Search NASA⌕ Search

SEARCH · Search NASA

Results for “Range Of Learning”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Dynamic Range and Sensitivity Requirements of Satellite Ocean Color Sensors: Learning from the Past

Sensor design and mission planning for satellite ocean color measurements requires careful consideration of the signal dynamic range and sensitivity (specifically here signal-to-noise ratio or SNR) so that small changes of ocean properties (e.g., surface chlorophyll-a concentrations or Chl) can be quantified while most measurements are not saturated. Past and current sensors used different signal levels, formats, and conventions to specify these critical parameters, making it difficult to make cross-sensor comparisons or to establish standards for future sensor design. The goal of this study is to quantify these parameters under uniform conditions for widely used past and current sensors in order to provide a reference for the design of future ocean color radiometers. Using measurements from the Moderate Resolution Imaging Spectroradiometer onboard the Aqua satellite (MODISA) under various solar zenith angles (SZAs), typical (L(sub typical)) and maximum (L(sub max)) at-sensor radiances from the visible to the shortwave IR were determined. The Ltypical values at an SZA of 45 deg were used as constraints to calculate SNRs of 10 multiband sensors at the same L(sub typical) radiance input and 2 hyperspectral sensors at a similar radiance input. The calculations were based on clear-water scenes with an objective method of selecting pixels with minimal cross-pixel variations to assure target homogeneity. Among the widely used ocean color sensors that have routine global coverage, MODISA ocean bands (1 km) showed 2-4 times higher SNRs than the Sea-viewing Wide Field-of-view Sensor (Sea-WiFS) (1 km) and comparable SNRs to the Medium Resolution Imaging Spectrometer (MERIS)-RR (reduced resolution, 1.2 km), leading to different levels of precision in the retrieved Chl data product. MERIS-FR (full resolution, 300 m) showed SNRs lower than MODISA and MERIS-RR with the gain in spatial resolution. SNRs of all MODISA ocean bands and SeaWiFS bands (except the SeaWiFS near-IR bands) exceeded those from prelaunch sensor specifications after adjusting the input radiance to L(sub typical). The tabulated L(sub typical), L(sub max), and SNRs of the various multiband and hyperspectral sensors under the same or similar radiance input provide references to compare sensor performance in product precision and to help design future missions such as the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission and the Pre-Aerosol-Clouds-Ecosystems (PACE) mission currently being planned by the U.S. National Aeronautics and Space Administration (NASA).

chlorophyll fluorescence↗
DOE OSTI2020

Seismic signal augmentation to improve generalization of deep neural networks

Deep learning has emerged as an effective approach for seismic data processing in general, and for earthquake monitoring in particular. The ability of deep learning models to generalize beyond the training and validation data is important for comprehensive earthquake monitoring; this ability furthermore depends on the availability of a sufficiently large and complete training dataset. However, this requirement can prove challenging to meet due to significant effort and time for data collection and labeling. Data augmentation provides an efficient and effective approach for increasing the dimension of training samples and improving generalization to unseen samples. In this paper, we present augmentation methods appropriate for seismic waveforms and demonstrate their ability to reduce bias and increase performance. Furthermore, these augmentation methods can be applied to a wide range of deep learning applications designed for seismic data.

58 GEOSCIENCES↗
NASA NTRS2005

The Different Wavelengths of Radio Science

Radio Science covers many different avenues. This summer I attempted to work in each of the different avenues to learn the full range of subjects covered by Radio Science. I began my summer by traveling to Greece for the 3rd International Planetary Probe Workshop (IPPW-3). I went as a co-author of the Doppler Wind Experiment (DWE) team paper. My first job when I returned from Greece was to update the Radio Science activities webpage. I then used Fast Fourier Transforms (FFT) to find radio signals in recorded Radio Science experimental data and determine frequencies and powers. I read about and ran Fortran code being used to determine wind measurements on Huygens. I formatted and revised the abstracts and data lengths for the DVD data sets. By performing these tasks, I also learned the Unix operating system as well as a small amount of shell programming.

