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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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Vector databases and Vector Embeddings-Review

Vector Databases and Vector Embeddings-Review

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Kukreja, Sanjay Kumar, Tarun Bharate, Vishal Purohit, Amit Dasgupta, Abhijit Guha, Debashis Sp Jain School of Global Management Department of Machine Learning Mumbai India EClerx Services Ltd. Coe AI-ML Chandigarh India EClerx Services Ltd. Coe AI-ML Pune India EClerx Services Ltd. Coe AI-ML Mumbai India Sp Jain School of Global Management Department of Data Science Mumbai India

ISBN: (纸本)9798350382914

This research paper aims to present a comprehensive survey of vector databases and vector embedding techniques. A concise overview of the evolution, architecture, advantages and challenges of vector databases are presented in this paper. To convert unstructured data into vectors, various embedding techniques with their in-depth technical description are also surveyed in this research paper. The existing vector databases' characteristics and features are described to select an appropriate vector database. The embedding and indexing techniques are thoroughly discussed to help the research community experiment with combining them to develop their application. The challenges of vector databases, like selecting distance metrics, dimensionality, data integrity, cost, etc. are presented. This information on vector database systems will help researchers to exhibit further advancements in their data science applications. © 2023 IEEE.

关键词： Embeddings

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A $4\times 4$ Millimeter-Wave Antenna Based on a Higher Order Mode in a Sub-mode Microwave SIW Strucutre for Recent 5G Applications

A $4\times 4$ Millimeter-Wave Antenna Based on a Higher Orde...

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European Conference on Antennas and Propagation, EuCAP

作者： Amjaad T. Altakhaineh Saqer S. Alja'afreh Chaoyun Song Data Science and Machine Learning Dept Pioneers International Academy Amman Jordan Dept of Electrical Engineering Mutah University Alkarak Jordan Department of Engineering King's College London London United Kingdom

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This paper presents a single structure, multi-port antenna for 5G millimeter wave (mmWave) applications. The proposed antenna is constructed by exploiting the higher-order modes of a fraction-mode microwave substrate-integrated waveguide (SIW) cavity at 28 GHz (quadrature mode substrate-integrated waveguide (QMSIW)). Such properties exhibit spatial multiplexing capabilities, giving the single cavity a MIMO capability. Firstly, two perpendicular T-shaped microstrip ports (Port 1 and Port 2) are installed to excite TE205 higher-order mode of the QMSIW cavity at 28 GHz. To extend the MIMO capability, a 4×4 MIMO QMSIW antenna is created by adding two extra coaxial feeding ports (Port 3 and Port 4), the higher-order modes of the cavity are reused by leveraging two orthogonal inverted L-shaped slots, in which each slot is excited using coaxial cables at 28 GHz. The proposed antenna exhibited a wide -10 dB impedance and > 11 dB isolation based on the simulation environment without using decoupling structures. Finally, we believe this work offers a promising contribution to present and future multi-port single antenna, MIMO, and polarization applications.

关键词： Phased arrays Microwave antennas Slot antennas 5G mobile communication Simulation Microwave communication Millimeter wave propagation Space division multiplexing Millimeter wave communication Substrates

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Assessing Potential of Organizations with Fuzzy Entropy

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Operations Research Forum 2023年第1期4卷 11页

作者： Aggarwal, Manish Krishankumar, R. Ravichandran, K.S. Senapati, T. Yager, R.R. School of Artificial Intelligence and Data Science IIT Jodhpur Jodhpur India Digital Humanities IIT Jodhpur Jodhpur India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Mathematics Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Coimbatore India School of Mathematics and Statistics Southwest University Chongqing Beibei 400715 China Machine Intelligence Institute Iona College New Rochelle 10801 NY United States

