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The performance of predicting human fixations in videos has been much enhanced with the help of development of the convolutional neural networks (CNN). In this paper, we propose a novel end-to-end neural network “SalSAC” for video saliency prediction, which uses the CNN-LSTM-Attention as the basic architecture and utilizes the information from both static and dynamic aspects. To better represent the static information of each frame, we first extract multi-level features of same size from different layers of the encoder CNN and calculate the corresponding multi-level attentions, then we randomly shuffle these attention maps among levels and multiply them to the extracted multi-level features respectively. Through this way, we leverage the attention consistency across different layers to improve the robustness of the network. On the dynamic aspect, we propose a correlation-based ConvLSTM to appropriately balance the influence of the current and preceding frames to the prediction. Experimental results on the DHF1K, Hollywood2 and UCF-sports datasets show that SalSAC outperforms many existing state-of-the-art methods.

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Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts’ heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design with high structural diversity and symmetry. Our model increases the generation validity by more than 700% compared to FTCP, one of the latest structure generators and by more than 45% compared to our previous CubicGAN model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1869 materials out of 2000 are successfully optimized and deposited into the Carolina Materials Database www.carolinamatdb.org, of which 39.6% have negative formation energy and 5.3% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potential synthesizability.

Various neural networks, including a single layer neural network (SLNN), a deep neural network (DNN) with multilayers, and a convolution neural network (CNN) have been developed and investigated to predict multiple molecular properties simultaneously. The data set of this work contains∼134 kilo molecules and their 15 properties (including rotational constant A, B, and C, dipole moment, isotropic polarizability, energy of HOMO, energy of LUMO, HOMO–LUMO gap energy, electronic spatial extent, zero point vibrational energy, internal energy at 0 K, internal energy at 298.15 K, enthalpy at 298.15 K, free energy at 298.15 K, and heat capacity at 298.15 K) at the hybrid density functional theory (DFT) level from the QM9 database. Coulomb matrix (CM) converted from the database representing every molecule uniquely and its eigenvalue are respectively used as the input of machine learning. The accuracies of predictions have been compared among SLNN, DNN and CNN by analyzing their mean absolute errors (MAEs). Using eigenvalues as input, both SLNN and DNN can give higher accuracy for the prediction of specific energy properties (U0 , U, H, and G). For the prediction of all 15 molecular properties at a time, DNN with a 3-layers network exhibits the best results using the full CM as input. The number of layers in DNN play a key role in the prediction of multiple molecular properties simultaneously. This work may provide possibility and guidance for the selection of different neural networks and input data forms for prediction and validation of multiple parameters according to different needs.

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