Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition

Recognizing facial action units (AUs) during spontaneous facial displays is a challenging problem. Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial AU recognition, where predefined and fixed convolution filter sizes are employed. In order to achieve the best performance, the optimal filter size is often empirically found by conducting extensive experimental validation. Such a training process suffers from expensive training cost, especially as the network becomes deeper. This paper proposes a novel Optimized Filter Size CNN (OFS-CNN), where the filter sizes and weights of all convolutional layers are learned simultaneously from the training data along with learning convolution filters. Specifically, the filter size is defined as a continuous variable, which is optimized by minimizing the training loss. Experimental results on two AU-coded spontaneous databases have shown that the proposed OFS-CNN is capable of estimating optimal filter size for varying image resolution and outperforms traditional CNNs with the best filter size obtained by exhaustive search. The OFS-CNN also beats the CNN using multiple filter sizes and more importantly, is much more efficient during testing with the proposed forward-backward propagation algorithm

A phage-displayed peptide recognizing porcine aminopeptidase N is a potent small molecule inhibitor of PEDV entry

Three phage-displayed peptides designated H, S and F that recognize porcine aminopeptidase N (pAPN), the cellular receptor of porcine transmissible gastroenteritis virus (TGEV) were able to inhibit cell infection by TGEV. These same peptides had no inhibitory effects on infection of Vero cells by porcine epidemic diarrhea virus (PEDV). However, when PEDV, TGEV and porcine pseudorabies virus were incubated with peptide H (HVTTTFAPPPPR), only infection of Vero cells by PEDV was inhibited. Immunofluorescence assays indicated that inhibition of PEDV infection by peptide H was independent of pAPN. Western blots demonstrated that peptide H interacted with PEDV spike protein and that pre-treatment of PEDV with peptide H led to a higher inhibition than synchronous incubation with cells. These results indicate direct interaction with the virus is necessary to inhibit infectivity. Temperature shift assays demonstrated that peptide H inhibited pre-attachment of the virus to the cells

Sparse Fast Fourier Transform and its application in intelligent diagnosis system of train rolling bearing

Healthy status monitoring of train bearing online is very meaningful work. But as traditional diagnosis system does, performing Fourier spectrum with the datum from more than 200 bearings in a marshalling train is an enormous challenge. Here a healthy status monitoring system of train rolling bearing based on Sparse Fast Fourier Transform (SFFT) is proposed. The monitoring system consists two sequential parts. First, extract fault features based on SFFT spectrum and other time-domain parameters. According to train bearing working environment, altogether 7 fault features are extracted in this paper. Another part is constructing a classifier based on BP neural network. Experimental results show that the system proposed here achieves gratifying results comparing with traditional fault diagnosis syste

Research on the Impact of Game Users’ Perceived Value on Satisfaction and Loyalty - Based on the Perspectives of Hedonic Value and Utilitarian Value

As Chinese game market growing mature, cultivating loyal game users has become the new goals for game companies. Based on the theory of game users experience, this paper constructs the structural model of customer with the variables of perceived value, customer satisfaction and customer loyalty and studies the relationship between the game users’ hedonic/utilitarian value and customer satisfaction/customer loyalty from the perspective of the game user utilitarian value and hedonic value. The study finds that the game users’ perceived value has a positive effect on customer satisfaction and customer loyalty; while hedonic value has a more significant effect on customer satisfaction than utilitarian value, the latter one has a greater significant effect on customer loyalty than the former one; customer satisfaction has a positive effect on customer loyalty; hedonic value and utilitarian value interact and influence with each other. Implication and recommendation of this research is that enhancing the hedonic and utilitarian value of game users by game companies which is one of the effective ways to improve game users’ satisfaction and loyalty

Wind power forecasts using Gaussian processes and numerical weather prediction

Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data