Plant diseases affect the health and yield of the crop which can in turn impact farmers economically and pose a threat to food security across the globe. To protect crops from disease and obtain high yield, many Agric...
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Data science-based techniques have been widely applied in studies related to COVID-19 spread prediction. In these studies, different modeling techniques have been deployed to estimate the current and future trajectori...
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We present a novel framework for learning system design with neural feature extractors. First, we introduce the feature geometry, which unifies statistical dependence and feature representations in a function space eq...
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We present a novel framework for learning system design with neural feature extractors. First, we introduce the feature geometry, which unifies statistical dependence and feature representations in a function space equipped with inner products. This connection defines function-space concepts on statistical dependence, such as norms, orthogonal projection, and spectral decomposition, exhibiting clear operational meanings. In particular, we associate each learning setting with a dependence component and formulate learning tasks as finding corresponding feature approximations. We propose a nesting technique, which provides systematic algorithm designs for learning the optimal features from data samples with off-the-shelf network architectures and optimizers. We further demonstrate multivariate learning applications, including conditional inference and multimodal learning, where we present the optimal features and reveal their connections to classical approaches.
Fast and efficient genome analysis can have a significant impact in areas such as scientific discovery and personalized medicine. Given the extensive data produced by sequencing machines, in-memory computing is consid...
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Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing *** goal is to find a Pareto front and as many equivalent Pareto optimal solutions as *** some evolutionary algorithms for them h...
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Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing *** goal is to find a Pareto front and as many equivalent Pareto optimal solutions as *** some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions *** this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision *** first extend a latest single-objective fireworks algorithm to handle *** we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special ***,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization *** results show that the proposed algorithm is superior to compared algorithms in solving ***,its runtime is less than its peers'.
Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is *** the developed model,the Adam stochastic g...
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Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is *** the developed model,the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 *** presented method can obtain more useful information than conventional point and interval ***,a prediction of the future complete probability distribution of wind power can be *** to the actual data forecast of wind power in the PJM network in the United States,the proposed probability density prediction approach can not only obtain more accurate point prediction results,it also obtains the complete probability density curve prediction results for wind *** with two other quantile regression methods,the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.
Piezoelectric energy (PE) harvesters can harvest vibration energy and convert it into electrical energy, which provides a promising solution to supply sustainable power for wireless sensor networks (WSN) applications....
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Edge computing, an extension of cloud and IoT technologies, introduces unique challenges for intrusion-detection systems (IDS). This study explores IDS architecture in the context of edge computing. We proposed a spec...
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Cyber-physical power systems' reliance on cyberspace makes them vulnerable to cyber-attacks, particularly false data injection attacks (FDIAs), where the aim is to alter the state estimation (SE) results by changi...
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The successful and complete downlink transmission of critical data from an unmanned aerial vehicle (UAV) base station, such as control command and intelligence information, is essential for ground users to perform spe...
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