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NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Hesham ElAbd↗

NASA NTRS1993

An overview of the Evaluation of Oxygen Interaction with Materials-third phase (EOIM-3) experiment: Space Shuttle Mission 46

The interaction of the atomic oxygen (AO) component of the low earth orbit (LEO) environment with spacecraft materials has been the subject of several flight experiments over the past 11 years. The effect of AO interactions with materials has been shown to be significant for long-lived spacecraft such as Space Station Freedom and has resulted in materials changes for externally exposed surfaces. The data obtained from previous flight experiments, augmented by limited ground-based evaluation, have been used to evaluate hardware performance and select materials. Questions pertaining to the accuracy of this data base remain, resulting from the use of long-term ambient density models to estimate the O-atom fluxes and fluences needed to calculate materials reactivity in short-term flight experiments. The EOIM-3 flight experiment was designed to produce benchmark AO reactivity data and was carried out during STS-46. Ambient density measurements were made with a quadrupole mass spectrometer which was calibrated for AO measurements in a unique ground-based test facility. The combination of these data with the predictions of ambient density models allows an assessment of the accuracy of measured reaction rates on a wide variety of materials, many of which had never been tested in LEO before. The mass spectrometer is also used to obtain a better definition of the local neutral and plasma environments resulting from interaction of the ambient atmosphere with various spacecraft surfaces. In addition, the EOIM-3 experiment was designed to produce information on the effects of temperature, mechanical stress, and solar exposure on the AO reactivity of a wide range of materials. An overview of the EOIM-3 methods and results are presented.

Leger, Lubert J.↗

NASA NTRS1998

X-33

In response to the Cooperative Agreement, Lockheed Martin Skunk Works has compiled an Annual Performance Report of the X-33/RLV Program. This report consists of individual reports from all industry team members, as well as NASA team centers. The first milestone was hand delivered to NASA MSFC. The second year has been one of significant accomplishment in which team members have demonstrated their ability to meet vital benchmarks while continuing on the technical adventure of the 20th century.

Source record↗

NASA NTRSDate not supplied

A Study of Parallel Scalability and Dynamic Workload Balancing in GlennICE

The Glenn Icing Computational Environment (GlennICE) is a computational tool designed to calculate ice growth on complex three-dimensional geometries. It utilizes user-supplied computational fluid dynamics solutions for the geometry of interest. Key developments include advancements in convergence of collection efficiency, trajectory optimization, and refinement methodology. These improvements have significantly enhanced GlennICE’s efficiency for practical engineering applications. A recent study focused on benchmarking GlennICE’s scalability in a parallel environment using static scheduling. Findings indicated a potential twofold increase in efficiency through workload balance enhancements. This paper presents an analysis of the solver’s new workload balancing improvements, incorporating shared memory and dynamic scheduling routines. Results demonstrate a highly efficient and consistent algorithm across high-performance computing clusters.

Computational Icing↗

NASA NTRSDate not supplied

A Study of Parallel Scalability and Dynamic Workload Balancing in GlennICE

Computational Icing↗

NASA NTRS2008

Benchmark Analysis of Pion Contribution from Galactic Cosmic Rays

Shielding strategies for extended stays in space must include a comprehensive resolution of the secondary radiation environment inside the spacecraft induced by the primary, external radiation. The distribution of absorbed dose and dose equivalent is a function of the type, energy and population of these secondary products. A systematic verification and validation effort is underway for HZETRN, which is a space radiation transport code currently used by NASA. It performs neutron, proton and heavy ion transport explicitly, but it does not take into account the production and transport of mesons, photons and leptons. The question naturally arises as to what is the contribution of these particles to space radiation. The pion has a production kinetic energy threshold of about 280 MeV. The Galactic cosmic ray (GCR) spectra, coincidentally, reaches flux maxima in the hundreds of MeV range, corresponding to the pion production threshold. We present results from the Monte Carlo code MCNPX, showing the effect of lepton and meson physics when produced and transported explicitly in a GCR environment.

Aghara, Sukesh K.↗

NASA NTRS1989

An evaluative model of system performance in manned teleoperational systems

Manned teleoperational systems are used in aerospace operations in which humans must interact with machines remotely. Manual guidance of remotely piloted vehicles, controling a wind tunnel, carrying out a scientific procedure remotely are examples of teleoperations. A four input parameter throughput (Tp) model is presented which can be used to evaluate complex, manned, teleoperations-based systems and make critical comparisons among candidate control systems. The first two parameters of this model deal with nominal (A) and off-nominal (B) predicted events while the last two focus on measured events of two types, human performance (C) and system performance (D). Digital simulations showed that the expression A(1-B)/C+D) produced the greatest homogeneity of variance and distribution symmetry. Results from a recently completed manned life science telescience experiment will be used to further validate the model. Complex, interacting teleoperational systems may be systematically evaluated using this expression much like a computer benchmark is used.

