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Design, Automation and Test in Europe Conference and Exhibition

作者： Sunyoung Park Hyunji Kim Hana Kim Ji-Hoon Kim Department of Electronic and Electrical Engineering Ewha Womans University Seoul Republic of Korea Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

In the realm of safety-critical Automotive System-on-Chip (SoC) design, memory functionality plays a crucial role in determining overall die yield due to its sizable footprint on the chip. Therefore, efficient methodologies for both post-manufacturing offline testing and real-time monitoring are essential to provide timely diagnostic feedback. This paper proses a real-time memory diagnostic data compression technique Pattern-Adaptive Two-Stage Bloom Filter (PA-2SBF) for automotive System-on-Chip (SoC) applications. PA-2SBF is designed to address the challenge of false positives incorporating frequently encountered failure patterns. Bloom filters, a probabilistic data structure renowned for their space-efficiency and quick approximate membership queries, are employed to expedite fault memory diagnosis information lookup and compression. Furthermore, failure patterns are considered to mitigate the false positive rate inherent in Bloom filters. The paper also presents a strategy for leveraging the compressed diagnostic information during run-time. Specifically, it exploits the quick lookup feature of Bloom filter to prohibit CPU access to defective memory regions, enhancing overall system reliability.

关键词： Memory management Data compression Information filters Probabilistic logic Data structures Real-time systems System-on-chip

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Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives

arXiv

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arXiv 2022年

作者： Liu, Xiaofeng Yoo, Chaehwa Xing, Fangxu Oh, Hyejin El Fakhri, Georges Kang, Je-Won Woo, Jonghye Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School BostonMA United States Dept. of Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of

Deep learning has become the method of choice to tackle real-world problems in different domains, partly because of its ability to learn from data and achieve impressive performance on a wide range of applications. However, its success usually relies on two assumptions: (i) vast troves of labeled datasets are required for accurate model fitting, and (ii) training and testing data are independent and identically distributed. Its performance on unseen target domains, thus, is not guaranteed, especially when encountering out-of-distribution data at the adaptation stage. The performance drop on data in a target domain is a critical problem in deploying deep neural networks that are successfully trained on data in a source domain. Unsupervised domain adaptation (UDA) is proposed to counter this, by leveraging both labeled source domain data and unlabeled target domain data to carry out various tasks in the target domain. UDA has yielded promising results on natural image processing, video analysis, natural language processing, time-series data analysis, medical image analysis, etc. In this review, as a rapidly evolving topic, we provide a systematic comparison of its methods and applications. In addition, the connection of UDA with its closely related tasks, e.g., domain generalization and out-of-distribution detection, has also been discussed. Furthermore, deficiencies in current methods and possible promising directions are highlighted. © 2022, CC BY.

关键词： Deep neural networks

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Unsupervised Domain Adaptation for Segmentation with Black-box Source Model

arXiv

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arXiv 2022年

作者： Liu, Xiaofeng Yoo, Chaehwa Xing, Fangxu Kuo, C.-C. Jay Fakhri, Georges El Kang, Je-Won Woo, Jonghye Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School BostonMA United States Dept. of Electronic and Electrical Engineering and Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of Dept. of Electrical and Computer Engineering University of Southern California Los AngelesCA United States

Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in a new domain. The training of conventional solutions usually relies on the existence of both source and target domain data. However, privacy of the large-scale and well-labeled data in the source domain and trained model parameters can become the major concern of cross center/domain collaborations. In this work, to address this, we propose a practical solution to UDA for segmentation with a black-box segmentation model trained in the source domain only, rather than original source data or a white-box source model. Specifically, we resort to a knowledge distillation scheme with exponential mixup decay (EMD) to gradually learn target-specific representations. In addition, unsupervised entropy minimization is further applied to regularization of the target domain confidence. We evaluated our framework on the BraTS 2018 database, achieving performance on par with white-box source model adaptation approaches. © 2022, CC BY.

