Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning

Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semisupervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local vertex features and graph topology in the convolutional layers. Although the GCN model compares favorably with other state-of-the-art methods, its mechanisms are not clear and it still requires a considerable amount of labeled data for validation and model selection. In this paper, we develop deeper insights into the GCN model and address its fundamental limits. First, we show that the graph convolution of the GCN model is actually a special form of Laplacian smoothing, which is the key reason why GCNs work, but it also brings potential concerns of over-smoothing with many convolutional layers. Second, to overcome the limits of the GCN model with shallow architectures, we propose both co-training and self-training approaches to train GCNs. Our approaches significantly improve GCNs in learning with very few labels, and exempt them from requiring additional labels for validation. Extensive experiments on benchmarks have verified our theory and proposals.Comment: AAAI-2018 Oral Presentatio

Fund family tournament and performance consequences: evidence from the UK fund industry

By applying tournament analysis to the UK Unit Trusts data, the results support significant risk shifting in the family tournament; i.e. interim winning managers tend to increase their level of risk exposure more than losing managers. It also shows that the risk-adjusted returns of the winners outperform those of the losers following the risk taking, which implies that risk altering can be regarded as an indication of managers’ superior ability. However, the tournament behaviour can still be a costly strategy for investors, since winners can be seen to beat losers in the observed returns due to the deterioration in the performance of their major portfolio holdings

A closed-loop operation to improve GMR sensor accuracy

Giant magnetoresistance (GMR) magnetic field sensors are compact, low power, high sensitivity devices that are low cost and have very simple supporting electronics. One of the disadvantages of GMR sensors can be their nonlinearity, hysteresis, and temperature-dependent output, which can reduce measurement accuracy. This paper presents an approach to improve the measurement accuracy of GMR sensors using a closed-loop circuit, which includes the sensor, a biasing coil, and a feedback circuit. The current in the biasing coil is actively changed to ensure that the component of magnetic field along the sensitive axis of the device is held constant, so that as the external magnetic field or orientation of the GMR sensor changes, the output of GMR sensor remains stable. In this way, the external magnetic field component along the sensitive axis of the device can be calculated by measuring the current in the biasing coil surrounding the GMR sensor, regardless of the hysteresis and nonlinearly of GMR sensor. The linearity and the accuracy of magnetic field measurements using a GMR sensor are significantly improved and a hardware prototype has been constructed and tested under a reference magnetic field