Accelerated training of bootstrap aggregation-based deep information extraction systems from cancer pathology reports
Not Available
60 APPLIED LIFE SCIENCES↗
SEARCH · Engineering Papers
Results for “network science”
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.
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Machine learning framework for predicting uranium enrichments from M400 CZT gamma spectra
A machine learning framework was developed for predicting uranium enrichments from M400 CZT gamma spectra. This framework leverages the availability of a large amount of measured M400 gamma spectra and uses a recently updated version of Gamma Detector Response and Analysis Software (GADRAS) for gamma spectrum analysis and generation. It also leverages the existing machine learning modules in Python for gamma spectrum data processing, curation, model training, benchmarking, and optimization of the deep machine learning models. The framework is used to develop a deep learning model to analyze gamma spectra from a set of U 3 O 8 samples with enrichments ranging from 0.31 to 93.17% and UF 6 cylinders with enrichments ranging from 0.2 to 4.95%, and the model performance is tested using a set of measured spectra and the respective declared enrichment values. Results show that the model can correctly classify 99.35% of the U 3 O 8 sample enrichments, and can predict the samples’ enrichments within an average absolute error of 0.099% (in percentage points of enrichment). For the UF 6 cylinders, the average absolute error was approximately 0.03%, with an accuracy of 98% in classifying discrete enrichment values of UF 6 samples. Finally, the results also show that the model has performed significantly better in terms of predicting enrichments in UF 6 cylinders based on measured gamma spectra than the GEM code, with a standard deviation (of the relative errors) of 2.23% (compared with the 11.51% value for the GEM code) based on results from a set of test data.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data
Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Essay: Quantum Sensing with Atomic, Molecular, and Optical Platforms for Fundamental Physics
Atomic, molecular, and optical (AMO) physics has been at the forefront of the development of quantum science while laying the foundation for modern technology. With the growing capabilities of quantum control of many atoms for engineered many-body states and quantum entanglement, a key question emerges: what critical impact will the second quantum revolution with ubiquitous applications of entanglement bring to bear on fundamental physics? In this Essay, we argue that a compelling long-term vision for fundamental physics and novel applications is to harness the rapid development of quantum information science to define and advance the frontiers of measurement physics, with strong potential for fundamental discoveries. As quantum technologies, such as fault-tolerant quantum computing and entangled quantum sensor networks, become much more advanced than today’s realization, we wonder what doors of basic science can these tools unlock. We anticipate that some of the most intriguing and challenging problems, such as quantum aspects of gravity, fundamental symmetries, or new physics beyond the minimal standard model, will be tackled at the emerging quantum measurement frontier. . Published by the American Physical Society 2024
Physics↗
Exploration of Novel Neuromorphic Methodologies for Materials Applications
Many of today's most interesting questions involve understanding and interpreting complex relationships within graph-based structures. For instance, in materials science, predicting material properties often relies on analyzing the intricate network of atomic interactions. Graph neural networks (GNNs) have emerged as a popular approach for these tasks; however, they suffer from limitations such as inefficient hardware utilization and over-smoothing. Recent advancements in neuromorphic computing offer promising solutions to these challenges. In this work, we evaluate two such neuromorphic strategies known as reservoir computing and hyperdimensional computing. We compare the performance of both approaches for bandgap classification and regression using a subset of the Materials Project dataset. Our results indicate recent advances in hyperdimensional computing can be applied effectively to better represent molecular graphs.
Gobin, Derek [George Mason University, Virginia]↗
Dynamic Rhizodeposition in the Woody Perennial Populus trichocarpa
Plants undergo physiological and metabolic changes that release specific molecules into the surrounding soil, a process collectively known as rhizodeposition. These compounds play crucial roles in plant‐microbe‐soil interactions, such as supporting plant development and resilience in changing environments. Under nutrient‐limited conditions, these plant‐derived compounds modify the rhizosphere environment, mobilizing otherwise inaccessible nutrients and recruiting stress‐adaptive microbial communities that support stress resilience. Currently, the chemical diversity of rhizodeposition has yet to be fully realized but is expected to be a complex mixture that includes soluble organic compounds excreted from root cells, along with products of root cell turnover, sloughed‐off root cap and border cells, and mucilage. Here, we developed a methodological and conceptual framework for an in‐depth measurement of rhizodeposition through critical advancements in untargeted metabolomics. Further, this approach provided foundational insights into the dynamic changes in rhizodeposition for the woody perennial Populus trichocarpa and rhizodeposit profiles varying by genotype, time, location, and environment. More broadly, this study provides a framework that will help formulate the next steps to effectively study rhizodeposition.