关键词： Distillation

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Multilayered LSTM with Parameter Transfer for Vehicle Speed Data Imputation

Multilayered LSTM with Parameter Transfer for Vehicle Speed ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Jungmin Kwon Chaeyeon Cha Hyunggon Park Dept. Electronic and Electrical Engineering Ewha Womans University Seoul Republic of Korea Smart Factory Multidisciplinary Program Ewha Womans University Seoul Republic of Korea The Alan Turing Institute London The United Kingdom

In this paper, we propose a multilayered Long ShortTerm Memory (LSTM) architecture with parameter transfer for a traffic speed data imputation in the vehicle to infrastructure (V2I) networks. We consider a scenario in V2I networks where a Road Side Unit (RSU) on the road cannot temporarily collect the traffic speed data because of its malfunction, which causes services that use the traffic speed data at the RSU to be unavailable. Therefore, it is imperative to develop an efficient and low complexity data imputation algorithm for uninterrupted and seamless V2I services. We propose an architecture that uses a multilayered LSTM (M-LSTM) network with parameter transfers, which can explicitly consider the characteristics of temporal dependency in the traffic speed data. The temporal dependency of the traffic speed data enables the parameters trained from each LSTM layer to be transferred to its adjacent LSTM layer and used for its parameter training, thereby significantly reducing the overall training and imputing complexity. Our simulation and experiment results confirm that the time for training and data imputation can be significantly reduced while maintaining imputation accuracy.

关键词： Training Vehicle-to-infrastructure Roads Road side unit Data models Complexity theory Time complexity

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A Deep Learning Model of Amyloid-β Diffusion Simulation

A Deep Learning Model of Amyloid-β Diffusion Simulation

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2024 International Conference on electronics, Information, and Communication, ICEIC 2024

作者： Jeong, ByeongChang Kim, Daegyeom Jeong, Hyun-Ghang Han, Cheol E. Korea University Alzheimer's Disease Neuroimaging Initiative Dept. of Electronic and Information Engineering Sejong Korea Republic of Korea University Alzheimer's Disease Neuroimaging Initiative Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Sejong Korea Republic of Korea University College of Medicine Alzheimer's Disease Neuroimaging Initiative Dept. of Psychiatry Seoul Korea Republic of

ISBN: (纸本)9798350371888

Alzheimer's disease (AD) is a severe neurodegenerative disease characterized by ongoing brain tissue decline. Since amyloid-β accumulation is supposed as a primary cause of AD progression, mathematical models have been recently employed to simulate amyloid-β diffusion in our brain. These models require parameter estimation through observed data and an iterative process to update parameters, although increased complexity can hinder their optimization. To overcome these difficulties, we converted the mathematical model of amyloid-β diffusion into a deep learning model, combining multi-layer perceptron (MLP) and graph convolutional neural network (GCN). The proposed model well predicted the change of accumulation level of amyloid-β with a high accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Algorithm-Hardware Co-Design for Wearable BCIs: An Evolution from Linear Algebra to Transformers

Algorithm-Hardware Co-Design for Wearable BCIs: An Evolution...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Sunyoung Park Wooseok Byun Minkyu Je Ji-Hoon Kim Department of Electronic and Electrical Engineering Ewha Womans University Seoul Republic of Korea Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea SAPEON Korea Inc Seongnam Republic of Korea School of Electrical Engineering KAIST Daejeon Republic of Korea

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Recent advancements in brain-computer interface (BCI) technology for steady-state visual evoked potential (SSVEP)-based target identification have shifted from traditional linear algebra (LA) techniques to more sophisticated neural network (NN) approaches, driven by their increased accuracy and consistent performance across different subjects. However, adopting NN-based algorithms has introduced complexities in wearable BCI systems, mainly due to their extensive parameter sets that demand significant memory capacity. Moreover, the computational intensity of these models requires reevaluating hardware architectures. Additionally, the advent of Transformer-based models has further advanced the state of the art, providing even higher accuracy and reduced variability in cross-subject performance, placing greater demands on hardware resources. This paper provides an overview of recent algorithmic progress in SSVEP-based target identification. Also, it proposes considerations for the hardware architecture needed to efficiently support the computation of cutting-edge Transformer-based models in wearable BCIs from the perspective of algorithm-hardware co-design.

关键词： Visualization Accuracy Computational modeling Computer architecture Artificial neural networks Linear algebra Transformers

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A Deep Learning Model of Amyloid-${\boldsymbol{\beta }}$ Diffusion Simulation

A Deep Learning Model of Amyloid-${\boldsymbol{\beta }}$ Dif...

