Conditional information and inference in response‐adaptive allocation designs

Core Collection database of Web of Scienceresource investigated the poorly developed problem of addictivebehaviour of children as a part of the ecological environment of modern childhood. In three clusters-"addictive child", "addiction", "ecological environment of childhood”, (as conditional information data groups), scientific-metric space indices were analyzed, namely -citation and content offull-text publications. We identified the leading trends of research works. They show that the addictioncomponent becomes increasingly important in ecological construct of childhood environment

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A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud

10.21203/rs.3.rs-69957/v1 ◽

2020 ◽

Author(s):

Wenjie Liu ◽

Ying Zhang ◽

Zhiliang Deng ◽

Jiaojiao Zhao ◽

Lian Tong

Keyword(s):

Machine Learning ◽

Quantum Circuit ◽

Machine Learning Algorithms ◽

Generation Process ◽

Generative Adversarial Network ◽

Computing Platform ◽

Adversarial Network ◽

Quantum Machine Learning ◽

Huge Impact ◽

Conditional Information

Abstract As an emerging field that aims to bridge the gap between human activities and computing systems, human-centered computing (HCC) in cloud, edge, fog has had a huge impact on the artificial intelligence algorithms. The quantum generative adversarial network (QGAN) is considered to be one of the quantum machine learning algorithms with great application prospects, which also should be improved to conform to the human-centered paradigm. The generation process of QGAN is relatively random and the generated model does not conform to the human-centered concept, so it is not quite suitable for real scenarios. In order to solve these problems, a hybrid quantum-classical conditional generative adversarial network (QCGAN) algorithm is proposed, which is a knowledge-driven human-computer interaction computing mode in cloud. The purpose of stabilizing the generation process and the interaction between human and computing process is achieved by inputting conditional information in the generator and discriminator. The generator uses the parameterized quantum circuit with an all-to-all connected topology, which facilitates the tuning of network parameters during the training process. The discriminator uses the classical neural network, which effectively avoids the ”input bottleneck” of quantum machine learning. Finally, the BAS training set is selected to conduct experiment on the quantum cloud computing platform. The result shows that the QCGAN algorithm can effectively converge to the Nash equilibrium point after training and perform human-centered classification generation tasks.

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