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arXiv 2023年

作者： Chen, Shirui Jiang, Linxing Preston Rao, Rajesh P.N. Shea-Brown, Eric Department of Applied Mathematics University of Washington Seattle United States Paul G. Allen School of Computer Science & Engineering University of Washington Seattle United States Center for Neurotechnology University of Washington Seattle United States Department of Applied Mathematics Computational Neuroscience Center University of Washington Seattle United States

In sampling-based Bayesian models of brain function, neural activities are assumed to be samples from probability distributions that the brain uses for probabilistic computation. However, a comprehensive understanding of how mechanistic models of neural dynamics can sample from arbitrary distributions is still lacking. We use tools from functional analysis and stochastic differential equations to explore the minimum architectural requirements for recurrent neural circuits to sample from complex distributions. We first consider the traditional sampling model consisting of a network of neurons whose outputs directly represent the samples (sampler-only network). We argue that synaptic current and firing-rate dynamics in the traditional model have limited capacity to sample from a complex probability distribution. We show that the firing rate dynamics of a recurrent neural circuit with a separate set of output units can sample from an arbitrary probability distribution. We call such circuits reservoir-sampler networks (RSNs). We propose an efficient training procedure based on denoising score matching that finds recurrent and output weights such that the RSN implements Langevin sampling. We empirically demonstrate our model's ability to sample from several complex data distributions using the proposed neural dynamics and discuss its applicability to developing the next generation of sampling-based Bayesian brain models. Copyright © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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Hybrid Attention with CNN-BiLSTM and CBAM for Efficient Wearable Activity Recognition

Hybrid Attention with CNN-BiLSTM and CBAM for Efficient Wear...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

The use of widespread sensors for automatic activity recognition has become a dynamic area of research due to its broad applications in healthcare, fitness monitoring, and assisted living. To efficiently identify daily and sports activities from sensor data, this study suggests a hybrid deep learning model that integrates convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) networks, and convolutional block attention modules (CBAM). The model starts by extracting spatial features from the sensor data using CNN layers. These spatial features are then employed to offer temporal context information to the BiLSTM network. To conclude, the CBAM attention mechanism is applied to guide the model’s focus to the most informative segments of the BiLSTM feature maps. This approach enables the accurate recognition of patterns in both complex and daily sports activities. The effectiveness of the proposed hybrid model is evaluated using the UCI-DSA, a publicly available benchmark dataset that includes sports and daily activities collected through wearable motion sensors. In contrast to existing approaches, the outcomes reveal that a unified framework involving CNN, BiLSTM, and CBAM achieves outstanding performance in activity recognition, exceeding $99 \%$ in both accuracy and F1-score while maintaining computational efficiency.

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Feasibility of extracting the proton weak charge from quantum-control measurements of atomic parity violation on the 2s−3s or 2s−4s transition in hydrogen

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Physical Review A 2024年第1期109卷 012808-012808页

作者： Jiguang Li (李冀光) Andrei Derevianko D. S. Elliott Institute of Applied Physics and Computational Mathematics Beijing 100088 China Department of Physics University of Nevada Reno Nevada 89557 USA Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA

We explore the feasibility of extracting electroweak observables from a measurement of atomic parity violation in hydrogen. Our proposed quantum-control scheme focuses on the 2s−3s or 2s−4s transitions in hydrogen. This work is motivated by the recently observed anomaly in the W-boson mass, which may substantially modify the Standard Model value of the proton weak charge. We also study the accuracy of the previously employed approximations in computing parity-violating effects in hydrogen.

关键词： Atomic & molecular structure Coherent control W & Z bosons

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Analysis of Deep Learning Algorithms for Prawn Aquaculture in a Challenging Environment 6

Analysis of Deep Learning Algorithms for Prawn Aquaculture i...

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6th International Conference on applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Zariful, Wafiq Tashim, Najeebah Az-Zahra Lim, Tiong Hoo Basri, Aida Maryam Chuprat, Suriayati Putra, Seno Adi Liu, Pengcheng Fakhrurroja, Hanif Universiti Teknologi Brunei Electrical and Electronic Engineering Bandar Seri Begawan Brunei Universiti Teknologi Brunei School of Applied Sciences and Mathematics Bandar Seri Begawan Brunei Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Johor Bahru Malaysia Telkom Universiti School of Industrial and System Engineering Bandung Indonesia University of York Department of Computer Science York United Kingdom Telkom University Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798350317428

