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On the particle approximation of lagged Feynman–Kac formulae

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Stochastic Processes and their Applications 2025年 188卷

作者： Awadelkarim, Elsiddig Caffarel, Michel Del Moral, Pierre Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia University of Toulouse CNRS-Lab. de Chimie et Physique Quantiques Toulouse31062 France Centre de Recherche Inria Bordeaux Sud-Ouest Talence33405 France School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

In this paper we examine the numerical approximation of the limiting invariant measure associated with Feynman–Kac formulae. These are expressed in a discrete time formulation and are associated with a Markov chain and a potential function. The typical application considered here is the computation of eigenvalues associated with non-negative operators as found, for example, in physics or particle simulation of rare-events. We focus on a novel lagged approximation of this invariant measure, based upon the introduction of a ratio of time-averaged Feynman–Kac marginals associated with a positive operator iterated l∈N times;a lagged Feynman–Kac formula. This estimator and its approximation using Diffusion Monte Carlo (DMC) are commonly used in the physics literature. In short, DMC is an iterative algorithm involving N∈N particles or walkers simulated in parallel, that undergo sampling and resampling operations. In this work, it is shown that for the DMC approximation of the lagged Feynman–Kac formula, one has an almost sure characterization of the L1-error as the time parameter (iteration) goes to infinity and this is at most of O(exp{−κl}/N), for κ>0. In addition a non-asymptotic in time, and time uniform L1−bound is proved which is O(l/N). We also prove a novel central limit theorem to give a characterization of the exact asymptotic in time variance. This analysis demonstrates that the strategy used in physics, namely, to run DMC with N and l small and, for long time enough, is mathematically justified. Our results also suggest how one should choose N and l in practice. We emphasize that these results are not restricted to physical applications;they have broad relevance to the general problem of particle simulation of the Feynman–Kac formula, which is utilized in a great variety of scientific and engineering fields. © 2025 Elsevier B.V.

关键词： Iterative methods

Automatic facial expression recognition in an image sequence using conditional random field

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Automatic facial expression recognition in an image sequence...

International Symposium on Computational Intelligence and Informatics

作者： Mohamad Roshanzamir Mahdi Roshanzamir Abdolreza Mirzaei Mohammad Tayarani Darbandy Afshin Shoeibi Roohallah Alizadehsani Fahime Khozeimeh Abbas Khosravi Department of Computer Engineering Fasa University Fasa Iran Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran School of Architecture Islamic Azad University Taft Taft Iran Internship in Health Data Analytics Program AI-enabled Processes (AIP) Research Centre Macquarie University Sydney Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia

ISBN: (纸本)9798350398830

Facial expression recognition is one of the fields that nowadays has attracted the attention of many researchers. It is possible to automate facial expression recognition using artificial intelligence methods. This will be of great help to researchers, especially in areas such as psychology. Automatic facial recognition can be derived from a static image of facial expression, but a better and more efficient way to do this is through a sequence of images. In this paper, a new method is proposed to automatically detect facial expressions from a sequence of images. Each sequence of facial images begins with a face neutral state and ends with one of the six main emotions. Motion vectors are extracted from the sequence using optical flow algorithm. These vectors are then used to train the conditional random field and finally to automatically determine the emotion. In this paper, in addition to the basic conditional random field, the hidden dynamic conditional random field is also investigated. Additionally, the effect of changing some parameters of these algorithms such as different optimization methods has been investigated. Given that a facial expression is recognized during a sequence of images, random field-based methods can be used for efficient classification of facial expressions reaching accuracy (more than 90%) competitive with the best existing methods for facial expression recognition.

关键词： Image recognition Mechatronics Face recognition Heuristic algorithms Psychology Optimization methods Feature extraction

On Time Uniform Wong-Zakai Approximation Theorems

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arXiv 2023年

作者： Del Moral, Pierre Hu, Shulan Jasra, Ajay Ruzayqat, Hamza Wang, Xinyu Institut de Mathematiques de Bordeaux Bordeaux33405 France School of Statistics and Mathematics Zhongnan University of Economics and Law China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Wenlan School of Business Zhongnan University of Economics and Law China

We consider the long time behavior of Wong-Zakai approximations of stochastic differential equations. These piecewise smooth diffusion approximations are of great importance in many areas, such as those with ordinary differential equations associated to random smooth fluctuations;e.g. robust filtering problems. In many examples, the mean error estimate bounds that have been derived in the literature can grow exponentially with respect to the time horizon. We show in a simple example that indeed mean error estimates do explode exponentially in the time parameter, i.e. in that case a Wong-Zakai approximation is only useful for extremely short time intervals. Under spectral conditions, we present some quantitative time-uniform convergence theorems, i.e. time-uniform mean error bounds, yielding what seems to be the first results of this type for Wong-Zakai diffusion *** Codes 60H35(Primary), 65C30(Secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： Brownian movement

