41,704 research outputs found
Large Zero Autocorrelation Zone of Golay Sequences and -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 -ary Golay sequence and -QAM
Golay complementary sequence with a large zero autocorrelation zone, where
is an arbitrary even integer and 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
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