Language Change and Value Orientations in Chinese Culture

We contend in this paper that language shapes as well as reflects social reality, thought patterns, and value/belief systems of a culture. We substantiate our claim by closely examining Chinese family value orientations and their relationships with language expressions. The linguistic features and cultural implications of the value orientations are explicated. We further investigate the impact of changes in the use of language on the social reality and thought patterns of Chinese culture. We conclude that language and culture are intertwined. The interplay between language and culture creates infinite discursive possibilities and multi-dimensional and ever changing human experiences. [China Media Research. 2011; 7(3): 56-63

Optical Monitoring of the Seyfert Galaxy NGC 4151 and Possible Periodicities in the Historical Light Curve

We report B, V, and R band CCD photometry of the Seyfert galaxy NGC 4151 obtained with the 1.0-m telescope at Weihai Observatory of Shandong University and the 1.56-m telescope at Shanghai Astronomical Observatory from 2005 December to 2013 February. Combining all available data from literature, we have constructed a historical light curve from 1910 to 2013 to study the periodicity of the source using three different methods (the Jurkevich method, the Lomb-Scargle periodogram method and the Discrete Correlation Function method). We find possible periods of P_1=4\pm0.1, P_2=7.5\pm0.3 and P_3=15.9\pm0.3 yr.Comment: 8 pages, 5 figures, Accepted by Research in Astronomy and Astrophysic

Provable learning of quantum states with graphical models

The complete learning of an $n$-qubit quantum state requires samples exponentially in $n$. Several works consider subclasses of quantum states that can be learned in polynomial sample complexity such as stabilizer states or high-temperature Gibbs states. Other works consider a weaker sense of learning, such as PAC learning and shadow tomography. In this work, we consider learning states that are close to neural network quantum states, which can efficiently be represented by a graphical model called restricted Boltzmann machines (RBMs). To this end, we exhibit robustness results for efficient provable two-hop neighborhood learning algorithms for ferromagnetic and locally consistent RBMs. We consider the $L_p$-norm as a measure of closeness, including both total variation distance and max-norm distance in the limit. Our results allow certain quantum states to be learned with a sample complexity \textit{exponentially} better than naive tomography. We hence provide new classes of efficiently learnable quantum states and apply new strategies to learn them

Information-Theoretic Measure of Genuine Multi-Qubit Entanglement

We consider pure quantum states of N qubits and study the genuine N-qubit entanglement that is shared among all the N qubits. We introduce an information-theoretic measure of genuine N-qubit entanglement based on bipartite partitions. When N is an even number, this measure is presented in a simple formula, which depends only on the purities of the partially reduced density matrices. It can be easily computed theoretically and measured experimentally. When N is an odd number, the measure can also be obtained in principle.Comment: 5 pages, 2 figure

Topological data analysis of Chinese stocks’ dynamic correlations under major public events

Topological data analysis has been acknowledged as one of the most successful mathematical data analytic methodologies in many fields. Additionally, it has also been gradually applied in financial time series analysis and proved effective in exploring the topological features of such data. We select 100 stocks from China’s markets and construct point cloud data for topological data analysis. We detect critical dates from the Lp-norms of the persistence landscapes. Our results reveal the dates are highly consistent with the transition time of some major events in the sample period. We compare the correlations and statistical properties of stocks before and during the events via complex networks to describe the markets’ situation. The strength and variation of links among stocks are clearly different during the major events. We also investigate the neighborhood features of stocks from topological perspectives. This helps identify the important stocks and explore their situations under each event. Finally, we cluster the stocks based on the neighborhood features, which exhibit the heterogeneity impact on stocks of the different events. Our work demonstrates that topological data analysis has strong applicability in the dynamic correlations of stocks