A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks

In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used in machine learning for data compaction and efficient learning by eliminating the curse of dimensionality. There exist many solutions for feature selection when the data are located at a central location. However, it becomes extremely challenging to perform the same when the data are distributed across a large number of peers or machines. Centralizing the entire dataset or portions of it can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, dynamic nature of the data/network, and privacy concerns. The solution proposed in this paper allows us to perform feature selection in an asynchronous fashion with a low communication overhead where each peer can specify its own privacy constraints. The algorithm works based on local interactions among participating nodes. We present results on real-world dataset in order to test the performance of the proposed algorithm.     

Estimation of relative evapotranspiration from NOAA PAL to derive growth characteristics in India

Clear‐sky dekadal relative evapotranspiration (RET) was derived using the surface energy‐balance approach applied to 10‐day composite NOAA PAL (8 km×8 km) datasets over the Indian landmass. This was further used to differentiate between growth characteristics for an irrigated intensive agriculture over a northern India state (e.g. Punjab) and a rainfed ill‐posed agriculture over a central India state (e.g. Madhya Pradesh) using time‐series data sets for five growing years (June–April): 1996–1997, 1997–1998, 1998–1999, 1999–2000, and 2000–2001. The triangular scatter between RET and normalized difference vegetation index (NDVI) showed that the minimum RET increases linearly with NDVI producing a ‘basal line’ that represents relative canopy transpiration only. A clear distinction in scatter was found between the two contrasting agro‐ecosystems showing a higher RET or root zone wetness in irrigated than rainfed systems. In rainfed rice‐growing regions, an inverse correlation (0.6–0.75) was found between RET and the Keetch–Byram meteorological drought index (KBDI), and a substantial reduction in RET was also found in a sub‐normal (2000) compared with a normal (1999) monsoon season. RET estimates were found to be most sensitive to atmospheric transmissivity followed by other land‐surface radiation budget inputs, such as NDVI, LST, and albedo. Error propagation due to three surface parameters is the opposite of that for transmissivity. The maximum possible error in clear‐sky NOAA PAL RET was estimated to be 12–15%. This test study would be helpful in deriving RET using optical and thermal data from a suite of current and future Indian geostationary satellite sensors for monitoring growing conditions.     

Direct Growth of Germanene at Interfaces between Van der Waals Materials and Ag(111)

Germanene, a 2D honeycomb germanium crystal, is grown at graphene/Ag(111) and hexagonal boron nitride (h-BN)/Ag(111) interfaces by segregating germanium atoms. A simple annealing process in N₂ or H₂/Ar at ambient pressure leads to the formation of germanene, indicating that an ultrahigh-vacuum condition is not necessary. The grown germanene is stable in air and uniform over the entire area covered with a van der Waals (vdW) material. As an important finding, it is necessary to use a vdW material as a cap layer for the present germanene growth method since the use of an Al₂O₃ cap layer results in no germanene formation. The present study also proves that Raman spectroscopy in air is a powerful tool for characterizing germanene at the interfaces, which is concluded by multiple analyses including first-principles density functional theory calculations. The direct growth of h-BN-capped germanene on Ag(111), which is demonstrated in the present study, is considered to be a promising technique for the fabrication of future germanene-based electronic devices.     

Multi-step Training of a Generalized Linear Classifier

Neural Processing Letters - We propose a multi-step training method for designing generalized linear classifiers. First, an initial multi-class linear classifier is found through regression. Then...     

Mechanical and thermal behaviour of biodegradable composites based on polycaprolactone with pine cone particle

Polycaprolactone (PCL) was reinforced with natural fibres as they not only permit a substantial reduction of the material costs, but also play a role as reinforcement in mechanical properties. This work was focused on the estimation of mechanical and thermal behaviour based on PCL and Pine Cone particles (PCP) filler at different weight percentages (0, 5, 10, 15, 30 and 45 wt%). Tests results indicated considerable improvement in mechanical properties, corresponding to a gain in impact strength and % elongation of 6 and 9.2% at 15 wt% particle loading, respectively. Some decrease in thermal stability was observed for composites with increasing filler content where as composite at 15% PCP was not significantly affected. Lower melting and crystallization enthalpies and higher crystallinity values were obtained for bio-composites compared with neat PCL. Some decrease in thermal stability and increase in oxygen and water vapour barrier properties were also observed for composites with increasing filler content.     

Bioresponse to nanotubes: surface chemistry of carbon nanotubes impacts the growth and expression of water channel protein in tomato plants (small 15/2012)

A tomato plant grows on medium supplemented with multi-walled carbon nanotubes. The correlations between the level of aggregation, the type of functional group on the surface of applied carbon nanotubes, plant growth performance and the expression of tomato water channel protein in the cell membranes of roots exposed to multi-walled carbon nanotubes are documented and discussed by A. S. Biris, M. V. Khodakovskaya, and co-workers.     

二阶 Newton 法训练径向基函数神经网络的算法研究

提出了一种混合加权距离测量（weighted distance measure ,weighted DM ）参数的构建和训练RBF（radial basis function）神经网络的两步批处理算法。该算法在引进了 DM 系数参数的基础上,采用Newton 法分别对径向基函数的覆盖参数、均值向量参数、加权距离测度系数以及输出权值进行了优化,并在优化过程中利用 OLS（orthogonal least squares）法来求解 New ton 法的方程组。通过实验数据,不仅分析了 New ton 法优化的各个参数向量对 RBF 网络训练的影响,而且比较了混合优化加权 DM 与RLS‐RBF（recursive least square RBF neural network）网络训练算法的收敛性和计算成本。所得到的结论表明整合了优化参数的加权 DM‐RBF 网络训练算法收敛速度比 RLS‐RBF 网络训练算法更快,而且具有比 LM‐RBF （Levenberg‐Marquardt RBF ）训练算法更小的计算成本,从而说明 OLS 求解的Newton 法对优化 RBF 网络参数具有重要应用价值。