Fluctuation Effects on the Transport Properties of Unitary Fermi Gases

In this letter, we investigate the fluctuation effects on the transport properties of unitary Fermi gases in the vicinity of the superfluid transition temperature $T_c$. Based on the time-dependent Ginzburg-Landau formalism of the BEC-BCS crossover, we investigate both the residual resistivity below $T_c$ induced by phase slips and the paraconductivity above $T_c$ due to pair fluctuations. These two effects have been well studied in the weak coupling BCS superconductor, and here we generalize them to the unitary regime of ultracold Fermi gases. We find that while the residual resistivity below $T_c$ increases as one approaches the unitary limit, consistent with recent experiments, the paraconductivity exhibits non-monotonic behavior. Our results can be verified with the recently developed transport apparatus using mesoscopic channels.Comment: 8 pages and 4 figures including supplementary material

Evolution of Higgs mode in a Fermion Superfluid with Tunable Interactions

In this letter we present a coherent picture for the evolution of Higgs mode in both neutral and charged $s$-wave fermion superfluids, as the strength of attractive interaction between fermions increases from the BCS to the BEC regime. In the case of neutral fermionic superfluid, such as ultracold fermions, the Higgs mode is pushed to higher energy while at the same time, gradually loses its spectral weight as interaction strength increases toward the BEC regime, because the system is further tuned away from Lorentz invariance. On the other hand, when damping is taken into account, Higgs mode is significantly broadened due to coupling to phase mode in the whole BEC-BCS crossover. In the charged case of electron superconductor, the Anderson-Higgs mechanism gaps out the phase mode and suppresses the coupling between the Higgs and the phase modes, and consequently, stabilizes the Higgs mode.Comment: 5 figures, 9 pages, including supplementary materia

Wideband Optical Filters with Small Gap Coupled Subwavelength Metal Structures

In this letter, we show that the bandwidth of optical band-stop filters made of subwavelength metal structures can be significantly increased by the strong plasmonic near-field coupling through the corners of the periodic metal squares. The effect of small gap coupling on the spectral bandwidth is investigated by varying the gap size between the metal squares. An equivalent transmission line model is used to fit the transmission and reflection spectra of the metal filters. The transmission line model can characterize well the metal structures with the gap size larger than the near-field decay length. However, it fails to model the transmission and reflection spectra when the gap size reaches the decay range of the near-field in the small gaps

Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications

Developing an intelligent vehicle which can perform human-like actions requires the ability to learn basic driving skills from a large amount of naturalistic driving data. The algorithms will become efficient if we could decompose the complex driving tasks into motion primitives which represent the elementary compositions of driving skills. Therefore, the purpose of this paper is to segment unlabeled trajectory data into a library of motion primitives. By applying a probabilistic inference based on an iterative Expectation-Maximization algorithm, our method segments the collected trajectories while learning a set of motion primitives represented by the dynamic movement primitives. The proposed method utilizes the mutual dependencies between the segmentation and representation of motion primitives and the driving-specific based initial segmentation. By utilizing this mutual dependency and the initial condition, this paper presents how we can enhance the performance of both the segmentation and the motion primitive library establishment. We also evaluate the applicability of the primitive representation method to imitation learning and motion planning algorithms. The model is trained and validated by using the driving data collected from the Beijing Institute of Technology intelligent vehicle platform. The results show that the proposed approach can find the proper segmentation and establish the motion primitive library simultaneously