Enhanced visibility of graphene: effect of one-dimensional photonic crystal

We investigate theoretically the light reflectance of a graphene layer prepared on the top of one-dimensional Si/SiO2 photonic crystal (1DPC). It is shown that the visibility of the graphene layers is enhanced greatly when 1DPC is added, and the visibility can be tuned by changing the incident angle and light wavelengths. This phenomenon is caused by the absorption of the graphene layer and the enhanced reflectance of the 1DPC.Comment: 4 pages, 4 figures. published, ApplPhysLett_91_18190

Physical Primitive Decomposition

Objects are made of parts, each with distinct geometry, physics, functionality, and affordances. Developing such a distributed, physical, interpretable representation of objects will facilitate intelligent agents to better explore and interact with the world. In this paper, we study physical primitive decomposition---understanding an object through its components, each with physical and geometric attributes. As annotated data for object parts and physics are rare, we propose a novel formulation that learns physical primitives by explaining both an object's appearance and its behaviors in physical events. Our model performs well on block towers and tools in both synthetic and real scenarios; we also demonstrate that visual and physical observations often provide complementary signals. We further present ablation and behavioral studies to better understand our model and contrast it with human performance.Comment: ECCV 2018. Project page: http://ppd.csail.mit.edu

Optical fiber sensors for coal mine shaft integrity and equipment condition monitoring

Shaft is an important structure of mine. Deep mining increases mine pressure, induces shaft deformation and affects mine normal lifting. How to improve the inspection efficiency, reduce the maintenance cost and ensure the normal operation of the shaft is an important problem facing the mine. The paper introduces the optical fiber sensing technology to monitor the equipment status of the main shaft, puts forward the implementation scheme of the optical fiber monitoring of shaft deformation, and sets up a shaft equipment condition monitoring system based on the optical fiber sensing technology. It can realize equipment displacement monitoring, strain monitoring and vibration signal monitoring in the process of shaft operation. Comprehensive on-line monitoring of shaft running state can be realized, which opens up a new method for shaft deformation monitoring technology. Fiber optic sensing monitoring technology is of great significance to the safe operation of shaft

Bihamiltonian Cohomologies and Integrable Hierarchies I: A Special Case

We present some general results on properties of the bihamiltonian cohomologies associated to bihamiltonian structures of hydrodynamic type, and compute the third cohomology for the bihamiltonian structure of the dispersionless KdV hierarchy. The result of the computation enables us to prove the existence of bihamiltonian deformations of the dispersionless KdV hierarchy starting from any of its infinitesimal deformations.Comment: 43 pages. V2: the accepted version, to appear in Comm. Math. Phy

Magneto-optical characteristics of magnetic nanowire arrays in anodic aluminium oxide templates

Nanocomposite films consisting of regularly ordered iron nanowires embedded in anodic aluminum oxide templates have been fabricated and their magneto-optical properties studied by determining the four Stokes parameters of the transmitted laser beam (λ=670 nm), originally linearly polarized and at normal incidence to the film surfaces. The results of the nanowire arrays are found to be considerably different from that of bulk iron. While an increase in diameter of the nanowire leads to a substantial increase in the values of the Faraday rotation angles per unit length at a fixed value of the magnetic fields, they are substantially less than that of bulk iron, indicating that the effective media theory may not be directly applicable

Video Object Detection with an Aligned Spatial-Temporal Memory

We introduce Spatial-Temporal Memory Networks for video object detection. At its core, a novel Spatial-Temporal Memory module (STMM) serves as the recurrent computation unit to model long-term temporal appearance and motion dynamics. The STMM's design enables full integration of pretrained backbone CNN weights, which we find to be critical for accurate detection. Furthermore, in order to tackle object motion in videos, we propose a novel MatchTrans module to align the spatial-temporal memory from frame to frame. Our method produces state-of-the-art results on the benchmark ImageNet VID dataset, and our ablative studies clearly demonstrate the contribution of our different design choices. We release our code and models at http://fanyix.cs.ucdavis.edu/project/stmn/project.html