One of the latest modern communication devices is a mobile device seriously affected by multiple malware. Malware is a virus software installed automatically by hackers on various computing devices. Malware corrupts t...
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This paper introduces SEAN, a novel anomaly detection algorithm designed for real-time applications in predictive maintenance. SEAN leverages an ensemble-based approach to deliver competitive performance while drastic...
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Temporal difference (TD) learning algorithms with neural network function parameterization have well-established empirical success in many practical large-scale reinforcement learning tasks. However, theoretical under...
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Temporal difference (TD) learning algorithms with neural network function parameterization have well-established empirical success in many practical large-scale reinforcement learning tasks. However, theoretical understanding of these algorithms remains challenging due to the nonlinearity of the action-value approximation. In this paper, we develop an improved non-asymptotic analysis of the neural TD method with a general L-layer neural network. New proof techniques are developed and an improved new Õ(ϵ-1) sample complexity is derived. To our best knowledge, this is the first finite-time analysis of neural TD that achieves an Õ(ϵ-1) complexity under the Markovian sampling, as opposed to the best known Õ(ϵ-2) complexity in the existing literature. Copyright 2024 by the author(s)
It is known that the left tail asymptotic for supercritical branching processes in the Schröder case satisfies a power law multiplied by some multiplicatively periodic function. We provide an explicit expression ...
For the solutions Φ(z) of functional equations Φ(z) = P(z) + Φ(Q(z)), we derive a complete asymptotic of power series coefficients. As an application, we improve significantly an asymptotic of the number of 2,3-tre...
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The classical Galton–Watson process works with a fixed probability of fission at each time step. One of the generalizations is that the probabilities depend on time. We consider one of the most complex and interestin...
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With the rapid digitization of Electronic Health Records (EHRs), fast and adaptive data anonymization methods have become increasingly important. While tools from topological data analysis (TDA) have been proposed to ...
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In this paper, we propose 1-bit weighted Σ quantization schemes of mixed order as a technique for digital halftoning. These schemes combine weighted Σ schemes of different orders for two-dimensional signals so one c...
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In recent years,convolutional neural networks(CNNs)have demonstrated their effectiveness in predicting bulk parameters,such as effective diffusion,directly from pore-space *** offer significant computational advantage...
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In recent years,convolutional neural networks(CNNs)have demonstrated their effectiveness in predicting bulk parameters,such as effective diffusion,directly from pore-space *** offer significant computational advantages over traditional methods,making them particularly ***,the current literature primarily focuses on fully saturated porous media,while the partially saturated case is also of high interest for various *** saturated conditions present more complex geometries for diffusive transport,making the prediction task more *** CNNs tend to lose robustness and accuracy with lower saturation *** this paper,we overcome this limitation by introducing a CNN,which conveniently fuses diffusion prediction and a well-established morphological model that describes phase distributions in partially saturated porous *** demonstrate the ability of our CNN to perform accurate predictions of relative diffusion directly from full pore-space ***,we compare our predictions with well-established relations such as the one by Millington–Quirk.
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