Blue micro-LED with a red/green blended polymer film for 3.5-Gbps visible light communication employing adaptive SNR-Flattening Algorithm

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Next Nanotechnology

Next Nanotechnology 2024年 5卷

作者： Ho, Pin-Wei Cheng, Chih-Hsien Liao, Yu-Sheng Chi, Yu-Chieh Sadhu, Annada Sankar Matsumoto, Atsushi Akahane, Kouichi Chen, Li-Yin Kuo, Hao-Chung Lin, Gong-Ru Graduate Institute of Photonics and Optoelectronics and Department of Electrical Engineering National Taiwan University Taipei 106319 Taiwan National Institute of Information and Communications Technology Koganei Tokyo 184-8795 Japan SANWAY OPTOELECTRONICS TECH. CORP New Taipei City 235601 Taiwan Department of Photonics Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan International Ph.D. Program in Photonics (UST) College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei 11492 Taiwan NTU-Tektronix Joint Research Center Tektronix Inc. and National Taiwan University Taipei 106319 Taiwan

By the semipolar blue single GaN μ-LED and blended Ir(piq)2(acac) + CC-MP5 polymer thin film color converter, a high-speed white-light μ-LED-based system is built up for the applications of short-distance VLC. The characteristics and properties of both devices are analyzed to understand the requirements for transmission and illumination. By selecting the growing orient, the influence of QCSE is reduced in this GaN μ-LED. Meanwhile, possessing the low reflection characteristic, it is beneficial for signal modulation. For the polymer thin film color converter with a lifetime of 7.8 ns, low surface reflection and high conversion efficiency are thought of good properties. Color-converted cool white light has a CCT of around 7000 K and high color accuracy with a CRI of about 90. The APD-combined frequency responses of the GaN μ-LED and GaN μ-LED + polymer are measured as 750 MHz and 600 MHz, respectively. After the optimization by utilizing the adaptive SNR-flattening pre-emphasis algorithm, the transmission performance of the white-light μ-LED is significantly promoted. For NRZ-OOK encoding, a 1.4 Gbps 0.15-m free-space transmission is achieved with a rising time of 656.33 ps, a falling time of 493.32 ps, and a Q-factor of 4.75. Besides, in more advanced data formats, the performance of this white-light μ-LED can be better highlighted. For the same 0.15-m free-space VLC, a high-speed 3 Gbps broadband 8-QAM-OFDM transmission is fulfilled with an EVM of 23.9%, an average SNR of 12.5, and a BER below 3.8 × 10^-3;while to the best of our knowledge, a record 3.5 Gbps BL-DMT transmission is implemented as well. This white-light μ-LED can also be integrated into large-scale arrays for multi-functional VLC applications. © 2024 The Authors

关键词： Adaptive SNR-Flattening algorithm Blue micro-LED Red/Green blended polymer Visible light communication

Shaping Integrity: Why Generative Artificial Intelligence Does Not Have to Undermine Education

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arXiv 2024年

作者： Tan, Myles Joshua Toledo Maravilla, Nicholle Mae Amor Tan Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL32611 United States Department of Epidemiology College of Public Health & Health Professions College of Medicine University of Florida GainesvilleFL32610 United States Biology Program College of Arts and Sciences University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Natural Sciences College of Arts and Sciences University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City6100 Philippines Yo-Vivo Corporation Negros Occidental Bacolod City6100 Philippines

This paper examines the role of generative artificial intelligence (GAI) inpromoting academic integrity within educational settings. It explores how AIcan be ethically integrated into classrooms to enhance learning experiences,foster intrinsic motivation, and support voluntary behavior change amongstudents. By analyzing established ethical frameworks and educational theoriessuch as deontological ethics, consequentialism, constructivist learning, andSelf-Determination Theory (SDT), the paper argues that GAI, when usedresponsibly, can enhance digital literacy, encourage genuine knowledgeconstruction, and uphold ethical standards in education. This researchhighlights the potential of GAI to create enriching, personalized learningenvironments that prepare students to navigate the complexities of the modernworld ethically and effectively. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation

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arXiv 2021年

作者： Jo, Jeonghee Kwak, Bumju Lee, Byunghan Yoon, Sungroh Interdisciplinary Program in Bioinformatics Seoul National University Recommendation Team Kakao Corporation Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Department of Electrical and Computer Engineering Seoul National University

