While the recent literature has seen a surge in the study of constrained bandit problems, all existing methods for these begin by assuming the feasibility of the underlying problem. We initiate the study of testing su...
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While the recent literature has seen a surge in the study of constrained bandit problems, all existing methods for these begin by assuming the feasibility of the underlying problem. We initiate the study of testing such feasibility assumptions, and in particular address the problem in the linear bandit setting, thus characterising the costs of feasibility testing for an unknown linear program using bandit feedback. Concretely, we test if ∃x : Ax ≥ 0 for an unknown A ∈ m×d, by playing a sequence of actions xt ∈ d, and observing Axt + noise in response. By identifying the hypothesis as determining the sign of the value of a minimax game, we construct a novel test based on low-regret algorithms and a nonasymptotic law of iterated logarithms. We prove that this test is reliable, and adapts to the 'signal level,' Γ, of any instance, with mean sample costs scaling as Õ(d2/Γ2). We complement this by a minimax lower bound of Ω(d/Γ2) for sample costs of reliable tests, dominating prior asymptotic lower bounds by capturing the dependence on d, and thus elucidating a basic insight missing in the extant literature on such problems. Copyright 2024 by the author(s)
This article proposes three-level (TL) buck-boost direct ac-ac converters based on switching-cell configuration with coupled magnetics. The proposed converters use only six active switches and can produce noninverting...
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In this letter, we depart from the widely-used gradient descent-based hierarchical federated learning (FL) algorithms to develop a novel hierarchical FL framework based on the alternating direction method of multiplie...
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Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power *** wide deployment of phasor measurement units(PMUs)promotes the devel...
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Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power *** wide deployment of phasor measurement units(PMUs)promotes the development of data-driven methods for RAS *** paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU *** grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power *** the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are *** studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against measurement uncertainties of the TTEDNN *** show that the TTEDNN model performs best among existing deep learning ***,the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.
In industrial inspection, the detection of surface defects - such as scratches, dents, or other defects - is crucial for ensuring product quality. However, the limited availability of annotated images of such defects ...
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Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computati...
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Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.
Perovskite solar cells represent a revolutionary class of photovoltaic devices that have gained substantial attention for their exceptional performance and potential to provide an affordable and efficient solution for...
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Perovskite solar cells represent a revolutionary class of photovoltaic devices that have gained substantial attention for their exceptional performance and potential to provide an affordable and efficient solution for harnessing solar energy. These cells utilize perovskite-structured materials, typically hybrid organicinorganic lead halide compounds, as the light-absorbing layer.
The goal of this study is to build a self-driving robot that can effectively navigate mazes by utilizing sophisticated computer vision algorithms with ROS2. Fusion 360 is used to create the robot model, and ROS2 launc...
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Distribution grids in individual countries inside the European Union have different properties, while the grid operators use different approaches to integrate photovoltaic (PV) systems into the grid, which influences ...
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Scanning microscopy systems, such as confocal and multiphoton microscopy, are powerful imaging tools for probing deep into biological tissue. However, scanning systems have an inherent trade-off between acquisition ti...
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