Floquet engineering of correlated tunneling in the Bose-Hubbard model with ultracold atoms

We report on the experimental implementation of tunable occupation-dependent tunneling in a Bose-Hubbard system of ultracold atoms via time-periodic modulation of the on-site interaction energy. The tunneling rate is inferred from a time-resolved measurement of the lattice site occupation after a quantum quench. We demonstrate coherent control of the tunneling dynamics in the correlated many-body system, including full suppression of tunneling as predicted within the framework of Floquet theory. We find that the tunneling rate explicitly depends on the atom number difference in neighboring lattice sites. Our results may open up ways to realize artificial gauge fields that feature density dependence with ultracold atoms.Comment: 8 pages, 9 figure

Classification of lidar measurements using supervised and unsupervised machine learning methods

While it is relatively straightforward to automate the processing of lidar signals, it is more difficult to choose periods of good measurements to process. Groups use various ad hoc procedures involving either very simple (e.g. signal-to-noise ratio) or more complex procedures (e.g. Wing et al. 2018) to perform a task that is easy to train humans to perform but is time-consuming. Here, we use machine learning techniques to train the machine to sort the measurements before processing. The presented method is generic and can be applied to most lidars. We test the techniques using measurements from the Purple Crow Lidar (PCL) system located in London, Canada. The PCL has over 200 000 raw profiles in Rayleigh and Raman channels available for classification. We classify raw (level-0) lidar measurements as clear sky profiles with strong lidar returns, bad profiles, and profiles which are significantly influenced by clouds or aerosol loads. We examined different supervised machine learning algorithms including the random forest, the support vector machine, and the gradient boosting trees, all of which can successfully classify profiles. The algorithms were trained using about 1500 profiles for each PCL channel, selected randomly from different nights of measurements in different years. The success rate of identification for all the channels is above 95 %. We also used the t-SNE) method, which is an unsupervised algorithm, to cluster our lidar profiles. Because the t-SNE is a data-driven method in which no labelling of the training set is needed, it is an attractive algorithm to find anomalies in lidar profiles. The method has been tested on several nights of measurements from the PCL measurements. The t-SNE can successfully cluster the PCL data profiles into meaningful categories. To demonstrate the use of the technique, we have used the algorithm to identify stratospheric aerosol layers due to wildfires

Observation of many-body long-range tunneling after a quantum quench

Quantum tunneling constitutes one of the most fundamental processes in nature. We observe resonantly-enhanced long-range quantum tunneling in one-dimensional Mott-insulating Hubbard chains that are suddenly quenched into a tilted configuration. Higher-order many-body tunneling processes occur over up to five lattice sites when the tilt per site is tuned to integer fractions of the Mott gap. Starting from a one-atom-per-site Mott state the response of the many-body quantum system is observed as resonances in the number of doubly occupied sites and in the emerging coherence in momentum space. Second- and third-order tunneling shows up in the transient response after the tilt, from which we extract the characteristic scaling in accordance with perturbation theory and numerical simulations.Comment: 22 pages, 7 figure

Preparation and spectroscopy of a metastable Mott insulator state with attractive interactions

We prepare and study a metastable attractive Mott insulator state formed with bosonic atoms in a three-dimensional optical lattice. Starting from a Mott insulator with Cs atoms at weak repulsive interactions, we use a magnetic Feshbach resonance to tune the interactions to large attractive values and produce a metastable state pinned by attractive interactions with a lifetime on the order of 10 seconds. We probe the (de-)excitation spectrum via lattice modulation spectroscopy, measuring the interaction dependence of two- and three-body bound state energies. As a result of increased on-site three-body loss we observe resonance broadening and suppression of tunneling processes that produce three-body occupation.Comment: 7 pages, 6 figure

