Deep Learning-based Limited Feedback Designs for MIMO Systems

We study a deep learning (DL) based limited feedback methods for multi-antenna systems. Deep neural networks (DNNs) are introduced to replace an end-to-end limited feedback procedure including pilot-aided channel training process, channel codebook design, and beamforming vector selection. The DNNs are trained to yield binary feedback information as well as an efficient beamforming vector which maximizes the effective channel gain. Compared to conventional limited feedback schemes, the proposed DL method shows an 1 dB symbol error rate (SER) gain with reduced computational complexity.Comment: to appear in IEEE Wireless Commun. Let

Covariation in Plant Functional Traits and Soil Fertility within Two Species-Rich Forests

The distribution of plant species along environmental gradients is expected to be predictable based on organismal function. Plant functional trait research has shown that trait values generally vary predictably along broad-scale climatic and soil gradients. This work has also demonstrated that at any one point along these gradients there is a large amount of interspecific trait variation. The present research proposes that this variation may be explained by the local-scale sorting of traits along soil fertility and acidity axes. Specifically, we predicted that trait values associated with high resource acquisition and growth rates would be found on soils that are more fertile and less acidic. We tested the expected relationships at the species-level and quadrat-level (20×20 m) using two large forest plots in Panama and China that contain over 450 species combined. Predicted relationships between leaf area and wood density and soil fertility were supported in some instances, but the majority of the predicted relationships were rejected. Alternative resource axes, such as light gradients, therefore likely play a larger role in determining the interspecific variability in plant functional traits in the two forests studied

Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees

Data accessibility statement: Full census data are available upon reasonable request from the ForestGEO data portal, http://ctfs.si.edu/datarequest/ We thank Margie Mayfield, three anonymous reviewers and Jacob Weiner for constructive comments on the manuscript. This study was financially supported by the National Key R&D Program of China (2017YFC0506100), the National Natural Science Foundation of China (31622014 and 31570426), and the Fundamental Research Funds for the Central Universities (17lgzd24) to CC. XW was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB3103). DS was supported by the Czech Science Foundation (grant no. 16-26369S). Yves Rosseel provided us valuable suggestions on using the lavaan package conducting SEM analyses. Funding and citation information for each forest plot is available in the Supplementary Information Text 1.Peer reviewedPostprin

Influence of Tool Shape on Surface Quality of Monocrystalline Nickel Nanofabrication

In this paper, the influence of tool shape on the surface quality of monocrystalline nickel nanofabrication is studied. The research mainly adopts the method of molecular dynamics simulation, through the statistics of the atomic coordinates of the machined surface, then calculates the influence of different tool rake angles on the surface roughness of monocrystalline nickel. It is concluded that the surface roughness distribution is ‘W’ when the rake angle of the diamond tool changes from −45° to +45°. When analyzing the relationship between the tool shape and the processing temperature, it is found that when the clearance angle of the tool reaches a certain range, the clearance angle is further increased, and the temperature of the workpiece does not change during machining. Therefore, a large number of simulations were carried out, and it was concluded that there is a critical clearance angle, and the critical clearance angle of the tool in the research conditions is 8–10°

Spatial variation in community structure of a subtropical evergreen broad-leaved forest: Implications for sampling design

With the full survey data for a 24-ha subtropical evergreen broad-leaved forest dynamics plot, we evaluated spatial variation in forest structure characteristics (basal area and aboveground biomass), and calculated the minimal sample size and total sampling area necessary to estimate the forest structure characteristics within 20% (+/- 10%) of the observed values with 95% probability for particular quadrat sizes by using a computer program that is designed to simulate the sampling process by allowing different sized quadrats to be randomly located within the sampling region. We found that (1) based on the 600 20 mx20 m subplots, basal area and aboveground biomass displayed a high degree of variation, with respective coefficients of variation of 27% and 31%; (2) based on the computer simulation analysis, the variability of basal area and aboveground biomass decreased with increasing quadrat size. The number of quadrats required to achieve the specified degree of precision dropped sharply with the increase of quadrat size. However, the total sampling area increased with increasing quadrat size, suggesting that using several small quadrats across the sampling area is more efficient than using fewer larger quadrats. Results of this study are valuable for evaluating the reliability of previous research and may assist researchers in designing effective sampling strategies for future field surveys, particularly in subtropical evergreen broad-leaved forests in China