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Machine Learning With data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo Ronan Fablet Didier Lucor Bertrand Iooss Julien Brajard Dunhui Xiao Tijana Janjic Weiping Ding Yike Guo Alberto Carrassi Marc Bocquet Rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFD)to geoscience and climate ***,much effort has been given in combining DA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

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A Residual Multi-Scale Convolutional Neural Network with Transformers for Speech Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 915-932页

作者： Yan, Tianhao Meng, Hao Parada-Cabaleiro, Emilia Tao, Jianhua Li, Taihao Schuller, Bjorn W. Zhejiang Lab Research Center for Data Hub and Security Hangzhou311121 China Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin150001 China Johannes Kepler University Linz Institute of Computational Perception Linz4040 Austria Tsinghua University Department of Automation Beijing100084 China UCAS Hangzhou Institute for Advanced Study Hangzhou310024 China University of Augsburg Chair of Embedded Intelligence for Health Care & Wellbeing Augsburg86159 Germany Imperial College London Group on Language Audio & Music LondonSW7 2AZ United Kingdom

The great variety of human emotional expression as well as the differences in the ways they perceive and annotate them make Speech Emotion Recognition (SER) an ambiguous and challenging task. With the development of deep learning, long-term progress has been made in SER systems. However, the existing convolutional neural networks present certain limitations, such as their inability to well capture features, which contain important emotional information. Moreover, the position encoding in the Transformer structure is relatively fixed and only encodes the time domain dimension, which cannot effectively obtain the position information of discriminative features in the frequency domain dimension. In order to overtake these limitations, we propose an end-to-end Residual Multi-Scale Convolutional Neural Networks (RMSCNN) with Transformer model network. Simultaneously, to further validate the effectiveness of RMSCNN in extracting multi-scale features and delivering pertinent emotion localization data, we developed the RMSC_down network in conjunction with the Wav2Vec 2.0 model. The results of the prediction of Arousal, Valence and Dominance on the popular corpora demonstrate the superiority and robustness of our approach for SER , showing an improvement of the recognition accuracy in the public dataset MSP-Podcast 1.9 version. The code is available at this GitHub. © 2010-2012 IEEE.

关键词： Convolutional neural networks

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LinkMed: Entity Recognition and Relation Extraction from Clinical Notes in Spanish

LinkMed: Entity Recognition and Relation Extraction from Cli...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Muñoz-Castro, Carlos Carvallo, Andrés Rojas, Matías Aracena, Claudio Guerra, Rodrigo Pizarro, Benjamín Dunstan, Jocelyn National Center for Artificial Intelligence Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Chile Department of Computer Science Pontificia Universidad Católica de Chile Chile Institute for Mathematical and Computational Engineering Pontificia Universidad Católica de Chile Chile Center for Mathematical Modeling Universidad de Chile Chile Millenium Institute Foundational Research on Data Chile

Relation extraction is an essential component of Natural Language Processing (NLP) and significantly influences information retrieval and structured information extraction. Within clinical notes, the task is needed to establish connections among illnesses, therapies, indications, and other medical concepts. Motivated by the above, in this work, we propose a two-step model approach for entity linking;in the first step, we solve entity recognition, and in the second, a relation classification approach. We evaluated our approach in a Spanish corpus of the TESTLINK challenge in IberLEF2023 (Iberian Languages Evaluation Forum), comprising 81 clinical notes to train and 80 clinical notes to test. Our results show competitive performance with a precision of 0.47, recall of 0.43, and F1-score of 0.45, presenting an effective strategy for relation extraction from clinical notes in Spanish. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Information retrieval

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Generalising Violence Detection with a New Near-Real-World Violence dataset

Generalising Violence Detection with a New Near-Real-World V...

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International Conference on Advancement in Electrical and Electronic engineering (ICAEEE)

作者： Mahmudul Haque Hussain Nyeem Tareque Bashar Ovi Al Nahid Md. Sabbir Hossain Molla Md. Tanjim Mahmud Tuhin Fardin Shahab Ayat Subah Alam Saadia Binte Alam Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Department of Electrical Electronic and Communication Engineering (EECE) Military Institute of Science and Technology (MIST) Mirpur Cantonment Dhaka Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Detecting and classifying violence is crucial for public safety and addressing societal violence. DL models have greatly improved the automation of violence detection systems by effectively capturing intricate visual patterns. However, the quality and diversity of the training data greatly impact the effectiveness of these models. Existing datasets may be biased towards specific situations, limiting their practical use. To address this limitation, we introduce the Movie Clip (MC) dataset to enhance the generalisability of Automated Violence Detection and Classification (AVDC) systems. The MC dataset encompasses a broad spectrum of near-real-world violent actions, incorporating diverse demographics, environmental circumstances, and cultural elements extracted from movies. Consequently, it accurately reflects the complexity and diversity of real-world violent scenarios. The potential of the new dataset is investigated against its existing counterparts, like Hockey Fight (HF) and AIRTLab datasets. These datasets are used to train the ConvLSTM models based on the VGG16 and VGG19 architectures. The proposed dataset significantly improves AVDC model generalisation, outperforming the generalisability of current datasets, thereby advancing violence detection and facilitating the development of more robust and efficient AVDC systems.

