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arXiv 2024年

作者： Fang, Jie Chen, Rui Sharp, David Renzi, Enrico M. Manna, Arnab Kala, Abhinav Mann, Sander A. Yao, Kan Munley, Christopher Rarick, Hannah Tang, Andrew Pumulo, Sinabu Zheng, Yuebing Menon, Vinod M. Alù, Andrea Majumdar, Arka Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Department of Physics University of Washington SeattleWA98195 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States Walker Department of Mechanical Engineering Texas Materials Institute The University of Texas at Austin AustinTX78712 United States Department of Materials Science and Engineering University of Washington SeattleWA98195 United States Department of Physics City College of New York New YorkNY10031 United States

High-quality (Q)-factor optical resonators with extreme temporal coherence are of both technological and fundamental importance in optical metrology1,2, continuous-wave lasing3-5, and semiconductor quantum optics6-8. Despite extensive efforts in designing high-Q resonators across different spectral regimes, the experimental realization of very large Q-factors at visible wavelengths remains challenging due to the small feature size that is sensitive to fabrication imperfections9-12, and thus is typically implemented in integrated photonics1-4,9,10. In the pursuit of free-space optics with the benefits of large space-bandwidth product and massive parallel operations13,14, here we design and fabricate a visible-wavelength etch-free metasurface with minimized fabrication defects and experimentally demonstrate a million-scale ultrahigh-Q resonance. A new laser-scanning momentum-space-resolved spectroscopy technique with extremely high spectral and angular resolution is developed to characterize the record-high Qfactor as well as the dispersion of the million-Q resonance in free space. By integrating monolayer WSe2 into our ultrahigh-Q meta-resonator, we further demonstrate laser-like highly unidirectional and narrow-linewidth exciton emission, albeit without any operating power density threshold. Under continuous-wave laser pumping, we observe pump-power-dependent linewidth narrowing at room temperature, indicating the potential of our meta-optics platform in controlling coherent quantum light-sources. Our result also holds great promise for applications like optical sensing, spectral filtering, and few-photon nonlinear optics. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous wave lasers

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Economics of Semantic Communication System: An Auction Approach

arXiv

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arXiv 2022年

作者： Liew, Zi Qin Du, Hongyang Yang, Wei Lim, Bryan Xiong, Zehui Niyato, Dusit Miao, Chunyan Kim, Dong In Alibaba-NTU Singapore Joint Research Institute Nanyang Technological Univerity Singapore The School of Computer Science and Engineering The Energy Research Institute NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Nanyang Technological University Singapore639798 Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meaning of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called "semantic model trading". Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called "semantic information trading". To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this paper, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them achieve higher utilities than the baseline method. Copyright © 2022, The Authors. All rights reserved.

关键词： Commerce

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Multiplexed Serial Imaging of Immunotherapy Response with Serrs Nanoprobes

Multiplexed Serial Imaging of Immunotherapy Response with Se...

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IEEE International Symposium on biomedical Imaging

作者： Naxhije Berisha Konstantinos Plakas Matthieu Gigoux Taha Merghoub Chrysafis Andreou Hunter College of the City University of New York New York NY USA PhD Program in Chemistry The Graduate Center of the City University of New York NY USA Memorial Sloan Kettering Cancer Center New York NY USA Department of Chemistry University at Buffalo The State University of New York Buffalo NY USA Weill Cornell Medicine New York NY USA Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

With the advent of new and promising cancer therapies, and with increased interest in precision medicine, there is an urgent need to develop non-invasive technologies that evaluate heterogeneity in treatment response. We demonstrate an 8-plex imaging platform based on surface enhanced resonance Raman scattering nanoparticles (SERRS NPs) to monitor the expression of 4 immunomarkers before and after treatment with checkpoint blockade therapy with anti-CTLA4 and anti-PD-1 antibodies in immunotherapy models of colon (CT26) and breast (4T1) cancers. We found that CT26 tumors showed an early treatment response, while the typically refractory 4T1 tumors demonstrated an unexpected treatment response, suggesting that the nanoparticles may have synergistically contributed to the treatment. Serial imaging of multiple markers during therapy can help predict treatment efficacy and uncover new theranostic adjuvants.

关键词： Nanoparticles Multiplexing Precision medicine Raman scattering Immunotherapy Resonance Surface treatment

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Reduce Fingerprint Construction for Positioning IoT Devices Based on Generative Adversarial Nets 1

Reduce Fingerprint Construction for Positioning IoT Devices ...

