arXiv subjects
Publications and source records attributed to Michael J. Kurtz.
For decades, the NASA Astrophysics Data System (ADS) has been the foundational digital library for astronomy. As ADS becomes the Science Explorer (SciX), the team prioritizes preserving this trusted experience while increasing their support for space and Earth sciences broadly, without diluting its commitment to astrophysics.
Real-time process monitoring requires methods that extract actionable information from high-dimensional time-series data. In this work, we present a new approach for process monitoring that combines tools of topological data analysis (TDA) and machine learning. In the proposed approach, we represent multivariate time-series data as manifolds and use topological descriptors to summarize the structure of such data; we then use a neural ordinary differential equation to learn the dynamic evolution of the topological structure of the system. Using real data from an industrial process, we show that this trajectory-based event detection approach is effective at detecting diverse types of events. We contrast this approach against reconstruction-based approaches such as principal component analysis and autoencoders and against a trajectory-based approach that uses Koopman autoencoders.
As many research papers in astronomy have been written since the beginning of the 21st century as had been written previously. This exponential growth has been accompanied by substantial changes in the structure of astrophysics research, which organizations perform it and where they are located. Using data from the Smithsonian/NASA Astrophysics Data System/Science Explorer (ADS/SciX) we have obtained an article number and citation based set of metrics as a function of the institutional affiliation of the first author; nearly every organization which has produced recent astronomy research is included. We use these data to examine changes in where astronomy research is being done. We demonstrate how to create custom rankings for the organizations. We develop a dashboard of key performance indicators (KPI) to examine the relative and absolute changes in the research performance for each of the 1949 organizations which have produced at least one first authored, refereed astronomy journal article since 1997. We also present KPI dashboards for 65 countries and three regions.
Open-source Large Language Models enable projects such as NASA SciX (i.e., NASA ADS) to think out of the box and try alternative approaches for information retrieval and data augmentation, while respecting data copyright and users' privacy. However, when large language models are directly prompted with questions without any context, they are prone to hallucination. At NASA SciX we have developed an experiment where we created semantic vectors for our large collection of abstracts and full-text content, and we designed a prompt system to ask questions using contextual chunks from our system. Based on a non-systematic human evaluation, the experiment shows a lower degree of hallucination and better responses when using Retrieval Augmented Generation. Further exploration is required to design new features and data augmentation processes at NASA SciX that leverages this technology while respecting the high level of trust and quality that the project holds.
The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing. At ADASS 2021, we introduced astroBERT, a machine learning language model tailored to the text used in astronomy papers in ADS. In this work we: - announce the first public release of the astroBERT language model; - show how astroBERT improves over existing public language models on astrophysics specific tasks; - and detail how ADS plans to harness the unique structure of scientific papers, the citation graph and citation context, to further improve astroBERT.
The NASA Astrophysics Data System (ADS), a critical research service for the astrophysics community, strives to provide the most accessible and inclusive environment for the discovery and exploration of the astronomical literature. Part of this goal involves creating a digital platform that can accommodate everybody, including those with disabilities that would benefit from alternative ways to present the information provided by the website. NASA ADS follows the official Web Content Accessibility Guidelines (WCAG) standard for ensuring accessibility of all its applications, striving to exceed this standard where possible. Through the use of both internal audits and external expert review based on these guidelines, we have identified many areas for improving accessibility in our current web application, and have implemented a number of updates to the UI as a result of this. We present an overview of some current web accessibility trends, discuss our experience incorporating these trends in our web application, and discuss the lessons learned and recommendations for future projects.
The existing search tools for exploring the NASA Astrophysics Data System (ADS) can be quite rich and empowering (e.g., similar and trending operators), but researchers are not yet allowed to fully leverage semantic search. For example, a query for "results from the Planck mission" should be able to distinguish between all the various meanings of Planck (person, mission, constant, institutions and more) without further clarification from the user. At ADS, we are applying modern machine learning and natural language processing techniques to our dataset of recent astronomy publications to train astroBERT, a deeply contextual language model based on research at Google. Using astroBERT, we aim to enrich the ADS dataset and improve its discoverability, and in particular we are developing our own named entity recognition tool. We present here our preliminary results and lessons learned.
We announce the public release of 141,531 moderate-dispersion optical spectra of 72,247 objects acquired over the past 25 years with the FAST Spectrograph on the Fred L. Whipple Observatory 1.5-meter Tillinghast telescope. We describe the data acquisition and processing so that scientists can understand the spectra. We highlight some of the largest FAST survey programs, and make recommendations for use. The spectra have been placed in a Virtual Observatory accessible archive and are ready for download.
Second Order Operators (SOOs) are database functions which form secondary queries based on attributes of the objects returned in an initial query; they can provide powerful methods to investigate complex, multipartite information graphs. The NASA Astrophysics Data System (ADS) has implemented four SOOs, reviews, useful, trending, and similar which use the citations, references, downloads, and abstract text. This tutorial describes these operators in detail, both alone and in conjunction with other functions. It is intended for scientists and others who wish to make fuller use of the ADS database. Basic knowledge of the ADS is assumed.
In this whitepaper we advocate that the Planetary Science (PS) community build a discipline-specific digital library, in collaboration with the existing astronomy digital library, ADS. We suggest that the PS data archives increase their level of curation to allow for direct linking between the archival data and the derived journal articles. And we suggest that a new component of the PS information infrastructure be created to collate and curate information on features and objects in our solar system, beginning with the USGS/IAU Gazetteer of Planetary Nomenclature.
