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Measuring 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1394-1395页

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Cho, Min Gee Qian, Yiwen Gu, Junsi Rozeveld, Steve Ercius, Peter Fessler, Jeffrey A. Xu, Ting Scott, Mary Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann Arbor MI United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL United States Advanced Photon Source Argonne National Laboratory Lemont IL United States Department of Materials Science and Engineering University of California at Berkeley Berkeley CA United States National Center for Electron Microscopy Lawrence Berkeley National Laboratory Berkeley CA United States Dow Chemical Co. Midland MI United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Materials Science Division Lawrence Berkeley National Laboratory Berkeley CA United States Applied Physics Program University of Michigan Ann Arbor MI United States

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Convergence of energy-based learning in linear resistive networks

arXiv

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arXiv 2025年

作者： Huijzer, Anne-Men Chaffey, Thomas Besselink, Bart van Waarde, Henk J. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands CogniGron - Groningen Cognitive Systems and Materials Center Netherlands Control Group Department of Engineering University of Cambridge United Kingdom School of Electrical and Computer Engineering University of Sydney Australia

Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a first step in this direction by analysing a particular energy-based learning algorithm, Contrastive Learning, applied to a network of linear adjustable resistors. It is shown that, in this setup, Contrastive Learning is equivalent to projected gradient descent on a convex function, for any step size, giving a guarantee of convergence for the *** Codes 65K10, 68T05, 93B30, 93D99 © 2025, CC BY-SA.

关键词： Contrastive Learning

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Diabetic Retinopathy Detection: A PySpark-Driven Approach with VGG 16 Feature Extraction and MLP Classification

Diabetic Retinopathy Detection: A PySpark-Driven Approach wi...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Toka Nabil Sama Ayman Samaa Ahmed Mawada Nagy Muhammad Megahed Abdulmoniem Elsaeed Monier Ashraf Khaled M. Fouad Ibrahim Abdelbaky School of Infromation Technology and Computer Science Nile University Giza Egypt CS program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computer Science and Engineering New Mansoura University Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

The current study used cutting-edge techniques to experimentally test the early diagnosis of diabetes via retinal scans. The goal was to enable effective disease prediction and management by facilitating quick and precise medical diagnostics. Three processes were involved in the development of a Diabetic Retinopathy (DR) diagnosis tool: feature extraction, feature reduction, and image classification. The research employed Apache Spark, a distributed computing framework, to manage large datasets and enhance the performance of the multilayer perceptron (MLP) model via hyperparameter tuning and cross validation. Utilizing resources more effectively and achieving faster training times were made possible by Apache Spark. To support data-driven decision-making, the study also emphasized the significance of distributed platforms for analyzing large amounts of real-time diabetic data. To produce discriminative features for classification, the VGG16 architecture was employed for feature extraction. In the last epoch, the MLP model performed remarkably well, with an accuracy of 97%. The study also underlined the value of distributed platforms for data-driven decision-making by analyzing substantial volumes of real-time diabetes data.

关键词： Diabetic retinopathy Computational modeling Distributed databases Cluster computing Machine learning Multilayer perceptrons Feature extraction Retina Real-time systems Medical diagnosis

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Deep Learning Based Facial Emotion Recognition using Multiple Layers Model

Deep Learning Based Facial Emotion Recognition using Multipl...

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2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Sandra, Lidia Heryadi, Yaya Lukas Suparta, Wayan Wibowo, Antoni Krida Wacana Christian University Psychology Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665499699

A Facial Emotion Recognition (FER) system is an important tool to be implemented in any psychology academic field and beyond. This paper aims to show a system of Facial Emotion Recognition that can be done using the multiple layers model of ResN et50 which is an ANN. The dataset is an array that consists of 7 different emotions represented by the numbers 0-6. The emotion included in the dataset is 'anger', 'disgust', 'fear', 'happiness', 'sadness', 'shock', and 'neutrality'. This research found that the highest accuracy in the application of the model was recorded at 65% and the test was 60% to recognize facial emotion from a graphical input. © 2021 IEEE.

关键词： Emotion Recognition

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Reliable Data Transmission through Private CBRS Networks

arXiv

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arXiv 2023年

作者： Kuo, Hsun-Yu Liu, Szu-Yu Huang, Chin-Ya Chen, Yu-Chi Xie, Meng-Hua The Bachelor Degree Program of Applied Science and Technology National Taiwan University of Science and Technology Taiwan The Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan

We consider the use of a domain proxy assisted private citizen broadband radio service (CBRS) network and propose a Maximum Transmission Continuity (MTC) scheme to transmit Internet of Things (IoT) data reliably. MTC dynamically allocates available CBRS channels to sustain the continuity of data transmission without violating the channel access requirements. MTC allocates the granted CBRS channels according to the priority of each user, the instant channel access status, interference among users, and the fairness. The simulation results demonstrate the improvement in managing reliable IoT data transmission in the private CBRS network. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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EEG-based Driver Drowsiness Classification via Calibration-Free Framework with Domain Generalization

EEG-based Driver Drowsiness Classification via Calibration-F...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Kim, Dong-Young Han, Dong-Kyun Jeong, Ji-Hoon Lee, Seong-Whan Korea University Dept. Artificial Intelligence Seoul Korea Republic of Korea University Dept. Brain and Cognitive Engineering Seoul Korea Republic of Chungbuk National University School of Computer Science Cheongju Korea Republic of

