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A Trusted Reputation Management Scheme for Cross-Chain Transactions

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Sensors 2023年第13期23卷 6033页

作者： Chen, Kuongho Lee, Lin-Fa Chiu, Wayne Su, Chunhua Yeh, Kuo-Hui Chao, Han-Chieh ezPay Co. Ltd Taipei11578 Taiwan Department of Information Management National Dong Hwa University Hualien97401 Taiwan Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby2800 Denmark Division of Computer Science University of Aizu Fukushima965-0006 Japan Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Institute of Computer Science and Innovation UCSI University Kuala Lumpur5600 Malaysia

Blockchain has become a well-known, secured, decentralized datastore in many domains, including medical, industrial, and especially the financial field. However, to meet the requirements of different fields, platforms that are built on blockchain technology must provide functions and characteristics with a wide variety of options. Although they may share similar technology at the fundamental level, the differences among them make data or transaction exchange challenging. Cross-chain transactions have become a commonly utilized function, while at the same time, some have pointed out its security loopholes. It is evident that a secure transaction scheme is desperately needed. However, what about those nodes that do not behave? It is clear that not only a secure transaction scheme is necessary, but also a system that can gradually eliminate malicious players is of dire need. At the same time, integrating different blockchain systems can be difficult due to their independent architectures, and cross-chain transactions can be at risk if malicious attackers try to control the nodes in the cross-chain system. In this paper, we propose a dynamic reputation management scheme based on the past transaction behaviors of nodes. These behaviors serve as the basis for evaluating a node’s reputation to support the decision on malicious behavior and enable the system to intercept it in a timely manner. Furthermore, to establish a reputation index with high precision and flexibility, we integrate Particle Swarm Optimization (PSO) into our proposed scheme. This allows our system to meet the needs of a wide variety of blockchain platforms. Overall, the article highlights the importance of securing cross-chain transactions and proposes a method to prevent misbehavior by evaluating and managing node reputation. © 2023 by the authors.

关键词： Blockchain

Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs

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arXiv 2023年

作者： Hu, Zheyuan Yang, Zhouhao Wang, Yezhen Karniadakis, George Em Kawaguchi, Kenji Division of Applied Mathematics Brown University ProvidenceRI02912 United States Department of Computer Science National University of Singapore Singapore119077 Singapore Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

Physics-Informed Neural Networks (PINNs) have triggered a paradigm shift in scientific computing, leveraging mesh-free properties and robust approximation capabilities. While proving effective for low-dimensional partial differential equations (PDEs), the computational cost of PINNs remains a hurdle in high-dimensional scenarios. This is particularly pronounced when computing high-order and high-dimensional derivatives in the physics-informed loss. Randomized Smoothing PINN (RS-PINN) introduces Gaussian noise for stochastic smoothing of the original neural net model, enabling the use of Monte Carlo methods for derivative approximation, which eliminates the need for costly automatic differentiation. Despite its computational efficiency, especially in the approximation of high-dimensional derivatives, RS-PINN introduces biases in both loss and gradients, negatively impacting convergence, especially when coupled with stochastic gradient descent (SGD) algorithms. We present a comprehensive analysis of biases in RS-PINN, attributing them to the nonlinearity of the Mean Squared Error (MSE) loss as well as the intrinsic nonlinearity of the PDE itself. We propose tailored bias correction techniques, delineating their application based on the order of PDE nonlinearity. The derivation of an unbiased RS-PINN allows for a detailed examination of its advantages and disadvantages compared to the biased version. Specifically, the biased version has a lower variance and runs faster than the unbiased version, but it is less accurate due to the bias. To optimize the bias-variance trade-off, we combine the two approaches in a hybrid method that balances the rapid convergence of the biased version with the high accuracy of the unbiased version. In addition to methodological contributions, we present an enhanced implementation of RS-PINN. Extensive experiments on diverse high-dimensional PDEs, including Fokker-Planck, Hamilton-Jacobi-Bellman (HJB), viscous Burgers’, Allen-Cahn, and Sine

