Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. However, less focus has been put on featu...
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The question of what kind of convolutional neural network (CNN) structure performs well is fascinating. In this work, we move toward the answer with one more step by connecting zero stability and model performance. Sp...
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Speaker extraction seeks to extract the clean speech of a target speaker from a multi-talker mixture speech. There have been studies to use a pre-recorded speech sample or face image of the target speaker as the speak...
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In this paper, we study the nurse rostering problem that considers multiple units and many soft time-related constraints. An efficient branch and price solution approach that relies on a fast algorithm to solve the pr...
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Beam tracking is crucial for maintaining stable data transmission in unmanned aerial vehicle (UAV) communications. However, a communication link can be disrupted by frequent switching of narrow beams between a base st...
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Within the context of cleaner production, enhancing energy efficiency and sustainability has emerged as a central focus in supercomputing center development. To address the challenges in predicting energy consumption,...
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Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically foc...
Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically focus on super resolution with sufficient training data. However, restricted by data acquisition conditions, certain hyperspectral images or band images are very different to obtain, resulted in insufficient training data. In order to solve this problem, a new hyperspectral image super resolution method is proposed in this paper in an effort to conduct the super resolution task over insufficient (sparse) training data, by applying the recently introduced ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation method. Particularly, the training dataset is divided into several subsets. For the subsets with sufficient training data, the relevant ANFIS models are trained using standard ANFIS learning algorithm, while for the subsets with sparse training data, the corresponding ANFIS models are interpolated through the use of ANFIS interpolation. Experimental results indicate that compared with the methods using sufficient training data, the proposed method can achieve very similar result, showing its effectiveness for situations where only sparse training data is available.
Multimodal fake news is more deceptive than unimodal content and often has adverse social and economic impacts. However, most existing methods learn modal features from a single perspective, without considering simult...
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Out-of-distribution (OOD) detection is essential in identifying test samples that deviate from the in-distribution (ID) data upon which a standard network is trained, ensuring network robustness and reliability. This ...
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The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of the quantum state is experimentally infeasible d...
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The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of the quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of the quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and the quantum approximate optimization algorithm (QAOA). It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for a QAOA with a noisy control Hamiltonian. This robust control optimization model can be trained to compensate for the uncertainties in practical quantum computing.
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