Pre-trained language models (PLMs) leverage chains-of-thought (CoT) to simulate human reasoning and inference processes, achieving proficient performance in multi-hop QA. However, a gap persists between PLMs' reas...
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multi-disease diagnosis of fundus image is a great challenge even though sophisticated computer-aided tools are available and ophthalmologist experience is to par. Clinical tests capture abnormalities and anatomical s...
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Recently, neural network-based inverse models have been used for multi-objective optimization. The basic idea is to approximate the mapping from the Pareto front to the Pareto set. In general, inverse modeling from a ...
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Tremor is a common disorder associated with neurological conditions such as multiple Sclerosis. In this paper, an approach for suppressing tremor signals was developed by combining linearization action and Repetitive ...
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Considering the impact of background noise, the traditional bridge crack detection method cannot completely extract the crack features and effectively fuse them, resulting in low crack detection accuracy, false and mi...
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We introduce a formalism called SLAM (Social Laws on ATL Models) for defining social laws. Such social laws can constrain the behaviour of a multi-agent system. Importantly, these social laws can use any ATL formula a...
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In this paper, we study the obstacle avoidance problem of second-order nonlinear multi-agent systems (MASs) with directed graph based on event-triggered control. Firstly, the consensus requirement is accomplished by u...
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Human pose estimation in crowded scenes has always been a challenging task in bottom-up multi-person pose estimation. To improve the accuracy of pose estimation in dense crowds, we propose an improved bottom-up human ...
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Object detection in adverse weather conditions, such as dense fog, poses a critical challenge for computer vision systems in applications like autonomous driving, surveillance, and robotics. Image quality degradation ...
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The use of deep learning algorithms for vehicle detection and speed estimate in traffic surveillance systems is investigated in this research study. Convolutional Neural Networks (CNNs) are the main tool used in this ...
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