The emerging of Digital Twin (DT) technology facilitates the further development of industrial automation. However, real-time and accurate DTs modeling and updating require massive communication and computing resource...
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ISBN:
(纸本)9798350358513;9798350358520
The emerging of Digital Twin (DT) technology facilitates the further development of industrial automation. However, real-time and accurate DTs modeling and updating require massive communication and computing resources, which poses a challenge to limited resources. Edge computing as a distributed computing architecture offers the possibility of high-efficient resource scheduling in DTs. Motivated by this gap, this paper aim to solve the problem of real-time and high fidelity DTs modeling and updating. First, we represent the computing tasks of DTs in the form of Heterogeneous computing Task Graph (HCTG). Then, a Hierarchical Attention Mechanism (HAT) is proposed to obtain the latent representation vectors of the HCTG. Finally, we design Markov Decision Process (MDP), and propose Deep Reinforcement learning (DRL)-based computing task scheduling approach (HAT-DRL) to satisfy the minimum total completion time requirement of different DTs. Experimental results demonstrate that the proposed algorithm has promising scheduling performance and outperforms other task scheduling algorithms.
At the age of artificial intelligence, the inheritance and development of innovation culture are crucial for constructing an innovative country, and they are major components of cultural intelligence computing. The po...
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Linear systems of equations can be found in various mathematical domains, as well as in the field of machine learning. By employing noisy intermediate-scale quantum devices, variational solvers promise to accelerate f...
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ISBN:
(纸本)9798331541378
Linear systems of equations can be found in various mathematical domains, as well as in the field of machine learning. By employing noisy intermediate-scale quantum devices, variational solvers promise to accelerate finding solutions for large systems. Although there is a wealth of theoretical research on these algorithms, only fragmentary implementations exist. To fill this gap, we have developed the variational-lse-solver framework, which realizes existing approaches in literature, and introduces several enhancements. The user-friendly interface is designed for researchers that work at the abstraction level of identifying and developing end-to-end applications.
With the continuous development and maturity of internet technology, many traditional industries are gradually being influenced or even subverted by the internet. As a traditional humanities, literature teaching has a...
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The research aimed to demonstrate how using cloud computing tools as technology to learn the English language can enhance communication skills. This improvement allows individuals to better express actions, emotions, ...
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By using an autoencoder as a dimension reduction tool, an Autoencoder-embedded teaching-learning Based Optimization (ATLBO) has been proved to be effective in solving high-dimensional computationally expensive problem...
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Scientific computing is an essential tool for scientific discovery and engineering design, and its computational cost is always a main concern in practice. To accelerate scientific computing, it is a promising approac...
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Scientific computing is an essential tool for scientific discovery and engineering design, and its computational cost is always a main concern in practice. To accelerate scientific computing, it is a promising approach to use machine learning (especially meta-learning) techniques for selecting hyperparameters of traditional numerical methods. There have been numerous proposals to this direction, but many of them require automatic-differentiable numerical methods. However, in reality, many practical applications still depend on well-established but non-automatic-differentiable legacy codes, which prevents practitioners from applying the state-of-the-art research to their own problems. To resolve this problem, we propose a non-intrusive methodology with a novel gradient estimation technique to combine machine learning and legacy numerical codes without any modification. We theoretically and numerically show the advantage of the proposed method over other baselines and present applications of accelerating established non-automatic-differentiable numerical solvers implemented in PETSc, a widely used open-source numerical software library.
In this paper, the application of Virtual Reality (VR) technology in college Chinese teaching is deeply studied, aiming at discussing and evaluating the influence of innovative teaching methods in VR environment on co...
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Deep learning algorithms, such as those used in Reinforcement learning, often require large quantities of data to train effectively. In most cases, the availability of data is not a significant issue. However, for som...
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ISBN:
(纸本)9798331541378
Deep learning algorithms, such as those used in Reinforcement learning, often require large quantities of data to train effectively. In most cases, the availability of data is not a significant issue. However, for some contexts, such as in autonomous cyber defence, we require data efficient methods. Recently, Quantum Machine learning and Boltzmann Machines have been proposed as solutions to this challenge. In this work we build upon the pre-existing work to extend the use of Deep Boltzmann Machines to the cutting edge algorithm Proximal Policy Optimisation in a Reinforcement learning cyber defence environment. We show that this approach, when solved using a D -WAVE quantum annealer, can lead to a two -fold increase in data efficiency. We therefore expect it to be used by the machine learning and quantum communities who are hoping to capitalise on data -efficient Reinforcement learning methods.
The fast development and use of artificial intelligence (AI) and its penetration to almost every profession in the Age of AI need support in popularizing AI education to nurture many professionals. The design includes...
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ISBN:
(纸本)9781665453318
The fast development and use of artificial intelligence (AI) and its penetration to almost every profession in the Age of AI need support in popularizing AI education to nurture many professionals. The design includes a hierarchy of education in AI from graduate school level to undergraduate level and then to middle and elementary schools need to be established. It first builds up AI experts in a supportive environment for both teaching and conducting research at the university level with enough critical mass to provide leadership in AI education in a degree program of AI on its own. The AI degree program nurtures AI professionals as well as future AI schoolteachers and may also provide AI training to existing schoolteachers. Having AI schoolteachers, AI education programs may be implemented in all schools starting from an early age at the 3rd grade through 12th grade. Here, AI experts from the university and experienced schoolteachers may collaborate to design the curriculum. One may develop an AI learning and teaching management system to support self-directed learning as well as collaborative learning to rapidly popularize AI education. In addition, learning through projects from an early age will nurture future innovators. Also, competitions and student presentations at conferences provide much motivation and training for the young learners.
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