Coherent heteronuclear spin dynamics in an ultracold spin-1 mixture

We report the observation of coherent heteronuclear spin dynamics driven by inter-species spin-spin interaction in an ultracold spinor mixture, which manifests as periodical and well correlated spin oscillations between two atomic species. In particular, we investigate the magnetic field dependence of the oscillations and find a resonance behavior which depends on {\em both} the linear and quadratic Zeeman effects and the spin-dependent interaction. We also demonstrate a unique knob for controlling the spin dynamics in the spinor mixture with species-dependent vector light shifts. Our finds are in agreement with theoretical simulations without any fitting parameters.Comment: 13 pages including the supplementary materia

Learning Local Feature Descriptor with Motion Attribute for Vision-based Localization

In recent years, camera-based localization has been widely used for robotic applications, and most proposed algorithms rely on local features extracted from recorded images. For better performance, the features used for open-loop localization are required to be short-term globally static, and the ones used for re-localization or loop closure detection need to be long-term static. Therefore, the motion attribute of a local feature point could be exploited to improve localization performance, e.g., the feature points extracted from moving persons or vehicles can be excluded from these systems due to their unsteadiness. In this paper, we design a fully convolutional network (FCN), named MD-Net, to perform motion attribute estimation and feature description simultaneously. MD-Net has a shared backbone network to extract features from the input image and two network branches to complete each sub-task. With MD-Net, we can obtain the motion attribute while avoiding increasing much more computation. Experimental results demonstrate that the proposed method can learn distinct local feature descriptor along with motion attribute only using an FCN, by outperforming competing methods by a wide margin. We also show that the proposed algorithm can be integrated into a vision-based localization algorithm to improve estimation accuracy significantly.Comment: This paper will be presented on IROS1

Mixing and separation of liquid-liquid two-phase in a novel cyclone reactor of isobutane alkylation catalyzed by ionic liquid

To improve the existing problems of the traditional isobutane alkylation catalyzed by ionic liquid reactors， a novel liquid-liquid cyclone reactor has been designed for the liquid-liquid heterogeneous reaction. Compared with the traditional hydrocyclone, the novel cyclone reactor consists of two inlets for light phase and heavy phase respectively. The light phase is injected into the reactor through two symmetric tangential slots in the inlet, while the heavy phase inlet is the axial entry with guide vane. The trajectory and residence time distribution (RTD) of the light phase could influence the reaction process and the products quality. In order to study the contact-mixing and separation mechanism of liquid-liquid in the novel cyclone reactor, the trajectory and residence time distribution in the reactor were investigated. The simulation using species transport equation and experiment were performed under oil-water system. The tangential and radial dispersion process of oil was observed in the simulation. The simulation results show that the mean residence time of the oil is between 0.6s~1.0s under different operating parameters. The oil flow in the reactor is not a smooth flow or a complete mixing flow judging from the dimensionless variance. The separation efficiency in simulated method was higher than 99%. The volume fraction of water in the overflow mixture was lower than 5%. And the deviation between the simulated and experimental results was no more than 5%, which indicates that the simulated results are reliable and accurate

The Chain Flexibility Effects on the Self-assembly of Diblock Copolymer in Thin Film

We investigate the effects of chain flexibility on the self-assembly behavior of symmetric diblock copolymers (BCPs) when they are confined as a thin film between two surfaces. Employing worm-like chain (WLC) self-consistent field theory, we study the relative stability of parallel (L$_{\parallel}$) and perpendicular (L$_{\perp}$) orientations of BCP lamellar phases, ranging in chain flexibility from flexible Gaussian chains to semi-flexible and rigid chains. For flat and neutral bounding surfaces (no surface preference for one of the two BCP components), the stability of the L$_{\perp}$ lamellae increases with chain rigidity. When the top surface is flat and the bottom substrate is corrugated, increasing the surface roughness enhances the stability of the L$_{\perp}$ lamellae for flexible Gaussian chains. However, an opposite behavior is observed for rigid chains, where the L$_{\perp}$ stability decreases as the substrate roughness increases. We further show that as the substrate roughness increases, the critical value of the substrate preference, $u^{*}$, corresponding to an L$_{\perp}$-to-L$_{\parallel}$ transition, decreases for rigid chains, while it increases for flexible Gaussian chains. Our results highlight the physical mechanism of tailoring the orientation of lamellar phases in thin-film setups. This is of importance, in particular, for short (semi-flexible or rigid) chains that are in high demand in emerging nanolithography and other industrial applications