This paper presents a general methodology for deriving information-theoretic generalization bounds for learning algorithms. The main technical tool is a probabilistic decorrelation lemma based on a change of measure a...
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The exponential growth of big data has led to the widespread adoption of Hadoop clusters for storing and processing large volumes of data. Efficient management of resources within these clusters is crucial for achievi...
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Due to rapid advancements in technology, the financial sector has experienced significant changes in the last few decades. In particular, financial technology (FinTech) has revolutionized the financial services indust...
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To ensure the safety and fluidity of air traffic, the air traffic controller applies a set of rules dictated by the ICAO, called separation rules. For this, the controller must first undergo training based in part on ...
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We consider the problem of selecting all the minimal-size subsets of multivariate time-series (TS) variables whose past leads to an optimal predictive model for the future (forecasting) of a given target variable (mul...
There is a possibility that deepfakes will increase a great deal of false information in a very realistic way, but it seems that today such technology appears to be extremely dangerous. While generative Artificial Int...
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In this paper, we address control problems for a mathematical model describing the dynamic Coulomb frictional contact between a thermo-electro-viscoelastic body and a rigid foundation that is electrically and thermall...
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This study addresses the fixed-time-synchronized control problem of perturbed multi-input multioutput(MIMO) systems. In the task of fixed-time-synchronized control, different dimensions of the output signal in MIMO sy...
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This study addresses the fixed-time-synchronized control problem of perturbed multi-input multioutput(MIMO) systems. In the task of fixed-time-synchronized control, different dimensions of the output signal in MIMO systems are required to reach the desired value simultaneously within a fixed time *** MIMO system is categorized into two cases: the input-dimension-dominant and the state-dimensiondominant cases. The classification is defined according to the dimension of system signals and, more importantly, the capability of converging at the same time. For each kind of MIMO system, sufficient Lyapunov conditions for fixed-time-synchronized convergence are explored, and the corresponding robust sliding mode controllers are designed. Moreover, perturbations are compensated using the super-twisting technique. The brake control of the vertical takeoff and landing aircraft is considered to verify the proposed method for the input-dimension-dominant case, which shows the essential advantages of decreasing the energy consumption and the output trajectory length. Furthermore, comparative numerical simulations are performed to show the semi-time-synchronized property for the state-dimension-dominant case.
Despite notable progress in enhancing the capability of machine learning against distribution shifts, training data quality remains a bottleneck for cross-distribution generalization. Recently, from a data-centric per...
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Despite notable progress in enhancing the capability of machine learning against distribution shifts, training data quality remains a bottleneck for cross-distribution generalization. Recently, from a data-centric perspective, there have been considerable efforts to improve model performance through refining the preparation of training data. Inspired by realistic scenarios, this paper addresses a practical requirement of acquiring training samples from various domains on a limited budget to facilitate model generalization to target test domain with distribution shift. Our empirical evidence indicates that the advance in data acquisition can significantly benefit the model performance on shifted data. Additionally, by leveraging unlabeled test domain data, we introduce a Domain-wise Active Acquisition framework. This framework iteratively optimizes the data acquisition strategy as training samples are accumulated, theoretically ensuring the effective approximation of test distribution. Extensive real-world experiments demonstrate our proposal's advantages in machine learning applications. The code is available at https://***/dongbaili/DAA. Copyright 2024 by the author(s)
Air pollution is a global issue with profound implications to human health and environmental sustainability. Especially, PM2.5, which refers to particulate matter with a size of 2.5 microns or smaller, poses significa...
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