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Physics-Guided neural networks for distributed Sparse Gas Source Localization Using Poisson's Equation and Green's Function Method 32

Physics-Guided Neural Networks for Distributed Sparse Gas So...

引用

32nd European Signal processing conference (EUSIPCO)

作者： Ruiz, Victor Scott Prieto Shutin, Dmitriy Wiedemann, Thomas Hinsen, Patrick German Aerosp Ctr DLR Muenchner Str 20 D-82234 Wessling Germany Tech Univ Munich Munich Inst Robot & Machine Intelligence Percept Intelligent Syst Munich Germany

ISBN: (纸本)9789464593617;9798331519773

Finding sources or leaks of airborne material in Chemical, Biological, Radiological, or Nuclear (CBRN) accidents is crucial for effective disaster response. This paper makes use of sparse Bayesian learning (SBL) to cooperatively estimate source locations based on measurements by multiple robots or a sensor network. The SBL approach facilitates the identification of sparse source support, indirectly providing information about the number of sources and their locations. To achieve this, we introduce a novel method that includes a trained surrogated model for the gas dispersion process described by a Partial Differential Equation (PDE). Namely, a Physics-Guided neural network (PGNN) is employed to approximate a parameterized Green's function of the PDE. The obtained approximation is integrated into a gradient-based optimization process. The proposed method allows estimating super-resolution arbitrary source locations, eliminating constraints to a specific grid. Further, the newly proposed PGNN surrogate model comes with the advantage that the approach can be extended to cases where no analytic Green's function is available. Simulation results demonstrate the effectiveness of the proposed approach, showcasing its potential for enhanced airborne material detection in CBRN scenarios.

关键词： Poisson equation

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High-resolution image generation using Artificial Intelligence and diffusion modelling 32

High-resolution image generation using Artificial Intelligen...

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32nd Euromicro International conference on Parallel, distributed and network-Based processing (PDP)

作者： Scoles, Amanda Sionis, Giulia Otero, Beatriz Utrera, Gladys Univ Politecnica Catalunya BarcelonaTECH Escola Tecn Super Engn Telecomunicacio Barcelona Barcelona 08034 Spain Univ Politecnica Catalunya BarcelonaTECH Comp Architecture Dept Barcelona 08034 Spain

ISBN: (纸本)9798350363074;9798350363081

Super-Resolution via Repeated Refinement (SR3) is a state-of-the-art super-resolution algorithm based on diffusion model that can enhance the resolution of images. This method is used to pre-trained models on large datasets and can be used for various tasks without requiring training from scratch. Training SR3 from scratch using the ImageNet dataset involves a complex process that requires substantial computational resources and expertise. The idea is applied the trained SR3 model to new images by feeding the low-resolution inputs and obtaining the high-resolution outputs. It's important to note that training SR3 from scratch is a resource-intensive process that requires powerful GPUs and significant computation time. If you do not have access to such resources, an alternative is to use pre-trained models that are already available and fine-tune them on specific datasets or tasks. The paper shows the result of comparing the resolution of the preprocessed images using a significantly smaller number of images to perform the training with those obtained using the pre-trained model. The results obtained show acceptable results without having to perform on large datasets minimizing the computation time to obtain the resolution of images.

关键词： SR3 Diffusion model Deep neural network Artificial intelligence Super-resolution

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Federated Learning for network Traffic Prediction 23

Federated Learning for Network Traffic Prediction

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23rd International-Federation-for-Information-processing (IFIP) networking conference (IFIP networking)

作者： Behera, Sadananda Panda, Saroj Kumar Panayiotou, Tania Ellinas, Georgios Natl Inst Technol Rourkela Dept Elect & Commun Engn Sundargarh Odisha India LTIMindtree Bangalore India Univ Cyprus KIOS CoE Nicosia Cyprus Univ Cyprus Dept Elect & Comp Engn Nicosia Cyprus

ISBN: (纸本)9798350390605;9783903176638

network traffic prediction is imperative for effective network planning, decision-making, and optimization, leveraging the inherent predictability observed in traffic patterns. Although various machine learning models, such as deep neural networks (NNs) with recurrent units, including long-short-term memory (LSTM) NNs, have shown promise in this area, they are conventionally centralized trained, overlooking the potential benefits of distributed training techniques at the network edge. Exploiting advances in distributed training could provide certain advantages to the problem at hand, such as improved privacy preservation, reduced processing times, and lower bandwidth usage. Thus, this work proposes a federated learning (FL) framework for predicting network traffic, utilizing LSTMs for local model training. Simulation results validate the effectiveness of the proposed model, as this distributed implementation demonstrates high traffic prediction accuracy on real traffic traces, comparable to the traditional centralized approach, while safeguarding data privacy.

关键词： Federated learning network Traffic Prediction Machine Learning

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ACTIVE LEARNING FOR SOUND EVENT CLASSIFICATION USING BAYESIAN neural networkSWITH GAUSSIAN VARIATIONAL POSTERIOR 49

ACTIVE LEARNING FOR SOUND EVENT CLASSIFICATION USING BAYESIA...

