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Static Hopf Solitons and Knotted Emergent Fields in Solid-State Noncentrosymmetric Magnetic Nanostructures

Physical Review Letters 2018年第18期121卷 187201-187201页

作者： Jung-Shen B. Tai (戴榮身) Ivan I. Smalyukh Department of Physics University of Colorado Boulder Colorado 80309 USA Materials Science and Engineering Program Soft Materials Research Center and Department of Electrical Computer and Energy Engineering University of Colorado Boulder Colorado 80309 USA Renewable and Sustainable Energy Institute National Renewable Energy Laboratory and University of Colorado Boulder Colorado 80309 USA

Two-dimensional topological solitons, commonly called Skyrmions, are extensively studied in solid-state magnetic nanostructures and promise many spintronics applications. However, three-dimensional topological solitons dubbed hopfions have not been demonstrated as stable spatially localized structures in solid-state magnetic materials. Here we model the existence of such static solitons with different Hopf index values in noncentrosymmetric solid magnetic nanostructures with a perpendicular interfacial magnetic anisotropy. We show how this surface anisotropy, along with the Dzyaloshinskii-Moriya interactions and the geometry of nanostructures, stabilize hopfions. We demonstrate knots in emergent field lines and computer simulate Lorentz transmission electron microscopy images of such solitonic configurations to guide their experimental discovery in magnetic solids.

关键词： Magnetism Chiral magnets Solitons Topology

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Machine Learning-Based Statistical Hypothesis Testing for Fault Detection

Machine Learning-Based Statistical Hypothesis Testing for Fa...

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Conference on Control and Fault-Tolerant Systems (SysTol)

作者： Radhia Fazai Majdi Mansouri Kamal Abodayeh Mohamed Trabelsi Hazem Nounou Mohamed Nounou Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Department of Mathematical Sciences Prince Sultan University Riyadh Saudi Arabia Department of Electronic and Communications Engineering Kuwait College of Science and Technology Kuwait Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (纸本)9781728103815

This paper elaborates the development of machine learning approach merged with statistical hypothesis testing aimed at enhancing the operation of photovoltaic (PV) systems by developing intelligent PV fault detection framework. Fault detection in PV systems is important to ensure optimal energy harvesting and reliable power production because PV systems usually operate in a harsh outdoor environment and tend to suffer various faults. In this paper, therefore, special attention is paid to detection of various faults during different modes of operation. The proposed approach merges the benefits of machine learning technique (MLT) with statistical hypothesis testing to enhance the fault detection and monitoring of PV systems. The proposed technique will be effective in monitoring the PV faults under both normal and abnormal conditions. For the framework developed, the modeling phase is addressed using MLT and the faults are detected using the generalized likelihood ratio test (GLRT) chart. The MLT is used to compute the residuals monitored and the GLRT chart is applied to the monitored residuals evaluated for fault detection purposes. The developed MLT-based GLRT algorithm is implemented and validated using both simulated and real PV data. The results are evaluated in terms of false alarm rates (FAR), missed detection rates (MDR) and computation time.

关键词： Ground penetrating radar Monitoring Circuit faults Testing Photovoltaic systems Fault detection

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Optimization Process of Glycerol Esterification Using Real Time Adaptive Control

Optimization Process of Glycerol Esterification Using Real T...

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International Conference on Advanced computer science and Information Systems

作者： Iwan Aang Soenandi Ani Suryani Taufik Djatna Irzaman Post Graduate Program of Agro-industrial Technology Bogor Agricultural University Dramaga Indonesia Lecturer Faculty of Engineering and Computer Science Krida Wacana Christian University Post Graduate Program of Agro-industrial Technology Bogor Agricultural University. Dramaga Indonesia Post Graduate Program of Agro-industrial Technology and Graduate Program of Computer Science Bogor Agricultural University. Dramaga Indonesia Department of Physics Faculty Mathematics and Natural Sciences Bogor Agricultural University. Dramaga Indonesia

ISBN: (纸本)9781509003648

The synthesis reaction used in esterification needs high energy consumption and a precise processing time to get the best yield of target. In this study, a model was formulated to optimize glycerol esterification process by minimizing the time needed for the process and maximizing the yield of Mono-glycerides. This optimization has gained importance for boosting the esterification industry and improving the production efficiency. Optimization through adaptive monitoring and control has provided significant advances in the process efficiency, a lower energy consumption and a better product quality. This paper presents the optimization with a computational algorithm in real time and adaptive control (RTAC), as compared to the conventional (traditional) methods to monitor and control of glycerol esterification processes. The identification of esterification status based on temperature and time are evaluated to strengthen the optimization. An adaptive method as feature selection to select wavelength IR sensors at specified intervals was carried out with Relief algorithm and Adaptive Pillar K-means clustering method to set the parameter control was proposed in this paper. Many combinations were evaluated from real time condition process, to achieve the best optimization results. The experimental results demonstrate that real time adaptive control can be achieved by using three clusters, which are heating up, stabilizing and finishing. In RTAC, each cluster has its own parameter to set the control point by the servo motor that was attached to magnetic stirrer-heater. By using optimization parameter for each cluster, esterification process time can be shortened by 15-20 minutes with a higher yield (7% or more), lower range stirrer rotation (300rpm-450 rpm) and a lower final temperature of 200℃ - 210℃.

