Universal behavior of giant electroresistance in epitaxial La0.67Ca0.33MnO3 thin films

We report a giant resistance drop induced by dc electrical currents in La0.67Ca0.33MnO3 epitaxial thin films. Resistance of the patterned thin films decreases exponentially with increasing current and a maximum drop shows at the temperature of resistance peak Tp. Variation of resistance with current densities can be scaled below and above Tp, respectively. This work can be useful for the future applications of electroresistance.Comment: 13 pages, 4 figure

Laser-catalyzed spin-exchange process in a Bose-Einstein condensate

We show theoretically that it is possible to optically control collective spin-exchange processes in spinor Bose condensates through virtual photoassociation. The interplay between optically induced spin exchange and spin-dependent collisions provides a flexible tool for the control of atomic spin dynamics, including enhanced or inhibited quantum spin oscillations, the optically-induced ferromagnetic-to-antiferromagnetic transition, and coherent matter-wave spin conversion.Comment: 4 pages, 4 figure

The mean velocity of two-state models of molecular motor

The motion of molecular motor is essential to the biophysical functioning of living cells. In principle, this motion can be regraded as a multiple chemical states process. In which, the molecular motor can jump between different chemical states, and in each chemical state, the motor moves forward or backward in a corresponding potential. So, mathematically, the motion of molecular motor can be described by several coupled one-dimensional hopping models or by several coupled Fokker-Planck equations. To know the basic properties of molecular motor, in this paper, we will give detailed analysis about the simplest cases: in which there are only two chemical states. Actually, many of the existing models, such as the flashing ratchet model, can be regarded as a two-state model. From the explicit expression of the mean velocity, we find that the mean velocity of molecular motor might be nonzero even if the potential in each state is periodic, which means that there is no energy input to the molecular motor in each of the two states. At the same time, the mean velocity might be zero even if there is energy input to the molecular motor. Generally, the velocity of molecular motor depends not only on the potentials (or corresponding forward and backward transition rates) in the two states, but also on the transition rates between the two chemical states

Is perpendicular magnetic anisotropy essential to all-optical ultrafast spin reversal in ferromagnets?

All-optical spin reversal presents a new opportunity for spin manipulations, free of a magnetic field. Most of all-optical-spin-reversal ferromagnets are found to have a perpendicular magnetic anisotropy (PMA), but it has been unknown whether PMA is necessary for the spin reversal. Here we theoretically investigate magnetic thin films with either PMA or in-plane magnetic anisotropy (IMA). Our results show that the spin reversal in IMA systems is possible, but only with a longer laser pulse and within a narrow laser parameter region. The spin reversal does not show a strong helicity dependence where the left- and right-circularly polarized light lead to the identical results. By contrast, the spin reversal in PMA systems is robust, provided both the spin angular momentum and laser field are strong enough while the magnetic anisotropy itself is not too strong. This explains why experimentally the majority of all-optical spin-reversal samples are found to have strong PMA and why spins in Fe nanoparticles only cant out of plane. It is the laser-induced spin-orbit torque that plays a key role in the spin reversal. Surprisingly, the same spin-orbit torque results in laser-induced spin rectification in spin-mixed configuration, a prediction that can be tested experimentally. Our results clearly point out that PMA is essential to the spin reversal, though there is an opportunity for in-plane spin reversal.Comment: 20 pages, 4 figures and one tabl

Switching ferromagnetic spins by an ultrafast laser pulse: Emergence of giant optical spin-orbit torque

Faster magnetic recording technology is indispensable to massive data storage and big data sciences. {All-optical spin switching offers a possible solution}, but at present it is limited to a handful of expensive and complex rare-earth ferrimagnets. The spin switching in more abundant ferromagnets may significantly expand the scope of all-optical spin switching. Here by studying 40,000 ferromagnetic spins, we show that it is the optical spin-orbit torque that determines the course of spin switching in both ferromagnets and ferrimagnets. Spin switching occurs only if the effective spin angular momentum of each constituent in an alloy exceeds a critical value. Because of the strong exchange coupling, the spin switches much faster in ferromagnets than weakly-coupled ferrimagnets. This establishes a paradigm for all-optical spin switching. The resultant magnetic field (65 T) is so big that it will significantly reduce high current in spintronics, thus representing the beginning of photospintronics.Comment: 12 page2, 6 figures. Accepted to Europhysics Letters (2016). Extended version with the supplementary information. Contribution from Indiana State University,Europhysics Letters (2016

Effect of disorder with long-range correlation on transport in graphene nanoribbon

Transport in disordered armchair graphene nanoribbons (AGR) with long-range correlation between quantum wire contact is investigated by transfer matrix combined with Landauer's formula. Metal-insulator transition is induced by disorder in neutral AGR. Thereinto, the conductance is one conductance quantum for metallic phase and exponentially decays otherwise when the length of AGR is infinity and far longer than its width. Similar to the case of long-range disorder, the conductance of neutral AGR first increases and then decreases while the conductance of doped AGR monotonically decreases, as the disorder strength increases. In the presence of strong disorder, the conductivity depends monotonically and non-monotonically on the aspect ratio for heavily doped and slightly doped AGR respectively.Comment: 6 pages, 8 figures; J. Phys: Condensed Matter (May 2012

Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction

Longitudinal analysis is important in many disciplines, such as the study of behavioral transitions in social science. Only very recently, feature selection has drawn adequate attention in the context of longitudinal modeling. Standard techniques, such as generalized estimating equations, have been modified to select features by imposing sparsity-inducing regularizers. However, they do not explicitly model how a dependent variable relies on features measured at proximal time points. Recent graphical Granger modeling can select features in lagged time points but ignores the temporal correlations within an individual's repeated measurements. We propose an approach to automatically and simultaneously determine both the relevant features and the relevant temporal points that impact the current outcome of the dependent variable. Meanwhile, the proposed model takes into account the non-{\em i.i.d} nature of the data by estimating the within-individual correlations. This approach decomposes model parameters into a summation of two components and imposes separate block-wise LASSO penalties to each component when building a linear model in terms of the past $\tau$ measurements of features. One component is used to select features whereas the other is used to select temporal contingent points. An accelerated gradient descent algorithm is developed to efficiently solve the related optimization problem with detailed convergence analysis and asymptotic analysis. Computational results on both synthetic and real world problems demonstrate the superior performance of the proposed approach over existing techniques.Comment: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 201