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In Search of Rogue Waves: A Novel Proposal Distribution for Parallelized Rejection sampling of the Truncated KdV Gibbs Measure

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STUDIES IN APPLIED MATHEMATICS 2025年第3期154卷

作者： Moore, Nicholas J. Foerster, Brendan Colgate Univ Dept Math Hamilton NY 13346 USA

The Gibbs ensemble of the truncated KdV (TKdV) equation has been shown to accurately describe the anomalous wave statistics observed in laboratory experiments, in particular the emergence of extreme events. Here, we introduce a novel proposal distribution that facilitates efficient rejection sampling of the TKdV Gibbs measure. Within parameter regimes accessible to laboratory experiments and capable of producing extreme events, the proposal distribution generates 1-6 orders of magnitude more accepted samples than does a naive, uniform distribution. When equipped with the new proposal distribution, a simple rejection algorithm enjoys key advantages over a Markov chain Monte Carlo algorithm, include better parallelization properties and generation of uncorrelated samples.

关键词： extreme events rogue waves sampling algorithm

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LieCConv: An Image Classification algorithm Based on Lie Group Convolutional Neural Network

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NEURAL PROCESSING LETTERS 2025年第1期57卷 1-21页

作者： Zhang, Yunjie Luo, Xizhao Tao, Chongben Qin, Bo Yang, Anjia Cao, Feng Soochow Univ Sch Comp Sci & Technol 333 Ganjiang East Rd Suzhou 215008 Jiangsu Peoples R China Suzhou Univ Sci & Technol Sch Elect & Informat Engn 99 Xuefu Rd Suzhou 215009 Jiangsu Peoples R China Renmin Univ China Sch Informat 59 Zhongguancun St Beijing 100872 Peoples R China Jinan Univ Coll Cyber Secur 855 East Xingyedadao Ave Guangzhou 511443 Guangdong Peoples R China Shanxi Univ Sch Comp & Informat Technol Sch Big Data 92 Wucheng Rd Taiyuan 030006 Shanxi Peoples R China

In Lie group convolutional neural networks (LG-CNNs), the calculation and storage of Lie group distances have quadratic space complexity. In order to improve the memory utilization efficiency of LG-CNNs, a novel Lie group convolutional neural network called LieCConv is proposed. LieCConv utilizes an innovative sampling algorithm and a linear space complexity calculation and storage approach for Lie group distances, substantially enhancing network memory efficiency. Firstly, LieCConv employs a novel sampling algorithm called array-neighborhood sampling (ANS) in the downsampling stage. ANS only requires neighborhood information to obtain an excellent sample set with a low threshold of use. The sample set generated by ANS reflects the distribution of the original set. Then, LieCConv adopts a batch calculation and storage scheme for Lie group distances, which effectively declines the space complexity of calculating and storing Lie group distances from quadratic complexity to linear complexity, reducing the memory consumption during training. Finally, the contrast between ANS and farthest point sampling was presented, demonstrating that ANS better captures the distribution characteristics of the original dataset. The memory usage of LieCConv and LieConv was compared, revealing that LieCConv reduces the memory usage for calculating and storing Lie group distances to less than 500 MB. And the performance of LieCConv was evaluated on RotMNIST, RotFashionMNIST and TT100K, validating that LieCConv is universal and effective.

关键词： Lie group Convolutional neural network sampling algorithm Image classification

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A Survey of sampling Methods for Hyperspectral Remote Sensing: Addressing Bias Induced by Random sampling

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REMOTE SENSING 2025年第8期17卷 1373-1373页

作者： Decker, Kevin T. Borghetti, Brett J. Air ForceInst Technol Dept Elect & Comp Engn 2950 Hobson Way Wright Patterson AFB OH 45433 USA

Identified as early as 2000, the challenges involved in developing and assessing remote sensing models with small datasets remain, with one key issue persisting: the misuse of random sampling to generate training and testing data. This practice often introduces a high degree of correlation between the sets, leading to an overestimation of model generalizability. Despite the early recognition of this problem, few researchers have investigated its nuances or developed effective sampling techniques to address it. Our survey highlights that mitigation strategies to reduce this bias remain underutilized in practice, distorting the interpretation and comparison of results across the field. In this work, we introduce a set of desirable characteristics to evaluate sampling algorithms, with a primary focus on their tendency to induce correlation between training and test data, while also accounting for other relevant factors. Using these characteristics, we survey 146 articles, identify 16 unique sampling algorithms, and evaluate them. Our evaluation reveals two broad archetypes of sampling techniques that effectively mitigate correlation and are suitable for model development.