Malecha, Jessica L.↗
DOE OSTI2014

WE-A-16A-01: International Medical Physics Symposium: Increasing Access to Medical Physics Education/Training and Research Excellence

In response to a world in which cancer is a growing global health challenge, there is now a greater need for US Medical Physicists and other Radiation Oncology professionals across institutions to work together and be more globally engaged in the fight against cancer. There are currently many opportunities for Medical Physicists to contribute to alleviating this pressing need, especially in helping enhance access to Medical Physics Education/training and Research Excellence across international boundaries, particularly for low and middle-income countries (LMIC), which suffer from a drastic shortage of accessible knowledge and quality training programs in radiotherapy. Many Medical Physicists are not aware of the range of opportunities that even with small effort could have a high impact. Faculty at the two CAMPEP-accredited Medical Physics Programs in New England: the University of Massachusetts Lowell and Harvard Medical School have developed a growing alliance to increase Access to Medical Physics Education/training and Research Excellence (AMPERE), and facilitate greater active involvement of U.S. Medical Physicists in helping the global fight against cancer and cancer disparities. In this symposium, AMPERE Alliance members and partners from Europe and Africa will present and discuss the growing global cancer challenge, the dearth of knowledge, research, and other barriers to providing life-saving radiotherapy in LMIC, mechanisms for meeting these challenges, the different opportunities for participation by Medical Physicists, including students and residents, and how participation can be facilitated to increase AMPERE for global health. Learning Objectives: To learn about the growing global cancer challenge, areas of greatest need and limitations to accessing knowledge and quality radiotherapy training programs, especially in LMIC; To learn about the range of opportunities for Medical Physicists, including students and residents, to work together in global health to help increase AMPERE and alleviate the growing global burden of cancer; To present and discuss a new model for harmonizing Medical Physics Training across countries and how this model (UMass and Heidelberg) could be extended to LMIC in collaboration with the IAEA; To highlight a new platform and program for facilitating contributions by Medical Physicists to increase AMPERE towards the elimination of global cancer disparities. Challenges in Cancer Control in Africa Twalib A. Ngoma, MD, Professor, Executive Director, Ocean Road Cancer Institute, Dar Es Salaam, Tanzania Cancer care in Africa is beset by lack of attention, political will, cancer registries, cancer plans, human resources, financial resources and treatment facilities.. As a result of this, cancer patients in Africa are far more likely to die of their disease than those in developed countries. According to data from the WHO 750,000 new cancer cases occur in Africa every year and this number is predicted to rise by 70% by 2020. To make matters worse, an estimated 75% of cancer patients in Africa have advanced or incurable cancers at diagnosis making palliative care the most realistic approach to their management. Furthermore, Cancer prevention is nearly nonexistent, cancer detection is rare and treatment usually comes too late and is inefficient. The overall mortality-to-incidence ratio for men with cancer in the Africa is 0.75 compared with 0.54 in the developed world while the ratios for women in Africa, is 0.65 compared with 0.45 for women in the developed world. There is also limited access to radiotherapy. According to the International Atomic Energy Agency (IAEA), whilst developed countries usually have one radiotherapy machine per 250,000 people, most African nations have only one machine per ten million people. The above numbers are alarming and speak for themselves. The only solution to improve this alarming situation is to address the major challenges which African countries face in provision of cancer services which include but not limited to lack of cancer registries, lack of funding, lack of human resources, lack of radiotherapy machines, lack of cancer drugs and lack of accessible and affordable cancer screening, early diagnosis, treatment or palliative care services. Since there are considerable differences among African countries, in my presentation I will share with the audience how we address cancer control challenges in Tanzania in general and specifically in radiation oncology. The African continent cancer plan 2013 2017 Folakemi Odedina, PhD, Professor and Director of Health Disparities, UF Health Cancer Center, University of Florida The burden of cancer is rising in Africa, in addition to current heavy burden of communicable, and other non-cancer related non—communicable diseases. Conquering cancer in Africa will require a comprehensive collaborative approach with cancer clinicians, scientists, patients, advocates, policy makers and community leaders working hand-in-hand at the local, state, national, and continent levels with the primary mission: To reduce the number of deaths from cancer and improve the quality of life of cancer patients, survivors and caregivers. Unfortunately, less than 40% of African countries have a credible cancer control policy and program. The African Organization for Research and Training in Cancer (AORTIC) decided to create an African Cancer Plan to provide cost-effective strategies that can be employed throughout the continent to fight cancer. Based on the African proverb that “It takes a village to raise a child”, the Cancer Plan provides specific strategies that can be used by individuals, employers, organizations and policy-makers to fight cancer. In addition, we have provided overarching strategies to address cancer in Africa and targeted 5-year plan for prostate, breast, cervix, lung and liver cancers. In developing this Cancer Plan, our primary goal is to decrease cancer incidence and mortality in Africa. This goal can only be achieved by stakeholders and dedicated individuals to lead and implement the strategies outlined in this plan. If you are interested in partnering with AORTIC to reduce the burden of cancer in Africa, please send an email to info@aortic-africa.org. Synergies in research and clinical care through international collaboration Thomas Bortfeld and David Gierga, Massachusetts General Hospital, Harvard Medical School, MA Medical Physics relies on high technology that is not distributed equally. The whole spectrum of Medical Physics technologies is not available at every hospital or research institute, and not even in every high income country. One example is heavy ion therapy equipment which is currently only available in Japan, Germany and Italy. There is also a large global variation in terms of research infrastructure and focus. A student of Medical physics cannot gain broad experience, certainly not hands-on experience, by staying at one place only. While it is debatable what a good trade-off between breadth and depth in Medical Physics education is, it is generally agreed upon that some breadth is necessary. Researchers in Medical Physics have to cross borders if they need specific technologies for their projects. Therefore it is self-evident that international programs in Medical Physics education and research make sense. Yet, very few programs of this type exist. In this presentation we will report on our own experience of pursuing an international career in Medical Physics, with international student programs, and with the international exchange of researchers. We will present new or planned opportunities such as the medical beamline at CERN in Geneva. We will also report on the synergies in clinical care through international collaborations between partners in high and low income countries. One example is the partnership of the MGH/Harvard Medical School community with the oncology community and government of Botswana to form the BOTSOGO (BOTSwana Oncology Global Outreach) initiative. This collaborative effort in oncology care was spurred by existing relationships in HIV/AIDS research and care delivery developed within the Botswana-Harvard AIDS Institute Partnership (BHP). The initial efforts of the BOTSOGO initiative have been organized as follows: 1) on-site visits to share expertise in clinical cancer care for capacity building purposes (e.g. cervical brachytherapy), 2) developing a forum for multi-disciplinary case discussions and education and 3) relationship building with local stakeholders for long-term sustainability and growth. An international system for the certification of medical physicists Raymond K. Wu, Chairman, IOMP Professional Relations Committee; Chairman, AAPM Exchange Scientist Program Subcommittee An international system for the certification of medical physicists is an important issue. The International Organization for Medical Physicists (IOMP) has in collaboration with a number of member countries established the International Medical Physics Certification Board (IMPCB) to address this issue, and to provide a mechanism to mark the milestone for the professional development of clinical medical physicists. Raymond Wu, PhD, is the CEO of IMPCB and the Chairman of the IOMP Professional Relations Committee. He is the invited speaker recommended by IOMP to give a talk on this important subject. He will give the latest update of the work of IMPCB, its near term goals, and pathways to the goals. He will also discuss the importance of such an International System of certification in the training/education of next generation Medical Physicists, including those in low and middle income countries (LMIC) where such training is crucial in the fight against cancer. Learning Objectives: Understand the certification program as described in the IOMP Policy Statements. Understand the plan of the IMPCB to establish the accreditation process of national certification programs. Understand the goals of this international collaborative effort and the potential impacts to the quality of clinical medical physics practice. Medical Physics Education Across Continents: The UMass Lowell and Heidelberg University Joint Coordination Effort Erno Sajo, Director of Medical Physics, University of Massachusetts at Lowell Medical Physics education has unique flavors across institutions within the US and shows significant differences across continents. In the latter, even the definition of Medical Physics may differ. Not only is there a difference in topical coverage, but often what is considered a cohesive topic in one institution, and taught as a single course, is fragmented among several other courses in the other institution due to a different philosophy. Because of the regulatory and certification requirements, these differences impact the mobility of medical physicists across continents. As a result, physicists who wish to practice in the US or Canada but have completed their education elsewhere often find that they have to take remedial courses or even obtain a new degree in Medical Physics despite the fact that they already have one. Outreach to developing countries, therefore, is even more difficult. The University of Massachusetts Lowell and Heidelberg University