Assessing the performance of organizations in the near future has been a challenging problem because of the factors of subjective assessment of the uncertainty. To cater to such applications, a novel form of fuzzy entropy is proposed to unify the concepts of fuzzy entropy and the conventional Shannon’s entropy. The proposed entropy provides a direct and convenient framework to quantify the uncertainty associated with a firm in the near future. It is also shown that the proposed form is amenable in extending the fuzzy entropy functions to the probabilistic-fuzzy domain. Thus, the proposed form of fuzzy entropy finds a special significance in the human decision-making problems. A case study illustrates the applicability of the proposed functions in assessment of future performance of a firm. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Attitudinal character Entropy Fuzzy logic Probabilistic-fuzzy Uncertainty

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Sharp empirical Bernstein bounds for the variance of bounded random variables

arXiv

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arXiv 2025年

作者： Martinez-Taboada, Diego Ramdas, Aaditya Department of Statistics & Data Science United States Machine Learning Department Carnegie Mellon University United States

Much recent effort has focused on deriving "empirical Bernstein" confidence sets for the mean µ of bounded random variables, that adapts to unknown variance V(X). In this paper, we provide fully empirical upper and lower confidence sets for the variance V(X), that adapt to both unknown µ and V[(X − µ)2]. Our bounds hold under constant conditional variance and mean, making them suitable for sequential decision making contexts. The results are instantiated for both the batch setting (where the sample size is fixed) and the sequential setting (where the sample size is a stopping time). We show that the first order width of the confidence intervals exactly matches that of the oracle Bernstein inequality;thus, our empirical Bernstein bounds are "sharp". We compare our bounds to a widely used concentration inequality based on self-bounding random variables, showing both the theoretical gains and improved empirical performance of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Random variables

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learning Latent Trajectories in Developmental Time Series with Hidden-Markov Optimal Transport 29th

Learning Latent Trajectories in Developmental Time Series w...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Halmos, Peter Gold, Julian Liu, Xinhao Raphael, Benjamin J. Department of Computer Science Princeton University 35 Olden St PrincetonNJ08544 United States Center for Statistics and Machine Learning Princeton University 26 Prospect Ave PrincetonNJ08544 United States

ISBN: (纸本)9783031902512

Deriving the sequence of transitions between cell types, or differentiation events, that occur during organismal development is one of the fundamental challenges in developmental biology. Single-cell and spatial sequencing of samples from different developmental timepoints provide data to investigate differentiation but inferring a sequence of differentiation events requires: (1) finding trajectories, or ancestor:descendant relationships, between cells from consecutive timepoints;(2) coarse-graining these trajectories into a differentiation map, or collection of transitions between cell types, rather than individual cells. We introduce Hidden-Markov Optimal Transport (HM-OT), an algorithm that simultaneously groups cells into cell types and learns transitions between these cell types from single-cell or spatial developmental time series. HM-OT uses low-rank optimal transport to simultaneously align samples in a time series and learn a sequence of clusterings and a differentiation map with minimal total transport cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cell culture

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Automating the Selection of Proxy Variables of Unmeasured Confounders

arXiv

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arXiv 2024年

作者： Xie, Feng Chen, Zhengming Luo, Shanshan Miao, Wang Cai, Ruichu Geng, Zhi Department of Applied Statistics Beijing Technology and Business University Beijing China School of Computer Science Guangdong University of Technology Guangzhou510006 China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Probability and Statistics Peking University Beijing China

Recently, interest has grown in the use of proxy variables of unobserved confounding for inferring the causal effect in the presence of unmeasured confounders from observational data. One difficulty inhibiting the practical use is finding valid proxy variables of unobserved confounding to a target causal effect of interest. These proxy variables are typically justified by background knowledge. In this paper, we investigate the estimation of causal effects among multiple treatments and a single outcome, all of which are affected by unmeasured confounders, within a linear causal model, without prior knowledge of the validity of proxy variables. To be more specific, we first extend the existing proxy variable estimator, originally addressing a single unmeasured confounder, to accommodate scenarios where multiple unmeasured confounders exist between the treatments and the outcome. Subsequently, we present two different sets of precise identifiability conditions for selecting valid proxy variables of unmeasured confounders, based on the second-order statistics and higher-order statistics of the data, respectively. Moreover, we propose two data-driven methods for the selection of proxy variables and for the unbiased estimation of causal effects. Theoretical analysis demonstrates the correctness of our proposed algorithms. Experimental results on both synthetic and real-world data show the effectiveness of the proposed approach. © 2024, CC BY.

