Multi-dimensional radiative transfer to analyze Hanle effect in Ca {\sc ii} K line at 3933 \AA\,

Radiative transfer (RT) studies of the linearly polarized spectrum of the Sun (the second solar spectrum) have generally focused on the line formation, with an aim to understand the vertical structure of the solar atmosphere using one-dimensional (1D) model atmospheres. Modeling spatial structuring in the observations of the linearly polarized line profiles requires the solution of multi-dimensional (multi-D) polarized RT equation and a model solar atmosphere obtained by magneto-hydrodynamical (MHD) simulations of the solar atmosphere. Our aim in this paper is to analyze the chromospheric resonance line Ca {\sc ii} K at 3933 \AA\ using multi-D polarized RT with Hanle effect and partial frequency redistribution in line scattering. We use an atmosphere which is constructed by a two-dimensional snapshot of the three-dimensional MHD simulations of the solar photosphere, combined with columns of an 1D atmosphere in the chromosphere. This paper represents the first application of polarized multi-D RT to explore the chromospheric lines using multi-D MHD atmospheres, with PRD as the line scattering mechanism. We find that the horizontal inhomogeneities caused by MHD in the lower layers of the atmosphere are responsible for strong spatial inhomogeneities in the wings of the linear polarization profiles, while the use of horizontally homogeneous chromosphere (FALC) produces spatially homogeneous linear polarization in the line core

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance. Model-based algorithms, in principle, can provide for much more efficient learning, but have proven difficult to extend to expressive, high-capacity models such as deep neural networks. In this work, we demonstrate that medium-sized neural network models can in fact be combined with model predictive control (MPC) to achieve excellent sample complexity in a model-based reinforcement learning algorithm, producing stable and plausible gaits to accomplish various complex locomotion tasks. We also propose using deep neural network dynamics models to initialize a model-free learner, in order to combine the sample efficiency of model-based approaches with the high task-specific performance of model-free methods. We empirically demonstrate on MuJoCo locomotion tasks that our pure model-based approach trained on just random action data can follow arbitrary trajectories with excellent sample efficiency, and that our hybrid algorithm can accelerate model-free learning on high-speed benchmark tasks, achieving sample efficiency gains of 3-5x on swimmer, cheetah, hopper, and ant agents. Videos can be found at https://sites.google.com/view/mbm

Polarized Line Formation in Multi-Dimensional Media.III. Hanle Effect with Partial Frequency Redistribution

In the previous two papers, namely, \citet{anuknn11} and \citet{anuetal11} we solved the polarized radiative transfer (RT) equation in multi-dimensional (multi-D) geometries, with partial frequency redistribution (PRD) as the scattering mechanism. We assumed Rayleigh scattering as the only source of linear polarization ($Q/I, U/I$) in both these papers. In this paper we extend these previous works to include the effect of weak oriented magnetic fields (Hanle effect) on line scattering. We generalize the technique of Stokes vector decomposition in terms of the irreducible spherical tensors $\mathcal{T}^K_Q$, developed in \citet{anuknn11}, to the case of RT with Hanle effect. A fast iterative method of solution (based on the Stabilized Preconditioned Bi-Conjugate-Gradient technique), developed in \citet{anuetal11}, is now generalized to the case of RT in magnetized three-dimensional media. We use the efficient short-characteristics formal solution method for multi-D media, generalized appropriately to the present context. The main results of this paper are the following: (1) A comparison of emergent $(I, Q/I, U/I)$ profiles formed in one-dimensional (1D) media, with the corresponding emergent, spatially averaged profiles formed in multi-D media, shows that in the spatially resolved structures, the assumption of 1D may lead to large errors in linear polarization, especially in the line wings. (2) The multi-D RT in semi-infinite non-magnetic media causes a strong spatial variation of the emergent $(Q/I, U/I)$ profiles, which is more pronounced in the line wings. (3) The presence of a weak magnetic field modifies the spatial variation of the emergent $(Q/I, U/I)$ profiles in the line core, by producing significant changes in their magnitudes.Comment: 31 pages, 14 figures, Submitted to ApJ, Under revie

Effect of cross-redistribution on the resonance scattering polarization of O {\sc i} line at 1302 \AA\,

Oxygen is the most abundant element on the Sun after Hydrogen and Helium. The intensity spectrum of resonance lines of neutral Oxygen namely O {\sc i} (1302, 1305 and 1306 \AA\,) has been studied in the literature for chromospheric diagnostics. In this paper we study the resonance scattering polarization in the O {\sc i} line at 1302 \AA\, using two-dimensional radiative transfer in a composite atmosphere constructed using a two-dimensional magneto-hydrodynamical snapshot in the photosphere and columns of the one-dimensional FALC atmosphere in the chromosphere. The methods developed by us recently in a series of papers to solve multi-dimensional polarized radiative transfer have been incorporated in our new code POLY2D which we use for our analysis. We find that multi-dimensional radiative transfer including XRD effects is important in reproducing the amplitude and shape of scattering polarization signals of the O {\sc i} line at 1302 \AA\,

Combining depth and intensity images to produce enhanced object detection for use in a robotic colony

Robotic colonies that can communicate with each other and interact with their ambient environments can be utilized for a wide range of research and industrial applications. However amongst the problems that these colonies face is that of the isolating objects within an environment. Robotic colonies that can isolate objects within the environment can not only map that environment in de-tail, but interact with that ambient space. Many object recognition techniques ex-ist, however these are often complex and computationally expensive, leading to overly complex implementations. In this paper a simple model is proposed to isolate objects, these can then be recognize and tagged. The model will be using 2D and 3D perspectives of the perceptual data to produce a probability map of the outline of an object, therefore addressing the defects that exist with 2D and 3D image techniques. Some of the defects that will be addressed are; low level illumination and objects at similar depths. These issues may not be completely solved, however, the model provided will provide results confident enough for use in a robotic colony