SANet: Structure-Aware Network for Visual Tracking

Convolutional neural network (CNN) has drawn increasing interest in visual tracking owing to its powerfulness in feature extraction. Most existing CNN-based trackers treat tracking as a classification problem. However, these trackers are sensitive to similar distractors because their CNN models mainly focus on inter-class classification. To address this problem, we use self-structure information of object to distinguish it from distractors. Specifically, we utilize recurrent neural network (RNN) to model object structure, and incorporate it into CNN to improve its robustness to similar distractors. Considering that convolutional layers in different levels characterize the object from different perspectives, we use multiple RNNs to model object structure in different levels respectively. Extensive experiments on three benchmarks, OTB100, TC-128 and VOT2015, show that the proposed algorithm outperforms other methods. Code is released at http://www.dabi.temple.edu/~hbling/code/SANet/SANet.html.Comment: In CVPR Deep Vision Workshop, 201

Bullion, bills and arbitrage: exchange markets in fourteenth- to seventeenth century Europe

Two drawbacks of current empirical studies on late medieval financial market integration are: the use of low frequency data; and the lack of a benchmark for comparison. As a result, there is a tendency to underestimate the degree of integration and one has no clear idea about whether the estimated degree of integration is high or low by the standards of the time. Consequently, there is not yet a satisfactory answer as to how integrated and efficient financial markets were in the late Middle Ages and early modern era. In tackling these two problems, this thesis employs monthly and weekly exchange rates to measure the degree of exchange market integration and the results are judged using the speed of communication as a benchmark since the flow of information played a critical role in financial arbitrage. Therefore, this thesis is able to show that exchange markets were already well integrated in the late fourteenth century. From then to the late seventeenth century, the high speed of adjustment to profitable opportunities was maintained, but the transaction costs associated with arbitrage fell over time. The reduction in transaction cost may be attributed to the financial innovations that took place in the sixteenth century. This thesis also finds that the type of information related to shocks received by economic agents had a decisive impact on the speed of price adjustment. The more explicit the information, the more efficiently the market responded to shocks

Can a second order bandpass sigma delta modulator achieve high signal-to-noise ratio for lowpass inputs

Institutively, second order SDMs usually achieve lower SNR than high order ones because high order loop filters can achieve better noise shaping characteristics. Moreover, the signal transfer function should be designed to have large values and the noise transfer function should be designed to have small values at the passband of loop filters in order to achieve good noise shaping characteristics, so SNR should be high if input signal bands match passbands of loop filters and low otherwise. Based on this argument, one may expect that SNR will be low when input signals have lowpass characteristics while loop filters have bandpass characteristics. However, since the above argument is based on the noise shaping theory which is formulated using a linear model, while quantizers in SDMs are nonlinear components, the linear model may not explain nonlinear system behaviors. In this letter, a counterexample is given to illustrate that a second order bandpass interpolative SDM may also give a very high SNR for lowpass inputs