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Robust event classification using imperfect real-world PMU Data

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arXiv 2021年

作者： Liu, Yunchuan Yang, Lei Ghasemkhani, Amir Livani, Hanif Centeno, Virgilio A. Chen, Pin-Yu Zhang, Junshan Department of Computer Science and Engineering University of Nevada Reno RenoNV89557 United States Computer Science and Engineering California State University San Bernardino San BernardinoCA92407 United States Department of Electrical and Biomedical Engineering University of Nevada Reno RenoNV89557 United States Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University BlacksburgVA24061 United States IBM Thomas J. Watson Research Center Yorktown HeightsNY10598 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

—This paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data);in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs;and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Based on the proposed framework, we develop a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data. © 2021, CC BY.

关键词： Phasor measurement units

Manipulation of Optical Force-Induced Micro-Assemblies at the Air-Liquid Interface

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Manipulation of Optical Force-Induced Micro-Assemblies at th...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Nicholas Carlisle Martin A.K. Williams Catherine P. Whitby Volker Nock Jack L.Y. Chen Ebubekir Avci School of Food and Advanced Technology Massey University Palmerston North New Zealand MacDiarmid Institute for Advanced Materials and Nanotechnology Wellington New Zealand School of Fundamental Sciences Massey University Palmerston North New Zealand Department of Electrical and Computer Engineering University of Canterbury Christchurch New Zealand Centre for Biomedical and Chemical Sciences Auckland University of Technology Auckland New Zealand

Colloidal particles trapped by a focused laser at the air-liquid interface provide an interesting assembly dynamic. In this study, we demonstrated manipulating optical force-induced swarms via dynamic locomotion of assemblies built with holographic optical tweezers. This manipulation approach builds the foundation for autonomous control of building assemblies at the air-liquid interface, which is the first time optical micro-robots have performed this feat. Our proposed semi-autonomous control allows users to produce small dynamic secondary assemblies at the interface, which are transported to and merged with a main static assembly. This static-dynamic approach grows assemblies up to ~2.1 times larger than conventional methods. Manipulation and control of large-scale optical force-induced assemblies in real-time to create re-configurable swarms has the potential to lead the development of new technology and approaches for complex tasks, such as the development of new material, transportation of biological matter, studying biofilm formation created by bacteria colonies at the air-liquid interface, and more.

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Energy-efficient knowledge distillation for spiking Neural Networks

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arXiv 2021年

作者： Lee, Dongjin Park, Seongsik Kim, Jongwan Doh, Wuhyeong Yoon, Sungroh Department of Electrical and Computer Engineering Institute of New Media and Communications Interdisciplinary Program in Artificial Intelligence Automation and Systems Research Institute Seoul National University Korea Republic of

Spiking neural networks (SNNs) have been gaining interest as energy-efficient alternatives of conventional artificial neural networks (ANNs) due to their event-driven computation. Considering the future deployment of SNN models to constrained neuromorphic devices, many studies have applied techniques originally used for ANN model compression, such as network quantization, pruning, and knowledge distillation, to SNNs. Among them, existing works on knowledge distillation reported accuracy improvements of student SNN model. However, analysis on energy efficiency, which is also an important feature of SNN, was absent. In this paper, we thoroughly analyze the performance of the distilled SNN model in terms of accuracy and energy efficiency. In the process, we observe a substantial increase in the number of spikes, leading to energy inefficiency, when using the conventional knowledge distillation methods. Based on this analysis, to achieve energy efficiency, we propose a novel knowledge distillation method with heterogeneous temperature parameters. We evaluate our method on two different datasets and show that the resulting SNN student satisfies both accuracy improvement and reduction of the number of spikes. On MNIST dataset, our proposed student SNN achieves up to 0.09% higher accuracy and produces 65% less spikes compared to the student SNN trained with conventional knowledge distillation method. We also compare the results with other SNN compression techniques and training methods. © 2021, CC BY.

关键词： Energy efficiency

Collaborative multi-resource allocation in terrestrial-satellite network towards 6G

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arXiv 2021年

作者： Fu, Shu Gao, Jie Zhao, Lian School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Xi'an710071 China Department of Electrical and Computer Engineering Marquette University MilwaukeeWI532331 United States Department of Electrical Computer and Biomedical Engineering Ryerson University ONM5B 2K3 Canada

Terrestrial-satellite networks are envisioned to play a significant role in the sixth-generation (6G) wireless networks. In such networks, hot air balloons are useful as they can relay the signals between satellites and ground stations. Most existing works assume that the hot air balloons are deployed at the same height with the same minimum elevation angle to the satellites, which may not be practical due to possible route conflict with airplanes and other flight equipment. In this paper, we consider a TSN containing hot air balloons at different heights and with different minimum elevation angles, which creates the challenge of non-uniform available serving time for the communication between the hot air balloons and the satellites. Jointly considering the caching, computing, and communication (3C) resource management for both the ground-balloon-satellite links and inter-satellite laser links, our objective is to maximize the network energy efficiency. Firstly, by proposing a tapped water-filling algorithm, we schedule the traffic to relay among satellites according to the available serving time of satellites. Then, we generate a series of configuration matrices, based on which we formulate the relationship of relay time and the power consumption involved in the relay among satellites. Finally, the integrated system model of TSN is built and solved by geometric programming with Taylor series approximation. Simulation results demonstrate the effectiveness of our proposed scheme. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

