<p>With the rapid development of computer, artificial intelligence and big data technology, artificial neural networks have become one of the most powerful machine learning algorithms. In the practice, most of the applications of artificial neural networks use back propagation neural network and its variation. Besides the back propagation neural network, various neural networks have been developing in order to improve the performance of standard models. Though neural networks are well known method in the research of real estate, there is enormous space for future research in order to enhance their function. Some scholars combine genetic algorithm, geospatial information, support vector machine model, particle swarm optimization with artificial neural networks to appraise the real estate, which is helpful for the existing appraisal technology. The mass appraisal of real estate in this paper includes the real estate valuation in the transaction and the tax base valuation in the real estate holding. In this study we focus on the theoretical development of artificial neural networks and mass appraisal of real estate, artificial neural networks model evolution and algorithm improvement, artificial neural networks practice and application, and review the existing literature about artificial neural networks and mass appraisal of real estate. Finally, we provide some suggestions for the mass appraisal of China's real estate.</p>

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Today, every country's economic policies now are causing inflation and strengthening Inflation Expectation. Without doubt, comprehending appropriately Inflation Expectation effects, especial its money-supply effect, is the important basis to manage Inflation Expectation and suppress inflation. The paper first introduces briefly rational expectation and its econometric expression; gives monetary supply's response to inflation expected by rational expectation by the model combined Cagan model and Lucas microeconomic rational expectation equation; points out that estimating and researching the mechanism of Inflation Expectation in China and its influence to monetary supply when managing InflationExpectation is crucially necessary for achieve the goal of the economic policy.

Optical label switching (OLS) is a promising solution for rapidly growing packet based internet traffic and data switching in data center. Most proposed schemes apply orthogonal modulation, which superimpose a nonamplitude modulated label signal onto a Manchester coded or pulse position modulated (PPM) ASK payload signal. The extinction ratio (ER) has to be reduced for overlaid signal. To avoid the quality degradation of payload signal from low ER, a differential phase shift keying (DPSK) over inverse PPM is proposed in this paper. Due to the high mark ratio of the inverse PPM, the proposed DPSK over inverse PPM modulation achieves a high ER for the ASK signal while induces limited crosstalk to the DPSK signal. Compare with the previously proposed schemes, the test results show the proposed DPSK over inverse 4PPM modulation achieves an optimum ER improvement of at least 6 dB to support an extended of at least 10.5 km.

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