Dynamics of many-body localization in the presence of particle loss

At long times, residual couplings to the environment become relevant even in the most isolated experiments, a crucial difficulty for the study of fundamental aspects of many-body dynamics. A particular example is many-body localization in a cold-atom setting, where incoherent photon scattering introduces both dephasing and particle loss. Whereas dephasing has been studied in detail and is known to destroy localization already on the level of non-interacting particles, the effect of particle loss is less well understood. A difficulty arises due to the 'non-local' nature of the loss process, complicating standard numerical tools using matrix product decomposition. Utilizing symmetries of the Lindbladian dynamics, we investigate the particle loss on both the dynamics of observables, as well as the structure of the density matrix and the individual states. We find that particle loss in the presence of interactions leads to dissipation and a strong suppression of the (operator space) entanglement entropy. Our approach allows for the study of the interplay of dephasing and loss for pure and mixed initial states to long times, which is important for future experiments using controlled coupling of the environment

Color pattern complexity in dwarf minke whales (Balaenoptera acutorostrata) of the northern Great Barrier Reef of Australia

Complex color patterning is a characteristic feature of dwarf minke whales (DMWs; Balaenoptera acutorostrata) which has been used to photographically identify (photo-ID) individuals and to research an aggregation on Australia's Great Barrier Reef (GBR). DMW color patterns have been described and applied in various studies, but a detailed and systematic analysis of their complexity is yet to be performed. Here, we applied a novel categorization tool to assess the variation, asymmetry, and association of several DMW color pattern elements, subelements, and their character states. Proportions, hierarchical clustering, and multiple correspondence analysis revealed a high level of asymmetric color pattern variation, with white markings dominant and associated on the right of the body. Our results will improve the citizen science driven photo-ID of this little-known cetacean as labor-intensive manual methods transition to more efficient automated approaches. Such advancement will be challenging, yet beneficial for broader research into the poorly understood areas of DMW life history, evolution, genetics, social structure, and feeding. This could also potentially allow investigation into the functional significance of their color patterns

Structure and function of gene regulatory networks associated with worker sterility in honeybees.

A characteristic of eusocial bees is a reproductive division of labor in which one or a few queens monopolize reproduction, while her worker daughters take on reproductively altruistic roles within the colony. The evolution of worker reproductive altruism involves indirect selection for the coordinated expression of genes that regulate personal reproduction, but evidence for this type of selection remains elusive. In this study, we tested whether genes coexpressed under queen-induced worker sterility show evidence of adaptive organization within a model brain transcriptional regulatory network (TRN). If so, this structured pattern would imply that indirect selection on nonreproductive workers has influenced the functional organization of genes within the network, specifically to regulate the expression of sterility. We found that literature-curated sets of candidate genes for sterility, ranging in size from 18 to 267, show strong evidence of clustering within the three-dimensional space of the TRN. This finding suggests that our candidate sets of genes for sterility form functional modules within the living bee brain\u27s TRN. Moreover, these same gene sets colocate to a single, albeit large, region of the TRN\u27s topology. This spatially organized and convergent pattern contrasts with a null expectation for functionally unrelated genes to be haphazardly distributed throughout the network. Our meta-genomic analysis therefore provides first evidence for a truly social transcriptome that may regulate the conditional expression of honeybee worker sterility

“AI’s gonna have an impact on everything in society, so it has to have an impact on public health”: a fundamental qualitative descriptive study of the implications of artificial intelligence for public health

Background: Our objective was to determine the impacts of artificial intelligence (AI) on public health practice. Methods: We used a fundamental qualitative descriptive study design, enrolling 15 experts in public health and AI from June 2018 until July 2019 who worked in North America and Asia. We conducted in-depth semi-structured interviews, iteratively coded the resulting transcripts, and analyzed the results thematically. Results: We developed 137 codes, from which nine themes emerged. The themes included opportunities such as leveraging big data and improving interventions; barriers to adoption such as confusion regarding AI’s applicability, limited capacity, and poor data quality; and risks such as propagation of bias, exacerbation of inequity, hype, and poor regulation. Conclusions: Experts are cautiously optimistic about AI’s impacts on public health practice, particularly for improving disease surveillance. However, they perceived substantial barriers, such as a lack of available expertise, and risks, including inadequate regulation. Therefore, investment and research into AI for public health practice would likely be beneficial. However, increased access to high-quality data, research and education regarding the limitations of AI, and development of rigorous regulation are necessary to realize these benefits