59 BASIC BIOLOGICAL SCIENCES↗
Conference Grant Report
The University of California San Diego (UCSD), hosted the High Energy-Density Science Summer School from July 28 – August 11, 2019 on the UC San Diego campus. The goal of the Summer School series was to introduce new talent to the breadth of the U.S. High Energy Density Science (HEDS) community through lectures, engaging workshops, and discussion sessions with leaders in academia and the national laboratories. The objectives are to inspire young scientists to pursue graduate and professional careers in the fields of high energy density science, teach them fundamental HED science and critical skills, and grant them the opportunity to network with leading academic and national laboratory researchers. Our focus was to attract promising early-career students from across the country.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Basic Research Needs in Quantum Computing and Networking
Employing quantum mechanical resources in computing, information processing, and networking opens the door to potential exponential advantages over classical counterparts. However, quantifying and realizing such advantages poses extensive scientific and engineering challenges. Department of Energy (DOE) investments have driven steady progress in addressing such challenges. Recently developed quantum algorithms offer asymptotic exponential advantages in speed or accuracy for fundamental scientific problems. These problems include simulating physical systems, solving systems of linear equations, differential equations, and optimization problems. Empirical demonstrations on nascent quantum hardware suggest better performance on contrived computational tasks than classical analogs. However, the requirements for a quantum computer or network to demonstrate an end-to-end rigorously quantifiable performance improvement over classical analogs remains a grand challenge, especially for problems of practical value. In particular, what will be required for quantum technology to ultimately exhibit scalable, rigorous, and transformative performance advantages for practical applications? In July 2023, DOE’s Advanced Scientific Computing Research program in the Office of Science convened the Workshop on Basic Research Needs in Quantum Computing and Networking, where major opportunities and grand challenges were identified. The following five priority research directions (PRDs) were identified as a result of the workshop.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
GROWdb US River Systems - Samples
GROW Overview We developed the Genome Resolved Open Watersheds database (GROWdb), which aims to increase genomic sampling and understanding of global river microbiomes. An emphasis of GROWdb is to create a publicly available and ever-expanding microbial genome database that is focused on rivers while being interoperable with databases from other ecosystems. GROWdb is based on a network-of-networks approach to move beyond a small collection of well-studied rivers, towards a spatially distributed, global network of systematic observations. GROWdb represents the first microbial, river-focused resource parsed at various scales from genes to MAGs to community level including expression and potential based measurements that will be of interest to microbiologists, ecologists, geochemists, hydrologists, and modelers. Dataset Acknowledgement GROWdb contains data from various research campaigns, please acknowledge the following data generators, as appropriate: WHONDRS derived genomes or samples - include this statement in your acknowledgements: “This study used data from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) under the River Corridor Science Focus Area (SFA) at the Pacific Northwest National Laboratory (PNNL) that was generated at the U.S. Department of Energy (DOE) Joint Genome Institute User Facility. PNNL is operated by Battelle Memorial Institute for the U.S. DOE under Contract No. DE-AC05-76RL01830. The SFA is supported by the U.S. DOE, Office of Biological and Environmental Research (BER), Environmental System Science (ESS) Program.” Total Samples loaded onto this Narrative: 178 Note: Not all GROW samples may be loaded into KBase Data Availability The data underlying GROWdb are accessible across various platforms to ensure all levels of data structure are widely available. First, all reads and MAGs are publicly hosted on National Center for Biotechnology (NCBI) under Bioproject PRJNA946291. Second, all data related data presented here including MAG annotations, extended data tables, phylogenetic tree files, antibiotic resistance gene database files, and MAG abundance tables are available in Zenodo (link). Beyond the flat database files listed above, our aim for GROWdb was to maximize data use by making the data available in searchable and interactive platforms including the National Microbiome Data Collaborative (NMDC) data portal, the Department of Energy’s Systems Biology Knowledgebase (KBase), and a GROW specific user interface released here, GROWdb Explorer. Each platform provides different ways to interact with GROWdb: NMDC GROWdb formed a pilot project for the NMDC. Specifically, individual GROWdb datasets (metagenomes, metatranscriptomes, etc) are easily accessible and searchable through the NMDC data portal, where they are systematically connected to each other and to a rich suite of sample information and standard analysis results, following Findable, Accessible, Interoperable, and Reusable (FAIR) data practices. KBase GROWdb is publicly available within KBase, including samples (this Narrative), MAGs, and corresponding genome scale metabolic models. Access within KBase allows for immediate access and reuse of data, including comparison to private data using KBase’s 500+ analysis tools. Other linked narratives in KBase: GROW Metagenome Assembled Genomes (MAGs) GROW Metabolic Models GROWdb Explorer GROWdb data is also explorable through a graphical user interface built through the Colorado State University Geospatial Centroid (https://geocentroid.shinyapps.io/GROWdatabase/), allowing users to search and graph microbial and spatial data simultaneously. In summary, this microbial genome resource represents the first publicly available genome collection from rivers and offers data that can be leveraged across microbiome studies. GROWdb is an expanding repository to incorporate and unify global river multi-omic data for the future.
