In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its **...
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In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its *** current WMC solvers work on Conjunctive Normal Form(CNF)***,CNF is not a natural representation for human-being in many *** by the stronger expressive power of Pseudo-Boolean(PB)formulas than CNF,we propose to perform WMC on PB *** on a recent dynamic programming algorithm framework called ADDMC for WMC,we implement a weighted PB counting tool *** compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD,ExactMC,D4,and ADDMC,where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF *** experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas.
Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D *** algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivo...
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Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D *** algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivotal in the imaging *** approaches painstakingly designed networks to directly map compressive measurements to HSIs,resulting in the lack of interpretability without exploiting the imaging *** some recent works have introduced the deep unfolding framework for explainable reconstruction,the performance of these methods is still limited by the weak information transmission between iterative *** this paper,we propose a Memory-Augmented deep Unfolding Network,termed MAUN,for explainable and accurate HSI ***,MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm,introducing an extra momentum incorporation step for each iteration to alleviate the information ***,to exploit the high correlation of intermediate images from neighboring iterations,we customize a cross-stage transformer(CSFormer)as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features,which is the first attempt to model the long-distance dependencies between iteration *** experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and *** code is publicly available at https://***/HuQ1an/MAUN.
The Internet of Things (IoT) and blockchain are two emerging information technologies that will significantly impact the lives and production patterns of people. When they meet together, blockchain services can be use...
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The main theme of this book is network analysis and architecture design for network planning. While there are various types of networks, this book specifically focuses on computer networks for data communication. Ther...
Interference source localization with high accuracy and time efficiency is of crucial importance for protecting spectrum resources. Due to the flexibility of unmanned aerial vehicles(UAVs), exploiting UAVs to locate t...
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Interference source localization with high accuracy and time efficiency is of crucial importance for protecting spectrum resources. Due to the flexibility of unmanned aerial vehicles(UAVs), exploiting UAVs to locate the interference source has attracted intensive research interests. The off-the-shelf UAV-based interference source localization schemes locate the interference sources by employing the UAV to keep searching until it arrives at the target. This obviously degrades time efficiency of localization. To balance the accuracy and the efficiency of searching and localization, this paper proposes a multi-UAV-based cooperative framework alone with its detailed scheme, where search and remote localization are iteratively performed with a swarm of UAVs. For searching, a low-complexity Q-learning algorithm is proposed to decide the direction of flight in every time interval for each UAV. In the following remote localization phase, a fast Fourier transformation based location prediction algorithm is proposed to estimate the location of the interference source by fusing the searching result of different UAVs in different time intervals. Numerical results reveal that in the proposed scheme outperforms the stateof-the-art schemes, in terms of the accuracy, the robustness and time efficiency of localization.
Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human *** enable learning by brain and machine,it is essential to accurately identify and correct the predict...
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Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human *** enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.
As an important computer vision task that can be used in many areas, facial expression recognition (FER) has been widely studied which much progress has been obtained especially when deep learning (DL) approaches have...
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An ultra-wideband (UWB) slotted compact Vivaldi antenna with a microstrip line feed was evaluated for microwave imaging (MI) applications. The recommended FR4 substrate-based Vivaldi antenna is 50×50×1.5 mm3...
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The text representation issue in text classification is thoroughly examined in this paper, with a focus on handling massive volumes of text data. The paper suggests the word vector weighting model based on WT-GloVe in...
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Recent model recovery methods in federated unlearning (FUL) either rely on additional communication with the remaining clients or require large amounts of high-quality data from the server for training, overlooking sc...
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