Upper limits of water contents in olivine and orthopyroxene of equilibrated chondrites and several achondrites

Hydroxyl defects in nominally anhydrous minerals (NAMs) were potential carriers of water in the early Solar System and might have contributed to the accretion of terrestrial water. To better understand this, we have conducted a nanoscale secondary ion mass spectrometry survey of water contents in olivine and orthopyroxene from a set of equilibrated ordinary chondrites of the L and LL groups (Baszkówka, Bensour, Kheneg Ljouâd, and Tuxtuac) and several ultramafic achondrites (Zakłodzie, Dhofar 125, Northwest Africa [NWA] 4969, NWA 6693, and NWA 7317). For calibration, we used terrestrial olivine and orthopyroxene with H2O contents determined by Fourier transform infrared. Our 99.7% (~3SD) detection limits are 3.6–5.4 ppmw H2O for olivine and 7.7–10.9 ppmw H2O for orthopyroxene. None of the meteoritic samples studied consistently shows water contents above the detection limits. A few exceptions slightly above the detection limits are suspected of terrestrial contamination by ferric oxyhydroxides. If the meteorite samples investigated accreted in the presence of small amounts of water ice, the upper limits of water contents provided by our survey suggest that the retention of hydrogen during thermal metamorphism and differentiation was ineffective. We suggest that loss occurred through combinations of low internal pressures, high permeability along grain boundaries, and speciation of hydrogen into reduced compounds such as H2 and methane, which are less soluble in NAMs than in water

DeepGAR: Deep Graph Learning for Analogical Reasoning

Analogical reasoning is the process of discovering and mapping correspondences from a target subject to a base subject. As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a corresponding subgraph (i.e., correspondence) in the target graph that is aligned with the base graph. However, incorporating deep learning for SMT is still under-explored due to several obstacles: 1) the combinatorial complexity of searching for the correspondence in the target graph; 2) the correspondence mining is restricted by various cognitive theory-driven constraints. To address both challenges, we propose a novel framework for Analogical Reasoning (DeepGAR) that identifies the correspondence between source and target domains by assuring cognitive theory-driven constraints. Specifically, we design a geometric constraint embedding space to induce subgraph relation from node embeddings for efficient subgraph search. Furthermore, we develop novel learning and optimization strategies that could end-to-end identify correspondences that are strictly consistent with constraints driven by the cognitive theory. Extensive experiments are conducted on synthetic and real-world datasets to demonstrate the effectiveness of the proposed DeepGAR over existing methods.Comment: 22nd IEEE International Conference on Data Mining (ICDM 2022

Open-ended Commonsense Reasoning with Unrestricted Answer Scope

Open-ended Commonsense Reasoning is defined as solving a commonsense question without providing 1) a short list of answer candidates and 2) a pre-defined answer scope. Conventional ways of formulating the commonsense question into a question-answering form or utilizing external knowledge to learn retrieval-based methods are less applicable in the open-ended setting due to an inherent challenge. Without pre-defining an answer scope or a few candidates, open-ended commonsense reasoning entails predicting answers by searching over an extremely large searching space. Moreover, most questions require implicit multi-hop reasoning, which presents even more challenges to our problem. In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision. The reasoning paths can help to identify the most precise answer to the commonsense question. We conduct experiments on two commonsense benchmark datasets. Compared to other approaches, our proposed method achieves better performance both quantitatively and qualitatively.Comment: Findings of EMNLP 202

Preservation of primordial signatures of water in highly-shocked ancient lunar rocks

Spurred by the discovery of water in lunar volcanic glasses about a decade ago, the accessory mineral apatite became the primary target to investigate the abundance and source of lunar water. This is due to its ability to contain significant amounts of OH in its structure, along with the widespread presence of apatite in lunar rocks. There is a general understanding that crustal cumulate rocks of the lunar magnesian (Mg) suite are better candidates for recording the original isotopic compositions of volatile elements in their parental melts compared to eruptive rocks, such as mare basalts. Consequently, water-bearing minerals in Mg-suite rocks are thought to be ideal candidates for discerning the primary hydrogen isotopic composition of water in the lunar interior. Mg-suite rocks and most other Apollo samples that were collected at the lunar surface display variable degrees of shock-deformation. In this study, we have investigated seven Apollo 17 Mg-suite samples that include troctolite, gabbro and norite lithologies, in order to understand if shock processes affected the water abundances and/or H isotopic composition of apatite. The measured water contents in apatite grains range from 31 to 964 ppm, with associated δD values varying between −535 ±134‰ and +147 ±194‰(2σ). Considering the full dataset, there appears to be no correlation between H2O and δD of apatite and the level of shock each apatite grain has experienced. However, the lowest δD was recorded by individual, water-poor (∼100 ppm H2O), regardless of the complexity of the shock-induced nanostructures, there appears to be no evidence of water-loss or alteration in their δD. The weighted average δD value of 24 such water-rich apatites is −192 ±71‰, and, of all 36 analyzed spots is −209 ±47‰, indistinguishable from that of other KREEPy lunar lithologies or the Earth’s deep mantle. Despite experiencing variable degrees of shock-deformation at a later stage in lunar history, water-rich apatite in some of the earliest-formed lunar crustal material appears to retain the original isotopic signature of H in the Moon