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Single-photon quantum effects in biomolecules: Quantum biological spectroscopy

Nature Photonics 2025年第2期19卷 129-130页

作者： Lee, Luke P. Harvard Medical School Harvard University Brigham and Women’s Hospital BostonMA United States Department of Bioengineering University of California Berkeley BerkeleyCA United States Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA United States Department of Biophysics Institute of Quantum Biophysics Sungkyunkwan University Suwon Korea Republic of Department of Chemistry and Nanoscience Ewha Womans University Seoul Korea Republic of

The effects of magnetic fields on quantum entangled biomolecules are detected using a highly sensitive magneto-fluorescence fluctuation microspectroscopy technique. This marks an important advance in experimental quan... 详细信息

关键词： Quantum entanglement

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Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation 39

Battling the Non-stationarity in Time Series Forecasting via...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Kim, HyunGi Kim, Siwon Mok, Jisoo Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of AIIS ASRI INMC Seoul National University Korea Republic of

ISBN: (纸本)157735897X

Deep Neural Networks have spearheaded remarkable advancements in time series forecasting (TSF), one of the major tasks in time series modeling. Nonetheless, the non-stationarity of time series undermines the reliability of pre-trained source time series forecasters in mission-critical deployment settings. In this study, we introduce a pioneering test-time adaptation framework tailored for TSF (TSF-TTA). TAFAS, the proposed approach to TSF-TTA, flexibly adapts source forecasters to continuously shifting test distributions while preserving the core semantic information learned during pre-training. The novel utilization of partially-observed ground truth and gated calibration module enables proactive, robust, and model-agnostic adaptation of source forecasters. Experiments on diverse benchmark datasets and cutting-edge architectures demonstrate the efficacy and generality of TAFAS, especially in long-term forecasting scenarios that suffer from significant distribution shifts. Copyright © 2025, Association for the Advancement of Artificial Intelligence.

关键词： Deep neural networks

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Reconfiguration of Power Distribution Systems in the Presence of Voltage-Dependent Loads

Reconfiguration of Power Distribution Systems in the Presenc...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Mahdavi, Meisam Schmitt, Konrad Bayne, Stephen Chamana, Manohar University of Jaén Department of Electrical Engineering Jaén Linares Spain Texas Tech University Electrical and Computer Engineering Department LubbockTX United States Texas Tech University Renewable Energy Program LubbockTX United States

ISBN: (纸本)9781665490719

Load power can be changed by voltage fluctuation of network buses, in which reconfiguring the topology of distribution systems impacts the magnitude of bus voltages. Only a few papers have considered voltage-dependent loads in their reconfiguration models but introduced nonlinear formulations or highly approximated linear approaches. While nonlinear models can be solved by metaheuristic algorithms without guaranteeing the optimality of solutions or may be implemented by commercial nonlinear solvers, they require intensive and time-consuming computations. Moreover, linearized models are highly approximated by piecewise linear functions with many unknown parameters. Thus, this paper presents the effective design of the reconfiguration problems, including voltage dependency of loads, which can be solved using commercial linear solvers. The proposed models are precise enough to find accurate results for the reconfiguration problem and are adequately fast to converge to optimal solutions. The results show that the proposed solutions not only reduce active power losses significantly but increase the minimum voltage of the system effectively. © 2023 IEEE.

关键词： Power quality

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Super-Mossian Dielectrics for Nanophotonics

Super-Mossian Dielectrics for Nanophotonics

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Wang, Xielin Doiron, Chloe F. Li, Zhichao Khurgin, Jacob B. Naik, Gururaj V. Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States

Searching for high-index dielectrics, we identify materials that break the index upper bound set by Moss' rule. We highlight the promise of such super-Mossian materials by demonstrating nanophotonic devices made o... 详细信息

ISBN: (纸本)9781957171258

关键词： Layered semiconductors

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Online 3D Bin Packing for Novel Objects Based on Deep Reinforcement Learning

Online 3D Bin Packing for Novel Objects Based on Deep Reinfo...

