In large scale systems, real-time monitoring of hardware and software resources is a crucial means for any management purpose. In architectures consisting of thousands of servers and hundreds of thousands of component...
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In large scale systems, real-time monitoring of hardware and software resources is a crucial means for any management purpose. In architectures consisting of thousands of servers and hundreds of thousands of component resources, the amount of data monitored at high sampling frequencies represents an overhead on system performance and communication, while reducing sampling may cause quality degradation. We present a real-time adaptive algorithm for scalable data monitoring that is able to adapt the frequency of sampling and data updating for a twofold goal: to minimize computational and communication costs, to guarantee that reduced samples do not affect the accuracy of information about resources. Experiments carried out on heterogeneous data traces referring to synthetic and real environments confirm that the proposed adaptive approach reduces utilization and communication overhead without penalizing the quality of data with respect to existing monitoring algorithms.
Roughgarden, Vassilvitskii, and Wang (JACM 18) recently introduced a novel framework for proving lower bounds for Massively Parallel Computation using techniques from boolean function complexity. We extend their frame...
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CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans Apart) can be used to protect data from auto bots. Countless kinds of CAPTCHAs are thus designed, while we most frequently utilize text-base...
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The recently proposed HR-calculus has enabled rigorous derivation of quaternion-valued adaptive filtering algorithms, and has also introduced several equivalent forms of the quaternion least mean square (QLMS). This w...
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ISBN:
(纸本)9781467303217
The recently proposed HR-calculus has enabled rigorous derivation of quaternion-valued adaptive filtering algorithms, and has also introduced several equivalent forms of the quaternion least mean square (QLMS). This work aims to address the uniqueness of the solutions to the stochastic gradient optimisation problems, and to provide a unified framework for the derivation and analysis of quaternion least mean square algorithms. In doing so, we assess and compare the properties of the adaptive algorithms in the context of their convergence and steady state performances. For generality, the convergence properties of both QLMS and its widely linear extension, the WL-QLMS are illuminated.
This paper presents a comparative study of two adaptive algorithms, Linear Constraint Minimum Variance (LMCV) and the Minimum Variance Distortionless Response (MVDR) applied to the smart antenna system. These algorith...
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This paper presents a comparative study of two adaptive algorithms, Linear Constraint Minimum Variance (LMCV) and the Minimum Variance Distortionless Response (MVDR) applied to the smart antenna system. These algorithms are studied in terms of power while tracking the desired signal. In this case, we used a system with three upcoming signals containing the desired signal, whose position changes constantly, and two signals considered as interference. The simulation result shows that the MVDR technique provides better results than LMCV by creating a beam forming that follows the desired signal through its movement preserving an adequate power.
We present an adaptive version of the Multi-Index Monte Carlo method, introduced by Haji-Ali, Nobile and Tempone (2016), for simulating PDEs with coefficients that are random fields. A classical technique for sampling...
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adaptive algorithms are extensively employed in the field of deep learning owing to their rapid convergence *** is the most common adaptive algorithm among ***,it has revealed that Adam has a poor generalization *** i...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
adaptive algorithms are extensively employed in the field of deep learning owing to their rapid convergence *** is the most common adaptive algorithm among ***,it has revealed that Adam has a poor generalization *** is an algorithm based on Adam with exact stepsize *** introduces a new second-order momentum that corrects Adam's second-order *** algorithms still fail to converge during training due to the presence of instability and extreme learning *** this paper,we propose a new adaptive and momental bounded algorithm,called AdaBMod,which can effectively mitigate the sudden large learning rate problem and is especially suitable for training deep neural *** setting an adaptive finite learning rate in AdaBelief algorithm,the obtained AdaBMod can effectively eliminate the problem of high learning rate in the late training of neural networks,so as to make the training process more *** simulation experiments on deep neural network tasks also show that our proposed AdaBMod algorithm eliminates the large learning rate during the training *** results are also better than other current state-of-the-art optimizers.
Deep brain stimulation (DBS) has proven to be an effective treatment for Parkinson’s disease (PD). adaptive control strategies offer the potential to improve efficacy, limit side effects and save battery consumption ...
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Voltage stability refers to the ability of a power system to maintain acceptable voltages among all buses under normal operating conditions and after a disturbance. In this paper, a measurement-based voltage stability...
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This paper deals with the problem of two-dimensional autoregressive (AR) estimation from noisy observations. The Yule-Walker equations are solved using adaptive steepest descent (SD) algorithm. Performance comparisons...
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