The Agile manifesto encourages us to value individuals and interactions over processes and tools, while Scrum, the most adopted Agile development methodology, is essentially based on roles, events, artifacts, and the rules that bind them together (i.e., processes). Moreover, it is generally proclaimed that whenever a Scrum project does not succeed, the reason is because Scrum was not implemented correctly and not because Scrum may have its own flaws. This grants irrefutability to the methodology, discouraging deviations to fit the actual needs and peculiarities of the developers. In particular, the members of the NASA ADS team are highly creative and autonomous whose motivation can be affected if their freedom is too strongly constrained. We present our experience following Agile principles, reusing certain Scrum elements and seeking the satisfaction of the team members, while rapidly reacting/keeping the project in line with our stakeholders expectations.
Some of the most exciting and promising areas of Astronomy research today are found at the boundaries of the discipline: the search for Exoplanets and Multi-Messenger Astronomy. In order to achieve breakthroughs in these research fields over the next decade, innovation and expansion of the digital information infrastructure which supports this research is required. Astronomy has been well-served by the existence of an open, distributed network of data centers and archives. However, institutional barriers and differing research cultures have prevented cross-disciplinary collaborations, creating fragmented knowledge and stove-piped research activities. This must change in order for the broader community of scientists to work together and solve our most ambitious decadal challenges. Interdisciplinary inquiry is best supported by bringing researchers together at the information discovery level. In order to cross the traditional disciplinary silos we must allow scientists both to explore new ideas and to gain access to new data and knowledge. This is best enabled by providing discovery platforms which allow them to explore and connect different research threads in the literature, identify communities of experts, access and analyze the related published datasets, measurements and catalogs.
The new NASA Astrophysics Data System (ADS) is designed with a serviceoriented architecture (SOA) that consists of multiple customized Apache Solr search engine instances plus a collection of microservices, containerized using Docker, and deployed in Amazon Web Services (AWS). For complex systems, like the ADS, this loosely coupled architecture can lead to a more scalable, reliable and resilient system if some fundamental questions are addressed. After having experimented with different AWS environments and deployment methods, we decided in December 2017 to go with Kubernetes as our container orchestration. Defining the best strategy to properly setup Kubernetes has shown to be challenging: automatic scaling services and load balancing traffic can lead to errors whose origin is difficult to identify, monitoring and logging the activity that happens across multiple layers for a single request needs to be carefully addressed, and the best workflow for a Continuous Integration and Delivery (CI/CD) system is not self-evident. We present here how we tackle these challenges and our plans for the future.
Conceptually exoplanet research has one foot in the discipline of Astrophysics and the other foot in Planetary Science. Research strategies for exoplanets will require efficient access to data and information from both realms. Astrophysics has a sophisticated, well integrated, distributed information system with archives and data centers which are interlinked with the technical literature via the Astrophysics Data System (ADS). The information system for Planetary Science does not have a central component linking the literature with the observational and theoretical data. Here we propose that the Committee on an Exoplanet Science Strategy recommend that this linkage be built, with the ADS playing the role in Planetary Science which it already plays in Astrophysics. This will require additional resources for the ADS, and the Planetary Data System (PDS), as well as other international collaborators
What is "intelligent" information retrieval? Essentially this is asking what is intelligence, in this article I will attempt to show some of the aspects of human intelligence, as related to information retrieval. I will do this by the device of a semi-imaginary Oracle. Every Observatory has an oracle, someone who is a distinguished scientist, has great administrative responsibilities, acts as mentor to a number of less senior people, and as trusted advisor to even the most accomplished scientists, and knows essentially everyone in the field. In an appendix I will present a brief summary of the Statistical Factor Space method for text indexing and retrieval, and indicate how it will be used in the Astrophysics Data System Abstract Service. 2018 Keywords: Personal Digital Assistant; Supervised Topic Models
Scientific publications have evolved several features for mitigating vocabulary mismatch when indexing, retrieving, and computing similarity between articles. These mitigation strategies range from simply focusing on high-value article sections, such as titles and abstracts, to assigning keywords, often from controlled vocabularies, either manually or through automatic annotation. Various document representation schemes possess different cost-benefit tradeoffs. In this paper, we propose to model different representations of the same article as translations of each other, all generated from a common latent representation in a multilingual topic model. We start with a methodological overview on latent variable models for parallel document representations that could be used across many information science tasks. We then show how solving the inference problem of mapping diverse representations into a shared topic space allows us to evaluate representations based on how topically similar they are to the original article. In addition, our proposed approach provides means to discover where different concept vocabularies require improvement.
In this paper we provide an update concerning the operations of the NASA Astrophysics Data System (ADS), its services and user interface, and the content currently indexed in its database. As the primary information system used by researchers in Astronomy, the ADS aims to provide a comprehensive index of all scholarly resources appearing in the literature. With the current effort in our community to support data and software citations, we discuss what steps the ADS is taking to provide the needed infrastructure in collaboration with publishers and data providers. A new API provides access to the ADS search interface, metrics, and libraries allowing users to programmatically automate discovery and curation tasks. The new ADS interface supports a greater integration of content and services with a variety of partners, including ORCID claiming, indexing of SIMBAD objects, and article graphics from a variety of publishers. Finally, we highlight how librarians can facilitate the ingest of gray literature that they curate into our system.
Bibliometric indicators, citation counts and/or download counts are increasingly being used to inform personnel decisions such as hiring or promotions. These statistics are very often misused. Here we provide a guide to the factors which should be considered when using these so-called quantitative measures to evaluate people. Rules of thumb are given for when begin to use bibliometric measures when comparing otherwise similar candidates.