Learn to Fly Test Setup and Concept of Operations

The NASA Learn-to-Fly (L2F) project recently completed a series of flight demonstrations of its learning algorithm for flight control at Fort A. P. Hill in Virginia. This paper discusses the test setup and concept of operations (ConOps) used by the L2F team. Unmanned airframe demonstrators for testing the research algorithms included a modified commercial off-the-shelf subscale powered airplane, plus four gliders two of which had an unconventional configuration and were fabricated using a rapid prototyping technique. Avionics system similarities and differences between the test aircraft are described, as well as ground testing in preparation for flight. The ConOps discussion includes the development of a tethered helium balloon drop launch technique for the glider demonstrators. This launch method was chosen for its potential to be inexpensive and allow for rapid turn-around for multiple glider launches but it also presented challenges, such as balloon tether avoidance, high angle of attack, low dynamic pressure initial conditions, and susceptibility to winds. A remotely piloted approach employing high-end hobbyist radio controlled (R/C) hardware was used for the powered demonstrator. This approach accommodated the interaction between the R/C flight system and the research flight control computer, engaging the L2F algorithm at varying initial conditions and artificially reducing the aircraft stability to stress the algorithm

Aircraft Configured for Flight in an Atmosphere Having Low Density

An aircraft is configured for flight in an atmosphere having a low density. The aircraft includes a fuselage, a pair of wings, and a rear stabilizer. The pair of wings extends from the fuselage in opposition to one another. The rear stabilizer extends from the fuselage in spaced relationship to the pair of wings. The fuselage, the wings, and the rear stabilizer each present an upper surface opposing a lower surface. The upper and lower surfaces have X, Y, and Z coordinates that are configured for flight in an atmosphere having low density

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 research.Peer reviewe

Current status and new features of the Consensus Coding Sequence database

The Consensus Coding Sequence (CCDS) projec

Current status and new features of the Consensus Coding Sequence database.

The Consensus Coding Sequence (CCDS) project (http://www.ncbi.nlm.nih.gov/CCDS/) is a collaborative effort to maintain a dataset of protein-coding regions that are identically annotated on the human and mouse reference genome assemblies by the National Center for Biotechnology Information (NCBI) and Ensembl genome annotation pipelines. Identical annotations that pass quality assurance tests are tracked with a stable identifier (CCDS ID). Members of the collaboration, who are from NCBI, the Wellcome Trust Sanger Institute and the University of California Santa Cruz, provide coordinated and continuous review of the dataset to ensure high-quality CCDS representations. We describe here the current status and recent growth in the CCDS dataset, as well as recent changes to the CCDS web and FTP sites. These changes include more explicit reporting about the NCBI and Ensembl annotation releases being compared, new search and display options, the addition of biologically descriptive information and our approach to representing genes for which support evidence is incomplete. We also present a summary of recent and future curation targets