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2023 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2023

作者： Chien, Shao-Yu Wong, Ching-Chang Tamkang University College of Engineering Doctoral Program in Robotics New Taipei City25137 Taiwan Tamkang University Electrical and Computer Engineering Department New Taipei City25137 Taiwan

ISBN: (纸本)9798350302714

In this paper, a method using deep reinforcement learning is proposed to deal with the 3D online bin packing problem. The packing objects are not limited to several specific or fixed cuboid objects, but are composed of more than a thousand objects and randomly generated cuboids, which make the trained policy network can handle novel unknown objects. In addition, the posture of the object in the box can be any angle, not limited to horizontal and vertical. In the proposed method, four voxel maps are used as inputs, and a Soft Actor-Critic (SAC) algorithm is used to train a policy network. On the other hand, in order to deal with various objects with irregular shapes, a packing task simulator with physics engine enable the policy network to learn the state of falling and stacking objects. In terms of training environment of deep reinforcement learning, the proposed method can be applied to boxes of different sizes because of the scalable image information. Moreover, a reward function and a training strategy with gradually increasing difficulty are proposed to effectively improve the learning of policy network. In terms of experimental results, the results on a random object bin packing task in a simulator illustrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Reinforcement learning

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Quantitative phase imaging(QPI)through random diffusers using a diffractive optical network

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Light(Advanced Manufacturing) 2023年第3期4卷 206-221页

作者： Yuhang Li Yi Luo Deniz Mengu Bijie Bai Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCalifornia 90095USA Bioengineering Department University of CaliforniaLos AngelesCalifornia 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCalifornia 90095USA

Quantitative phase imaging(QPI)is a label-free computational imaging technique used in various fields,including biology and medical *** QPI systems typically rely on digital processing using iterative algorithms for phase retrieval and image ***,we report a diffractive optical network trained to convert the phase information of input objects positioned behind random diffusers into intensity variations at the output plane,all-optically performing phase recovery and quantitative imaging of phase objects completely hidden by unknown,random phase *** QPI diffractive network is composed of successive diffractive layers,axially spanning in total~70λ,where is the illumination wavelength;unlike existing digital image reconstruction and phase retrieval methods,it forms an all-optical processor that does not require external power beyond the illumination beam to complete its QPI reconstruction at the speed of light *** all-optical diffractive processor can provide a low-power,high frame rate and compact alternative for quantitative imaging of phase objects through random,unknown diffusers and can operate at different parts of the electromagnetic spectrum for various applications in biomedical imaging and *** presented QPI diffractive designs can be integrated onto the active area of standard CCD/CMOS-based image sensors to convert an existing optical microscope into a diffractive QPI microscope,performing phase recovery and image reconstruction on a chip through light diffraction within passive structured layers.

关键词： Quantitative phase imaging Optical neural network Diffractive deep neural network Diffusive media All-optical computing

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Modified Isolated SEPIC Converter for EV Charging and High Voltage Applications

Modified Isolated SEPIC Converter for EV Charging and High V...

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2023 International Conference on Power, Instrumentation, Energy and Control, PIECON 2023

作者： Ahmad, Javed Lin, Chang-Hua Khan, Mohammad Suhail Liu, Hwa-Dong Fahad, Mohammad National Taiwan University of Science and Technology College of Electrical Engineering and Computer Science Department of Electrical Engineering Taipei Taiwan National Taiwan Normal University Undergraduate Program of Vehicle and Energy Engineering Taipei Taiwan

ISBN: (纸本)9798350399769

This research article presents a modified isolated SEPIC converter suitable for high-power applications. It uses a transformer instead of coupled inductor for isolation between the input power source and the output load. The steady-state analysis is explained in detail. Thermal loss analysis is performed by using PLECS software. Further, a hardware prototype is developed to verify the performance aspects of the proposed converter. © 2023 IEEE.

关键词： High frequency transformers

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Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios

arXiv

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arXiv 2024年

作者： Ma, Shihan Hu, Bo Jia, Tianyu Clarke, Alexander Kenneth Zicher, Blanka Caillet, Arnault H. Farina, Dario Príncipe, José C. Department of Bioengineering Imperial College London United Kingdom Department of Electrical and Computer Engineering University of Florida United States

The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroencephalography (EEG) and electromyography (EMG) recordings. However, current approaches for capturing high-level and contextual connectivity between these recordings have important limitations. Here, we present a novel application of statistical dependence estimators based on orthonormal decomposition of density ratios to model the relationship between cortical and muscle oscillations. Our method extends from traditional scalar-valued measures by learning eigenvalues, eigenfunctions, and projection spaces of density ratios from realizations of the signal, addressing the interpretability, scalability, and local temporal dependence of cortico-muscular connectivity. We experimentally demonstrate that eigenfunctions learned from cortico-muscular connectivity can accurately classify movements and subjects. Moreover, they reveal channel and temporal dependencies that confirm the activation of specific EEG channels during movement. Our code is available at https://***/bohu615/corticomuscular-eigen-encoder. Copyright © 2024, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Analysis of Brain Excitability After Transcranial Direct Current Stimulation and Brain-computer Interface Based on Motor Imagery on a Post-stroke Patient 9th

Analysis of Brain Excitability After Transcranial Direct Cu...

