Are Real Exchange Rates Nonlinear with a Unit Root? Evidence on Purchasing Power Parity for China: A Note

This article applies the threshold autoregressive model proposed by Caner and Hansen (2001) to examine both linearity and stationarity of China's real exchange rate vis-à-vis her 9 trading partner countries over the period of January 1986 to October 2009. Two main conclusions are drawn. Firstly, the empirical results indicate that China's real exchange is a nonlinear process. Secondly, a unit root in real exchange rate was found for most of the cases under study. This result provides no support for purchasing power parity for China relative to their major trading partner countries.Threshold Autoregressive Model; Linearity and Stationarity, Purchasing Power Parity; Threshold Unit Root Test

A combined analysis of PandaX, LUX, and XENON1T experiments within the framework of dark matter effective theory

Weakly interacting massive particles are a widely well-probed dark matter candidate by the dark matter direct detection experiments. Theoretically, there are a large number of ultraviolet completed models that consist of a weakly interacting massive particle dark matter. The variety of models makes the comparison with the direct detection data complicated and often non-trivial. To overcome this, in the non-relativistic limit, the effective theory was developed in the literature which works very well to significantly reduce the complexity of dark matter-nucleon interactions and to better study the nuclear response functions. In the effective theory framework for a spin-1/2 dark matter, we combine three independent likelihood functions from the latest PandaX, LUX, and XENON1T data, and give a joint limit on each effective coupling. The astrophysical uncertainties of the dark matter distribution are also included in the likelihood. We further discuss the isospin violating cases of the interactions. Finally, for both dimension-five and dimension-six effective theories above the electroweak scale, we give updated limits of the new physics mass scales.Comment: 33 pages, 11 figures, PandaX run10 data included and version accepted in JHEP, "code is available at the LikeDM website, https://likedm.hepforge.org/

Detecting Slow Wave Sleep Using a Single EEG Signal Channel

Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods

Data preprocessing for artificial neural network applications in prioritizing railroad projects â a practical experience in Taiwan

[[abstract]]Financial constraints necessitate the tradeoff among proposed railroad projects, so that the project priorities for implementation and budget allocation need to be determined by the ranking mechanisms in the government. At present, the Taiwan central government prioritizes funding allocations primarily using the analytic hierarchy process (AHP), a methodology that permits the synthesizing of subjective judgments systematically and logically into objective consensus. However, due to the coopetition and heterogeneity of railway projects, the proper priorities of railroad projects could not be always evaluated by the AHP. The decision makers prefer subjective judgments to referring to the AHP evaluation re- sults. This circumstance not only decreased the AHP advantages, but also raised the risk of the policies. A method to con- sider both objective measures and subjective judgments of project attributes can help reduce this problem. Accordingly, combining the AHP with the artificial neural network (ANN) methodologies would theoretically be a proper solution to bring a ranking predication model by creating the obscure relations between objective measures by the AHP and subjec- tive judgments. However, the inconsistency between the AHP evaluation and subjective judgments resulted in the inferior soundness of the AHP/ANN ranking forecast model. To overcome this problem, this study proposes the data prepro- cessing method (DPM) to calculate the correlation coefficient value using the subjective and objective ranking incidence matrixes; according to the correlation coefficient value, the consistency between the AHP rankings and subjective judg- ments of railroad projects can be evaluated and improved, so that the forecast accuracy of the AHP/ANN ranking forecast model can also be enhanced. Based on this concept, a practical railroad project ranking experience derived from the Insti- tute of Transportation of Taiwan is illustrated in this paper to reveal the feasibility of applying the DPM to the AHP/ANN ranking prediction model.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]電子版[[countrycodes]]LT