关键词： Higher order statistics

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How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise?

arXiv

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arXiv 2023年

作者： Kostin, Julia Krahmer, Felix Stöger, Dominik Technical University of Munich Department of Mathematics Germany Munich Center for Machine Learning Germany Technical University of Munich Munich Data Science Institute Germany Germany

In this paper, we study the problem of recovering two unknown signals from their convolution, which is commonly referred to as blind deconvolution. Reformulation of blind deconvolution as a low-rank recovery problem has led to multiple theoretical recovery guarantees in the past decade due to the success of the nuclear norm minimization heuristic. In particular, in the absence of noise, exact recovery has been established for sufficiently incoherent signals contained in lower-dimensional subspaces. However, if the convolution is corrupted by additive bounded noise, the stability of the recovery problem remains much less understood. In particular, existing reconstruction bounds involve large dimension factors and therefore fail to explain the empirical evidence for dimension-independent robustness of nuclear norm minimization. Recently, theoretical evidence has emerged for ill-posed behavior of low-rank matrix recovery for sufficiently small noise levels. In this work, we develop improved recovery guarantees for blind deconvolution with adversarial noise which exhibit square-root scaling in the noise level. Hence, our results are consistent with existing counterexamples which speak against linear scaling in the noise level as demonstrated for related low-rank matrix recovery problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Recovery

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Method for Finding an Investment Strategy in the Case of a Sparse Covariance Matrix

Method for Finding an Investment Strategy in the Case of a S...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Victor Gorelik Tatiana Zolotova Department of Simulation Systems and Operations Research Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russia Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia

An optimality principle is proposed for making investment decisions based on efficiency and risk assessments with a sparse covariance matrix. The method is implemented as a program with a graphical interface and demon... 详细信息

ISBN: (数字)9798350375718

ISBN: (纸本)9798350375725

关键词： Large-scale systems Sparse matrices Risk management Covariance matrices Stock markets Portfolios Investment

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Enhancing Agricultural Yield Predictions with Real-Time IoT Sensor data and machine learning Integration

Enhancing Agricultural Yield Predictions with Real-Time IoT ...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Chandiraprakash. N A. Chinnasamy M Ashok Department of Artificial intelligence and machine learning Malla Reddy College of Engineering Hyderabad India Department of Data Science and Business Systems school of computing SRMIST kattankulathur campus Chennai India Malla Reddy College of Engineering Hyderabad India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

This research extends previous studies on the effects of climate change on agricultural yield predictions by integrating advanced machine learning (ML) methods with real time environmental sensing data. It builds on the basic usage of Long Short-Term Memory (LSTM) and Random Forest (RF) methods by employing real time data streams from IoT based field sensors that measure soil moisture, nutrient levels and pest activity. The aim is to enhance the prediction model's adaption to changing climatic circumstances, therefore increasing their utility in agricultural management. This study proposes a dynamic adaptation feature that uses real time data to make timely adjustments to crop management predictions. Moreover, the integration of Convolutional Neural Network (CNN) with LSTM is being investigated to increase the spatial and temporal accuracy of these predictions. By integrating conventional climatic data with detailed micro environmental elements from IoT sensors, this method obtains a aspects which affects the crop growth. This study also focuses on developing a userfriendly interface that allows farmers to easily access predictive developments and recommendations, allowing for more informed decision making. This technique is predicted to make substantial advances in precision agriculture, giving more effective methods for regulating agricultural productivity in dynamic climatic variability. These results may improve food security and sustainability by optimizing resource utilization and reducing the environmental impact of farming operations.

关键词： Precision agriculture Accuracy Decision making Crops Predictive models Real-time systems Sensors Convolutional neural networks Long short term memory Farming

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