4,365 research outputs found
On the Computation Power of Name Parameterization in Higher-order Processes
Parameterization extends higher-order processes with the capability of
abstraction (akin to that in lambda-calculus), and is known to be able to
enhance the expressiveness. This paper focuses on the parameterization of
names, i.e. a construct that maps a name to a process, in the higher-order
setting. We provide two results concerning its computation capacity. First,
name parameterization brings up a complete model, in the sense that it can
express an elementary interactive model with built-in recursive functions.
Second, we compare name parameterization with the well-known pi-calculus, and
provide two encodings between them.Comment: In Proceedings ICE 2015, arXiv:1508.0459
A New RSSI-based Centroid Localization Algorithm by Use of Virtual Reference Tags
A good design of node location is critical for efficient
and effective wireless communications. This paper presents an
improved algorithm, in order to solve the low localization
accuracy caused by traditional centroid algorithm. The
improved algorithm combined with VIRE system and
traditional centroid algorithm. The VIRE algorithm is
introduced and the signal propagation model is utilized to
construct virtual reference tags in the location area. Simulation shows that this further developed algorithm has further improved the accuracy of positioning up to 35.12% compared
to the traditional centroid algorithm. It is concluded that this algorithm can further improve the locating accuracy in comparison with the original centroid algorithm
Artificial intelligence and automation in valvular heart diseases
Artificial intelligence (AI) is gradually changing every aspect of social life, and healthcare is no exception. The clinical procedures that were supposed to, and could previously only be handled by human experts can now be carried out by machines in a more accurate and efficient way. The coming era of big data and the advent of supercomputers provides great opportunities to the development of AI technology for the enhancement of diagnosis and clinical decision-making. This review provides an introduction to AI and highlights its applications in the clinical flow of diagnosing and treating valvular heart diseases (VHDs). More specifically, this review first introduces some key concepts and subareas in AI. Secondly, it discusses the application of AI in heart sound auscultation and medical image analysis for assistance in diagnosing VHDs. Thirdly, it introduces using AI algorithms to identify risk factors and predict mortality of cardiac surgery. This review also describes the state-of-the-art autonomous surgical robots and their roles in cardiac surgery and intervention
Profitability of contrarian strategies in the Chinese stock market
This paper reexamines the profitability of loser, winner and contrarian
portfolios in the Chinese stock market using monthly data of all stocks traded
on the Shanghai Stock Exchange and Shenzhen Stock Exchange covering the period
from January 1997 to December 2012. We find evidence of short-term and
long-term contrarian profitability in the whole sample period when the
estimation and holding horizons are 1 month or longer than 12 months and the
annualized returns of contrarian portfolios increases with the estimation and
holding horizons. We perform subperiod analysis and find that the long-term
contrarian effect is significant in both bullish and bearish states while the
short-term contrarian effect disappears in bullish states. We compare the
performance of contrarian portfolios based on different grouping manners in the
estimation period and unveil that decile grouping outperforms quintile grouping
and tertile grouping, which is more evident and robust in the long run.
Generally, loser portfolios and winner portfolios have positive returns and
loser portfolios perform much better than winner portfolios. Both loser and
winner portfolios in bullish states perform better than those in the whole
sample period. In contrast, loser and winner portfolios have smaller returns in
bearish states in which loser portfolio returns are significant only in the
long term and winner portfolio returns become insignificant. These results are
robust to the one-month skipping between the estimation and holding periods and
for the two stock exchanges. Our findings show that the Chinese stock market is
not efficient in the weak form. These findings also have obvious practical
implications for financial practitioners.Comment: 24 pages (including 4 figures and 9 tables) + 5 supplementary figures
+ 10 supplementary table
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