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In 1996, Hatano and Nelson proposed a non-Hermitian lattice model containing an imaginary Peierls phase [], which subsequent analyses revealed to be an instance of a new class of topological systems. Here, we experimentally realize a continuum analog to this model containing an imaginary gauge potential using a homogeneous spin-orbit coupled Bose-Einstein condensate (BEC). Non-Hermiticity is introduced by adding tunable spin-dependent loss via microwave coupling to a subspace with spontaneous emission. We demonstrate that the resulting Heisenberg equations of motion for position and momentum depend explicitly on the system’s phase-space distribution. First, we observe collective nonreciprocal transport in real space, with a “self-acceleration” that decreases with the BEC’s spatial extent, consistent with non-Hermitian Gross-Pitaevskii simulations. We then examine localized edge states: the relatively strong interactions in our BEC suppress the formation of topological edge states, yielding instead highly excited states localized by an interplay between self-acceleration and wave function spreading. Finally, we confirm that our non-Hermitian description remains valid at all times by comparing it to a multilevel master equation treatment. 

We experimentally and theoretically investigate the anisotropic speed of sound of an atomic superfluid (SF) Bose-Einstein condensate in a 1D optical lattice. Because the speed of sound derives from the SF density, this implies that the SF density is itself anisotropic. We find that the speed of sound is decreased by the optical lattice, and the SF density is concomitantly reduced. This reduction is accompanied by the appearance of a zero entropy normal fluid in the purely Bose condensed phase. The reduction in SF density—first predicted [A. J. Leggett, Phys. Rev. Lett. 25, 1543 (1970).] in the context of supersolidity—results from the coexistence of superfluidity and density modulations, but is agnostic about the origin of the modulations. We additionally measure the moment of inertia of the system in a scissors mode experiment, demonstrating the existence of rotational flow. As such we shed light on some supersolid properties using imposed, rather than spontaneously formed, density order. 

The precise control of direct current (dc) magnetic fields is crucial in a wide range of experimental platforms, from ultracold quantum gases and nuclear magnetic resonance to precision measurements. In each of these cases, the Zeeman effect causes quantum states to shift in energy as a function of the magnetic field. The development of low-noise current sources is essential because electromagnets are the preferred tool to dynamically control the magnetic field. Here, we describe an ultra-low noise bipolar current source using pairs of complementary n- and p-channel metal–oxide–semiconductor field-effect transistors controlled by zero-drift operational amplifiers. Our source has a 90 kHz inherent bandwidth and provides current from −20 to 20 A with noise (0.1 Hz to 100 kHz) of 140 µA at ±20 A. 

Automated or semi-automated pavement condition data collection is replacing manual data collection in many state and local highway agencies due to its advantages of reducing labor, time, and cost. However, the practical experience of highway agencies indicates that there are still data quality issues with the pavement condition data collected using existing image and sensor-based data collection technologies. This study aims to investigate the implementation experiences and issues of automated or semi-automated pavement condition surveys. An online questionnaire survey was conducted, along with scheduled virtual/phone interviews to gather information from government, industry, and academia about the state of the practice and state of the art. Open questions about the data quality and quality control & quality assurance (QC/QA) were used to receive first-hand inputs from highway agencies and pavement experts. The study has compiled the following observations: (1) Highway agencies urgently need a uniform data collection protocol for automated data collection; (2) the current QA requires too much human intervention; (3) cost ($100–$200 per mile) is a significant burden for state and local agencies; (4) the main issues regarding data quality are data inconsistencies and discrepancies; (5) agencies expect a greater accuracy once the image processing algorithms are improved using artificial intelligence technologies; and (6) existing automated data collection methods are not available for project-level data collection.