Deep Learning plays a vital role in various domains, spanning surveillance, agriculture, and healthcare. However, the current landscape lacks comprehensive comparative studies, underscoring the needs to explore various convolutional neural networks (CNNs) for aquaculture industries. This study conducts a comprehensive comparative analysis on state-of-the-art object detection models to identify the most suitable approach for detecting freshwater prawns, specifically in challenging turbid and murky water conditions as there is a lack of comparison studies for underwater images of freshwater prawns. The evaluated models, including YOLOv8, Faster R-CNN (FRCNN), Single Shot Detector (SSD), and EfficientDet, are assessed based on the following metrics namely inference time, accuracy, image cropping size, and model memory footprint. Using a dataset comprising 2976 manually collected images of freshwater prawns, augmented to a total of 7148 images, our results demonstrate that YOLOv8 outperforms its counterparts, achieving an impressive mean Average Precision (mAP) accuracy of 83.71% with an inference time of 32.9 milliseconds. This positions YOLOv8 as a dependable choice for real-time object detection in challenging underwater environments and contributes to addressing the limited research comparing YOLOv8 with other popular object detection algorithms, thereby aiding model selection for similar object detection applications. © 2023 IEEE.

关键词： Object detection

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Detecting Face-Touching Gestures with Smartwatches and Deep Learning Networks

Detecting Face-Touching Gestures with Smartwatches and Deep ...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Research Group in Statistical Learning and Inference Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Everyday actions like scratching one's nose or resting the chin on one's hand may facilitate the spread of germs and diseases. Detecting these gestures holds the potential for innovative health monitoring and disease prevention applications. However, the identification of face-touching poses challenges due to the variety of human gestures and the limitations in accuracy associated with wearable sensors. This study introduces an original deep-learning system to identify face-touching gestures using standard smartwatches' inertial measurement unit sensors. The system on the smartwatch captures and pre-processes multi-channel time-series data from the accelerometer to generate robust input features. We utilize a benchmark, the Face Touching dataset, to evaluate the recognition performance of deep learning networks, including our proposed network. Our approach proposes a hybrid deep residual network architecture tailored for sequence-based gesture classification using signals from the smartwatch sensors. In our experiments, the developed deep learning framework achieves an impressive F1-score of 97.53% in detecting face-touch gestures. The suggested system takes a step forward in advancing the practical applications of face-touch gesture detection to smartwatch-based health sensing and disease prevention.

关键词： Deep learning Measurement units Prevention and mitigation Face recognition Signal processing Sensor systems Telecommunications

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Optimising Electric Charging Station Management for Green Transportation with Cloud-Based E-GraphSage Models

Optimising Electric Charging Station Management for Green Tr...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Shubhangi Shivaji Manjare V. K. Somasekhar Srinivas A. Thangam M. Udayakumar Jitendra Singh Soumya Mishra Department of Electrical Engineering DKTE Society's Textile and Engineering Institute Ichalakaranji India Department of Mathematics School of Liberal Arts and Sciences Mohan Babu University Tirupati India Department of Mathematics Pondicherry University-Community College Pondicherry University Community College Lawspet India Department of Mechanical Engineering University of Technology and Applied Sciences Salalah Oman Department of Computer Science and Engineering SRM Institute of Science and Technology Ghaziabad India Department of Electrical Engineering Quality Assurance Cell KIIT DU Bhubaneswar India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The rapid expansion of EVs in recent years has caught the attention of markets, policymakers, the general public, and academia. Issues continue to arise despite the progress. The numerous complaints regarding the low battery life and long charging periods contribute to the problem of electric vehicle charging being an annoyance that discourages more people from purchasing these vehicles. The proposed procedure consists of three stages, which are preprocessing, feature extraction, and training the model. To ensure that the input data can be loaded into the developed model more effectively, preprocessing is an essential step. We begin by extracting three types of charging station features-geographic, traffic, and area-in order to address the data sparsity challenge. We used E-GraphSAGE to train the model. This cutting-edge method outperforms both AdaBoost and Random Forest in terms of average accuracy, coming in at 93.55%.

关键词： Training Accuracy Sensitivity Computational modeling Charging stations Feature extraction Electric vehicle charging Data models Data mining Load modeling

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QCA based design of cost-efficient code converter with temperature stability and energy efficiency analysis

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Materials Today: Proceedings 2022年 49卷 3585-3594页

作者： Dhrubajyoti Bhowmik Jayanta Pal Munesh Chandra Apu Kumar Saha Nitendra Kumar Department of Computer Science and Engineering NIT Agartala India Department of Information Technology Tripura University India Department of Mathematics NIT Agartala India Department of Applied Sciences and Humanities IIMT College of Engineering Greater Noida India

