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On the particle approximation of lagged Feynman–Kac formulae

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 Thuwal 23955-6900 Saudi Arabia University of Toulouse CNRS-Lab. de Chimie et Physique Quantiques Toulouse 31062 France Centre de Recherche Inria Bordeaux Sud-Ouest Talence 33405 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： Diffusion Monte Carlo Eigenvalue approximation Feynman–Kac formula

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Variational Transformer Ansatz for the Density Operator of Steady States in Dissipative Quantum Many-Body Systems

arXiv

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arXiv 2025年

作者： Wei, Lu Jia, Zhian Wang, Yufeng Kaszlikowski, Dagomir Ling, Haibin Department of Applied Mathematics & Statistics Stony Brook University Stony BrookNY11794 United States Program of Data Science Stony Brook University Stony BrookNY11794 United States Centre for Quantum Technologies National University of Singapore SG 117543 Singapore Department of Physics National University of Singapore SG 117543 Singapore Department of Computer Science Stony Brook University Stony BrookNY11794 United States

The transformer architecture, known for capturing long-range dependencies and intricate patterns, has extended beyond natural language processing. Recently, it has attracted significant attention in quantum information and condensed matter physics. In this work, we propose the transformer density operator ansatz for determining the steady states of dissipative quantum many-body systems. By vectorizing the density operator as a many-body state in a doubled Hilbert space, the transformer encodes the amplitude and phase of the state’s coefficients, with its parameters serving as variational variables. Our design preserves translation invariance while leveraging attention mechanisms to capture diverse long-range correlations. We demonstrate the effectiveness of our approach by numerically calculating the steady states of dissipative Ising and Heisenberg spin chain models, showing that our method achieves excellent accuracy in predicting steady states. © 2025, CC BY.

关键词： Hilbert spaces

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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Enhanced Performance and data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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A Bayesian-based approach for extracting the pion charge radius from electron-electron scattering data

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Chinese Physics C 2021年第8期45卷 12-27页

作者： Alam A Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara UniversityJakarta 11480Indonesia Computer Science Department BINUS Graduate Program-Master of Computer ScienceBina Nusantara UniversityJakarta 11480Indonesia

In this study,we utilize a potentially versatile Bayesian parameter approach to compute the value of the pion charge radius and quantify its uncertainty from several experimental e^(+)e^(-) datasets for the pion vector form *** employ dispersion relations to model the pion vector form factor to extract the *** model selection is used to determine the order of polynomial appearing in the form factor formulation that can be supported by the data,adapting the computation of Bayes evidence and Bayesian effective complexity based on Occam's *** findings indicate that five out of six used datasets favor the nine-parameter model for radius extraction,and accordingly,we average the radii from the *** some inconsistencies with the most updated radius values,our approach may serve as a more intuitive method of addressing parameter estimations in dispersion theory.

关键词： Bayesian complexity dispersion hadron pion

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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Computational Intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), IEEE Symposium on

作者： Matthew Andres Moreno Anika Ranjan Emily Dolson Luis Zaman Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and AI in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this “hereditary stratigraphy” approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation “surface”-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation “column”-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions.

关键词： Surface reconstruction Accuracy Annotations Heuristic algorithms Computational modeling Biological system modeling Stratigraphy Phylogeny History Bars

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Evaluating masked self-supervised learning frameworks for 3D dental model segmentation tasks

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Scientific Reports 2025年第1期15卷 1-15页

作者： Krenmayr, Lucas von Schwerin, Reinhold Schaudt, Daniel Riedel, Pascal Hafner, Alexander Geserick, Marc Cooperative Doctoral Program for Data Science and Analytics University of Ulm Ulm 89081 Germany Department of Computer Science University of Applied Sciences Ulm 89081 Germany smyl tp GmbH Ulm 89073 Germany

The application of deep learning using dental models is crucial for automated computer-aided treatment planning. However, developing highly accurate models requires a substantial amount of accurately labeled data. Obtaining this data is challenging, especially in the medical domain. Masked self-supervised learning has shown great promise in overcoming the challenge of data scarcity. However, its effectiveness has not been well explored in the 3D domain, particularly on dental models. In this work, we investigate the applicability of the four recently published masked self-supervised learning frameworks-Point-BERT, Point-MAE, Point-GPT, and Point-M2AE-for improving downstream tasks such as tooth and brace segmentation. These frameworks were pre-trained on a proprietary dataset of over 4000 unlabeled 3D dental models and fine-tuned using the publicly available Teeth3DS dataset for tooth segmentation and a self-constructed braces segmentation dataset. Through a set of experiments we demonstrate that pre-training can enhance the performance of downstream tasks, especially when training data is scarce or imbalanced—a critical factor for clinical usability. Our results show that the benefits are most noticeable when training data is limited but diminish as more labeled data becomes available, providing insights into when and how this technique should be applied to maximize its effectiveness. © The Author(s) 2025.

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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2025 IEEE Symposium on Computational Intelligence in Artificial Life and Cooperative Intelligent Systems, ALIFE-CIS 2025

作者： Moreno, Matthew Andres Ranjan, Anika Dolson, Emily Zaman, Luis University of Michigan Department of Ecology and Evolutionary Biology Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and Ai in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Michigan State University Department of Computer Science and Engineering East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (纸本)9798331508371

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this 'hereditary stratigraphy' approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation 'surface'-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation 'column'-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions. © 2025 IEEE.

关键词： Biostratigraphy

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Advantages of two quantum programming platforms in quantum computing and quantum chemistry

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Journal of cheminformatics 2025年第1期17卷 77页

作者： Pei-Hua Wang Wei-Yeh Wu Che-Yu Lee Jia-Cheng Hong Yufeng Jane Tseng Undergraduate Program in Intelligent Computing and Big Data Chung Yuan Christian University No. 200 Zhongbei Road Taoyuan 320314 Taiwan. Quantum Information Center Chung Yuan Christian University No. 200 Zhongbei Road Taoyuan 320314 Taiwan. Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Physics Division National Center for Theoretical Sciences No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Center for Quantum Science and Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw.

Quantum computing is at the forefront of technological advancement and has the potential to revolutionize various fields, including quantum chemistry. Choosing an appropriate quantum programming language becomes critical as quantum education and research increase. In this paper, we comprehensively compare two leading quantum programming languages, Qiskit and PennyLane, focusing on their suitability for teaching and research. We delve into their basic and advanced usage, examine their learning curves, and evaluate their capabilities in quantum computing experiments. We also demonstrate using a quantum programming language to build a half adder and a machine learning model. Our study reveals that each language has distinct advantages. While PennyLane excels in research applications due to its flexibility to adjust parameters in detail and access multiple sources of real quantum devices, Qiskit stands out in education because of its web-based graphical user interface and smaller code size. The codes and the dataset used in the studies are available at https://***/wangpeihua1231/quantum-programming-platform .

关键词： PennyLane Qiskit Quantum chemistry Quantum computing Quantum programming language

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