关键词： Stochastic systems

Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks

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arXiv 2023年

作者： Hu, Zheyuan Shi, Zekun Karniadakis, George Em Kawaguchi, Kenji Department of Computer Science National University of Singapore Singapore119077 Singapore Division of Applied Mathematics Brown University ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

Physics-Informed Neural Networks (PINNs) have proven effective in solving partial differential equations (PDEs), especially when some data are available by seamlessly blending data and physics. However, extending PINNs to high-dimensional and even high-order PDEs encounters significant challenges due to the computational cost associated with automatic differentiation in the residual loss function calculation. Herein, we address the limitations of PINNs in handling high-dimensional and high-order PDEs by introducing the Hutchinson Trace Estimation (HTE) method. Starting with the second-order high-dimensional PDEs, which are ubiquitous in scientific computing, HTE is applied to transform the calculation of the entire Hessian matrix into a Hessian vector product (HVP). This approach not only alleviates the computational bottleneck via Taylor-mode automatic differentiation but also significantly reduces memory consumption from the Hessian matrix to an HVP's scalar output. We further showcase HTE's convergence to the original PINN loss and its unbiased behavior under specific conditions. Comparisons with the Stochastic Dimension Gradient Descent (SDGD) highlight the distinct advantages of HTE, particularly in scenarios with significant variability and variance among dimensions. We further extend the application of HTE to higher-order and higher-dimensional PDEs, specifically addressing the biharmonic equation. By employing tensor-vector products (TVP), HTE efficiently computes the colossal tensor associated with the fourth-order high-dimensional biharmonic equation, saving memory and enabling rapid computation. The effectiveness of HTE is illustrated through experimental setups, demonstrating comparable convergence rates with SDGD under memory and speed constraints. Additionally, HTE proves valuable in accelerating the Gradient-Enhanced PINN (gPINN) version as well as the Biharmonic equation. Overall, HTE opens up a new capability in scientific machine learning for tackl

关键词： Stochastic systems

GLOBAL WELL-POSEDNESS OF THE 3D PRIMITIVE EQUATIONS WITH HORIZONTAL VISCOSITY AND VERTICAL DIFFUSIVITY II: CLOSE TO H1 INITIAL DATA

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arXiv 2024年

作者： Cao, Chongsheng Li, Jinkai Titi, Edriss S. Wang, Dong Department of Mathematics Florida International University University Park MiamiFL33199 United States South China Research Center for Applied Mathematics and Interdisciplinary Studies School of Mathematical Sciences South China Normal University Guangzhou510631 China Department of Mathematics Texas A&M University College StationTX77843 United States Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot76100 Israel

MSC Codes 26D10, 35Q35, 35Q86, 76D03, 76D05, 86A05, 86A10In this paper, we consider the initial-boundary value problem to the three-dimensional primitive equations for the oceanic and atmospheric dynamics with only horizontal eddy viscosities in the horizontal momentum equations and only vertical diffusivity in the temperature equation in the domain Ω = M ×(−h, h), with M = (0, 1) × (0, 1). Global well-posedness of strong solutions is established, for any initial data (v0, T0) H1(Ω) ∩ L∞(Ω) with (∂zv0, ∇HT0) Lq(Ω) and v0L1z(Bq,21 (M)), for some q (2, ∞), by using delicate energy estimates and maximal regularity estimate in the anisotropic setting. Copyright © 2024, The Authors. All rights reserved.

关键词： Viscosity

A Cautionary Note on Poli's Stability Condition for Particle Swarm Optimization

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A Cautionary Note on Poli's Stability Condition for Particle...

