Demonstration of Palm Vein Pattern Biometric Recognition by Machine Learning

This paper aims to demonstrate the extraction of palm vein pattern features by local binary pattern (LBP) and its different recognition rate by two types of classification methods. The first classification method is by K-nearest neighbour (KNN) while the second method is support vector machine (SVM). Whilst SVM is optimized for direct classification between two classes, the KNN is best for multi-class classification. Based on the biometric recognition framework shared in this paper, both techniques shared comparable performance in terms of the recognition rate. The difference in the recognition rate can only be seen if the LBP features extracted for the classification are different. In general, higher recognition rate can be achieved for palm vein pattern biometric system if all LBP bins are used for the classification, compared to if only selected features are used for the purpose. Best recognition rate that can be achieved by the three datasets demonstrated in this paper are 60%, 70% and 100% respectively for the CASIA, PolyU and self-dataset

Spin Coulomb drag in the two-dimensional electron liquid

We calculate the spin-drag transresistivity $\rho_{\uparrow \downarrow}(T)$ in a two-dimensional electron gas at temperature $T$ in the random phase approximation. In the low-temperature regime we show that, at variance with the three-dimensional low-temperature result [$\rho_{\uparrow\downarrow}(T) \sim T^2$], the spin transresistivity of a two-dimensional {\it spin unpolarized} electron gas has the form $\rho_{\uparrow\downarrow}(T) \sim T^2 \ln T$. In the spin-polarized case the familiar form $\rho_{\uparrow\downarrow}(T) =A T^2$ is recovered, but the constant of proportionality $A$ diverges logarithmically as the spin-polarization tends to zero. In the high-temperature regime we obtain $\rho_{\uparrow \downarrow}(T) = -(\hbar / e^2) (\pi^2 Ry^* /k_B T)$ (where $Ry^*$ is the effective Rydberg energy) {\it independent} of the density. Again, this differs from the three-dimensional result, which has a logarithmic dependence on the density. Two important differences between the spin-drag transresistivity and the ordinary Coulomb drag transresistivity are pointed out: (i) The $\ln T$ singularity at low temperature is smaller, in the Coulomb drag case, by a factor $e^{-4 k_Fd}$ where $k_F$ is the Fermi wave vector and $d$ is the separation between the layers. (ii) The collective mode contribution to the spin-drag transresistivity is negligible at all temperatures. Moreover the spin drag effect is, for comparable parameters, larger than the ordinary Coulomb drag effect.Comment: 6 figures; various changes; version accepted for publicatio

Constrained spin dynamics description of random walks on hierarchical scale-free networks

We study a random walk problem on the hierarchical network which is a scale-free network grown deterministically. The random walk problem is mapped onto a dynamical Ising spin chain system in one dimension with a nonlocal spin update rule, which allows an analytic approach. We show analytically that the characteristic relaxation time scale grows algebraically with the total number of nodes $N$ as $T \sim N^z$. From a scaling argument, we also show the power-law decay of the autocorrelation function C_{\bfsigma}(t)\sim t^{-\alpha}, which is the probability to find the Ising spins in the initial state {\bfsigma} after $t$ time steps, with the state-dependent non-universal exponent $\alpha$. It turns out that the power-law scaling behavior has its origin in an quasi-ultrametric structure of the configuration space.Comment: 9 pages, 6 figure

Effects of Electron-Electron and Electron-Phonon Interactions in Weakly Disordered Conductors and Heterostuctures

We investigate quantum corrections to the conductivity due to the interference of electron-electron (electron-phonon) scattering and elastic electron scattering in weakly disordered conductors. The electron-electron interaction results in a negative $T^2 \ln T$-correction in a 3D conductor. In a quasi-two-dimensional conductor, $d < v_F/T$ ($d$ is the thickness, $v_F$ is the Fermi velocity), with 3D electron spectrum this correction is linear in temperature and differs from that for 2D electrons (G. Zala et. al., Phys. Rev.B {\bf 64}, 214204 (2001)) by a numerical factor. In a quasi-one-dimensional conductor, temperature-dependent correction is proportional to $T^2$. The electron interaction via exchange of virtual phonons also gives $T^2$-correction. The contribution of thermal phonons interacting with electrons via the screened deformation potential results in $T^4$-term and via unscreened deformation potential results in $T^2$-term. The interference contributions dominate over pure electron-phonon scattering in a wide temperature range, which extends with increasing disorder.Comment: 6 pages, 2figure

