On the Cosmic Evolution of Fe/Mg in QSO Absorption Line Systems

We investigate the variation of the ratio of the equivalent widths of the FeII$\lambda$2600 line to the MgII$\lambda\lambda$2796,2803 doublet as a function of redshift in a large sample of absorption lines drawn from the JHU-SDSS Absorption Line Catalog. We find that despite large scatter, the observed ratio shows a trend where the equivalent width ratio $\mathcal{R}\equiv W_{\rm FeII}/W_{\rm MgII}$ decreases monotonically with increasing redshift $z$ over the range $0.55 \le z \le 1.90$. Selecting the subset of absorbers where the signal-to-noise ratio of the MgII equivalent width $W_{\rm MgII}$ is $\ge$3 and modeling the equivalent width ratio distribution as a gaussian, we find that the mean of the gaussian distribution varies as $\mathcal{R}\propto (-0.045\pm0.005)z$. We discuss various possible reasons for the trend. A monotonic trend in the Fe/Mg abundance ratio is predicted by a simple model where the abundances of Mg and Fe in the absorbing clouds are assumed to be the result of supernova ejecta and where the cosmic evolution in the SNIa and core-collapse supernova rates is related to the cosmic star-formation rate. If the trend in $\mathcal{R}$ reflects the evolution in the abundances, then it is consistent with the predictions of the simple model.Comment: 10 pages, 4 figures, final version published in MNRA

Propagation Networks for Model-Based Control Under Partial Observation

There has been an increasing interest in learning dynamics simulators for model-based control. Compared with off-the-shelf physics engines, a learnable simulator can quickly adapt to unseen objects, scenes, and tasks. However, existing models like interaction networks only work for fully observable systems; they also only consider pairwise interactions within a single time step, both restricting their use in practical systems. We introduce Propagation Networks (PropNet), a differentiable, learnable dynamics model that handles partially observable scenarios and enables instantaneous propagation of signals beyond pairwise interactions. Experiments show that our propagation networks not only outperform current learnable physics engines in forward simulation, but also achieve superior performance on various control tasks. Compared with existing model-free deep reinforcement learning algorithms, model-based control with propagation networks is more accurate, efficient, and generalizable to new, partially observable scenes and tasks.Comment: Accepted to ICRA 2019. Project Page: http://propnet.csail.mit.edu Video: https://youtu.be/ZAxHXegkz4

Design of Phononic Crystal Tethers for Frequency-selective Quality Factor Enhancement in AlN Piezoelectric-on-silicon Resonators

AbstractIn this work, we experimentally demonstrate frequency-selective improvement of unloaded quality factor (Qu) using one-dimensional (1D) phononic crystal (PnC) ring tethers in Aluminium Nitride (AlN) thin-film piezoelectric-on-silicon (TPoS) micromechanical resonators. We show that the 1D-PnC tethers help boost Qu by 3 times specifically at the desired resonant modes that lie in the PnC stopband but not for resonant modes lying outside the PnC stopband. These results show that the 1D-PnCs serve as frequency-selective acoustic reflectors

Power output, microstructure, and microchemical analysis of high surface area Pd and Ti cathodes obtained by thermal treatment

Palladium and titanium foils were heated over a Bunsen burner flame to obtain a topography with a high surface area to volume ratio in the resulting metal oxide. These microstructures are hypothesized to form via spinodal decomposition, rather than classical recrystallization and grain growth. For metals that can undergo significant sorption of hydrogen isotopes, an increased surface area naturally leads to an increased hydrogen (and/or deuterium) loading capacity. Electrolysis experiments conducted in acidified, either light or heavy water, using the heat treated metal cathodes, showed anomalous elements, including V and Fe, from Ti cathodes after heat treatment and electrolysis. Following electrolysis, anomalous Ag was detected in the center of the dark pit on the Pd cathode. Power output from electrolysis with a heat treated Pd cathode will be compared with that from electrolysis with a Pd cathode in the cold rolled condition

DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs

A major difficulty of solving continuous POMDPs is to infer the multi-modal distribution of the unobserved true states and to make the planning algorithm dependent on the perceived uncertainty. We cast POMDP filtering and planning problems as two closely related Sequential Monte Carlo (SMC) processes, one over the real states and the other over the future optimal trajectories, and combine the merits of these two parts in a new model named the DualSMC network. In particular, we first introduce an adversarial particle filter that leverages the adversarial relationship between its internal components. Based on the filtering results, we then propose a planning algorithm that extends the previous SMC planning approach [Piche et al., 2018] to continuous POMDPs with an uncertainty-dependent policy. Crucially, not only can DualSMC handle complex observations such as image input but also it remains highly interpretable. It is shown to be effective in three continuous POMDP domains: the floor positioning domain, the 3D light-dark navigation domain, and a modified Reacher domain.Comment: IJCAI 202

Visual Object Networks: Image Generation with Disentangled 3D Representation

Recent progress in deep generative models has led to tremendous breakthroughs in image generation. However, while existing models can synthesize photorealistic images, they lack an understanding of our underlying 3D world. We present a new generative model, Visual Object Networks (VON), synthesizing natural images of objects with a disentangled 3D representation. Inspired by classic graphics rendering pipelines, we unravel our image formation process into three conditionally independent factors---shape, viewpoint, and texture---and present an end-to-end adversarial learning framework that jointly models 3D shapes and 2D images. Our model first learns to synthesize 3D shapes that are indistinguishable from real shapes. It then renders the object's 2.5D sketches (i.e., silhouette and depth map) from its shape under a sampled viewpoint. Finally, it learns to add realistic texture to these 2.5D sketches to generate natural images. The VON not only generates images that are more realistic than state-of-the-art 2D image synthesis methods, but also enables many 3D operations such as changing the viewpoint of a generated image, editing of shape and texture, linear interpolation in texture and shape space, and transferring appearance across different objects and viewpoints.Comment: NeurIPS 2018. Code: https://github.com/junyanz/VON Website: http://von.csail.mit.edu

The Berkeley High Resolution Tropospheric NO_2 product

We describe upgrades to the Berkeley High Resolution (BEHR) NO2 satellite retrieval product. BEHR v3.0B builds on the NASA version 3 standard Ozone Monitoring Instrument (OMI) tropospheric NO_2 product to provide a high spatial resolution product for a domain covering the continental United States and lower Canada that is consistent with daily variations in the 12km a priori NO_2 profiles. Other improvements to the BEHR v3.0 product include surface reflectance and elevation, and factors affecting the NO_2 a priori profiles such as lightning and anthropogenic emissions.- We describe the retrieval algorithm in detail and evaluate the impact of changes to the algorithm between v2.1C and v3.0B on the retrieved NO_2 vertical column densities (VCDs). Not surprisingly, we find that, on average, the changes to the a priori NO_2 profiles and the update to the new NASA slant column densities have the greatest impact on the retrieved VCDs. More significantly, we find that using daily a priori profiles results in greater average VCDs than using monthly profiles in regions and times with significant lightning activity. The BEHR product is available as four subproducts on the University of California DASH repository, using monthly a priori profiles at native OMI pixel resolution (https://doi.org/10.6078/D1N086) and regridded to 0.05° × 0.05° (https://doi.org/10.6078/D1RQ3G) and using daily a priori profiles at native OMI (https://doi.org/10.6078/D1WH41) and regridded (https://doi.org/10.6078/D12D5X) resolutions. The subproducts using monthly profiles are currently available from January 2005 to July 2017, and will be expanded to more recent years. The subproducts using daily profiles are currently available for years 2005–2010 and 2012–2014; 2011 and 2015 on will be added as the necessary input data are simulated for those years