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Guohua, classical Chinese painting, has not lost its significance and is part of the modern art of the PRC. Liu Haisu, a Chinese artist and teacher, combined the laws of guohua and European art in his work. Basing on these principles, he sought to reform art education in China. He is considered one of the iconic figures in the history of the fine arts of the Celestial Empire, but his personality is little known in Russian art history. This paper focuses on the creativity pathway of Liu Haisu, the outcome of which was return to mainstream traditional Chinese guohua painting. It is the first work that systematizes and introduces to the research community foreign publications about Liu Haisu, which are practically unknown in Russia. Studying these sources makes it possible to understand what principles the modern art of the PRC is based on, the genesis of which is analyzed from the standpoint of Liu Haisu’s career as a painter. The paper explores the painter’s creativity heritage, representing a synthesis between Eastern and Western approaches to fine arts.

Hierarchical temporal memory is an emerging machine learning technology that aims to model the structural and algorithmic properties of the neocortex. It is particularly suitable for learning and predicting sequential data. However, when dealing with long time series or complex sequences, the accuracy is relatively lower than desired. In this paper, a novel hierarchical temporal memory based on recurrent learning unit is proposed, where a feedback mechanism is involved into the model. The original cell is extended with a recurrent unit to capture long temporal dependencies of synaptic connections between neurons. The temporal pooler algorithm is then modified to adapt to the recurrent learning unit, and the supervised gradient information is combined with the Hebbian synaptogenesis learning rule in speeding up the training. The prototype of the proposed hierarchical temporal memory is implemented and extensive experiments are carried out on two public datasets under various settings. Experimental results show that the proposed model obtains an accuracy increase by up to 32% and a perplexity drop by up to 14% on sequence prediction and text generation tasks, respectively, which indicates the hierarchical temporal memory with recurrent feedback outperforms the original model on sequence learning. [ABSTRACT FROM AUTHOR]

Bitcoin market had a significant momentum phenomenon before the launch of Futures, and then it turned into an insignificant reversal effect. After Covid-19 appeared, the momentum effect and reversal effect disappeared. The advent of bitcoin futures has increased how investors respond to information. With the outbreak of COVID-19, investor interest in Bitcoin as a safe-haven asset has increased the effectiveness of the price. We estimate the speed of signal diffusion in the bitcoin market, and the results support that effective response to information is the essential mechanism for the disappearance of momentum effect. [ABSTRACT FROM AUTHOR]