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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Evaluating the Effectiveness of the 2002-2003 NASA SCIence Files(TM) Program

NASA SCIence Files (tm) is a research-, inquiry-, and standards-based, integrated mathematics, science, and technology series of 60-minute instructional distance learning (television and web-based) programs for students in grades 3-5. Respondents who evaluated the programs in the 2002-2003 NASA SCIence Files (tm) series reported that (1) they used the programs in the series; (2) the goals and objectives for the series were met; (3) the programs were aligned with the national mathematics, science, and technology standards; (4) the program content was developmentally appropriate for grade level; and (5) the programs in the series enhanced and enriched the teaching of mathematics, science, and technology.

Pinelli, Thomas E.↗
NASA NTRS2004

Results of a Telephone Survey of Television Station Managers Concerning the NASA SCI Files(TM) and NASA CONNECT(TM)

A telephone survey of television station managers concerning 2 instructional television programs, the NASA SCI Files(TM) and NASA CONNECT(TM), offered by the NASA Langley Center for Distance Learning (CDL) was conducted. Using a 4-point scale, with 4 being very satisfied, survey participants reported that they were either very satisfied (77.1 percent) or satisfied (19.9 percent) with the overall (educational and technical) quality of the NASA SCI Files(TM). Using a 4-point scale, with 4 being very satisfied, survey participants reported that they were either very satisfied (77.9 percent) or satisfied (19.1 percent) with the overall (educational and technical) quality of NASA CONNECT(TM) .

Pinelli, Thomas E.↗
NASA NTRS2004

Assessing the Effectiveness of a Mathematics-Focused, Instructional Technology Program for Grades 6-8: A 5-Year Trend Analysis of NASA CONNECT(tm) Evaluation Data

NASA CONNECT is a research-, inquiry, and standards-based, integrated mathematics, science, and technology series of 30-minute instructional distance learning (television and web-based) programs for students in grades 6 8. Respondents who evaluated the programs in the series over the first five seasons (1998-99 through 2002-03) reported that (1) they used the programs in the series; (2) the goals and objectives for the series were met; (3) the programs were aligned with the national mathematics, science, and technology standards; (4) the program content was developmentally appropriate for the grade level; and (5) the programs in the series enhanced and enriched the teaching of mathematics, science, and technology.

Glassman, Nanci A.↗
NASA NTRS2005

Evaluating the Effectiveness of the 2003-2004 NASA SCIence Files(trademark) Program

The NASA SCI Files is an Emmy award-winning series of instructional programs for grades 3-5. Produced by the NASA Center for Distance Learning, programs in the series are research-, inquiry-, standards-, teacher- and technology-based. Each NASA SCI Files program (1) integrates mathematics, science, and technology; (2) uses Problem-Based Learning (PBL) to enhance and enrich the teaching and learning of science; (3) emphasizes science as inquiry and the scientific method; (4) motivates students to become critical thinkers and active problem solvers; and (5) uses NASA research, facilities, and personnel to raise student awareness of careers and to exhibit the "real-world" application of mathematics, science, and technology. In April 2004, 1,500 randomly selected registered users of the NASA SCI Files were invited to complete a survey containing a series of questions. A total of 263 surveys were received. This report contains the quantitative and qualitative results of that survey.

