Large Zero Autocorrelation Zone of Golay Sequences and 4q4^q-QAM Golay Complementary Sequences

Sequences with good correlation properties have been widely adopted in modern communications, radar and sonar applications. In this paper, we present our new findings on some constructions of single $H$-ary Golay sequence and $4^q$-QAM Golay complementary sequence with a large zero autocorrelation zone, where $H\ge 2$ is an arbitrary even integer and $q\ge 2$ is an arbitrary integer. Those new results on Golay sequences and QAM Golay complementary sequences can be explored during synchronization and detection at the receiver end and thus improve the performance of the communication system

A New Distribution-Random Limit Normal Distribution

This paper introduces a new distribution to improve tail risk modeling. Based on the classical normal distribution, we define a new distribution by a series of heat equations. Then, we use market data to verify our model

Entry, Exit and the Dynamics of Productivity Growth in Chinese Manufacturing Industry

In this paper we have attempted to examine aspects of the competitive selection process, firms’ entry, survival and exit, in an important sector of Chinese manufacturing, looking in particular for changes resulting from the latest stage of reform, dubbed the transition to the “socialist market economy”. These dynamic processes may be becoming increasingly important for the continuing growth of manufacturing, as the agricultural sector as a source of surplus labour begins to decline.Entry, Exit, Survival, Productivity, Economic Reform, Chinese Enterprises

Improved Dropout for Shallow and Deep Learning

Dropout has been witnessed with great success in training deep neural networks by independently zeroing out the outputs of neurons at random. It has also received a surge of interest for shallow learning, e.g., logistic regression. However, the independent sampling for dropout could be suboptimal for the sake of convergence. In this paper, we propose to use multinomial sampling for dropout, i.e., sampling features or neurons according to a multinomial distribution with different probabilities for different features/neurons. To exhibit the optimal dropout probabilities, we analyze the shallow learning with multinomial dropout and establish the risk bound for stochastic optimization. By minimizing a sampling dependent factor in the risk bound, we obtain a distribution-dependent dropout with sampling probabilities dependent on the second order statistics of the data distribution. To tackle the issue of evolving distribution of neurons in deep learning, we propose an efficient adaptive dropout (named \textbf{evolutional dropout}) that computes the sampling probabilities on-the-fly from a mini-batch of examples. Empirical studies on several benchmark datasets demonstrate that the proposed dropouts achieve not only much faster convergence and but also a smaller testing error than the standard dropout. For example, on the CIFAR-100 data, the evolutional dropout achieves relative improvements over 10\% on the prediction performance and over 50\% on the convergence speed compared to the standard dropout.Comment: In NIPS 201