243 research outputs found

    Quantum Hall States in Graphene from Strain-Induced Nonuniform Magnetic Fields

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    We examine strain-induced quantized Landau levels in graphene. Specifically, arc-bend strains are found to cause nonuniform pseudomagnetic fields. Using an effective Dirac model which describes the low-energy physics around the nodal points, we show that several of the key qualitative properties of graphene in a strain-induced pseudomagnetic field are different compared to the case of an externally applied physical magnetic field. We discuss how using different strain strengths allows us to spatially separate the two components of the pseudospinor on the different sublattices of graphene. These results are checked against a tight-binding calculation on the graphene honeycomb lattice, which is found to exhibit all the features described. Furthermore, we find that introducing a Hubbard repulsion on the mean-field level induces a measurable polarization difference between the A and the B sublattices, which provides an independent experimental test of the theory presented here.Comment: 9 pages, 8 figures. Updated to version that appears in PR

    CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

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    Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in terms of adapting to dynamic traffic environment and coordinating thousands of agents including vehicles and pedestrians. A key factor in the success of modern reinforcement learning relies on a good simulator to generate a large number of data samples for learning. The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios. This motivates us to create a new traffic simulator CityFlow with fundamentally optimized data structures and efficient algorithms. CityFlow can support flexible definitions for road network and traffic flow based on synthetic and real-world data. It also provides user-friendly interface for reinforcement learning. Most importantly, CityFlow is more than twenty times faster than SUMO and is capable of supporting city-wide traffic simulation with an interactive render for monitoring. Besides traffic signal control, CityFlow could serve as the base for other transportation studies and can create new possibilities to test machine learning methods in the intelligent transportation domain.Comment: WWW 2019 Demo Pape

    Phenotype and functional evaluation of ex vivo generated antigen-specific immune effector cells with potential for therapeutic applications

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    Ex vivo activation and expansion of lymphocytes for adoptive cell therapy has demonstrated great success. To improve safety and therapeutic efficacy, increased antigen specificity and reduced non-specific response of the ex vivo generated immune cells are necessary. Here, using a complete protein-spanning pool of pentadecapeptides of the latent membrane protein 2A (LMP2A) of Epstein-Barr virus (EBV), a weak viral antigen which is associated with EBV lymphoproliferative diseases, we investigated the phenotype and function of immune effector cells generated based on IFN-γ or CD137 activation marker selection and dendritic cell (DC) activation. These ex vivo prepared immune cells exhibited a donor- and antigen-dependent T cell response; the IFN-γ-selected immune cells displayed a donor-related CD4- or CD8-dominant T cell phenotype; however, the CD137-enriched cells showed an increased ratio of CD4 T cells. Importantly, the pentadecapeptide antigens accessed both class II and class I MHC antigen processing machineries and effectively activated EBV-specific CD4 and CD8 T cells. Phenotype and kinetic analyses revealed that the IFN-γ and the CD137 selections enriched more central memory T (Tcm) cells than did the DC-activation approach, and after expansion, the IFN-γ-selected effector cells showed the highest level of antigen-specificity and effector activities. While all three approaches generated immune cells with comparable antigen-specific activities, the IFN-γ selection followed by ex vivo expansion produced high quality and quantity of antigen-specific effector cells. Our studies presented the optimal approach for generating therapeutic immune cells with potential for emergency and routine clinical applications

    LiCROM: Linear-Subspace Continuous Reduced Order Modeling with Neural Fields

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    Linear reduced-order modeling (ROM) simplifies complex simulations by approximating the behavior of a system using a simplified kinematic representation. Typically, ROM is trained on input simulations created with a specific spatial discretization, and then serves to accelerate simulations with the same discretization. This discretization-dependence is restrictive. Becoming independent of a specific discretization would provide flexibility to mix and match mesh resolutions, connectivity, and type (tetrahedral, hexahedral) in training data; to accelerate simulations with novel discretizations unseen during training; and to accelerate adaptive simulations that temporally or parametrically change the discretization. We present a flexible, discretization-independent approach to reduced-order modeling. Like traditional ROM, we represent the configuration as a linear combination of displacement fields. Unlike traditional ROM, our displacement fields are continuous maps from every point on the reference domain to a corresponding displacement vector; these maps are represented as implicit neural fields. With linear continuous ROM (LiCROM), our training set can include multiple geometries undergoing multiple loading conditions, independent of their discretization. This opens the door to novel applications of reduced order modeling. We can now accelerate simulations that modify the geometry at runtime, for instance via cutting, hole punching, and even swapping the entire mesh. We can also accelerate simulations of geometries unseen during training. We demonstrate one-shot generalization, training on a single geometry and subsequently simulating various unseen geometries

    Advanced microscopy to elucidate cardiovascular injury and regeneration: 4D light-sheet imaging

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    The advent of 4-dimensional (4D) light-sheet fluorescence microscopy (LSFM) has provided an entry point for rapid image acquisition to uncover real-time cardiovascular structure and function with high axial resolution and minimal photo-bleaching/-toxicity. We hereby review the fundamental principles of our LSFM system to investigate cardiovascular morphogenesis and regeneration after injury. LSFM enables us to reveal the micro-circulation of blood cells in the zebrafish embryo and assess cardiac ventricular remodeling in response to chemotherapy-induced injury using an automated segmentation approach. Next, we review two distinct mechanisms underlying zebrafish vascular regeneration following tail amputation. We elucidate the role of endothelial Notch signaling to restore vascular regeneration after exposure to the redox active ultrafine particles (UFP) in air pollutants. By manipulating the blood viscosity and subsequently, endothelial wall shear stress, we demonstrate the mechanism whereby hemodynamic shear forces impart both mechanical and metabolic effects to modulate vascular regeneration. Overall, the implementation of 4D LSFM allows for the elucidation of mechanisms governing cardiovascular injury and regeneration with high spatiotemporal resolution

    Advanced microscopy to elucidate cardiovascular injury and regeneration: 4D light-sheet imaging

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    The advent of 4-dimensional (4D) light-sheet fluorescence microscopy (LSFM) has provided an entry point for rapid image acquisition to uncover real-time cardiovascular structure and function with high axial resolution and minimal photo-bleaching/-toxicity. We hereby review the fundamental principles of our LSFM system to investigate cardiovascular morphogenesis and regeneration after injury. LSFM enables us to reveal the micro-circulation of blood cells in the zebrafish embryo and assess cardiac ventricular remodeling in response to chemotherapy-induced injury using an automated segmentation approach. Next, we review two distinct mechanisms underlying zebrafish vascular regeneration following tail amputation. We elucidate the role of endothelial Notch signaling to restore vascular regeneration after exposure to the redox active ultrafine particles (UFP) in air pollutants. By manipulating the blood viscosity and subsequently, endothelial wall shear stress, we demonstrate the mechanism whereby hemodynamic shear forces impart both mechanical and metabolic effects to modulate vascular regeneration. Overall, the implementation of 4D LSFM allows for the elucidation of mechanisms governing cardiovascular injury and regeneration with high spatiotemporal resolution
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