Caton, Randall H.↗
NASA NTRS2009

Transitioning NPOESS Data to Weather Offices: The SPoRT Paradigm with EOS Data

Real-time satellite information provides one of many data sources used by NWS weather forecast offices (WFOs) to diagnose current weather conditions and to assist in short-term forecast preparation. While GOES satellite data provides relatively coarse spatial resolution coverage of the continental U.S. on a 10-15 minute repeat cycle, polar orbiting imagery has the potential to provide snapshots of weather conditions at high-resolution in many spectral channels. Additionally, polar orbiting sounding data can provide additional information on the thermodynamic structure of the atmosphere in data sparse regions of at asynoptic observation times. The NASA Short-term Prediction Research and Transition (SPoRT) project has demonstrated the utility of polar orbiting MODIS and AIRS data on the Terra and Aqua satellites to improve weather diagnostics and short-term forecasting on the regional and local scales. SPoRT scientists work directly forecasters at selected WFOS in the Southern Region (SR) to help them ingest these unique data streams into their AWIPS system, understand how to use the data (through on-site and distance learn techniques), and demonstrate the utility of these products to address significant forecast problems. This process also prepares forecasters for the use of similar observational capabilities from NPOESS operational sensors. NPOESS environmental data records (EDRs) from the Visible 1 Infrared Imager I Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrlS) and Advanced Technology Microwave Sounder (ATMS) instruments and additional value-added products produced by NESDIS will be available in near real-time and made available to WFOs to extend their use of NASA EOS data into the NPOESS era. These new data streams will be integrated into the NWs's new AWIPS II decision support tools. The AWIPS I1 system to be unveiled in WFOs in 2009 will be a JAVA-based decision support system which preserves the functionality of the existing systems and offers unique development opportunities for new data sources and applications in the Service Orientated Architecture ISOA) environment. This paper will highlight some of the SPoRT activities leading to the integration of VllRS and CrIS/ATMS data into the display capabilities of these new systems to support short-term forecasting problems at WFOs.

Jedlovec, Gary↗
NASA NTRS2010

Sun-Earth Day - Teaching Heliophysics Through Education Technology

Sun-Earth Day (SED) is an Education and Outreach program supported by the U.S, National Aeronautics and Space Administration (NASA). The intent of the program is to teach students and the general public about Heliophysics (the science of the study of the Sun, how it varies, and how solar dynamics affect the rest of the solar system, especially the Earth). The program was begun ten years ago. Each year since that time a particular day has been designated as "Sun-Earth Day ,,. Usually the day of the spring equinox (March 20 or 21) is Sun-Earth Day, but other days have been used as well. Each year a theme is chosen relating to Heliophysics and events reflecting that theme are planned not only for Sun-Earth Day, but for the entire year. From the very beginning educational technology was emphasized in the events in order to effectively reach wide audiences with the SED message. The main approach has been to have a "webcast" related to each year's theme, often from a location that supports the theme as well. For example, a webcast took place from the Mayan pyramids at Chichen Itza, Mexico to highlight the theme of "Ancient Observatories, Timeless Knowledge". Webcasts were not the only technology employed, however. Many of the themes centered on the dynamic nature of the Sun and the effects that solar storms can have on interplanetary space and in our day-to-day life on Earth. Activities for tracking when solar storms happen and how they affect the Earth were developed and brought together in an educational package called Space Weather Action Centers. This project is explained in more detail in another presentation in this session being given by Norma Teresinha Oliveira Reis. Recent Sun-Earth Days have utilized "social networking" technologies to reach widespread groups on the internet. Podcasts, Vodcasts, Facebook, Twitter, and Second Life are the types of network technologies being employed now. The NASA Distance learning Network is another method for bringing Sun-Earth Day events and training to widespread educators and classrooms in order to magnify the reach of Sun-Earth Day. Examples of the technologies will be shown along with an assessment of their effectiveness.

Thieman, J.↗
NASA NTRS1996

Telemedicine: An Application in Search of Users

Telemedicine involves the use of telecommunication technologies as a medium for the provision of medical information and services to consumers at sites that are at a distance from the provider. The concept encompasses everything from the telephone system to high-speed, wide-bandwidth transmission with use of fiberoptics, satellites, or a combination of terrestrial and satellite-communication technologies. The peripheral software could be as simple as a typewriter used to type a letter requesting an opinion or as complex as high-capacity parallel processing computers and imaging devices. Although the definition includes telephone, facsimile, and distance learning, the term "Telemedicine" is currently used as a generic label for remote consultation and diagnosis. Telemedicine is not a medical subspecialty but a facilitator of all medical and surgical specialties.