Haines, Richard F.↗

NASA NTRS2000

Benchmark Active Controls Technology (BACT) Wing CFD Results

The Benchmark Active Controls Technology (BACT) wing test (see chapter 8E) provides data for the validation of aerodynamic, aeroelastic, and active aeroelastic control simulation codes. These data provide a rich database for development and validation of computational aeroelastic and aeroservoelastic methods. In this vein, high-level viscous CFD analyses of the BACT wing have been performed for a subset of the test conditions available in the dataset. The computations presented in this section investigate the aerodynamic characteristics of the rigid clean wing configuration as well as simulations of the wing with a static and oscillating aileron and spoiler deflection. Two computational aeroelasticity codes extensively used at NASA Langley Research Center are implemented in this simulation. They are the ENS3DAE and CFL3DAE computational aeroelasticity programs. Both of these methods solve the three-dimensional compressible Navier-Stokes equations for both rigid and flexible vehicles, but they use significantly different approaches to the solution 6f the aerodynamic equations of motion. Detailed descriptions of both methods are presented in the following section.

Schuster, David M.↗

NASA NTRS2002

Performance Analysis of Multilevel Parallel Applications on Shared Memory Architectures

In this paper we describe how to apply powerful performance analysis techniques to understand the behavior of multilevel parallel applications. We use the Paraver/OMPItrace performance analysis system for our study. This system consists of two major components: The OMPItrace dynamic instrumentation mechanism, which allows the tracing of processes and threads and the Paraver graphical user interface for inspection and analyses of the generated traces. We describe how to use the system to conduct a detailed comparative study of a benchmark code implemented in five different programming paradigms applicable for shared memory

Jost, Gabriele↗

NASA NTRS2004

Active learning in the presence of unlabelable examples

We propose a new active learning framework where the expert labeler is allowed to decline to label any example. This may be necessary because the true label is unknown or because the example belongs to a class that is not part of the real training problem. We show that within this framework, popular active learning algorithms (such as Simple) may perform worse than random selection because they make so many queries to the unlabelable class. We present a method by which any active learning algorithm can be modified to avoid unlabelable examples by training a second classifier to distinguish between the labelable and unlabelable classes. We also demonstrate the effectiveness of the method on two benchmark data sets and a real-world problem.

active learning↗

NASA NTRS2012

Implementation of Finite Volume based Navier Stokes Algorithm Within General Purpose Flow Network Code

This paper describes a finite volume based numerical algorithm that allows multi-dimensional computation of fluid flow within a system level network flow analysis. There are several thermo-fluid engineering problems where higher fidelity solutions are needed that are not within the capacity of system level codes. The proposed algorithm will allow NASA's Generalized Fluid System Simulation Program (GFSSP) to perform multi-dimensional flow calculation within the framework of GFSSP s typical system level flow network consisting of fluid nodes and branches. The paper presents several classical two-dimensional fluid dynamics problems that have been solved by GFSSP's multi-dimensional flow solver. The numerical solutions are compared with the analytical and benchmark solution of Poiseulle, Couette and flow in a driven cavity.

Schallhorn, Paul↗

NASA NTRS2014

Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset

Benchmarking of prognostic algorithms has been challenging due to limited availability of common datasets suitable for prognostics. In an attempt to alleviate this problem several benchmarking datasets have been collected by NASA's prognostic center of excellence and made available to the Prognostics and Health Management (PHM) community to allow evaluation and comparison of prognostics algorithms. Among those datasets are five C-MAPSS datasets that have been extremely popular due to their unique characteristics making them suitable for prognostics. The C-MAPSS datasets pose several challenges that have been tackled by different methods in the PHM literature. In particular, management of high variability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabilities of prognostics algorithms. More than 70 publications have used the C-MAPSS datasets for developing data-driven prognostic algorithms. The C-MAPSS datasets are also shown to be well-suited for development of new machine learning and pattern recognition tools for several key preprocessing steps such as feature extraction and selection, failure mode assessment, operating conditions assessment, health status estimation, uncertainty management, and prognostics performance evaluation. This paper summarizes a comprehensive literature review of publications using C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches.