关键词： Optimization Real Time Adaptive Control Glycerol Esterification

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Microstructural analysis and electro-optic properties of thick epitaxial BaTiO3 films integrated on silicon (001)

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Physical Review Materials 2022年第9期6卷 095201-095201页

作者： Marc Reynaud Zuoming Dong Hyoju Park Wente Li Agham B. Posadas Jamie H. Warner Daniel Wasserman Alexander A. Demkov Department of Physics University of Texas at Austin Austin Texas 78712 USA Department of Electrical and Computer Engineering University of Texas at Austin Austin Texas 78758 USA Walker Department of Mechanical Engineering University of Texas at Austin Austin Texas 78712 USA Materials Science and Engineering Program and Texas Materials Institute The University of Texas at Austin Texas 78712 USA

Recent advances in epitaxial oxide deposition have enabled the fabrication of thick films of ferroelectric perovskite BaTiO3 capable of providing a robust electro-optic response via the Pockels effect in silicon photonic devices. We report a microstructure analysis of such films integrated on Si(001) by molecular beam epitaxy, showing how the crystallographic and polarization orientation change as a function of thickness. The measured electro-optic properties of the film correlate well with the microstructural analysis and demonstrate the potential of Si-integrated BaTiO3 for silicon photonics. An effective Pockels coefficient of up to 268 pm/V has been demonstrated in 110-nm-thick films in transmission measurements and 352 pm/V in waveguide measurements.

关键词： Ferroelectricity Photonics Epitaxy

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Improving Electrically Evoked Compound Action Potential Based on Electrical Field Imaging and Electrode Interfaces

Improving Electrically Evoked Compound Action Potential Base...

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IEEE Conference on Electromagnetic Field Computation

作者： Charles T.M. Choi Chun Ting Ke Jelani Lawrence Alexander C. C. Wang Department of Electronics and Electrical Engineering College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electrical and Control Engineering College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Electrical Engineering & Computer Science International Graduate Program College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350348958

ISBN: (纸本)9798350348965

The electrically evoked compound action potential (ECAP) has been used in various clinical studies and has become a key physiological signal for cochlear implants (CI). This study used four sensing electrodes to record ECAP signals based on the alternating polarity approach. An electrical field imaging (EFI) result based on the finite element method was used to obtain the interface impedance, then ECAP simulation results were computed and compared with a patient's clinical ECAP measurements. Preliminary modeling results show that the interface impedance obtained by this EFI-based technique can improve the simulation accuracy of the ECAP model. The ECAP modeling result will be compared with clinical ECAP measurements to validate the model in the full paper.

关键词： Electrodes Action potentials Impedance measurement Computational modeling Simulation Imaging Finite element analysis

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Multi-choice Dialogue-Based Reading Comprehension with Knowledge and Key Turns

arXiv

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arXiv 2020年

作者： Li, Junlong Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Multi-choice machine reading comprehension (MRC) requires models to choose the correct answer from candidate options given a passage and a question. Our research focuses dialogue-based MRC, where the passages are multi-turn dialogues. It suffers from two challenges, the answer selection decision is made without support of latently helpful commonsense, and the multi-turn context may hide considerable irrelevant information. This work thus makes the first attempt to tackle those two challenges by extracting substantially important turns and utilizing external knowledge to enhance the representation of context. In this paper, the relevance of each turn to the question are calculated to choose key turns. Besides, terms related to the context and the question in a knowledge graph are extracted as external knowledge. The original context, question and external knowledge are encoded with the pre-trained language model, then the language representation and key turns are combined together with a will-designed mechanism to predict the answer. Experimental results on a DREAM dataset show that our proposed model achieves great improve- Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge graph

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Robust Coupling between Structural and Electronic Transitions in a Mott Material

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Physical Review Letters 2019年第5期122卷 057601-057601页

作者： Yoav Kalcheim Nikita Butakov Nicolas M. Vargas Min-Han Lee Javier del Valle Juan Trastoy Pavel Salev Jon Schuller Ivan K. Schuller Department of Physics and Center for Advanced Nanoscience University of California San Diego La Jolla California 92093 USA Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara California 93106 USA Material Science and Engineering Program University of California San Diego La Jolla California 92093 USA

The interdependences of different phase transitions in Mott materials are fundamental to the understanding of the mechanisms behind them. One of the most important relations is between the ubiquitous structural and electronic transitions. Using IR spectroscopy, optical reflectivity, and x-ray diffraction, we show that the metal-insulator transition is coupled to the structural phase transition in V2O3 films. This coupling persists even in films with widely varying transition temperatures and strains. Our findings are in contrast to recent experimental findings and theoretical predictions. Using V2O3 as a model system, we discuss the pitfalls in measurements of the electronic and structural states of Mott materials in general, calling for a critical examination of previous work in this field. Our findings also have important implications for the performance of Mott materials in next-generation neuromorphic computing technology.

