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Telepresence, IEEE Conference on

作者： Yujin An Daniel Mitchell John Lathrop David Flynn Soon-Jo Chung Division of Engineering and Applied Science California Institute of Technology Autonomous Systems and Connectivity Division University of Glasgow Glasgow U.K.

ISBN: (数字)9798331528249

ISBN: (纸本)9798331528256

Brain-computer interfaces (BCI) have the potential to provide transformative control in prosthetics, assistive technologies (wheelchairs), robotics, and human-computer interfaces. While Motor Imagery (MI) offers an intuitive approach to BCI control, its practical implementation is often limited by the requirement for expensive devices, extensive training data, and complex algorithms, leading to user fatigue and reduced accessibility. In this paper, we demonstrate that effective MI-BCI control of a mobile robot in real-world settings can be achieved using a fine-tuned Deep Neural Network (DNN) with a sliding window, eliminating the need for complex feature extractions for real-time robot control. The fine-tuning process optimizes the convolutional and attention layers of the DNN to adapt to each user’s daily MI data streams, reducing training data by 70% and minimizing user fatigue from extended data collection. Using a low-cost (~$3k), 16-channel, non-invasive, open-source electroencephalogram (EEG) device, four users teleoperated a quadruped robot over three days. The system achieved 78% accuracy on a single-day validation dataset and maintained a 75% validation accuracy over three days without extensive retraining from day-to-day. For real-world robot command classification, we achieved an average of 62% accuracy. By providing empirical evidence that MI-BCI systems can maintain performance over multiple days with reduced training data to DNN and a low-cost EEG device, our work enhances the practicality and accessibility of BCI technology. This advancement makes BCI applications more feasible for real-world scenarios, particularly in controlling robotic systems.

关键词： Accuracy Telepresence Wheelchairs Training data Artificial neural networks Motors Fatigue Electroencephalography Brain-computer interfaces Mobile robots

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Highly efficient visible and near-IR photon pair generation with thin-film lithium niobate

Optica Quantum

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Optica Quantum 2024年第2期2卷 103-109页

作者： Nathan A. Harper Emily Y. Hwang Ryoto Sekine Luis Ledezma Christian Perez Alireza Marandi Scott K. Cushing Division of Chemistry and Chemical Engineering California Institute of Technologyhttps://***/05dxps055 Pasadena California 91125 USA Department of Applied Physics and Materials Science California Institute of Technology Pasadena California 91125 USA Department of Electrical Engineering California Institute of Technology Pasadena California 91125 USA California State University Los Angeles California 90032 USA

Efficient on-chip entangled photon pair generation at telecom wavelengths is an integral aspect of emerging quantum optical technologies, particularly for quantum communication and computing. However, moving to shorter wavelengths enables the use of more accessible silicon detector technology, and opens up applications in imaging and spectroscopy. Here, we present high brightness ((1.6 ± 0.3) × 10 9 pairs/s/mW/nm) visible–near-IR photon pair generation in a periodically poled lithium niobate nanophotonic waveguide. The degenerate spectrum of the photon pairs is centered at 811 nm with a bandwidth of 117 nm when pumped with a spectrally multimode laser diode. The measured on-chip source efficiency of (2.3 ± 0.5) × 10 11 pairs/s/mW is on par with source efficiencies at telecom wavelengths and is also orders of magnitude higher than the efficiencies of other visible sources implemented in bulk crystal or diffused waveguide-based technologies. Further improvements in the brightness and efficiencies are possible by pumping the device with a single-frequency laser, which would also shrink the pair bandwidth. These results represent the shortest wavelength of photon pairs generated in a nanophotonic waveguide reported to date by nearly an octave.

关键词： Periodically poled lithium niobate Photonic entanglement Quantum communications Quantum computation Quantum information Quantum technology

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aterials structure-property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes

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npj Computational Materials 2022年第1期 574-580页

作者： Dan Guevarra Lan Zhou Matthias H.Richter Aniketa Shinde Di Chen Carla P.Gomes John M.Gregoire Division of Engineering and Applied Science and Liquid Sunlight Alliance California Institute of TechnologyPasadenaCAUSA Department of Computer Science Cornell UniversityIthacaNYUSA

Properties can be tailored by tuning composition in high-order composition *** spaces with complex phase behavior,modeling the properties as a function of composition and phase distribution remains a formidable *** present materials structure–property factorization(MSPF)as an approach to automate modeling of such data and identify synergistic phase *** is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks(DRNets)to matrix factorization-based modeling of the representative properties of each phase in a *** is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation,which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity *** the measured photoactivity to a learned model for non-interacting phases,synergistic phase interactions are identified to guide further photoactivity optimization and *** identifies synergistic interactions of a BiVO_(4)-like phase with both Cu_(2)V_(2)O_(7)-like and CuV_(2)O_(6)-like phases,creating avenues for understanding complex photoelectrocatalysts.