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Daniel H. Von Eschwege Andries P. Engelbrecht Department of Industrial Engineering Stellenbosch University Stellenbosch South Africa Department of Industrial Engineering and Computer Science Division Stellenbosch University Stellenbosch South Africa Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology Kuwait Kuwait

Particle swarm optimization (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. PSO performance depends on the use of a suitable control parameter (CP) configuration, which governs the trade-off between exploration and exploitation in the swarm. Various methods of adapting or tuning CPs exist, but many result in exploding particle velocities and an unstable search process. Poli's stability condition ensures convergence in the mathematical limit, and is often used to inform CP configuration. However, this study shows that since it does not place any practical convergence constraints, it cannot be used to guarantee a stable search process. Velocity explosion occurs nonetheless and can lead to floating-point overflow and numerical instability. The investigation into various CP configurations across diverse functions and measurements of particle velocities provides empirical evidence of velocity explosion, and cautions against the assumption that enforcing Poli's criterion guarantees stability. The findings underline the need for comprehensive understanding of CP tuning and stability conditions in PSO, as well as the crucial role of empirical evidence in evaluating the real-world performance of swarm intelligence algorithms.

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Detecting Bone Lesions in X-Ray Under Diverse Acquisition Conditions

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arXiv 2022年

作者： Zimbalist, Tal Rosen, Ronnie Peri-Hanania, Keren Caspi, Yaron Rinott, Bar Zeltser-Dekel, Carmel Bercovich, Eyal Eldar, Yonina C. Bagon, Shai Department of Computer Science & Applied Mathematics Weizmann Institute of Science Rehovot Israel Medical Imaging Division Rambam Health Care Campus Haifa Israel

The diagnosis of primary bone tumors is challenging, as the initial complaints are often non-specific. Early detection of bone cancer is crucial for a favorable prognosis. Incidentally, lesions may be found on radiographs obtained for other reasons. However, these early indications are often missed. In this work, we propose an automatic algorithm to detect bone lesions in conventional radiographs to facilitate early diagnosis. Detecting lesions in such radiographs is challenging: first, the prevalence of bone cancer is very low;any method must show high precision to avoid a prohibitive number of false alarms. Second, radiographs taken in health maintenance organizations (HMOs) or emergency departments (EDs) suffer from inherent diversity due to different X-ray machines, technicians and imaging protocols. This diversity poses a major challenge to any automatic analysis method. We propose to train an off-the-shelf object detection algorithm to detect lesions in radiographs. The novelty of our approach stems from a dedicated preprocessing stage that directly addresses the diversity of the data. The preprocessing consists of self-supervised region-of-interest detection using vision transformer (ViT), and a foreground-based histogram equalization for contrast enhancement to relevant regions only. We evaluate our method via a retrospective study that analyzes bone tumors on radiographs acquired from January 2003 to December 2018 under diverse acquisition protocols. Our method obtains 82.43% sensitivity at 1.5% false-positive rate and surpasses existing preprocessing methods. For lesion detection, our method achieves 82.5% accuracy and an IoU of 0.69. The proposed preprocessing method enables to effectively cope with the inherent diversity of radiographs acquired in HMOs and EDs. © 2022, CC BY-NC-ND.

关键词： Tumors

EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants

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arXiv 2025年

作者： Cappello, Franck Madireddy, Sandeep Underwood, Robert Getty, Neil Chia, Nicholas Lee-Ping Ramachandra, Nesar Nguyen, Josh Keçeli, Murat Mallick, Tanwi Li, Zilinghan Ngom, Marieme Zhang, Chenhui Yanguas-Gil, Angel Antoniuk, Evan Kailkhura, Bhavya Tian, Minyang Du, Yufeng Ting, Yuan-Sen Wells, Azton Nicolae, Bogdan Maurya, Avinash Mustafa Rafique, M. Huerta, Eliu Li, Bo Foster, Ian Stevens, Rick Mathematics and Computer Science Division Argonne National Laboratory United States Data Science and Learning Division Argonne National Laboratory United States Computational Science Division Argonne National Laboratory United States Applied Materials Division Argonne National Laboratory United States Department of Computer Science The University of Chicago United States University of Pennsylvania United States Lawrence Livermore National Laboratory United States The Ohio State University United States Rochester Institute of Technology United States Massachusetts Institute of Technology United States