关键词： Crystal growth Crystal orientation Crystal structure Crystal symmetry Crystallization Electronic structure Peierls transition Phase transitions Spin state transition Strain Antiferromagnets Mott insulators Stimuli-responsive materials Strongly correlated systems Thin films Cavity resonators Crystallography Fourier transform infrared spectroscopy Resistivity measurements Scanning techniques Sputtering Strain engineering X-ray diffraction

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Lateralized Learning for Multi-Class Visual Classification Tasks

arXiv

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arXiv 2023年

作者： Siddique, Abubakar Browne, Will N. Grimshaw, Gina M. The School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand The Faculty of Engineering School of Electrical Engineering & Robotics Queensland University of Technology Brisbane4000 Australia The Cognitive the School of Psychology Victoria University of Wellington PO Box 600 Wellington6140 New Zealand

The majority of computer vision algorithms fail to find higher-order (abstract) patterns in an image so are not robust against adversarial attacks, unlike human lateralized vision. Deep learning considers each input pixel in a homogeneous manner such that different parts of a "locality-sensitive hashing table" are often not connected, meaning higher-order patterns are not discovered. Hence these systems are not robust against noisy, irrelevant, and redundant data, resulting in the wrong prediction being made with high confidence. Conversely, vertebrate brains afford heterogeneous knowledge representation through lateralization, enabling modular learning at different levels of abstraction. This work aims to verify the effectiveness, scalability, and robustness of a lateralized approach to real-world problems that contain noisy, irrelevant, and redundant data. The experimental results of multi-class (200 classes) image classification show that the novel system effectively learns knowledge representation at multiple levels of abstraction making it more robust than other state-of-the-art techniques. Crucially, the novel lateralized system outperformed all the state-of-the-art deep learning-based systems for the classification of normal and adversarial images by 19.05% − 41.02% and 1.36% − 49.22%, respectively. Findings demonstrate the value of heterogeneous and lateralized learning for computer vision applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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Stochastic policy gradient reinforcement learning on a simple 3D biped

Stochastic policy gradient reinforcement learning on a simpl...

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： R. Tedrake T.W. Zhang H.S. Seung Computer Science & Artificial Intelligence Lab Center for Bits & Atoms Massachusetts Institute of Technology Cambridge MA USA Department of Mechanical Engineering Brain & Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA Howard Hughes Medical Institute Brain & Cognitive Sciences Center for Bits & Atoms Massachusetts Institute of Technology Cambridge MA USA

We present a learning system which is able to quickly and reliably acquire a robust feedback control policy for 3D dynamic walking from a blank-slate using only trials implemented on our physical robot. The robot begins walking within a minute and learning converges in approximately 20 minutes. This success can be attributed to the mechanics of our robot, which are modeled after a passive dynamic walker, and to a dramatic reduction in the dimensionality of the learning problem. We reduce the dimensionality by designing a robot with only 6 internal degrees of freedom and 4 actuators, by decomposing the control system in the frontal and sagittal planes, and by formulating the learning problem on the discrete return map dynamics. We apply a stochastic policy gradient algorithm to this reduced problem and decrease the variance of the update using a state-based estimate of the expected cost. This optimized learning system works quickly enough that the robot is able to continually adapt to the terrain as it walks.

关键词： Stochastic processes Legged locomotion Learning systems Robust control Feedback control Robots Actuators Control systems State estimation Costs

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Instruction-Driven Game Engine: A Poker Case Study

arXiv

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arXiv 2024年

作者： Wu, Hongqiu Liu, Xingyuan Wang, Yan Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Tencent China

The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game descriptions and generate game-play processes. The IDGE allows users to create games simply by natural language instructions, which significantly lowers the barrier for game development. We approach the learning process for IDGEs as a Next State Prediction task, wherein the model autoregressively predicts the game states given player actions. The computation of game states must be precise;otherwise, slight errors could corrupt the game-play experience. This is challenging because of the gap between stability and diversity. To address this, we train the IDGE in a curriculum manner that progressively increases its exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, which not only supports a wide range of poker variants but also allows for highly individualized new poker games through natural language inputs. This work lays the groundwork for future advancements in transforming how games are created and played. Copyright © 2024, The Authors. All rights reserved.

关键词： Game design

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