关键词： Training Visualization Atmospheric modeling Training data Hafnium Motion pictures Public security

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Joint Multi-Scale Contextual and Noise Suppression for Group Emotion Recognition

Joint Multi-Scale Contextual and Noise Suppression for Group...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wangdong Guo Qing Zhu Qirong Mao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Group Emotion Recognition (GER) seeks to identify emotional states within multi-person groups. The complexity of the environment and the reliance on a single group emotion label often result in noisy individuals with inconsistent emotional expressions, hampering accurate emotion classification. Mainstream GER networks attempt to downweight noisy individuals, but when their numbers are high and over-relying on a single group label, the negative impact of noise on individuals remains significant, further complicating classification and diminishing accuracy. To address these limitations, we propose the Multi-Scale Contextual and Noise Suppression Model (MCon-NSM), a novel framework that enhances GER by capturing fine-grained interaction contexts and collaboratively suppressing noise at both the individual and group label levels. Extensive experiments on the GAF series datasets demonstrate that our method achieves results comparable to state-of-the-art techniques, validating its effectiveness in mitigating noise in GER.

关键词： Emotion recognition Analytical models Accuracy Noise Noise reduction Speech recognition Complexity theory Noise measurement Speech processing Context modeling

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High-Performance computational Approach for Inverse Design of Passive Photonic Components

High-Performance Computational Approach for Inverse Design o...

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2022 Laser Applications Conference, LAC 2022

作者： Minin, Iurii Matveev, Sergey Skoltech Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Bolshoy Boulevard 30 bld.1 Moscow121205 Russia Fryazino Branch of Kotel’nikov Institute of Radio-Engineering and Electronics of RAS Vvedenskiy sq. 1 Moscow Region Fryazino141190 Russia Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Leninskie Gory 1-52 2nd educational building 6th floor room 663 Moscow119991 Russia Marchuk Institute of Numerical Mathematics RAS Moscow Russia

We propose novel approach for high-performance computations of electric field distributions which uses Green’s Function Integral Equation Method, memcopy-free GMRES, fast (FFT-based) matvecs and can work 10 times fas... 详细信息

ISBN: (纸本)9781957171203

关键词： Integral equations

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Benchmarks of Cuda-Based GMRES Solver for Toeplitz and Hankel Matrices and Applications to Topology Optimization of Photonic Components

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computational Mathematics and Modeling 2021年第4期32卷 438-452页

作者： Minin, Iu. B. Matveev, S.A. Fedorov, M.V. Zacharov, I.E. Rykovanov, S.G. Skoltech Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russian Federation Fryazino Branch of Kotel’nikov Institute of Radio-Engineering and Electronics of Russian Academy of Sciences Moscow Fryazino Russian Federation Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow Russian Federation Marchuk Institute of Numerical Mathematics Russian Academy of Sciences Moscow Russian Federation Sirius University of Science and Technology Krasnodar Krai Sochi Russian Federation

Generalized Minimal Residual Method (GMRES) was benchmarked on many types of GPUs for solving linear systems based on dense and sparse matrices. However, there are still no GMRES implementation benchmarks on Tesla V100 compared to GTX 1080 Ti ones or even for Toeplitz-like matrices. The introduced software consists of a Python module and a C++ library which enable to manage streams for concurrent computations of separated linear systems on a GPU (and GPUs). The GMRES solver is parallelized for running on a NVIDIA GPGPU accelerator. The parallelization efficiency is explored when GMRES is applied to solve (Helmholtz equation) linear systems based on the use of Green’s Function Integral Equation Method (GFIEM) for computing electric field distribution in the design domain. The proposed implementation shew the maximal speedup of 55 (t¯=0.017s) and of 125 (t¯=0.77s) for 1024 × 1024 (on GTX 1080 Ti) and 8192 × 8192 (on Tesla V100) dense Toeplitz matrices generated from GFIEM. 1024 × 1024 resolution provides accuracy 6.1% that can be acceptable according to testing and demonstrating on gradient computations and topology optimization. We open up possibilities for robust topology optimization of passive photonic integrated components. That has the advantage, e. g., of faster and more accurate designing photonic components on a PC without a supercomputer. © 2022, Springer science+Business Media, LLC, part of Springer Nature.

关键词： CUDA C++ GMRES GPU Helmholtz equation parallelization performance Python module Toeplitz-like matrix topology optimization

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Switching Millimeter Wave Channels Using Fuzzy Controller System

Switching Millimeter Wave Channels Using Fuzzy Controller Sy...