A molecule is a complex of heterogeneous components, and the spatial arrangements of these components determine the whole molecular properties and characteristics. With the advent of deep learning in computational chemistry, several studies have focused on how to predict molecular properties based on molecular configurations. Message passing neural network provides an effective framework for capturing molecular geometric features with the perspective of a molecule as a graph. However, most of these studies assumed that all heterogeneous molecular features, such as atomic charge, bond length, or other geometric features always contribute equivalently to the target prediction, regardless of the task type. In this study, we propose a dual-branched neural network for molecular property prediction based on message-passing framework. Our model learns heterogeneous molecular features with different scales, which are trained flexibly according to each prediction target. In addition, we introduce a discrete branch to learn single atom features without local aggregation, apart from message-passing steps. We verify that this novel structure can improve the model performance with faster convergence in most targets. The proposed model outperforms other recent models with sparser representations. Our experimental results indicate that in the chemical property prediction tasks, the diverse chemical nature of targets should be carefully considered for both model performance and generalizability. © 2021, CC BY.

关键词： Deep learning

On the Particle Approximation of Lagged Feynman-Kac Formulae

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arXiv 2024年

作者： Awadelkarim, Elsiddig Caffarel, Michel Moral, Pierre Del Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia University of Toulouse CNRS-Lab. de Chimie et Physique Quantiques Toulouse31062 France Centre de Recherche Inria Bordeaux Sud-Ouest Talence33405 France School of Data Science The Chinese University Hong Kong Shenzhen China

In this paper we examine the numerical approximation of the limiting invariant measure associated with Feynman-Kac formulae. These are expressed in a discrete time formulation and are associated with a Markov chain and a potential function. The typical application considered here is the computation of eigenvalues associated with non-negative operators as found, for example, in physics or particle simulation of rare-events. We focus on a novel lagged approximation of this invariant measure, based upon the introduction of a ratio of time-averaged Feynman-Kac marginals associated with a positive operator iterated l ∈ N times;a lagged Feynman-Kac formula. This estimator and its approximation using Diffusion Monte Carlo (DMC) have been extensively employed in the physics literature. In short, DMC is an iterative algorithm involving N ∈ N particles or walkers simulated in parallel, that undergo sampling and resampling operations. In this work, it is shown that for the DMC approximation of the lagged Feynman-Kac formula, one has an almost sure characterization of the L1-error as the time parameter (iteration) goes to infinity and this is at most of O(exp{−κl}/N), for_ κ > 0. In addition a non-asymptotic in time, and time uniform L1−bound is proved which is O(l/√N). We also prove a novel central limit theorem to give a characterization of the exact asymptotic in time variance. This analysis demonstrates that the strategy used in physics, namely, to run DMC with N and l small and, for long time enough, is mathematically justified. Our results also suggest how one should choose N and l in practice. We emphasize that these results are not restricted to physical applications;they have broad relevance to the general problem of particle simulation of the Feynman-Kac formula, which is utilized in a great variety of scientific and engineering fields. © 2024, CC BY.

关键词： Eigenvalues and eigenfunctions

Deep learning-based photodamage reduction on harmonic generation microscope at low-level optical power

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Journal of Biophotonics 2024年第1期17卷 e202300285-e202300285页

作者： Shen, Yi-Jiun Liao, En-Yu Tai, Tsung-Ming Liao, Yi-Hua Sun, Chi-Kuang Lee, Cheng-Kuang See, Simon Chen, Hung-Wen International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering and Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei Taiwan NVIDIA AI Technology Center NVIDIA Taipei Taiwan Department of Dermatology National Taiwan University Hospital and College of Medicine National Taiwan University Taipei Taiwan Institute of Photonics Technologies National Tsing Hua University Hsinchu Taiwan

The trade-off between high-quality images and cellular health in optical bioimaging is a crucial problem. We demonstrated a deep-learning-based power-enhancement (PE) model in a harmonic generation microscope (HGM), including second harmonic generation (SHG) and third harmonic generation (THG). Our model can predict high-power HGM images from low-power images, greatly reducing the risk of phototoxicity and photodamage. Furthermore, the PE model trained only on normal skin data can also be used to predict abnormal skin data, enabling the dermatopathologist to successfully identify and label cancer cells. The PE model shows potential for in-vivo and ex-vivo HGM imaging. © 2023 Wiley-VCH GmbH.

关键词： Harmonic generation

Artificial Intelligence for Digital and Computational Pathology

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arXiv 2023年

作者： Song, Andrew H. Jaume, Guillaume Williamson, Drew F.K. Lu, Ming Y. Vaidya, Anurag Miller, Tiffany R. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Harvard Medical School BostonMA United States

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis;however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data. © 2023, CC BY.

关键词： Pathology