Segregation by Race in Public Schools Retrospect and Prospect

Solar energy conversion has been intensively studied in past decades and has been shown to be greatly effective for solving the serious environmental pollution and energy shortage problems. Photoelectrocatalysis and photovoltaics have been considered as the two main approaches for solar energy conversion and utilization, which are generally involved with nanostructured materials and/or catalytic processes, greatly affecting the efficiencies for solar energy conversion. Then, it is necessary to understand the relationship between the physical and chemical properties of nanomaterials and their performances for solar energy conversion. It is also important to explore the fundamentals in catalytic processes for solar energy conversion and make breakthrough in design and synthesis of nanomaterials or nanostructures, characterization of material properties, and performance of novel devices and systems. The aim of this special issue is to present some recent progress in the field of advanced catalysis and nanostructure design for solar energy conversion. A brief summary of all accepted papers is provided below

Research on Video Behaviour Recognition Methods Based on Deep Learning

Currently, in the field of computer vision, video behaviour recognition has become a hot content and has been applied in many emerging industries. This paper explores the application of deep learning method in video behaviour recognition technology by taking the multi-temporal information fusion recognition method as an example, and investigates the practical application value of this method by constructing a model of artificial neural network and taking an experimental approach, in order to have positive significance to the development of intelligent human-computer interaction technology in China

Evolution of magnetic fields and mass flow in a decaying active region

Five days of coordinated observation were carried out from 24–29 September, 1987 at Big Bear and Huairou Solar Observatories. Longitudinal magnetic fields of an αp sunspot active region were observed almost continuously by the two observatories. In addition, vector magnetic fields, photospheric and chromospheric Doppler velocity fields of the active region were also observed at Huairou Solar Observatory. We studied the evolution of magnetic fields and mass motions of the active region and obtained the following results: (1) There are two kinds of Moving Magnetic Features (MMFs). (a) MMFs with the same magnetic polarity as the center sunspot. These MMFs carry net flux from the spot, move through the moat, and accumulate at the moat's outer boundary. (b) MMFs in pairs of mixed polarity. These MMFs are not responsible for the decay of the spot since they do not carry away the net flux. MMFs in category (b) move faster than those of (a). (2) The speed of the mixed polarity MMFs is larger than the outflow measured by photospheric Dopplergrams. The uni-polar MMFs are moving at about the same speed as the Doppler outflow. (3) The chromospheric velocity is in approximately the opposite direction from the photospheric velocity. The photospheric Doppler flow is outward; chromospheric flow is inward. We also found evidence that downward flow appears in the photospheric umbra; in the chromosphere there is an upflow

Learning Robust Features for Recognition of Emotions in Images and Videos

University of Technology Sydney. Faculty of Engineering and Information Technology.Today, recognition of emotions in images and videos has attracted increasing research attention. In terms of video emotion recognition, most existing approaches are based on spatial features extracted from video frames. The performance of these approaches is mainly restricted due to the broad affective gap between spatial image features and high-level emotions. To bridge the affective gap, we propose to recognize emotions with kernelized features. A polynomial kernel function is constructed based on rewritten the equation of the discrete Fourier transform as the linear kernel. Moreover, we propose to apply the sparse representation method to kernelized features to reduce the impact of noise contained in video frames. This method can further help contribute to performance improvement. In the second work, we develop a weighted sum pooling method for video emotion representation. We present an end-to-end deep network for simultaneously image emotion classification and emotion intensity map prediction. The proposed network is build based on the feature pyramid network. The class activation mapping technique is utilized to generate pseudo intensity maps to train the network. The proposed network is first trained on a large-scale image emotion dataset and then used to extracted features and intensity maps for video frames. We empirically show that this approach is effective to improve recognition performance. Recent work has shown that using local region information helps to improve image emotion recognition performance. In the third work, we develop an end-to-end deep neural network for image emotion recognition by utilizing emotion intensity. The proposed network is composed of an intensity prediction stream and a classification stream. The class activation mapping technique is used to generated pseudo intensity maps to guide the intensity prediction network for emotion intensity learning. The predicted intensity maps are integrated to the classification stream for final recognition. The two streams are trained cooperatively with each other to improve the overall performance. In the fourth work, we present a dual pattern learning network architecture with adversarial adaptation (DPLAANet). Unlike conventional networks, the proposed architecture has two input branches. The dual input structure allows the network to have a considerably large number of image pairs for training. This can help address the overfitting issue due to limited training data. Moreover, we introduce to use the adversarial training approach to reduce the domain difference between training data and test data. The experimental results show that the DPLAANets are effective for several benchmark datasets

SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks

Graph convolutional networks have been successfully applied in various graph-based tasks. In a typical graph convolutional layer, node features are updated by aggregating neighborhood information. Repeatedly applying graph convolutions can cause the oversmoothing issue, i.e., node features at deep layers converge to similar values. Previous studies have suggested that oversmoothing is one of the major issues that restrict the performance of graph convolutional networks. In this paper, we propose a stochastic regularization method to tackle the oversmoothing problem. In the proposed method, we stochastically scale features and gradients (SSFG) by a factor sampled from a probability distribution in the training procedure. By explicitly applying a scaling factor to break feature convergence, the oversmoothing issue is alleviated. We show that applying stochastic scaling at the gradient level is complementary to that applied at the feature level to improve the overall performance. Our method does not increase the number of trainable parameters. When used together with ReLU, our SSFG can be seen as a stochastic ReLU activation function. We experimentally validate our SSFG regularization method on three commonly used types of graph networks. Extensive experimental results on seven benchmark datasets for four graph-based tasks demonstrate that our SSFG regularization is effective in improving the overall performance of the baseline graph networks