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49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shishkini, Stepan Hollosi, Danilo Goetze, Stefan Doclo, Simon Fraunhofer Inst Digital Media Technol IDMT Oldenburg Branch Hearing Speech & Audio Technol H Oldenburg Germany Univ Sheffield Dept Comp Sci Speech & Hearing SPandH Sheffield S Yorkshire England Carl von Ossietzky Univ Oldenburg Dept Med Phys & Acoust Oldenburg Germany Carl von Ossietzky Univ Oldenburg Cluster Excellence Hearing4all Oldenburg Germany

ISBN: (纸本)9798350344868;9798350344851

Manual annotation of audio material is cumbersome. Active learning aims at minimizing the annotation effort by iteratively selecting an acquisition batch of unlabeled data, asking a human to annotate the selected data and re-training a classifier until an annotation budget is depleted. In this paper we propose the Gaussian-dense active learning (GDAL) algorithm to train a sound event classifier. The classifier is a Bayesian neural network where the weights are normally distributed. This is in contrast to conventional neural networks where weights are not distributed, but have assigned values. The Bayesian nature of the classifier empowers GDAL to select acquisition batches from a set of unlabeled audio clips based on their estimated informativeness. Evaluation results on the UrbanSound8k dataset show that GDAL outperforms a state-of-the-art algorithm based on medoid active learning for all considered annotation budgets and an algorithm based on dropout active learning for sufficiently large annotation budgets.

关键词： sound event classification active learning Bayesian neural networks PANN embeddings

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Murmuration: On-the-fly DNN Adaptation for SLO-Aware distributed Inference in Dynamic Edge Environments 24

Murmuration: On-the-fly DNN Adaptation for SLO-Aware Distrib...

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53rd International conference on Parallel processing (ICPP)

作者： Lin, Jieyu Li, Minghao Zhang, Sai Qian Leon-Garcia, Alberto Univ Toronto Toronto ON Canada NYU New York NY USA

ISBN: (纸本)9798400717932

The proliferation of Virtual and Augmented Reality (VR/AR) and the Internet of Things (IoT) applications is driving the demand for efficient Deep neural network (DNN) inference at the edge. These applications often impose stringent Service Level Objectives (SLOs), such as latency or accuracy, that must be met under the constraints of limited resources and dynamic network conditions. In this study, we explore a novel approach to DNN inference across multiple edge devices, incorporating both model customization and partitioning dynamically, to better align with these constraints and SLOs. Unlike conventional methods that employ a single fixed DNN network, our system, termed Murmuration, combines one-shot neural Architecture Search (NAS) and Reinforcement Learning (RL) to dynamically customize and partition DNN models. This approach adapts in real-time to the capabilities of the edge devices, network conditions, and varying SLO requirements. The design of Murmuration allows it to effectively navigate the large search space defined by DNN models, network delays, and bandwidth, offering a significant improvement in managing trade-offs between accuracy and latency. We implemented and evaluated Murmuration using a variety of edge devices. The results show that our approach outperforms state-of-the-art methods in terms of inference accuracy by up to 5% or latency by up to 6.7x. With the flexibility of model customization, Murmuration can meet SLO under a wider range of network delays and bandwidths, improving SLO compliance rate by up to 52%.

关键词： DNN distributed Inference Model Partitioning Reinforcement Learning One-shot NAS Service Level Objective (SLO)

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Applications of Large-Scale Quantum neural networks Based on distributed Quantum Computing 16

Applications of Large-Scale Quantum Neural Networks Based on...

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16th International conference on Wireless Communications and Signal processing, WCSP 2024

作者： Kan, Yin He, Junyuan Xue, Cheng Anhui University AHU-IAI Ai Joint Laboratory Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Hefei China Institute of Artificial Intelligence Comprehensive National Science Center Hefei China

ISBN: (纸本)9798350390643

This research investigates the limitations of current quantum hardware in fulfilling the computational demands of quantum neural networks. It introduces an optimized circuit computed method leveraging bit splitting within the framework of distributed quantum computing. The research exemplifies this approach by decomposing a 25-qubit variational quantum circuit into several smaller 5-qubit circuits, thereby enabling distributed quantum computations. A hybrid quantum-classical neural network architecture employing this methodology was implemented and evaluated on selected computer vision datasets. The results were promising, affirming the viability of the approach. The successful implementation of this architecture suggests that future distributed quantum computing systems will likely consist of scalable, heterogeneous quantum systems interconnected through an extensive quantum network. This configuration is anticipated to significantly enhance computational power and versatility, expanding the scope of quantum computing applications in real-world scenarios. This advancement not only validates the feasibility of distributed quantum architectures but also underscores their potential to revolutionize various technological domains through enhanced computational capabilities. © 2024 IEEE.

关键词： Quantum electronics

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Spiking neural network for Microseismic Events Detection Using distributed Acoustic Sensing Data 6th

Spiking Neural Network for Microseismic Events Detection Usi...

