Effect of Impurities and Effective Masses on Spin-Dependent Electrical Transport in Ferromagnet-Normal Metal-Ferromagnet Hybrid Junctions

The effect of nonmagnetic impurities and the effective masses on the spin-dependent transport in a ferromagnet-normal metal-ferromagnet junction is investigated on the basis of a two-band model. Our results show that impurities and the effective masses of electrons in two ferromagnetic electrodes have remarkable effects on the behaviors of the conductance, namely, both affect the oscillating amplitudes, periods, as well as the positions of the resonant peaks of the conductance considerably. The impurity tends to suppress the amplitudes of the conductance, and makes the spin-valve effect less obvious, but under certain conditions the phenomenon of the so-called impurity-induced resonant tunneling is clearly observed. The impurity and the effective mass both can lead to nonmonotonous oscillation of the junction magnetoresistance (JMR) with the incident energy and the thickness of the normal metal. It is also observed that a smaller difference of the effective masses of electrons in two ferromagnetic electrodes would give rise to a larger amplitude of the JMR.Comment: Revtex, 10 figure

Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning

Group-level emotion recognition (GER) is an inseparable part of human behavior analysis, aiming to recognize an overall emotion in a multi-person scene. However, the existing methods are devoted to combing diverse emotion cues while ignoring the inherent uncertainties under unconstrained environments, such as congestion and occlusion occurring within a group. Additionally, since only group-level labels are available, inconsistent emotion predictions among individuals in one group can confuse the network. In this paper, we propose an uncertainty-aware learning (UAL) method to extract more robust representations for GER. By explicitly modeling the uncertainty of each individual, we utilize stochastic embedding drawn from a Gaussian distribution instead of deterministic point embedding. This representation captures the probabilities of different emotions and generates diverse predictions through this stochasticity during the inference stage. Furthermore, uncertainty-sensitive scores are adaptively assigned as the fusion weights of individuals' face within each group. Moreover, we develop an image enhancement module to enhance the model's robustness against severe noise. The overall three-branch model, encompassing face, object, and scene component, is guided by a proportional-weighted fusion strategy and integrates the proposed uncertainty-aware method to produce the final group-level output. Experimental results demonstrate the effectiveness and generalization ability of our method across three widely used databases.Comment: 11 pages,3 figure

Quantum anti-Zeno effect without rotating wave approximation

In this paper, we systematically study the spontaneous decay phenomenon of a two-level system under the influences of both its environment and continuous measurements. In order to clarify some well-established conclusions about the quantum Zeno effect (QZE) and the quantum anti-Zeno effect (QAZE), we do not use the rotating wave approximation (RWA) in obtaining an effective Hamiltonian. We examine various spectral distributions by making use of our present approach in comparison with other approaches. It is found that with respect to a bare excited state even without the RWA, the QAZE can still happen for some cases, e.g., the interacting spectra of hydrogen. But for a physical excited state, which is a renormalized dressed state of the atomic state, the QAZE disappears and only the QZE remains. These discoveries inevitably show a transition from the QZE to the QAZE as the measurement interval changes.Comment: 14 pages, 8 figure