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 i...
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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.
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 ...
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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.
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 g...
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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.
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) param...
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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.
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 tim...
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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.
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 t...
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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.
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...
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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.
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 prop...
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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.
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 consump...
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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%.
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 ...
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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.
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