Recent advancements have positioned AI, and particularly Large Language Models (LLMs) as transformative tools for scientific research, capable of addressing complex tasks that require reasoning, problem-solving, and decision-making. Their exceptional capabilities suggest their potential as scientific research assistants, but also highlight the need for holistic, rigorous, and domain-specific evaluation to assess effectiveness in real-world scientific applications. This paper describes a multifaceted methodology for Evaluating AI models as scientific Research Assistants (EAIRA) developed at Argonne National Laboratory. This methodology incorporates four primary classes of evaluations. 1) Multiple Choice Questions to assess factual recall;2) Open Response to evaluate advanced reasoning and problem-solving skills;3) Lab-Style Experiments involving detailed analysis of capabilities as research assistants in controlled environments;and 4) Field-Style Experiments to capture researcher-LLM interactions at scale in a wide range of scientific domains and applications. These complementary methods enable a comprehensive analysis of LLM strengths and weaknesses with respect to their scientific knowledge, reasoning abilities, and adaptability. Recognizing the rapid pace of LLM advancements, we designed the methodology to evolve and adapt so as to ensure its continued relevance and applicability. This paper describes the methodology’s state at the end of February 2025. Although developed within a subset of scientific domains, the methodology is designed to be generalizable to a wide range of scientific domains. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision making

Automated Ensemble Multimodal Machine Learning for Healthcare

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IEEE Journal of Biomedical and Health Informatics 2025年第6期PP卷 PP页

作者： Imrie, Fergus Denner, Stefan Brunschwig, Lucas S. Maier-Hein, Klaus Van Der Schaar, Mihaela University of Oxford Department of Statistics Oxford United Kingdom Heidelberg University Faculty of Mathematics and Computer Science Germany École Polytechnique Fédérale de Lausanne Switzerland Division of Medical Image Computing Germany Heidelberg University Hospital Pattern Analysis and Learning Group Department of Radiation Oncology Germany Heidelberg Germany University of Cambridge Department of Applied Mathematics and Theoretical Physics United Kingdom

The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. This stands in stark contrast with clinician decision-making which employs diverse information from multiple sources. While several multimodal machine learning approaches exist, significant challenges in developing multimodal systems remain that are hindering clinical adoption. In this paper, we introduce a multimodal framework, AutoPrognosis-M, that enables the integration of structured clinical (tabular) data and medical imaging using automated machine learning. AutoPrognosis-M incorporates 17 imaging models, including convolutional neural networks and vision transformers, and three distinct multimodal fusion strategies. In an illustrative application using a multimodal skin lesion dataset, we highlight the importance of multimodal machine learning and the power of combining multiple fusion strategies using ensemble learning. We have open-sourced our framework as a tool for the community and hope it will accelerate the uptake of multimodal machine learning in healthcare and spur further innovation. © 2013 IEEE.

关键词： Adversarial machine learning

Score-fPINN: Fractional Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck-Lévy Equations

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arXiv 2024年

作者： Hu, Zheyuan Zhang, Zhongqiang Karniadakis, George Em Kawaguchi, Kenji Department of Computer Science National University of Singapore Singapore119077 Singapore Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA01609 United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

We introduce an innovative approach for solving high-dimensional Fokker-Planck-Lévy (FPL) equations in modeling non-Brownian processes across disciplines such as physics, finance, and ecology. We utilize a fractional score function and Physical-informed neural networks (PINN) to lift the curse of dimensionality (CoD) and alleviate numerical overflow from exponentially decaying solutions with dimensions. The introduction of a fractional score function allows us to transform the FPL equation into a second-order partial differential equation without fractional Laplacian and thus can be readily solved with standard physics-informed neural networks (PINNs). We propose two methods to obtain a fractional score function: fractional score matching (FSM) and score-fPINN for fitting the fractional score function. While FSM is more cost-effective, it relies on known conditional distributions. On the other hand, score-fPINN is independent of specific stochastic differential equations (SDEs) but requires evaluating the PINN model's derivatives, which may be more costly. We conduct our experiments on various SDEs and demonstrate numerical stability and effectiveness of our method in dealing with high-dimensional problems, marking a significant advancement in addressing the CoD in FPL equations. © 2024, CC BY.

关键词： Cost effectiveness