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1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020

作者： Tarekegn, Getaneh Berie Juang, Rong-Terng Lin, Hsin-Piao Munaye, Yirga Yayeh Belay Adege, Abebe National Taipei University of Technology Department of International Graduate Program in Electrical Engineering and Computer Science Taipei Taiwan Feng Chia University Department of Electronic Engineering Taichung Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei Taiwan Debre Markos University Department of Information Technology Debre Markos Ethiopia

ISBN: (纸本)9781665404839

Fingerprint-based positioning is popular and applicable for Internet of Things (IoT) applications to offer seamless, intelligent and adaptive location-aware services for IoT devices. However, it takes time and cost to build the radio-map. This paper proposed deep convolutional Generative adversarial nets (DCGANs) to minimize the site survey time and cost, and to mitigate signal fluctuations. The radio-map was designed for receiving radio signals from detectable wireless local area network (WLAN) and cellular networks in scalable environments. The proposed fingerprinting-based positioning is a sequential combination of the hybrid support vector machine and long short-term memory algorithms. The experimental results indicate that the proposed method achieves a promising and reasonable positioning performance for IoT devices in scalable wireless environments. © 2020 IEEE.

关键词： Support vector machines

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Transfer learning approach to modeling multichannel gate-all-around nanosheet field-effect transistors under work function fluctuations

引用

engineering Applications of Artificial Intelligence 2025年 148卷

作者： Dash, Sagarika Li, Yiming Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Artificial Intelligence Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Microelectronics National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan

Deep learning (DL) has significantly advanced various industries, including semiconductors, by providing sophisticated methods for analyzing emerging device data. Transfer learning (TL), a prominent DL topology, leverages knowledge from a pre-trained model to improve the performance of a specific target task. This study aims to apply TL to predict the impact of work function fluctuation (WKF) on the variability of threshold voltage (σVTH) in gate-all-around silicon nanosheet field-effect transistors (GAA Si NS FETs). We used TL to transfer knowledge from models trained on 1-channel (1ch) GAA Si NS FETs to more complex 4-channel (4ch) counterparts. The prediction accuracy is evaluated by using the mean square error(MSE), root mean square error (RMSE), and the mean absolute error(MAE) metrics. Our TL approach achieved an error rate of less than 1%, demonstrating high accuracy in predicting WKF effects on σ VTH of 4ch GAA Si NS FETs using only 500 samples. This study presents a pioneering application of TL using a one-dimensional (1D) convolutional neural network (CNN) long short-term memory (LSTM) topology in semiconductor device technology. The results effectively address the challenge of predicting σ VTH variations in advanced GAA Si NS FETs because of WKF, which eliminates the need for extensive semiconductor device simulations. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

引用

Scientific Reports 2023年第1期13卷 1-10页

作者： Kuo, Tzu-Yun Wang, Jen-Hung Huang, Yung-Wen Sung, Ting-Yi Chen, Ching-Tai Department of Biochemical Science and Technology National Taiwan University Taipei 10617 Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Statistical Science Academia Sinica Taipei 11529 Taiwan Institute of Information Science Academia Sinica Taipei 11529 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 11221 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Department of Bioinformatics and Biomedical Engineering Asia University Taichung 41354 Taiwan Center for Precision Health Research Asia University Taichung 41354 Taiwan

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms;its executable files, user manual, and sample data sets are freely available at https://***/comics/Software_*** . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline. © 2023, Springer Nature Limited.

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Towards Robust and Scalable Dispatch Modeling of Long-Duration Energy Storage

arXiv

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arXiv 2024年

作者： Guerra, Omar J. Dalvi, Sourabh Thatte, Amogh A. Cowiestoll, Brady Jorgenson, Jennie Hodge, Bri-Mathias National Renewable Energy Laboratory 15013 Denver West Parkway GoldenCO80401 United States Colorado School of Mines Advanced Energy Systems Graduate Program 1500 Illinois Street GoldenCO80401 United States Renewable and Sustainable Energy Institute University of Colorado BoulderCO80309 United States Department of Electrical Computer & Energy Engineering University of Colorado BoulderCO80309 United States

Energy storage technologies, including short-duration, long-duration, and seasonal storage, are seen as technologies that can facilitate the integration of larger shares of variable renewable energy, such as wind and solar photovoltaics, in power systems. However, despite recent advances in the techno-economic modeling of energy storage (particularly for short-duration applications), the operation and economics of long-duration energy storage are still incomplete in power systems modeling platforms. For instance, existing modeling approaches for long-duration storage are often based either on an oversimplified representation of power system operations or limited representation of storage technologies, e.g., evaluation of only a single application. This manuscript presents an overview of the challenges of modeling long-duration energy storage technologies, as well as a discussion regarding the capabilities and limitations of existing approaches. We used two test power systems with high shares of both solar photovoltaics- and wind (70% - 90% annual variable renewable energy shares) to assess long-duration energy storage dispatch approaches. Our results estimate that better dispatch modeling of long-duration energy storage could increase the associated operational value by 4% - 14% and increase the standard capacity credit by 14% - 34%. Thus, a better long-duration energy storage dispatch could represent significant cost saving opportunities for electric utilities and system operators. In addition, existing long-duration dispatch modeling approaches were tested in terms of both improved system value (e.g., based on production cost and standard capacity credit) and scalability (e.g., based on central processing unit time and peak memory usage). Both copper plate and nodal representations of the power system were considered. Although the end volume target dispatch approach, i.e., based on mid-term scheduling, showed promising performance in terms of both improved system

关键词： Scalability

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Recurrence plots and convolutional neural networks applied to nonintrusive load monitoring

Recurrence plots and convolutional neural networks applied t...

