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We describe a portable container which allows specification of the probabilistic relations between several variables. The applications motivating this work are mainly scientific inference problems.
Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.
Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.
The evolution of ISO C++ standards increasingly serves the needs of scientific computing, offering potential benefits for developing portable applications. The recent revisions of C++ programming language, for instance, introduces a suite of algorithms capable of being executed on accelerators. Although this approach may not yield best performance, it can present a viable balance between code productivity and computational efficiency. In this report, we discuss the implementation of the HISQ operator utilizing a range of features from the C++17/20/23 standards and include an assessment of their performance.
Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming.
The Fifth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: automation for Space Station; intelligent control, testing, and fault diagnosis; robotics and vision; planning and scheduling; simulation, modeling, and tutoring; development tools and automatic programming; knowledge representation and acquisition; and knowledge base/data base integration.
Selenium is a crucial earth-abundant and non-toxic semiconductor with a wide range of applications across the semiconductor industries. Selenium has drawn attention from scientific communities for its wide range of applicability: from photovoltaics to imaging devices. Its usage as a photosensitive material largely involves the synthesis of the amorphous phase (a-Se) via various experimental techniques. However, the ground state crystalline phase of this material, known as the trigonal selenium (\textit{t}-Se), is not extensively studied for its optimum electronic and optical properties. In this work, we present density functional theory (DFT) based systematic studies on the ultra-thin $(10\overline{1}0)$ surface slabs of \textit{t}-Se. We report the surface energies, work function, electronic and optical properties as a function of number of layers for $(10\overline{1}0)$ surface slabs to access its suitability for applications as a photosensitive material.
The United States National Aeronautics and Space Administration (NASA) has initiated a five-year study of a Surface Deformation and Change Observing System Architecture. The goal of the study is to define an observing system that would meet the recommendations of the 2017 US National Academy of Sciences, Engineering, and Medicine Decadal Survey for developing an affordable synthetic aperture radar-based system or systems that can address fundamental scientific questions about the nature of hazards, disasters, landscape change, and serve applications communities as well. The study is to consider the existing international constellation of SAR systems, both civil and commercial, as well as new technologies that could be employed to address scientific and applications objectives and to improve affordability. The study will be conducted in four phases: 1) expanding the architectural trade space; 2) assessing the science and applications value of each identified architecture; 3) down selecting to three final candidates and performing detailed assessments; 4) final reporting and preparation for mission formulation. The first phase is approximately 2 years in duration, and will concentrate on the scope of research and application areas to be addressed, defining the possible architectures to consider, and beginning the assessment of their performance. The study team comprises five NASA centers. Broad participation from the research, applications, technology, and commercial sectors is planned.
Abstract The general method of graph coarsening or graph reduction has been a remarkably useful and ubiquitous tool in scientific computing and it is now just starting to have a similar impact in machine learning. The goal of this paper is to take a broad look into coarsening techniques that have been successfully deployed in scientific computing and see how similar principles are finding their way in more recent applications related to machine learning. In scientific computing, coarsening plays a central role in algebraic multigrid methods as well as the related class of multilevel incomplete LU factorizations. In machine learning, graph coarsening goes under various names, e.g., graph downsampling or graph reduction. Its goal in most cases is to replace some original graph by one which has fewer nodes, but whose structure and characteristics are similar to those of the original graph. As will be seen, a common strategy in these methods is to rely on spectral properties to define the coarse graph.
Nuclear fission reactors are abundant sources of antineutrinos for neutrino physics experiments. The flux and spectrum of antineutrinos emitted by a reactor can indicate its activity and composition, suggesting potential applications of neutrino measurements beyond fundamental scientific studies that may be valuable to society. The utility of reactor antineutrinos for applications and fundamental science is dependent on the availability of precise predictions of these emissions. For example, in the last decade, disagreements between reactor antineutrino measurements and models have inspired revision of reactor antineutrino calculations and standard nuclear databases as well as searches for new fundamental particles not predicted by the Standard Model of particle physics. Past predictions and descriptions of the methods used to generate them are documented to varying degrees in the literature, with different modeling teams incorporating a range of methods, input data, and assumptions. The resulting difficulty in accessing or reproducing past models and reconciling results from differing approaches complicates the future study and application of reactor antineutrinos. The CONFLUX (Calculation Of Neutrino FLUX) software framework is a neutrino prediction tool built with the goal of simplifying, standardizing, and democratizing the process of reactor antineutrino flux calculations. CONFLUX includes three primary methods for calculating the antineutrino emissions of nuclear reactors or individual beta decays that incorporate common nuclear data and beta decay theory. The software is prepackaged with the current nuclear databases, including ENDF.B/VIII, JEFF-3.3, and ENSDF, and it includes the capability to predict time-dependent reactor emissions, adjust nuclear database or beta decay inputs/assumptions, and propagate related sources of uncertainty. Here, this paper describes the CONFLUX software structure, details the methods used for flux and spectrum calculations, and provides examples of potential use cases.
Today's scientific high-performance computing applications and advanced instruments are producing vast volumes of data across a wide range of domains, which impose a serious burden on data transfer and storage. Error-bounded lossy compression has been developed and widely used in the scientific community because it not only can significantly reduce the data volumes but also can strictly control the data distortion based on the user-specified error bound. Existing lossy compressors, however, cannot offer ultrafast compression speed, which is highly demanded by numerous applications or use cases (such as in-memory compression and online instrument data compression). In this paper, we propose a novel ultrafast error-bounded lossy compressor that can obtain fairly high compression performance on both CPUs and GPUs and with reasonably high compression ratios. The key contributions are threefold. (1) We propose a generic error-bounded lossy compression framework---called SZx---that achieves ultrafast performance through its novel design comprising only lightweight operations such as bitwise and addition/subtraction operations, while still keeping a high compression ratio. (2) We implement SZx on both CPUs and GPUs and optimize the performance according to their architectures. (3) We perform a comprehensive evaluation with six real-world production-level scientific datasets on both CPUs and GPUs. Experiments show that SZx is 2~16x faster than the second-fastest existing error-bounded lossy compressor (either SZ or ZFP) on CPUs and GPUs, with respect to both compression and decompression.
Abstracts are provided for 115 patents and patent applications entered into the NASA scientific and technical information system during the period January 1986 through June 1986. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent application.
Abstracts are provided for 136 patents and patent applications entered into the NASA scientific and technical information system during the period July through December 1987. Each entry consists of a citation , an abstract, and in most cases, a key illustration selected from the patent or patent application.
Abstracts are cited for 130 patents and patent applications introduced into the NASA scientific and technical information system during the period of January 1981 through July 1981. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or application for patent.
Abstracts are cited for 165 patents and patent applications introduced into the NASA scientific and technical information system during the period July 1981 through December 1981. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application.
Abstracts are cited for 234 patents and patent applications introduced into the NASA scientific and technical information system during the period July 1982 through December 1982. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application.
Abstracts are cited for 129 patents and patent applications introduced into the NASA scientific and technical information system during the period January 1983 through June 1983. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application.
Abstracts are provided for 167 patents and patent applications entered into the NASA scientific and technical information system during the period July 1983 through December 1983. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application.