关键词： sampling algorithm generalization model assessment correlation remote sensing

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On sampling algorithms in multilevel QR factorization method for magnetoquasistatic analysis of integrated circuits over multilayered Lossy substrates

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2006年第9期25卷 1777-1792页

作者： Wang, Hao Gang Chan, Chi Hou Tsang, Leung Jandhyala, Vikram City Univ Hong Kong Wireless Commun Res Ctr Hong Kong Hong Kong Peoples R China Univ Washington Dept Elect Engn Seattle WA 98195 USA

This paper proposes improvements in the row and column samplings of the multilevel QR factorization method known as IES3, a fast integral equation solver previously proposed for efficient three-dimensional (3-D) parameter extraction. First, a rigorous Gram-Schmidt row sampling is developed to replace the row sampling algorithm in IES3, leading to a more stable algorithm. Second, to further enhance the efficiency of column sampling, a new scheme based on the idea of locating the interpolation points is presented. Error analyses indicate that the proposed schemes have higher accuracies than the original sampling in IES3, especially when the number of sampled points is small. The IES3 that uses one of these improved algorithms is called improved multilevel matrix QR factorization (IMLMQRF). These IMLMQRFs are applied in the magnetoquasistatic analysis of printed circuits on multilayered lossy medium for extractions of inductances and resistances. The frequency dependency of such parameters is also illustrated.

关键词： improved multilevel matrix QR factorization (IMLMQRF) large-scale EM simulation magnetoquasistatic radio frequency integrated circuits sampling algorithm

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sampling OF MASS SMS FILTERING algorithm BASED ON FREQUENT TIME-DOMAIN AREA

SAMPLING OF MASS SMS FILTERING ALGORITHM BASED ON FREQUENT T...

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3rd International Conference on Knowledge Discovery and Data Mining

作者： Xia Hu Fu Yan Univ Elect Sci & Technol China Sch Engn & Comp Sci Chengdu 610054 Peoples R China

ISBN: (纸本)9780769539232

With the rapid growth of the SMS, the filtration to all messages has been unable to meet the real-time processing requirement. In this paper, we propose a sampling of mass SMS filtering algorithm based on frequent time-domain area to solve this problem. First, we collect the long-running system log. And then analyze the time and domain features of the messages to generate the time-domain strategy. Finally we predict the potential spam messages' rate in different domain and different time, and carries on the filtration according to each rate separately. This algorithm can satisfy the real-time filtration requirement of the mass SMS stream, and meanwhile there is no significant reduction in spam.

关键词： Mass SMS filtering sampling algorithm frequent time-domain area spam message

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A Novel sampling-Based Optimal Motion Planning algorithm for Energy-Efficient Robotic Pick and Place

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ENERGIES 2023年第19期16卷 6910页

作者： Alam, Md Moktadir Nishi, Tatsushi Liu, Ziang Fujiwara, Tomofumi Univ Michigan Mech Engn 2350 Hayward Ann Arbor MI 48109 USA Okayama Univ Fac Environm Life Nat Sci & Technol 3-1-1 Tsushima NakaKita Ku Okayama 7008530 Japan

Energy usage in robotic applications is rapidly increasing as industrial robot installations grow. This research introduces a novel approach, using the rapidly exploring random tree (RRT)-based scheme for optimizing the robot's motion planning and minimizing energy consumption. sampling-based algorithms for path planning, such as RRT and its many other variants, are widely used in robotic motion planning due to their efficiency in solving complex high-dimensional problems efficiently. However, standard versions of these algorithms cannot guarantee that the generated trajectories are always optimum and mostly ignore the energy consumption in robotic applications. This paper proposes an energy-efficient industrial robotics motion planning approach using the novel flight cost-based RRT (FC-RRT*) algorithm in pick-and-place operation to generate nodes in a predetermined direction and then calculate energy consumption using the circle point method. After optimizing the motion trajectory, power consumption is computed for the rotary axes of a six degree of freedom (6DOF) serial type of industrial robot using the work-energy hypothesis for the rotational motion of a rigid body. The results are compared to the traditional RRT and RRT* (RRT-star) algorithm as well as the kinematic solutions. The experimental results of axis indexing tests indicate that by employing the sampling-based FC-RRT* algorithm, the robot joints consume less energy (1.6% to 16.5% less) compared to both the kinematic solution and the conventional RRT* algorithm.