Khandheria, Bijoy K.↗
NASA NTRS2012

Inspiring the Next Generation of Explorers: Scientist Involvement in the Expedition Earth and Beyond Program

Scientists, science experts, graduate and even undergraduate student researchers have a unique ability to inspire the next generation of explorers. These science, technology, engineering, and mathematics (STEM) experts can serve as role models for students and can help inspire them to consider future STEM-related careers. They have an exceptional ability to instill a sense of curiosity and fascination in the minds of students as they bring science to life in the classroom. Students and teachers are hungry for opportunities to interact with scientists. They feel honored when these experts take time out of their busy day to share their science, their expertise, and their stories. The key for teachers is to be cognizant of opportunities to connect their students with scientists. For scientists, the key is to know how to get involved, to have options for participation that involve different levels of commitment, and to work with educational specialists who can help facilitate their involvement. The Expedition Earth and Beyond (EEAB) Program, facilitated by the Astromaterials Research and Exploration Science (ARES) Directorate at the NASA Johnson Space Center, is an Earth and planetary science education program designed to inspire, engage, and educate teachers and students by getting them actively involved with NASA exploration, discovery, and the process of science. One of the main goals of the program is to facilitate student research in the classroom. The program uses astronaut photographs, provided through the ARES Crew Earth Observations (CEO) payload on the International Space Station (ISS) as the hook to help students gain an interest in a research topic. Student investigations can focus on Earth or involve comparative planetology. Student teams are encouraged to use additional imagery and data from Earth or planetary orbital spacecraft, or ground-based data collection tools, to augment the astronaut photography dataset. A second goal of the program is to provide opportunities for meaningful connections between scientists and classrooms. To do this, EEAB offers multiple opportunities for scientist involvement. One opportunity involves having scientists work as mentors for student teams conducting research. These student teams, ranging from grades 4 through 12, are able to obtain guidance, suggestions, and input from STEM experts as they conduct a research investigation. Another opportunity for scientist involvement is participation in Classroom Connection Distance Learning (DL) events. These DL events entail interactive and engaging presentations that enable STEM experts to share their expertise with students and teachers (grades 3 through 12) from all across the nation. A third opportunity for scientist involvement involves participation in virtual student team science presentations. Student teams have the opportunity to share their research and results by presenting it to science experts through the use of WebEx, an easy-to-use online conferencing tool. The impact STEM experts have on students in today s classrooms is powerful. They serve as role models to these students, and they open students eyes to a potential career path they may not have known existed otherwise. The more scientists and STEM experts we can connect with students, the greater the impact we can make as we strive to inspire and prepare our nation s next generation of explorers.

