Research on the Relationship Between Learning Motivation and Neural Activity in the Learning Process of Instructional Video:A NIRS Study

As the intrinsic driving force to promote learner’s learning,learning motivation is one of the key factors that affect learning engagement and efficiency.In terms of optimizing instructional videos and strengthening learning effects,it is particularly important to understand the cognitive neural mechanism and influencing factors of the changes of learning motivation.By using the near-infrared spectrometer technology,the paper has collected the state of neural activity while learners were learning different instructional videos,and has analyzed the relationship between the learning motivation of instructional videos and the state of neural activity in the learning process from the angle of cognitive neuroscience.It is found that both the intrinsic and extrinsic learning motivation of instructional videos will affect the state of neural activity in the learning process;the learning process will also affect the intensity of learning motivation,while the preparation of fine instructional videos will also cause the transfer of learning motivation.

Representations of Graph States with Neural Networks

Quantum many-body problem(QMBP)has become a hot topic in high energy physics and condensed matter physics.With the exponential increasing of the dimension of the Hilbert space,it becomes a big challenge to solve the QMBP even with the most powerful computers.With the rapid development of machine learning,artificial neural networks provide a powerful tool to represent or approximate quantum many-body states.In this paper,we aim to construct explicitly the neural network representations of graph states,without stochastic optimization of the network parameters.Our method shows constructively that all graph states can be represented precisely by proper neural networks originated from[Science,355,602(2017)]and formulated in[Sci.China-Phys.Mech.Astron.,63,210312(2020)].

Statistical mechanics and artificial intelligence to model the thermodynamic properties of pure and mixture of ionic liquids

In this paper, the volumetric properties of pure and mixture of ionic liquids are predicted using the developed statistical mechanical equation of state in different temperatures, pressures and mole fractions. The temperature dependent parameters of the equation of state have been calculated using corresponding state correlation based on only the density at 298.15 K as scaling constants. The obtained mean of deviations of modified equation of state for density of all pure ionic liquids for 1662 data points was 0.25%. In addition, the performance of the artificial neural network(ANN) with principle component analysis(PCA) based on back propagation training with28 neurons in hidden layer for predicting of behavior of binary mixtures of ionic liquids was investigated. The AADs of a collection of 568 data points for all binary systems using the EOS and the ANN at various temperatures and mole fractions are 1.03% and 0.68%, respectively. Moreover, the excess molar volume of all binary mixtures is predicted using obtained densities of EOS and ANN, and the results show that these properties have good agreement with literature.

Representations of hypergraph states with neural networks

The quantum many-body problem(QMBP) has become a hot topic in high-energy physics and condensed-matter physics. With an exponential increase in the dimensions of Hilbert space, it becomes very challenging to solve the QMBP, even with the most powerful computers. With the rapid development of machine learning, artificial neural networks provide a powerful tool that can represent or approximate quantum many-body states. In this paper, we aim to explicitly construct the neural network representations of hypergraph states. We construct the neural network representations for any k-uniform hypergraph state and any hypergraph state,respectively, without stochastic optimization of the network parameters. Our method constructively shows that all hypergraph states can be represented precisely by the appropriate neural networks introduced in [Science 355(2017) 602] and formulated in [Sci. China-Phys.Mech. Astron. 63(2020) 210312].

Neural network representations of quantum many-body states

Machine learning is currently the most active interdisciplinary field having numerous applications; additionally, machine-learning techniques are used to research quantum many-body problems. In this study, we first propose neural network quantum states(NNQSs) with general input observables and explore a few related properties, such as the tensor product and local unitary operation. Second, we determine the necessary and sufficient conditions for the representability of a general graph state using normalized NNQS. Finally, to quantify the approximation degree of a given pure state, we define the best approximation degree using normalized NNQSs. Furthermore, we observe that some N-qubit states can be represented by a normalized NNQS, such as separable pure states, Bell states and GHZ states.

Model-Based Adaptive Predictive Control with Visual Servo of a Rotary Crane System

This paper investigated the implementation of an adaptive predictive controller using nonlinear dynamic echo state neural （ESN） model for a rotary crane system by the visual servo method. The control sequences within the control horizon were described using cubic spline interpolation to enlarge the predictive horizon. Verification of the proposed scheme in the face of exogenous disturbances and modeling error with inaccurate string length was demonstrated by both simulations and experiments.