59 BASIC BIOLOGICAL SCIENCES↗
Deviations from maximal entanglement for eigenstates of the Sachdev-Ye-Kitaev model
We consider mid-spectrum eigenstates of the Sachdev-Ye-Kiteav (SYK) model. We prove that for subsystems whose size is a constant fraction of the system size, the entanglement entropy deviates from the maximum entropy by at least a positive constant. This result highlights the difference between the entanglement entropy of mid-spectrum eigenstates of the SYK model and that of random states.
Disordered Systems and Neural Networks (cond-mat.d↗
Anomalies of global symmetries on the lattice
't Hooft anomalies of global symmetries play a fundamental role in quantum many-body systems and quantum field theory (QFT). In this paper, we make a systematic analysis of lattice anomalies - the analog of 't Hooft anomalies in lattice systems - for which we give a precise definition. Crucially, a lattice anomaly is not a feature of a specific Hamiltonian, but rather is a topological invariant of the symmetry action. The controlled setting of lattice systems allows for a systematic and rigorous treatment of lattice anomalies, shorn of the technical challenges of QFT. We find that lattice anomalies reproduce the expected properties of QFT anomalies in many ways, but also have crucial differences. In particular, lattice anomalies and QFT anomalies are not, contrary to a common expectation, in one-to-one correspondence, and there can be non-trivial anomalies on the lattice that are infrared (IR) trivial: they admit symmetric trivial gapped ground states, and map to trivial QFT anomalies at low energies. Nevertheless, we show that lattice anomalies (including IR-trivial ones) have a number of interesting consequences in their own right, including connections to commuting projector models, phases of many-body localized (MBL) systems, and quantum cellular automata (QCA). We make substantial progress on the classification of lattice anomalies and develop several theoretical tools to characterize their consequences on symmetric Hamiltonians. Our work places symmetries of quantum many-body lattice systems into a unified theoretical framework and may also suggest new perspectives on symmetries in QFT.
Disordered Systems and Neural Networks (cond-mat.d↗
Space programs summary no. 37-45, volume VI FOR the period March 1, 1967 to April 30, 1967. Space exploration programs and space sciences
Space exploration programs and space sciences - Lunar program, Planetary-Interplanetary program, and Deep space network program
LUNAR PROGRAM↗
Space applications of artificial intelligence; Proceedings of the Annual Goddard Conference, Greenbelt, MD, May 16, 17, 1989
Theoretical and implementation aspects of AI systems for space applications are discussed in reviews and reports. Sections are devoted to planning and scheduling, fault isolation and diagnosis, data management, modeling and simulation, and development tools and methods. Particular attention is given to a situated reasoning architecture for space repair and replace tasks, parallel plan execution with self-processing networks, the electrical diagnostics expert system for Spacelab life-sciences experiments, diagnostic tolerance for missing sensor data, the integration of perception and reasoning in fast neural modules, a connectionist model for dynamic control, and applications of fuzzy sets to the development of rule-based expert systems.
Rash, James L.↗
Link Winds: A visual data analysis system and its application to the atmospheric ozone depletion problem
The Linked Windows Interactive Data System (LinkWinds) is a prototype visual data exploration system resulting from a NASA Jet Propulsion Laboratory (JPL) program of research into the application of graphical methods for rapidly accessing, displaying, and analyzing large multi variate multidisciplinary data sets. Running under UNIX it is an integrated multi-application executing environment using a data-linking paradigm to dynamically interconnect and control multiple windows containing a variety of displays and manipulators. This paradigm, resulting in a system similar to a graphical spreadsheet, is not only a powerful method for organizing large amounts of data for analysis, but leads to a highly intuitive, easy-to-learn user interface. It provides great flexibility in rapidly interacting with large masses of complex data to detect trends, correlations, and anomalies. The system, containing an expanding suite of non-domain-specific applications, provides for the ingestion of a variety of data base formats and hard -copy output of all displays. Remote networked workstations running LinkWinds may be interconnected, providing a multiuser science environment (MUSE) for collaborative data exploration by a distributed science team. The system is being developed in close collaboration with investigators in a variety of science disciplines using both archived and real-time data. It is currently being used to support the Microwave Limb Sounder (MLS) in orbit aboard the Upper Atmosphere Research Satellite (UARS). This paper describes the application of LinkWinds to this data to rapidly detect features, such as the ozone hole configuration, and to analyze correlations between chemical constituents of the atmosphere.