引用

2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Cavalca, Diego L. Fernandes, Ricardo A. S. Federal University of São Carlos Graduate Program in Computer Science São Carlos Brazil Federal University of São Carlos Electrical Engineering Department São Carlos Brazil

ISBN: (纸本)9781728155081

The need to increasingly promote the efficient use of electricity for residential consumers motivated some researches to obtain solutions that allow the identification of loads for use in demand response programs and in the energy consumption disaggregation. Recently, the use of Convolutional Neural Networks for load identification has been common. However, it is necessary to transform the temporal signal into an image. In this sense, the present paper proposes a novel feature engineering stage, based on Recurrence Plots, to obtain the images for Nonintrusive Load Monitoring. In order to prove its effectiveness, the Recurrence Plots were compared to the Gramian Angular Difference Fields, which had already been applied in this context. For this purpose, it was used the REDD dataset, in which only the low frequency data measured in the main distribution panel were considered. Individual load measurements were only used for the labelling of the 90-seconds windows. The Convolutional Neural Network was trained and validated, considering two different strategies (multi-label and binary models), obtaining an F1-score of 66.05% and 72.65%, respectively. © 2020 IEEE.

关键词： Machine learning

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More Optimal Fractional-Order Stochastic Gradient Descent for Non-Convex Optimization Problems

arXiv

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science Graduate Program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages fractional exponents to capture long-memory effects in optimization. However, its utility is often limited by the difficulty of tuning and stabilizing these exponents. We propose 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), which integrates the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to adapt the fractional exponent in a data-driven manner. By tracking model sensitivity and effective dimensionality, 2SEDFOSGD dynamically modulates the exponent to mitigate oscillations and hasten convergence. Theoretically, for onoconvex optimization problems, this approach preserves the advantages of fractional memory without the sluggish or unstable behavior observed in naïve fractional SGD. Empirical evaluations in Gaussian and α-stable noise scenarios using an autoregressive (AR) model highlight faster convergence and more robust parameter estimates compared to baseline methods, underscoring the potential of dimension-aware fractional techniques for advanced modeling and estimation tasks. © 2025, CC BY.

关键词： Convex optimization

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Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection

arXiv

引用

arXiv 2022年

作者： Yang, Zhenyu Lafata, Kyle Vaios, Eugene Hu, Zongsheng Mullikin, Trey Yin, Fang-Fang Wang, Chunhao Department of Radiation Oncology Duke University DurhamNC United States Medical Physics Graduate Program Duke Kunshan University Jiangsu Kunshan China Medical Physics Graduate Program Duke University DurhamNC United States Department of Radiology Duke University DurhamNC United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Radiation Physics The University of Texas MD Anderson Cancer Center HoustonTX United States The University of Texas MD Anderson Graduate School of Biomedical Science HoustonTX United States

Background: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In potential clinical applications, the uncertainty result reflects the algorithm's robustness and supports the confidence and trust of the segmentation result when the ground-truth result is absent. For commonly studied deep learning models, novel methods for quantifying segmentation uncertainty are in demand. Purpose: To develop a U-Net segmentation uncertainty quantification method based on spherical image projection of multi-parametric MRI (MP-MRI) in glioma segmentation. Methods: The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection-based U-Net (SPU-Net) segmentation model design, multiple independent segmentation predictions can be obtained from a single MRI. The final segmentation is the average of all available results, and the variation can be visualized as a pixel-wise uncertainty map. An uncertainty score was introduced to evaluate and compare the performance of uncertainty measurements. The proposed SPU-Net model was implemented on the basis of 369 glioma patients with MP-MRI scans (T1, T1-Ce, T2, and FLAIR). Three SPU-Net models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The SPU-Net model was compared with (1) the classic U-Net model with test-time augmentation (TTA) and (2) linear scaling-based U-Net (LSU-Net) segmentation models in terms of both segmentation accuracy (Dice coefficient, sensitivity, specificity, and accuracy) and segmentation uncertainty (uncertainty map and uncertainty score). Results: The developed SPU-Net model successfully achieved low uncertainty

关键词： Tumors

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