Interaction Corrections to Two-Dimensional Hole Transport in Large rsr_{s} Limit

The metallic conductivity of dilute two-dimensional holes in a GaAs HIGFET (Heterojunction Insulated-Gate Field-Effect Transistor) with extremely high mobility and large $r_{s}$ is found to have a linear dependence on temperature, consistent with the theory of interaction corrections in the ballistic regime. Phonon scattering contributions are negligible in the temperature range of our interest, allowing comparison between our measured data and theory without any phonon subtraction. The magnitude of the Fermi liquid interaction parameter $F_{0}^{\sigma}$ determined from the experiment, however, decreases with increasing $r_{s}$ for r_{s}\agt22, a behavior unexpected from existing theoretical calculations valid for small $r_{s}$.Comment: 6 pages, 4 figure

Transport Properties of Random Walks on Scale-Free/Regular-Lattice Hybrid Networks

We study numerically the mean access times for random walks on hybrid disordered structures formed by embedding scale-free networks into regular lattices, considering different transition rates for steps across lattice bonds ($F$) and across network shortcuts ($f$). For fast shortcuts ($f/F\gg 1$) and low shortcut densities, traversal time data collapse onto an universal curve, while a crossover behavior that can be related to the percolation threshold of the scale-free network component is identified at higher shortcut densities, in analogy to similar observations reported recently in Newman-Watts small-world networks. Furthermore, we observe that random walk traversal times are larger for networks with a higher degree of inhomogeneity in their shortcut distribution, and we discuss access time distributions as functions of the initial and final node degrees. These findings are relevant, in particular, when considering the optimization of existing information networks by the addition of a small number of fast shortcut connections.Comment: 8 pages, 6 figures; expanded discussions, added figures and references. To appear in J Stat Phy

Metallicity and its low temperature behavior in dilute 2D carrier systems

We theoretically consider the temperature and density dependent transport properties of semiconductor-based 2D carrier systems within the RPA-Boltzmann transport theory, taking into account realistic screened charged impurity scattering in the semiconductor. We derive a leading behavior in the transport property, which is exact in the strict 2D approximation and provides a zeroth order explanation for the strength of metallicity in various 2D carrier systems. By carefully comparing the calculated full nonlinear temperature dependence of electronic resistivity at low temperatures with the corresponding asymptotic analytic form obtained in the $T/T_F \to 0$ limit, both within the RPA screened charged impurity scattering theory, we critically discuss the applicability of the linear temperature dependent correction to the low temperature resistivity in 2D semiconductor structures. We find quite generally that for charged ionized impurity scattering screened by the electronic dielectric function (within RPA or its suitable generalizations including local field corrections), the resistivity obeys the asymptotic linear form only in the extreme low temperature limit of $T/T_F \le 0.05$. We point out the experimental implications of our findings and discuss in the context of the screening theory the relative strengths of metallicity in different 2D systems.Comment: We have substantially revised this paper by adding new materials and figures including a detailed comparison to a recent experimen

The fate of carbon in a mature forest under carbon dioxide enrichment

Atmospheric carbon dioxide enrichment (eCO2) can enhance plant carbon uptake and growth1 5, thereby providing an important negative feedback to climate change by slowing the rate of increase of the atmospheric CO2 concentration6. Although evidence gathered from young aggrading forests has generally indicated a strong CO2 fertilization effect on biomass growth3 5, it is unclear whether mature forests respond to eCO2 in a similar way. In mature trees and forest stands7 10, photosynthetic uptake has been found to increase under eCO2 without any apparent accompanying growth response, leaving the fate of additional carbon fixed under eCO2 unclear4,5,7 11. Here using data from the first ecosystem-scale Free-Air CO2 Enrichment (FACE) experiment in a mature forest, we constructed a comprehensive ecosystem carbon budget to track the fate of carbon as the forest responded to four years of eCO2 exposure. We show that, although the eCO2 treatment of +150 parts per million (+38 per cent) above ambient levels induced a 12 per cent (+247 grams of carbon per square metre per year) increase in carbon uptake through gross primary production, this additional carbon uptake did not lead to increased carbon sequestration at the ecosystem level. Instead, the majority of the extra carbon was emitted back into the atmosphere via several respiratory fluxes, with increased soil respiration alone accounting for half of the total uptake surplus. Our results call into question the predominant thinking that the capacity of forests to act as carbon sinks will be generally enhanced under eCO2, and challenge the efficacy of climate mitigation strategies that rely on ubiquitous CO2 fertilization as a driver of increased carbon sinks in global forests. © 2020, The Author(s), under exclusive licence to Springer Nature Limited

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

Effect of event selection on jetlike correlation measurement in d+Au collisions at sNN=200 GeV

AbstractDihadron correlations are analyzed in sNN=200 GeV d+Au collisions classified by forward charged particle multiplicity and zero-degree neutral energy in the Au-beam direction. It is found that the jetlike correlated yield increases with the event multiplicity. After taking into account this dependence, the non-jet contribution on the away side is minimal, leaving little room for a back-to-back ridge in these collisions