Superfluid response in electron-doped cuprate superconductors

We propose a weakly coupled two-band model with $d_{x^2-y^2}$ pairing symmetry to account for the anomalous temperature dependence of superfluid density $\rho_s$ in electron-doped cuprate superconductors. This model gives a unified explanation to the presence of a upward curvature in $\rho_s$ near $T_c$ and a weak temperature dependence of $\rho_s$ in low temperatures. Our work resolves a discrepancy in the interpretation of different experimental measurements and suggests that the pairing in electron-doped cuprates has predominately $d_{x^2-y^2}$ symmetry in the whole doping range.Comment: 4 pages, 3 figures, title changed and references adde

Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT

Wireless sensor networks (WSN) are fundamental to the Internet of Things (IoT) by bridging the gap between the physical and the cyber worlds. Anomaly detection is a critical task in this context as it is responsible for identifying various events of interests such as equipment faults and undiscovered phenomena. However, this task is challenging because of the elusive nature of anomalies and the volatility of the ambient environments. In a resource-scarce setting like WSN, this challenge is further elevated and weakens the suitability of many existing solutions. In this paper, for the first time, we introduce autoencoder neural networks into WSN to solve the anomaly detection problem. We design a two-part algorithm that resides on sensors and the IoT cloud respectively, such that (i) anomalies can be detected at sensors in a fully distributed manner without the need for communicating with any other sensors or the cloud, and (ii) the relatively more computation-intensive learning task can be handled by the cloud with a much lower (and configurable) frequency. In addition to the minimal communication overhead, the computational load on sensors is also very low (of polynomial complexity) and readily affordable by most COTS sensors. Using a real WSN indoor testbed and sensor data collected over 4 consecutive months, we demonstrate via experiments that our proposed autoencoder-based anomaly detection mechanism achieves high detection accuracy and low false alarm rate. It is also able to adapt to unforeseeable and new changes in a non-stationary environment, thanks to the unsupervised learning feature of our chosen autoencoder neural networks.Comment: 6 pages, 7 figures, IEEE ICC 201

Parametric study of turbine NGV blade lean and vortex design

The effects of blade lean and vortex design on the aerodynamics of a turbine entry nozzle guide vane (NGV) are considered using computational fluid dynamics. The aim of the work is to address some of the uncertainties which have arisen from previous studies where conflicting results have been reported for the effect on the NGV. The configuration was initially based on the energy efficient engine turbine which also served as the validation case for the computational method. A total of 17 NGV configurations were evaluated to study the effects of lean and vortex design on row efficiency and secondary kinetic energy. The distribution of mass flow ratio is introduced as an additional factor in the assessment of blade lean effects. The results show that in the turbine entry NGV, the secondary flow strength is not a dominant factor that determines NGV losses and therefore the changes of loading distribution due to blade lean and the associated loss mechanisms should be regarded as a key factor. Radial mass flow redistribution under different NGV lean and twist is demonstrated as an addition key factor influencing row efficiency

In-plane ferromagnetism in charge-ordering Na0.55CoO2Na_{0.55}CoO_2

The magnetic and transport properties are systematically studied on the single crystal $Na_{0.55}CoO_2$ with charge ordering and divergency in resistivity below 50 K. A long-range ferromagnetic ordering is observed in susceptibility below 20 K with the magnetic field parallel to Co-O plane, while a negligible behavior is observed with the field perpendicular to the Co-O plane. It definitely gives a direct evidence for the existence of in-plane ferromagnetism below 20 K. The observed magnetoresistance (MR) of 30 % at the field of 6 T at low temperatures indicates an unexpectedly strong spin-charge coupling in triangle lattice systems.Comment: 4 pages, 5 figure

Hysteresis and Anisotropic Magnetoresistance in Antiferromagnetic Nd2−xCexCuO4Nd_{2-x}Ce_xCuO_{4}

The out-of-plane resistivity ($\rho_c$) and magnetoresistivity (MR) are studied in antiferromangetic (AF) $Nd_{2-x}Ce_xCuO_{4}$ single crystals, which have three types of noncollinear antiferromangetic spin structures. The apparent signatures are observed in $\rho_c(T)$ measured at the zero-field and 14 T at the spin structure transitions, giving a definite evidence for the itinerant electrons directly coupled to the localized spins. One of striking feature is an anisotropy of the MR with a fourfold symmetry upon rotating the external field (B) within ab plane in the different phases, while twofold symmetry at spin reorientation transition temperatures. The intriguing thermal hysteresis in $\rho_c(T,B)$ and magnetic hysteresis in MR are observed at spin reorientation transition temperatures.Comment: 4 pages, 4 figure

Optimizing Hartree-Fock orbitals by the density-matrix renormalization group

We have proposed a density-matrix renormalization group (DMRG) scheme to optimize the one-electron basis states of molecules. It improves significantly the accuracy and efficiency of the DMRG in the study of quantum chemistry or other many-fermion system with nonlocal interactions. For a water molecule, we find that the ground state energy obtained by the DMRG with only 61 optimized orbitals already reaches the accuracy of best quantum Monte Carlo calculation with 92 orbitals.Comment: published version, 4 pages, 4 figure