Graff, Paige↗
NASA NTRS2016

Virtual Microscope Views of the Apollo 11 and 12 Lunar Samples

The Apollo virtual microscope is a means of viewing, over the Internet, polished thin sections of every rock in the Apollo lunar sample collections via software, duplicating many of the functions of a petrological microscope, is described. Images from the Apollo 11 and 12 missions may be viewed at: www.virtualmicroscope.org/content/apollo. Introduction: During the six NASA missions to the Moon from 1969-72 a total of 382 kilograms of rocks and soils, often referred to as "the legacy of Apollo", were collected and returned to Earth. A unique collection of polished thin sections (PTSs) was made from over 400 rocks by the Lunar Sample Curatorial Facility at the Johnson Spacecraft Center (JSC), Houston. These materials have been available for loan to approved PIs but of course they can't be simultaneously investigated by several researchers unless they are co-located or the sample is passed back and forward between them by mail/hand carrying which is inefficient and very risky for irreplaceable material. When The Open University (OU), the world's largest Distance Learning Higher Education Establishment found itself facing a comparable problem (how to supply thousands of undergraduate students with an interactive petrological microscope and a personal set of thin sections), it decided to develop a software tool called the Virtual Microscope (VM). As a result it is now able to make the unique and precious collection of Apollo specimens universally available as a resource for concurrent study by anybody in the world's Earth and Planetary Sciences community. Herein, we describe the first steps of a collaborative project between OU and the Johnson Space Center (JSC) Curatorial Facility to record a PTS for every lunar rock, beginning with those collected by the Apollo 11 and 12 missions. Method: Production of a virtual microscope dedicated to a particular theme divides into four main parts - photography, image processing, building and assembly of virtual microscope components, and publication on a website. Two large research quality microscopes are used to collect all the images required for a virtual microscope. The first is part of an integrated package that utilizes Leica PowerMosaic software and a motorised XYZ stage to generate large area mosaics. It includes a fast acquisition camera and depending on the PTS size normally is used to produce seamless mosaic images consisting of 100-500 individual photographs. If the sample is suitable, three mosaics of each sample are recorded - plane polarised light, between crossed polars and reflected light. In order for the VM to be a true petrological microscope it is necessary to recreate the features of a rotating stage and perform observations using filters to produce polarised light. Thus the petrological VM includes the capability of seeing changes in optical properties (pleochroism and birefringence) during rotation allowing mineral identification. The second microscope in the system provides the functions of the rotating stage. To this microscope we have added a robotically controlled motor to acquire seventy-two images (5 degree intervals) in plane polarised light and between crossed polars. To process the images acquired from the two microscopes involves a combination of proprietary software (Photoshop) and our own in-house code. The final stage involves assembling all the components in an HTML5 environment. Pathfinder investigations: We have undertaken a number of pilot studies to demonstrate the efficacy of the petrological microscope with lunar samples. The first was to make available on-line images collected from the Educational Package of Apollo samples provided by NASA to the UK STFC (Science and Technical Facilities Council) for loan as educational material e.g. for schools. The real PTSs of the samples are now no longer sent out to schools removing the risks associated with transport, accidental breakage and eliminating the possibility of loss. The availability of lunar sample VM-related material was further extended to include twenty-eight specimens from all of the Apollo missions. Some of these samples were made more generally available through an ibook entitled "Moon Rocks: an introduction to the Geology of the Moon," free from the Apple Bookstore. Research possibilities: Although the Virtual Microscope was originally conceived as a teaching aid and was later recognised as a means of public outreach and engagement, we now realize that it also has enormous potential as a high level research tool. Following discussions with the JSC Curators we have received Curation and Analysis Planning Team for Extraterrestrial Materials (CAPTEM) permission to embark on a programme of digitizing the entire lunar sample PTS collection for all three of the above purposes. By the time of the 47th Lunar and Planetary Science Conference (LPSC) we will have completed 81 rocks collected during the Apollo 11 and 12 missions and the data, with cross-links to the Lunar Sample Compendium will go live on the Web at the 47th LPSC. The VM images of the Apollo 11 (41 VM images) and 12 (40 VM images) missions can be viewed at: http:/www.virtualmicroscope.org/content/apollo. The lunar sample VM will enable large numbers of skilled/unskilled microscopists (professional and amateur researchers, educators and students, enthusiasts and the simply curious non-scientists) to share the information from a single sample. It will mean that all the PTSs already cut, even historical ones, could be available for new joint investigations or private study. The scientific return from the collection will increase exponentially as a result of further debate and discussion. Simultaneously the VM will remove the need for making unnecessary multiple samplings, avoid consignment of delicate/breakable specimens (all of which are priceless) to insecure mail/courier services and reduce direct labour and indirect costs, travel budgets and unproductive travelling time necessary for co-location of collaborating researchers. For the future we have already recognized further potential for virtual technology. There is nothing that a petrologist likes more than to see the original rock as a hand specimen. It is entirely possible to recreate virtual hand specimens with 3-D hard and software, already developed for viewing fossils, located within the Curatorial Facility, http://curator.jsc.nasa.gov/lunar/lsc/index.cfm.

Gibson, E. K.↗
DOE OSTI2024

Distance preserving machine learning for uncertainty aware accelerator capacitance predictions

Abstract Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard for this task; however, they can struggle with large, high-dimensional datasets. Combining deep neural networks with Gaussian process approximation techniques has shown promising results, but dimensionality reduction through standard deep neural network layers is not guaranteed to maintain the distance information necessary for Gaussian process models. We build on previous work by comparing the use of the singular value decomposition against a spectral-normalized dense layer as a feature extractor for a deep neural Gaussian process approximation model and apply it to a capacitance prediction problem for the High Voltage Converter Modulators in the Oak Ridge Spallation Neutron Source. Our model shows improved distance preservation and predicts in-distribution capacitance values with less than 1% error.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14