recently completed a joint coordination effort, in which they identified topics that are common versus complementary in their medical physics curricula. A student exchange program was developed that permits students to take any of the common topics at the other institution while taking complementary courses as electives, which count towards their degree requirements at their home institution. Thesis research is also mutually accepted. When properly documented, in this way CAMPEP recommendations can be met across the institutions. Therefore, students participating in this program satisfy both the American Board of Radiology (ABR) requirements and the European regulatory requirements. The framework on which this collaboration rests and the cross-comparison methods developed therein may be implemented in other exchange programs and thus a similar approach can be adopted in outreach programs with developing countries. IAEA PACT Program and opportunities for support and collaboration Susan Morgan, Program Coordinator, International Atomic Energy Agency, Vienna, Austria In response to the developing world's cancer crisis, the International Atomic Energy Agency (IAEA) established the Program of Action for Cancer Therapy (PACT) in 2004 to fully realize the public health impact obtained through global partnerships in cancer control and technology transfer in radiation medicine. PACT's vision strives for global partnerships to confront the cancer crisis in developing countries, notably with our sister United Nations agency, the World Health Organization (WHO), and our Joint Programme on Cancer Control established in 2009. The IAEA, through PACT, the WHO, the International Agency for Research on Cancer (IARC) and other cancer-related organizations work together to make a coordinated global response in supporting low and middle income (LMI) IAEA Member States in the implementation of comprehensive national cancer control programmes. PACT's goals are: To build global partnerships of cancer-related organizations committed to addressing the challenge of cancer in LMI Member States in all its aspects; To mobilize resources from charitable trusts, foundations, and others in public and private sectors sources to assist LMI Member States to develop and implement their radiation medicine capacities within a national cancer control programme (NCCP); and, To ensure the effective and sustainable transfer of radiation medicine technologies or knowledge to all LMI Member States where unmet needs exist. PACT work focuses on: imPACT: Assessing Cancer Burden PMDS: Developing Global Partnerships VUCCnet: Promoting Cancer Control Training AGaRT: Making Radiotherapy Accessible Facilitating increased participation and professional development of Medical Physicists and other Radiation Oncology professionals in global health Wilfred Ngwa, Harvard Medical School, University of Massachusetts Lowell, MA The 2014 World Health Organization (WHO) Cancer report highlights an alarming increase in the global burden of cancer. It also highlights what it terms “the cancer divide”, or disparities, evinced by a substantially higher cancer burden in low and middle income countries (LMIC) in Asia, Central/South America and Africa. The WHO even predicts a potential African cancer epidemic by 2020 if significant progress is not made in global cancer control efforts. Evidence that collaborative global health approaches have led to major progress in controlling infectious diseases including in LMIC suggests that similar approaches will be useful for non-communicable diseases like cancer. In consonance with this, leaders in cancer policy from the USA and 14 economically diverse countries recently concluded that successful campaigns to control cancers with existing methods and to improve current strategies will increasingly depend onconcerted multinational collaborations (Sci Transl Med 5, p. 175, 2013). Hence there is growing urgency for increasing collaborative global cancer Care Research and Education (CaRE), as well as support for greater effectiveness of already existing initiatives involving partners from different nations, diverse economic and cultural backgrounds. The good news is that there is a growing awareness of the importance of global health and growing interest including amongst Medical Physicists and other Radiation oncology (RadOnc) professionals to participate in global health. However, many are unaware of currently existing opportunities for participation that even with small effort could have a high impact. Over 50% of cancer patients in the developed world depend on RadOnc professionals for their treatment, and hence participation of RadOnc professionals in global health efforts in the global fight against cancer is crucial. It is also important that the next generation of RadOnc professionals (students, and residents) be trained with a global perspective, to be global health leaders in cancer CaRE. This presentation will highlight a novel platform for enhancing participation and professional development of Medical Physicists and other RadOnc professionals in global health. Ways in which this platform can facilitate contributions by Medical Physicists and other RadOnc Professionals, students and residents in global health towards the elimination of global cancer disparities will be discussed. This will be followed by a panel discussion by some of the pioneers/leaders in collaborative global cancer CaRE on the growing cancer burden, challenges and opportunities for greater active involvement and professional development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Conformational spread drives the evolution of the calcium–calmodulin protein kinase II

