2,469 research outputs found
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
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ORACLE GUIDED INCREMENTAL SAT SOLVING TO REVERSE ENGINEER CAMOUFLAGED CIRCUITS
This study comprises two tasks. The ļ¬rst is to implement gate-level circuit camouļ¬age techniques. The second is to implement the Oracle-guided incremental de-camouļ¬age algorithm and apply it to the camouļ¬aged designs.
The circuit camouļ¬age algorithms are implemented in Python, and the Oracle- guided incremental de-camouļ¬age algorithm is implemented in C++. During this study, I evaluate the Oracle-guided de-camouļ¬age tool (Solver, in short) performance by de-obfuscating the ISCAS-85 combinational benchmarks, which are camouļ¬aged by the camouļ¬age algorithms. The results show that Solver is able to eļ¬ciently de-obfuscate the ISCAS-85 benchmarks regardless of camouļ¬aging 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 camouļ¬age style
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
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
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