Uncertainty↗

NASA NTRS2005

Experimental Results from the Active Aeroelastic Wing Wind Tunnel Test Program

The Active Aeroelastic Wing (AAW) program is a cooperative effort among NASA, the Air Force Research Laboratory and the Boeing Company, encompassing flight testing, wind tunnel testing and analyses. The objective of the AAW program is to investigate the improvements that can be realized by exploiting aeroelastic characteristics, rather than viewing them as a detriment to vehicle performance and stability. To meet this objective, a wind tunnel model was crafted to duplicate the static aeroelastic behavior of the AAW flight vehicle. The model was tested in the NASA Langley Transonic Dynamics Tunnel in July and August 2004. The wind tunnel investigation served the program goal in three ways. First, the wind tunnel provided a benchmark for comparison with the flight vehicle and various levels of theoretical analyses. Second, it provided detailed insight highlighting the effects of individual parameters upon the aeroelastic response of the AAW vehicle. This parameter identification can then be used for future aeroelastic vehicle design guidance. Third, it provided data to validate scaling laws and their applicability with respect to statically scaled aeroelastic models.

Heeg, Jennifer↗

NASA NTRS1992

Defining the performance gap: Conducting a self-assessment

This paper presents two different approaches to performing self-assessments of continuous improvement activities. Case Study 1 describes the activities performed by JSC to assess the implementation of continuous improvement efforts at the NASA Center. The JSC approach included surveys administered to randomly selected NASA personnel and personal interviews with NASA and contractor management personnel. Case Study 2 describes the continuous improvement survey performed by the JSC Safety, Reliability, and Quality Assurance (SR&QA) organization. This survey consisted of a short questionnaire (50 questions) administered to all NASA and contractor SR&QA personnel. The questionnaire is based on the eight categories of the President's Award for Quality and Productivity Improvement. It is designed to objectively determine placement on the TQ benchmark and identify a roadmap for improvement.

Braymer, Susan A.↗

NASA NTRS1988

ART/Ada design project, phase 1. Task 3 report: Test plan

The plan is described for the integrated testing and benchmark of Phase Ada based ESBT Design Research Project. The integration testing is divided into two phases: (1) the modules that do not rely on the Ada code generated by the Ada Generator are tested before the Ada Generator is implemented; and (2) all modules are integrated and tested with the Ada code generated by the Ada Generator. Its performance and size as well as its functionality is verified in this phase. The target platform is a DEC Ada compiler on VAX mini-computers and VAX stations running the VMS operating system.

Allen, Bradley P.↗

NASA NTRS2002

Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization

We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front.

Lohn, Jason D.↗

NASA NTRS2005

Parallelization of Lower-Upper Symmetric Gauss-Seidel Method for Chemically Reacting Flow

Development of technologies for exploration of the solar system has revived an interest in computational simulation of chemically reacting flows since planetary probe vehicles exhibit non-equilibrium phenomena during the atmospheric entry of a planet or a moon as well as the reentry to the Earth. Stability in combustion is essential for new propulsion systems. Numerical solution of real-gas flows often increases computational work by an order-of-magnitude compared to perfect gas flow partly because of the increased complexity of equations to solve. Recently, as part of Project Columbia, NASA has integrated a cluster of interconnected SGI Altix systems to provide a ten-fold increase in current supercomputing capacity that includes an SGI Origin system. Both the new and existing machines are based on cache coherent non-uniform memory access architecture. Lower-Upper Symmetric Gauss-Seidel (LU-SGS) relaxation method has been implemented into both perfect and real gas flow codes including Real-Gas Aerodynamic Simulator (RGAS). However, the vectorized RGAS code runs inefficiently on cache-based shared-memory machines such as SGI system. Parallelization of a Gauss-Seidel method is nontrivial due to its sequential nature. The LU-SGS method has been vectorized on an oblique plane in INS3D-LU code that has been one of the base codes for NAS Parallel benchmarks. The oblique plane has been called a hyperplane by computer scientists. It is straightforward to parallelize a Gauss-Seidel method by partitioning the hyperplanes once they are formed. Another way of parallelization is to schedule processors like a pipeline using software. Both hyperplane and pipeline methods have been implemented using openMP directives. The present paper reports the performance of the parallelized RGAS code on SGI Origin and Altix systems.

Yoon, Seokkwan↗