The calcium calmodulin (Ca 2+ /CaM) dependent protein kinase II (CaMKII) decodes Ca 2+ frequency oscillations. The CaMKIIα isoform is predominantly expressed in the brain and has a central role in learning. I matched residue and organismal evolution with collective motions deduced from the atomic structure of the human CaMKIIα holoenzyme to learn how its ring architecture abets function. Protein dynamic simulations showed its peripheral kinase domains (KDs) are conformationally coupled via lateral spread along the central hub. The underlying β-sheet motions in the hub or association domain (AD) were deconvolved into dynamic couplings based on mutual information. They mapped onto a coevolved residue network to partition the AD into two distinct sectors. A second, energetically stressed sector was added to ancient bacterial enzyme dimers for assembly of the ringed hub. The continued evolution of the holoenzyme after AD–KD fusion targeted the sector’s ring contacts coupled to the KD. Among isoforms, the α isoform emerged last and, it alone, mutated rapidly after the poikilotherm–homeotherm jump to match the evolution of memory. The correlation between dynamics and evolution of the CaMKII AD argues single residue substitutions fine-tune hub conformational spread. The fine-tuning could increase CaMKIIα Ca 2+ frequency response range for complex learning functions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Attention-based quantum tomography

Abstract With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. There has been a growing interest in approaching the problem of quantum state reconstruction using generative neural network models. Here we propose the ‘attention-based quantum tomography’ (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state. AQT is based on the model proposed in ‘Attention is all you need’ by Vaswani et al (2017 NIPS ) that is designed to learn long-range correlations in natural language sentences and thereby outperform previous natural language processing (NLP) models. We demonstrate not only that AQT outperforms earlier neural-network-based quantum state reconstruction on identical tasks but that AQT can accurately reconstruct the density matrix associated with a noisy quantum state experimentally realized in an IBMQ quantum computer. We speculate the success of the AQT stems from its ability to model quantum entanglement across the entire quantum system much as the attention model for NLP captures the correlations among words in a sentence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Study of Clustering Techniques and Hierarchical Matrix Formats for Kernel Ridge Regression

We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Using hierarchical matrix approximations for the kernel matrix the memory requirements, the number of floating point operations, and the execution time are drastically reduced compared to standard dense linear algebra routines. We consider both the general H matrix hierarchical format as well as Hierarchically Semi-Separable (HSS) matrices. Furthermore, we investigate the impact of several preprocessing and clustering techniques on the hierarchical matrix compression. Effective clustering of the input leads to a ten-fold increase in efficiency of the compression. The algorithms are implemented using the STRUMPACK solver library. These results confirm that - with correct tuning of the hyperparameters - classification using kernel ridge regression with the compressed matrix does not lose prediction accuracy compared to the exact - not compressed - kernel matrix and that our approach can be extended to O(1M) datasets, for which computation with the full kernel matrix becomes prohibitively expensive. We present numerical experiments in a distributed memory environment up to 1,024 processors of the NERSC's Cori supercomputer using well-known datasets to the machine learning community that range from dimension 8 up to 784.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Sparse Tensor Benchmark Suite for CPUs and GPUs

Tensor computations present significant performance chal- lenges that impact a wide spectrum of applications ranging from machine learning, healthcare analytics, social network analysis, data mining to quantum chemistry and signal processing. Efforts to improve the perfor- mance of tensor computations include exploring data layout, execution scheduling, and parallelism in common tensor kernels. This work presents a benchmark suite for arbitrary-order sparse tensor kernels using state- of-the-art tensor formats: coordinate (COO) and hierarchical coordinate (HiCOO) on CPUs and GPUs. It presents a set of reference tensor kernel implementations that are compatible with real-world tensors and power law tensors extended from synthetic graph generation techniques. We also propose Roofline performance models for these kernels to provide insights of computer platforms from sparse tensor view. This benchmark suite along with the synthetic tensor generator is publicly available.