Two-Stage Fine-Tuning: A Novel Strategy for Learning Class-Imbalanced Data

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arXiv 2022年

作者： ValizadehAslani, Taha Shi, Yiwen Wang, Jing Ren, Ping Zhang, Yi Hu, Meng Zhao, Liang Liang, Hualou Department of Electrical and Computer Engineering College of Engineering Drexel University PhiladelphiaPA United States College of Computing and Informatics Drexel University PhiladelphiaPA United States Office of Research and Standards Office of Generic Drugs Center for Drug Evaluation and Research United States Food and Drug Administration Silver SpringMD United States School of Biomedical Engineering Science and Health Systems Drexel University PhiladelphiaPA United States

Classification on long-tailed distributed data is a challenging problem, which suffers from serious class-imbalance and hence poor performance on tail classes with only a few samples. Owing to this paucity of samples, learning on the tail classes is especially challenging for the fine-tuning when transferring a pretrained model to a downstream task. In this work, we present a simple modification of standard fine-tuning to cope with these challenges. Specifically, we propose a two-stage fine-tuning: we first fine-tune the final layer of the pretrained model with class-balanced reweighting loss, and then we perform the standard fine-tuning. Our modification has several benefits: (1) it leverages pretrained representations by only fine-tuning a small portion of the model parameters while keeping the rest untouched;(2) it allows the model to learn an initial representation of the specific task;and importantly (3) it protects the learning of tail classes from being at a disadvantage during the model updating. We conduct extensive experiments on synthetic datasets of both two-class and multi-class tasks of text classification as well as a real-world application to ADME (i.e., absorption, distribution, metabolism, and excretion) semantic labeling. The experimental results show that the proposed two-stage fine-tuning outperforms both fine-tuning with conventional loss and fine-tuning with a reweighting loss on the above datasets. © 2022, CC BY.

关键词： Semantics

Ultraefficient thermoacoustic conversion through a split ring resonator

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Advanced Photonics 2020年第3期2卷 69-77页

作者： Lu Lan Yueming Li Tiffany Yang-Tran Ying Jiang Yingchun Cao Ji-Xin Cheng Boston University Department of Biomedical EngineeringBostonMassachusettsUnited States Boston University Department of Mechanical EngineeringBostonMassachusettsUnited States Boston University Department of Electrical and Computer EngineeringBostonMassachusettsUnited States Boston University Photonics Center BostonMassachusettsUnited States

Microwaves,which have a∼10-cm wavelength,can penetrate deeper into tissue than photons,heralding exciting deep tissue applications such as modulation or imaging via the thermoacoustic *** conversion efficiency is however very low,even with an exogenous contrast *** break this low-conversion limit,using a split ring resonator to effectively collect and confine the microwaves into a submillimeter hot spot for ultrasound emission and achieve a conversion efficiency over 2000 times higher than other reported thermoacoustic contrast ***,the frequency of emitted ultrasound can be precisely tuned and multiplexed by modulation of the microwave *** performance is inaccessible by a piezoelectric-based transducer or a photoacoustic emitter and,therefore,split ring resonators open up new opportunities to study the frequency response of cells in ultrasonic *** applications in deep tissue localization,a split ring resonator can be used as a wireless,battery-free ultrasound beacon placed under a breast phantom.

关键词： thermoacoustic effect metamaterial photoacoustic effect split ring resonator ultrasound

Fine-Tuning BERT for Automatic ADME Semantic Labeling in FDA Drug Labeling to Enhance Product-Specific Guidance Assessment

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arXiv 2022年

作者： Shi, Yiwen Wang, Jing Ren, Ping ValizadehAslani, Taha Zhang, Yi Hu, Meng Liang, Hualou College of Computing and Informatics Drexel University PhiladelphiaPA United States Office of Research and Standards Office of Generic Drugs Center for Drug Evaluation and Research United States Food and Drug Administration Silver Spring MD United States Department of Electrical and Computer Engineering College of Engineering Drexel University PhiladelphiaPA United States School of Biomedical Engineering Science and Health Systems Drexel University PhiladelphiaPA United States

Product-specific guidances (PSGs) recommended by the United States Food and Drug Administration (FDA) are instrumental to promote and guide generic drug product development. To assess a PSG, the FDA assessor needs to take extensive time and effort to manually retrieve supportive drug information of absorption, distribution, metabolism, and excretion (ADME) from the reference listed drug labeling. In this work, we leveraged the state-of-the-art pre-trained language models to automatically label the ADME paragraphs in the pharmacokinetics section from the FDA-approved drug labeling to facilitate PSG assessment. We applied a transfer learning approach by fine-tuning the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model to develop a novel application of ADME semantic labeling, which can automatically retrieve ADME paragraphs from drug labeling instead of manual work. We demonstrated that fine-tuning the pre-trained BERT model can outperform the conventional machine learning techniques, achieving up to 11.6% absolute F1 improvement. To our knowledge, we were the first to successfully apply BERT to solve the ADME semantic labeling task. We further assessed the relative contribution of pre-training and fine-tuning to the overall performance of the BERT model in the ADME semantic labeling task using a series of analysis methods such as attention similarity and layer-based ablations. Our analysis revealed that the information learned via fine-tuning is focused on task-specific knowledge in the top layers of the BERT, whereas the benefit from the pre-trained BERT model is from the bottom layers. © 2022, CC BY.

