A Surface Scientist's view on Spectroscopic Ellipsometry

none1noneMaurizio CanepaCanepa, Maurizi

Selection of the InSight Landing Site

The selection of the Discovery Program InSight landing site took over four years from initial identification of possible areas that met engineering constraints, to downselection via targeted data from orbiters (especially Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) and High-Resolution Imaging Science Experiment (HiRISE) images), to selection and certification via sophisticated entry, descent and landing (EDL) simulations. Constraints on elevation (≤−2.5 km for sufficient atmosphere to slow the lander), latitude (initially 15°S–5°N and later 3°N–5°N for solar power and thermal management of the spacecraft), ellipse size (130 km by 27 km from ballistic entry and descent), and a load bearing surface without thick deposits of dust, severely limited acceptable areas to western Elysium Planitia. Within this area, 16 prospective ellipses were identified, which lie ∼600 km north of the Mars Science Laboratory (MSL) rover. Mapping of terrains in rapidly acquired CTX images identified especially benign smooth terrain and led to the downselection to four northern ellipses. Acquisition of nearly continuous HiRISE, additional Thermal Emission Imaging System (THEMIS), and High Resolution Stereo Camera (HRSC) images, along with radar data confirmed that ellipse E9 met all landing site constraints: with slopes \u3c15° at 84 m and 2 m length scales for radar tracking and touchdown stability, low rock abundance (\u3c10 %) to avoid impact and spacecraft tip over, instrument deployment constraints, which included identical slope and rock abundance constraints, a radar reflective and load bearing surface, and a fragmented regolith ∼5 m thick for full penetration of the heat flow probe. Unlike other Mars landers, science objectives did not directly influence landing site selection

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press