关键词： synergistic phase property

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Recent advances and applications of deep learning methods in materials science

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npj Computational Materials 2022年第1期8卷 548-573页

作者： Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza Ryan Cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm Shyue Ping Ong Chris Wolverton Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD20899USA Theiss Research La JollaCA92037USA DeepMaterials LLC Silver SpringMD20906USA Material Measurement Science Division National Institute of Standards and TechnologyGaithersburgMD20899USA Department of NanoEngineering University of California San DiegoSan DiegoCA92093USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Materials Science and Engineering Carnegie Mellon UniversityPittsburghPA15213USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL60208USA Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL60208USA Department of Applied Physics and Applied Mathematics and the Data Science Institute Fu Foundation School of Engineering and Applied SciencesColumbia UniversityNew YorkNY10027USA

Deep learning(DL)is one of the fastest-growing topics in materials data science.with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data *** allows analysis of unstructured data and automated identification of *** recent development of large materials databases has fueled the application of DL methods in atomistic prediction in *** contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL *** this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language *** each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and *** conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science.

关键词： learning limitations textual

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High-field transport and hot-electron noise in GaAs from first-principles calculations: Role of two-phonon scattering

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Physical Review B 2022年第24期106卷 245201-245201页

作者： Peishi S. Cheng Jiace Sun Shi-Ning Sun Alexander Y. Choi Austin J. Minnich Division of Engineering and Applied Science California Institute of Technology Pasadena 91125 CA United States

High-field charge transport in semiconductors is of fundamental interest and practical importance. While the ab initio treatment of low-field transport is well developed, the treatment of high-field transport is much less so, particularly for multiphonon processes that are reported to be relevant in GaAs. Here, we report a calculation of the high-field transport properties and current power spectral density (PSD) of hot electrons in GaAs from first principles including on-shell two-phonon (2ph) scattering. The on-shell 2ph scattering rates are found to qualitatively alter the high-field distribution function by increasing both the momentum and energy relaxation rates as well as contributing markedly to intervalley scattering. This finding reconciles a long-standing discrepancy regarding the strength of intervalley scattering in GaAs as inferred from transport and optical studies. The characteristic nonmonotonic trend of PSD with electric field is not predicted at this level of theory. Our work shows how ab initio calculations of high-field transport and noise may be used as a stringent test of the electron-phonon interaction in semiconductors.

关键词： Electrical conductivity Electron relaxation Electron-phonon coupling First-principles calculations Noise Transport phenomena Boltzmann theory

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Identifiability of Product of Experts Models

arXiv

引用

arXiv 2023年

作者： Gordon, Spencer L. Kant, Manav Ma, Eric Schulman, Leonard J. Staicu, Andrei The Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States

Product of experts (PoE) are layered networks in which the value at each node is an AND (or product) of the values (possibly negated) at its inputs. These were introduced as a neural network architecture that can efficiently learn to generate high-dimensional data which satisfy many low-dimensional constraints-thereby allowing each individual expert to perform a simple task. PoEs have found a variety of applications in learning. We study the problem of identifiability of a product of experts model having a layer of binary latent variables, and a layer of binary observables that are iid conditional on the latents. The previous best upper bound on the number of observables needed to identify the model was exponential in the number of parameters. We show: (a) When the latents are uniformly distributed, the model is identifiable with a number of observables equal to the number of parameters (and hence best possible). (b) In the more general case of arbitrarily distributed latents, the model is identifiable for a number of observables that is still linear in the number of parameters (and within a factor of two of best-possible). The proofs rely on root interlacing phenomena for some special three-term *** Codes 62E10, 62F99, 68T05 © 2023, CC BY.

关键词： Clustering algorithms

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Motor Imagery Teleoperation of a Mobile Robot Using a Low-Cost Brain-Computer Interface for Multi-Day Validation

arXiv

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arXiv 2024年

作者： An, Yujin Mitchell, Daniel Lathrop, John Flynn, David Chung, Soon-Jo The Division of Engineering and Applied Science California Institute of Technology United States The Autonomous Systems and Connectivity Division University of Glasgow Glasgow United Kingdom