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Drowsy driving causes severe road traffic accidents and significantly threatens road driving. Recently, electroencephalogram (EEG)-based drowsiness state classification has gained attention in the field of brain-computer interface (BCI). Because of the inter-and intra-subject variability of EEG signals, EEG-based drowsiness state classification is still challenging in developing an estimator applicable to unseen subjects. Generally, calibration sessions are required to tune the model with subject-specific data. In this paper, we propose an EEG-based driver drowsiness state (i.e., alert and drowsy) classification framework that improves the generalization performance to unseen subjects. Style features of multi-domain instances are mixed to generate unseen domains, and the distance of labels within classes is minimized to learn robust representations. Experiments were conducted on EEG data acquired from a drowsy driving experiment in a simulated-driving environment. Our proposed framework achieved an accuracy of 77.26%, an F1-score of 0.6266, and a recall of 0.6813 across eleven subjects in leave-one-subject-out cross-validation. The experimental results showed an improvement in the generalization performance for novel target subjects in driver drowsiness state classification and demonstrated the potential for calibration-free BCI. © 2022 IEEE.

关键词： Electroencephalography

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IoE Knowledge Flow Model in Smart Cities

IoE Knowledge Flow Model in Smart Cities

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Paes, Vitor C. Pessoa, Clinton H. M. Farias Da Costa, Viviane C. Oliveira, Luiz F. S. De Souza, Jano M. Federal University of Rio de Janeiro Systems Engineering and Computer Science Program Rio de Janeiro Brazil Federal Institute of Rio de Janeiro Niterói Brazil

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

With a growing demand for new technologies, concepts such as the Internet of Everything (IoE) - in which smart sensors (humans and machines) connect, communicate, and share information from the surrounding environment - are gaining notoriety. Thus, this study focuses on developing a knowledge flow model in the IoE scenario for Smart Cities, including how knowledge is disseminated in these intelligent environments in human-to-machine interactions. The study considers a knowledge-based taxonomy defined for IoE environments and supports the full realization of IoE applications through a knowledge management strategy. The proposed IoE Knowledge Flow Model contributes to identification of knowledge flows related to IoE architecture layers in smart city applications. © 2022 IEEE.

关键词： Internet of Everything

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High Resolution of City-Level Climate Simulation by GPU with Multi-physical Phenomena 18th

High Resolution of City-Level Climate Simulation by GPU with...

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18th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2021

作者： Watanabe, Koei Kikuchi, Kohei Boku, Taisuke Sato, Takuto Kusaka, Hiroyuki Master’s Program in Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Tsukubashi Japan Center for Computational Sciences University of Tsukuba Tsukubashi Tennodi Japan

ISBN: (纸本)9783030935702

In this paper, we describe the Graphics Processing Unit (GPU) implementation of our City-LES code on detailed large eddy simulations, including the multi-physical phenomena on fluid dynamics, heat absorption and reflection by surface and building materials, cloud effects, and even sunlight effect. Because a detailed simulation involving these phenomena is required for analyses at the street level and several meters of resolution, the computation amount is enormous, and ordinary CPU computation cannot provide sufficient performance. Therefore, we implemented the entire code on GPU clusters with large-scale computing. We applied OpenACC coding to incrementally implement relatively easy programming and eliminate data transfers between the CPU and GPU memories. Based on this research, we determined that the elimination of data transfers is effective, even in the case where a part of the code execution on the GPU is slower than the CPU, owing to the absence of spatial parallelism. The objective of this study is to perform a complete climate simulation on a few square-kilometers field around the Tokyo Station, considering the finest resolution of the original highlighted area of the Marathon race in the Olympic Games Tokyo 2020. We successfully transferred the entire code to the GPU to provide approximately eight times the performance of CPU-only computation on multi-GPU per node with a large scale cluster. © 2022, IFIP International Federation for Information Processing.

关键词： Graphics processing unit

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Acoustic Scene Classification Using Perceptually Weighted Log Mel Spectrogram and Buttom-up Broadcast Neural Network

Acoustic Scene Classification Using Perceptually Weighted Lo...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Riyad Ali Ahmad Abuirbaiha Chang-Hsing Lee Cheng Chang Lien Ph.D. Program in Engineering Science Chung Hua University Hsinchu Taiwan Bachelor Program in Industrial Artificial Intelligence Ming Chi University of Technology New Taipei City Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

The study explores various 2D feature representations including spectrogram, MFCC spectrogram, log Mel-spectrogram, and the perceptual weighted log Mel-spectrogram (PW-LMSP) for acoustic scene classification (ASC). These 2D feature representations were classified using a bottom-up broadcast neural network (BBNN). The experimental results have shown that PW-LMSP outperforms other 2D representations. Further, the proposed method (PW-LMSP with BBNN) achieves accuracies of 91.2% and 91.6% respectively on the DCASE2018 and DCASE2019 development datasets, outperforming all the submissions in DCASE2018 and DCASE2019, as well as state-of-the-art approaches.

关键词： Scene classification Accuracy Neural networks LoRa Feature extraction Acoustics Mel frequency cepstral coefficient

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Long short-Term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control 7

Long short-Term memory (LSTM) model-based reinforcement lear...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Wijaya, Santo Heryadi, Yaya Arifin, Yulyani Suparta, Wayan Lukas Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta55281 Indonesia Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance. © 2022 Elsevier B.V.. All rights reserved.

关键词： Model predictive control

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