The impact of monetary policy shocks on income inequality: a tale of two countries

The easing monetary policy after the global financial crisis triggered wide concerns on the responses of income inequality. In this paper, we investigate impact of monetary policy shocks on income inequality. We propose a general equilibrium model and show that monetary policies could affect income inequality by affecting the earnings of high-income households in financial markets and business operations. Using a TVP-FAVAR model, we find contradictory distributional effects of monetary policy shocks in China and the US. Specifically, expansionary monetary policy shocks persistently increase income inequality in China but decrease income inequality in the US. Moreover, the impacts are volatile in the short-term, but stabilise after 10 periods. The investigation on the responses of top 1% and bottom 50% income share confirms the finding of contradictory distributional effects of monetary policy shocks

An expert PI controller with dead time compensation of monitor AGC in hot strip mill

Hot strip rolling production is a high-speed process which requires high-speed control and communication system, but because of the long distance between the delivery stand of the finishing mill and the gauge meter, dead time occurs when strip is transported from the site of the actuator to another location where the gauge meter takes its reading, which seriously affects the thickness control effect. According to the process model which is developed based on the measured data, a filtered Smith predictor is applied to predict the thickness deviation of the finishing mill. At the same time, an expert PI controller based on feature information is proposed for the strip thinning during looper rising and coiler biting period and the strip thickening during the tension loss period of the strip tail end. As a result, the thickness accuracy has been improved by about 1.06% at a steady rolling speed and about 1.23% in acceleration and deceleration

Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with sophisticated convolution implementation and deep supervision. In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image segmentation, (2) prediction of the class labels of the objects within the image, and (3) classification of the scene the image belonging to. While these three sub-tasks are trained to optimize their individual loss functions of different perceptual levels, we propose to let them interact by the task-task context ensemble. Moreover, we propose a novel sync-regularization to penalize the deviation between the outputs of the pixel-wise segmentation and the class prediction tasks. These effective regularizations help FCN utilize context information comprehensively and attain accurate semantic segmentation, even though the number of the images for training may be limited in many biomedical applications. We have successfully applied our framework to three diverse 2D/3D medical image datasets, including Robotic Scene Segmentation Challenge 18 (ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus Glaucoma Challenge (REFUGE18). We have achieved top-tier performance in all three challenges.Comment: IEEE Transactions on Medical Imagin

Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

Machine learning based solutions have been successfully employed for automatic detection of malware in Android applications. However, machine learning models are known to lack robustness against inputs crafted by an adversary. So far, the adversarial examples can only deceive Android malware detectors that rely on syntactic features, and the perturbations can only be implemented by simply modifying Android manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new highly-effective attack that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK using a substitute model. Based on the transferability concept, the perturbations that successfully deceive the substitute model are likely to deceive the original models as well. We develop an automated tool to generate the adversarial examples without human intervention to apply the attacks. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK's Dalvik bytecode rather than Android manifest to evade from recent detectors. We evaluated the proposed manipulation methods for adversarial examples by using the same datasets that Drebin and MaMadroid (5879 malware samples) used. Our results show that, the malware detection rates decreased from 96% to 1% in MaMaDroid, and from 97% to 1% in Drebin, with just a small distortion generated by our adversarial examples manipulation method.Comment: 15 pages, 11 figure