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NASA NTRS1995

Statistical and linguistic features of DNA sequences

We present evidence supporting the idea that the DNA sequence in genes containing noncoding regions is correlated, and that the correlation is remarkably long range--indeed, base pairs thousands of base pairs distant are correlated. We do not find such a long-range correlation in the coding regions of the gene. We resolve the problem of the "non-stationary" feature of the sequence of base pairs by applying a new algorithm called Detrended Fluctuation Analysis (DFA). We address the claim of Voss that there is no difference in the statistical properties of coding and noncoding regions of DNA by systematically applying the DFA algorithm, as well as standard FFT analysis, to all eukaryotic DNA sequences (33 301 coding and 29 453 noncoding) in the entire GenBank database. We describe a simple model to account for the presence of long-range power-law correlations which is based upon a generalization of the classic Levy walk. Finally, we describe briefly some recent work showing that the noncoding sequences have certain statistical features in common with natural languages. Specifically, we adapt to DNA the Zipf approach to analyzing linguistic texts, and the Shannon approach to quantifying the "redundancy" of a linguistic text in terms of a measurable entropy function. We suggest that noncoding regions in plants and invertebrates may display a smaller entropy and larger redundancy than coding regions, further supporting the possibility that noncoding regions of DNA may carry biological information.

Non-NASA Center↗

NASA NTRS1982

A Study of Feature Extraction Using Divergence Analysis of Texture Features

An empirical study of texture analysis for feature extraction and classification of high spatial resolution remotely sensed imagery (10 meters) is presented in terms of specific land cover types. The principal method examined is the use of spatial gray tone dependence (SGTD). The SGTD method reduces the gray levels within a moving window into a two-dimensional spatial gray tone dependence matrix which can be interpreted as a probability matrix of gray tone pairs. Haralick et al (1973) used a number of information theory measures to extract texture features from these matrices, including angular second moment (inertia), correlation, entropy, homogeneity, and energy. The derivation of the SGTD matrix is a function of: (1) the number of gray tones in an image; (2) the angle along which the frequency of SGTD is calculated; (3) the size of the moving window; and (4) the distance between gray tone pairs. The first three parameters were varied and tested on a 10 meter resolution panchromatic image of Maryville, Tennessee using the five SGTD measures. A transformed divergence measure was used to determine the statistical separability between four land cover categories forest, new residential, old residential, and industrial for each variation in texture parameters.

Hallada, W. A.↗

NASA NTRS1994

Coding For Compression Of Low-Entropy Data

Improved method of encoding digital data provides for efficient lossless compression of partially or even mostly redundant data from low-information-content source. Method of coding implemented in relatively simple, high-speed arithmetic and logic circuits. Also increases coding efficiency beyond that of established Huffman coding method in that average number of bits per code symbol can be less than 1, which is the lower bound for Huffman code.

Yeh, Pen-Shu↗

NASA NTRS1981

Entropy encoding applied to a video DM bit stream

This paper investigates bandwidth compression of delta modulated video signals using a variety of novel techniques. The video signal is encoded at 16 MHz with an adaptive delta modulator (ADM) using the Song video mode algorithm. One method of achieving bandwidth compression is through the detection and reduction of steady state patterns in the DM bit stream. Further reduction is obtained by deleting the horizontal and vertical drive information. Finally, a modified ADM algorithm is introduced to increase the frequency of steady state patterns. When the modified ADM is combined with entropy coding a 30-50% data compression can be realized.

Barba, J.↗

NASA NTRS2003

Real-Time Optical Monitoring and Simulations of Gas Phase Kinetics in InN Vapor Phase Epitaxy at High Pressure