Brain-computer interfaces (BCI) have the potential to provide transformative control in prosthetics, assistive technologies (wheelchairs), robotics, and human-computer interfaces. While Motor Imagery (MI) offers an intuitive approach to BCI control, its practical implementation is often limited by the requirement for expensive devices, extensive training data, and complex algorithms, leading to user fatigue and reduced accessibility. In this paper, we demonstrate that effective MI-BCI control of a mobile robot in real-world settings can be achieved using a fine-tuned Deep Neural Network (DNN) with a sliding window, eliminating the need for complex feature extractions for real-time robot control. The fine-tuning process optimizes the convolutional and attention layers of the DNN to adapt to each user’s daily MI data streams, reducing training data by 70% and minimizing user fatigue from extended data collection. Using a low-cost (∼$3k), 16-channel, non-invasive, open-source electroencephalogram (EEG) device, four users teleoperated a quadruped robot over three days. The system achieved 78% accuracy on a single-day validation dataset and maintained a 75% validation accuracy over three days without extensive retraining from day-to-day. For real-world robot command classification, we achieved an average of 62% accuracy. By providing empirical evidence that MI-BCI systems can maintain performance over multiple days with reduced training data to DNN and a low-cost EEG device, our work enhances the practicality and accessibility of BCI technology. This advancement makes BCI applications more feasible for real-world scenarios, particularly in controlling robotic systems. © 2024, CC BY-NC-SA.

关键词： Mobile robots

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Charge transport in BAs and the role of two-phonon scattering

arXiv

引用

arXiv 2023年

作者： Esho, Iretomiwa Minnich, Austin J. Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States

The semiconductor BAs has drawn significant interest due to experimental reports of simultaneous high thermal conductivity and ambipolar charge mobility. The ab initio prediction of high electron and hole mobility assumed the dominance of charge carrier scattering by one phonon. Recently, higher-order electron-phonon scattering processes in polar and non-polar semiconductors have been reported to have a non-negligible impact on charge transport properties, suggesting they may play a role in BAs as well. Here, we report an ab initio study of two-phonon electron and hole scattering processes in BAs. We find that inclusion of these higher-order processes reduces the computed room temperature electron and hole mobility in BAs by around 40% from the one-phonon value, resulting in an underestimate of experimental values by a similar percentage. We suggest an experimental approach to test these predictions using luminescence spectroscopy that is applicable to the defective samples which are presently available. © 2023, CC BY.

关键词： Electrons

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Forecasting subcritical cylinder wakes with Fourier Neural Operators

arXiv

引用

arXiv 2023年

作者： Renn, Peter I. Wang, Cong Lale, Sahin Li, Zongyi Anandkumar, Anima Gharib, Morteza Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States

We apply Fourier neural operators (FNOs), a state-of-the-art operator learning technique, to forecast the temporal evolution of experimentally measured velocity fields. FNOs are a recently developed machine learning method capable of approximating solution operators to systems of partial differential equations through data alone. The learned FNO solution operator can be evaluated in milliseconds, potentially enabling faster-than-real-time modeling for predictive flow control in physical systems. Here we use FNOs to predict how physical fluid flows evolve in time, training with particle image velocimetry measurements depicting cylinder wakes in the subcritical vortex shedding regime. We train separate FNOs at Reynolds numbers ranging from Re = 240 to Re = 3060 and study how increasingly turbulent flow phenomena impact prediction accuracy. We focus here on a short prediction horizon of ten non-dimensionalized time-steps, as would be relevant for problems of predictive flow control. We find that FNOs are capable of accurately predicting the evolution of experimental velocity fields throughout the range of Reynolds numbers tested (L2 norm error Copyright © 2023, The Authors. All rights reserved.

关键词： Flow control

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Investigation of Drain Noise in Cryogenic InP High Electron Mobility Transistors Using On-wafer S-parameter and Microwave Noise Characterization

Investigation of Drain Noise in Cryogenic InP High Electron ...

引用

2022 Compound Semiconductor Week, CSW 2022

作者： Gabritchidze, Bekari Esho, Iretomiwa Cleary, Kieran A. Readhead, Anthony C. Minnich, Austin J. California Institute of Technology Division of Physics Mathematics and Astronomy PasadenaCA91125 United States University of Crete Department of Physics HeraklionGR-7003 Greece California Institute of Technology Division of Engineering and Applied Science PasadenaCA91125 United States

ISBN: (数字)9781665453400

ISBN: (纸本)9781665453400

We report the on-wafer characterization of S-parameters and microwave noise (T50) of discrete InP HEMTs over a range of physical temperatures, 40 K - 300 K. From these data, we extract a small-signal model and the drain noise temperature (Td) at each bias and temperature. We find that T50 exhibits a temperature dependence that is incompatible with a fixed Td. In contrast, explaining the noise measurements requires Td to change from ~2500 K at room temperature (RT) to ~400 K at cryogenic temperatures. This trend is consistent with the predictions of a theory of drain noise based on real-space transfer of electrons from the channel to the barrier [5]. © 2022 IEEE.

关键词： High electron mobility transistors

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