Abstract The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-residue distance predictions to enhance EMA, during the 2020 CASP14 experiment, we integrated several new inter-residue distance features with the existing model quality assessment features in several deep learning methods to predict the quality of protein structural models. According to the evaluation of performance in selecting the best model from the models of CASP14 targets, our three multi-model predictors of estimating model accuracy (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) achieve the averaged loss of 0.073, 0.079, and 0.081, respectively, in terms of the global distance test score (GDT-TS). The three methods are ranked first, second, and third out of all 68 CASP14 predictors. MULTICOM-DEEP, the single-model predictor of estimating model accuracy (EMA), is ranked within top 10 among all the single-model EMA methods according to GDT-TS score loss. The results demonstrate that inter-residue distance features are valuable inputs for deep learning to predict the quality of protein structural models. However, larger training datasets and better ways of leveraging inter-residue distance information are needed to fully explore its potentials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Augmenting Graph Convolution with Distance Preserving Embedding for Improved Learning

Graph convolution incorporates topological information of a graph into learning. Message passing corresponds to traversal of a local neighborhood in classical graph algorithms. We show that incorporating additional global structures, such as shortest paths, through distance preserving embedding can improve performance. Our approach, Gavotte, significantly improves the performance of a range of popular graph neu-ral networks such as GCN, GA T,Graph SAGE, and GCNII for transductive learning. Gavotte also improves the performance of graph neural networks for full-supervised tasks, albeit to a smaller degree. As high-quality embeddings are generated by Gavotte as a by-product, we leverage clustering algorithms on these embed dings to augment the training set and introduce Gavotte+. Our results of Gavotte+ on datasets with very few labels demonstrate the advantage of augmenting graph convolution with distance preserving embedding.

Cong, Guojing↗
DOE OSTI2021

Distributed Inference with Sparse and Quantized Communication

Here, we consider the problem of distributed inference where agents in a network observe a stream of private signals generated by an unknown state, and aim to uniquely identify this state from a finite set of hypotheses. We focus on scenarios where communication between agents is costly, and takes place over channels with finite bandwidth. To reduce the frequency of communication, we develop a novel event-triggered distributed learning rule that is based on the principle of diffusing low beliefs on each false hypothesis. Building on this principle, we design a trigger condition under which an agent broadcasts only those components of its belief vector that have adequate innovation, to only those neighbors that require such information. We prove that our rule guarantees convergence to the true state exponentially fast almost surely despite sparse communication, and that it has the potential to significantly reduce information flow from uninformative agents to informative agents. Next, to deal with finite-precision communication channels, we propose a distributed learning rule that leverages the idea of adaptive quantization. We show that by sequentially refining the range of the quantizers, every agent can learn the truth exponentially fast almost surely, while using just 1 bit to encode its belief on each hypothesis. For both our proposed algorithms, we rigorously characterize the trade-offs between communication-efficiency and the learning rate.

42 ENGINEERING↗
DOE OSTI2021

Predicting images for the dynamics of stellar clusters ( π-DOC ): a deep learning framework to predict mass, distance, and age of globular clusters

ABSTRACT Dynamical mass estimates of simple systems such as globular clusters (GCs) still suffer from up to a factor of 2 uncertainty. This is primarily due to the oversimplifications of standard dynamical models that often neglect the effects of the long-term evolution of GCs. Here, we introduce a new approach to measure the dynamical properties of GCs, based on the combination of a deep-learning framework and the large amount of data from direct N-body simulations. Our algorithm, π-DOC (Predicting Images for the Dynamics Of stellar Clusters) is composed of two convolutional networks, trained to learn the non-trivial transformation between an observed GC luminosity map and its associated mass distribution, age, and distance. The training set is made of V-band luminosity and mass maps constructed as mock observations from N-body simulations. The tests on π-DOC demonstrate that we can predict the mass distribution with a mean error per pixel of 27 per cent, and the age and distance with an accuracy of 1.5 Gyr and 6 kpc, respectively. In turn, we recover the shape of the mass-to-light profile and its global value with a mean error of 12 per cent, which implies that we efficiently trace mass segregation. A preliminary comparison with observations indicates that our algorithm is able to predict the dynamical properties of GCs within the limits of the training set. These encouraging results demonstrate that our deep-learning framework and its forward modelling approach can offer a rapid and adaptable tool competitive with standard dynamical models.

Chardin, Jonathan↗
DOE OSTI2022

Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation

While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation—an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used with Rosetta to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model’s generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deep Learning Denoising Applied to Regional Distance Seismic Data in Utah

Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ~5 db, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ~0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ~2-5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ~15 db.

58 GEOSCIENCES↗