Exchange rate pass through into domestic prices in mainland China

The present study sets up an empirical framework to study the exchange rate pass through (ERPT) issue in China\u27s domestic markets, within the Chinese economic reform period from 1978 till present. The results show a relative low degree of pass-through to consumer and retail prices, but high degree of pass-through to producer and purchasing prices. It suggests that the degree of ERPT tends to diminish along the price chain. In addition, the results also show an increasing trend of the degree of ERPT in recent years. The speed of price reaction to exchange rate shocks may be quicker in recent years as well. Overall, this study reveals a relatively complete picture of the ERPT in China\u27s domestic markets

Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels

Variational methods for parameter estimation are an active research area, potentially offering computationally tractable heuristics with theoretical performance bounds. We build on recent work that applies such methods to network data, and establish asymptotic normality rates for parameter estimates of stochastic blockmodel data, by either maximum likelihood or variational estimation. The result also applies to various sub-models of the stochastic blockmodel found in the literature.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1124 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

ORACLE GUIDED INCREMENTAL SAT SOLVING TO REVERSE ENGINEER CAMOUFLAGED CIRCUITS

This study comprises two tasks. The first is to implement gate-level circuit camouflage techniques. The second is to implement the Oracle-guided incremental de-camouflage algorithm and apply it to the camouflaged designs. The circuit camouflage algorithms are implemented in Python, and the Oracle- guided incremental de-camouflage algorithm is implemented in C++. During this study, I evaluate the Oracle-guided de-camouflage tool (Solver, in short) performance by de-obfuscating the ISCAS-85 combinational benchmarks, which are camouflaged by the camouflage algorithms. The results show that Solver is able to efficiently de-obfuscate the ISCAS-85 benchmarks regardless of camouflaging style, and is able to do so 10.5x faster than the best existing approaches. And, based on Solver, this study also measures the de-obfuscation runtime for each camouflage style

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224x224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102x faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.Comment: This manuscript is the accepted version for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015. See Changelo