A high spatial resolution synchrotron Mössbauer study of the Tazewell IIICD and Esquel pallasite meteorites.

Metallic phases in the Tazewell IIICD iron and Esquel pallasite meteorites were examined using 57Fe synchrotron Mössbauer spectroscopy. Spatial resolution of ~10-20 μm was achieved, together with high throughput, enabling individual spectra to be recorded in less than 1 h. Spectra were recorded every 5-10 μm, allowing phase fractions and hyperfine parameters to be traced along transects of key microstructural features. The main focus of the study was the transitional region between kamacite and plessite, known as the "cloudy zone." Results confirm the presence of tetrataenite and antitaenite in the cloudy zone as its only components. However, both phases were also found in plessite, indicating that antitaenite is not restricted exclusively to the cloudy zone, as previously thought. The confirmation of paramagnetic antitaenite as the matrix phase of the cloudy zone contrasts with recent observations of a ferromagnetic matrix phase using X-ray photoemission electron spectroscopy. Possible explanations for the different results seen using these techniques are proposed.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007 - 2013) / ERC Grant Agreement No. 320750

Raman and infrared spectra of dimethyl ether 13C-isotopologue (CH3O13CH3) from a CCSD(T) potential energy surface

So far, no experimental data of the infrared and Raman spectra of 13C isotopologue of dimethyl ether are available. With the aim of providing some clues of its low-lying vibrational bands and with the hope of contributing in a next spectral analysis, a number of vibrational transition frequencies below 300 cm−1 of the infrared spectrum and around 400 cm−1 of the Raman spectrum have been predicted and their assignments were proposed. Calculations were carried out through an ab initio three dimensional potential energy surface based on a previously reported one for the most abundant dimethyl ether isotopologue (M. Villa et al., J. Phys. Chem. A 115 (2011) 13573). The potential function was vibrationally corrected and computed with a highly correlated CCSD(T) method involving the COC bending angle and the two large amplitude CH3 internal rotation degrees of freedom. Also, the Hamiltonian parameters could represent a support for the spectral characterization of this species. Although the computed vibrational term values are expected to be very accurate, an empirical adjustment of the Hamiltonian has been performed with the purpose of anticipating some workable corrections to any possible divergence of the vibrational frequencies. Also, the symmetry breaking derived from the isotopic substitution of 13C in the dimethyl ether was taken into account when the symmetrization procedure was applied

Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning

We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control. The Grounded Semantic Mapping Network (GSMN) is a fully-differentiable neural network architecture that builds an explicit semantic map in the world reference frame by incorporating a pinhole camera projection model within the network. The information stored in the map is learned from experience, while the local-to-world transformation is computed explicitly. We train the model using DAggerFM, a modified variant of DAgger that trades tabular convergence guarantees for improved training speed and memory use. We test GSMN in virtual environments on a realistic quadcopter simulator and show that incorporating an explicit mapping and grounding modules allows GSMN to outperform strong neural baselines and almost reach an expert policy performance. Finally, we analyze the learned map representations and show that using an explicit map leads to an interpretable instruction-following model.Comment: To appear in Robotics: Science and Systems (RSS), 201

Adsorption pathways of boron on clay and their implications for boron cycling on land and in the ocean

Reversible adsorption and isotope fractionation of boron on the surface of clay minerals is a key process that impacts boron isotope cycling in porewater, rivers and the ocean. However, the differences in boron isotope fractionation factors between various clay minerals and their dependence on fluid chemistry are not well known. We performed two sets of experiments, using solutions of pure water with added boron and seawater, to explore the isotope behavior during adsorption of boron onto kaolinite, smectite and illite. We found that the amount of sorbed boron increases with ionic strength of solutions and is proportional to the cation exchange capacity of a given clay mineral. Maximum adsorption is observed in alkaline seawater, which we attribute to the efficient fixation of magnesium-borate ion pairs onto negatively charged surface sites. Isotopic fractionation is modestly different between clays and demonstrates that clay surfaces preferentially sorb borate, even when the concentration of borate in solution is low. In both pure water and seawater, adsorbed complexes retain the isotopic composition of their dissolved precursors (borate or boric acid) with minimal isotopic fractionation. In other words, isotopic composition of adsorbed boron is set by the ability of clays to adsorb boron from an already fractionated boron pool rather than specific fractionation associated with the complexation reaction. Our experimental results allow us to provide revised constraints on the adsorbed boron being transported in terrestrial fluids and the ocean

3D-MVP: 3D Multiview Pretraining for Robotic Manipulation

Recent works have shown that visual pretraining on egocentric datasets using masked autoencoders (MAE) can improve generalization for downstream robotics tasks. However, these approaches pretrain only on 2D images, while many robotics applications require 3D scene understanding. In this work, we propose 3D-MVP, a novel approach for 3D multi-view pretraining using masked autoencoders. We leverage Robotic View Transformer (RVT), which uses a multi-view transformer to understand the 3D scene and predict gripper pose actions. We split RVT's multi-view transformer into visual encoder and action decoder, and pretrain its visual encoder using masked autoencoding on large-scale 3D datasets such as Objaverse. We evaluate 3D-MVP on a suite of virtual robot manipulation tasks and demonstrate improved performance over baselines. We also show promising results on a real robot platform with minimal finetuning. Our results suggest that 3D-aware pretraining is a promising approach to improve sample efficiency and generalization of vision-based robotic manipulation policies. We will release code and pretrained models for 3D-MVP to facilitate future research. Project site: https://jasonqsy.github.io/3DMV

Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects

We address the problem of building digital twins of unknown articulated objects from two RGBD scans of the object at different articulation states. We decompose the problem into two stages, each addressing distinct aspects. Our method first reconstructs object-level shape at each state, then recovers the underlying articulation model including part segmentation and joint articulations that associate the two states. By explicitly modeling point-level correspondences and exploiting cues from images, 3D reconstructions, and kinematics, our method yields more accurate and stable results compared to prior work. It also handles more than one movable part and does not rely on any object shape or structure priors. Project page: https://github.com/NVlabs/DigitalTwinArtComment: CVPR 202