Deep Bilateral Learning for Real-Time Image Enhancement

Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new slicing node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.Comment: 12 pages, 14 figures, Siggraph 201

Ab initio variational approach for evaluating lattice thermal conductivity

We present a first-principles theoretical approach for evaluating the lattice thermal conductivity based on the exact solution of the Boltzmann transport equation. We use the variational principle and the conjugate gradient scheme, which provide us with an algorithm faster than the one previously used in literature and able to always converge to the exact solution. Three-phonon normal and umklapp collision, isotope scattering and border effects are rigorously treated in the calculation. Good agreement with experimental data for diamond is found. Moreover we show that by growing more enriched diamond samples it is possible to achieve values of thermal conductivity up to three times larger than the commonly observed in isotopically enriched diamond samples with 99.93% C12 and 0.07 C13

Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: method and comparison with synthesis inversion

A four-dimensional variational (4D-Var) data assimilation system for inverse modelling of atmospheric methane emissions is presented. The system is based on the TM5 atmospheric transport model. It can be used for assimilating large volumes of measurements, in particular satellite observations and quasi-continuous in-situ observations, and at the same time it enables the optimization of a large number of model parameters, specifically grid-scale emission rates. Furthermore, the variational method allows to estimate uncertainties in posterior emissions. Here, the system is applied to optimize monthly methane emissions over a 1-year time window on the basis of surface observations from the NOAA-ESRL network. The results are rigorously compared with an analogous inversion by Bergamaschi et al. (2007), which was based on the traditional synthesis approach. The posterior emissions as well as their uncertainties obtained in both inversions show a high degree of consistency. At the same time we illustrate the advantage of 4D-Var in reducing aggregation errors by optimizing emissions at the grid scale of the transport model. The full potential of the assimilation system is exploited in Meirink et al. (2008), who use satellite observations of column-averaged methane mixing ratios to optimize emissions at high spatial resolution, taking advantage of the zooming capability of the TM5 model

Improved Potential Energy Surface of Ozone Constructed Using the Fitting by Permutationally Invariant Polynomial Function

New global potential energy surface for the ground electronic state of ozone is constructed at the complete basis set level of the multireference configuration interaction theory. A method of fitting the data points by analytical permutationally invariant polynomial function is adopted. A small set of 500 points is preoptimized using the old surface of ozone. In this procedure the positions of points in the configuration space are chosen such that the RMS deviation of the fit is minimized. New ab initio calculations are carried out at these points and are used to build new surface. Additional points are added to the vicinity of the minimum energy path in order to improve accuracy of the fit, particularly in the region where the surface of ozone exhibits a shallow van der Waals well. New surface can be used to study formation of ozone at thermal energies and its spectroscopy near the dissociation threshold

The M87 Black Hole Mass From Gas-Dynamical Models Of Space Telescope Imaging Spectrograph Observations

The supermassive black hole of M87 is one of the most massive black holes known and has been the subject of several stellar and gas-dynamical mass measurements; however, the most recent revision to the stellar-dynamical black hole mass measurement is a factor of about two larger than the previous gas-dynamical determinations. Here, we apply comprehensive gas-dynamical models that include the propagation of emission-line profiles through the telescope and spectrograph optics to new Space Telescope Imaging Spectrograph observations from the Hubble Space Telescope. Unlike the previous gas-dynamical studies of M87, we map out the complete kinematic structure of the emission-line disk within similar to 40 pc from the nucleus, and find that a small amount of velocity dispersion internal to the gas disk is required to match the observed line widths. We examine a scenario in which the intrinsic velocity dispersion provides dynamical support to the disk, and determine that the inferred black hole mass increases by only 6%. Incorporating this effect into the error budget, we ultimately measure a mass of M-BH = (3.5(-0.7)(+0.9)) x 10(9)M circle dot (68% confidence). Our gas-dynamical black hole mass continues to differ from the most recent stellar-dynamical mass by a factor of two, underscoring the need for carrying out more cross-checks between the two main black hole mass measurement methods.NSF Astronomy and Astrophysics Postdoctoral Fellowship 1102845Space Telescope Science Institute 12162NASA NAS 5-26555NSF AST-1108835Astronom