Quantum dot Cellular Automata (QCA) are a prominent nanotechnology that is widely employed in digital circuits and systems. In comparison to complementary metal–oxide semiconductor (CMOS) technology, QCA is a remarkable and challenging alternative with many attractive aspects such as fast execution and low power use. To accomplish all arithmetic processes, code converters are the fundamental unit of information change. Most of the converters, out of many designed for far in QCA, did not considered temperature stability, energy dissipation which may contribute to propagate as a non-operational circuit in various temperatures. This paper proposes a novel concept for a binary-to-gray code and a BCD-to-Excess 3 code converter based on QCA. The promised structure diminishes the number of cells, area utilized to make the design cost effective. The fundamental purpose of this research is to give a temperature stable and energy efficient converter design. The operability of the introduced design is verified with the QCADesigner2.0.3 tool and the temperature stability factor and energy waste are evaluated with QCADesignerE2.0.3.

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on computer Aided Systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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State-space models are accurate and efficient neural operators for dynamical systems

arXiv

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arXiv 2024年

作者： Hu, Zheyuan Daryakenari, Nazanin Ahmadi Shen, Qianli Kawaguchi, Kenji Em Karniadakis, George Department of Computer Science National University of Singapore Singapore119077 Singapore Center for Biomedical Engineering School of Engineering Brown University ProvidenceRI02912 United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

Physics-informed machine learning (PIML) has emerged as a promising alternative to classical methods for predicting dynamical systems, offering faster and more generalizable solutions. However, existing models, including recurrent neural networks (RNNs), transformers, and neural operators, face challenges such as long-time integration, long-range dependencies, chaotic dynamics, and extrapolation, to name a few. To this end, this paper introduces state-space models implemented in Mamba for accurate and efficient dynamical system operator learning. Mamba addresses the limitations of existing architectures by dynamically capturing long-range dependencies and enhancing computational efficiency through reparameterization techniques. To extensively test Mamba and compare against another 11 baselines, we introduce several strict extrapolation testbeds that go beyond the standard interpolation benchmarks. We demonstrate Mamba’s superior performance in both interpolation and challenging extrapolation tasks. Mamba consistently ranks among the top models while maintaining the lowest computational cost and exceptional extrapolation capabilities. Moreover, we demonstrate the good performance of Mamba for a real-world application in quantitative systems pharmacology for assessing the efficacy of drugs in tumor growth under limited data scenarios. Taken together, our findings highlight Mamba’s potential as a powerful tool for advancing scientific machine learning in dynamical systems modeling. (The code will be available at https://***/zheyuanhu01/State_ Space_Model_Neural_Operator upon acceptance.) © 2024, CC BY.

关键词： Recurrent neural networks

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A SUCCESSIVE TWO-STAGE METHOD FOR SPARSE GENERALIZED EIGENVALUE PROBLEMS

arXiv

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arXiv 2023年

作者： Li, Qia Liao, Jianmin Shen, Lixin Zhang, Na School of Computer Science and Engineering Guangdong Province Key Laboratory of Computational Science Sun Yat-sen University Guangdong Province Guangzhou510275 China Department of Mathematics Syracuse University SyracuseNY13244 United States Department of Applied Mathematics College of Mathematics and Informatics South China Agricultural University Guangdong Province Guangzhou510642 China

The Sparse Generalized Eigenvalue Problem (sGEP), a pervasive challenge in statistical learning methods including sparse principal component analysis, sparse Fisher's discriminant analysis, and sparse canonical correlation analysis, presents significant computational complexity due to its NP-hardness. The primary aim of sGEP is to derive a sparse vector approximation of the largest generalized eigenvector, effectively posing this as a sparse optimization problem. Conventional algorithms for sGEP, however, often succumb to local optima and exhibit significant dependency on initial points. This predicament necessitates a more refined approach to avoid local optima and achieve an improved solution in terms of sGEP's objective value, which we address in this paper through a novel successive two-stage method. The first stage of this method incorporates an algorithm for sGEP capable of yielding a stationary point from any initial point. The subsequent stage refines this stationary point by adjusting its support, resulting in a point with an enhanced objective value relative to the original stationary point. This support adjustment is achieved through a novel procedure we have named support alteration. The final point derived from the second stage then serves as the initial point for the algorithm in the first stage, creating a cyclical process that continues until a predetermined stopping criterion is satisfied. We also provide a comprehensive convergence analysis of this process. Through extensive experimentation under various settings, our method has demonstrated significant improvements in the objective value of sGEP compared to existing methodologies, underscoring its potential as a valuable tool in statistical learning and *** Codes 90C26, 90C32, 90C59, 90C90 Copyright © 2023, The Authors. All rights reserved.

关键词： Principal component analysis

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