关键词： Semantics

Cell Spatial Analysis in Crohn's Disease: Unveiling Local Cell Arrangement Pattern with Graph-based Signatures

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arXiv 2023年

作者： Bao, Shunxing Zhu, Sichen Kolachala, Vasantha L. Remedios, Lucas W. Hwang, Yeonjoo Sun, Yutong Deng, Ruining Cui, Can Li, Yike Li, Jia Roland, Joseph T. Liu, Qi Lau, Ken S. Kugathasan, Subra Qiu, Peng Wilson, Keith T. Coburn, Lori A. Landman, Bennett A. Huo, Yuankai Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States Division of Pediatric Gastroenterology Department of Pediatrics Pediatric Research Institute Children's Healthcare of Atlanta Emory University School of Medicine AtlantaGA United States Department of Computer Science Vanderbilt University NashvilleTN United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Surgery Vanderbilt University Medical Center NashvilleTN United States Department of Cell and Developmental Biology Vanderbilt University School of Medicine NashvilleTN United States Division of Gastroenterology Hepatology and Nutrition Department of Medicine Vanderbilt University Medical Center NashvilleTN United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Department of Otolaryngology-Head and Neck Surgery Vanderbilt University Medical Center NashvilleTN United States

Crohn's disease (CD) is a chronic and relapsing inflammatory condition that affects segments of the gastrointestinal tract. CD activity is determined by histological findings, particularly the density of neutrophils observed on Hematoxylin and Eosin stains (H&E) imaging. However, understanding the broader morphometry and local cell arrangement beyond cell counting and tissue morphology remains challenging. To address this, we characterize six distinct cell types from H&E images and develop a novel approach for the local spatial signature of each cell. Specifically, we create a 10-cell neighborhood matrix, representing neighboring cell arrangements for each individual cell. Utilizing t-SNE for non-linear spatial projection in scatter-plot and Kernel Density Estimation contour-plot formats, our study examines patterns of differences in the cellular environment associated with the odds ratio of spatial patterns between active CD and control groups. This analysis is based on data collected at the two research institutes. The findings reveal heterogeneous nearest-neighbor patterns, signifying distinct tendencies of cell clustering, with a particular focus on the rectum region. These variations underscore the impact of data heterogeneity on cell spatial arrangements in CD patients. Moreover, the spatial distribution disparities between the two research sites highlight the significance of collaborative efforts among healthcare organizations. All research analysis pipeline tools are available at https://***/MASILab/cellNN. © 2023, CC BY.

关键词： Cells

Efficient Noise Calculation in Deep Learning-based MRI Reconstructions

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arXiv 2025年

作者： Dalmaz, Onat Desai, Arjun D. Heckel, Reinhard Çukur, Tolga Chaudhari, Akshay S. Hargreaves, Brian A. Department of Electrical Engineering Stanford University StanfordCA United States Department of Radiology Stanford University StanfordCA United States Department of Computer Engineering Technical University of Munich Munich Germany Department of Electrical and Electronics Engineering Bilkent University Ankara Turkey Department of Biomedical Data Science Stanford University StanfordCA United States Department of Bioengineering Stanford University StanfordCA United States

Accelerated MRI reconstruction involves solving an ill-posed inverse problem where noise in acquired data propagates to the reconstructed images. Noise analyses are central to MRI reconstruction for providing an explicit measure of solution fidelity and for guiding the design and deployment of novel reconstruction methods. However, deep learning (DL)-based reconstruction methods have often overlooked noise propagation due to inherent analytical and computational challenges, despite its critical importance. This work proposes a theoretically grounded, memory-efficient technique to calculate voxel-wise variance for quantifying uncertainty due to acquisition noise in accelerated MRI reconstructions. Our approach approximates noise covariance using the DL network’s Jacobian, which is intractable to calculate. To circumvent this, we derive an unbiased estimator for the diagonal of this covariance matrix—voxel-wise variance—, and introduce a Jacobian sketching technique to efficiently implement it. We evaluate our method on knee and brain MRI datasets for both data- and physics-driven networks trained in supervised and unsupervised manners. Compared to empirical references obtained via Monte-Carlo simulations, our technique achieves near-equivalent performance while reducing computational and memory demands by an order of magnitude or more. Furthermore, our method is robust across varying input noise levels, acceleration factors, and diverse undersampling schemes, highlighting its broad applicability. Our work reintroduces accurate and efficient noise analysis as a central tenet of reconstruction algorithms, holding promise to reshape how we evaluate and deploy DL-based MRI. Our code will be made publicly available upon *** Codes 65C60, 94A08, 68T07 © 2025, CC BY-NC-ND.

关键词： Monte Carlo methods