A generalized asymptotic extraction solution for antennas in multilayered spherical media

An efficient model is developed to accelerate the convergence of the dyadic Green's function's (DGF) infinite summation when the source and observation points are placed in different layers of a dielectric sphere, thereby expediting computational analysis. The proposed procedure is based on asymptotic extraction principles in which the quasi-static images are extracted from the spectral domain DGF. The effectiveness of the approach is demonstrated in a method of moment model where a microstrip antenna as well as a conformal dipole array have been studied

Neural Face Editing with Intrinsic Image Disentangling

Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive. In this paper, we propose an end-to-end generative adversarial network that infers a face-specific disentangled representation of intrinsic face properties, including shape (i.e. normals), albedo, and lighting, and an alpha matte. We show that this network can be trained on "in-the-wild" images by incorporating an in-network physically-based image formation module and appropriate loss functions. Our disentangling latent representation allows for semantically relevant edits, where one aspect of facial appearance can be manipulated while keeping orthogonal properties fixed, and we demonstrate its use for a number of facial editing applications.Comment: CVPR 2017 ora

Analysis of optical near-field energy transfer by stochastic model unifying architectural dependencies

We theoretically and experimentally demonstrate energy transfer mediated by optical near-field interactions in a multi-layer InAs quantum dot (QD) structure composed of a single layer of larger dots and N layers of smaller ones. We construct a stochastic model in which optical near-field interactions that follow a Yukawa potential, QD size fluctuations, and temperature-dependent energy level broadening are unified, enabling us to examine device-architecture-dependent energy transfer efficiencies. The model results are consistent with the experiments. This study provides an insight into optical energy transfer involving inherent disorders in materials and paves the way to systematic design principles of nanophotonic devices that will allow optimized performance and the realization of designated functions

Coding of visual object features and feature conjunctions in the human brain

Peer reviewedPublisher PD

Structure of polyamidoamide dendrimers up to limiting generations : a mesoscale description

The polyamidoamide (PAMAM) class of dendrimers was one of the first dendrimers synthesized by Tomalia and co-workers at Dow. Since its discovery the PAMAMs have stimulated many discussions on the structure and dynamics of such hyperbranched polymers. Many questions remain open because the huge conformation disorder combined with very similar local symmetries have made it difficult to characterize experimentally at the atomistic level the structure and dynamics of PAMAM dendrimers. The higher generation dendrimers have also been difficult to characterize computationally because of the large size (294852 atoms for generation 11) and the huge number of conformations. To help provide a practical means of atomistic computational studies, we have developed an atomistically informed coarse-grained description for the PAMAM dendrimer. We find that a two-bead per monomer representation retains the accuracy of atomistic simulations for predicting size and conformational complexity, while reducing the degrees of freedom by tenfold. This mesoscale description has allowed us to study the structural properties of PAMAM dendrimer up to generation 11 for time scale of up to several nanoseconds. The gross properties such as the radius of gyration compare very well with those from full atomistic simulation and with available small angle x-ray experiment and small angle neutron scattering data. The radial monomer density shows very similar behavior with those obtained from the fully atomistic simulation. Our approach to deriving the coarse-grain model is general and straightforward to apply to other classes of dendrimers

Perspects in astrophysical databases

Astrophysics has become a domain extremely rich of scientific data. Data mining tools are needed for information extraction from such large datasets. This asks for an approach to data management emphasizing the efficiency and simplicity of data access; efficiency is obtained using multidimensional access methods and simplicity is achieved by properly handling metadata. Moreover, clustering and classification techniques on large datasets pose additional requirements in terms of computation and memory scalability and interpretability of results. In this study we review some possible solutions