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IEEE International Conference on RFID-Technology and Applications (RFID-TA)

作者： Esmail Abuhdima Abdulmajid Mrebit Naomi Rolle Jason Laing Nabeyou Tedessa Jian Liu Amirhossein Nazeri Gurcan Comert Chin-Tser Huang Pierluigi Pisu Engineering Technology ECPI University Newport news VA USA Engineering and Computer Science Benedict College Columbia USA Computer Science and Engineering University of South Carolina Columbia USA International Center for Automotive Clemson University Greenville USA Computational Data Science and Engineering North Carolina A&T State University Greensboro NC USA

ISBN: (数字)9798331540227

ISBN: (纸本)9798331540234

This paper introduces a novel approach for managing 5G millimeter-wave channel switching, addressing the critical need for high-speed, low-latency communication. While promising significantly higher transmission rates, millimeter-wave frequencies are highly susceptible to environmental conditions such as rain, snow, dust, and sand, leading to signal attenuation and connectivity disruptions. To counter these challenges, we propose a Fuzzy Controller for switching mm-wave channels, which utilizes advanced algorithms to ensure smooth and efficient channel transitions. Our model quantifies the impact of environmental conditions on 5G signal propagation, precisely simulating how rainfall influences signal strength. Unlike devices on the market that typically switch between two frequencies based on signal strength alone, our approach intelligently incorporates environmental data such as visibility and rain intensity to choose optimally from five different frequencies. Through extensive simulations and performance evaluations, we show that our approach ensures reliable connectivity while maintaining high data speeds and minimizing latency. This research sets a foundational platform for future investigations into robust millimeter-wave channel switching mechanisms for 5G networks, enhancing user experience and overall network efficiency.

关键词： Rain 5G mobile communication Snow Millimeter wave technology Switches Control systems Attenuation User experience Reliability Millimeter wave communication

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Guardian of the Ensembles: Introducing Pairwise Adversarially Robust Loss for Resisting Adversarial Attacks in DNN Ensembles

Guardian of the Ensembles: Introducing Pairwise Adversariall...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Shubhi Shukla Subhadeep Dalui Manaar Alam Shubhajit Datta Arijit Mondal Debdeep Mukhopadhyay Partha Pratim Chakrabarti Centre for Computational and Data Sciences IIT Kharagpur India Computer Science and Engineering Department IIT Kharagpur India Center for Cyber Security New York University Abu Dhabi UAE Department of Artificial Intelligence IIT Kharagpur India Computer Science and Engineering Department IIT Patna India

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Adversarial attacks rely on transferability, where an adversarial example (AE) crafted on a surrogate classifier tends to mislead a target classifier. Recent ensemble methods demonstrate that AEs are less likely to mislead multiple classifiers in an ensemble. This paper proposes a new ensemble training using a Pairwise Adversarially Robust Loss (PARL) that by construction produces an ensemble of classifiers with diverse decision boundaries. PARL utilizes outputs and gradients of each layer with respect to network parameters in every classifier within the ensemble simultaneously. PARL is demonstrated to achieve higher robustness against black-box transfer attacks than previous ensemble methods as well as adversarial training without adversely affecting clean example accuracy. Extensive experiments using standard Resnet20, WideResnet28-10 classifiers demonstrate the robustness of PARL against state-of-the-art adversarial attacks. While maintaining similar clean accuracy and lesser training time, the proposed architecture has a 24.8% increase in robust accuracy (∊= 0.07) from the state-of-the art method. Code is available at: https://***/shubhishukla10/PARL

关键词： Training Computer vision Accuracy Perturbation methods Neural networks Closed box Computer architecture Robustness Ensemble learning Standards

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CMM PLN at MentalRiskES: A Traditional Machine Learning Approach for Detection of Eating Disorders and Depression in Chat Messages

CMM PLN at MentalRiskES: A Traditional Machine Learning Appr...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Guerra, Rodrigo Pizarro, Benjamín Muñoz-Castro, Carlos Carvallo, Andrés Rojas, Matías Aracena, Claudio Dunstan, Jocelyn Center for Mathematical Modeling Universidad de Chile Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Chile Department of Computer Science Pontificia Universidad Católica de Chile Santiago Chile National Center for Artificial Intelligence Chile Institute for Mathematical and Computational Engineering Pontificia Universidad Católica de Chile Chile Millenium Institute Foundational Research on Data Chile

This paper describes our approaches to solving the MentalRiskES task, which belongs to the IberLEF (Iberian Languages Evaluation Forum) shared task. The task aims to identify the eating disorders and depression of a user using a series of Telegram messages. Our proposed system uses the traditional TFiDF method to represent the messages and then utilizes these representations as input for machine learning models. The best results for classification were obtained using the Naive Bayes classifier, while in the regression task, the best models were Gradient Boots Regressor and Linear Regressor. Despite its simplicity, we demonstrated that our traditional approaches can still achieve competitive results in recent NLP tasks, obtaining the best results in the case of detecting depression and eating disorders. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Natural language processing systems

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