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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. 

Online consumer reviews contain rich yet implicit information regarding consumers’ preferences for specific aspects of products/services. Extracting aspects from online consumer reviews has been recognized as a valuable step in performing fine-grained analytical tasks (e.g. aspect-based sentiment analysis). Extant approaches to aspect extraction are dominated by discrete models. Despite explosive research interests in continuous-space language models in recent years, these models have yet to be explored for the task of extracting product/service aspects from online consumer reviews. In addition, previous continuous-space models for information extraction have largely overlooked the role of semantic information embedded in texts. In this study, we propose an approach of aspect extraction that leverages semantic information from WordNet in conjunction of building continuous-space language models from review texts. The experiment results with online restaurant reviews demonstrate that the WordNet-guided continuous-space language models outperform both discrete models and continuous-space language models without incorporating the semantic information. The research findings have important implications for understanding consumer preferences and improving business performances.