic systems. These structures possess a universal approximation property that allows them to be used in feedback control of unknown systems without requirements for linearity in the system parameters or finding a regression matrix. Nonlinear nets can be linear or nonlinear in the tunable weight parameters. In the latter case weight tuning algorithms are not straightforward to obtain. Feedback control topologies and weight tuning algorithms are given here that guarantee closed-loop stability and bounded weights. Extensions are discussed to force control, backstepping control, and output feedback control, where dynamic nonlinear nets are required.

关键词： Intelligent feedback control neural networks neural network structures basis function networks fuzzy control fuzzy-neural networks universal approximation adaptive nonparametric control learning control systems

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