关键词： robot motion planning graph search energy efficiency trajectory generation sampling algorithm industry RRT

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Rapid earthquake loss assessment based on machine learning and representative sampling

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EARTHQUAKE SPECTRA 2022年第1期38卷 152-177页

作者： Stojadinovic, Zoran Kovacevic, Milos Marinkovic, Dejan Stojadinovic, Bozidar Univ Belgrade Fac Civil Engn Bulevar Kralja Aleksandra 73 Belgrade 11000 Serbia Swiss Fed Inst Technol Inst Struct Engn Zurich Switzerland

This article proposes a new framework for rapid earthquake loss assessment based on a machine learning damage classification model and a representative sampling algorithm. A random forest classification model predicts a damage probability distribution that, combined with an expert-defined repair cost matrix, enables the calculation of the expected repair costs for each building and, in aggregate, of direct losses in the earthquake-affected area. The proposed building representation does not include explicit information about the earthquake and the soil type. Instead, such information is implicitly contained in the spatial distribution of damage. To capture this distribution, a sampling algorithm, based on K-means clustering, is used to select a minimal number of buildings that represent the area of interest in terms of its seismic risk, independently of future earthquakes. To observe damage states in the representative set after an earthquake, the proposed framework utilizes a local network of trained damage assessors. The model is updated after each damage observation cycle, thus increasing the accuracy of the current loss assessment. The proposed framework is exemplified using the 2010 Kraljevo, Serbia earthquake dataset.

关键词： Earthquake loss assessment machine learning sampling algorithm damage state building type

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Efficiently sampling exchangeable Cuadras-Auge copulas in high dimensions

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INFORMATION SCIENCES 2009年第17期179卷 2872-2877页

作者： Mai, Jan-Frederik Scherer, Matthias Tech Univ Munich HVB Inst Math Finance D-85748 Garching Germany

An n-dimensional random vector is constructed whose survival copula is given by a copula that was first presented in Cuadras and Auge [C.M. Cuadras, J. Auge, A continuous general multivariate distribution and its properties, Communications in Statistics - Theory and Methods 10 (4) (1981) 339-353]. This construction adds a Poisson subordinator as mixing variable to initially independent exponentially distributed random variables. It is shown how the choice of Poisson process relates to the parameter of the induced Cuadras-Auge copula. Based on this construction, a sampling algorithm for this multivariate distribution is presented which has average computational efficiency O(n log log n). (C) 2008 Elsevier Inc. All rights reserved.

关键词： Cuadras-Auge copula sampling algorithm Poisson process

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User-Driven Adaptive sampling for Massive Internet of Things

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IEEE ACCESS 2020年 8卷 135798-135810页

作者： Kim-Hung, Le Le-Trung, Quan Vietnam Natl Univ Ho Chi Minh City Univ Informat Technol Fac Comp Networks & Commun Ho Chi Minh City 700000 Vietnam

Energy conservation techniques are crucial to achieving high reliability in the Internet of Things (IoT) services, especially in the Massive IoT (MIoT), which stringently requires cost-effective and low-energy consumption for battery-powered devices. Most of the proposed techniques generally assume that data acquiring and processing consume significantly lower than that of communication. Unfortunately, this assumption is incorrect in the MIoT scenario, which mostly involves the low-power wide-area network (LPWAN) and complex data sensing operations (e.g., biological and seismic sensing) using "power-hungry" sensors (e.g., gas sensors, seismometers). Thus, sensing actions may consume even more energy than transmission. In addition, none of them support end-users in controlling the trade-off between energy conservation and data precision. To deal with these issues, we propose an adaptive sampling algorithm that estimates the optimal sampling frequencies in real-time for IoT devices based on the changes of collected data. Given a user's saving desire, our algorithm could minimize the device's energy consumption while ensuring the precision of collected information. Practical experiments over IoT datasets have shown that our algorithm can reduce the number of acquired samples up to 20 times compared with a traditional fixed-rate approach at extremely low Normal Mean Error value around 3.45%.

关键词： Internet of Things Gas detectors Energy consumption Power demand Real-time systems Batteries Constrained devices energy efficiency massive internet of things sampling algorithm

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Polynomial calculation of the Shapley value based on sampling

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COMPUTERS & OPERATIONS RESEARCH 2009年第5期36卷 1726-1730页

作者： Castro, Javier Gomez, Daniel Tejada, Juan Univ Complutense Madrid Escuela Univ Estadst Dpto Estadst & IO 3 E-28040 Madrid Spain Univ Complutense Madrid Fac Ciencias Matemat Dpto Estadst & IO 1 E-28040 Madrid Spain

In this paper we develop a polynomial method based on sampling theory that can be used to estimate the Shapley value (or any semivalue) for cooperative games. Besides analyzing the complexity problem, we examine some desirable statistical properties of the proposed approach and provide some computational results. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Game theory Shapley value sampling algorithm

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