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For a homonuclear ion-atom system, we show that the exchange symmetry leads to special outcomes for ion transport that manifest themselves in ultracold experiments. We compute the two body charge hopping probabilities and rates, which are used to model charge hopping in the dynamics of an ultracold${}^{6/7}$Li⁺ion immersed within an ultracold gas of${}^{6/7}$Li atoms at micro-Kelvin temperatures. We show that the charge hopping and collisional diffusion compete, giving unique results leading to charge trapping in regions of high atomic density gradient. These investigations in ion-atom systems open a new approach to probe quantum phenomena in various systems with exchange symmetry.

 

A high efficiency, high brightness, and robust micro or sub-microscale red light emitting diode (LED) is an essential, yet missing, component of the emerging virtual reality and future ultrahigh resolution mobile displays. We report, for the first time, to our knowledge, the demonstration of an N-polar InGaN/GaN nanowire sub-microscale LED emitting in the red spectrum that can overcome the efficiency cliff of conventional red-emitting micro-LEDs. We show that the emission wavelengths of N-polar InGaN/GaN nanowires can be progressively shifted from yellow to orange and red, which is difficult to achieve for conventional InGaN quantum wells or Ga-polar nanowires. Significantly, the optical emission intensity can be enhanced by more than one order of magnitude by employing anin situannealing process of the InGaN active region, suggesting significantly reduced defect formation. LEDs with lateral dimensions as small as$\sim 0.75\mathrm{\mu m}$, consisting of approximately five nanowires, were fabricated and characterized, which are the smallest red-emitting LEDs ever reported, to our knowledge. A maximum external quantum efficiency$\sim 1.2\%$was measured, which is comparable to previously reported conventional quantum well micro-LEDs operating in this wavelength range, while our device sizes are nearly three to five orders of magnitude smaller in surface area.

 

Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration of recommenders in science gateways in order to spur research productivity,we present a novel “OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons). 

Neuroscientists are increasingly relying on high performance/throughput computing resources for experimentation on voluminous data, analysis and visualization at multiple neural levels. Though current science gateways provide access to computing resources, datasets and tools specific to the disciplines, neuroscientists require guided knowledge discovery at various levels to accomplish their research/education tasks. The guidance can help them to navigate them through relevant publications, tools, topic associations and cloud platform options as they accomplish important research and education activities. To address this need and to spur research productivity and rapid learning platform development, we present “OnTimeRecommend”, a novel recommender system that comprises of several integrated recommender modules through RESTful web services. We detail a neuroscience use case in a CyNeuro science gateway, and show how the OnTimeRecommend design can enable novice/expert user interfaces, as well as template-driven control of heterogeneous cloud resources.