The Mars Organic Molecule Analyzer (MOMA) instrument onboard the ESA/Roscosmos ExoMars rover (to launch in July, 2020) will analyze volatile and refractory organic compounds in martian surface and subsurface sediments. In this study, we describe the design, current status of development, and analytical capabilities of the instrument. Data acquired on preliminary MOMA flight-like hardware and experimental setups are also presented, illustrating their contribution to the overall science return of the mission. Key Words: Mars—Mass spectrometry—Life detection—Planetary instrumentation. Astrobiology 17, 655–685.

Using the electronic shell closure criteria, we propose a new electron counting rule that enables us to predict the size, composition, and structure of many hitherto unknown magic clusters consisting of hydrogen and aluminum atoms. This rule, whose validity is established through a synergy between first-principles calculations and anion-photoelectron spectroscopy experiments, provides a powerful basis for searching magic clusters consisting of hydrogen and simple metal atoms.

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The search for life and habitable environments on other Solar System bodies is a major motivator for planetary exploration. Due to the difficulty and significance of detecting extant or extinct extraterrestrial life in situ, several independent measurements from multiple instrument techniques will bolster the community's confidence in making any such claim. We demonstrate the detection of subsurface biosignatures using a suite of instrument techniques including IR reflectance spectroscopy, laser-induced breakdown spectroscopy, and scanning electron microscopy/energy dispersive X-ray spectroscopy. We focus our measurements on subterranean calcium carbonate field samples, whose biosignatures are analogous to those that might be expected on some high-interest astrobiology targets. In this work, we discuss the feasibility and advantages of using each of the aforementioned instrument techniques for the in situ search for biosignatures and present results on the autonomous characterization of biosignatures using multivariate statistical analysis techniques. Key Words: Biosignature suites-Caves-Mars-Life detection. Astrobiology 17, 1203-1218.

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Given questions regarding some prototypical situation -such as Name something that people usually do before they leave the house for work? -a human can easily answer them via acquired experiences. There can be multiple right answers for such questions, with some more common for a situation than others. This paper introduces a new question answering dataset for training and evaluating common sense reasoning capabilities of artificial intelligence systems in such prototypical situations. The training set is gathered from an existing set of questions played in a longrunning international game show -FAMILY-FEUD. The hidden evaluation set is created by gathering answers for each question from 100 crowd-workers. We also propose a generative evaluation task where a model has to output a ranked list of answers, ideally covering all prototypical answers for a question. After presenting multiple competitive baseline models, we find that human performance still exceeds model scores on all evaluation metrics with a meaningful gap, supporting the challenging nature of the task.