Learning distance to subspace for the nearest subspace methods in high-dimensional data classification

The nearest subspace methods (NSM) are a category of classification methods widely applied to classify high-dimensional data. In this paper, we propose to improve the classification performance of NSM through learning tailored distance metrics from samples to class subspaces. The learned distance metric is termed as ‘learned distance to subspace’ (LD2S). Using LD2S in the classification rule of NSM can make the samples closer to their correct class subspaces while farther away from their wrong class subspaces. In this way, the classification task becomes easier and the classification performance of NSM can be improved. The superior classification performance of using LD2S for NSM is demonstrated on three real-world high-dimensional spectral datasets

Effect of finite Coulomb interaction on full counting statistics of electronic transport through single-molecule magnet

We study the full counting statistics (FCS) in a single-molecule magnet (SMM) with finite Coulomb interaction $U$. For finite $U$ the FCS, differing from $U\rightarrow \infty$, shows a symmetric gate-voltage-dependence when the coupling strengths with two electrodes are interchanged, which can be observed experimentally just by reversing the bias-voltages. Moreover, we find that the effect of finite $U$ on shot noise depends on the internal level structure of the SMM and the coupling asymmetry of the SMM with two electrodes as well. When the coupling of the SMM with the incident-electrode is stronger than that with the outgoing-electrode, the super-Poissonian shot noise in the sequential tunneling regime appears under relatively small gate-voltage and relatively large finite $U$, and dose not for $U\rightarrow \infty$; while it occurs at relatively large gate-voltage for the opposite coupling case. The formation mechanism of super-Poissonian shot noise can be qualitatively attributed to the competition between fast and slow transport channels.Comment: 28 pages, 7 figures, Revised version. Accepted for publication in Physics Letters

Microscopic theory of single-electron tunneling through molecular-assembled metallic nanoparticles

We present a microscopic theory of single-electron tunneling through metallic nanoparticles connected to the electrodes through molecular bridges. It combines the theory of electron transport through molecular junctions with the description of the charging dynamics on the nanoparticles. We apply the theory to study single-electron tunneling through a gold nanoparticle connected to the gold electrodes through two representative benzene-based molecules. We calculate the background charge on the nanoparticle induced by the charge transfer between the nanoparticle and linker molecules, the capacitance and resistance of molecular junction using a first-principles based Non-Equilibrium Green's Function theory. We demonstrate the variety of transport characteristics that can be achieved through ``engineering'' of the metal-molecule interaction.Comment: To appear in Phys. Rev.

Inclinations and black hole masses of Seyfert 1 galaxies

A tight correlation of black hole mass and central velocity dispersion has been found recently for both active and quiescent galaxies. By applying this correlation, we develop a simple method to derive the inclination angles for a sample of 11 Seyfert 1 galaxies that have both measured central velocity dispersions and black hole masses estimated by reverberation mapping. These angles, with a mean value of 36 degree that agrees well with the result obtained by fitting the iron K$\alpha$ lines of Seyfert 1s observed with ASCA, provide further support to the orientation-dependent unification scheme of AGN. A positive correlation of the inclinations with observed FWHMs of H$\beta$ line and a possible anti-correlation with the nuclear radio-loudness have been found. We conclude that more accurate knowledge on inclinations and broad line region dynamics is needed to improve the black hole mass determination of AGN with the reverberation mapping technique.Comment: 12 pages including 4 figures, accepted for publication in The Astrophysical Journal Letter

Stem-root flow effect on soil–atmosphere interactions and uncertainty assessments

Abstract. Soil water can rapidly enter deeper layers via vertical redistribution of soil water through the stem–root flow mechanism. This study develops the stem–root flow parameterization scheme and coupled this scheme with the Simplified Simple Biosphere model (SSiB) to analyze its effects on land–atmospheric interactions. The SSiB model was tested in a single column mode using the Lien Hua Chih (LHC) measurements conducted in Taiwan and HAPEX-Mobilhy (HAPEX) measurements in France. The results show that stem–root flow generally caused a decrease in the moisture content at the top soil layer and moistened the deeper soil layers. Such soil moisture redistribution results in significant changes in heat flux exchange between land and atmosphere. In the humid environment at LHC, the stem–root flow effect on transpiration was minimal, and the main influence on energy flux was through reduced soil evaporation that led to higher soil temperature and greater sensible heat flux. In the Mediterranean environment of HAPEX, the stem–root flow significantly affected plant transpiration and soil evaporation, as well as associated changes in canopy and soil temperatures. However, the effect on transpiration could either be positive or negative depending on the relative changes in the moisture content of the top soil vs. deeper soil layers due to stem–root flow and soil moisture diffusion processes

The matched subspace detector with interaction effects

This paper aims to propose a new hyperspectral target-detection method termed the matched subspace detector with interaction effects (MSDinter). The MSDinter introduces “interaction effects” terms into the popular matched subspace detector (MSD), from regression analysis in multivariate statistics and the bilinear mixing model in hyperspectral unmixing. In this way, the interaction between the target and the surrounding background, which should have but not yet been considered by the MSD, is modelled and estimated, such that superior performance of target detection can be achieved. Besides deriving the MSDinter methodologically, we also demonstrate its superiority empirically using two hyperspectral imaging datasets

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM

Mathematics from China to Virginia by Way of Singapore

Our article follows from an interesting concurrence of mathematical and educational lines. At least the concurrence seems so to us and we hope that those who read on will agree. The lines or streams are a joint minimester program at St. Catherine’s and St. Christopher‘s Schools, an interest in problem solving, and a Singapore connection. We shall describe the lines first and then describe the mathematics that we found at their intersection