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9th Latin American Congress on Biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical engineering, CBEB 2022

作者： Silva, Leticia Lima, Jéssica Schreider, Sheila Delisle-Rodriguez, Denis Krishnan, Sridhar Bastos-Filho, Teodiano Postgraduate Program in Electrical Engineering Universidade Federal do Espirito Santo Vitoria Brazil Postgraduate Program in Biotechnology Universidade Federal do Espirito Santo Vitoria Brazil Edmond and Lily Safra International Institute of Neurosciences Instituto Santos Dumont Macaiba Brazil Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada

ISBN: (纸本)9783031494062

Stroke is a neurological syndrome that may cause severe cognitive and motor impairments for survival. Alternative rehabilitation techniques have been developed to recover lower-limb movements and gait of post-stroke patients, such as Motor Imagery (MI)-based Brain-computer Interfaces (BCIs), Virtual Reality (VR), and robotic devices. Transcranial Direct Current Stimulation (tDCS) is a non-invasive technique to stimulate the brain and modulate cortical excitability. This study aims to analyze the short-term effects of tDCS compared to sham stimulation before a lower-limb MI-BCI pedaling training (through a robotic monocycle) task in a patient with subacute stroke. We studied a post-stroke patient with 2 months of ischemic stroke and right hemiparesis who was submitted to the following tasks: active pedaling, sham tDCS and MI-BCI, and tDCS and MI-BCI. We evaluated the relative power around the foot area (Cz electrode), and a significant (p-value © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Brain computer interface

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TransNetOCT: An Efficient Transformer-Based Model for 3D-OCT Segmentation Using Prior Shape 27th

TransNetOCT: An Efficient Transformer-Based Model for 3D-OCT...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Elsharkawy, Mohamed Abdelhalim, Ibrahim Ghazal, Mohammed Haq, Mohammad Z. Haq, Rayan Mahmoud, Ali Sandhu, Harpal S. Thanos, Aristomenis El-Baz, Ayman Department of Bioengineering University of Louisville LouisvilleKY United States Electrical and Computer Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates School of Medicine University of Louisville LouisvilleKY United States Legacy Devers Eye Institute PortlandOR United States

ISBN: (纸本)9783031781971

Segmentation of retinal layers in three-dimensional optical coherence tomography (3D-OCT) images plays a pivotal role in disease identification and prognosis. For instance, analyzing variations in layer curvature within 3D-OCT images offers crucial insights into age-related macular degeneration (AMD) progression. This paper presents TransNetOCT, a novel approach employing a transformer architecture for 3D-OCT volume segmentation. The method delineates 3D-OCT scans into twelve surfaces, outlining the background and eleven layers. Initially, we segment the central macular B-scan, identifiable by the fovea, using a joint Markov Gibbs random field model. This model integrates shape, intensity, and spatial characteristics across the twelve retinal surfaces. Subsequently, a probability prior shape algorithm is applied to the adjacent slices, utilizing the middle segmented B-scan image as a reference. TransNetOCT is then trained on the central slice along with the extracted probability prior shapes of adjacent slices. An evaluation of 85 patients, including those with normal, early, and intermediate AMD OCT scans, demonstrates the method’s effectiveness. It achieves an Absolute Sum of Surface Distance (ASSD) of 1.3149 and Mean Absolute Surface Distance (MASD) of 1.7091, outperforming well-known models like the UNet and Feature Pyramid Network (FPN). Specifically, it surpasses UNet and FPN with a 78% reduction for ASSD and a 76% reduction for MASD, respectively, and a 48% reduction for both ASSD and MASD, respectively. These results highlight its superior capability in segmenting a broader spectrum of retinal layers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical coherence tomography

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