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This assessment of visual communication integrates the optical design of the image-gathering device with the digital processing for image coding and restoration. Results show that informationally optimized image gathering ordinarily can be relied upon to maximize the information efficiency of decorrelated data and the visual quality of optimally restored images.
A long-running difficulty with conventional game theory has been how to modify it to accommodate the bounded rationality of all red-world players. A recurring issue in statistical physics is how best to approximate joint probability distributions with decoupled (and therefore far more tractable) distributions. This paper shows that the same information theoretic mathematical structure, known as Product Distribution (PD) theory, addresses both issues. In this, PD theory not only provides a principle formulation of bounded rationality and a set of new types of mean field theory in statistical physics; it also shows that those topics are fundamentally one and the same.
The noise properties of the radar image formation process are used in the present modeling of a communication channel in which the desired target properties are the information transmitted, and the final image represents the received signal. The average information rate over this communication channel is calculated together with appropriate bounds and approximations, and is found to be small on a per-sample basis. As a result, many samples must be averaged to allow for the discrimination, or classification, of several levels of target reflectivity. These information rate properties are consistent with known results concerning target detection and image quality in speckle, and the rate is applicable to the definition of radar image radiometric resolution. Radiometric resolution is functionally related to the degree of noncoherent averaging performed by the sensor.
In this paper, we will present a new methodology that measures the "worth" of deploying an additional testing instrument (sensor) in terms of the amount of information that can be retrieved from such measurement. This quantity is obtained using a probabilistic model of RLV's that has been partially developed in the NASA Ames Research Center. A number of correlated attributes are identified and used to obtain the worth of deploying a sensor in a given test point from an information-theoretic viewpoint. Once the information-theoretic worth of sensors is formulated and incorporated into our general model for IHM performance, the problem can be formulated as a constrained optimization problem where reliability and operational safety of the system as a whole is considered. Although this research is conducted specifically for RLV's, the proposed methodology in its generic form can be easily extended to other domains of systems health monitoring.
The primary objective of the NASA Earth-Sun Exploration Technology Office is to understand the observed Earth climate variability, thus enabling the determination and prediction of the climate's response to both natural and human-induced forcing. We are currently developing a suite of computational tools that will allow researchers to calculate, from data, a variety of information-theoretic quantities such as mutual information, which can be used to identify relationships among climate variables, and transfer entropy, which indicates the possibility of causal interactions. Our tools estimate these quantities along with their associated error bars, the latter of which is critical for describing the degree of uncertainty in the estimates. This work is based upon optimal binning techniques that we have developed for piecewise-constant, histogram-style models of the underlying density functions. Two useful side benefits have already been discovered. The first allows a researcher to determine whether there exist sufficient data to estimate the underlying probability density. The second permits one to determine an acceptable degree of round-off when compressing data for efficient transfer and storage. We also demonstrate how mutual information and transfer entropy can be applied so as to allow researchers not only to identify relations among climate variables, but also to characterize and quantify their possible causal interactions.
Over the past 20 to 25 years theoretical chemistry (particularly theoretical chemical kinetics) has played an increasingly important role in developing chemical kinetics models for combustion. Theoretical methods of obtaining rate parameters are now competitive in accuracy with experiment, particularly for small molecules. Moreover, theoretical methods can deal with conditions that experiments frequently cannot. In addition to increased accuracy, theory has rejuvenated methods and discovered phenomena that were completely unappreciated, or at least underappreciated, in the 20th century. Our primary interest here is in molecular-level issues, i.e. in calculating rate and transport parameters. However, dealing with kinetics models that involve thousands of reactions and hundreds of species is important for practical applications and is relatively new to the 21st century. Theory, in a general sense, and theoretical methods development have a role to play here too. We discuss in this review all these topics in some detail with an emphasis on issues and methods that have emerged in the last 20 years or so. Even so, our review is selective, rather than comprehensive, out of necessity.