Li, Jiajia↗
DOE OSTI2022

The Imperial Valley Dark Fiber Project: Toward Seismic Studies Using DAS and Telecom Infrastructure for Geothermal Applications

We report that the Imperial Valley is a seismically active basin occupying the southern end of the Salton trough, an area of rapid extension, high heat flow, and abundant geothermal resources. This report describes an ongoing large-scale distributed acoustic sensing (DAS) recording study acquiring high-density seismic data on an array between Calipatria and Imperial, California. This 27 km array, operating on dark fiber since 9 November 2020, has recorded a wealth of local seismic events as well as ambient noise. The goal of the broader Imperial Valley Dark Fiber project is to evaluate passive DAS as a tool for geothermal exploration and monitoring. This report is intended to provide installation information, noise characteristics, and metadata for future studies utilizing the data set. Because of the relatively small number of basin-scale DAS studies that have been conducted to date, we also provide a range of lessons learned during the deployment to assist future researchers exploring this acquisition strategy.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Incorporation of Physics Phenomenology into an Adaptive Algorithm Framework (Final Report)

We attempt to incorporate prior physics knowledge into a machine learning architecture at an applied application level (empirical) as opposed to the level of fundamental physics (first principles). The purpose of this work is to allow for the application of methods of physics-informed machine learning to a broad range of national security problems, while enhancing the trust in machine-learned models by decreasing the “black box” nature of such methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1994

Instructable autonomous agents

In contrast to current intelligent systems, which must be laboriously programmed for each task they are meant to perform, instructable agents can be taught new tasks and associated knowledge. This thesis presents a general theory of learning from tutorial instruction and its use to produce an instructable agent. Tutorial instruction is a particularly powerful form of instruction, because it allows the instructor to communicate whatever kind of knowledge a student needs at whatever point it is needed. To exploit this broad flexibility, however, a tutorable agent must support a full range of interaction with its instructor to learn a full range of knowledge. Thus, unlike most machine learning tasks, which target deep learning of a single kind of knowledge from a single kind of input, tutorability requires a breadth of learning from a broad range of instructional interactions. The theory of learning from tutorial instruction presented here has two parts. First, a computational model of an intelligent agent, the problem space computational model, indicates the types of knowledge that determine an agent's performance, and thus, that should be acquirable via instruction. Second, a learning technique, called situated explanation specifies how the agent learns general knowledge from instruction. The theory is embodied by an implemented agent, Instructo-Soar, built within the Soar architecture. Instructo-Soar is able to learn hierarchies of completely new tasks, to extend task knowledge to apply in new situations, and in fact to acquire every type of knowledge it uses during task performance - control knowledge, knowledge of operators' effects, state inferences, etc. - from interactive natural language instructions. This variety of learning occurs by applying the situated explanation technique to a variety of instructional interactions involving a variety of types of instructions (commands, statements, conditionals, etc.). By taking seriously the requirements of flexible tutorial instruction, Instructo-Soar demonstrates a breadth of interaction and learning capabilities that goes beyond previous instructable systems, such as learning apprentice systems. Instructo-Soar's techniques could form the basis for future 'instructable technologies' that come equipped with basic capabilities, and can be taught by novice users to perform any number of desired tasks.

