Search

Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs trained using domain-focused corpora perform better on specialized tasks. Inspired by this insight, we developed INDUS, a comprehensive suite of LLMs tailored for the closely-related domains of Earth science, biology, physics, heliophysics, planetary sciences and astrophysics, and trained using curated scientific corpora drawn from diverse data sources. The suite of models include: (1) an encoder model trained using domain-specific vocabulary and corpora to address NLP tasks, (2) a contrastive-learning based text embedding model trained using a diverse set of datasets to address information retrieval tasks and (3) smaller versions of these models created using knowledge distillation for applications which have latency or resource constraints. We also created three new scientific benchmark datasets, CLIMATE-CHANGE NER (entity-recognition), NASA-QA (extractive QA) and NASA-IR (IR) to accelerate research in these multi-disciplinary fields. We show that our models outperform both general-purpose (RoBERTa) and domain-specific (SCIBERT) encoders on these new tasks as well as existing tasks in the domains of interest. Furthermore, we demonstrate the use of these models in two industrial settings -- as a retrieval model for large-scale vector search applications and in automatic content tagging systems., Comment: EMNLP 2024 (Industry Track)

The state government of West Virginia has been motivating a reform of high school science and technology education by actively funding projects that lead to innovative computer-based teaching techniques. Faculties at Marshall University (Huntington, West Virginia) have been active participants in this reform, and have been developing computer-based interactive multimedia models for high school students. These models are designed to promote and enhance high school science education, particularly in remote Appalachian high schools that lack ready access to either extensive laboratory equipment or computer resources, and to help high school teachers utilize computer technology in the classroom. The primary benefit of the models for students is that they learn the scientific processes of making their own hypotheses, and proving or disproving them using inferences, analysis, and critical thinking skills. (AEF)

Emerging capabilities to integrate instruments on smallsats, airborne platforms and in situ devices into an intelligent, distributed observing strategy show great promise for measuring Earth science natural phenomena and physical processes that have not previously been characterized. To reduce the threshold for success in deploying such an intelligent, integrated observing strategy, a ground-based testbed system is proposed. Virtually all of the technologies needed for using such a tool have matured to the point of being used, individually. Virtually none of the technologies have been deployed, working together. The technologies to be deployed should be integrated into a working "breadboard" where the components can be debugged and performance and behavior characterized and tuned-up. A system of this complexity should not be expected to work without full integration and experimental characterization. Further, and perhaps more importantly, in order to successfully propose a space-based element to this strategy, teams must convince the relevant science community that the risk is low enough to warrant the investment. The main benefit of the testbed is to retire the risk of integrating these new technologies and increase the Technology Readiness Level (TRL) of each component as well as the System Readiness Level (SRL) of the integrated system.

With the advancement of space hardware technologies such as smaller spacecraft, component and instrument miniaturization and high performance space processors, and with the advancement of software technologies in artificial intelligence, big data analysis and autonomous decision making, Earth Science is looking at novel ways to observe phenomena that previously could not have been studied or would have been too expensive to study with traditional missions. In particular, the New Observing Strategies (NOS) component of the NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program aims at leveraging these novel technologies as well as low cost and easy access to space to acquire multi-temporal or simultaneous multi-angular, multi-locations, multi-resolution and multi-spectral observations that will provide better multi-source measurements and will build a more dynamic and comprehensive picture of Earth Science phenomena that need to be studied and analyzed. For applications such as water resources management, air quality monitoring, biodiversity studies or disaster management, NOS will integrate the use of small instruments, small spacecraft, constellations of spacecraft and networks of sensors to design new missions that will provide the necessary measurements to improve future forecast and science modeling systems.Measurement acquisition will therefore be approached as a system of systems rather than on a mission basis, and a system of this complexity should not be expected to work without full integration and experimental characterization. Although most of the individual technologies enabling to link and coordinate multi-source observations are more or less mature, a few technologies need to be developed and all of them need to be integrated and tested as a system. In order for this validation to occur, the AIST Program is developing the NOS Testbed that includes 3 main goals:1.Validate novel NOS technologies, independently and as a system2.Demonstrate novel distributed operations concepts3.Socialize new Distributed Spacecraft Mission (DSM) and SensorWeb (SW) technologies and concepts to the science community by significantly retiring the risk of integrating these new technologies.The NOS Testbed will consist of multiple sensing nodes, simulated or actual, representing space, air and/or ground measurements, that are interconnected by a communications fabric (infrastructure that permits nodes to transmit and receive data between one another and interact with each other). Each node will be supported by hardware capabilities required to perform nodes monitoring and command & control, as well as intelligent "onboard" computing. The nodes will work together in a collaborative manner to demonstrate optimal science capabilities. The testbed will enable to validate technologies such as inter-node communication models, techniques and protocols; inter-node coordination; real-time data fusion and understanding; planning; sensor re-targeting; etc. Additionally, the testbed will have the capability to interact with various mission design tools, OSSEs and one or several forecast models. More details about the NOS Testbed will be presented at the confererence.

Cloud computing has the potential to bring high performance computing capabilities to the average science researcher. However, in order to take full advantage of cloud capabilities, the science data used in the analysis must often be reorganized. This typically involves sharding the data across multiple nodes to enable relatively fine-grained parallelism. This can be either via cloud-based file systems or cloud-enabled databases such as Cassandra, Rasdaman or SciDB. Since storing an extra copy of data leads to increased cost and data management complexity, NASA is interested in determining the benefits and costs of various cloud analytics methods for real Earth Observation cases. Accordingly, NASA's Earth Science Technology Office and Earth Science Data and Information Systems project have teamed with cloud analytics practitioners to run a benchmark comparison on cloud analytics methods using the same input data and analysis algorithms. We have particularly looked at analysis algorithms that work over long time series, because these are particularly intractable for many Earth Observation datasets which typically store data with one or just a few time steps per file. This post will present side-by-side cost and performance results for several common Earth observation analysis operations.