Understanding the kinetics of nucleation and coalescence of heteroepitaxial thin films is a crucial step in controlling a chemical vapor deposition process, since it defines the perfection of the heteroepitaxial film both in terms of extended defect formation and chemical integrity of the interface. The initial nucleation process also defines the film quality during the later stages of film growth. The growth of emerging new materials heterostructures such as InN or In-rich Ga(x)In(1-x)N require deposition methods operating at higher vapor densities due to the high thermal decomposition pressure in these materials. High nitrogen pressure has been demonstrated to suppress thermal decomposition of InN, but has not been applied yet in chemical vapor deposition or etching experiments. Because of the difficulty with maintaining stochiometry at elevated temperature, current knowledge regarding thermodynamic data for InN, e.g., its melting point, temperature-dependent heat capacity, heat and entropy of formation are known with far less accuracy than for InP, InAs and InSb. Also, no information exists regarding the partial pressures of nitrogen and phosphorus along the liquidus surfaces of mixed-anion alloys of InN, of which the InN(x)P(1-x) system is the most interesting option. A miscibility gap is expected for InN(x)P(1-x) pseudobinary solidus compositions, but its extent is not established at this point by experimental studies under near equilibrium conditions. The extension of chemical vapor deposition to elevated pressure is also necessary for retaining stoichiometric single phase surface composition for materials that are characterized by large thermal decomposition pressures at optimum processing temperatures.

Dietz, Nikolaus↗

NASA NTRS2000

Bayesian Fusion of Color and Texture Segmentations

In many applications one would like to use information from both color and texture features in order to segment an image. We propose a novel technique to combine "soft" segmentations computed for two or more features independently. Our algorithm merges models according to a mean entropy criterion, and allows to choose the appropriate number of classes for the final grouping. This technique also allows to improve the quality of supervised classification based on one feature (e.g. color) by merging information from unsupervised segmentation based on another feature (e.g., texture.)

Manduchi, Roberto↗

NASA NTRS2018

Uncertainty in Heart Rate Complexity Metrics Caused by R-Peak Perturbations

Heart rate complexity (HRC) is a proven metric for gaining insight into human stress and physiological deterioration. To calculate HRC, the detection of the exact instance of when the heart beats, the R-peak, is necessary. Electrocardiogram (ECG) signals can often be corrupted by environmental noise (e.g., from electromagnetic interference, movement artifacts), which can potentially alter the HRC measurement, producing erroneous inputs which feed into decision support models. Current literature has only investigated how HRC is affected by noise when R-peak detection errors occur (false positives and false negatives). However, the numerical methods used to calculate HRC are also sensitive to the specific location of the fiducial point of the R-peak. This raises many questions regarding how this fiducial point is altered by noise, the resulting impact on the measured HRC, and how we can account for noisy HRC measures as inputs into our decision models. This work uses Monte Carlo simulations to systematically add white and pink noise at different permutations of signal-to-noise ratios (SNRs), time segments, sampling rates, and HRC measurements to characterize the influence of noise on the HRC measure by altering the fiducial point of the R-peak. Using the generated information from these simulations provides improved decision processes for system design which address key concerns such as permutation entropy being a more precise, reliable, less biased, and more sensitive measurement for HRC than sample and approximate entropy.

Napoli, Nicholas J.↗

NASA NTRS2019

Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.

US Army collaboration↗

NASA NTRS1979

Feature selection via entropy minimization: An example using LANDSAT satellite data

The author has identified the following significant results. The minimum entropy model may provide several useful advantages over traditional techniques for processing LANDSAT data. Total computer time to conduct a complete pattern recognition process is reduced. Subjective (transformed image), as well as statistically derived information is made available to the analyst/user much earlier in the analysis process. A rapid feedback loop in which numerous training set combinations can be tested for difference and representativeness is available. Additional tests of LANDSAT data processing using the minimum entropy model are clearly justified.

Zandonella, A.↗

NASA NTRS1994

Beyond maximum entropy: Fractal pixon-based image reconstruction

We have developed a new Bayesian image reconstruction method that has been shown to be superior to the best implementations of other methods, including Goodness-of-Fit (e.g. Least-Squares and Lucy-Richardson) and Maximum Entropy (ME). Our new method is based on the concept of the pixon, the fundamental, indivisible unit of picture information. Use of the pixon concept provides an improved image model, resulting in an image prior which is superior to that of standard ME.

Puetter, R. C.↗

NASA NTRS1972

Solid-gas phase equilibria and thermodynamic properties of cadmium selenide.

Accurate vapor pressures are determined through direct weight loss measurements using the Knudsen effusion technique. The experimental data are evaluated by establishing the mode of vaporization and determining the heat capacity of cadmium selenide at elevated temperatures. Additional information is obtained through a second- and third-law evaluation of data, namely, the heat of formation and the absolute entropy of cadmium selenide. A preferential loss of selenium during the initial heating of CdSe is observed, which leads to a deviation in stoichiometry.