Huffman, Scott Bradley↗
DOE OSTI2019

The Machine Learning landscape of top taggers

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Complete and Correct Transfer of Information (CACTI)

Many distributed systems, file transfer mechanisms, and message passing systems offer reliability mechanisms such as acknowledgements, retries, and durability. While these tools may be “good enough” for their typical use cases, they may not offer sufficient coverage for the wide range of faults that impact data transfers and communication. A gap in the reliability measures may lead to some small amount of data loss. Some high-consequence systems cannot tolerate the loss or corruption of even a single record. We present seven principles that will counter a wide range of faults and protect against data loss and corruption. These principles bring together lessons learned from a wide range of technologies and can inform appropriate system design and application usage. These principles will help readers reason on how prevent data loss in a multi-hop pipeline and how to properly use tools that may have a deficiency in reliability.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Strategies Which Foster Broad Use and Deployment of Earth and Space Science Informal and Formal Education Resources

Education projects supported by federal agencies and carried out by a wide range of organizations foster learning about Earth and Space systems science in a wide array of venues. Across these agencies a range of strategies are employed to ensure that effective materials are created for these diverse venues. And that these materials are deployed broadly so that a large spectrum of the American Public, both adults and children alike, can learn and become excited by the Earth and space system science. This session will highlight some of those strategies and will cover representative examples to illustrate the effectiveness of the strategies. Invited speakers from selected formal and informal educational efforts will anchor this session. Speakers with representative examples are encouraged to submit abstracts for the session to showcase the strategies which they use.

Meeson, Blanche W.↗
DOE OSTI2018

Decoupling Mesoscale Functional Response in PLZT across the Ferroelectric–Relaxor Phase Transition with Contact Kelvin Probe Force Microscopy and Machine Learning

Relaxor ferroelectrics exhibit a range of interesting material behavior, including high electromechanical response, polarization rotations, as well as temperature and electric field-driven phase transitions. The origin of this unusual functional behavior remains elusive due to limited knowledge on polarization dynamics at the nanoscale. Piezoresponse force microscopy and associated switching spectroscopy provide access to local electromechanical properties on the micro- and nanoscale, which can help to address some of these gaps in our knowledge. However, these techniques are inherently prone to artefacts caused by signal contributions emanating from electrostatic interactions between tip and sample. Understanding functional behavior of complex, disordered systems like relaxor materials with unknown electromechanical properties therefore requires a technique that allows distinguishing between electromechanical and electrostatic response. Here, contact Kelvin probe force microscopy (cKPFM) is used to gain insight into the evolution of local electromechanical and capacitive properties of a representative relaxor material lead lanthanum zirconate across the phase transition from a ferroelectric to relaxor state. The obtained multidimensional data set was processed using an unsupervised machine learning algorithm to detect variations in functional response across the probed area and temperature range. Further analysis showed the formation of two separate cKPFM response bands below 50 °C, providing evidence for polarization switching. At higher temperatures only one band is observed, indicating an electrostatic origin of the measured response. In addition, the junction potential difference, which was extracted from the cKPFM data, becomes independent of the temperature in the relaxor state. As a result, the combination of this multidimensional voltage spectroscopy technique and machine learning allows to identify the origin of the measured functional response and to decouple ferroelectric from electrostatic phenomena necessary to understand the functional behavior of complex, disordered systems like relaxor materials.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Neural controller for adaptive movements with unforeseen payloads

A theory and computer simulation of a neural controller that learns to move and position a link carrying an unforeseen payload accurately are presented. The neural controller learns adaptive dynamic control from its own experience. It does not use information about link mass, link length, or direction of gravity, and it uses only indirect uncalibrated information about payload and actuator limits. Its average positioning accuracy across a large range of payloads after learning is 3 percent of the positioning range. This neural controller can be used as a basis for coordinating any number of sensory inputs with limbs of any number of joints. The feedforward nature of control allows parallel implementation in real time across multiple joints.

Kuperstein, Michael↗
DOE OSTI2022

Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements

Light detection and ranging (LiDAR) measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations, which encompass a breadth of atmospheric stability regimes and rotor thrust coefficients. The LiDAR measurements are clustered through the k-means algorithm, which enables identifying the most representative realizations of wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters. Considering the large number of LiDAR samples collected to probe the wake velocity field, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data and the associated supervisory control and data acquisition and meteorological data enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability. Furthermore, the cluster analysis of the LiDAR data allows for the identification of systematic off-design operations with a certain yaw misalignment of the turbine rotor with the mean wind direction.

17 WIND ENERGY↗