The theory of Compressive Sensing (CS) has garnered significant attention in recent years due to its distinct advantages in mitigating the high sidelobe of Random Frequency and Pulse Repetition Interval Agile (RFPA) r...
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Applying the noisy channel model to search query spelling correction requires an error model and a language model. Typically, the error model relies on a weighted string edit distance measure. The weights can be learn...
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This paper presents a structure-coupled sparse Bayesian learning method for building layout reconstruction using through-the-wall radar. We characterize the azimuth continuity of the wall and two-dimensional extensibi...
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Maximizing monotone submodular functions under cardinality constraints is a classic optimization task with several applications in data mining and machine learning. In this paper we study this problem in a dynamic env...
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Maximizing monotone submodular functions under cardinality constraints is a classic optimization task with several applications in data mining and machine learning. In this paper we study this problem in a dynamic environment with consistency constraints: elements arrive in a streaming fashion and the goal is maintaining a constant approximation to the optimal solution while having a stable solution (i.e., the number of changes between two consecutive solutions is bounded). We provide algorithms in this setting with different trade-offs between consistency and approximation quality. We also complement our theoretical results with an experimental analysis showing the effectiveness of our algorithms in real-world instances. Copyright 2024 by the author(s)
Aiming at the problem of high energy consumption of air conditioning system of UAV nest on tower, a low power optimization control method based on expectationmaximization (EM) algorithm is proposed. Based on the anal...
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In this paper, we present a multivariate bounded Kotz mixture model (BKMM) for data modeling when the data lies in a bounded support region. In BKMM, parameter estimation is performed by maximizing the log-likelihood ...
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Positron Emission Tomography (PET) is a medical imaging modality relying on numerical methods that integrate the statistical properties of the measurements and prior assumptions about the images. In order to maximize ...
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Within the contemporary context of smart city infrastructures, the Internet of Medical Things (IoMT) has the capacity to instigate a revolution in urban monitoring. Despite recent advancements, conventional face recog...
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In biology, constructing gene co-expression networks presents a significant research challenge, largely due to the high dimensionality of the data and the heterogeneity of the samples. Furthermore, observations from t...
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Social networks have been a common medium for spreading information among users. Inspired by this phenomenon, business companies consider 'hiring' some influential users in the social network to expand product...
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