733 research outputs found
Shot noise of spin current and spin transfer torque
We report the theoretical investigation of noise spectrum of spin current and
spin transfer torque for non-colinear spin polarized transport in a spin-valve
device which consists of normal scattering region connected by two
ferromagnetic electrodes. Our theory was developed using non-equilibrium
Green's function method and general non-linear and
relations were derived as a function of angle between magnetization of
two leads. We have applied our theory to a quantum dot system with a resonant
level coupled with two ferromagnetic electrodes. It was found that for the MNM
system, the auto-correlation of spin current is enough to characterize the
fluctuation of spin current. For a system with three ferromagnetic layers,
however, both auto-correlation and cross-correlation of spin current are needed
to characterize the noise spectrum of spin current. Furthermore, the spin
transfer torque and the torque noise were studied for the MNM system. For a
quantum dot with a resonant level, the derivative of spin torque with respect
to bias voltage is proportional to when the system is far away
from the resonance. When the system is near the resonance, the spin transfer
torque becomes non-sinusoidal function of . The derivative of noise
spectrum of spin transfer torque with respect to the bias voltage
behaves differently when the system is near or far away from the resonance.
Specifically, the differential shot noise of spin transfer torque is a
concave function of near the resonance while it becomes convex
function of far away from resonance. For certain bias voltages, the
period becomes instead of . For small , it
was found that the differential shot noise of spin transfer torque is very
sensitive to the bias voltage and the other system parameters.Comment: 15pages, 6figure
Mass hierarchy sensitivity of medium baseline reactor neutrino experiments with multiple detectors
We report the neutrino mass hierarchy (MH) sensitivity of medium baseline
reactor neutrino experiments with multiple detectors. Sensitivity of
determining the MH can be significantly improved by adding a near detector and
combining both the near and far detectors. The size of the sensitivity
improvement is related to accuracy of the individual mass-splitting
measurements and requires strict control on the relative energy scale
uncertainty of the near and far detectors. We study the impact of both baseline
and target mass of the near detector on the combined sensitivity. A
figure-of-merit is defined to optimize the baseline and target mass of the near
detector and the optimal selections are 13~km and 4~kton
respectively for a far detector with the 20~kton target mass and 52.5~km
baseline. As typical examples of future medium baseline reactor neutrino
experiments, the optimal location and target mass of the near detector are
selected for JUNO and RENO-50. Finally, we discuss distinct effects of the
neutrino spectrum uncertainty for setups of a single detector and double
detectors, which indicate that the spectrum uncertainty can be well constrained
in the presence of the near detector.Comment: 7 pages, 9 figure
Research of Oil Product Secondary Distribution Optimization Based on Collaborative Distribution
AbstractDuring peak seasons, the petrol company's oil supply capacity is insufficient, therefore, with limited trucks, adjusting the distribution quantity of petrol station and formulating an effective distribution route can minimize the total cost and maximize the vehicle utilization. In this paper we observe the extension of the multi-depot half open vehicle routing problem with time windows (MDHOVRPTW) in oil product secondary distribution. Based on the characteristics of secondary distribution and MDHOVRPTW problem, this paper formulates oil distribution model intra-area with distribution quantity and distribution routing as decision variables. A proposed algorithm is applied to solve this model and result compared with the traditional non-cooperative method to verify the effectiveness of collaborative distribution
The Impact of Different Power Structures on The Cross-Boder e-Retail Supply Chain With An O2O Dual- Channel
In this paper, considering a cross-border e-retail supply chain composed by a foreign supplier and a cross-border e-retailer, we study the impact of different power structures on the supply chain members’ pricing and profits by establishing foreign supplier Stackelberg (FSS), cross-border e-retailer Stackelberg (CES) and vertical Nash (VN) game model on the basis of discussing O2O dual-channel retail mode and pricing decision. The results show that: i) the cross-border e-retailer prefer to choose the centralized pricing mode and will gain more profit than that in the decentralized pricing mode under the condition of O2O dual-channel retailing. ii) The impact of Stackelberg game on dual channel pricing of the cross-border e-retailer is identical, but the impact of three games on foreign supplier’s pricing is significant, (i.e., the wholesale price of the foreign supplier becomes smaller with the game dominance decreased gradually). iii) The impact of three games on cross-border electronic supply chain members’ profits is significant (i.e., members’ profits become smaller with the game dominance decreased gradually. In addition, the impact of Stackelberg game on supply chain total profits is identical. However, the supply chain total profits under Vertical Nash game are more than Stackelberg game
Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds
Urban modeling from LiDAR point clouds is an important topic in computer
vision, computer graphics, photogrammetry and remote sensing. 3D city models
have found a wide range of applications in smart cities, autonomous navigation,
urban planning and mapping etc. However, existing datasets for 3D modeling
mainly focus on common objects such as furniture or cars. Lack of building
datasets has become a major obstacle for applying deep learning technology to
specific domains such as urban modeling. In this paper, we present a
urban-scale dataset consisting of more than 160 thousands buildings along with
corresponding point clouds, mesh and wire-frame models, covering 16 cities in
Estonia about 998 Km2. We extensively evaluate performance of state-of-the-art
algorithms including handcrafted and deep feature based methods. Experimental
results indicate that Building3D has challenges of high intra-class variance,
data imbalance and large-scale noises. The Building3D is the first and largest
urban-scale building modeling benchmark, allowing a comparison of supervised
and self-supervised learning methods. We believe that our Building3D will
facilitate future research on urban modeling, aerial path planning, mesh
simplification, and semantic/part segmentation etc
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