Scanning tunneling microscopy and spectroscopy of nanoscale twisted bilayer graphene

Nanoscale twisted bilayer graphene (TBG) is quite instable and will change its structure to Bernal (or AB-stacking) bilayer with a much lower energy. Therefore, the lack of nanoscale TBG makes its electronic properties not accessible in experiment up to now. In this work, a special confined TBG is obtained in the overlaid area of two continuous misoriented graphene sheets. The width of the confined region of the TBG changes gradually from about 22 nm to 0 nm. By using scanning tunnelling microscopy, we studied carefully the structure and the electronic properties of the nanoscale TBG. Our results indicate that the low-energy electronic properties, including twist-induced van Hove singularities (VHSs) and spatial modulation of local density-of-state, are strongly affected by the translational symmetry breaking of the nanoscale TBG. Whereas, the electronic properties above the energy of the VHSs are almost not influenced by the quantum confinement even when the width of the TBG is reduced to only a single moire spot.Comment: 4 Figure

Human motion data refinement unitizing structural sparsity and spatial-temporal information

Human motion capture techniques (MOCAP) are widely applied in many areas such as computer vision, computer animation, digital effect and virtual reality. Even with professional MOCAP system, the acquired motion data still always contains noise and outliers, which highlights the need for the essential motion refinement methods. In recent years, many approaches for motion refinement have been developed, including signal processing based methods, sparse coding based methods and low-rank matrix completion based methods. However, motion refinement is still a challenging task due to the complexity and diversity of human motion. In this paper, we propose a data-driven-based human motion refinement approach by exploiting the structural sparsity and spatio-temporal information embedded in motion data. First of all, a human partial model is applied to replace the entire pose model for a better feature representation to exploit the abundant local body posture. Then, a dictionary learning which is for special task of motion refinement is designed and applied in parallel. Meanwhile, the objective function is derived by taking the statistical and locality property of motion data into account. Compared with several state-of-art motion refine methods, the experimental result demonstrates that our approach outperforms the competitors

Preparation of graphene film reinforced CoCrFeNiMn high-entropy alloy matrix composites with strength-plasticity synergy via flake powder metallurgy method

Inspired by the design principle of pearl structure, a bottom-up flake powder self-assembly arrangement strategy, flake powder metallurgy, is used to prepare graphene films (GFs) reinforced CoCrFeNiMn high-entropy alloy (HEA) matrix composites with a pearl laminated structure. Flaky HEA powder was prepared by ball milling method and homogeneously mixed with Ni plated GFs. Vacuum hot-press sintering (VHPS) technique was carried out to solidify the mixed powders to obtain composites with uniform distribution of GFs(Ni) and flaky HEA. The results show that the bottom-up preparation strategy can effectively fabricate bionic laminated HEA matrix composites, and the composites have a distinct pearly laminated structure. The tensile strength of the composites with 5 vol% GFs(Ni) content reached 834.04 MPa, and the elongation reached 26.58 %. The compressive strength in parallel and perpendicular laminar directions reached 2069.66 MPa and 2418.45 MPa at 50 % strain, respectively. The laminated GFs(Ni)/HEA matrix composites possessed excellent strength and maintained good plasticity. In this study, the strengthening and toughening mechanism of the laminated GFs(Ni)/HEA matrix composites is discussed in detail, and the results show that the laminated structure and GFs(Ni) are favorable for the hardening and strengthening of the HEA matrix

Venice: Exploring Server Architectures for Effective Resource Sharing

Consolidated server racks are quickly becoming the backbone of IT infrastructure for science, engineering, and business, alike. These servers are still largely built and organized as when they were distributed, individual entities. Given that many fields increasingly rely on analytics of huge datasets, it makes sense to support flexible resource utilization across servers to improve cost-effectiveness and performance. We introduce Venice, a family of data-center server architectures that builds a strong communication substrate as a first-class resource for server chips. Venice provides a diverse set of resource-joining mechanisms that enables user programs to efficiently leverage non-local resources. To better understand the implications of design decisions about system support for resource sharing we have constructed a hardware prototype that allows us to more accurately measure end-to-end performance of at-scale applications and to explore tradeoffs among performance, power, and resource-sharing transparency. We present results from our initial studies analyzing these tradeoffs when sharing memory, accelerators, or NICs. We find that it is particularly important to reduce or hide latency, that data-sharing access patterns should match the features of the communication channels employed, and that inter-channel collaboration can be exploited for better performance

Investigation of bio-aerosol dispersion in a tunnel-ventilated poultry house

Bio-aerosol concentrations in poultry houses must be controlled to provide adequate air quality for both birds and workers. High concentrations of airborne bio-aerosols would affect the environmental sustainability of the production and create environmental hazards to the surroundings via the ventilation systems. Previous studies demonstrate that several factors including the age of the birds, the housing configuration, the humidity and temperature would strongly affect the indoor concentration of bio-aerosols. However, limited studies are performed in the literature to investigate the bio-aerosol dispersion pattern inside poultry buildings. In order to fill a gap of the understanding of the bio-aerosol dispersion behavior, experimental measurements of the indoor bio-aerosol distribution are performed in a tunnel-ventilated poultry house in this paper. Meanwhile a three-dimensional computational fluid dynamics (CFD) model is built and validated to further investigate the effect of flow pattern, turbulence and vortex on the dispersion and deposition of the bio-aerosols. Furthermore, bio-aerosols with various diameters are also examined in the CFD model. It is found that higher concentrations of bio-aerosols are detected at the rear part of the house and strong turbulent flow resulting from the ventilation inlets enhances the diffusion and dispersion of bio-aerosols. Local vortex or disturbed flow is responsible for higher local concentration due to the re-suspension of settled bio-aerosols, which suggests that careful attentions should be paid to these locations during cleaning and disinfection. Results from present study contribute to the optimization of design and operation of the poultry houses from the standing point of reducing airborne bio-aerosol concentrations

Single-image mesh reconstruction and pose estimation via generative normal map

We present a unified learning framework for recovering both 3D mesh and camera pose of the object from a single image. Our approach learns to recover outer shape and surface geometric details of the mesh without relying on 3D supervision. We adopt multi-view normal maps as the 2D supervision so that the silhouette and geometric details information can be transferred to neural network. A normal mismatch based objective function is introduced to train the network, and the camera pose is parameterized into the objective, it integrates pose estimation with the mesh reconstruction in a same optimization procedure. We demonstrate the abilities of the proposed approach in generating 3D mesh and estimating camera pose with qualitative and quantitative experiments