Fabrication, modeling, and characterization of form-birefringent nanostructures

A 490-nm-deep nanostructure with a period of 200 nm was fabricated in a GaAs substrate by use of electron-beam lithography and dry-etching techniques. The form birefringence of this microstructure was studied numerically with rigorous coupled-wave analysis and compared with experimental measurements at a wavelength of 920 nm. The numerically predicted phase retardation of 163.3 degrees was found to be in close agreement with the experimentally measured result of 162.5 degrees, thereby verifying the validity of our numerical modeling. The fabricated microstructures show extremely large artificial anisotropy compared with that available in naturally birefringent materials and are useful for numerous polarization optics applications

Separating Reflection and Transmission Images in the Wild

The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms. State-of-the-art methods can remove reflections on synthetic data and in controlled scenarios. However, they are based on strong assumptions and do not generalize well to real-world images. Contrary to a common misconception, real-world images are challenging even when polarization information is used. We present a deep learning approach to separate the reflected and the transmitted components of the recorded irradiance, which explicitly uses the polarization properties of light. To train it, we introduce an accurate synthetic data generation pipeline, which simulates realistic reflections, including those generated by curved and non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.Comment: accepted at ECCV 201

PSTM / NSOM modeling by 2-D quadridirectional eigenmode expansion

A two-dimensional (2-D) model for photon-scanning tunneling microscopy (PSTM) of integrated optical devices is evaluated. The simulations refer to a setup where the optical field in the vicinity of the sample is probed by detecting the optical power that is transferred via evanescent or radiative coupling to the tapered tip of an optical fiber close to the sample surface. Scanning the tip across the surface leads to a map of the local optical field in the sample. As a step beyond the mere analysis of the sample device, simulations are considered that include the sample as well as the probe tip. An efficient semianalytical simulation technique based on quadridirectional eigenmode expansions is applied. Results for a series of configurations, where slab waveguides with different types of corrugations serve as samples, allow assessment of the relation between the PSTM signal and the local field distribution in the sample. A reasonable qualitative agreement was observed between these computations and a previous experimental PSTM investigation of a waveguide Bragg grating

Models of magnetized neutron star atmospheres: thin atmospheres and partially ionized hydrogen atmospheres with vacuum polarization

Observed X-ray spectra of some isolated magnetized neutron stars display absorption features, sometimes interpreted as ion cyclotron lines. Modeling the observed spectra is necessary to check this hypothesis and to evaluate neutron star parameters.We develop a computer code for modeling magnetized neutron star atmospheres in a wide range of magnetic fields (10^{12} - 10^{15} G) and effective temperatures (3 \times 10^5 - 10^7 K). Using this code, we study the possibilities to explain the soft X-ray spectra of isolated neutron stars by different atmosphere models. The atmosphere is assumed to consist either of fully ionized electron-ion plasmas or of partially ionized hydrogen. Vacuum resonance and partial mode conversion are taken into account. Any inclination of the magnetic field relative to the stellar surface is allowed. We use modern opacities of fully or partially ionized plasmas in strong magnetic fields and solve the coupled radiative transfer equations for the normal electromagnetic modes in the plasma. Spectra of outgoing radiation are calculated for various atmosphere models: fully ionized semi-infinite atmosphere, thin atmosphere, partially ionized hydrogen atmosphere, or novel "sandwich" atmosphere (thin atmosphere with a hydrogen layer above a helium layer. Possibilities of applications of these results are discussed. In particular, the outgoing spectrum using the "sandwich" model is constructed. Thin partially ionized hydrogen atmospheres with vacuum polarization are shown to be able to improve the fit to the observed spectrum of the nearby isolated neutron star RBS 1223 (RX J1308.8+2127).Comment: Accepted for publications in Astronomy and Astrophysics, 9 pages, 12 figure

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. Surrogate models are widely used in the field of simulation sciences to efficiently analyze computationally expensive simulation models. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic