Electron-electron scatttering in Sn-doped indium oxide thick films

We have measured the low-field magnetoresistances (MRs) of a series of Sn-doped indium oxide thick films in the temperature $T$ range 4--35 K. The electron dephasing rate $1/\tau_{\varphi}$ as a function of $T$ for each film was extracted by comparing the MR data with the three-dimensional (3D) weak-localization theoretical predictions. We found that the extracted $1/\tau_{\varphi}$ varies linearly with $T^{3/2}$. Furthermore, at a given $T$, $1/\tau_{\varphi}$ varies linearly with $k_F^{-5/2}l^{-3/2}$, where $k_{F}$ is the Fermi wavenumber, and $l$ is the electron elastic mean free path. These features are well explained in terms of the small-energy-transfer electron-electron scattering time in 3D disordered conductors. This electron dephasing mechanism dominates over the electron-phonon ($e$-ph) scattering process because the carrier concentrations in our films are $\sim$ 3 orders of magnitude lower than those in typical metals, which resulted in a greatly suppressed $e$-ph relaxation rate.Comment: 5 pages, 3 figure

Impact of Foreign Capital on Economic Growth in Developing Countries: A Debatable Issue in India

Foreign capital is increasingly becoming a significant resource to spur economic growth in many developing countries. This present study used time series data from 1978 to 2014 to investigate the impact of foreign capital on economic growth in India. We found that external debt and foreign remittances lead positively towards economic growth on one hand while on the other hand ODA and FDI negatively affects economic growth in India. The popularly believed argument that foreign capital can help boost economic growth remains an arguable issue in India. Keywords: India, Foreign Capital, Economic Growt

Can Foreign Remittances Accelerate Economic Growth? An Empirical Analysis for China

Using Johansen’s co-integration test, this paper is conducted to investigate the effect of foreign remittances on economic growth by collecting time series data from 1982 to 2015 from the world data bank. Results indicate that there is a long run relationship between foreign remittances and economic development. Furthermore study applies error correction model ECM to detect the short run adjustment in variables to attain equilibrium in the long run. The results show that remittances have a significant positive impact on China economic growth both in the long run and in the short run, indicating that remittances can accelerate China economic growth. The conclusion suggests that remittances in China are currently profit- driven and a considerable share of remittances has already entered production investment. Keywords: Foreign remittances, Economic growth , Time Series Analysi

A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification

$k$ Nearest Neighbors ($k$NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we propose a new graph-based $k$NN algorithm which can effectively handle both Gaussian distributed data and nonlinear manifold distributed data. To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by constructing an $R$-level nearest-neighbor strengthened tree over the graph, and then compute a TRW matrix for similarity measurement purposes. After this, the nearest neighbors are identified according to the TRW matrix and the class label of a query point is determined by the sum of all the TRW weights of its nearest neighbors. To deal with online situations, we also propose a new algorithm to handle sequential samples based a local neighborhood reconstruction. Comparison experiments are conducted on both synthetic data sets and real-world data sets to demonstrate the validity of the proposed new $k$NN algorithm and its improvements to other version of $k$NN algorithms. Given the widespread appearance of manifold structures in real-world problems and the popularity of the traditional $k$NN algorithm, the proposed manifold version $k$NN shows promising potential for classifying manifold-distributed data.Comment: 32 pages, 12 figures, 7 table