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Gallium-based heterogeneous catalysts originally developed for commercial use in propane dehydroaromatization have been explored extensively as potential replacements for Pt- and Cr-based catalysts used in propane dehydrogenation for on-demand propylene production. In a large number of experimental and theoretical studies, active sites with a variety of Ga nuclearities, coordination environments, and oxidation states have been proposed. Isolated Ga(I) ions are often invoked, despite the scarcity of well-defined molecular species and their well-documented instability. In this study, we investigate the appearance of a high-intensity, low-energy white line at the Ga K-edge upon reduction of Ga/HZSM-5 by H 2 at a temperature of ca. 500 °C, accompanied by a dramatic reduction in the extended X-ray absorption fine structure (EXAFS) intensity. In contrast, Ga/γ-Al 2 O 3 does not show such behavior. In order to lay a rigorous foundation for characterizing these types of systems and to establish experimental signatures for the elusive Ga(I) oxidation state, we recorded Ga K-edge X-ray absorption spectra [including high-energy-resolution fluorescence detection-X-ray absorption near-edge spectroscopy (HERFD-XANES)] for several well-defined molecular and crystalline Ga(I) compounds. XANES is essential to establishing the presence of Ga(I), despite the overlap in edge energies with organoGa(III) compounds, because Ga(I)-containing oxide materials show very weak EXAFS scattering. Compared to the XANES of trigonal Ga(III)-containing materials, Ga(I) spectra display a significantly more intense white line feature. Theoretical simulations agree well with this experimental observation and reveal that the strong XANES intensity originates from the superposition of transitions to several empty, nearly degenerate p-like states. Finally, these signatures provide compelling evidence for assigning the intense white line and dramatic loss of EXAFS intensity in Ga/HZSM-5 to the near-quantitative reduction of Ga(III) to Ga(I), while the weaker white line and conventional EXAFS signal of Ga/γ-Al 2 O 3 point to, at most, a minor fraction of Ga(I) sites.
SUMMARY Plant responses to environmental change are mediated via changes in cellular metabolomes. However, <5% of signals obtained from liquid chromatography tandem mass spectrometry (LC‐MS/MS) can be identified, limiting our understanding of how metabolomes change under biotic/abiotic stress. To address this challenge, we performed untargeted LC‐MS/MS of leaves, roots, and other organs of Brachypodium distachyon (Poaceae) under 17 organ–condition combinations, including copper deficiency, heat stress, low phosphate, and arbuscular mycorrhizal symbiosis. We found that both leaf and root metabolomes were significantly affected by the growth medium. Leaf metabolomes were more diverse than root metabolomes, but the latter were more specialized and more responsive to environmental change. We found that 1 week of copper deficiency shielded the root, but not the leaf metabolome, from perturbation due to heat stress. Machine learning (ML)‐based analysis annotated approximately 81% of the fragmented peaks versus approximately 6% using spectral matches alone. We performed one of the most extensive validations of ML‐based peak annotations in plants using thousands of authentic standards, and analyzed approximately 37% of the annotated peaks based on these assessments. Analyzing responsiveness of each predicted metabolite class to environmental change revealed significant perturbations of glycerophospholipids, sphingolipids, and flavonoids. Co‐accumulation analysis further identified condition‐specific biomarkers. To make these results accessible, we developed a visualization platform on the Bio‐Analytic Resource for Plant Biology website ( https://bar.utoronto.ca/efp_brachypodium_metabolites/cgi‐bin/efpWeb.cgi ), where perturbed metabolite classes can be readily visualized. Overall, our study illustrates how emerging chemoinformatic methods can be applied to reveal novel insights into the dynamic plant metabolome and stress adaptation.
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Abstract not provided.
(Previously announced in STAR as N82-19731)
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The project of physics discovery is often equivalent to finding the most concise description of a physical system. The description with optimum predictive capability for a dataset generated by a physical system is one that minimizes both predictive error on the dataset and the complexity of the description. The discovery of the governing physics of a system can therefore be viewed as a mathematical optimization problem. We outline here a method to optimize the description of arbitrarily complex physical systems by minimizing the entropy of the description of the system. The Recursive Domain Partitioning (RDP) procedure finds the optimum partitioning of each physical domain into subdomains, and the optimum predictive function within each subdomain. Penalty functions are introduced to limit the complexity of the predictive function within each domain. Examples are shown in 1D and 2D. In 1D, the technique effectively discovers the elastic and plastic regions within a stress-strain curve generated by simulations of amorphous carbon material, while in 2D the technique discovers the free-flow region and the inertially-obstructed flow region in the simulation of fluid flow across a plate.
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