引用

International Conference on electronics, Information and Communications (ICEIC)

作者： ByeongChang Jeong Daegyeom Kim Hyun-Ghang Jeong Cheol E. Han Dept. of Electronic and Information Engineering The Alzheimer's Disease Neuroimaging Initiative Korea University Sejong Republic of Korea The Alzheimer's Disease Neuroimaging Initiative Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Republic of Korea Dept. of Psychiatry The Alzheimer's Disease Neuroimaging Initiative Korea University College of Medicine Seoul Republic of Korea

Alzheimer's disease (AD) is a severe neurodegenerative disease characterized by ongoing brain tissue decline. Since amyloid- $\beta$ accumulation is supposed as a primary cause of AD progression, mathematical models have been recently employed to simulate amyloid- $\beta$ diffusion in our brain. These models require parameter estimation through observed data and an iterative process to update parameters, although increased complexity can hinder their optimization. To overcome these difficulties, we converted the mathematical model of amyloid- $\beta$ diffusion into a deep learning model, combining multi-layer perceptron (MLP) and graph convolutional neural network (GCN). The proposed model well predicted the change of accumulation level of amyloid- $\beta$ with a high accuracy.

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Deep STI: Deep Stochastic Time-series Imputation on electronic Health Records

Deep STI: Deep Stochastic Time-series Imputation on Electron...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ming-Che Cheng Yi-Hsien Hsieh Te-Cheng Hsu Tung-Hung Su Che Lin Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Dept. of Internal Medicine Division of Gastroenterolgy and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

electronic Health Records (EHRs) are a cornerstone of modern healthcare analytics, offering rich datasets for various disease analyses through advanced deep learning algorithms. However, the pervasive issue of missing values in EHRs significantly hampers the development and performance of these models. Addressing this challenge is crucial for enhancing clinical decision-making and patient care. Existing methods for handling missing data, ranging from simple imputation to more sophisticated approaches, often fall short of capturing the temporal dynamics inherent in EHRs. To bridge this gap, we introduce the Deep Stochastic Time-series Imputation (Deep STI) algorithm, an innovative end-to-end deep learning model that seamlessly integrates a sequence-to-sequence generative network with a prediction network. Deep STI is designed to leverage the observed time-series data in EHRs, learning to infer missing values from the temporal context with high accuracy. We evaluated Deep STI on the liver cancer data from the National Taiwan University Hospital (NTUH), Taiwan. Our results showed that Deep STI achieved better 5-year hepatocellular carcinoma predictions (19.21% in the area under the precision-recall curve) than extreme gradient boosting (18.15%) and Transformer (18.09%). The ablation study also illustrates the efficacy of our generative architecture design compared to regular imputations. This approach not only promises to improve the reliability of disease analysis in the presence of incomplete data but also sets a new standard for utilizing EHRs in predictive healthcare. Our work aims to advance the field of healthcare analytics and open new avenues for research in deep learning applications to EHRs.

关键词： Deep learning Time series analysis Stochastic processes Predictive models Transformers Imputation Reliability electronic medical records Standards Diseases

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Author Correction: Cortical astrocytes modulate dominance behavior in male mice by regulating synaptic excitatory and inhibitory balance

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Nature neuroscience 2023年第12期26卷 2250页

作者： Kyungchul Noh Woo-Hyun Cho Byung Hun Lee Dong Wook Kim Yoo Sung Kim Keebum Park Minkyu Hwang Ellane Barcelon Yoon Kyung Cho C Justin Lee Bo-Eun Yoon Se-Young Choi Hye Yoon Park Sang Beom Jun Sung Joong Lee Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. Department of Physics and Astronomy Seoul National University Seoul Republic of Korea. Department of Molecular Biology Dankook University Cheonan Republic of Korea. Department of Electronic and Electrical Engineering Ewha Womans University Seoul Republic of Korea. Center for Cognition and Sociality Institute for Basic Science (IBS) Daejeon Republic of Korea. Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA. Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea. Department of Brain & Cognitive Sciences Ewha Womans University Seoul Republic of Korea. Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. sjlee87@snu.ac.kr.

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