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6th International conference on Soft Computing and Data Mining (SCDM)

作者： Bin Shahabudin, Mohd Safuwan Krishnan, Nor Farisha Binti Muhamad Mausor, Farahida Hanim Binti Univ Teknol PETRONAS Seri Iskandar 32610 Perak Malaysia

ISBN: (纸本)9783031669644;9783031669651

Microseismic detection events are critical in monitoring subsurface activities, including hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage and reservoir characterization, to guarantee safe and efficient energy extraction. This presents significant challenges as it generates large amounts of data. Despite the recent machine learning techniques being increasingly integrated into fiber-optic distributed acoustic sensor (DAS) systems to enhance their intelligent recognition capabilities, there is still a need to solve this. In some research, it has been observed that computational speed is time-consuming and overfitting, thus necessitating a more extensive investigation of this analysis. This study proposes a novel approach using DAS data to enhance microseismic event detection's precision, overfitting issue, and interpretability. This approach utilizes a specifically designed neural network architecture. The deep learning approach is highly effective for the real-time management of the substantial amounts of data recorded by DAS equipment. Three phases of research methodology are proposed. The contribution of this research is that spiking neural network architecture for microseismic detection will bring advancements in microseismic monitoring.

关键词： Deep Learning Signal processing Microseismic Event Detection Spiking neural network distributed Acoustic Sensing

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A Discrete-Time Collaborative Neurodynamic Approach to distributed Global Optimization 13

A Discrete-Time Collaborative Neurodynamic Approach to Distr...

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13th International conference on Intelligent Control and Information processing, ICICIP 2025

作者： Huang, Haoen Zeng, Zhigang Wang, Jun School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science Department of Data Science City University of HongKong Kowloon Hong Kong

ISBN: (纸本)9798331516147

In this paper, a discrete-time projection neural network with an adaptive step size (DPNN) is proposed for distributed global optimization. The DPNN is proven to be convergent to a Karush-Kuhn-Tucker point. Several DPNNs are utilized in a collaborative neurodynamic framework for solving distributed global optimization problem. The efficacy of the collaborative neurodynamic approach with DPNNs is demonstrated through simulation results. © 2025 IEEE.

关键词： neural network models

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Non-Linear Coded Computation for distributed CNN Inference: A Learning-based Approach 44

Non-Linear Coded Computation for Distributed CNN Inference: ...

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44th IEEE International conference on distributed Computing Systems (ICDCS)

作者： Wang, Haoyu Liu, Yiyu Xu, Chunhui Liu, Xing Tan, Ming Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9798350354720;9798350354713

distributed inference enables multiple distributed computing devices to cooperatively perform statistical inference or data analysis tasks in order to reduce the inference delay. Due to the device heterogeneity in terms of processing capacities, some devices may experience slower processing speeds or even fail to compute the inference task. This leads to a larger inference delay or sometimes inference task failure. In this work, we propose a novel learning-based coded inference framework for convolutional neural networks (CNNs) by introducing redundant computing devices. By using an encoder to determine the inference inputs of redundant devices and an decoder to reconstruct the convolutional segment result of the CNNs, we show that our framework can significantly enhance the resilience and efficiency of distributed inference systems. By deploying a robust encoding-decoding scheme, our framework dynamically compensates for devices with sub-optimal performance or computational failures. This ensures the overall system to maintain high levels of accuracy and reduce inference latency, even in heterogeneous device environments. Our evaluation results reveal the profound impact of our proposed framework on enhancing distributed inference tasks for CNNs. Based on the experiments conducted on image classification across various realistic datasets, we have demonstrated that our framework maintains high accuracy levels, even in scenarios where some distributed devices encounter failures, validating the robustness of our proposed approach.

关键词： distributed inference coded computation convolutional neural network fault tolerance redundancy

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network Intrusion Detection using Machine Learning on Resource-Constrained Edge Devices

Network Intrusion Detection using Machine Learning on Resour...

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International Joint conference on neural networks (IJCNN)

作者： Lidholm, Pontus Markovic, Tijana Leon, Miguel Strandberg, Per Erik Westermo Network Technol AB Res & Dev Vasteras Sweden Malardalen Univ Sch Innovat Design & Engn Vasteras Sweden

ISBN: (纸本)9798350359329;9798350359312

The rapid growth of the Internet has led to the evolution of sophisticated security threats that exploit vulnerabilities within networks. The defence mechanisms must quickly adapt to these new threats to ensure that networks stay secure. One possible mechanism is to use Machine Learning (ML) algorithms to detect malicious activities. The edge devices that control and manage the network, such as routers, already have access to the data that is flowing through the network and may utilize its own computational resources to host ML algorithms and use them to detect intrusions. This paper presents a system for network intrusion detection which is deployed to an edge device and evaluated for live binary classification of network traffic. Different ML algorithms (Decision Tree, Random Forest, and Artificial neural network) are evaluated on existing datasets (Westermo and CIC-IDS-2017). Flow-based data pre-processing is performed and different labeling strategies and flow durations are used and compared. The most effective version of each algorithm is implemented and deployed on the Westermo Lynx-3510 routing-capable network switch and system performance is assessed across various scenarios with simulated network attacks. The experiments showed that Random Forest is the best option, closely followed by Decision Tree.

关键词： Intrusion Detection Machine Learning Edge Computing Embedded System

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