Jacobson, Allan S.↗
Trade-Off Analysis Report
NASAs Glenn Research Center (GRC) defines and develops advanced technology for high priority national needs in communications technologies for application to aeronautics and space. GRC tasked Computer Networks and Software Inc. (CNS) to examine protocols and architectures for an In-Space Internet Node. CNS has developed a methodology for network reference models to support NASAs four mission areas: Earth Science, Space Science, Human Exploration and Development of Space (REDS), Aerospace Technology. CNS previously developed a report which applied the methodology, to three space Internet-based communications scenarios for future missions. CNS conceptualized, designed, and developed space Internet-based communications protocols and architectures for each of the independent scenarios. GRC selected for further analysis the scenario that involved unicast communications between a Low-Earth-Orbit (LEO) International Space Station (ISS) and a ground terminal Internet node via a Tracking and Data Relay Satellite (TDRS) transfer. This report contains a tradeoff analysis on the selected scenario. The analysis examines the performance characteristics of the various protocols and architectures. The tradeoff analysis incorporates the results of a CNS developed analytical model that examined performance parameters.
Dhas, Chris↗
Ethernet for Space Flight Applications
NASA's Goddard Space Flight Center (GSFC) is adapting current data networking technologies to fly on future spaceflight missions. The benefits of using commercially based networking standards and protocols have been widely discussed and are expected to include reduction in overall mission cost, shortened integration and test (I&T) schedules, increased operations flexibility, and hardware and software upgradeability/scalability with developments ongoing in the commercial world. The networking effort is a comprehensive one encompassing missions ranging from small University Explorer (UNEX) class spacecraft to large observatories such as the Next Generation Space Telescope (NGST). Mission aspects such as flight hardware and software, ground station hardware and software, operations, RF communications, and security (physical and electronic) are all being addressed to ensure a complete end-to-end system solution. One of the current networking development efforts at GSFC is the SpaceLAN (Spacecraft Local Area Network) project, development of a space-qualifiable Ethernet network. To this end we have purchased an IEEE 802.3-compatible 10/100/1000 Media Access Control (MAC) layer Intellectual Property (IP) core and are designing a network node interface (NNI) and associated network components such as a switch. These systems will ultimately allow the replacement of the typical MIL-STD-1553/1773 and custom interfaces that inhabit most spacecraft. In this paper we will describe our current Ethernet NNI development along with a novel new space qualified physical layer that will be used in place of the standard interfaces. We will outline our plans for development of space qualified network components that will allow future spacecraft to operate in significant radiation environments while using a single onboard network for reliable commanding and data transfer. There will be a brief discussion of some issues surrounding system implications of a flight Ethernet. Finally, we will show an onboard network architecture for a proposed new mission using Ethernet for science data transport.
Webb, Evan↗
Lights Out Operations of a Space, Ground, Sensorweb
We have been operating an autonomous, integrated sensorweb linking numerous space and ground sensors in 24/7 operations since 2004. This sensorweb includes elements of space data acquisition (MODIS, GOES, and EO-1), space asset retasking (EO-1), integration of data acquired from ground sensor networks with on-demand ground processing of data into science products. These assets are being integrated using web service standards from the Open Geospatial Consortium. Future plans include extension to fixed and mobile surface and subsurface sea assets as part of the NSF's ORION Program.
Chien, Steve↗
GeneLab: NASA's Open Access, Collaborative Platform for Systems Biology and Space Medicine
NASA is investing in GeneLab1 (http:genelab.nasa.gov), a multi-year effort to maximize utilization of the limited resources to conduct biological and medical research in space, principally aboard the International Space Station (ISS). High-throughput genomic, transcriptomic, proteomic or other omics analyses from experiments conducted on the ISS will be stored in the GeneLab Data Systems (GLDS), an open-science information system that will also include a biocomputation platform with collaborative science capabilities, to enable the discovery and validation of molecular networks.
Berrios, Daniel C.↗