Sigai, A. G.↗

NASA NTRS1998

On the Information Content of Program Traces

Program traces are used for analysis of program performance, memory utilization, and communications as well as for program debugging. The trace contains records of execution events generated by monitoring units inserted into the program. The trace size limits the resolution of execution events and restricts the user's ability to analyze the program execution. We present a study of the information content of program traces and develop a coding scheme which reduces the trace size to the limit given by the trace entropy. We apply the coding to the traces of AIMS instrumented programs executed on the IBM SPA and the SCSI Power Challenge and compare it with other coding methods. Our technique shows size of the trace can be reduced by more than a factor of 5.

Frumkin, Michael↗

NASA NTRS1986

Remote boundary conditions for unsteady multidimensional aerodynamic computations

The behavior of gas dynamic flows which are perturbations of a uniform stream in terms of information transfer across artificial (computational) boundaries remote from the source of disturbance are discussed. A set of boundary conditions is derived involving vorticity, entropy, and pressure-velocity relationships derived from bicharacteristic equations.

Roe, Philip L.↗

NASA NTRS1989

Remote boundary conditions for unsteady multidimensional aerodynamic computations

Roe, P. L.↗

NASA NTRS1982

Quantiles, parametric-select density estimation, and bi-information parameter estimators

A quantile-based approach to statistical analysis and probability modeling of data is presented which formulates statistical inference problems as functional inference problems in which the parameters to be estimated are density functions. Density estimators can be non-parametric (computed independently of model identified) or parametric-select (approximated by finite parametric models that can provide standard models whose fit can be tested). Exponential models and autoregressive models are approximating densities which can be justified as maximum entropy for respectively the entropy of a probability density and the entropy of a quantile density. Applications of these ideas are outlined to the problems of modeling: (1) univariate data; (2) bivariate data and tests for independence; and (3) two samples and likelihood ratios. It is proposed that bi-information estimation of a density function can be developed by analogy to the problem of identification of regression models.

Parzen, E.↗

NASA NTRS1984

Reversibility and stability of information processing systems

Classical and quantum models of dynamically reversible computers are considered. Instabilities in the evolution of the classical 'billiard ball computer' are analyzed and shown to result in a one-bit increase of entropy per step of computation. 'Quantum spin computers', on the other hand, are not only microscopically, but also operationally reversible. Readoff of the output of quantum computation is shown not to interfere with this reversibility. Dissipation, while avoidable in principle, can be used in practice along with redundancy to prevent errors.

Zurek, W. H.↗

NASA NTRS1996

CROSS: A GDSS for the Evaluation and Prioritization of Engineering Support Requests and Advanced Technology Projects at NASA

Objective evaluation and prioritization of engineering support requests (ESRs) is a difficult task at the Kennedy Space Center (KSC) Shuttle Project Engineering Office. The difficulty arises from the complexities inherent in the evaluation process and the lack of structured information. The purpose of this project is to implement the consensus ranking organizational support system (CROSS), a multiple criteria decision support system (DSS) developed at KSC that captures the decision maker's beliefs through a series of sequential, rational, and analytical processes. CROSS utilizes the analytic hierarchy process (AHP), subjective probabilities, entropy concept, and maximize agreement heuristic (MAH) to enhance the decision maker's intuition in evaluation ESRs. Some of the preliminary goals of the project are to: (1) revisit the structure of the ground systems working team (GWST) steering committee, (2) develop a template for ESR originators to provide more comple and consistent information to the GSWT steering committee members to eliminate the need for a facilitator, (3) develop an objective and structured process for the initial screening of ESRs, (4) extensive training of the stakeholders and the GWST steering committee to eliminate the need for a facilitator, (5) automate the process as much as possible, (6) create an environment to compile project success factor data on ESRs and move towards a disciplined system that could be used to address supportability threshold issues at the KSC, and (7) investigate the possibility of an organization-wide implementation of CROSS.

Tavana, Madjid↗

NASA NTRS1993

Generalised squeezing and information theory approach to quantum entanglement

It is shown that the usual one- and two-mode squeezing are based on reducible representations of the SU(1,1) group. Generalized squeezing is introduced with the use of different SU(1,1) rotations on each irreducible sector. Two-mode squeezing entangles the modes and information theory methods are used to study this entanglement. The entanglement of three modes is also studied with the use of the strong subadditivity property of the entropy.

Vourdas, A.↗